Merge branch 'develop' into amd-develop

example/ck_tile/remod.py

+import pathlib
+from pathlib import Path
+import subprocess
+import os
+import copy
+
+all_files = []
+for p in sorted(Path("./").rglob("*")):
+    if p.suffix in ['.hpp', '.cpp']:
+        all_files.append(pathlib.PurePath(p))
+            
+
+# formatting
+for x in all_files:
+    subprocess.Popen(f'dos2unix {str(x)}', shell=True)
+    cmd = f'clang-format-12 -style=file -i {str(x)}'
+    #for xp in x.parents:
+    #print(get_file_base(x))
+    subprocess.Popen(cmd, shell=True)
+
+#print(all_files)

include/ck/tensor_description/multi_index_transform.hpp

@@ -1951,4 +1951,89 @@ struct Modulo
         printf("}");
     }
 };
+
+template <typename LowLengths>
+struct Xor
+{
+    using LowerIndex = MultiIndex<2>;
+    using UpperIndex = MultiIndex<2>;
+
+    using UpLengths = LowLengths;
+
+    UpLengths up_lengths_;
+
+    __host__ __device__ constexpr Xor() : up_lengths_{} {}
+
+    __host__ __device__ constexpr Xor(const LowLengths& low_lengths) : up_lengths_{low_lengths} {}
+
+    __host__ __device__ static constexpr index_t GetNumOfLowerDimension() { return 2; }
+
+    __host__ __device__ static constexpr index_t GetNumOfUpperDimension() { return 2; }
+
+    __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() == 2 && UpIdx::Size() == 2,
+                      "wrong! inconsistent # of dimension");
+
+        idx_low(Number<0>{}) = idx_up[Number<0>{}];
+
+        idx_low(Number<1>{}) =
+            idx_up[Number<1>{}] ^ (idx_up[Number<0>{}] % up_lengths_[Number<1>{}]);
+    }
+
+    template <typename LowIdxDiff,
+              typename UpIdxDiff,
+              typename LowIdx,
+              typename UpIdx,
+              index_t Hack>
+    __host__ __device__ void UpdateLowerIndex(LowIdxDiff& idx_diff_low,
+                                              const UpIdxDiff&,
+                                              LowIdx& idx_low,
+                                              const UpIdx& idx_up,
+                                              Number<Hack>) const
+    {
+        static_assert(LowIdxDiff::Size() == 2 && UpIdxDiff::Size() == 2 && LowIdx::Size() == 2 &&
+                          UpIdx::Size() == 2,
+                      "wrong! inconsistent # of dimension");
+
+        const auto idx_low_old = idx_low;
+
+        CalculateLowerIndex(idx_low, idx_up);
+
+        idx_diff_low = idx_low - idx_low_old;
+    }
+
+    __host__ __device__ static constexpr bool IsValidUpperIndexAlwaysMappedToValidLowerIndex()
+    {
+        return true;
+    }
+
+    template <typename UpIdx>
+    __host__ __device__ static constexpr bool
+    IsValidUpperIndexMappedToValidLowerIndex(const UpIdx& /* idx_up */)
+    {
+        return true;
+    }
+
+    __host__ __device__ static constexpr bool IsKnownAtCompileTime()
+    {
+        return is_known_at_compile_time<UpLengths>::value;
+    }
+
+    __host__ __device__ void Print() const
+    {
+        printf("Xor{");
+
+        //
+        printf("up_lengths_: ");
+        print(up_lengths_);
+        printf(", ");
+
+        printf("}");
+    }
+};
 } // namespace ck

include/ck/tensor_description/multi_index_transform_helper.hpp

@@ -127,4 +127,10 @@ __host__ __device__ constexpr auto make_modulo_transform(const Modulus& modulus,
 {
     return Modulo<Modulus, UpLength>{modulus, up_length};
 }
+
+template <typename LowLengths>
+__host__ __device__ constexpr auto make_xor_transform(const LowLengths& low_lengths)
+{
+    return Xor<LowLengths>{low_lengths};
+}
 } // namespace ck

include/ck/tensor_operation/gpu/block/blockwise_gemm_pipeline_xdlops.hpp

@@ -960,13 +960,13 @@ struct BlockwiseGemmXdlops_pipeline_v4
     static constexpr auto a_thread_desc_ = make_naive_tensor_descriptor(
         make_tuple(Number<MRepeat>{}, I1, Number<KRepeat>{}, Number<KPack>{}),
         make_tuple(
-            Number<KPack>{}, Number<KPack * MRepeat * KPack>{}, Number<MRepeat * KPack>{}, I1));
+            Number<KPack>{}, Number<KRepeat * MRepeat * KPack>{}, Number<MRepeat * KPack>{}, I1));
 
     // B[N0, N1, N2, KPack]
     static constexpr auto b_thread_desc_ = make_naive_tensor_descriptor(
         make_tuple(Number<NRepeat>{}, I1, Number<KRepeat>{}, Number<KPack>{}),
         make_tuple(
-            Number<KPack>{}, Number<KPack * MRepeat * KPack>{}, Number<MRepeat * KPack>{}, I1));
+            Number<KPack>{}, Number<KRepeat * NRepeat * KPack>{}, Number<NRepeat * KPack>{}, I1));
 
     // C[M, N, NumRegXdlops]
     static constexpr auto c_thread_desc_ = make_naive_tensor_descriptor_packed(

include/ck/tensor_operation/gpu/block/blockwise_gemm_pipeline_xdlops_base.hpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include "ck/utility/common_header.hpp"
+#include "ck/utility/blkgemmpipe_scheduler.hpp"
+#include "ck/tensor_operation/gpu/thread/threadwise_tensor_slice_transfer.hpp"
+#include "ck/tensor_operation/gpu/warp/xdlops_gemm.hpp"
+#include "ck/tensor_description/tensor_adaptor.hpp"
+
+namespace ck {
+
+template <index_t BlockSize,
+          typename ADataType,
+          typename BDataType,
+          typename ComputeDataType,
+          typename AccDataType,
+          typename ATileDesc,
+          typename BTileDesc,
+          typename AMmaTileDesc,
+          typename BMmaTileDesc,
+          index_t ABlockTransferSrcScalarPerVector,
+          index_t BBlockTransferSrcScalarPerVector,
+          index_t MPerBlock,
+          index_t NPerBlock,
+          index_t KPerBlock,
+          index_t MPerXDL,
+          index_t NPerXDL,
+          index_t MRepeat,
+          index_t NRepeat,
+          index_t KPack,
+          bool TransposeC = false>
+struct BlockwiseGemmXdlops_pipeline_base
+{
+    static constexpr auto I0 = Number<0>{};
+    static constexpr auto I1 = Number<1>{};
+    static constexpr auto I2 = Number<2>{};
+    static constexpr auto I3 = Number<3>{};
+
+    using ThisThreadBlock = ThisThreadBlock<BlockSize>;
+
+    // Hardcode to 64, as HIP-provided "warpSize" would return 32 on RDNA GPUs.
+    static constexpr index_t WaveSize = 64;
+
+    static constexpr index_t A_K0 = ATileDesc{}.GetLength(I0);
+    static constexpr index_t B_K0 = BTileDesc{}.GetLength(I0);
+    static constexpr index_t A_K1 = ATileDesc{}.GetLength(I2);
+    static constexpr index_t B_K1 = BTileDesc{}.GetLength(I2);
+
+    static constexpr auto xdlops_gemm =
+        XdlopsGemm<ComputeDataType, MPerXDL, NPerXDL, KPack, ComputeDataType, TransposeC>{};
+
+    static constexpr index_t AMmaKStride = KPack;
+    static constexpr index_t BMmaKStride = KPack;
+
+    static constexpr index_t KPerThread = KPerBlock / xdlops_gemm.K0PerXdlops;
+    static constexpr index_t KRepeat    = KPerThread / KPack;
+
+    static constexpr index_t MWaves = MPerBlock / (MRepeat * MPerXDL);
+    static constexpr index_t NWaves = NPerBlock / (NRepeat * NPerXDL);
+
+    using HotLoopInstList =
+        ck::BlockwiseGemmXdlops_pipeline_hotloop_inst<BlockSize,
+                                                      MPerBlock,
+                                                      NPerBlock,
+                                                      KPerBlock,
+                                                      ABlockTransferSrcScalarPerVector,
+                                                      BBlockTransferSrcScalarPerVector,
+                                                      A_K1,
+                                                      B_K1,
+                                                      A_K1,
+                                                      B_K1,
+                                                      MRepeat,
+                                                      NRepeat,
+                                                      MPerXDL,
+                                                      NPerXDL,
+                                                      xdlops_gemm.KPerXdlops>;
+
+    static_assert(KPerThread % KPack == 0,
+                  "Wrong KPack setting; try increasing KPerThread or decreasing KPack");
+
+    StaticBufferTupleOfVector<AddressSpaceEnum::Vgpr,
+                              AccDataType,
+                              MRepeat * NRepeat,
+                              xdlops_gemm.GetRegSizePerXdlops(),
+                              true>
+        c_thread_buf_;
+
+    __host__ __device__ constexpr auto& GetCThreadBuffer() { return c_thread_buf_; }
+
+    __device__ static auto GetWaveIdx()
+    {
+        const index_t thread_id = ThisThreadBlock::GetThreadId();
+
+        constexpr auto threadid_to_wave_idx_adaptor = make_single_stage_tensor_adaptor(
+            make_tuple(make_merge_transform(make_tuple(MWaves, NWaves, WaveSize))),
+            make_tuple(Sequence<0, 1, 2>{}),
+            make_tuple(Sequence<0>{}));
+
+        return threadid_to_wave_idx_adaptor.CalculateBottomIndex(make_multi_index(thread_id));
+    }
+
+    __device__ static auto CalculateAThreadOriginDataIndex()
+    {
+        const auto wave_idx = GetWaveIdx();
+
+        const auto waveId_m = wave_idx[I0];
+
+        const auto xdlops_a_idx = xdlops_gemm.CalculateAThreadOriginDataIndex();
+
+        return make_tuple(0, waveId_m, xdlops_a_idx[I1], KPerThread * xdlops_a_idx[I0]);
+    }
+
+    __device__ static auto CalculateBThreadOriginDataIndex()
+    {
+        const auto wave_idx = GetWaveIdx();
+
+        const auto waveId_n = wave_idx[I1];
+
+        const auto xdlops_b_idx = xdlops_gemm.CalculateBThreadOriginDataIndex();
+
+        return make_tuple(0, waveId_n, xdlops_b_idx[I1], KPerThread * xdlops_b_idx[I0]);
+    }
+
+    template <index_t m0, index_t n0, index_t xdlops_i, index_t blk_i>
+    __device__ static auto
+        CalculateCThreadOriginDataIndex(Number<m0>, Number<n0>, Number<xdlops_i>, Number<blk_i>)
+    {
+        const auto wave_idx = GetWaveIdx();
+
+        const auto waveId_m = wave_idx[I0];
+        const auto waveId_n = wave_idx[I1];
+
+        const auto blk_idx = xdlops_gemm.GetBeginOfThreadBlk(xdlops_i, blk_i);
+
+        constexpr auto mrepeat_mwave_mperxdl_to_m_adaptor = make_single_stage_tensor_adaptor(
+            make_tuple(make_unmerge_transform(make_tuple(MRepeat, MWaves, MPerXDL))),
+            make_tuple(Sequence<0>{}),
+            make_tuple(Sequence<0, 1, 2>{}));
+
+        constexpr auto nrepeat_nwave_nperxdl_to_n_adaptor = make_single_stage_tensor_adaptor(
+            make_tuple(make_unmerge_transform(make_tuple(NRepeat, NWaves, NPerXDL))),
+            make_tuple(Sequence<0>{}),
+            make_tuple(Sequence<0, 1, 2>{}));
+
+        const index_t c_thread_m = mrepeat_mwave_mperxdl_to_m_adaptor.CalculateBottomIndex(
+            make_tuple(m0, waveId_m, blk_idx[I0]))[I0];
+        const index_t c_thread_n = nrepeat_nwave_nperxdl_to_n_adaptor.CalculateBottomIndex(
+            make_tuple(n0, waveId_n, blk_idx[I1]))[I0];
+
+        return make_tuple(c_thread_m, c_thread_n);
+    }
+
+    template <index_t m0, index_t n0, index_t xdlops_i, index_t blk_i>
+    __device__ static auto
+        CalculateCThreadOriginDataIndex8D(Number<m0>, Number<n0>, Number<xdlops_i>, Number<blk_i>)
+    {
+        const auto wave_idx = GetWaveIdx();
+
+        const auto waveId_m = wave_idx[I0];
+        const auto waveId_n = wave_idx[I1];
+
+        const auto blk_idx = xdlops_gemm.GetBeginOfThreadBlk4D(xdlops_i, blk_i);
+
+        return make_tuple(
+            m0, n0, waveId_m, waveId_n, blk_idx[I0], blk_idx[I1], blk_idx[I2], blk_idx[I3]);
+    }
+
+    using Tuple4 = decltype(CalculateAThreadOriginDataIndex());
+
+    __host__ __device__
+    BlockwiseGemmXdlops_pipeline_base(Tuple4 a_origin = CalculateAThreadOriginDataIndex(),
+                                      Tuple4 b_origin = CalculateBThreadOriginDataIndex())
+        : a_thread_copy_(a_origin), b_thread_copy_(b_origin)
+    {
+        static_assert(AMmaTileDesc::IsKnownAtCompileTime() && BMmaTileDesc::IsKnownAtCompileTime(),
+                      "wrong! Desc should be known at compile-time");
+
+        static_assert(ThisThreadBlock::GetNumOfThread() == MWaves * NWaves * WaveSize,
+                      "ThisThreadBlock::GetNumOfThread() != MWaves * NWaves * WaveSize\n");
+
+        static_assert(MPerBlock % (MPerXDL * MRepeat) == 0 && NPerBlock % (NPerXDL * NRepeat) == 0,
+                      "wrong!");
+    }
+
+    // transposed XDL output supporting C_xdl' = B_xdl' * A_xdl'
+    __host__ __device__ static constexpr auto GetCThreadDescriptor_M0_N0_M1_N1_M2_N2_N3_N4()
+    {
+        constexpr auto c_m0_m1_m2_n_tblk_lens = xdlops_gemm.GetCM0M1M2NThreadBlkLengths();
+
+        constexpr auto M0 = c_m0_m1_m2_n_tblk_lens[I0];
+        constexpr auto M1 = c_m0_m1_m2_n_tblk_lens[I1];
+        constexpr auto M2 = c_m0_m1_m2_n_tblk_lens[I2];
+        constexpr auto N  = c_m0_m1_m2_n_tblk_lens[I3];
+
+        return make_naive_tensor_descriptor_packed(
+            make_tuple(Number<MRepeat>{}, Number<NRepeat>{}, I1, I1, N, M0, M1, M2));
+    }
+
+    // XDL output supporting C_xdl = A_xdl * B_xdl
+    __host__ __device__ static constexpr auto GetCThreadDescriptor_M0_N0_M1_N1_M2_M3_M4_N2()
+    {
+        constexpr auto c_m0_m1_m2_n_tblk_lens = xdlops_gemm.GetCM0M1M2NThreadBlkLengths();
+
+        constexpr auto M0 = c_m0_m1_m2_n_tblk_lens[I0];
+        constexpr auto M1 = c_m0_m1_m2_n_tblk_lens[I1];
+        constexpr auto M2 = c_m0_m1_m2_n_tblk_lens[I2];
+        constexpr auto N  = c_m0_m1_m2_n_tblk_lens[I3];
+
+        return make_naive_tensor_descriptor_packed(
+            make_tuple(Number<MRepeat>{}, Number<NRepeat>{}, I1, I1, M0, M1, M2, N));
+    }
+
+    __host__ __device__ static constexpr auto GetCThreadDescriptor_G_M0_N0_M1_N1_M2_M3_M4_N2()
+    {
+        constexpr auto c_m0_m1_m2_n_tblk_lens = xdlops_gemm.GetCM0M1M2NThreadBlkLengths();
+
+        constexpr auto M0 = c_m0_m1_m2_n_tblk_lens[I0];
+        constexpr auto M1 = c_m0_m1_m2_n_tblk_lens[I1];
+        constexpr auto M2 = c_m0_m1_m2_n_tblk_lens[I2];
+        constexpr auto N  = c_m0_m1_m2_n_tblk_lens[I3];
+
+        return make_naive_tensor_descriptor_packed(
+            make_tuple(I1, Number<MRepeat>{}, Number<NRepeat>{}, I1, I1, M0, M1, M2, N));
+    }
+
+    // transposed XDL output supporting C_xdl' = B_xdl' * A_xdl'
+    __host__ __device__ static constexpr auto GetCBlockDescriptor_M0_N0_M1_N1_M2_N2_N3_N4()
+    {
+        constexpr auto c_block_desc_m0_n0_m1_n1_m2_n2 =
+            make_naive_tensor_descriptor_packed(make_tuple(Number<MRepeat>{},
+                                                           Number<NRepeat>{},
+                                                           Number<MWaves>{},
+                                                           Number<NWaves>{},
+                                                           Number<MPerXDL>{},
+                                                           Number<NPerXDL>{}));
+
+        return xdlops_gemm.MakeCDescriptor_M0_N0_M1_N1_M2_N2_N3_N4(c_block_desc_m0_n0_m1_n1_m2_n2);
+    }
+
+    // XDL output supporting C_xdl = A_xdl * B_xdl
+    __host__ __device__ static constexpr auto GetCBlockDescriptor_M0_N0_M1_N1_M2_M3_M4_N2()
+    {
+        constexpr auto c_block_desc_m0_n0_m1_n1_m2_n2 =
+            make_naive_tensor_descriptor_packed(make_tuple(Number<MRepeat>{},
+                                                           Number<NRepeat>{},
+                                                           Number<MWaves>{},
+                                                           Number<NWaves>{},
+                                                           Number<MPerXDL>{},
+                                                           Number<NPerXDL>{}));
+
+        return xdlops_gemm.MakeCDescriptor_M0_N0_M1_N1_M2_M3_M4_N2(c_block_desc_m0_n0_m1_n1_m2_n2);
+    }
+
+    __host__ __device__ static constexpr auto GetCBlockDescriptor_G_M0_N0_M1_N1_M2_M3_M4_N2()
+    {
+        constexpr auto c_block_desc_g_m0_n0_m1_n1_m2_n2 =
+            make_naive_tensor_descriptor_packed(make_tuple(I1,
+                                                           Number<MRepeat>{},
+                                                           Number<NRepeat>{},
+                                                           Number<MWaves>{},
+                                                           Number<NWaves>{},
+                                                           Number<MPerXDL>{},
+                                                           Number<NPerXDL>{}));
+
+        return xdlops_gemm.MakeCDescriptor_G_M0_N0_M1_N1_M2_M3_M4_N2(
+            c_block_desc_g_m0_n0_m1_n1_m2_n2);
+    }
+
+    template <typename CGridDesc_M_N>
+    __host__ __device__ static constexpr auto
+    MakeCGridDescriptor_M0_N0_M1_N1_M2_M3_M4_N2(const CGridDesc_M_N& c_grid_desc_m_n)
+    {
+        const auto M = c_grid_desc_m_n.GetLength(I0);
+        const auto N = c_grid_desc_m_n.GetLength(I1);
+
+        const auto c_grid_desc_m0_n0_m1_n1_m2_n2 = transform_tensor_descriptor(
+            c_grid_desc_m_n,
+            make_tuple(make_unmerge_transform(make_tuple(M / (MWaves * MPerXDL), MWaves, MPerXDL)),
+                       make_unmerge_transform(make_tuple(N / (NWaves * NPerXDL), NWaves, NPerXDL))),
+            make_tuple(Sequence<0>{}, Sequence<1>{}),
+            make_tuple(Sequence<0, 2, 4>{}, Sequence<1, 3, 5>{}));
+
+        return xdlops_gemm.MakeCDescriptor_M0_N0_M1_N1_M2_M3_M4_N2(c_grid_desc_m0_n0_m1_n1_m2_n2);
+    }
+
+    template <typename CGridDesc_G_M_N>
+    __host__ __device__ static constexpr auto
+    MakeCGridDescriptor_G_M0_N0_M1_N1_M2_M3_M4_N2(const CGridDesc_G_M_N& c_grid_desc_g_m_n)
+    {
+        const auto G = c_grid_desc_g_m_n.GetLength(I0);
+        const auto M = c_grid_desc_g_m_n.GetLength(I1);
+        const auto N = c_grid_desc_g_m_n.GetLength(I2);
+
+        const auto c_grid_desc_g_m0_n0_m1_n1_m2_n2 = transform_tensor_descriptor(
+            c_grid_desc_g_m_n,
+            make_tuple(make_pass_through_transform(G),
+                       make_unmerge_transform(make_tuple(M / (MWaves * MPerXDL), MWaves, MPerXDL)),
+                       make_unmerge_transform(make_tuple(N / (NWaves * NPerXDL), NWaves, NPerXDL))),
+            make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
+            make_tuple(Sequence<0>{}, Sequence<1, 3, 5>{}, Sequence<2, 4, 6>{}));
+
+        return xdlops_gemm.MakeCDescriptor_G_M0_N0_M1_N1_M2_M3_M4_N2(
+            c_grid_desc_g_m0_n0_m1_n1_m2_n2);
+    }
+
+    static constexpr AMmaTileDesc a_block_desc_m0_m1_m2_k;
+    static constexpr BMmaTileDesc b_block_desc_n0_n1_n2_k;
+
+    protected:
+    // M1, N1 as double buffer index
+    // Read buffer + Compute buffer
+    // A[M0, M1, M2, KPack]
+    static constexpr auto a_thread_desc_ = make_naive_tensor_descriptor(
+        make_tuple(Number<MRepeat>{}, I1, Number<KRepeat>{}, Number<KPack>{}),
+        make_tuple(
+            Number<KPack>{}, Number<KRepeat * MRepeat * KPack>{}, Number<MRepeat * KPack>{}, I1));
+
+    // B[N0, N1, N2, KPack]
+    static constexpr auto b_thread_desc_ = make_naive_tensor_descriptor(
+        make_tuple(Number<NRepeat>{}, I1, Number<KRepeat>{}, Number<KPack>{}),
+        make_tuple(
+            Number<KPack>{}, Number<KRepeat * NRepeat * KPack>{}, Number<NRepeat * KPack>{}, I1));
+
+    // C[M, N, NumRegXdlops]
+    static constexpr auto c_thread_desc_ = make_naive_tensor_descriptor_packed(
+        make_tuple(Number<MRepeat>{}, Number<NRepeat>{}, xdlops_gemm.GetRegSizePerXdlops()));
+
+    using AThreadCopy = ThreadwiseTensorSliceTransfer_v4<ADataType,
+                                                         ComputeDataType,
+                                                         decltype(a_block_desc_m0_m1_m2_k),
+                                                         decltype(a_thread_desc_),
+                                                         Sequence<1, 1, 1, KPack>,
+                                                         Sequence<0, 1, 2, 3>,
+                                                         3,
+                                                         A_K1,
+                                                         A_K1>;
+
+    using BThreadCopy = ThreadwiseTensorSliceTransfer_v4<BDataType,
+                                                         ComputeDataType,
+                                                         decltype(b_block_desc_n0_n1_n2_k),
+                                                         decltype(b_thread_desc_),
+                                                         Sequence<1, 1, 1, KPack>,
+                                                         Sequence<0, 1, 2, 3>,
+                                                         3,
+                                                         B_K1,
+                                                         B_K1>;
+
+    AThreadCopy a_thread_copy_;
+    BThreadCopy b_thread_copy_;
+};
+
+} // namespace ck

