[Perf][Bwd-weights]Lds re-layout to avoid ds read/write bank conflict and...

[Perf][Bwd-weights]Lds re-layout to avoid ds read/write bank conflict and balance ds ops with address calculations (#190)

* add some instance to develop

* avoid bank conflicts for wrw for all instance

* add small K1 test

* delete some unused instance

* reset buffer load oob and ds memcpy to default option

* remove useless instances

* remove redandunt space

* remove printf code

* clang-format-10 change

* fix clang format for the other files

* add bank length computation

* add template to distinguish the instance that need lds padding for wrw

* use rocm5.1 as docker

* use integer value for GEMM test

* 1. move dedicated transform into gridwisegemm's head file. 2. make lds tensor params a struct templete. 3. remove useless code

* use a new gridwise gemm header for bwd-weight

* revert gridwise gemm v2r4r2

* change foramt

* rename kernel invoker
Co-authored-by: Chao Liu <chao.liu2@amd.com>

include/ck/tensor_operation/gpu/device/device_conv2d_backward_weight_xdl_c_shuffle_nhwc_kyxc_nhwk.hpp

@@ -11,7 +11,7 @@
 #include "tensor_layout.hpp"
 #include "tensor_descriptor.hpp"
 #include "tensor_descriptor_helper.hpp"
-#include "gridwise_gemm_xdlops_v2r4r2.hpp"
+#include "gridwise_gemm_xdlops_bwd_weight.hpp"
 
 namespace ck {
 namespace tensor_operation {
@@ -81,6 +81,20 @@ struct DeviceConv2dBwdWeightXdl_C_Shuffle_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_
     static constexpr auto K1Number     = Number<K1>{};
     static constexpr auto GemmK1Number = K1Number;
 
+    // Bytes per 32 lds bank: 32 * 4 bytes
+    static constexpr auto BankLength = 128;
+    static constexpr auto ElePerBank = BankLength / sizeof(ADataType);
+
+    // M1 & M0
+    static constexpr auto ABlockLdsM1PerBlock = ElePerBank / K1;
+    static constexpr auto ABlockLdsM0PerBlock = MPerBlock / ABlockLdsM1PerBlock;
+    static constexpr auto ABlockLdsM1Padding  = 4;
+
+    // N1 & N0
+    static constexpr auto BBlockLdsN1PerBlock = ElePerBank / K1;
+    static constexpr auto BBlockLdsN0PerBlock = NPerBlock / BBlockLdsN1PerBlock;
+    static constexpr auto BBlockLdsN1Padding  = 4;
+
     static auto
     MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N(ck::index_t N,
                                                     ck::index_t K,
@@ -205,7 +219,7 @@ struct DeviceConv2dBwdWeightXdl_C_Shuffle_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_
     using CGridDesc_M_N     = remove_cvref_t<decltype(ABCGridDescs{}[I2])>;
 
     // GridwiseGemm
-    using GridwiseGemm = GridwiseGemm_bk0mk1_bk0nk1_mn_xdlops_v2r4r2<
+    using GridwiseGemm = GridwiseGemm_bk0mk1_bk0nk1_mn_xdlops_bwd_weight<
         BlockSize,
         ADataType, // TODO: distinguish A/B datatype
         AccDataType,
@@ -233,6 +247,9 @@ struct DeviceConv2dBwdWeightXdl_C_Shuffle_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_
         ABlockTransferDstScalarPerVector_K1,
         false, // AThreadTransferSrcResetCoordinateAfterRun,
         ABlockLdsAddExtraM,
+        ABlockLdsM1PerBlock,
+        ABlockLdsM0PerBlock,
+        ABlockLdsM1Padding,
         BBlockTransferThreadClusterLengths_K0_N_K1,
         BBlockTransferThreadClusterArrangeOrder,
         BBlockTransferSrcAccessOrder,
@@ -241,12 +258,17 @@ struct DeviceConv2dBwdWeightXdl_C_Shuffle_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_
         BBlockTransferDstScalarPerVector_K1,
         false, // BThreadTransferSrcResetCoordinateAfterRun,
         BBlockLdsAddExtraN,
+        BBlockLdsN1PerBlock,
+        BBlockLdsN0PerBlock,
+        BBlockLdsN1Padding,
         CShuffleMXdlPerWavePerShuffle,
         CShuffleNXdlPerWavePerShuffle,
         CBlockTransferScalarPerVector_NWaveNPerXdl,
-        CBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock>;
+        CBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
+        true,
+        true>;
 
-    using GridwiseGemmAtomicAdd = GridwiseGemm_bk0mk1_bk0nk1_mn_xdlops_v2r4r2<
+    using GridwiseGemmAtomicAdd = GridwiseGemm_bk0mk1_bk0nk1_mn_xdlops_bwd_weight<
         BlockSize,
         ADataType, // TODO: distinguish A/B datatype
         AccDataType,
@@ -274,6 +296,9 @@ struct DeviceConv2dBwdWeightXdl_C_Shuffle_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_
         ABlockTransferDstScalarPerVector_K1,
         false, // AThreadTransferSrcResetCoordinateAfterRun,
         ABlockLdsAddExtraM,
+        ABlockLdsM1PerBlock,
+        ABlockLdsM0PerBlock,
+        ABlockLdsM1Padding,
         BBlockTransferThreadClusterLengths_K0_N_K1,
         BBlockTransferThreadClusterArrangeOrder,
         BBlockTransferSrcAccessOrder,
@@ -282,10 +307,15 @@ struct DeviceConv2dBwdWeightXdl_C_Shuffle_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_
         BBlockTransferDstScalarPerVector_K1,
         false, // BThreadTransferSrcResetCoordinateAfterRun,
         BBlockLdsAddExtraN,
+        BBlockLdsN1PerBlock,
+        BBlockLdsN0PerBlock,
+        BBlockLdsN1Padding,
         CShuffleMXdlPerWavePerShuffle,
         CShuffleNXdlPerWavePerShuffle,
         CBlockTransferScalarPerVector_NWaveNPerXdl,
-        CBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock>;
+        CBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
+        true,
+        true>;
     // Argument
     using CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock =
         decltype(GridwiseGemm::MakeCGridDesc_MBlock_MPerBlock_NBlock_NPerBlock(CGridDesc_M_N{}));
@@ -465,7 +495,7 @@ struct DeviceConv2dBwdWeightXdl_C_Shuffle_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_
             {
                 if(kbatch == 1)
                 {
-                    const auto kernel = kernel_gemm_xdlops_v2r4r2<
+                    const auto kernel = kernel_gemm_xdlops_bwd_weight<
                         GridwiseGemm,
                         ADataType, // TODO: distiguish A/B datatype
                         CDataType,
@@ -482,7 +512,7 @@ struct DeviceConv2dBwdWeightXdl_C_Shuffle_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_
                 }
                 else
                 {
-                    const auto kernel = kernel_gemm_xdlops_v2r4r2<
+                    const auto kernel = kernel_gemm_xdlops_bwd_weight<
                         GridwiseGemmAtomicAdd,
                         ADataType, // TODO: distiguish A/B datatype
                         CDataType,
@@ -502,7 +532,7 @@ struct DeviceConv2dBwdWeightXdl_C_Shuffle_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_
             {
                 if(kbatch == 1)
                 {
-                    const auto kernel = kernel_gemm_xdlops_v2r4r2<
+                    const auto kernel = kernel_gemm_xdlops_bwd_weight<
                         GridwiseGemm,
                         ADataType, // TODO: distiguish A/B datatype
                         CDataType,
@@ -519,7 +549,7 @@ struct DeviceConv2dBwdWeightXdl_C_Shuffle_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_
                 }
                 else
                 {
-                    const auto kernel = kernel_gemm_xdlops_v2r4r2<
+                    const auto kernel = kernel_gemm_xdlops_bwd_weight<
                         GridwiseGemmAtomicAdd,
                         ADataType, // TODO: distiguish A/B datatype
                         CDataType,

include/ck/tensor_operation/gpu/grid/gridwise_gemm_xdlops_bwd_weight.hpp

+#pragma once
+
+#include "common_header.hpp"
+#include "multi_index_transform_helper.hpp"
+#include "tensor_descriptor.hpp"
+#include "tensor_descriptor_helper.hpp"
+#include "blockwise_gemm_xdlops.hpp"
+#include "thread_group_tensor_slice_transfer_v4r1.hpp"
+#include "thread_group_tensor_slice_transfer_v6r1.hpp"
+#include "threadwise_tensor_slice_transfer.hpp"
+
+namespace ck {
+
+// Implementation of "Merge" transformation primitive that uses division and mod. It is supposed to
+// be used for low_lengths that are known at compile time and are power of 2, otherwise performance
+// will be very bad
+template <typename LowLengths>
+struct Merge_v4_no_carry
+{
+    static constexpr index_t NDimLow = LowLengths::Size();
+
+    using LowerIndex = MultiIndex<NDimLow>;
+    using UpperIndex = MultiIndex<1>;
+
+    using LowLengthsScan =
+        decltype(container_reverse_exclusive_scan(LowLengths{}, math::multiplies{}, Number<1>{}));
+
+    using UpLengths =
+        decltype(make_tuple(container_reduce(LowLengths{}, math::multiplies{}, Number<1>{})));
+
+    LowLengths low_lengths_;
+    LowLengthsScan low_lengths_scan_;
+    UpLengths up_lengths_;
+
+    __host__ __device__ constexpr Merge_v4_no_carry() = default;
+
+    __host__ __device__ constexpr Merge_v4_no_carry(const LowLengths& low_lengths)
+        : low_lengths_{low_lengths},
+          low_lengths_scan_{
+              container_reverse_exclusive_scan(low_lengths, math::multiplies{}, Number<1>{})},
+          up_lengths_{make_tuple(container_reduce(low_lengths, math::multiplies{}, Number<1>{}))}
+    {
+        static_assert(LowerIndex::Size() == NDimLow, "wrong!");
+    }
+
+    __host__ __device__ static constexpr index_t GetNumOfLowerDimension() { return NDimLow; }
+
+    __host__ __device__ static constexpr index_t GetNumOfUpperDimension() { return 1; }
+
+    __host__ __device__ constexpr const auto& GetUpperLengths() const { return up_lengths_; }
+
+    template <typename LowIdx, typename UpIdx>
+    __host__ __device__ constexpr void CalculateLowerIndex(LowIdx& idx_low,
+                                                           const UpIdx& idx_up) const
+    {
+        static_assert(LowIdx::Size() == NDimLow && UpIdx::Size() == 1,
+                      "wrong! inconsistent # of dimension");
+
+        index_t tmp = idx_up[Number<0>{}];
+
+        // division and mod
+        static_for<0, NDimLow - 1, 1>{}([&](auto i) {
+            idx_low(i) = tmp / this->low_lengths_scan_[i];
+            tmp %= this->low_lengths_scan_[i];
+        });
+
+        idx_low(Number<NDimLow - 1>{}) = tmp;
+    }
+
+    template <typename LowIdxDiff,
+              typename UpIdxDiff,
+              typename LowIdx,
+              typename UpIdx,
+              index_t Hack>
+    __host__ __device__ void UpdateLowerIndex(LowIdxDiff& idx_diff_low,
+                                              const UpIdxDiff& idx_up_diff,
+                                              LowIdx& idx_low,
+                                              const UpIdx& idx_up_new,
+                                              Number<Hack>) const
+    {
+        static_assert(LowIdxDiff::Size() == NDimLow && UpIdxDiff::Size() == 1 &&
+                          LowIdx::Size() == NDimLow && UpIdx::Size() == 1,
+                      "wrong! inconsistent # of dimension");
+
+        constexpr auto I0   = Number<0>{};
+        constexpr auto INm1 = Number<NDimLow - 1>{};
+
+        index_t tmp = idx_up_new[I0];
+
+        idx_low(INm1)      = tmp;
+        idx_diff_low(INm1) = idx_up_diff[I0];
+    }
+
+    __host__ __device__ static constexpr bool IsLinearTransform() { return false; }
+
+    __host__ __device__ static constexpr bool IsValidUpperIndexAlwaysMappedToValidLowerIndex()
+    {
+        return true;
+    }
+
+    __host__ __device__ static constexpr bool IsKnownAtCompileTime()
+    {
+        return is_known_at_compile_time<LowLengths>::value &&
+               is_known_at_compile_time<LowLengthsScan>::value &&
+               is_known_at_compile_time<UpLengths>::value;
+    }
+
+    template <typename UpIdx>
+    __host__ __device__ static constexpr bool
+    IsValidUpperIndexMappedToValidLowerIndex(const UpIdx& /* idx_up */)
+    {
+        return true;
+    }
+
+    __host__ __device__ void Print() const
+    {
+        printf("{");
+        printf("Merge_v3_direct_division_mod_wrw, ");
+        printf("low_lengths_ ");
+        print_multi_index(low_lengths_);
+        printf("low_lengths_scan_ ");
+        print_multi_index(low_lengths_scan_);
+        printf("up_lengths_ ");
+        print_multi_index(up_lengths_);
+        printf("}");
+    }
+};
+
+template <typename LowLengths>
+__host__ __device__ constexpr auto make_merge_transform_v4_no_carry(const LowLengths& low_lengths)
+{
+    return Merge_v4_no_carry<LowLengths>{low_lengths};
+}
+
+template <typename GridwiseGemm,
+          typename FloatAB,
+          typename FloatC,
+          typename AGridDesc_B_K0_M_K1,
+          typename BGridDesc_B_K0_N_K1,
+          typename CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock,
+          typename AElementwiseOperation,
+          typename BElementwiseOperation,
+          typename CElementwiseOperation,
+          typename CBlockClusterAdaptor,
+          bool HasMainKBlockLoop>
+__global__ void
+#if CK_USE_LAUNCH_BOUNDS
+    __launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
+#endif
+        kernel_gemm_xdlops_bwd_weight(const FloatAB* __restrict__ p_a_grid,
+                                      const FloatAB* __restrict__ p_b_grid,
+                                      FloatC* __restrict__ p_c_grid,
+                                      const AGridDesc_B_K0_M_K1 a_b_k0_m_k1_grid_desc,
+                                      const BGridDesc_B_K0_N_K1 b_b_k0_n_k1_grid_desc,
+                                      const CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock
+                                          c_grid_desc_mblock_mperblock_nblock_nperblock,
+                                      const AElementwiseOperation a_element_op,
+                                      const BElementwiseOperation b_element_op,
+                                      const CElementwiseOperation c_element_op,
+                                      const CBlockClusterAdaptor c_block_cluster_adaptor)
+{
+#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx908__) || defined(__gfx90a__))
+    constexpr index_t shared_block_size =
+        GridwiseGemm::GetSharedMemoryNumberOfByte() / sizeof(FloatAB);
+
+    __shared__ FloatAB p_shared_block[shared_block_size];
+
+    GridwiseGemm::template Run<HasMainKBlockLoop>(p_a_grid,
+                                                  p_b_grid,
+                                                  p_c_grid,
+                                                  p_shared_block,
+                                                  a_b_k0_m_k1_grid_desc,