include/ck/tensor_operation/gpu/block/blockwise_gemm_pipeline_xdlops_selector.hpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include "ck/tensor_operation/gpu/block/blockwise_gemm_pipeline_xdlops_v1.hpp"
+#include "ck/tensor_operation/gpu/block/blockwise_gemm_pipeline_xdlops_v2.hpp"
+#include "ck/tensor_operation/gpu/block/blockwise_gemm_pipeline_xdlops_v3.hpp"
+#include "ck/tensor_operation/gpu/block/blockwise_gemm_pipeline_xdlops_v4.hpp"
+#include "ck/tensor_operation/gpu/block/blockwise_gemm_pipeline_xdlops_v5.hpp"
+
+namespace ck {
+
+enum struct BlockGemmPipelineVersion
+{
+    v1, // Naive
+    v2, // Mem
+    v3, // Comp
+    v4, // Comp, double lds buffer
+    v5, // Comp, double global prefetch register buffer
+};
+
+template <BlockGemmPipelineVersion BlkGemmPipelineVer,
+          BlockGemmPipelineScheduler BlkGemmPipeSche,
+          index_t BlockSize,
+          typename ADataType,
+          typename BDataType,
+          typename ComputeDataType,
+          typename AccDataType,
+          typename ATileDesc,
+          typename BTileDesc,
+          typename AMmaTileDesc,
+          typename BMmaTileDesc,
+          index_t ABlockTransferSrcScalarPerVector,
+          index_t BBlockTransferSrcScalarPerVector,
+          index_t MPerBlock,
+          index_t NPerBlock,
+          index_t KPerBlock,
+          index_t MPerXDL,
+          index_t NPerXDL,
+          index_t MRepeat,
+          index_t NRepeat,
+          index_t KPack>
+constexpr auto BlockGemmPipeline_Selector()
+{
+    if constexpr(BlkGemmPipelineVer == BlockGemmPipelineVersion::v1)
+    {
+        return BlockwiseGemmXdlops_pipeline_v1<BlkGemmPipeSche,
+                                               BlockSize,
+                                               ADataType,
+                                               BDataType,
+                                               ComputeDataType,
+                                               AccDataType,
+                                               ATileDesc,
+                                               BTileDesc,
+                                               AMmaTileDesc,
+                                               BMmaTileDesc,
+                                               ABlockTransferSrcScalarPerVector,
+                                               BBlockTransferSrcScalarPerVector,
+                                               MPerBlock,
+                                               NPerBlock,
+                                               KPerBlock,
+                                               MPerXDL,
+                                               NPerXDL,
+                                               MRepeat,
+                                               NRepeat,
+                                               KPack>{};
+    }
+    else if constexpr(BlkGemmPipelineVer == BlockGemmPipelineVersion::v2)
+    {
+        return BlockwiseGemmXdlops_pipeline_v2<BlkGemmPipeSche,
+                                               BlockSize,
+                                               ADataType,
+                                               BDataType,
+                                               ComputeDataType,
+                                               AccDataType,
+                                               ATileDesc,
+                                               BTileDesc,
+                                               AMmaTileDesc,
+                                               BMmaTileDesc,
+                                               ABlockTransferSrcScalarPerVector,
+                                               BBlockTransferSrcScalarPerVector,
+                                               MPerBlock,
+                                               NPerBlock,
+                                               KPerBlock,
+                                               MPerXDL,
+                                               NPerXDL,
+                                               MRepeat,
+                                               NRepeat,
+                                               KPack>{};
+    }
+    else if constexpr(BlkGemmPipelineVer == BlockGemmPipelineVersion::v3)
+    {
+        return BlockwiseGemmXdlops_pipeline_v3<BlkGemmPipeSche,
+                                               BlockSize,
+                                               ADataType,
+                                               BDataType,
+                                               ComputeDataType,
+                                               AccDataType,
+                                               ATileDesc,
+                                               BTileDesc,
+                                               AMmaTileDesc,
+                                               BMmaTileDesc,
+                                               ABlockTransferSrcScalarPerVector,
+                                               BBlockTransferSrcScalarPerVector,
+                                               MPerBlock,
+                                               NPerBlock,
+                                               KPerBlock,
+                                               MPerXDL,
+                                               NPerXDL,
+                                               MRepeat,
+                                               NRepeat,
+                                               KPack>{};
+    }
+    else if constexpr(BlkGemmPipelineVer == BlockGemmPipelineVersion::v4)
+    {
+        return BlockwiseGemmXdlops_pipeline_v4<BlkGemmPipeSche,
+                                               BlockSize,
+                                               ADataType,
+                                               BDataType,
+                                               ComputeDataType,
+                                               AccDataType,
+                                               ATileDesc,
+                                               BTileDesc,
+                                               AMmaTileDesc,
+                                               BMmaTileDesc,
+                                               ABlockTransferSrcScalarPerVector,
+                                               BBlockTransferSrcScalarPerVector,
+                                               MPerBlock,
+                                               NPerBlock,
+                                               KPerBlock,
+                                               MPerXDL,
+                                               NPerXDL,
+                                               MRepeat,
+                                               NRepeat,
+                                               KPack>{};
+    }
+    else if constexpr(BlkGemmPipelineVer == BlockGemmPipelineVersion::v5)
+    {
+        return BlockwiseGemmXdlops_pipeline_v5<BlkGemmPipeSche,
+                                               BlockSize,
+                                               ADataType,
+                                               BDataType,
+                                               ComputeDataType,
+                                               AccDataType,
+                                               ATileDesc,
+                                               BTileDesc,
+                                               AMmaTileDesc,
+                                               BMmaTileDesc,
+                                               ABlockTransferSrcScalarPerVector,
+                                               BBlockTransferSrcScalarPerVector,
+                                               MPerBlock,
+                                               NPerBlock,
+                                               KPerBlock,
+                                               MPerXDL,
+                                               NPerXDL,
+                                               MRepeat,
+                                               NRepeat,
+                                               KPack>{};
+    }
+    else
+    {
+        std::cerr << "BlockGemmPipeline configuration is not available" << std::endl;
+    }
+}
+
+} // namespace ck