+                                                  b_b_k0_n_k1_grid_desc,
+                                                  c_grid_desc_mblock_mperblock_nblock_nperblock,
+                                                  a_element_op,
+                                                  b_element_op,
+                                                  c_element_op,
+                                                  c_block_cluster_adaptor);
+#else
+    ignore = p_a_grid;
+    ignore = p_b_grid;
+    ignore = p_c_grid;
+    ignore = a_b_k0_m_k1_grid_desc;
+    ignore = b_b_k0_n_k1_grid_desc;
+    ignore = c_grid_desc_mblock_mperblock_nblock_nperblock;
+    ignore = a_element_op;
+    ignore = b_element_op;
+    ignore = c_element_op;
+    ignore = c_block_cluster_adaptor;
+#endif // end of if (defined(__gfx908__) || defined(__gfx90a__))
+}
+
+template <index_t BlockSize,
+          typename FloatAB,
+          typename FloatAcc,
+          typename FloatC,
+          InMemoryDataOperationEnum CGlobalMemoryDataOperation,
+          typename AGridDesc_B_K0_M_K1,
+          typename BGridDesc_B_K0_N_K1,
+          typename CMNGridDesc,
+          typename AElementwiseOperation,
+          typename BElementwiseOperation,
+          typename CElementwiseOperation,
+          index_t MPerBlock,
+          index_t NPerBlock,
+          index_t K0PerBlock,
+          index_t MPerXDL,
+          index_t NPerXDL,
+          index_t K1Value,
+          index_t MRepeat,
+          index_t NRepeat,
+          typename ABlockTransferThreadClusterLengths_K0_M_K1,
+          typename ABlockTransferThreadClusterArrangeOrder,
+          typename ABlockTransferSrcAccessOrder,
+          index_t ABlockTransferSrcVectorDim,
+          index_t ABlockTransferSrcScalarPerVector,
+          index_t ABlockTransferDstScalarPerVector_K1,
+          bool AThreadTransferSrcResetCoordinateAfterRun,
+          bool ABlockLdsExtraM,
+          index_t ABlockLdsM1PerBlock,
+          index_t ABlockLdsM0PerBlock,
+          index_t ABlockLdsM1Padding,
+          typename BBlockTransferThreadClusterLengths_K0_N_K1,
+          typename BBlockTransferThreadClusterArrangeOrder,
+          typename BBlockTransferSrcAccessOrder,
+          index_t BBlockTransferSrcVectorDim,
+          index_t BBlockTransferSrcScalarPerVector,
+          index_t BBlockTransferDstScalarPerVector_K1,
+          bool BThreadTransferSrcResetCoordinateAfterRun,
+          bool BBlockLdsExtraN,
+          index_t BBlockLdsN1PerBlock,
+          index_t BBlockLdsN0PerBlock,
+          index_t BBlockLdsN1Padding,
+          index_t CShuffleMRepeatPerShuffle,
+          index_t CShuffleNRepeatPerShuffle,
+          index_t CBlockTransferScalarPerVector_NWaveNPerXDL,
+          typename CBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
+          bool ABlockLdsExtraM1Wrw = false,
+          bool BBlockLdsExtraN1Wrw = false>
+struct GridwiseGemm_bk0mk1_bk0nk1_mn_xdlops_bwd_weight
+{
+    static constexpr auto I0 = Number<0>{};
+    static constexpr auto I1 = Number<1>{};
+    static constexpr auto I2 = Number<2>{};
+    static constexpr auto I3 = Number<3>{};
+    static constexpr auto I4 = Number<4>{};
+    static constexpr auto I5 = Number<5>{};
+    static constexpr auto I6 = Number<6>{};
+    static constexpr auto I7 = Number<7>{};
+
+    // K1 should be Number<...>
+    static constexpr auto K1 = Number<K1Value>{};
+
+    using ThisThreadBlock = ThisThreadBlock<BlockSize>;
+
+    // M0/M1/M1Padding
+    static constexpr auto M1PerBlock = Number<ABlockLdsM1PerBlock>{};
+    static constexpr auto M0PerBlock = Number<ABlockLdsM0PerBlock>{};
+    static constexpr auto M1Padding  = Number<ABlockLdsM1Padding>{};
+
+    // N0/N1/N1Padding
+    static constexpr auto N1PerBlock = Number<BBlockLdsN1PerBlock>{};
+    static constexpr auto N0PerBlock = Number<BBlockLdsN0PerBlock>{};
+    static constexpr auto N1Padding  = Number<BBlockLdsN1Padding>{};
+
+    __host__ __device__ static constexpr auto GetABlockDescriptor_K0PerBlock_MPerBlock_K1()
+    {
+        constexpr auto max_lds_align = K1;
+
+        // A matrix in LDS memory, dst of blockwise copy
+        constexpr auto a_block_desc_k0_m_k1 = [&]() {
+            if constexpr(ABlockLdsExtraM)
+            {
+                if constexpr(ABlockLdsExtraM1Wrw)
+                {
+                    constexpr auto a_block_desc_k0_m0_m1_k1 = make_naive_tensor_descriptor(
+                        make_tuple(
+                            Number<K0PerBlock>{}, Number<M0PerBlock>{}, Number<M1PerBlock>{}, K1),
+                        make_tuple(Number<M0PerBlock>{} * (Number<M1PerBlock>{} * K1 + M1Padding),
+                                   Number<M1PerBlock>{} * K1 + M1Padding,
+                                   K1,
+                                   I1));
+
+                    constexpr auto a_block_desc_k0_m_k1_tmp = transform_tensor_descriptor(
+                        a_block_desc_k0_m0_m1_k1,
+                        make_tuple(make_pass_through_transform(Number<K0PerBlock>{}),
+                                   make_merge_transform_v3_division_mod(
+                                       make_tuple(Number<M0PerBlock>{}, Number<M1PerBlock>{})),
+                                   make_pass_through_transform(K1)),
+                        make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3>{}),
+                        make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
+
+                    return a_block_desc_k0_m_k1_tmp;
+                }
+                else
+                {
+                    return make_naive_tensor_descriptor(
+                        make_tuple(Number<K0PerBlock>{}, Number<MPerBlock>{}, K1),
+                        make_tuple(Number<MPerBlock + 1>{} * K1, K1, I1));
+                }
+            }
+            else
+            {
+                return make_naive_tensor_descriptor_aligned(
+                    make_tuple(Number<K0PerBlock>{}, Number<MPerBlock>{}, K1), max_lds_align);
+            }
+        }();
+
+        return a_block_desc_k0_m_k1;
+    }
+
+    __host__ __device__ static constexpr auto GetABlockDescriptor_Batch_K0PerBlock_MPerBlock_K1()
+    {
+        constexpr auto max_lds_align = K1;
+
+        // A matrix in LDS memory, dst of blockwise copy
+        constexpr auto a_block_desc_b_k0_m_k1 = [&]() {
+            if constexpr(ABlockLdsExtraM)
+            {
+                if constexpr(ABlockLdsExtraM1Wrw)
+                {
+                    constexpr auto a_block_desc_b_k0_m0_m1_k1 = make_naive_tensor_descriptor(
+                        make_tuple(Number<1>{},
+                                   Number<K0PerBlock>{},
+                                   Number<M0PerBlock>{},
+                                   Number<M1PerBlock>{},
+                                   K1),
+                        make_tuple(Number<K0PerBlock>{} * Number<M0PerBlock>{} *
+                                       (Number<M1PerBlock>{} * K1 + M1Padding),
+                                   Number<M0PerBlock>{} * (Number<M1PerBlock>{} * K1 + M1Padding),
+                                   Number<M1PerBlock>{} * K1 + M1Padding,
+                                   K1,
+                                   I1));
+
+                    constexpr auto a_block_desc_b_k0_m_k1_tmp = transform_tensor_descriptor(
+                        a_block_desc_b_k0_m0_m1_k1,
+                        make_tuple(make_pass_through_transform(Number<1>{}),
+                                   make_pass_through_transform(Number<K0PerBlock>{}),
+                                   make_merge_transform_v4_no_carry(
+                                       make_tuple(Number<M0PerBlock>{}, Number<M1PerBlock>{})),
+                                   make_pass_through_transform(K1)),
+                        make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2, 3>{}, Sequence<4>{}),
+                        make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
+
+                    return a_block_desc_b_k0_m_k1_tmp;
+                }
+                else
+                {
+                    return make_naive_tensor_descriptor(
+                        make_tuple(Number<1>{}, Number<K0PerBlock>{}, Number<MPerBlock>{}, K1),
+                        make_tuple(Number<K0PerBlock>{} * Number<MPerBlock + 1>{} * K1,
+                                   Number<MPerBlock + 1>{} * K1,
+                                   K1,
+                                   I1));
+                }
+            }
+            else
+            {
+                return make_naive_tensor_descriptor_aligned(
+                    make_tuple(Number<1>{}, Number<K0PerBlock>{}, Number<MPerBlock>{}, K1),
+                    max_lds_align);
+            }
+        }();
+
+        return a_block_desc_b_k0_m_k1;
+    }
+
+    __host__ __device__ static constexpr auto GetBBlockDescriptor_K0PerBlock_NPerBlock_K1()
+    {
+        constexpr auto max_lds_align = K1;
+
+        // B matrix in LDS memory, dst of blockwise copy
+        constexpr auto b_block_desc_k0_n_k1 = [&]() {
+            if constexpr(BBlockLdsExtraN)
+            {
+                if constexpr(BBlockLdsExtraN1Wrw)
+                {
+                    constexpr auto b_block_desc_k0_n0_n1_k1 = make_naive_tensor_descriptor(
+                        make_tuple(
+                            Number<K0PerBlock>{}, Number<N0PerBlock>{}, Number<N1PerBlock>{}, K1),
+                        make_tuple(Number<N0PerBlock>{} * (Number<N1PerBlock>{} * K1 + N1Padding),
+                                   Number<N1PerBlock>{} * K1 + N1Padding,
+                                   K1,
+                                   I1));
+
+                    constexpr auto b_block_desc_k0_n_k1_tmp = transform_tensor_descriptor(
+                        b_block_desc_k0_n0_n1_k1,
+                        make_tuple(make_pass_through_transform(Number<K0PerBlock>{}),
+                                   make_merge_transform_v3_division_mod(
+                                       make_tuple(Number<N0PerBlock>{}, Number<N1PerBlock>{})),
+                                   make_pass_through_transform(K1)),
+                        make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3>{}),
+                        make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
+
+                    return b_block_desc_k0_n_k1_tmp;
+                }
+                else
+                {
+                    return make_naive_tensor_descriptor(
+                        make_tuple(Number<K0PerBlock>{}, Number<NPerBlock>{}, K1),
+                        make_tuple(Number<NPerBlock + 1>{} * K1, K1, I1));
+                }
+            }
+            else
+            {
+                return make_naive_tensor_descriptor_aligned(
+                    make_tuple(Number<K0PerBlock>{}, Number<NPerBlock>{}, K1), max_lds_align);
+            }
+        }();
+
+        return b_block_desc_k0_n_k1;
+    }
+
+    __host__ __device__ static constexpr auto GetBBlockDescriptor_Batch_K0PerBlock_NPerBlock_K1()
+    {
+        constexpr auto max_lds_align = K1;
+
+        // B matrix in LDS memory, dst of blockwise copy
+        constexpr auto b_block_desc_b_k0_n_k1 = [&]() {
+            if constexpr(BBlockLdsExtraN)
+            {
+                if constexpr(BBlockLdsExtraN1Wrw)
+                {
+                    constexpr auto b_block_desc_b_k0_n0_n1_k1 = make_naive_tensor_descriptor(
+                        make_tuple(Number<1>{},
+                                   Number<K0PerBlock>{},
+                                   Number<N0PerBlock>{},
+                                   Number<N1PerBlock>{},
+                                   K1),
+                        make_tuple(Number<K0PerBlock>{} * Number<N0PerBlock>{} *
+                                       (Number<N1PerBlock>{} * K1 + N1Padding),
+                                   Number<N0PerBlock>{} * (Number<N1PerBlock>{} * K1 + N1Padding),
+                                   Number<N1PerBlock>{} * K1 + N1Padding,
+                                   K1,
+                                   I1));
+
+                    constexpr auto b_block_desc_b_k0_n_k1_tmp = transform_tensor_descriptor(
+                        b_block_desc_b_k0_n0_n1_k1,
+                        make_tuple(make_pass_through_transform(Number<1>{}),
+                                   make_pass_through_transform(Number<K0PerBlock>{}),
+                                   make_merge_transform_v4_no_carry(
+                                       make_tuple(Number<N0PerBlock>{}, Number<N1PerBlock>{})),
+                                   make_pass_through_transform(K1)),
+                        make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2, 3>{}, Sequence<4>{}),
+                        make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
+
+                    return b_block_desc_b_k0_n_k1_tmp;
+                }
+                else
+                {
+                    return make_naive_tensor_descriptor(
+                        make_tuple(Number<1>{}, Number<K0PerBlock>{}, Number<NPerBlock>{}, K1),
+                        make_tuple(Number<K0PerBlock>{} * Number<NPerBlock + 1>{} * K1,
+                                   Number<NPerBlock + 1>{} * K1,
+                                   K1,
+                                   I1));
+                }
+            }
+            else
+            {
+                return make_naive_tensor_descriptor_aligned(
+                    make_tuple(Number<1>{}, Number<K0PerBlock>{}, Number<NPerBlock>{}, K1),
+                    max_lds_align);
+            }
+        }();
+
+        return b_block_desc_b_k0_n_k1;
+    }
+
+    __host__ __device__ static constexpr index_t GetSharedMemoryNumberOfByte()
+    {
+        constexpr auto max_lds_align = K1;
+
+        // A matrix in LDS memory, dst of blockwise copy
+        constexpr auto a_b_k0_m_k1_block_desc = GetABlockDescriptor_Batch_K0PerBlock_MPerBlock_K1();
+