include/ck/tensor_operation/gpu/block/blockwise_gemm_pipeline_xdlops_v1.hpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include "ck/tensor_operation/gpu/block/blockwise_gemm_pipeline_xdlops_base.hpp"
+
+namespace ck {
+
+// Naive pipeline with lowest resource request per WGP
+// GlobalPrefetchStages: 1
+// LocalPreFillStages: 1
+// LocalPreFetchStages: 0
+// LocalSharedMemoryBuffer: 1
+
+template <BlockGemmPipelineScheduler BlkGemmPipelineVer,
+          index_t BlockSize,
+          typename ADataType,
+          typename BDataType,
+          typename ComputeDataType,
+          typename AccDataType,
+          typename ATileDesc,
+          typename BTileDesc,
+          typename AMmaTileDesc,
+          typename BMmaTileDesc,
+          index_t ABlockTransferSrcScalarPerVector,
+          index_t BBlockTransferSrcScalarPerVector,
+          index_t MPerBlock,
+          index_t NPerBlock,
+          index_t KPerBlock,
+          index_t MPerXDL,
+          index_t NPerXDL,
+          index_t MRepeat,
+          index_t NRepeat,
+          index_t KPacks>
+struct BlockwiseGemmXdlops_pipeline_v1
+{
+};
+
+template <index_t BlockSize,
+          typename ADataType,
+          typename BDataType,
+          typename ComputeDataType,
+          typename AccDataType,
+          typename ATileDesc,
+          typename BTileDesc,
+          typename AMmaTileDesc,
+          typename BMmaTileDesc,
+          index_t ABlockTransferSrcScalarPerVector,
+          index_t BBlockTransferSrcScalarPerVector,
+          index_t MPerBlock,
+          index_t NPerBlock,
+          index_t KPerBlock,
+          index_t MPerXDL,
+          index_t NPerXDL,
+          index_t MRepeat,
+          index_t NRepeat,
+          index_t KPack
+          // ,bool TransposeC //disable transposec right now...
+          >
+struct BlockwiseGemmXdlops_pipeline_v1<BlockGemmPipelineScheduler::Intrawave,
+                                       BlockSize,
+                                       ADataType,
+                                       BDataType,
+                                       ComputeDataType,
+                                       AccDataType,
+                                       ATileDesc,
+                                       BTileDesc,
+                                       AMmaTileDesc,
+                                       BMmaTileDesc,
+                                       ABlockTransferSrcScalarPerVector,
+                                       BBlockTransferSrcScalarPerVector,
+                                       MPerBlock,
+                                       NPerBlock,
+                                       KPerBlock,
+                                       MPerXDL,
+                                       NPerXDL,
+                                       MRepeat,
+                                       NRepeat,
+                                       KPack>
+    : BlockwiseGemmXdlops_pipeline_base<BlockSize,
+                                        ADataType,
+                                        BDataType,
+                                        ComputeDataType,
+                                        AccDataType,
+                                        ATileDesc,
+                                        BTileDesc,
+                                        AMmaTileDesc,
+                                        BMmaTileDesc,
+                                        ABlockTransferSrcScalarPerVector,
+                                        BBlockTransferSrcScalarPerVector,
+                                        MPerBlock,
+                                        NPerBlock,
+                                        KPerBlock,
+                                        MPerXDL,
+                                        NPerXDL,
+                                        MRepeat,
+                                        NRepeat,
+                                        KPack>
+
+{
+    using Base = BlockwiseGemmXdlops_pipeline_base<BlockSize,
+                                                   ADataType,
+                                                   BDataType,
+                                                   ComputeDataType,
+                                                   AccDataType,
+                                                   ATileDesc,
+                                                   BTileDesc,
+                                                   AMmaTileDesc,
+                                                   BMmaTileDesc,
+                                                   ABlockTransferSrcScalarPerVector,
+                                                   BBlockTransferSrcScalarPerVector,
+                                                   MPerBlock,
+                                                   NPerBlock,
+                                                   KPerBlock,
+                                                   MPerXDL,
+                                                   NPerXDL,
+                                                   MRepeat,
+                                                   NRepeat,
+                                                   KPack>;
+    using Base::I0;
+    using Base::KRepeat;
+    using Base::xdlops_gemm;
+
+    using Base::CalculateCThreadOriginDataIndex;
+    using Base::CalculateCThreadOriginDataIndex8D;
+    using Base::GetCBlockDescriptor_G_M0_N0_M1_N1_M2_M3_M4_N2;
+    using Base::GetCBlockDescriptor_M0_N0_M1_N1_M2_M3_M4_N2;
+    using Base::GetCBlockDescriptor_M0_N0_M1_N1_M2_N2_N3_N4;
+    using Base::GetCThreadBuffer;
+    using Base::GetCThreadDescriptor_G_M0_N0_M1_N1_M2_M3_M4_N2;
+    using Base::GetCThreadDescriptor_M0_N0_M1_N1_M2_M3_M4_N2;
+    using Base::GetCThreadDescriptor_M0_N0_M1_N1_M2_N2_N3_N4;
+    using Base::MakeCGridDescriptor_G_M0_N0_M1_N1_M2_M3_M4_N2;
+    using Base::MakeCGridDescriptor_M0_N0_M1_N1_M2_M3_M4_N2;
+
+    using Base::a_block_desc_m0_m1_m2_k;
+    using Base::b_block_desc_n0_n1_n2_k;
+
+    using Base::AMmaKStride;
+    using Base::BMmaKStride;
+
+    static constexpr index_t PrefetchStages  = 1;
+    static constexpr index_t PrefillStages   = 1;
+    static constexpr index_t GlobalBufferNum = 1;
+
+    __host__ static constexpr bool BlockHasHotloop(index_t num_loop)
+    {
+        return num_loop > PrefetchStages;
+    }
+
+    __host__ static constexpr TailNumber BlockLoopTailNum(index_t num_loop)
+    {
+        ignore = num_loop;
+        return TailNumber::Full;
+    }
+
+    template <bool HasMainLoop,
+              TailNumber TailNum,
+              typename AGridDesc,
+              typename ABlockDesc,
+              typename ABlockTransfer,
+              typename AGridBuffer,
+              typename ABlockBuffer,
+              typename ABlockTransferStep,
+              typename BGridDesc,
+              typename BBlockDesc,
+              typename BBlockTransfer,
+              typename BGridBuffer,
+              typename BBlockBuffer,
+              typename BBlockTransferStep,
+              typename CThreadBuffer>
+    __device__ void Run(const AGridDesc& a_grid_desc,
+                        const ABlockDesc& a_block_desc,
+                        ABlockTransfer& a_blockwise_copy,
+                        const AGridBuffer& a_grid_buf,
+                        ABlockBuffer& a_block_buf,
+                        const ABlockTransferStep& a_block_copy_step,
+                        const BGridDesc& b_grid_desc,
+                        const BBlockDesc& b_block_desc,
+                        BBlockTransfer& b_blockwise_copy,
+                        const BGridBuffer& b_grid_buf,
+                        BBlockBuffer& b_block_buf,
+                        const BBlockTransferStep& b_block_copy_step,
+                        CThreadBuffer& c_thread_buf,
+                        index_t num_loop) const
+    {
+        auto a_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataType>(
+            a_thread_desc_.GetElementSpaceSize());
+        auto b_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataType>(
+            b_thread_desc_.GetElementSpaceSize());
+
+        // Global prefetch 1
+        a_blockwise_copy.RunRead(a_grid_desc, a_grid_buf);
+        b_blockwise_copy.RunRead(b_grid_desc, b_grid_buf);
+
+        a_blockwise_copy.MoveSrcSliceWindow(a_grid_desc, a_block_copy_step);
+        b_blockwise_copy.MoveSrcSliceWindow(b_grid_desc, b_block_copy_step);
+
+        // Local prefill 1
+        a_blockwise_copy.RunWrite(a_block_desc, a_block_buf);
+        b_blockwise_copy.RunWrite(b_block_desc, b_block_buf);
+
+        // Initialize C
+        c_thread_buf.Clear();
+
+        // main body
+        if constexpr(HasMainLoop)
+        {
+            index_t i = 0;
+            do
+            {
+                // -------------------------------------------------------------------------------------------
+                a_blockwise_copy.RunRead(a_grid_desc, a_grid_buf);
+                b_blockwise_copy.RunRead(b_grid_desc, b_grid_buf);
+
+                a_blockwise_copy.MoveSrcSliceWindow(a_grid_desc, a_block_copy_step);
+                b_blockwise_copy.MoveSrcSliceWindow(b_grid_desc, b_block_copy_step);
+
+                block_sync_lds();
+                static_for<0, KRepeat, 1>{}([&](auto k) {
+                    static_for<0, MRepeat, 1>{}([&](auto m0) {
+                        a_thread_copy_.Run(a_block_desc_m0_m1_m2_k,
+                                           make_tuple(m0, I0, I0, Number<k * AMmaKStride>{}),
+                                           a_block_buf,
+                                           a_thread_desc_,
+                                           make_tuple(m0, I0, k, I0),
+                                           a_thread_buf);
+                        static_for<0, NRepeat, 1>{}([&](auto n0) {
+                            b_thread_copy_.Run(b_block_desc_n0_n1_n2_k,
+                                               make_tuple(n0, I0, I0, Number<k * BMmaKStride>{}),
+                                               b_block_buf,
+                                               b_thread_desc_,
+                                               make_tuple(n0, I0, k, I0),
+                                               b_thread_buf);
+                        });
+                    });
+                });
+
+                static_for<0, KRepeat, 1>{}([&](auto k0) {
+                    static_for<0, MRepeat, 1>{}([&](auto m0) {
+                        static_for<0, NRepeat, 1>{}([&](auto n0) {
+                            vector_type<ComputeDataType, KPack> a_thread_vec;
+                            vector_type<ComputeDataType, KPack> b_thread_vec;
+
+                            static_for<0, KPack, 1>{}([&](auto ik) {
+                                a_thread_vec.template AsType<ComputeDataType>()(ik) =
+                                    a_thread_buf[Number<a_thread_desc_.CalculateOffset(
+                                        make_tuple(m0, I0, k0, ik))>{}];
+                                b_thread_vec.template AsType<ComputeDataType>()(ik) =
+                                    b_thread_buf[Number<b_thread_desc_.CalculateOffset(
+                                        make_tuple(n0, I0, k0, ik))>{}];
+                            });
+
+                            using mfma_input_type =
+                                typename vector_type<ComputeDataType,
+                                                     xdlops_gemm.K1PerXdlops>::type;
+
+                            constexpr index_t c_offset =
+                                c_thread_desc_.CalculateOffset(make_tuple(m0, n0, 0));
+
+                            xdlops_gemm.template Run(
+                                a_thread_vec.template AsType<mfma_input_type>(),
+                                b_thread_vec.template AsType<mfma_input_type>(),
+                                c_thread_buf.GetVectorTypeReference(Number<c_offset>{}));
+                        });
+                    });
+                });
+
+                block_sync_lds();
+                a_blockwise_copy.RunWrite(a_block_desc, a_block_buf);
+                b_blockwise_copy.RunWrite(b_block_desc, b_block_buf);
+
+                i += 1;
+            } while(i < (num_loop - 1));
+        }
+
+        // tail
+        if constexpr(TailNum == TailNumber::Full)
+        {
+            block_sync_lds();
+            static_for<0, KRepeat, 1>{}([&](auto k) {
+                static_for<0, MRepeat, 1>{}([&](auto m0) {
+                    a_thread_copy_.Run(a_block_desc_m0_m1_m2_k,
+                                       make_tuple(m0, I0, I0, Number<k * AMmaKStride>{}),
+                                       a_block_buf,
+                                       a_thread_desc_,
+                                       make_tuple(m0, I0, k, I0),
+                                       a_thread_buf);
+                    static_for<0, NRepeat, 1>{}([&](auto n0) {
+                        b_thread_copy_.Run(b_block_desc_n0_n1_n2_k,
+                                           make_tuple(n0, I0, I0, Number<k * BMmaKStride>{}),
+                                           b_block_buf,
+                                           b_thread_desc_,
+                                           make_tuple(n0, I0, k, I0),
+                                           b_thread_buf);
+                    });
+                });
+            });
+
+            static_for<0, KRepeat, 1>{}([&](auto k0) {
+                static_for<0, MRepeat, 1>{}([&](auto m0) {
+                    static_for<0, NRepeat, 1>{}([&](auto n0) {
+                        vector_type<ComputeDataType, KPack> a_thread_vec;
+                        vector_type<ComputeDataType, KPack> b_thread_vec;
+
+                        static_for<0, KPack, 1>{}([&](auto ik) {
+                            a_thread_vec.template AsType<ComputeDataType>()(ik) =
+                                a_thread_buf[Number<a_thread_desc_.CalculateOffset(
+                                    make_tuple(m0, I0, k0, ik))>{}];
+                            b_thread_vec.template AsType<ComputeDataType>()(ik) =
+                                b_thread_buf[Number<b_thread_desc_.CalculateOffset(
+                                    make_tuple(n0, I0, k0, ik))>{}];
+                        });
+
+                        using mfma_input_type =
+                            typename vector_type<ComputeDataType, xdlops_gemm.K1PerXdlops>::type;
+
+                        constexpr index_t c_offset =
+                            c_thread_desc_.CalculateOffset(make_tuple(m0, n0, 0));
+
+                        xdlops_gemm.template Run(
+                            a_thread_vec.template AsType<mfma_input_type>(),
+                            b_thread_vec.template AsType<mfma_input_type>(),
+                            c_thread_buf.GetVectorTypeReference(Number<c_offset>{}));
+                    });
+                });
+            });
+        }
+    }
+
+    protected:
+    using Base::a_thread_copy_;
+    using Base::a_thread_desc_;
+    using Base::b_thread_copy_;
+    using Base::b_thread_desc_;
+    using Base::c_thread_desc_;
+};
+
+template <index_t BlockSize,
+          typename ADataType,
+          typename BDataType,
+          typename ComputeDataType,
+          typename AccDataType,
+          typename ATileDesc,
+          typename BTileDesc,
+          typename AMmaTileDesc,
+          typename BMmaTileDesc,
+          index_t ABlockTransferSrcScalarPerVector,
+          index_t BBlockTransferSrcScalarPerVector,
+          index_t MPerBlock,
+          index_t NPerBlock,
+          index_t KPerBlock,
+          index_t MPerXDL,
+          index_t NPerXDL,
+          index_t MRepeat,
+          index_t NRepeat,
+          index_t KPack
+          // ,bool TransposeC //disable transposec right now...
+          >
+struct BlockwiseGemmXdlops_pipeline_v1<BlockGemmPipelineScheduler::Interwave,
+                                       BlockSize,
+                                       ADataType,
+                                       BDataType,
+                                       ComputeDataType,
+                                       AccDataType,
+                                       ATileDesc,
+                                       BTileDesc,
+                                       AMmaTileDesc,
+                                       BMmaTileDesc,
+                                       ABlockTransferSrcScalarPerVector,
+                                       BBlockTransferSrcScalarPerVector,
+                                       MPerBlock,
+                                       NPerBlock,
+                                       KPerBlock,
+                                       MPerXDL,
+                                       NPerXDL,
+                                       MRepeat,
+                                       NRepeat,
+                                       KPack>
+    : BlockwiseGemmXdlops_pipeline_base<BlockSize,
+                                        ADataType,
+                                        BDataType,
+                                        ComputeDataType,
+                                        AccDataType,
+                                        ATileDesc,
+                                        BTileDesc,
+                                        AMmaTileDesc,
+                                        BMmaTileDesc,
+                                        ABlockTransferSrcScalarPerVector,
+                                        BBlockTransferSrcScalarPerVector,
+                                        MPerBlock,
+                                        NPerBlock,
+                                        KPerBlock,
+                                        MPerXDL,