+        // B matrix in LDS memory, dst of blockwise copy
+        constexpr auto b_b_k0_n_k1_block_desc = GetBBlockDescriptor_Batch_K0PerBlock_NPerBlock_K1();
+
+        // LDS allocation for A and B: be careful of alignment
+        constexpr auto a_block_space_size = math::integer_least_multiple(
+            a_b_k0_m_k1_block_desc.GetElementSpaceSize(), max_lds_align);
+
+        constexpr auto b_block_space_size = math::integer_least_multiple(
+            b_b_k0_n_k1_block_desc.GetElementSpaceSize(), max_lds_align);
+
+        constexpr auto c_block_size =
+            GetCBlockDescriptor_MBlock_MPerBlock_NBlock_NPerBlock().GetElementSpaceSize();
+
+        return math::max((a_block_space_size + b_block_space_size) * sizeof(FloatAB),
+                         c_block_size * sizeof(FloatC));
+    }
+
+    // block_id to matrix tile idx (m0, n0) mapping are controlled by {M01, N01}
+    __host__ __device__ static constexpr bool
+    CheckValidity(const AGridDesc_B_K0_M_K1& a_b_k0_m_k1_grid_desc,
+                  const BGridDesc_B_K0_N_K1& b_b_k0_n_k1_grid_desc,
+                  const CMNGridDesc& c_m_n_grid_desc,
+                  index_t M01,
+                  index_t N01)
+    {
+        static_assert(is_known_at_compile_time<remove_cv_t<decltype(K1)>>::value,
+                      "wrong! K1 need to be known at compile-time");
+
+        static_assert((MPerBlock % (MPerXDL * MRepeat) == 0) &&
+                          (NPerBlock % (NRepeat * NPerXDL)) == 0,
+                      "Invalid tuning param!");
+
+        const auto M      = a_b_k0_m_k1_grid_desc.GetLength(I2);
+        const auto N      = b_b_k0_n_k1_grid_desc.GetLength(I2);
+        const auto K0     = a_b_k0_m_k1_grid_desc.GetLength(I1);
+        const auto KBatch = a_b_k0_m_k1_grid_desc.GetLength(I0);
+
+        if(!(M == c_m_n_grid_desc.GetLength(I0) && N == c_m_n_grid_desc.GetLength(I1) &&
+             K0 == b_b_k0_n_k1_grid_desc.GetLength(I1) &&
+             K1 == a_b_k0_m_k1_grid_desc.GetLength(I3) &&
+             K1 == b_b_k0_n_k1_grid_desc.GetLength(I3) &&
+             KBatch == b_b_k0_n_k1_grid_desc.GetLength(I0)))
+            return false;
+
+        if(!(M % MPerBlock == 0 && N % NPerBlock == 0 && K0 % K0PerBlock == 0))
+            return false;
+
+        // check M01, N01
+        constexpr auto M1 = Number<MPerBlock>{};
+        constexpr auto N1 = Number<NPerBlock>{};
+
+        const auto M0 = M / M1;
+        const auto N0 = N / N1;
+
+        if(!(M0 % M01 == 0 && N0 % N01 == 0))
+            return false;
+
+        // TODO: also check validity of all components (blockwise-copy, threadwise-copy, etc)
+        return true;
+    }
+
+    __host__ __device__ static constexpr index_t
+    CalculateGridSize(const CMNGridDesc& c_m_n_grid_desc, index_t KBatch)
+    {
+        const auto M = c_m_n_grid_desc.GetLength(I0);
+        const auto N = c_m_n_grid_desc.GetLength(I1);
+
+        const index_t grid_size = (M / MPerBlock) * (N / NPerBlock) * KBatch;
+
+        return grid_size;
+    }
+
+    __host__ __device__ static constexpr bool CalculateHasMainK0BlockLoop(index_t K0)
+    {
+        const bool has_main_k0_block_loop = K0 > K0PerBlock;
+
+        return has_main_k0_block_loop;
+    }
+
+    __host__ __device__ static constexpr auto
+    MakeCGridDesc_MBlock_MPerBlock_NBlock_NPerBlock(const CMNGridDesc& c_m_n_grid_desc)
+    {
+        const auto M = c_m_n_grid_desc.GetLength(I0);
+        const auto N = c_m_n_grid_desc.GetLength(I1);
+
+        const auto MBlock = M / MPerBlock;
+        const auto NBlock = N / NPerBlock;
+
+        return transform_tensor_descriptor(
+            c_m_n_grid_desc,
+            make_tuple(make_unmerge_transform(make_tuple(MBlock, Number<MPerBlock>{})),
+                       make_unmerge_transform(make_tuple(NBlock, Number<NPerBlock>{}))),
+            make_tuple(Sequence<0>{}, Sequence<1>{}),
+            make_tuple(Sequence<0, 1>{}, Sequence<2, 3>{}));
+    }
+
+    // return block_id to C matrix tile idx (m0, n0) mapping
+    __host__ __device__ static constexpr auto MakeCBlockClusterAdaptor(
+        const CMNGridDesc& c_m_n_grid_desc, index_t M01, index_t N01, index_t KBatch)
+    {
+        const auto M = c_m_n_grid_desc.GetLength(I0);
+        const auto N = c_m_n_grid_desc.GetLength(I1);
+
+        constexpr auto M1 = Number<MPerBlock>{};
+        constexpr auto N1 = Number<NPerBlock>{};
+
+        const auto M0 = M / M1;
+        const auto N0 = N / N1;
+
+        const auto M00 = M0 / M01;
+        const auto N00 = N0 / N01;
+
+        const auto kbatch_m00_m01_n00_n01_to_m0_n0_block_cluster_adaptor =
+            make_single_stage_tensor_adaptor(
+                make_tuple(make_pass_through_transform(KBatch),
+                           make_unmerge_transform(make_tuple(M00, M01)),
+                           make_unmerge_transform(make_tuple(N00, N01))),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
+                make_tuple(Sequence<0>{}, Sequence<1, 3>{}, Sequence<2, 4>{}));
+
+        const auto c_blockid_to_kbatch_m00_m01_n00_n01_block_cluster_adaptor =
+            make_single_stage_tensor_adaptor(
+                make_tuple(make_merge_transform(make_tuple(KBatch, M00, N00, M01, N01))),
+                make_tuple(Sequence<0, 1, 2, 3, 4>{}),
+                make_tuple(Sequence<0>{}));
+
+        const auto c_blockid_to_kbatch_m0_n0_block_cluster_adaptor =
+            chain_tensor_adaptors(kbatch_m00_m01_n00_n01_to_m0_n0_block_cluster_adaptor,
+                                  c_blockid_to_kbatch_m00_m01_n00_n01_block_cluster_adaptor);
+
+        return c_blockid_to_kbatch_m0_n0_block_cluster_adaptor;
+    }
+
+    __host__ __device__ static constexpr auto
+    GetCBlockDescriptor_MBlock_MPerBlock_NBlock_NPerBlock()
+    {
+        constexpr index_t MWave = MPerBlock / (MRepeat * MPerXDL);
+        constexpr index_t NWave = NPerBlock / (NRepeat * NPerXDL);
+
+        return make_naive_tensor_descriptor_packed(
+            make_tuple(I1,
+                       Number<CShuffleMRepeatPerShuffle * MWave * MPerXDL>{},
+                       I1,
+                       Number<CShuffleNRepeatPerShuffle * NWave * NPerXDL>{}));
+    }
+
+    using CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock =
+        decltype(MakeCGridDesc_MBlock_MPerBlock_NBlock_NPerBlock(CMNGridDesc{}));
+    using CBlockClusterAdaptor = decltype(MakeCBlockClusterAdaptor(CMNGridDesc{}, 1, 1, 1));
+
+    template <bool HasMainKBlockLoop>
+    __device__ static void Run(const FloatAB* __restrict__ p_a_grid,
+                               const FloatAB* __restrict__ p_b_grid,
+                               FloatC* __restrict__ p_c_grid,
+                               FloatAB* __restrict__ p_shared_block,
+                               const AGridDesc_B_K0_M_K1& a_b_k0_m_k1_grid_desc,
+                               const BGridDesc_B_K0_N_K1& b_b_k0_n_k1_grid_desc,
+                               const CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock&