+                                        NPerXDL,
+                                        MRepeat,
+                                        NRepeat,
+                                        KPack>
+
+{
+    using Base = BlockwiseGemmXdlops_pipeline_base<BlockSize,
+                                                   ADataType,
+                                                   BDataType,
+                                                   ComputeDataType,
+                                                   AccDataType,
+                                                   ATileDesc,
+                                                   BTileDesc,
+                                                   AMmaTileDesc,
+                                                   BMmaTileDesc,
+                                                   ABlockTransferSrcScalarPerVector,
+                                                   BBlockTransferSrcScalarPerVector,
+                                                   MPerBlock,
+                                                   NPerBlock,
+                                                   KPerBlock,
+                                                   MPerXDL,
+                                                   NPerXDL,
+                                                   MRepeat,
+                                                   NRepeat,
+                                                   KPack>;
+    using Base::A_K1;
+    using Base::B_K1;
+    using Base::I0;
+    using Base::I1;
+    using Base::KPerThread;
+    using Base::xdlops_gemm;
+
+    using Base::CalculateCThreadOriginDataIndex;
+    using Base::CalculateCThreadOriginDataIndex8D;
+    using Base::GetCBlockDescriptor_G_M0_N0_M1_N1_M2_M3_M4_N2;
+    using Base::GetCBlockDescriptor_M0_N0_M1_N1_M2_M3_M4_N2;
+    using Base::GetCBlockDescriptor_M0_N0_M1_N1_M2_N2_N3_N4;
+    using Base::GetCThreadBuffer;
+    using Base::GetCThreadDescriptor_G_M0_N0_M1_N1_M2_M3_M4_N2;
+    using Base::GetCThreadDescriptor_M0_N0_M1_N1_M2_M3_M4_N2;
+    using Base::GetCThreadDescriptor_M0_N0_M1_N1_M2_N2_N3_N4;
+    using Base::MakeCGridDescriptor_G_M0_N0_M1_N1_M2_M3_M4_N2;
+    using Base::MakeCGridDescriptor_M0_N0_M1_N1_M2_M3_M4_N2;
+
+    using Base::a_block_desc_m0_m1_m2_k;
+    using Base::b_block_desc_n0_n1_n2_k;
+
+    static constexpr index_t NumMacClusters  = CK_EXPERIMENTAL_INTER_WAVE_SCHEDULING_MAC_CLUSTERS;
+    static constexpr index_t KPerInnerLoop   = math::max(KPerThread / NumMacClusters, KPack);
+    static constexpr index_t KRepeat         = KPerThread / KPerInnerLoop;
+    static constexpr index_t PrefetchStages  = 1;
+    static constexpr index_t PrefillStages   = 1;
+    static constexpr index_t GlobalBufferNum = 1;
+    __host__ static constexpr bool BlockHasHotloop(index_t num_loop)
+    {
+        return num_loop > PrefetchStages;
+    }
+
+    __host__ static constexpr TailNumber BlockLoopTailNum(index_t num_loop)
+    {
+        ignore = num_loop;
+        return TailNumber::Full;
+    }
+
+    template <bool HasMainLoop,
+              TailNumber TailNum,
+              typename AGridDesc,
+              typename ABlockDesc,
+              typename ABlockTransfer,
+              typename AGridBuffer,
+              typename ABlockBuffer,
+              typename ABlockTransferStep,
+              typename BGridDesc,
+              typename BBlockDesc,
+              typename BBlockTransfer,
+              typename BGridBuffer,
+              typename BBlockBuffer,
+              typename BBlockTransferStep,
+              typename CThreadBuffer>
+    __device__ void Run(const AGridDesc& a_grid_desc,
+                        const ABlockDesc& a_block_desc,
+                        ABlockTransfer& a_blockwise_copy,
+                        const AGridBuffer& a_grid_buf,
+                        ABlockBuffer& a_block_buf,
+                        const ABlockTransferStep& a_block_copy_step,
+                        const BGridDesc& b_grid_desc,
+                        const BBlockDesc& b_block_desc,
+                        BBlockTransfer& b_blockwise_copy,
+                        const BGridBuffer& b_grid_buf,
+                        BBlockBuffer& b_block_buf,
+                        const BBlockTransferStep& b_block_copy_step,
+                        CThreadBuffer& c_thread_buf,
+                        index_t num_loop) const
+    {
+        auto a_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataType>(
+            a_thread_desc_.GetElementSpaceSize());
+        auto b_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataType>(
+            b_thread_desc_.GetElementSpaceSize());
+
+        // Global prefetch 1
+        a_blockwise_copy.RunRead(a_grid_desc, a_grid_buf);
+        b_blockwise_copy.RunRead(b_grid_desc, b_grid_buf);
+
+        a_blockwise_copy.MoveSrcSliceWindow(a_grid_desc, a_block_copy_step);
+        b_blockwise_copy.MoveSrcSliceWindow(b_grid_desc, b_block_copy_step);
+
+        // Local prefill 1
+        a_blockwise_copy.RunWrite(a_block_desc, a_block_buf);
+        b_blockwise_copy.RunWrite(b_block_desc, b_block_buf);
+
+        // Initialize C
+        c_thread_buf.Clear();
+
+        // main body
+        if constexpr(HasMainLoop)
+        {
+            index_t i = 0;
+            do
+            {
+                // -------------------------------------------------------------------------------------------
+                a_blockwise_copy.RunRead(a_grid_desc, a_grid_buf);
+                b_blockwise_copy.RunRead(b_grid_desc, b_grid_buf);
+
+                a_blockwise_copy.MoveSrcSliceWindow(a_grid_desc, a_block_copy_step);
+                b_blockwise_copy.MoveSrcSliceWindow(b_grid_desc, b_block_copy_step);
+
+                block_sync_lds();
+                static_for<0, KRepeat, 1>{}([&](auto k0) {
+                    static_for<0, MRepeat, 1>{}([&](auto m0) {
+                        a_thread_copy_.Run(a_block_desc_m0_m1_m2_k,
+                                           make_tuple(m0, I0, I0, Number<k0 * KPerInnerLoop>{}),
+                                           a_block_buf,
+                                           a_thread_desc_,
+                                           make_tuple(m0, I0, k0, I0),
+                                           a_thread_buf);
+                        static_for<0, NRepeat, 1>{}([&](auto n0) {
+                            b_thread_copy_.Run(b_block_desc_n0_n1_n2_k,
+                                               make_tuple(n0, I0, I0, Number<k0 * KPerInnerLoop>{}),
+                                               b_block_buf,
+                                               b_thread_desc_,
+                                               make_tuple(n0, I0, k0, I0),
+                                               b_thread_buf);
+                        });
+                    });
+                    __builtin_amdgcn_sched_barrier(0);
+                    // NOTE: Synchronize threads in a workgroup at the start of each MAC cluster,
+                    // but except the first, as we can shorten non-MAC cluster a bit and there's no
+                    // observable negative impact. The desired effect is waves in a workgroup
+                    // executing MAC in sync. This avoids some out-of-sync waves hijacking MAC
+                    // resource from other workgroups and reducing the chance of latency hiding by
+                    // waiting for the rest of the workgroup at the eventual sync point.
+                    if constexpr(k0.value != 0 || KRepeat == 1)
+                    {
+                        __builtin_amdgcn_s_barrier();
+                        __builtin_amdgcn_sched_barrier(0);
+                    }
+                    static_for<0, KPerInnerLoop, KPack>{}([&](auto k_) {
+                        static_for<0, MRepeat, 1>{}([&](auto m0) {
+                            static_for<0, NRepeat, 1>{}([&](auto n0) {
+                                vector_type<ComputeDataType, KPack> a_thread_vec;
+                                vector_type<ComputeDataType, KPack> b_thread_vec;
+
+                                static_for<0, KPack, 1>{}([&](auto ik) {
+                                    a_thread_vec.template AsType<ComputeDataType>()(ik) =
+                                        a_thread_buf[Number<a_thread_desc_.CalculateOffset(
+                                            make_tuple(m0, I0, k0, k_ + ik))>{}];
+                                    b_thread_vec.template AsType<ComputeDataType>()(ik) =
+                                        b_thread_buf[Number<b_thread_desc_.CalculateOffset(
+                                            make_tuple(n0, I0, k0, k_ + ik))>{}];
+                                });
+
+                                using mfma_input_type =
+                                    typename vector_type<ComputeDataType,
+                                                         xdlops_gemm.K1PerXdlops>::type;
+
+                                constexpr index_t c_offset =
+                                    c_thread_desc_.CalculateOffset(make_tuple(m0, n0, 0));
+
+                                // The block_sync_lds() here performs double duty:
+                                // A) safeguard against data hazard because barrier from
+                                // blockwise_gemm is moved here B) reduce VMEM FIFO congestion by
+                                // applying small delays to different wavefronts It is performed
+                                // near the end of MAC cluster to minimize lgkmcnt penalty
+                                if constexpr(k0.value == KRepeat - 1 &&
+                                             k_.value == KPerInnerLoop - KPack &&
+                                             m0.value == MRepeat - 1 && n0.value == NRepeat - 1)
+                                {
+                                    __builtin_amdgcn_sched_barrier(0);
+                                    block_sync_lds();
+                                    __builtin_amdgcn_sched_barrier(0);
+                                }
+                                xdlops_gemm.template Run(
+                                    a_thread_vec.template AsType<mfma_input_type>(),
+                                    b_thread_vec.template AsType<mfma_input_type>(),
+                                    c_thread_buf.GetVectorTypeReference(Number<c_offset>{}));
+                                if constexpr(k_.value == 0 && m0.value == 0 && n0.value == 0)
+                                {
+                                    __builtin_amdgcn_sched_barrier(0);
+                                    __builtin_amdgcn_s_setprio(1);
+                                    __builtin_amdgcn_sched_barrier(0);
+                                }
+                            });
+                        });
+                    });
+                    __builtin_amdgcn_sched_barrier(0);
+                    __builtin_amdgcn_s_setprio(0);
+                    __builtin_amdgcn_sched_barrier(0);
+                });
+
+                // block_sync_lds();
+                a_blockwise_copy.RunWrite(a_block_desc, a_block_buf);
+                b_blockwise_copy.RunWrite(b_block_desc, b_block_buf);
+
+                i += 1;
+            } while(i < (num_loop - 1));
+        }
+
+        // tail
+        if constexpr(TailNum == TailNumber::Full)
+        {
+            block_sync_lds();
+            static_for<0, KRepeat, 1>{}([&](auto k0) {
+                static_for<0, MRepeat, 1>{}([&](auto m0) {
+                    a_thread_copy_.Run(a_block_desc_m0_m1_m2_k,
+                                       make_tuple(m0, I0, I0, Number<k0 * KPerInnerLoop>{}),
+                                       a_block_buf,
+                                       a_thread_desc_,
+                                       make_tuple(m0, I0, k0, I0),
+                                       a_thread_buf);
+                    static_for<0, NRepeat, 1>{}([&](auto n0) {
+                        b_thread_copy_.Run(b_block_desc_n0_n1_n2_k,
+                                           make_tuple(n0, I0, I0, Number<k0 * KPerInnerLoop>{}),
+                                           b_block_buf,
+                                           b_thread_desc_,
+                                           make_tuple(n0, I0, k0, I0),
+                                           b_thread_buf);
+                    });
+                });
+
+                __builtin_amdgcn_sched_barrier(0);
+                if constexpr(k0.value != 0 || KRepeat == 1)
+                {
+                    __builtin_amdgcn_s_barrier();
+                    __builtin_amdgcn_sched_barrier(0);
+                }
+                static_for<0, KPerInnerLoop, KPack>{}([&](auto k_) {
+                    static_for<0, MRepeat, 1>{}([&](auto m0) {
+                        static_for<0, NRepeat, 1>{}([&](auto n0) {
+                            vector_type<ComputeDataType, KPack> a_thread_vec;
+                            vector_type<ComputeDataType, KPack> b_thread_vec;
+
+                            static_for<0, KPack, 1>{}([&](auto ik) {
+                                a_thread_vec.template AsType<ComputeDataType>()(ik) =
+                                    a_thread_buf[Number<a_thread_desc_.CalculateOffset(
+                                        make_tuple(m0, I0, k0, k_ + ik))>{}];
+                                b_thread_vec.template AsType<ComputeDataType>()(ik) =
+                                    b_thread_buf[Number<b_thread_desc_.CalculateOffset(
+                                        make_tuple(n0, I0, k0, k_ + ik))>{}];
+                            });
+
+                            using mfma_input_type =
+                                typename vector_type<ComputeDataType,
+                                                     xdlops_gemm.K1PerXdlops>::type;
+
+                            constexpr index_t c_offset =
+                                c_thread_desc_.CalculateOffset(make_tuple(m0, n0, 0));
+
+                            if constexpr(k0.value == KRepeat - 1 &&
+                                         k_.value == KPerInnerLoop - KPack &&
+                                         m0.value == MRepeat - 1 && n0.value == NRepeat - 1)
+                            {
+                                __builtin_amdgcn_sched_barrier(0);
+                                block_sync_lds();
+                                __builtin_amdgcn_sched_barrier(0);
+                            }
+                            xdlops_gemm.template Run(
+                                a_thread_vec.template AsType<mfma_input_type>(),
+                                b_thread_vec.template AsType<mfma_input_type>(),
+                                c_thread_buf.GetVectorTypeReference(Number<c_offset>{}));
+                            if constexpr(k_.value == 0 && m0.value == 0 && n0.value == 0)
+                            {
+                                __builtin_amdgcn_sched_barrier(0);
+                                __builtin_amdgcn_s_setprio(1);
+                                __builtin_amdgcn_sched_barrier(0);
+                            }
+                        });
+                    });
+                });
+                __builtin_amdgcn_sched_barrier(0);
+                __builtin_amdgcn_s_setprio(0);
+                __builtin_amdgcn_sched_barrier(0);
+            });
+        }
+    }
+
+    protected:
+    // K->M loopover
+    static constexpr auto a_thread_desc_ = make_naive_tensor_descriptor(
+        make_tuple(Number<MRepeat>{}, I1, Number<KRepeat>{}, Number<KPerInnerLoop>{}),
+        make_tuple(Number<KPerInnerLoop>{},
+                   Number<KRepeat * MRepeat * KPerInnerLoop>{},
+                   Number<MRepeat * KPerInnerLoop>{},
+                   I1));
+
+    static constexpr auto b_thread_desc_ = make_naive_tensor_descriptor(
+        make_tuple(Number<NRepeat>{}, I1, Number<KRepeat>{}, Number<KPerInnerLoop>{}),
+        make_tuple(Number<KPerInnerLoop>{},
+                   Number<KRepeat * NRepeat * KPerInnerLoop>{},
+                   Number<NRepeat * KPerInnerLoop>{},
+                   I1));
+
+    using AThreadCopy = ThreadwiseTensorSliceTransfer_v4<ADataType,
+                                                         ComputeDataType,
+                                                         decltype(a_block_desc_m0_m1_m2_k),
+                                                         decltype(a_thread_desc_),
+                                                         Sequence<1, 1, 1, KPerInnerLoop>,
+                                                         Sequence<0, 1, 2, 3>,
+                                                         3,
+                                                         A_K1,
+                                                         A_K1>;
+
+    using BThreadCopy = ThreadwiseTensorSliceTransfer_v4<BDataType,
+                                                         ComputeDataType,
+                                                         decltype(b_block_desc_n0_n1_n2_k),
+                                                         decltype(b_thread_desc_),
+                                                         Sequence<1, 1, 1, KPerInnerLoop>,
+                                                         Sequence<0, 1, 2, 3>,
+                                                         3,
+                                                         B_K1,
+                                                         B_K1>;
+
+    AThreadCopy a_thread_copy_{Base::CalculateAThreadOriginDataIndex()};
+    BThreadCopy b_thread_copy_{Base::CalculateBThreadOriginDataIndex()};
+    using Base::c_thread_desc_;
+};
+
+} // namespace ck

include/ck/tensor_operation/gpu/block/blockwise_gemm_pipeline_xdlops_v2.hpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include "ck/tensor_operation/gpu/block/blockwise_gemm_pipeline_xdlops_base.hpp"
+
+namespace ck {
+
+// Maximum Global Memory throughput pipeline with >=32KB data in fly
+// GlobalPrefetchStages: >=2
+// LocalPreFillStages: 1
+// LocalPreFetchStages: 0
+// LocalSharedMemoryBuffer: 1
+
+template <BlockGemmPipelineScheduler BlkGemmPipelineVer,
+          index_t BlockSize,
+          typename ADataType,
+          typename BDataType,
+          typename ComputeDataType,
+          typename AccDataType,
+          typename ATileDesc,
+          typename BTileDesc,
+          typename AMmaTileDesc,
+          typename BMmaTileDesc,
+          index_t ABlockTransferSrcScalarPerVector,
+          index_t BBlockTransferSrcScalarPerVector,
+          index_t MPerBlock,
+          index_t NPerBlock,
+          index_t KPerBlock,
+          index_t MPerXDL,
+          index_t NPerXDL,
+          index_t MRepeat,
+          index_t NRepeat,
+          index_t KPacks>
+struct BlockwiseGemmXdlops_pipeline_v2
+{
+};
+
+template <index_t BlockSize,
+          typename ADataType,
+          typename BDataType,
+          typename ComputeDataType,
+          typename AccDataType,
+          typename ATileDesc,
+          typename BTileDesc,
+          typename AMmaTileDesc,
+          typename BMmaTileDesc,
+          index_t ABlockTransferSrcScalarPerVector,
+          index_t BBlockTransferSrcScalarPerVector,
+          index_t MPerBlock,
+          index_t NPerBlock,
+          index_t KPerBlock,
+          index_t MPerXDL,
+          index_t NPerXDL,
+          index_t MRepeat,
+          index_t NRepeat,
+          index_t KPack
+          // ,bool TransposeC //disable transposec right now...
+          >
+struct BlockwiseGemmXdlops_pipeline_v2<BlockGemmPipelineScheduler::Intrawave,
+                                       BlockSize,
+                                       ADataType,
+                                       BDataType,
+                                       ComputeDataType,
+                                       AccDataType,
+                                       ATileDesc,
+                                       BTileDesc,
+                                       AMmaTileDesc,
+                                       BMmaTileDesc,
+                                       ABlockTransferSrcScalarPerVector,
+                                       BBlockTransferSrcScalarPerVector,
+                                       MPerBlock,
+                                       NPerBlock,
+                                       KPerBlock,
+                                       MPerXDL,
+                                       NPerXDL,
+                                       MRepeat,
+                                       NRepeat,
+                                       KPack>
+    : BlockwiseGemmXdlops_pipeline_base<BlockSize,
+                                        ADataType,
+                                        BDataType,
+                                        ComputeDataType,
+                                        AccDataType,
+                                        ATileDesc,
+                                        BTileDesc,
+                                        AMmaTileDesc,
+                                        BMmaTileDesc,
+                                        ABlockTransferSrcScalarPerVector,
+                                        BBlockTransferSrcScalarPerVector,
+                                        MPerBlock,
+                                        NPerBlock,
+                                        KPerBlock,
+                                        MPerXDL,
+                                        NPerXDL,
+                                        MRepeat,
+                                        NRepeat,
+                                        KPack>
+
+{
+    using Base = BlockwiseGemmXdlops_pipeline_base<BlockSize,
+                                                   ADataType,
+                                                   BDataType,
+                                                   ComputeDataType,
+                                                   AccDataType,
+                                                   ATileDesc,
+                                                   BTileDesc,
+                                                   AMmaTileDesc,
+                                                   BMmaTileDesc,
+                                                   ABlockTransferSrcScalarPerVector,
+                                                   BBlockTransferSrcScalarPerVector,
+                                                   MPerBlock,
+                                                   NPerBlock,
+                                                   KPerBlock,
+                                                   MPerXDL,
+                                                   NPerXDL,
+                                                   MRepeat,
+                                                   NRepeat,
+                                                   KPack>;
+    using Base::I0;
+    using Base::KRepeat;
+    using Base::xdlops_gemm;
+
+    using Base::CalculateCThreadOriginDataIndex;
+    using Base::CalculateCThreadOriginDataIndex8D;
+    using Base::GetCBlockDescriptor_G_M0_N0_M1_N1_M2_M3_M4_N2;
+    using Base::GetCBlockDescriptor_M0_N0_M1_N1_M2_M3_M4_N2;
+    using Base::GetCBlockDescriptor_M0_N0_M1_N1_M2_N2_N3_N4;
+    using Base::GetCThreadBuffer;
+    using Base::GetCThreadDescriptor_G_M0_N0_M1_N1_M2_M3_M4_N2;
+    using Base::GetCThreadDescriptor_M0_N0_M1_N1_M2_M3_M4_N2;
+    using Base::GetCThreadDescriptor_M0_N0_M1_N1_M2_N2_N3_N4;
+    using Base::MakeCGridDescriptor_G_M0_N0_M1_N1_M2_M3_M4_N2;
+    using Base::MakeCGridDescriptor_M0_N0_M1_N1_M2_M3_M4_N2;
+
+    using Base::a_block_desc_m0_m1_m2_k;
+    using Base::b_block_desc_n0_n1_n2_k;
+
+    using Base::AMmaKStride;
+    using Base::BMmaKStride;
+
+    static constexpr index_t FullMemBandPrefetchStages = math::integer_divide_ceil(
+        32768 / (4 * warpSize / BlockSize),
+        (MPerBlock * sizeof(ADataType) + NPerBlock * sizeof(BDataType)) * KPerBlock);
+    static constexpr index_t PrefetchStages =
+        FullMemBandPrefetchStages >= 2
+            ? FullMemBandPrefetchStages <= 8 ? FullMemBandPrefetchStages : 8
+            : 2;
+
+    static constexpr index_t PrefillStages   = 1;
+    static constexpr index_t GlobalBufferNum = PrefetchStages;
+
+    __host__ static constexpr bool BlockHasHotloop(index_t num_loop)
+    {
+        return num_loop > PrefetchStages;
+    }
+
+    __host__ static constexpr TailNumber BlockLoopTailNum(index_t num_loop)
+    {
+        if(num_loop % PrefetchStages == 1)
+        {
+            return TailNumber::One;
+        }
+        else if(num_loop % PrefetchStages == 2)
+        {
+            return TailNumber::Two;
+        }
+        else if(num_loop % PrefetchStages == 3)
+        {
+            return TailNumber::Three;
+        }
+        else if(num_loop % PrefetchStages == 4)
+        {
+            return TailNumber::Four;
+        }
+        else if(num_loop % PrefetchStages == 5)
+        {
+            return TailNumber::Five;
+        }
+        else if(num_loop % PrefetchStages == 6)
+        {
+            return TailNumber::Six;
+        }
+        else if(num_loop % PrefetchStages == 7)
+        {
+            return TailNumber::Seven;
+        }
+        else
+        {
+            return TailNumber::Full;
+        }
+    }
+
+    template <bool HasMainLoop,
+              TailNumber TailNum,
+              typename AGridDesc,
+              typename ABlockDesc,
+              typename ABlockTransfer,
+              typename AGridBuffer,
+              typename ABlockBuffer,
+              typename ABlockTransferStep,
+              typename BGridDesc,
+              typename BBlockDesc,
+              typename BBlockTransfer,
+              typename BGridBuffer,
+              typename BBlockBuffer,
+              typename BBlockTransferStep,
+              typename CThreadBuffer>
+    __device__ void Run(const AGridDesc& a_grid_desc,
+                        const ABlockDesc& a_block_desc,
+                        ABlockTransfer& a_blockwise_copy,
+                        const AGridBuffer& a_grid_buf,
+                        ABlockBuffer& a_block_buf,
+                        const ABlockTransferStep& a_block_copy_step,
+                        const BGridDesc& b_grid_desc,
+                        const BBlockDesc& b_block_desc,
+                        BBlockTransfer& b_blockwise_copy,
+                        const BGridBuffer& b_grid_buf,
+                        BBlockBuffer& b_block_buf,
+                        const BBlockTransferStep& b_block_copy_step,
+                        CThreadBuffer& c_thread_buf,
+                        index_t num_loop) const
+    {
+        auto a_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataType>(
+            a_thread_desc_.GetElementSpaceSize());
+        auto b_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataType>(
+            b_thread_desc_.GetElementSpaceSize());
+
+        // Global prefetch 1
+        a_blockwise_copy.RunRead(a_grid_desc, a_grid_buf, I0);
+        b_blockwise_copy.RunRead(b_grid_desc, b_grid_buf, I0);
+
+        a_blockwise_copy.MoveSrcSliceWindow(a_grid_desc, a_block_copy_step);
+        b_blockwise_copy.MoveSrcSliceWindow(b_grid_desc, b_block_copy_step);
+
+        // Initialize C
+        c_thread_buf.Clear();
+
+        // Local prefill 1
+        a_blockwise_copy.RunWrite(a_block_desc, a_block_buf, I0);
+        b_blockwise_copy.RunWrite(b_block_desc, b_block_buf, I0);
+
+        // Global prefetch [2, PrefetchStages]
+        static_for<1, PrefetchStages, 1>{}([&](auto iprefetch) {
+            a_blockwise_copy.RunRead(a_grid_desc, a_grid_buf, iprefetch);
+            b_blockwise_copy.RunRead(b_grid_desc, b_grid_buf, iprefetch);
+
+            a_blockwise_copy.MoveSrcSliceWindow(a_grid_desc, a_block_copy_step);
+            b_blockwise_copy.MoveSrcSliceWindow(b_grid_desc, b_block_copy_step);
+        });
+
+        // main body
+        if constexpr(HasMainLoop)
+        {
+            index_t i = 0;
+            do
+            {
+                static_for<0, PrefetchStages, 1>{}([&](auto iprefetch) {
+                    // -------------------------------------------------------------------------------------------
+                    block_sync_lds();
+                    static_for<0, KRepeat, 1>{}([&](auto k) {
+                        static_for<0, MRepeat, 1>{}([&](auto m0) {
+                            a_thread_copy_.Run(a_block_desc_m0_m1_m2_k,
+                                               make_tuple(m0, I0, I0, Number<k * AMmaKStride>{}),
+                                               a_block_buf,
+                                               a_thread_desc_,
+                                               make_tuple(m0, I0, k, I0),
+                                               a_thread_buf);
+                            static_for<0, NRepeat, 1>{}([&](auto n0) {
+                                b_thread_copy_.Run(
+                                    b_block_desc_n0_n1_n2_k,
+                                    make_tuple(n0, I0, I0, Number<k * BMmaKStride>{}),
+                                    b_block_buf,
+                                    b_thread_desc_,
+                                    make_tuple(n0, I0, k, I0),
+                                    b_thread_buf);
+                            });
+                        });
+                    });
+
+                    static_for<0, KRepeat, 1>{}([&](auto k0) {
+                        static_for<0, MRepeat, 1>{}([&](auto m0) {
+                            static_for<0, NRepeat, 1>{}([&](auto n0) {
+                                vector_type<ComputeDataType, KPack> a_thread_vec;
+                                vector_type<ComputeDataType, KPack> b_thread_vec;
+
+                                static_for<0, KPack, 1>{}([&](auto ik) {
+                                    a_thread_vec.template AsType<ComputeDataType>()(ik) =
+                                        a_thread_buf[Number<a_thread_desc_.CalculateOffset(
+                                            make_tuple(m0, I0, k0, ik))>{}];
+                                    b_thread_vec.template AsType<ComputeDataType>()(ik) =
+                                        b_thread_buf[Number<b_thread_desc_.CalculateOffset(
+                                            make_tuple(n0, I0, k0, ik))>{}];
+                                });
+
+                                using mfma_input_type =
+                                    typename vector_type<ComputeDataType,
+                                                         xdlops_gemm.K1PerXdlops>::type;
+
+                                constexpr index_t c_offset =
+                                    c_thread_desc_.CalculateOffset(make_tuple(m0, n0, 0));
+
+                                xdlops_gemm.template Run(
+                                    a_thread_vec.template AsType<mfma_input_type>(),
+                                    b_thread_vec.template AsType<mfma_input_type>(),
+                                    c_thread_buf.GetVectorTypeReference(Number<c_offset>{}));
+                            });
+                        });
+                    });
+
+                    block_sync_lds();
+                    a_blockwise_copy.RunWrite(
+                        a_block_desc, a_block_buf, Number<(iprefetch + 1) % PrefetchStages>{});
+                    b_blockwise_copy.RunWrite(
+                        b_block_desc, b_block_buf, Number<(iprefetch + 1) % PrefetchStages>{});
+
+                    a_blockwise_copy.RunRead(a_grid_desc, a_grid_buf, iprefetch);
+                    b_blockwise_copy.RunRead(b_grid_desc, b_grid_buf, iprefetch);
+
+                    a_blockwise_copy.MoveSrcSliceWindow(a_grid_desc, a_block_copy_step);
+                    b_blockwise_copy.MoveSrcSliceWindow(b_grid_desc, b_block_copy_step);
+                });
+
+                i += PrefetchStages;
+            } while(i < (num_loop - PrefetchStages));
+        }
+
+        // tail
+
+        auto LoopTailFunc = [&](auto tail_num) {
+            static_for<1, tail_num, 1>{}([&](auto iprefetch) {
+                block_sync_lds();
+                static_for<0, KRepeat, 1>{}([&](auto k) {
+                    static_for<0, MRepeat, 1>{}([&](auto m0) {
+                        a_thread_copy_.Run(a_block_desc_m0_m1_m2_k,
+                                           make_tuple(m0, I0, I0, Number<k * AMmaKStride>{}),
+                                           a_block_buf,
+                                           a_thread_desc_,
+                                           make_tuple(m0, I0, k, I0),
+                                           a_thread_buf);
+                        static_for<0, NRepeat, 1>{}([&](auto n0) {
+                            b_thread_copy_.Run(b_block_desc_n0_n1_n2_k,
+                                               make_tuple(n0, I0, I0, Number<k * BMmaKStride>{}),
+                                               b_block_buf,
+                                               b_thread_desc_,
+                                               make_tuple(n0, I0, k, I0),
+                                               b_thread_buf);
+                        });
+                    });
+                });
+
+                static_for<0, KRepeat, 1>{}([&](auto k0) {
+                    static_for<0, MRepeat, 1>{}([&](auto m0) {
+                        static_for<0, NRepeat, 1>{}([&](auto n0) {
+                            vector_type<ComputeDataType, KPack> a_thread_vec;
+                            vector_type<ComputeDataType, KPack> b_thread_vec;
+
+                            static_for<0, KPack, 1>{}([&](auto ik) {
+                                a_thread_vec.template AsType<ComputeDataType>()(ik) =
+                                    a_thread_buf[Number<a_thread_desc_.CalculateOffset(
+                                        make_tuple(m0, I0, k0, ik))>{}];
+                                b_thread_vec.template AsType<ComputeDataType>()(ik) =
+                                    b_thread_buf[Number<b_thread_desc_.CalculateOffset(
+                                        make_tuple(n0, I0, k0, ik))>{}];
+                            });
+
+                            using mfma_input_type =
+                                typename vector_type<ComputeDataType,
+                                                     xdlops_gemm.K1PerXdlops>::type;
+
+                            constexpr index_t c_offset =
+                                c_thread_desc_.CalculateOffset(make_tuple(m0, n0, 0));
+
+                            xdlops_gemm.template Run(
+                                a_thread_vec.template AsType<mfma_input_type>(),
+                                b_thread_vec.template AsType<mfma_input_type>(),
+                                c_thread_buf.GetVectorTypeReference(Number<c_offset>{}));
+                        });
+                    });
+                });
+
+                block_sync_lds();
+                a_blockwise_copy.RunWrite(a_block_desc, a_block_buf, iprefetch);
+                b_blockwise_copy.RunWrite(b_block_desc, b_block_buf, iprefetch);
+            });
+
+            block_sync_lds();
+            static_for<0, KRepeat, 1>{}([&](auto k) {
+                static_for<0, MRepeat, 1>{}([&](auto m0) {
+                    a_thread_copy_.Run(a_block_desc_m0_m1_m2_k,
+                                       make_tuple(m0, I0, I0, Number<k * AMmaKStride>{}),
+                                       a_block_buf,
+                                       a_thread_desc_,
+                                       make_tuple(m0, I0, k, I0),
+                                       a_thread_buf);
+                    static_for<0, NRepeat, 1>{}([&](auto n0) {
+                        b_thread_copy_.Run(b_block_desc_n0_n1_n2_k,
+                                           make_tuple(n0, I0, I0, Number<k * BMmaKStride>{}),
+                                           b_block_buf,
+                                           b_thread_desc_,
+                                           make_tuple(n0, I0, k, I0),
+                                           b_thread_buf);
+                    });
+                });
+            });
+
+            static_for<0, KRepeat, 1>{}([&](auto k0) {
+                static_for<0, MRepeat, 1>{}([&](auto m0) {
+                    static_for<0, NRepeat, 1>{}([&](auto n0) {
+                        vector_type<ComputeDataType, KPack> a_thread_vec;
+                        vector_type<ComputeDataType, KPack> b_thread_vec;
+
+                        static_for<0, KPack, 1>{}([&](auto ik) {
+                            a_thread_vec.template AsType<ComputeDataType>()(ik) =
+                                a_thread_buf[Number<a_thread_desc_.CalculateOffset(
+                                    make_tuple(m0, I0, k0, ik))>{}];
+                            b_thread_vec.template AsType<ComputeDataType>()(ik) =
+                                b_thread_buf[Number<b_thread_desc_.CalculateOffset(
+                                    make_tuple(n0, I0, k0, ik))>{}];
+                        });
+
+                        using mfma_input_type =
+                            typename vector_type<ComputeDataType, xdlops_gemm.K1PerXdlops>::type;
+
+                        constexpr index_t c_offset =
+                            c_thread_desc_.CalculateOffset(make_tuple(m0, n0, 0));
+
+                        xdlops_gemm.template Run(
+                            a_thread_vec.template AsType<mfma_input_type>(),
+                            b_thread_vec.template AsType<mfma_input_type>(),
+                            c_thread_buf.GetVectorTypeReference(Number<c_offset>{}));
+                    });
+                });
+            });
+        };
+
+        if constexpr(TailNum == TailNumber::One)
+        {
+            block_sync_lds();
+            static_for<0, KRepeat, 1>{}([&](auto k) {
+                static_for<0, MRepeat, 1>{}([&](auto m0) {
+                    a_thread_copy_.Run(a_block_desc_m0_m1_m2_k,
+                                       make_tuple(m0, I0, I0, Number<k * AMmaKStride>{}),
+                                       a_block_buf,
+                                       a_thread_desc_,
+                                       make_tuple(m0, I0, k, I0),
+                                       a_thread_buf);
+                    static_for<0, NRepeat, 1>{}([&](auto n0) {
+                        b_thread_copy_.Run(b_block_desc_n0_n1_n2_k,
+                                           make_tuple(n0, I0, I0, Number<k * BMmaKStride>{}),
+                                           b_block_buf,
+                                           b_thread_desc_,
+                                           make_tuple(n0, I0, k, I0),
+                                           b_thread_buf);
+                    });
+                });
+            });
+
+            static_for<0, KRepeat, 1>{}([&](auto k0) {
+                static_for<0, MRepeat, 1>{}([&](auto m0) {
+                    static_for<0, NRepeat, 1>{}([&](auto n0) {
+                        vector_type<ComputeDataType, KPack> a_thread_vec;
+                        vector_type<ComputeDataType, KPack> b_thread_vec;
+
+                        static_for<0, KPack, 1>{}([&](auto ik) {
+                            a_thread_vec.template AsType<ComputeDataType>()(ik) =
+                                a_thread_buf[Number<a_thread_desc_.CalculateOffset(
+                                    make_tuple(m0, I0, k0, ik))>{}];
+                            b_thread_vec.template AsType<ComputeDataType>()(ik) =
+                                b_thread_buf[Number<b_thread_desc_.CalculateOffset(
+                                    make_tuple(n0, I0, k0, ik))>{}];
+                        });
+
+                        using mfma_input_type =
+                            typename vector_type<ComputeDataType, xdlops_gemm.K1PerXdlops>::type;
+
+                        constexpr index_t c_offset =
+                            c_thread_desc_.CalculateOffset(make_tuple(m0, n0, 0));
+
+                        xdlops_gemm.template Run(
+                            a_thread_vec.template AsType<mfma_input_type>(),
+                            b_thread_vec.template AsType<mfma_input_type>(),
+                            c_thread_buf.GetVectorTypeReference(Number<c_offset>{}));
+                    });
+                });
+            });
+        }
+        else if constexpr(TailNum == TailNumber::Two)
+        {
+            LoopTailFunc(Number<2>{});
+        }
+        else if constexpr(TailNum == TailNumber::Three)
+        {
+            LoopTailFunc(Number<3>{});
+        }
+        else if constexpr(TailNum == TailNumber::Four)
+        {
+            LoopTailFunc(Number<4>{});
+        }
+        else if constexpr(TailNum == TailNumber::Five)
+        {
+            LoopTailFunc(Number<5>{});
+        }
+        else if constexpr(TailNum == TailNumber::Six)
+        {
+            LoopTailFunc(Number<6>{});
+        }
+        else if constexpr(TailNum == TailNumber::Seven)
+        {
+            LoopTailFunc(Number<7>{});
+        }
+        else if constexpr(TailNum == TailNumber::Full)
+        {
+            LoopTailFunc(Number<PrefetchStages>{});
+        }
+    }
+
+    protected:
+    using Base::a_thread_copy_;
+    using Base::a_thread_desc_;
+    using Base::b_thread_copy_;
+    using Base::b_thread_desc_;
+    using Base::c_thread_desc_;
+};
+
+template <index_t BlockSize,
+          typename ADataType,
+          typename BDataType,
+          typename ComputeDataType,
+          typename AccDataType,
+          typename ATileDesc,
+          typename BTileDesc,
+          typename AMmaTileDesc,
+          typename BMmaTileDesc,
+          index_t ABlockTransferSrcScalarPerVector,
+          index_t BBlockTransferSrcScalarPerVector,
+          index_t MPerBlock,
+          index_t NPerBlock,
+          index_t KPerBlock,
+          index_t MPerXDL,
+          index_t NPerXDL,
+          index_t MRepeat,
+          index_t NRepeat,
+          index_t KPack
+          // ,bool TransposeC //disable transposec right now...
+          >
+struct BlockwiseGemmXdlops_pipeline_v2<BlockGemmPipelineScheduler::Interwave,
+                                       BlockSize,
+                                       ADataType,
+                                       BDataType,
+                                       ComputeDataType,
+                                       AccDataType,
+                                       ATileDesc,
+                                       BTileDesc,
+                                       AMmaTileDesc,
+                                       BMmaTileDesc,
+                                       ABlockTransferSrcScalarPerVector,
+                                       BBlockTransferSrcScalarPerVector,
+                                       MPerBlock,
+                                       NPerBlock,
+                                       KPerBlock,
+                                       MPerXDL,
+                                       NPerXDL,
+                                       MRepeat,
+                                       NRepeat,
+                                       KPack>
+    : BlockwiseGemmXdlops_pipeline_base<BlockSize,
+                                        ADataType,
+                                        BDataType,
+                                        ComputeDataType,
+                                        AccDataType,
+                                        ATileDesc,
+                                        BTileDesc,
+                                        AMmaTileDesc,
+                                        BMmaTileDesc,
+                                        ABlockTransferSrcScalarPerVector,
+                                        BBlockTransferSrcScalarPerVector,
+                                        MPerBlock,
+                                        NPerBlock,
+                                        KPerBlock,
+                                        MPerXDL,
+                                        NPerXDL,
+                                        MRepeat,
+                                        NRepeat,
+                                        KPack>
+
+{
+    using Base = BlockwiseGemmXdlops_pipeline_base<BlockSize,
+                                                   ADataType,
+                                                   BDataType,
+                                                   ComputeDataType,
+                                                   AccDataType,
+                                                   ATileDesc,
+                                                   BTileDesc,
+                                                   AMmaTileDesc,
+                                                   BMmaTileDesc,
+                                                   ABlockTransferSrcScalarPerVector,
+                                                   BBlockTransferSrcScalarPerVector,
+                                                   MPerBlock,
+                                                   NPerBlock,
+                                                   KPerBlock,
+                                                   MPerXDL,
+                                                   NPerXDL,
+                                                   MRepeat,
+                                                   NRepeat,
+                                                   KPack>;
+    using Base::A_K1;
+    using Base::B_K1;
+    using Base::I0;