+                                   c_grid_desc_mblock_mperblock_nblock_nperblock,
+                               const AElementwiseOperation& a_element_op,
+                               const BElementwiseOperation& b_element_op,
+                               const CElementwiseOperation& c_element_op,
+                               const CBlockClusterAdaptor& c_block_cluster_adaptor)
+    {
+        const auto a_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
+            p_a_grid, a_b_k0_m_k1_grid_desc.GetElementSpaceSize());
+        const auto b_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
+            p_b_grid, b_b_k0_n_k1_grid_desc.GetElementSpaceSize());
+        auto c_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
+            p_c_grid, c_grid_desc_mblock_mperblock_nblock_nperblock.GetElementSpaceSize());
+
+        const auto K0 = a_b_k0_m_k1_grid_desc.GetLength(I1);
+
+        // divide block work by [M, N]
+        const auto block_work_idx =
+            c_block_cluster_adaptor.CalculateBottomIndex(make_multi_index(get_block_1d_id()));
+
+        const index_t k_batch_id = block_work_idx[I0];
+
+        // HACK: this force m/n_block_data_idx_on_grid into SGPR
+        const index_t m_block_data_idx_on_grid =
+            __builtin_amdgcn_readfirstlane(block_work_idx[I1] * MPerBlock);
+
+        const index_t n_block_data_idx_on_grid =
+            __builtin_amdgcn_readfirstlane(block_work_idx[I2] * NPerBlock);
+
+        // lds max alignment
+        constexpr auto max_lds_align = K1;
+
+        // A matrix in LDS memory, dst of blockwise copy
+        constexpr auto a_k0_m_k1_block_desc = GetABlockDescriptor_K0PerBlock_MPerBlock_K1();
+
+        constexpr auto a_b_k0_m_k1_block_desc = GetABlockDescriptor_Batch_K0PerBlock_MPerBlock_K1();
+        // B matrix in LDS memory, dst of blockwise copy
+        constexpr auto b_k0_n_k1_block_desc = GetBBlockDescriptor_K0PerBlock_NPerBlock_K1();
+
+        constexpr auto b_b_k0_n_k1_block_desc = GetBBlockDescriptor_Batch_K0PerBlock_NPerBlock_K1();
+        // A matrix blockwise copy
+        auto a_blockwise_copy =
+            ThreadGroupTensorSliceTransfer_v4r1<ThisThreadBlock,
+                                                AElementwiseOperation,
+                                                ck::tensor_operation::element_wise::PassThrough,
+                                                InMemoryDataOperationEnum::Set,
+                                                Sequence<1, K0PerBlock, MPerBlock, K1>,
+                                                ABlockTransferThreadClusterLengths_K0_M_K1,
+                                                ABlockTransferThreadClusterArrangeOrder,
+                                                FloatAB,
+                                                FloatAB,
+                                                decltype(a_b_k0_m_k1_grid_desc),
+                                                decltype(a_b_k0_m_k1_block_desc),
+                                                ABlockTransferSrcAccessOrder,
+                                                Sequence<0, 2, 1, 3>,
+                                                ABlockTransferSrcVectorDim,
+                                                3,
+                                                ABlockTransferSrcScalarPerVector,
+                                                ABlockTransferDstScalarPerVector_K1,
+                                                1,
+                                                1,
+                                                AThreadTransferSrcResetCoordinateAfterRun,
+                                                true>(
+                a_b_k0_m_k1_grid_desc,
+                make_multi_index(k_batch_id, 0, m_block_data_idx_on_grid, 0),
+                a_element_op,
+                a_b_k0_m_k1_block_desc,
+                make_multi_index(0, 0, 0, 0),
+                ck::tensor_operation::element_wise::PassThrough{});
+
+        // B matrix blockwise copy
+        auto b_blockwise_copy =
+            ThreadGroupTensorSliceTransfer_v4r1<ThisThreadBlock,
+                                                BElementwiseOperation,
+                                                ck::tensor_operation::element_wise::PassThrough,
+                                                InMemoryDataOperationEnum::Set,
+                                                Sequence<1, K0PerBlock, NPerBlock, K1>,
+                                                BBlockTransferThreadClusterLengths_K0_N_K1,
+                                                BBlockTransferThreadClusterArrangeOrder,
+                                                FloatAB,
+                                                FloatAB,
+                                                decltype(b_b_k0_n_k1_grid_desc),
+                                                decltype(b_b_k0_n_k1_block_desc),
+                                                BBlockTransferSrcAccessOrder,
+                                                Sequence<0, 2, 1, 3>,
+                                                BBlockTransferSrcVectorDim,
+                                                3,
+                                                BBlockTransferSrcScalarPerVector,
+                                                BBlockTransferDstScalarPerVector_K1,
+                                                1,
+                                                1,
+                                                BThreadTransferSrcResetCoordinateAfterRun,
+                                                true>(
+                b_b_k0_n_k1_grid_desc,
+                make_multi_index(k_batch_id, 0, n_block_data_idx_on_grid, 0),
+                b_element_op,
+                b_b_k0_n_k1_block_desc,
+                make_multi_index(0, 0, 0, 0),
+                ck::tensor_operation::element_wise::PassThrough{});
+
+        // GEMM definition
+        //   c_mtx += transpose(a_mtx) * b_mtx
+        //     a_mtx[K0PerBlock, MPerBlock] is in LDS
+        //     b_mtx[K0PerBlock, NPerBlock] is in LDS
+        //     c_mtx[MPerBlock, NPerBlock] is distributed among threads, and saved in
+        //       register
+        // sanity check
+
+        constexpr index_t KPack =
+            math::max(K1, MfmaSelector<FloatAB, MPerXDL, NPerXDL>::selected_mfma.k_per_blk);
+
+        auto blockwise_gemm =
+            BlockwiseGemmXdlops_k0mk1_k0nk1_m0n0m1n1m2m3m4n2_v1<BlockSize,
+                                                                FloatAB,
+                                                                FloatAcc,
+                                                                decltype(a_k0_m_k1_block_desc),
+                                                                decltype(b_k0_n_k1_block_desc),
+                                                                MPerXDL,
+                                                                NPerXDL,