+    using Base::I1;
+    using Base::KPerThread;
+    using Base::xdlops_gemm;
+
+    using Base::CalculateCThreadOriginDataIndex;
+    using Base::CalculateCThreadOriginDataIndex8D;
+    using Base::GetCBlockDescriptor_G_M0_N0_M1_N1_M2_M3_M4_N2;
+    using Base::GetCBlockDescriptor_M0_N0_M1_N1_M2_M3_M4_N2;
+    using Base::GetCBlockDescriptor_M0_N0_M1_N1_M2_N2_N3_N4;
+    using Base::GetCThreadBuffer;
+    using Base::GetCThreadDescriptor_G_M0_N0_M1_N1_M2_M3_M4_N2;
+    using Base::GetCThreadDescriptor_M0_N0_M1_N1_M2_M3_M4_N2;
+    using Base::GetCThreadDescriptor_M0_N0_M1_N1_M2_N2_N3_N4;
+    using Base::MakeCGridDescriptor_G_M0_N0_M1_N1_M2_M3_M4_N2;
+    using Base::MakeCGridDescriptor_M0_N0_M1_N1_M2_M3_M4_N2;
+
+    using Base::a_block_desc_m0_m1_m2_k;
+    using Base::b_block_desc_n0_n1_n2_k;
+
+    static constexpr index_t NumMacClusters = CK_EXPERIMENTAL_INTER_WAVE_SCHEDULING_MAC_CLUSTERS;
+    static constexpr index_t KPerInnerLoop  = math::max(KPerThread / NumMacClusters, KPack);
+    static constexpr index_t KRepeat        = KPerThread / KPerInnerLoop;
+
+    static constexpr index_t FullMemBandPrefetchStages = math::integer_divide_ceil(
+        32768 / (4 * warpSize / BlockSize),
+        (MPerBlock * sizeof(ADataType) + NPerBlock * sizeof(BDataType)) * KPerBlock);
+    static constexpr index_t PrefetchStages =
+        FullMemBandPrefetchStages >= 2
+            ? FullMemBandPrefetchStages <= 8 ? FullMemBandPrefetchStages : 8
+            : 2;
+
+    static constexpr index_t PrefillStages   = 1;
+    static constexpr index_t GlobalBufferNum = PrefetchStages;
+
+    __host__ static constexpr bool BlockHasHotloop(index_t num_loop)
+    {
+        return num_loop > PrefetchStages;
+    }
+
+    __host__ static constexpr TailNumber BlockLoopTailNum(index_t num_loop)
+    {
+        if(num_loop % PrefetchStages == 1)
+        {
+            return TailNumber::One;
+        }
+        else if(num_loop % PrefetchStages == 2)
+        {
+            return TailNumber::Two;
+        }
+        else if(num_loop % PrefetchStages == 3)
+        {
+            return TailNumber::Three;
+        }
+        else if(num_loop % PrefetchStages == 4)
+        {
+            return TailNumber::Four;
+        }
+        else if(num_loop % PrefetchStages == 5)
+        {
+            return TailNumber::Five;
+        }
+        else if(num_loop % PrefetchStages == 6)
+        {
+            return TailNumber::Six;
+        }
+        else if(num_loop % PrefetchStages == 7)
+        {
+            return TailNumber::Seven;
+        }
+        else
+        {
+            return TailNumber::Full;
+        }
+    }
+
+    template <bool HasMainLoop,
+              TailNumber TailNum,
+              typename AGridDesc,
+              typename ABlockDesc,
+              typename ABlockTransfer,
+              typename AGridBuffer,
+              typename ABlockBuffer,
+              typename ABlockTransferStep,
+              typename BGridDesc,
+              typename BBlockDesc,
+              typename BBlockTransfer,
+              typename BGridBuffer,
+              typename BBlockBuffer,
+              typename BBlockTransferStep,
+              typename CThreadBuffer>
+    __device__ void Run(const AGridDesc& a_grid_desc,
+                        const ABlockDesc& a_block_desc,
+                        ABlockTransfer& a_blockwise_copy,
+                        const AGridBuffer& a_grid_buf,
+                        ABlockBuffer& a_block_buf,
+                        const ABlockTransferStep& a_block_copy_step,
+                        const BGridDesc& b_grid_desc,
+                        const BBlockDesc& b_block_desc,
+                        BBlockTransfer& b_blockwise_copy,
+                        const BGridBuffer& b_grid_buf,
+                        BBlockBuffer& b_block_buf,
+                        const BBlockTransferStep& b_block_copy_step,
+                        CThreadBuffer& c_thread_buf,
+                        index_t num_loop) const
+    {
+        auto a_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataType>(
+            a_thread_desc_.GetElementSpaceSize());
+        auto b_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataType>(
+            b_thread_desc_.GetElementSpaceSize());
+
+        // Global prefetch 1
+        a_blockwise_copy.RunRead(a_grid_desc, a_grid_buf, I0);
+        b_blockwise_copy.RunRead(b_grid_desc, b_grid_buf, I0);
+
+        a_blockwise_copy.MoveSrcSliceWindow(a_grid_desc, a_block_copy_step);
+        b_blockwise_copy.MoveSrcSliceWindow(b_grid_desc, b_block_copy_step);
+
+        // Initialize C
+        c_thread_buf.Clear();
+
+        // Local prefill 1
+        a_blockwise_copy.RunWrite(a_block_desc, a_block_buf, I0);
+        b_blockwise_copy.RunWrite(b_block_desc, b_block_buf, I0);
+
+        // Global prefetch [2, PrefetchStages]
+        static_for<1, PrefetchStages, 1>{}([&](auto iprefetch) {
+            a_blockwise_copy.RunRead(a_grid_desc, a_grid_buf, iprefetch);
+            b_blockwise_copy.RunRead(b_grid_desc, b_grid_buf, iprefetch);
+
+            a_blockwise_copy.MoveSrcSliceWindow(a_grid_desc, a_block_copy_step);
+            b_blockwise_copy.MoveSrcSliceWindow(b_grid_desc, b_block_copy_step);
+        });
+
+        // main body
+        if constexpr(HasMainLoop)
+        {
+            index_t i = 0;
+            do
+            {
+                static_for<0, PrefetchStages, 1>{}([&](auto iprefetch) {
+                    // -------------------------------------------------------------------------------------------
+                    block_sync_lds();
+                    static_for<0, KRepeat, 1>{}([&](auto k0) {
+                        static_for<0, MRepeat, 1>{}([&](auto m0) {
+                            a_thread_copy_.Run(a_block_desc_m0_m1_m2_k,
+                                               make_tuple(m0, I0, I0, Number<k0 * KPerInnerLoop>{}),
+                                               a_block_buf,
+                                               a_thread_desc_,
+                                               make_tuple(m0, I0, k0, I0),
+                                               a_thread_buf);
+                            static_for<0, NRepeat, 1>{}([&](auto n0) {
+                                b_thread_copy_.Run(
+                                    b_block_desc_n0_n1_n2_k,
+                                    make_tuple(n0, I0, I0, Number<k0 * KPerInnerLoop>{}),
+                                    b_block_buf,
+                                    b_thread_desc_,
+                                    make_tuple(n0, I0, k0, I0),
+                                    b_thread_buf);
+                            });
+                        });
+                        __builtin_amdgcn_sched_barrier(0);
+                        // NOTE: Synchronize threads in a workgroup at the start of each MAC
+                        // cluster, but except the first, as we can shorten non-MAC cluster a bit
+                        // and there's no observable negative impact. The desired effect is waves in
+                        // a workgroup executing MAC in sync. This avoids some out-of-sync waves
+                        // hijacking MAC resource from other workgroups and reducing the chance of
+                        // latency hiding by waiting for the rest of the workgroup at the eventual
+                        // sync point.
+                        if constexpr(k0.value != 0 || KRepeat == 1)
+                        {
+                            __builtin_amdgcn_s_barrier();
+                            __builtin_amdgcn_sched_barrier(0);
+                        }
+                        static_for<0, KPerInnerLoop, KPack>{}([&](auto k_) {
+                            static_for<0, MRepeat, 1>{}([&](auto m0) {
+                                static_for<0, NRepeat, 1>{}([&](auto n0) {
+                                    vector_type<ComputeDataType, KPack> a_thread_vec;
+                                    vector_type<ComputeDataType, KPack> b_thread_vec;
+
+                                    static_for<0, KPack, 1>{}([&](auto ik) {
+                                        a_thread_vec.template AsType<ComputeDataType>()(ik) =
+                                            a_thread_buf[Number<a_thread_desc_.CalculateOffset(
+                                                make_tuple(m0, I0, k0, k_ + ik))>{}];
+                                        b_thread_vec.template AsType<ComputeDataType>()(ik) =
+                                            b_thread_buf[Number<b_thread_desc_.CalculateOffset(
+                                                make_tuple(n0, I0, k0, k_ + ik))>{}];
+                                    });
+
+                                    using mfma_input_type =
+                                        typename vector_type<ComputeDataType,
+                                                             xdlops_gemm.K1PerXdlops>::type;
+
+                                    constexpr index_t c_offset =
+                                        c_thread_desc_.CalculateOffset(make_tuple(m0, n0, 0));
+
+                                    // The block_sync_lds() here performs double duty:
+                                    // A) safeguard against data hazard because barrier from
+                                    // blockwise_gemm is moved here B) reduce VMEM FIFO congestion
+                                    // by applying small delays to different wavefronts It is
+                                    // performed near the end of MAC cluster to minimize lgkmcnt
+                                    // penalty
+                                    if constexpr(k0.value == KRepeat - 1 &&
+                                                 k_.value == KPerInnerLoop - KPack &&
+                                                 m0.value == MRepeat - 1 && n0.value == NRepeat - 1)
+                                    {
+                                        __builtin_amdgcn_sched_barrier(0);
+                                        block_sync_lds();
+                                        __builtin_amdgcn_sched_barrier(0);
+                                    }
+                                    xdlops_gemm.template Run(
+                                        a_thread_vec.template AsType<mfma_input_type>(),
+                                        b_thread_vec.template AsType<mfma_input_type>(),
+                                        c_thread_buf.GetVectorTypeReference(Number<c_offset>{}));
+                                    if constexpr(k_.value == 0 && m0.value == 0 && n0.value == 0)
+                                    {
+                                        __builtin_amdgcn_sched_barrier(0);
+                                        __builtin_amdgcn_s_setprio(1);
+                                        __builtin_amdgcn_sched_barrier(0);
+                                    }
+                                });
+                            });
+                        });
+                        __builtin_amdgcn_sched_barrier(0);
+                        __builtin_amdgcn_s_setprio(0);
+                        __builtin_amdgcn_sched_barrier(0);
+                    });
+
+                    // block_sync_lds();
+                    a_blockwise_copy.RunWrite(
+                        a_block_desc, a_block_buf, Number<(iprefetch + 1) % PrefetchStages>{});
+                    b_blockwise_copy.RunWrite(
+                        b_block_desc, b_block_buf, Number<(iprefetch + 1) % PrefetchStages>{});
+
+                    a_blockwise_copy.RunRead(a_grid_desc, a_grid_buf, iprefetch);
+                    b_blockwise_copy.RunRead(b_grid_desc, b_grid_buf, iprefetch);
+
+                    a_blockwise_copy.MoveSrcSliceWindow(a_grid_desc, a_block_copy_step);
+                    b_blockwise_copy.MoveSrcSliceWindow(b_grid_desc, b_block_copy_step);
+                });
+                i += PrefetchStages;
+            } while(i < (num_loop - PrefetchStages));
+        }
+
+        // tail
+
+        auto LoopTailFunc = [&](auto tail_num) {
+            static_for<1, tail_num, 1>{}([&](auto iprefetch) {
+                block_sync_lds();
+                static_for<0, KRepeat, 1>{}([&](auto k0) {
+                    static_for<0, MRepeat, 1>{}([&](auto m0) {
+                        a_thread_copy_.Run(a_block_desc_m0_m1_m2_k,
+                                           make_tuple(m0, I0, I0, Number<k0 * KPerInnerLoop>{}),
+                                           a_block_buf,
+                                           a_thread_desc_,
+                                           make_tuple(m0, I0, k0, I0),
+                                           a_thread_buf);
+                        static_for<0, NRepeat, 1>{}([&](auto n0) {
+                            b_thread_copy_.Run(b_block_desc_n0_n1_n2_k,
+                                               make_tuple(n0, I0, I0, Number<k0 * KPerInnerLoop>{}),
+                                               b_block_buf,
+                                               b_thread_desc_,
+                                               make_tuple(n0, I0, k0, I0),
+                                               b_thread_buf);
+                        });
+                    });
+
+                    __builtin_amdgcn_sched_barrier(0);
+                    if constexpr(k0.value != 0 || KRepeat == 1)
+                    {
+                        __builtin_amdgcn_s_barrier();
+                        __builtin_amdgcn_sched_barrier(0);
+                    }
+                    static_for<0, KPerInnerLoop, KPack>{}([&](auto k_) {
+                        static_for<0, MRepeat, 1>{}([&](auto m0) {
+                            static_for<0, NRepeat, 1>{}([&](auto n0) {
+                                vector_type<ComputeDataType, KPack> a_thread_vec;
+                                vector_type<ComputeDataType, KPack> b_thread_vec;
+
+                                static_for<0, KPack, 1>{}([&](auto ik) {
+                                    a_thread_vec.template AsType<ComputeDataType>()(ik) =
+                                        a_thread_buf[Number<a_thread_desc_.CalculateOffset(
+                                            make_tuple(m0, I0, k0, k_ + ik))>{}];
+                                    b_thread_vec.template AsType<ComputeDataType>()(ik) =
+                                        b_thread_buf[Number<b_thread_desc_.CalculateOffset(
+                                            make_tuple(n0, I0, k0, k_ + ik))>{}];
+                                });
+
+                                using mfma_input_type =
+                                    typename vector_type<ComputeDataType,
+                                                         xdlops_gemm.K1PerXdlops>::type;
+
+                                constexpr index_t c_offset =
+                                    c_thread_desc_.CalculateOffset(make_tuple(m0, n0, 0));
+
+                                if constexpr(k0.value == KRepeat - 1 &&
+                                             k_.value == KPerInnerLoop - KPack &&
+                                             m0.value == MRepeat - 1 && n0.value == NRepeat - 1)
+                                {
+                                    __builtin_amdgcn_sched_barrier(0);
+                                    block_sync_lds();
+                                    __builtin_amdgcn_sched_barrier(0);
+                                }
+                                xdlops_gemm.template Run(
+                                    a_thread_vec.template AsType<mfma_input_type>(),
+                                    b_thread_vec.template AsType<mfma_input_type>(),
+                                    c_thread_buf.GetVectorTypeReference(Number<c_offset>{}));
+                                if constexpr(k_.value == 0 && m0.value == 0 && n0.value == 0)
+                                {
+                                    __builtin_amdgcn_sched_barrier(0);
+                                    __builtin_amdgcn_s_setprio(1);
+                                    __builtin_amdgcn_sched_barrier(0);
+                                }
+                            });
+                        });
+                    });
+                    __builtin_amdgcn_sched_barrier(0);
+                    __builtin_amdgcn_s_setprio(0);
+                    __builtin_amdgcn_sched_barrier(0);
+                });
+
+                a_blockwise_copy.RunWrite(a_block_desc, a_block_buf, iprefetch);
+                b_blockwise_copy.RunWrite(b_block_desc, b_block_buf, iprefetch);
+            });
+            block_sync_lds();
+            static_for<0, KRepeat, 1>{}([&](auto k0) {
+                static_for<0, MRepeat, 1>{}([&](auto m0) {
+                    a_thread_copy_.Run(a_block_desc_m0_m1_m2_k,
+                                       make_tuple(m0, I0, I0, Number<k0 * KPerInnerLoop>{}),
+                                       a_block_buf,
+                                       a_thread_desc_,
+                                       make_tuple(m0, I0, k0, I0),
+                                       a_thread_buf);
+                    static_for<0, NRepeat, 1>{}([&](auto n0) {
+                        b_thread_copy_.Run(b_block_desc_n0_n1_n2_k,
+                                           make_tuple(n0, I0, I0, Number<k0 * KPerInnerLoop>{}),
+                                           b_block_buf,
+                                           b_thread_desc_,
+                                           make_tuple(n0, I0, k0, I0),
+                                           b_thread_buf);
+                    });
+                });
+
+                __builtin_amdgcn_sched_barrier(0);
+                if constexpr(k0.value != 0 || KRepeat == 1)
+                {
+                    __builtin_amdgcn_s_barrier();
+                    __builtin_amdgcn_sched_barrier(0);
+                }
+                static_for<0, KPerInnerLoop, KPack>{}([&](auto k_) {
+                    static_for<0, MRepeat, 1>{}([&](auto m0) {
+                        static_for<0, NRepeat, 1>{}([&](auto n0) {
+                            vector_type<ComputeDataType, KPack> a_thread_vec;
+                            vector_type<ComputeDataType, KPack> b_thread_vec;
+
+                            static_for<0, KPack, 1>{}([&](auto ik) {
+                                a_thread_vec.template AsType<ComputeDataType>()(ik) =
+                                    a_thread_buf[Number<a_thread_desc_.CalculateOffset(
+                                        make_tuple(m0, I0, k0, k_ + ik))>{}];
+                                b_thread_vec.template AsType<ComputeDataType>()(ik) =
+                                    b_thread_buf[Number<b_thread_desc_.CalculateOffset(
+                                        make_tuple(n0, I0, k0, k_ + ik))>{}];
+                            });
+
+                            using mfma_input_type =
+                                typename vector_type<ComputeDataType,
+                                                     xdlops_gemm.K1PerXdlops>::type;
+
+                            constexpr index_t c_offset =
+                                c_thread_desc_.CalculateOffset(make_tuple(m0, n0, 0));
+
+                            if constexpr(k0.value == KRepeat - 1 &&
+                                         k_.value == KPerInnerLoop - KPack &&
+                                         m0.value == MRepeat - 1 && n0.value == NRepeat - 1)
+                            {
+                                __builtin_amdgcn_sched_barrier(0);
+                                block_sync_lds();
+                                __builtin_amdgcn_sched_barrier(0);
+                            }
+                            xdlops_gemm.template Run(
+                                a_thread_vec.template AsType<mfma_input_type>(),
+                                b_thread_vec.template AsType<mfma_input_type>(),
+                                c_thread_buf.GetVectorTypeReference(Number<c_offset>{}));
+                            if constexpr(k_.value == 0 && m0.value == 0 && n0.value == 0)
+                            {
+                                __builtin_amdgcn_sched_barrier(0);
+                                __builtin_amdgcn_s_setprio(1);
+                                __builtin_amdgcn_sched_barrier(0);
+                            }
+                        });
+                    });
+                });
+                __builtin_amdgcn_sched_barrier(0);
+                __builtin_amdgcn_s_setprio(0);
+                __builtin_amdgcn_sched_barrier(0);
+            });
+        };
+
+        if constexpr(TailNum == TailNumber::One)
+        {
+            block_sync_lds();
+            static_for<0, KRepeat, 1>{}([&](auto k0) {
+                static_for<0, MRepeat, 1>{}([&](auto m0) {
+                    a_thread_copy_.Run(a_block_desc_m0_m1_m2_k,
+                                       make_tuple(m0, I0, I0, Number<k0 * KPerInnerLoop>{}),
+                                       a_block_buf,
+                                       a_thread_desc_,
+                                       make_tuple(m0, I0, k0, I0),
+                                       a_thread_buf);
+                    static_for<0, NRepeat, 1>{}([&](auto n0) {
+                        b_thread_copy_.Run(b_block_desc_n0_n1_n2_k,
+                                           make_tuple(n0, I0, I0, Number<k0 * KPerInnerLoop>{}),
+                                           b_block_buf,
+                                           b_thread_desc_,
+                                           make_tuple(n0, I0, k0, I0),
+                                           b_thread_buf);
+                    });
+                });
+
+                __builtin_amdgcn_sched_barrier(0);
+                if constexpr(k0.value != 0 || KRepeat == 1)
+                {
+                    __builtin_amdgcn_s_barrier();
+                    __builtin_amdgcn_sched_barrier(0);
+                }
+                static_for<0, KPerInnerLoop, KPack>{}([&](auto k_) {
+                    static_for<0, MRepeat, 1>{}([&](auto m0) {
+                        static_for<0, NRepeat, 1>{}([&](auto n0) {
+                            vector_type<ComputeDataType, KPack> a_thread_vec;
+                            vector_type<ComputeDataType, KPack> b_thread_vec;
+
+                            static_for<0, KPack, 1>{}([&](auto ik) {
+                                a_thread_vec.template AsType<ComputeDataType>()(ik) =
+                                    a_thread_buf[Number<a_thread_desc_.CalculateOffset(
+                                        make_tuple(m0, I0, k0, k_ + ik))>{}];
+                                b_thread_vec.template AsType<ComputeDataType>()(ik) =
+                                    b_thread_buf[Number<b_thread_desc_.CalculateOffset(
+                                        make_tuple(n0, I0, k0, k_ + ik))>{}];
+                            });
+
+                            using mfma_input_type =
+                                typename vector_type<ComputeDataType,
+                                                     xdlops_gemm.K1PerXdlops>::type;
+
+                            constexpr index_t c_offset =
+                                c_thread_desc_.CalculateOffset(make_tuple(m0, n0, 0));
+
+                            if constexpr(k0.value == KRepeat - 1 &&
+                                         k_.value == KPerInnerLoop - KPack &&
+                                         m0.value == MRepeat - 1 && n0.value == NRepeat - 1)
+                            {
+                                __builtin_amdgcn_sched_barrier(0);
+                                block_sync_lds();
+                                __builtin_amdgcn_sched_barrier(0);
+                            }
+                            xdlops_gemm.template Run(
+                                a_thread_vec.template AsType<mfma_input_type>(),
+                                b_thread_vec.template AsType<mfma_input_type>(),
+                                c_thread_buf.GetVectorTypeReference(Number<c_offset>{}));
+                            if constexpr(k_.value == 0 && m0.value == 0 && n0.value == 0)
+                            {
+                                __builtin_amdgcn_sched_barrier(0);
+                                __builtin_amdgcn_s_setprio(1);
+                                __builtin_amdgcn_sched_barrier(0);
+                            }
+                        });
+                    });
+                });
+                __builtin_amdgcn_sched_barrier(0);
+                __builtin_amdgcn_s_setprio(0);
+                __builtin_amdgcn_sched_barrier(0);
+            });
+        }
+        else if constexpr(TailNum == TailNumber::Two)
+        {
+            LoopTailFunc(Number<2>{});
+        }
+        else if constexpr(TailNum == TailNumber::Three)
+        {
+            LoopTailFunc(Number<3>{});
+        }
+        else if constexpr(TailNum == TailNumber::Four)
+        {
+            LoopTailFunc(Number<4>{});
+        }
+        else if constexpr(TailNum == TailNumber::Five)
+        {
+            LoopTailFunc(Number<5>{});
+        }
+        else if constexpr(TailNum == TailNumber::Six)
+        {
+            LoopTailFunc(Number<6>{});
+        }
+        else if constexpr(TailNum == TailNumber::Seven)
+        {
+            LoopTailFunc(Number<7>{});
+        }
+        else if constexpr(TailNum == TailNumber::Full)
+        {
+            LoopTailFunc(Number<PrefetchStages>{});
+        }
+    }
+
+    protected:
+    // K->M loopover
+    static constexpr auto a_thread_desc_ = make_naive_tensor_descriptor(
+        make_tuple(Number<MRepeat>{}, I1, Number<KRepeat>{}, Number<KPerInnerLoop>{}),
+        make_tuple(Number<KPerInnerLoop>{},
+                   Number<KRepeat * MRepeat * KPerInnerLoop>{},
+                   Number<MRepeat * KPerInnerLoop>{},
+                   I1));
+
+    static constexpr auto b_thread_desc_ = make_naive_tensor_descriptor(
+        make_tuple(Number<NRepeat>{}, I1, Number<KRepeat>{}, Number<KPerInnerLoop>{}),
+        make_tuple(Number<KPerInnerLoop>{},
+                   Number<KRepeat * NRepeat * KPerInnerLoop>{},
+                   Number<NRepeat * KPerInnerLoop>{},
+                   I1));
+
+    using AThreadCopy = ThreadwiseTensorSliceTransfer_v4<ADataType,
+                                                         ComputeDataType,
+                                                         decltype(a_block_desc_m0_m1_m2_k),
+                                                         decltype(a_thread_desc_),
+                                                         Sequence<1, 1, 1, KPerInnerLoop>,
+                                                         Sequence<0, 1, 2, 3>,
+                                                         3,
+                                                         A_K1,
+                                                         A_K1>;
+
+    using BThreadCopy = ThreadwiseTensorSliceTransfer_v4<BDataType,
+                                                         ComputeDataType,
+                                                         decltype(b_block_desc_n0_n1_n2_k),
+                                                         decltype(b_thread_desc_),
+                                                         Sequence<1, 1, 1, KPerInnerLoop>,
+                                                         Sequence<0, 1, 2, 3>,
+                                                         3,
+                                                         B_K1,
+                                                         B_K1>;
+
+    AThreadCopy a_thread_copy_{Base::CalculateAThreadOriginDataIndex()};
+    BThreadCopy b_thread_copy_{Base::CalculateBThreadOriginDataIndex()};
+    using Base::c_thread_desc_;
+};
+
+} // namespace ck

include/ck/tensor_operation/gpu/block/blockwise_gemm_pipeline_xdlops_v3.hpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include "ck/tensor_operation/gpu/block/blockwise_gemm_pipeline_xdlops_base.hpp"
+
+namespace ck {
+
+// Compute optimized pipeline
+// GlobalPrefetchStages: 2
+// LocalPreFillStages: 1
+// LocalPreFetchStages: 1
+// LocalSharedMemoryBuffer: 1
+
+template <BlockGemmPipelineScheduler BlkGemmPipelineVer,
+          index_t BlockSize,
+          typename ADataType,
+          typename BDataType,
+          typename ComputeDataType,
+          typename AccDataType,
+          typename ATileDesc,
+          typename BTileDesc,
+          typename AMmaTileDesc,
+          typename BMmaTileDesc,
+          index_t ABlockTransferSrcScalarPerVector,
+          index_t BBlockTransferSrcScalarPerVector,
+          index_t MPerBlock,
+          index_t NPerBlock,
+          index_t KPerBlock,
+          index_t MPerXDL,
+          index_t NPerXDL,
+          index_t MRepeat,
+          index_t NRepeat,
+          index_t KPacks>
+struct BlockwiseGemmXdlops_pipeline_v3
+{
+};
+
+template <index_t BlockSize,
+          typename ADataType,
+          typename BDataType,
+          typename ComputeDataType,
+          typename AccDataType,
+          typename ATileDesc,
+          typename BTileDesc,
+          typename AMmaTileDesc,
+          typename BMmaTileDesc,
+          index_t ABlockTransferSrcScalarPerVector,
+          index_t BBlockTransferSrcScalarPerVector,
+          index_t MPerBlock,
+          index_t NPerBlock,
+          index_t KPerBlock,
+          index_t MPerXDL,
+          index_t NPerXDL,
+          index_t MRepeat,
+          index_t NRepeat,
+          index_t KPack
+          // ,bool TransposeC //disable transposec right now...
+          >
+struct BlockwiseGemmXdlops_pipeline_v3<BlockGemmPipelineScheduler::Intrawave,
+                                       BlockSize,
+                                       ADataType,
+                                       BDataType,
+                                       ComputeDataType,
+                                       AccDataType,
+                                       ATileDesc,
+                                       BTileDesc,
+                                       AMmaTileDesc,
+                                       BMmaTileDesc,
+                                       ABlockTransferSrcScalarPerVector,
+                                       BBlockTransferSrcScalarPerVector,
+                                       MPerBlock,
+                                       NPerBlock,
+                                       KPerBlock,
+                                       MPerXDL,
+                                       NPerXDL,
+                                       MRepeat,
+                                       NRepeat,
+                                       KPack>
+    : BlockwiseGemmXdlops_pipeline_base<BlockSize,
+                                        ADataType,
+                                        BDataType,
+                                        ComputeDataType,
+                                        AccDataType,
+                                        ATileDesc,
+                                        BTileDesc,
+                                        AMmaTileDesc,
+                                        BMmaTileDesc,
+                                        ABlockTransferSrcScalarPerVector,
+                                        BBlockTransferSrcScalarPerVector,
+                                        MPerBlock,
+                                        NPerBlock,
+                                        KPerBlock,
+                                        MPerXDL,
+                                        NPerXDL,
+                                        MRepeat,
+                                        NRepeat,
+                                        KPack>
+
+{
+    using Base = BlockwiseGemmXdlops_pipeline_base<BlockSize,
+                                                   ADataType,
+                                                   BDataType,
+                                                   ComputeDataType,
+                                                   AccDataType,
+                                                   ATileDesc,
+                                                   BTileDesc,
+                                                   AMmaTileDesc,
+                                                   BMmaTileDesc,
+                                                   ABlockTransferSrcScalarPerVector,
+                                                   BBlockTransferSrcScalarPerVector,
+                                                   MPerBlock,
+                                                   NPerBlock,
+                                                   KPerBlock,
+                                                   MPerXDL,
+                                                   NPerXDL,
+                                                   MRepeat,
+                                                   NRepeat,
+                                                   KPack>;
+    using Base::I0;
+    using Base::I1;
+    using Base::KRepeat;
+    using Base::xdlops_gemm;
+    using typename Base::HotLoopInstList;
+
+    using Base::CalculateCThreadOriginDataIndex;
+    using Base::CalculateCThreadOriginDataIndex8D;
+    using Base::GetCBlockDescriptor_G_M0_N0_M1_N1_M2_M3_M4_N2;
+    using Base::GetCBlockDescriptor_M0_N0_M1_N1_M2_M3_M4_N2;
+    using Base::GetCBlockDescriptor_M0_N0_M1_N1_M2_N2_N3_N4;
+    using Base::GetCThreadBuffer;
+    using Base::GetCThreadDescriptor_G_M0_N0_M1_N1_M2_M3_M4_N2;
+    using Base::GetCThreadDescriptor_M0_N0_M1_N1_M2_M3_M4_N2;
+    using Base::GetCThreadDescriptor_M0_N0_M1_N1_M2_N2_N3_N4;
+    using Base::MakeCGridDescriptor_G_M0_N0_M1_N1_M2_M3_M4_N2;
+    using Base::MakeCGridDescriptor_M0_N0_M1_N1_M2_M3_M4_N2;
+
+    using Base::a_block_desc_m0_m1_m2_k;
+    using Base::b_block_desc_n0_n1_n2_k;
+
+    using Base::AMmaKStride;
+    using Base::BMmaKStride;
+
+    static constexpr index_t PrefetchStages  = 2;
+    static constexpr index_t PrefillStages   = 1;
+    static constexpr index_t GlobalBufferNum = 1;
+
+    __host__ static constexpr bool BlockHasHotloop(index_t num_loop)
+    {
+        return num_loop > PrefetchStages;
+    }
+
+    __host__ static constexpr TailNumber BlockLoopTailNum(index_t num_loop)
+    {
+        ignore = num_loop;
+        return TailNumber::Full;
+    }
+
+    __device__ static constexpr auto HotLoopScheduler()
+    {
+        // A/B split schedule
+        // compiler is likely to use ds_read2 when instruction width smaller than 16bytes
+        constexpr auto num_ds_read_inst_a =
+            HotLoopInstList::A_LDS_Read_Width * sizeof(ADataType) == 16
+                ? HotLoopInstList::A_LDS_Read_Inst_Num
+                : HotLoopInstList::A_LDS_Read_Inst_Num / 2;
+        constexpr auto num_ds_read_inst_b =
+            HotLoopInstList::B_LDS_Read_Width * sizeof(BDataType) == 16
+                ? HotLoopInstList::B_LDS_Read_Inst_Num
+                : HotLoopInstList::B_LDS_Read_Inst_Num / 2;
+
+        constexpr auto num_ds_write_inst_a = HotLoopInstList::A_LDS_Write_Inst_Num;
+        constexpr auto num_ds_write_inst_b = HotLoopInstList::B_LDS_Write_Inst_Num;
+
+        constexpr auto num_buffer_load_inst_a = HotLoopInstList::A_Buffer_Load_Inst_Num;
+        constexpr auto num_buffer_load_inst_b = HotLoopInstList::B_Buffer_Load_Inst_Num;
+
+        constexpr auto num_mfma_inst = HotLoopInstList::C_MFMA_Inst_Num;
+
+        constexpr auto mfma_cycle = NPerXDL == 16 ? 16 : 32;
+        constexpr auto ds_read_a_issue_cycle =
+            HotLoopInstList::A_LDS_Read_Width * sizeof(ADataType) == 16 ? 8 : 4;
+        constexpr auto ds_read_b_issue_cycle =
+            HotLoopInstList::B_LDS_Read_Width * sizeof(BDataType) == 16 ? 8 : 4;
+        constexpr auto ds_read_a_mfma_rate =
+            (mfma_cycle - 8 + ds_read_a_issue_cycle - 1) / ds_read_a_issue_cycle;
+        constexpr auto ds_read_b_mfma_rate =
+            (mfma_cycle - 8 + ds_read_b_issue_cycle - 1) / ds_read_b_issue_cycle;
+
+        // stage 1
+        // Separate this part?
+        constexpr auto num_mfma_per_ds_read = sizeof(ComputeDataType) / sizeof(ADataType) >
+                                                      sizeof(ComputeDataType) / sizeof(BDataType)
+                                                  ? sizeof(ComputeDataType) / sizeof(ADataType)
+                                                  : sizeof(ComputeDataType) / sizeof(BDataType);
+        constexpr auto num_mfma_stage1 =
+            num_mfma_inst - num_mfma_per_ds_read * (num_ds_read_inst_a / ds_read_a_mfma_rate +
+                                                    num_ds_read_inst_b / ds_read_b_mfma_rate);
+        constexpr auto num_mfma_per_issue =
+            num_mfma_stage1 / (num_buffer_load_inst_a + num_buffer_load_inst_b);
+        constexpr auto num_dswrite_per_issue_a = num_ds_write_inst_a / num_buffer_load_inst_a;
+        constexpr auto num_dswrite_per_issue_b = num_ds_write_inst_b / num_buffer_load_inst_b;
+
+        static_for<0, num_buffer_load_inst_a, 1>{}([&](auto i) {
+            ignore = i;
+            static_for<0, num_dswrite_per_issue_a, 1>{}([&](auto idswrite) {
+                ignore = idswrite;
+                __builtin_amdgcn_sched_group_barrier(0x200, 1, 0); // DS write
+                __builtin_amdgcn_sched_group_barrier(0x008, 1, 0); // MFMA
+            });
+            __builtin_amdgcn_sched_group_barrier(0x020, 1, 0); // VMEM read
+            __builtin_amdgcn_sched_group_barrier(
+                0x008, num_mfma_per_issue - num_dswrite_per_issue_a, 0); // MFMA
+        });
+        static_for<0, num_buffer_load_inst_b, 1>{}([&](auto i) {
+            ignore = i;
+            static_for<0, num_dswrite_per_issue_b, 1>{}([&](auto idswrite) {
+                ignore = idswrite;
+                __builtin_amdgcn_sched_group_barrier(0x200, 1, 0); // DS write
+                __builtin_amdgcn_sched_group_barrier(0x008, 1, 0); // MFMA
+            });
+            __builtin_amdgcn_sched_group_barrier(0x020, 1, 0); // VMEM read
+            __builtin_amdgcn_sched_group_barrier(
+                0x008, num_mfma_per_issue - num_dswrite_per_issue_b, 0); // MFMA
+        });
+
+        // stage 2
+        static_for<0, num_ds_read_inst_a / ds_read_a_mfma_rate, 1>{}([&](auto i) {
+            ignore = i;
+            __builtin_amdgcn_sched_group_barrier(0x100, ds_read_a_mfma_rate, 0);  // DS read
+            __builtin_amdgcn_sched_group_barrier(0x008, num_mfma_per_ds_read, 0); // MFMA
+        });
+
+        static_for<0, num_ds_read_inst_b / ds_read_b_mfma_rate, 1>{}([&](auto i) {
+            ignore = i;
+            __builtin_amdgcn_sched_group_barrier(0x100, ds_read_b_mfma_rate, 0);  // DS read
+            __builtin_amdgcn_sched_group_barrier(0x008, num_mfma_per_ds_read, 0); // MFMA
+        });
+    }
+
+    template <bool HasMainLoop,
+              TailNumber TailNum,
+              typename AGridDesc,
+              typename ABlockDesc,
+              typename ABlockTransfer,
+              typename AGridBuffer,
+              typename ABlockBuffer,
+              typename ABlockTransferStep,
+              typename BGridDesc,
+              typename BBlockDesc,
+              typename BBlockTransfer,
+              typename BGridBuffer,
+              typename BBlockBuffer,
+              typename BBlockTransferStep,
+              typename CThreadBuffer>
+    __device__ void Run(const AGridDesc& a_grid_desc,
+                        const ABlockDesc& a_block_desc,
+                        ABlockTransfer& a_blockwise_copy,
+                        const AGridBuffer& a_grid_buf,
+                        ABlockBuffer& a_block_buf,
+                        const ABlockTransferStep& a_block_copy_step,
+                        const BGridDesc& b_grid_desc,
+                        const BBlockDesc& b_block_desc,
+                        BBlockTransfer& b_blockwise_copy,
+                        const BGridBuffer& b_grid_buf,
+                        BBlockBuffer& b_block_buf,
+                        const BBlockTransferStep& b_block_copy_step,
+                        CThreadBuffer& c_thread_buf,
+                        index_t num_loop) const
+    {
+        __builtin_amdgcn_sched_barrier(0);
+        auto a_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataType>(
+            a_thread_desc_.GetElementSpaceSize());
+        auto b_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataType>(
+            b_thread_desc_.GetElementSpaceSize());
+
+        // Global prefetch 1
+        a_blockwise_copy.RunRead(a_grid_desc, a_grid_buf);
+        b_blockwise_copy.RunRead(b_grid_desc, b_grid_buf);
+
+        a_blockwise_copy.MoveSrcSliceWindow(a_grid_desc, a_block_copy_step);
+        b_blockwise_copy.MoveSrcSliceWindow(b_grid_desc, b_block_copy_step);
+
+        // Local prefill 1
+        a_blockwise_copy.RunWrite(a_block_desc, a_block_buf);
+        b_blockwise_copy.RunWrite(b_block_desc, b_block_buf);
+
+        // Global prefetch 2
+        a_blockwise_copy.RunRead(a_grid_desc, a_grid_buf);
+        b_blockwise_copy.RunRead(b_grid_desc, b_grid_buf);
+
+        a_blockwise_copy.MoveSrcSliceWindow(a_grid_desc, a_block_copy_step);
+        b_blockwise_copy.MoveSrcSliceWindow(b_grid_desc, b_block_copy_step);
+
+        // Initialize C
+        c_thread_buf.Clear();
+
+        // Local prefetch 1
+        block_sync_lds();
+        static_for<0, KRepeat, 1>{}([&](auto k0) {
+            static_for<0, MRepeat, 1>{}([&](auto m0) {
+                a_thread_copy_.Run(a_block_desc_m0_m1_m2_k,
+                                   make_tuple(m0, I0, I0, Number<k0 * AMmaKStride>{}),
+                                   a_block_buf,
+                                   a_thread_desc_,
+                                   make_tuple(m0, I0, k0, I0),
+                                   a_thread_buf);
+            });
+            static_for<0, NRepeat, 1>{}([&](auto n0) {
+                b_thread_copy_.Run(b_block_desc_n0_n1_n2_k,
+                                   make_tuple(n0, I0, I0, Number<k0 * BMmaKStride>{}),
+                                   b_block_buf,
+                                   b_thread_desc_,
+                                   make_tuple(n0, I0, k0, I0),
+                                   b_thread_buf);
+            });
+        });
+
+        __builtin_amdgcn_sched_barrier(0);
+
+        // main body
+        if constexpr(HasMainLoop)
+        {
+            index_t i = 0;
+            do
+            {
+                block_sync_lds();
+
+                a_blockwise_copy.RunWrite(a_block_desc, a_block_buf);
+                b_blockwise_copy.RunWrite(b_block_desc, b_block_buf);
+
+                a_blockwise_copy.RunRead(a_grid_desc, a_grid_buf);
+                b_blockwise_copy.RunRead(b_grid_desc, b_grid_buf);
+
+                a_blockwise_copy.MoveSrcSliceWindow(a_grid_desc, a_block_copy_step);
+                b_blockwise_copy.MoveSrcSliceWindow(b_grid_desc, b_block_copy_step);
+
+                static_for<0, KRepeat, 1>{}([&](auto k0) {
+                    static_for<0, MRepeat, 1>{}([&](auto m0) {
+                        static_for<0, NRepeat, 1>{}([&](auto n0) {
+                            vector_type<ComputeDataType, KPack> a_thread_vec;
+                            vector_type<ComputeDataType, KPack> b_thread_vec;
+
+                            static_for<0, KPack, 1>{}([&](auto ik) {
+                                a_thread_vec.template AsType<ComputeDataType>()(ik) =
+                                    a_thread_buf[Number<a_thread_desc_.CalculateOffset(
+                                        make_tuple(m0, I0, k0, ik))>{}];
+                                b_thread_vec.template AsType<ComputeDataType>()(ik) =
+                                    b_thread_buf[Number<b_thread_desc_.CalculateOffset(
+                                        make_tuple(n0, I0, k0, ik))>{}];
+                            });
+
+                            using mfma_input_type =
+                                typename vector_type<ComputeDataType,
+                                                     xdlops_gemm.K1PerXdlops>::type;
+
+                            constexpr index_t c_offset =
+                                c_thread_desc_.CalculateOffset(make_tuple(m0, n0, 0));
+
+                            xdlops_gemm.template Run(
+                                a_thread_vec.template AsType<mfma_input_type>(),
+                                b_thread_vec.template AsType<mfma_input_type>(),
+                                c_thread_buf.GetVectorTypeReference(Number<c_offset>{}));
+                        });
+                    });
+                });
+
+                block_sync_lds();
+
+                static_for<0, KRepeat, 1>{}([&](auto k0) {
+                    static_for<0, MRepeat, 1>{}([&](auto m0) {
+                        a_thread_copy_.Run(a_block_desc_m0_m1_m2_k,
+                                           make_tuple(m0, I0, I0, Number<k0 * AMmaKStride>{}),
+                                           a_block_buf,
+                                           a_thread_desc_,
+                                           make_tuple(m0, I0, k0, I0),
+                                           a_thread_buf);
+                    });
+                    static_for<0, NRepeat, 1>{}([&](auto n0) {
+                        b_thread_copy_.Run(b_block_desc_n0_n1_n2_k,
+                                           make_tuple(n0, I0, I0, Number<k0 * BMmaKStride>{}),
+                                           b_block_buf,
+                                           b_thread_desc_,
+                                           make_tuple(n0, I0, k0, I0),
+                                           b_thread_buf);
+                    });
+                });
+
+                HotLoopScheduler();
+                __builtin_amdgcn_sched_barrier(0);
+
+                i += 1;
+            } while(i < (num_loop - 1));
+        }
+        // tail
+        if constexpr(TailNum == TailNumber::Full)
+        {
+            static_for<0, KRepeat, 1>{}([&](auto k0) {
+                static_for<0, MRepeat, 1>{}([&](auto m0) {
+                    static_for<0, NRepeat, 1>{}([&](auto n0) {
+                        vector_type<ComputeDataType, KPack> a_thread_vec;
+                        vector_type<ComputeDataType, KPack> b_thread_vec;
+
+                        static_for<0, KPack, 1>{}([&](auto ik) {
+                            a_thread_vec.template AsType<ComputeDataType>()(ik) =
+                                a_thread_buf[Number<a_thread_desc_.CalculateOffset(
+                                    make_tuple(m0, I0, k0, ik))>{}];
+                            b_thread_vec.template AsType<ComputeDataType>()(ik) =
+                                b_thread_buf[Number<b_thread_desc_.CalculateOffset(
+                                    make_tuple(n0, I0, k0, ik))>{}];
+                        });
+
+                        using mfma_input_type =
+                            typename vector_type<ComputeDataType, xdlops_gemm.K1PerXdlops>::type;
+
+                        constexpr index_t c_offset =
+                            c_thread_desc_.CalculateOffset(make_tuple(m0, n0, 0));
+
+                        xdlops_gemm.template Run(
+                            a_thread_vec.template AsType<mfma_input_type>(),
+                            b_thread_vec.template AsType<mfma_input_type>(),
+                            c_thread_buf.GetVectorTypeReference(Number<c_offset>{}));
+                    });
+                });
+            });
+            __builtin_amdgcn_sched_barrier(0);
+        }
+    }
+
+    protected:
+    using Base::a_thread_copy_;
+    using Base::a_thread_desc_;
+    using Base::b_thread_copy_;
+    using Base::b_thread_desc_;
+    using Base::c_thread_desc_;
+};
+
+} // namespace ck