+                                                                MRepeat,
+                                                                NRepeat,
+                                                                KPack>{};
+
+        auto c_thread_buf = blockwise_gemm.GetCThreadBuffer();
+
+        // LDS allocation for A and B: be careful of alignment
+        constexpr auto a_block_space_size =
+            math::integer_least_multiple(a_k0_m_k1_block_desc.GetElementSpaceSize(), max_lds_align);
+
+        FloatAB* p_a_block = p_shared_block;
+        FloatAB* p_b_block = p_shared_block + a_block_space_size;
+
+        constexpr auto a_block_slice_copy_step = make_multi_index(0, K0PerBlock, 0, 0);
+        constexpr auto b_block_slice_copy_step = make_multi_index(0, K0PerBlock, 0, 0);
+
+        auto a_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
+            p_a_block, a_k0_m_k1_block_desc.GetElementSpaceSize());
+        auto b_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
+            p_b_block, b_k0_n_k1_block_desc.GetElementSpaceSize());
+
+        // preload data into LDS
+        {
+            a_blockwise_copy.RunRead(a_b_k0_m_k1_grid_desc, a_grid_buf);
+            b_blockwise_copy.RunRead(b_b_k0_n_k1_grid_desc, b_grid_buf);
+
+            a_blockwise_copy.RunWrite(a_b_k0_m_k1_block_desc, a_block_buf);
+            b_blockwise_copy.RunWrite(b_b_k0_n_k1_block_desc, b_block_buf);
+        }
+
+        // Initialize C
+        c_thread_buf.Clear();
+
+        // main body
+        if constexpr(HasMainKBlockLoop)
+        {
+            index_t k0_block_data_begin = 0;
+
+            do
+            {
+                a_blockwise_copy.MoveSrcSliceWindow(a_b_k0_m_k1_grid_desc, a_block_slice_copy_step);
+                b_blockwise_copy.MoveSrcSliceWindow(b_b_k0_n_k1_grid_desc, b_block_slice_copy_step);
+
+                a_blockwise_copy.RunRead(a_b_k0_m_k1_grid_desc, a_grid_buf);
+
+                block_sync_lds();
+
+                b_blockwise_copy.RunRead(b_b_k0_n_k1_grid_desc, b_grid_buf);
+
+                blockwise_gemm.Run(a_block_buf, b_block_buf, c_thread_buf);
+
+                block_sync_lds();
+
+                a_blockwise_copy.RunWrite(a_b_k0_m_k1_block_desc, a_block_buf);
+                b_blockwise_copy.RunWrite(b_b_k0_n_k1_block_desc, b_block_buf);
+
+                k0_block_data_begin += K0PerBlock;
+            } while(k0_block_data_begin < (K0 - K0PerBlock));
+        }
+
+        // tail
+        {
+            block_sync_lds();
+
+            blockwise_gemm.Run(a_block_buf, b_block_buf, c_thread_buf);
+        }
+
+        // output: register to global memory
+        {
+            constexpr index_t MWave = MPerBlock / (MRepeat * MPerXDL);
+            constexpr index_t NWave = NPerBlock / (NRepeat * NPerXDL);
+
+            constexpr auto c_m0_n0_m1_n1_m2_m3_m4_n2_block_desc =
+                blockwise_gemm.GetCBlockDescriptor_M0_N0_M1_N1_M2_M3_M4_N2();
+
+            constexpr auto c_m0_n0_m1_n1_m2_m3_m4_n2_thread_desc =
+                blockwise_gemm.GetCThreadDescriptor_M0_N0_M1_N1_M2_M3_M4_N2();
+
+            constexpr auto M0 = c_m0_n0_m1_n1_m2_m3_m4_n2_block_desc.GetLength(I0);
+            constexpr auto N0 = c_m0_n0_m1_n1_m2_m3_m4_n2_block_desc.GetLength(I1);
+            constexpr auto M1 = c_m0_n0_m1_n1_m2_m3_m4_n2_block_desc.GetLength(I2);
+            constexpr auto N1 = c_m0_n0_m1_n1_m2_m3_m4_n2_block_desc.GetLength(I3);
+            constexpr auto M2 = c_m0_n0_m1_n1_m2_m3_m4_n2_block_desc.GetLength(I4);
+            constexpr auto M3 = c_m0_n0_m1_n1_m2_m3_m4_n2_block_desc.GetLength(I5);
+            constexpr auto M4 = c_m0_n0_m1_n1_m2_m3_m4_n2_block_desc.GetLength(I6);
+            constexpr auto N2 = c_m0_n0_m1_n1_m2_m3_m4_n2_block_desc.GetLength(I7);
+
+            constexpr auto c_block_desc_mblock_mperblock_nblock_nperblock =
+                GetCBlockDescriptor_MBlock_MPerBlock_NBlock_NPerBlock();
+
+            auto c_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
+                static_cast<FloatC*>(p_shared_block),
+                c_block_desc_mblock_mperblock_nblock_nperblock.GetElementSpaceSize());
+
+            static_assert(M1 == MWave, "");
+            static_assert(N1 == NWave, "");
+            static_assert(M2 * M3 * M4 == MPerXDL, "");
+            static_assert(N2 == NPerXDL, "");
+
+            constexpr auto c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2 = transform_tensor_descriptor(
+                c_block_desc_mblock_mperblock_nblock_nperblock,
+                make_tuple(
+                    make_freeze_transform(I0), // freeze mblock
+                    make_unmerge_transform(make_tuple(CShuffleMRepeatPerShuffle,
+                                                      M1,
+                                                      M2,
+                                                      M3,
+                                                      M4)), // M1 = MWave, M2 * M3 * M4 = MPerXDL
+                    make_freeze_transform(I0),              // freeze nblock
+                    make_unmerge_transform(make_tuple(CShuffleNRepeatPerShuffle,
+                                                      N1,
+                                                      N2))), // M1 = MWave, M2 * M3 * M4 = MPerXDL
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
+                make_tuple(
+                    Sequence<>{}, Sequence<0, 2, 4, 5, 6>{}, Sequence<>{}, Sequence<1, 3, 7>{}));
+
+            // calculate origin of thread output tensor on global memory
+            //     blockwise GEMM c matrix starting index
+            const auto c_thread_mtx_on_block =
+                blockwise_gemm.CalculateCThreadOriginDataIndex(I0, I0, I0, I0);
+
+            const index_t m_thread_data_on_block = c_thread_mtx_on_block[I0];
+            const index_t n_thread_data_on_block = c_thread_mtx_on_block[I1];
+
+            const auto m_thread_data_on_block_to_m0_m1_m2_m3_m4_adaptor =
+                make_single_stage_tensor_adaptor(
+                    make_tuple(make_merge_transform(make_tuple(M0, M1, M2, M3, M4))),
+                    make_tuple(Sequence<0, 1, 2, 3, 4>{}),
+                    make_tuple(Sequence<0>{}));
+
+            const auto m_thread_data_on_block_idx =
+                m_thread_data_on_block_to_m0_m1_m2_m3_m4_adaptor.CalculateBottomIndex(
+                    make_multi_index(m_thread_data_on_block));
+
+            const auto n_thread_data_on_block_to_n0_n1_n2_adaptor =
+                make_single_stage_tensor_adaptor(
+                    make_tuple(make_merge_transform(make_tuple(N0, N1, N2))),
+                    make_tuple(Sequence<0, 1, 2>{}),
+                    make_tuple(Sequence<0>{}));
+
+            const auto n_thread_data_on_block_idx =
+                n_thread_data_on_block_to_n0_n1_n2_adaptor.CalculateBottomIndex(
+                    make_multi_index(n_thread_data_on_block));
+
+            // VGPR to LDS
+            auto c_thread_copy_vgpr_to_lds =
+                ThreadwiseTensorSliceTransfer_v1r3<FloatAcc,
+                                                   FloatC,
+                                                   decltype(c_m0_n0_m1_n1_m2_m3_m4_n2_thread_desc),
+                                                   decltype(c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2),
+                                                   ck::tensor_operation::element_wise::PassThrough,
+                                                   Sequence<CShuffleMRepeatPerShuffle,
+                                                            CShuffleNRepeatPerShuffle,
+                                                            I1,
+                                                            I1,
+                                                            M2,
+                                                            I1,
+                                                            M4,
+                                                            I1>,
+                                                   Sequence<0, 1, 2, 3, 4, 5, 6, 7>,
+                                                   7,
+                                                   1,
+                                                   InMemoryDataOperationEnum::Set,
+                                                   1,
+                                                   true>{
+                    c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2,
+                    make_multi_index(0,
+                                     0,
+                                     m_thread_data_on_block_idx[I1],
+                                     n_thread_data_on_block_idx[I1],
+                                     m_thread_data_on_block_idx[I2],
+                                     m_thread_data_on_block_idx[I3],
+                                     m_thread_data_on_block_idx[I4],
+                                     n_thread_data_on_block_idx[I2]),
+                    ck::tensor_operation::element_wise::PassThrough{}};
+
+            // LDS to global
+            auto c_block_copy_lds_to_global = ThreadGroupTensorSliceTransfer_v6r1<
+                ThisThreadBlock,            // index_t BlockSize,
+                CElementwiseOperation,      // ElementwiseOperation,
+                CGlobalMemoryDataOperation, // DstInMemOp,
+                Sequence<1,
+                         CShuffleMRepeatPerShuffle * MWave * MPerXDL,
+                         1,
+                         CShuffleNRepeatPerShuffle * NWave * NPerXDL>, // BlockSliceLengths,
+                CBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
+                Sequence<0, 1, 2, 3>, // typename ThreadClusterArrangeOrder,
+                FloatC,               // typename SrcData,
+                FloatC,               // typename DstData,
+                decltype(c_block_desc_mblock_mperblock_nblock_nperblock),
+                decltype(c_grid_desc_mblock_mperblock_nblock_nperblock),
+                Sequence<0, 1, 2, 3>,                       // typename DimAccessOrder,
+                3,                                          // index_t VectorDim,
+                CBlockTransferScalarPerVector_NWaveNPerXDL, // index_t ScalarPerVector,
+                true,  // bool ThreadTransferSrcResetCoordinateAfterRun,
+                false> // bool ThreadTransferDstResetCoordinateAfterRun
+                {c_block_desc_mblock_mperblock_nblock_nperblock,
+                 make_multi_index(0, 0, 0, 0),
+                 c_grid_desc_mblock_mperblock_nblock_nperblock,
+                 make_multi_index(block_work_idx[I1], 0, block_work_idx[I2], 0),
+                 c_element_op};
+
+            constexpr auto mxdlperwave_forward_step =
+                make_multi_index(0, CShuffleMRepeatPerShuffle * MWave * MPerXDL, 0, 0);
+            constexpr auto nxdlperwave_forward_step =
+                make_multi_index(0, 0, 0, CShuffleNRepeatPerShuffle * NWave * NPerXDL);
+            constexpr auto nxdlperwave_backward_step =
+                make_multi_index(0, 0, 0, -CShuffleNRepeatPerShuffle * NWave * NPerXDL);
+
+            static_for<0, MRepeat, CShuffleMRepeatPerShuffle>{}([&](auto mxdlperwave_iter) {
+                constexpr auto mxdlperwave = mxdlperwave_iter;
+
+                static_for<0, NRepeat, CShuffleNRepeatPerShuffle>{}([&](auto nxdlperwave_iter) {
+                    constexpr bool nxdlperwave_forward_sweep =
+                        (mxdlperwave % (2 * CShuffleMRepeatPerShuffle) == 0);
+
+                    constexpr index_t nxdlperwave_value =
+                        nxdlperwave_forward_sweep
+                            ? nxdlperwave_iter
+                            : (NRepeat - nxdlperwave_iter - CShuffleNRepeatPerShuffle);
+
+                    constexpr auto nxdlperwave = Number<nxdlperwave_value>{};
+
+                    // make sure it's safe to do ds_write
+                    block_sync_lds();
+
+                    // VGPR to LDS
+                    c_thread_copy_vgpr_to_lds.Run(
+                        c_m0_n0_m1_n1_m2_m3_m4_n2_thread_desc,
+                        make_tuple(mxdlperwave, nxdlperwave, I0, I0, I0, I0, I0, I0),
+                        c_thread_buf,
+                        c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2,
+                        c_block_buf);
+
+                    // make sure it's safe to do ds_read
+                    block_sync_lds();
+
+                    // LDS to global
+                    c_block_copy_lds_to_global.Run(c_block_desc_mblock_mperblock_nblock_nperblock,
+                                                   c_block_buf,
+                                                   c_grid_desc_mblock_mperblock_nblock_nperblock,
+                                                   c_grid_buf);
+
+                    // move on nxdlperwave dimension
+                    if constexpr(nxdlperwave_forward_sweep &&
+                                 (nxdlperwave < NRepeat - CShuffleNRepeatPerShuffle))
+                    {
+                        c_block_copy_lds_to_global.MoveDstSliceWindow(
+                            c_grid_desc_mblock_mperblock_nblock_nperblock,
+                            nxdlperwave_forward_step);
+                    }
+                    else if constexpr((!nxdlperwave_forward_sweep) && (nxdlperwave > 0))
+                    {
+                        c_block_copy_lds_to_global.MoveDstSliceWindow(
+                            c_grid_desc_mblock_mperblock_nblock_nperblock,
+                            nxdlperwave_backward_step);
+                    }
+                });
+
+                // move on mxdlperwave dimension
+                if constexpr(mxdlperwave < MRepeat - CShuffleMRepeatPerShuffle)
+                {
+                    c_block_copy_lds_to_global.MoveDstSliceWindow(
+                        c_grid_desc_mblock_mperblock_nblock_nperblock, mxdlperwave_forward_step);
+                }
+            });
+        }
+    }
+}; // namespace ck
+
+} // namespace ck