include/ck/tensor_operation/gpu/block/blockwise_gemm_pipeline_xdlops_v4.hpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include "ck/tensor_operation/gpu/block/blockwise_gemm_pipeline_xdlops_base.hpp"
+
+namespace ck {
+
+// Compute optimimal pipeline with highest resource request
+// GlobalPrefetchStages: 4
+// LocalPreFillStages: 2
+// LocalPreFetchStages: 1
+// LocalSharedMemoryBuffer: 2
+
+template <BlockGemmPipelineScheduler BlkGemmPipelineVer,
+          index_t BlockSize,
+          typename ADataType,
+          typename BDataType,
+          typename ComputeDataType,
+          typename AccDataType,
+          typename ATileDesc,
+          typename BTileDesc,
+          typename AMmaTileDesc,
+          typename BMmaTileDesc,
+          index_t ABlockTransferSrcScalarPerVector,
+          index_t BBlockTransferSrcScalarPerVector,
+          index_t MPerBlock,
+          index_t NPerBlock,
+          index_t KPerBlock,
+          index_t MPerXDL,
+          index_t NPerXDL,
+          index_t MRepeat,
+          index_t NRepeat,
+          index_t KPacks>
+struct BlockwiseGemmXdlops_pipeline_v4
+{
+};
+
+template <index_t BlockSize,
+          typename ADataType,
+          typename BDataType,
+          typename ComputeDataType,
+          typename AccDataType,
+          typename ATileDesc,
+          typename BTileDesc,
+          typename AMmaTileDesc,
+          typename BMmaTileDesc,
+          index_t ABlockTransferSrcScalarPerVector,
+          index_t BBlockTransferSrcScalarPerVector,
+          index_t MPerBlock,
+          index_t NPerBlock,
+          index_t KPerBlock,
+          index_t MPerXDL,
+          index_t NPerXDL,
+          index_t MRepeat,
+          index_t NRepeat,
+          index_t KPack
+          // ,bool TransposeC //disable transposec right now...
+          >
+struct BlockwiseGemmXdlops_pipeline_v4<BlockGemmPipelineScheduler::Intrawave,
+                                       BlockSize,
+                                       ADataType,
+                                       BDataType,
+                                       ComputeDataType,
+                                       AccDataType,
+                                       ATileDesc,
+                                       BTileDesc,
+                                       AMmaTileDesc,
+                                       BMmaTileDesc,
+                                       ABlockTransferSrcScalarPerVector,
+                                       BBlockTransferSrcScalarPerVector,
+                                       MPerBlock,
+                                       NPerBlock,
+                                       KPerBlock,
+                                       MPerXDL,
+                                       NPerXDL,
+                                       MRepeat,
+                                       NRepeat,
+                                       KPack>
+    : BlockwiseGemmXdlops_pipeline_base<BlockSize,
+                                        ADataType,
+                                        BDataType,
+                                        ComputeDataType,
+                                        AccDataType,
+                                        ATileDesc,
+                                        BTileDesc,
+                                        AMmaTileDesc,
+                                        BMmaTileDesc,
+                                        ABlockTransferSrcScalarPerVector,
+                                        BBlockTransferSrcScalarPerVector,
+                                        MPerBlock,
+                                        NPerBlock,
+                                        KPerBlock,
+                                        MPerXDL,
+                                        NPerXDL,
+                                        MRepeat,
+                                        NRepeat,
+                                        KPack>
+
+{
+    using Base = BlockwiseGemmXdlops_pipeline_base<BlockSize,
+                                                   ADataType,
+                                                   BDataType,
+                                                   ComputeDataType,
+                                                   AccDataType,
+                                                   ATileDesc,
+                                                   BTileDesc,
+                                                   AMmaTileDesc,
+                                                   BMmaTileDesc,
+                                                   ABlockTransferSrcScalarPerVector,
+                                                   BBlockTransferSrcScalarPerVector,
+                                                   MPerBlock,
+                                                   NPerBlock,
+                                                   KPerBlock,
+                                                   MPerXDL,
+                                                   NPerXDL,
+                                                   MRepeat,
+                                                   NRepeat,
+                                                   KPack>;
+    using Base::I0;
+    using Base::I1;
+    using Base::KRepeat;
+    using Base::xdlops_gemm;
+    using typename Base::HotLoopInstList;
+
+    using Base::CalculateCThreadOriginDataIndex;
+    using Base::CalculateCThreadOriginDataIndex8D;
+    using Base::GetCBlockDescriptor_G_M0_N0_M1_N1_M2_M3_M4_N2;
+    using Base::GetCBlockDescriptor_M0_N0_M1_N1_M2_M3_M4_N2;
+    using Base::GetCBlockDescriptor_M0_N0_M1_N1_M2_N2_N3_N4;
+    using Base::GetCThreadBuffer;
+    using Base::GetCThreadDescriptor_G_M0_N0_M1_N1_M2_M3_M4_N2;
+    using Base::GetCThreadDescriptor_M0_N0_M1_N1_M2_M3_M4_N2;
+    using Base::GetCThreadDescriptor_M0_N0_M1_N1_M2_N2_N3_N4;
+    using Base::MakeCGridDescriptor_G_M0_N0_M1_N1_M2_M3_M4_N2;
+    using Base::MakeCGridDescriptor_M0_N0_M1_N1_M2_M3_M4_N2;
+
+    using Base::a_block_desc_m0_m1_m2_k;
+    using Base::b_block_desc_n0_n1_n2_k;
+
+    using Base::AMmaKStride;
+    using Base::BMmaKStride;
+
+    static constexpr index_t PrefetchStages  = 4;
+    static constexpr index_t PrefillStages   = 2;
+    static constexpr index_t GlobalBufferNum = 2;
+    static constexpr index_t HotloopUnroll   = 2;
+
+    __host__ static constexpr bool BlockHasHotloop(index_t num_loop)
+    {
+        return num_loop > PrefetchStages;
+    }
+
+    __host__ static constexpr TailNumber BlockLoopTailNum(index_t num_loop)
+    {
+        if(num_loop % HotloopUnroll == 1)
+        {
+            return TailNumber::Odd;
+        }
+        else
+        {
+            return TailNumber::Even;
+        }
+    }
+
+    template <typename ScheduleGroup>
+    __device__ static constexpr void HotLoopScheduler(ScheduleGroup schedule_group)
+    {
+        // TODO: Take data type into consideration as pipe ver 3
+        // A-B splited schedule
+        constexpr auto num_ds_read_inst_a =
+            HotLoopInstList::A_LDS_Read_Width * sizeof(ADataType) == 16
+                ? HotLoopInstList::A_LDS_Read_Inst_Num
+                : HotLoopInstList::A_LDS_Read_Inst_Num / 2;
+        constexpr auto num_ds_read_inst_b =
+            HotLoopInstList::B_LDS_Read_Width * sizeof(BDataType) == 16
+                ? HotLoopInstList::B_LDS_Read_Inst_Num
+                : HotLoopInstList::B_LDS_Read_Inst_Num / 2;
+
+        constexpr auto num_issue_a = HotLoopInstList::A_Buffer_Load_Inst_Num;
+        constexpr auto num_dswrite_per_issue_a =
+            (HotLoopInstList::A_LDS_Write_Inst_Num + num_issue_a - 1) / num_issue_a;
+        constexpr auto num_dsread_per_issue_a = num_ds_read_inst_a / num_issue_a;
+
+        constexpr auto num_issue_b = HotLoopInstList::B_Buffer_Load_Inst_Num;
+        constexpr auto num_dswrite_per_issue_b =
+            (HotLoopInstList::B_LDS_Write_Inst_Num + num_issue_b - 1) / num_issue_b;
+        constexpr auto num_dsread_per_issue_b = num_ds_read_inst_b / num_issue_b;
+
+        constexpr auto num_mfma_per_issue =
+            HotLoopInstList::C_MFMA_Inst_Num / (num_issue_a + num_issue_b);
+
+        static_for<0, num_issue_a, 1>{}([&](auto i) {
+            ignore = i;
+            static_for<0, num_dsread_per_issue_a, 1>{}([&](auto idsread) {
+                ignore = idsread;
+                __builtin_amdgcn_sched_group_barrier(0x100, 1, schedule_group); // DS read
+                __builtin_amdgcn_sched_group_barrier(0x008, 1, schedule_group); // MFMA
+            });
+
+            static_for<0, num_dswrite_per_issue_a, 1>{}([&](auto idswrite) {
+                ignore = idswrite;
+                __builtin_amdgcn_sched_group_barrier(0x200, 1, schedule_group); // DS write
+                __builtin_amdgcn_sched_group_barrier(0x008, 1, schedule_group); // MFMA
+            });
+
+            __builtin_amdgcn_sched_group_barrier(0x020, 1, schedule_group); // VMEM read
+            __builtin_amdgcn_sched_group_barrier(0x008,
+                                                 num_mfma_per_issue - num_dsread_per_issue_a -
+                                                     num_dswrite_per_issue_a,
+                                                 schedule_group); // MFMA
+        });
+
+        static_for<0, num_issue_b, 1>{}([&](auto i) {
+            ignore = i;
+            static_for<0, num_dsread_per_issue_b, 1>{}([&](auto idsread) {
+                ignore = idsread;
+                __builtin_amdgcn_sched_group_barrier(0x100, 1, schedule_group); // DS read
+                __builtin_amdgcn_sched_group_barrier(0x008, 1, schedule_group); // MFMA
+            });
+
+            static_for<0, num_dswrite_per_issue_b, 1>{}([&](auto idswrite) {
+                ignore = idswrite;
+                __builtin_amdgcn_sched_group_barrier(0x200, 1, schedule_group); // DS write
+                __builtin_amdgcn_sched_group_barrier(0x008, 1, schedule_group); // MFMA
+            });
+
+            __builtin_amdgcn_sched_group_barrier(0x020, 1, schedule_group); // VMEM read
+            __builtin_amdgcn_sched_group_barrier(0x008,
+                                                 num_mfma_per_issue - num_dsread_per_issue_a -
+                                                     num_dswrite_per_issue_b,
+                                                 schedule_group); // MFMA
+        });
+        __builtin_amdgcn_sched_barrier(0);
+    }
+
+    template <bool HasMainLoop,
+              TailNumber TailNum,
+              typename AGridDesc,
+              typename ABlockDesc,
+              typename ABlockTransfer,
+              typename AGridBuffer,
+              typename ABlockBuffer,
+              typename ABlockTransferStep,
+              typename BGridDesc,
+              typename BBlockDesc,
+              typename BBlockTransfer,
+              typename BGridBuffer,
+              typename BBlockBuffer,
+              typename BBlockTransferStep,
+              typename CThreadBuffer>
+    __device__ void Run(const AGridDesc& a_grid_desc,
+                        const ABlockDesc& a_block_desc,
+                        ABlockTransfer& a_blockwise_copy,
+                        const AGridBuffer& a_grid_buf,
+                        ABlockBuffer& a_block_buf,
+                        const ABlockTransferStep& a_block_copy_step,
+                        const BGridDesc& b_grid_desc,
+                        const BBlockDesc& b_block_desc,
+                        BBlockTransfer& b_blockwise_copy,
+                        const BGridBuffer& b_grid_buf,
+                        BBlockBuffer& b_block_buf,
+                        const BBlockTransferStep& b_block_copy_step,
+                        CThreadBuffer& c_thread_buf,
+                        index_t num_loop) const
+    {
+        auto a_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataType>(
+            a_thread_desc_.GetElementSpaceSize());
+        auto b_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataType>(
+            b_thread_desc_.GetElementSpaceSize());
+
+        StaticallyIndexedArray<decltype(a_thread_buf), Number<2>{}> a_thread_bufs;
+        StaticallyIndexedArray<decltype(b_thread_buf), Number<2>{}> b_thread_bufs;
+
+        // Global prefetch 1
+        a_blockwise_copy.RunRead(a_grid_desc, a_grid_buf, I0);
+        b_blockwise_copy.RunRead(b_grid_desc, b_grid_buf, I0);
+
+        a_blockwise_copy.MoveSrcSliceWindow(a_grid_desc, a_block_copy_step);
+        b_blockwise_copy.MoveSrcSliceWindow(b_grid_desc, b_block_copy_step);
+
+        // Global prefetch 2
+        a_blockwise_copy.RunRead(a_grid_desc, a_grid_buf, I1);
+        b_blockwise_copy.RunRead(b_grid_desc, b_grid_buf, I1);
+
+        a_blockwise_copy.MoveSrcSliceWindow(a_grid_desc, a_block_copy_step);
+        b_blockwise_copy.MoveSrcSliceWindow(b_grid_desc, b_block_copy_step);
+
+        // Local prefill 1
+        a_blockwise_copy.RunWrite(a_block_desc, a_block_buf.At(I0), I0);
+        b_blockwise_copy.RunWrite(b_block_desc, b_block_buf.At(I0), I0);
+
+        // Local prefill 2
+        a_blockwise_copy.RunWrite(a_block_desc, a_block_buf.At(I1), I1);
+        b_blockwise_copy.RunWrite(b_block_desc, b_block_buf.At(I1), I1);
+
+        // Local prefetch 1
+        block_sync_lds();
+        static_for<0, KRepeat, 1>{}([&](auto k) {
+            static_for<0, MRepeat, 1>{}([&](auto m0) {
+                a_thread_copy_.Run(a_block_desc_m0_m1_m2_k,
+                                   make_tuple(m0, I0, I0, Number<k * AMmaKStride>{}),
+                                   a_block_buf.At(I0),
+                                   a_thread_desc_,
+                                   make_tuple(m0, I0, k, I0),
+                                   a_thread_bufs(I0));
+                static_for<0, NRepeat, 1>{}([&](auto n0) {
+                    b_thread_copy_.Run(b_block_desc_n0_n1_n2_k,
+                                       make_tuple(n0, I0, I0, Number<k * BMmaKStride>{}),
+                                       b_block_buf.At(I0),
+                                       b_thread_desc_,
+                                       make_tuple(n0, I0, k, I0),
+                                       b_thread_bufs(I0));
+                });
+            });
+        });
+
+        // Global prefetch 3
+        a_blockwise_copy.RunRead(a_grid_desc, a_grid_buf, I0);
+        b_blockwise_copy.RunRead(b_grid_desc, b_grid_buf, I0);
+
+        a_blockwise_copy.MoveSrcSliceWindow(a_grid_desc, a_block_copy_step);
+        b_blockwise_copy.MoveSrcSliceWindow(b_grid_desc, b_block_copy_step);
+
+        // Global prefetch 4
+        a_blockwise_copy.RunRead(a_grid_desc, a_grid_buf, I1);
+        b_blockwise_copy.RunRead(b_grid_desc, b_grid_buf, I1);
+
+        a_blockwise_copy.MoveSrcSliceWindow(a_grid_desc, a_block_copy_step);
+        b_blockwise_copy.MoveSrcSliceWindow(b_grid_desc, b_block_copy_step);
+
+        // Initialize C
+        c_thread_buf.Clear();
+
+        // main body
+        if constexpr(HasMainLoop)
+        {
+            index_t i = 0;
+            // This hot loop has two legacy loopover, to implement the double local buffer strategy
+            do
+            {
+                auto LoopFunc = [&](auto lds_read_buf,
+                                    auto lds_read_reg_buf,
+                                    auto lds_write_buf,
+                                    auto vmem_buf,
+                                    auto mfma_reg_buf,
+                                    auto schedule_group) {
+                    block_sync_lds();
+
+                    static_for<0, KRepeat, 1>{}([&](auto k) {
+                        static_for<0, MRepeat, 1>{}([&](auto m0) {
+                            a_thread_copy_.Run(a_block_desc_m0_m1_m2_k,
+                                               make_tuple(m0, I0, I0, Number<k * AMmaKStride>{}),
+                                               a_block_buf.At(lds_read_buf),
+                                               a_thread_desc_,
+                                               make_tuple(m0, I0, k, I0),
+                                               a_thread_bufs(lds_read_reg_buf));
+                            static_for<0, NRepeat, 1>{}([&](auto n0) {
+                                b_thread_copy_.Run(
+                                    b_block_desc_n0_n1_n2_k,
+                                    make_tuple(n0, I0, I0, Number<k * BMmaKStride>{}),
+                                    b_block_buf.At(lds_read_buf),
+                                    b_thread_desc_,
+                                    make_tuple(n0, I0, k, I0),
+                                    b_thread_bufs(lds_read_reg_buf));
+                            });
+                        });
+                    });
+
+                    a_blockwise_copy.RunWrite(
+                        a_block_desc, a_block_buf.At(lds_write_buf), vmem_buf);
+                    b_blockwise_copy.RunWrite(
+                        b_block_desc, b_block_buf.At(lds_write_buf), vmem_buf);
+
+                    a_blockwise_copy.RunRead(a_grid_desc, a_grid_buf, vmem_buf);
+                    b_blockwise_copy.RunRead(b_grid_desc, b_grid_buf, vmem_buf);
+
+                    a_blockwise_copy.MoveSrcSliceWindow(a_grid_desc, a_block_copy_step);
+                    b_blockwise_copy.MoveSrcSliceWindow(b_grid_desc, b_block_copy_step);
+
+                    static_for<0, KRepeat, 1>{}([&](auto k0) {
+                        static_for<0, MRepeat, 1>{}([&](auto m0) {
+                            static_for<0, NRepeat, 1>{}([&](auto n0) {
+                                vector_type<ComputeDataType, KPack> a_thread_vec;
+                                vector_type<ComputeDataType, KPack> b_thread_vec;
+
+                                static_for<0, KPack, 1>{}([&](auto ik) {
+                                    a_thread_vec.template AsType<ComputeDataType>()(ik) =
+                                        a_thread_bufs[mfma_reg_buf]
+                                                     [Number<a_thread_desc_.CalculateOffset(
+                                                         make_tuple(m0, I0, k0, ik))>{}];
+                                    b_thread_vec.template AsType<ComputeDataType>()(ik) =
+                                        b_thread_bufs[mfma_reg_buf]
+                                                     [Number<b_thread_desc_.CalculateOffset(
+                                                         make_tuple(n0, I0, k0, ik))>{}];
+                                });
+
+                                using mfma_input_type =
+                                    typename vector_type<ComputeDataType,
+                                                         xdlops_gemm.K1PerXdlops>::type;
+
+                                constexpr index_t c_offset =
+                                    c_thread_desc_.CalculateOffset(make_tuple(m0, n0, 0));
+
+                                xdlops_gemm.template Run(
+                                    a_thread_vec.template AsType<mfma_input_type>(),
+                                    b_thread_vec.template AsType<mfma_input_type>(),
+                                    c_thread_buf.GetVectorTypeReference(Number<c_offset>{}));
+                            });
+                        });
+                    });
+
+                    HotLoopScheduler(schedule_group);
+                };
+
+                LoopFunc(I1, I1, I0, I0, I0, I0);
+                LoopFunc(I0, I0, I1, I1, I1, I0);
+
+                i += HotloopUnroll;
+            } while(i < (num_loop - PrefetchStages));
+        }
+
+        auto ReadWriteCompFunc = [&](auto lds_read_buf,
+                                     auto lds_read_reg_buf,
+                                     auto lds_write_buf,
+                                     auto vmem_buf,
+                                     auto mfma_reg_buf,
+                                     auto schedule_group) {
+            block_sync_lds();
+
+            static_for<0, KRepeat, 1>{}([&](auto k) {
+                static_for<0, MRepeat, 1>{}([&](auto m0) {
+                    a_thread_copy_.Run(a_block_desc_m0_m1_m2_k,
+                                       make_tuple(m0, I0, I0, Number<k * AMmaKStride>{}),
+                                       a_block_buf.At(lds_read_buf),
+                                       a_thread_desc_,
+                                       make_tuple(m0, I0, k, I0),
+                                       a_thread_bufs(lds_read_reg_buf));
+                    static_for<0, NRepeat, 1>{}([&](auto n0) {
+                        b_thread_copy_.Run(b_block_desc_n0_n1_n2_k,
+                                           make_tuple(n0, I0, I0, Number<k * BMmaKStride>{}),
+                                           b_block_buf.At(lds_read_buf),
+                                           b_thread_desc_,
+                                           make_tuple(n0, I0, k, I0),
+                                           b_thread_bufs(lds_read_reg_buf));
+                    });
+                });
+            });
+
+            a_blockwise_copy.RunWrite(a_block_desc, a_block_buf.At(lds_write_buf), vmem_buf);
+            b_blockwise_copy.RunWrite(b_block_desc, b_block_buf.At(lds_write_buf), vmem_buf);
+
+            static_for<0, KRepeat, 1>{}([&](auto k0) {
+                static_for<0, MRepeat, 1>{}([&](auto m0) {
+                    static_for<0, NRepeat, 1>{}([&](auto n0) {
+                        vector_type<ComputeDataType, KPack> a_thread_vec;
+                        vector_type<ComputeDataType, KPack> b_thread_vec;
+
+                        static_for<0, KPack, 1>{}([&](auto ik) {
+                            a_thread_vec.template AsType<ComputeDataType>()(ik) =
+                                a_thread_bufs[mfma_reg_buf][Number<a_thread_desc_.CalculateOffset(
+                                    make_tuple(m0, I0, k0, ik))>{}];
+                            b_thread_vec.template AsType<ComputeDataType>()(ik) =
+                                b_thread_bufs[mfma_reg_buf][Number<b_thread_desc_.CalculateOffset(
+                                    make_tuple(n0, I0, k0, ik))>{}];
+                        });
+
+                        using mfma_input_type =
+                            typename vector_type<ComputeDataType, xdlops_gemm.K1PerXdlops>::type;
+
+                        constexpr index_t c_offset =
+                            c_thread_desc_.CalculateOffset(make_tuple(m0, n0, 0));
+
+                        xdlops_gemm.template Run(
+                            a_thread_vec.template AsType<mfma_input_type>(),
+                            b_thread_vec.template AsType<mfma_input_type>(),
+                            c_thread_buf.GetVectorTypeReference(Number<c_offset>{}));
+                    });
+                });
+            });
+
+            HotLoopScheduler(schedule_group);
+        };
+
+        auto ReadCompFunc = [&](auto lds_read_buf,
+                                auto lds_read_reg_buf,
+                                auto mfma_reg_buf,
+                                auto schedule_group) {
+            block_sync_lds();
+
+            static_for<0, KRepeat, 1>{}([&](auto k) {
+                static_for<0, MRepeat, 1>{}([&](auto m0) {
+                    a_thread_copy_.Run(a_block_desc_m0_m1_m2_k,
+                                       make_tuple(m0, I0, I0, Number<k * AMmaKStride>{}),
+                                       a_block_buf.At(lds_read_buf),
+                                       a_thread_desc_,
+                                       make_tuple(m0, I0, k, I0),
+                                       a_thread_bufs(lds_read_reg_buf));
+                    static_for<0, NRepeat, 1>{}([&](auto n0) {
+                        b_thread_copy_.Run(b_block_desc_n0_n1_n2_k,
+                                           make_tuple(n0, I0, I0, Number<k * BMmaKStride>{}),
+                                           b_block_buf.At(lds_read_buf),
+                                           b_thread_desc_,
+                                           make_tuple(n0, I0, k, I0),
+                                           b_thread_bufs(lds_read_reg_buf));
+                    });
+                });
+            });
+
+            static_for<0, KRepeat, 1>{}([&](auto k0) {
+                static_for<0, MRepeat, 1>{}([&](auto m0) {
+                    static_for<0, NRepeat, 1>{}([&](auto n0) {
+                        vector_type<ComputeDataType, KPack> a_thread_vec;
+                        vector_type<ComputeDataType, KPack> b_thread_vec;
+
+                        static_for<0, KPack, 1>{}([&](auto ik) {
+                            a_thread_vec.template AsType<ComputeDataType>()(ik) =
+                                a_thread_bufs[mfma_reg_buf][Number<a_thread_desc_.CalculateOffset(
+                                    make_tuple(m0, I0, k0, ik))>{}];
+                            b_thread_vec.template AsType<ComputeDataType>()(ik) =
+                                b_thread_bufs[mfma_reg_buf][Number<b_thread_desc_.CalculateOffset(
+                                    make_tuple(n0, I0, k0, ik))>{}];
+                        });
+
+                        using mfma_input_type =
+                            typename vector_type<ComputeDataType, xdlops_gemm.K1PerXdlops>::type;
+
+                        constexpr index_t c_offset =
+                            c_thread_desc_.CalculateOffset(make_tuple(m0, n0, 0));
+
+                        xdlops_gemm.template Run(
+                            a_thread_vec.template AsType<mfma_input_type>(),
+                            b_thread_vec.template AsType<mfma_input_type>(),
+                            c_thread_buf.GetVectorTypeReference(Number<c_offset>{}));
+                    });
+                });
+            });
+
+            HotLoopScheduler(schedule_group);
+        };
+
+        auto CompFunc = [&](auto mfma_reg_buf) {
+            static_for<0, KRepeat, 1>{}([&](auto k0) {
+                static_for<0, MRepeat, 1>{}([&](auto m0) {
+                    static_for<0, NRepeat, 1>{}([&](auto n0) {
+                        vector_type<ComputeDataType, KPack> a_thread_vec;
+                        vector_type<ComputeDataType, KPack> b_thread_vec;
+
+                        static_for<0, KPack, 1>{}([&](auto ik) {
+                            a_thread_vec.template AsType<ComputeDataType>()(ik) =
+                                a_thread_bufs[mfma_reg_buf][Number<a_thread_desc_.CalculateOffset(
+                                    make_tuple(m0, I0, k0, ik))>{}];
+                            b_thread_vec.template AsType<ComputeDataType>()(ik) =
+                                b_thread_bufs[mfma_reg_buf][Number<b_thread_desc_.CalculateOffset(
+                                    make_tuple(n0, I0, k0, ik))>{}];
+                        });
+
+                        using mfma_input_type =
+                            typename vector_type<ComputeDataType, xdlops_gemm.K1PerXdlops>::type;
+
+                        constexpr index_t c_offset =
+                            c_thread_desc_.CalculateOffset(make_tuple(m0, n0, 0));
+
+                        xdlops_gemm.template Run(
+                            a_thread_vec.template AsType<mfma_input_type>(),
+                            b_thread_vec.template AsType<mfma_input_type>(),
+                            c_thread_buf.GetVectorTypeReference(Number<c_offset>{}));
+                    });
+                });
+            });
+        };
+        // tail
+        if constexpr(TailNum == TailNumber::Odd)
+        {
+            ReadWriteCompFunc(I1, I1, I0, I0, I0, I1);
+            ReadCompFunc(I0, I0, I1, I1);
+            CompFunc(I0);
+        }
+        else if constexpr(TailNum == TailNumber::Even)
+        {
+            ReadWriteCompFunc(I1, I1, I0, I0, I0, I1);
+            ReadWriteCompFunc(I0, I0, I1, I1, I1, I1);
+            ReadCompFunc(I1, I1, I0, I1);
+            CompFunc(I1);
+        }
+    }
+
+    protected:
+    using Base::a_thread_copy_;
+    using Base::a_thread_desc_;
+    using Base::b_thread_copy_;
+    using Base::b_thread_desc_;
+    using Base::c_thread_desc_;
+};
+
+} // namespace ck

include/ck/tensor_operation/gpu/block/blockwise_gemm_pipeline_xdlops_v5.hpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include "ck/tensor_operation/gpu/block/blockwise_gemm_pipeline_xdlops_base.hpp"
+
+namespace ck {
+
+// Compute optimized pipeline
+// GlobalPrefetchStages: 3
+// LocalPreFillStages: 1
+// LocalPreFetchStages: 1
+// LocalSharedMemoryBuffer: 2
+
+template <BlockGemmPipelineScheduler BlkGemmPipelineVer,
+          index_t BlockSize,
+          typename ADataType,
+          typename BDataType,
+          typename ComputeDataType,
+          typename AccDataType,
+          typename ATileDesc,
+          typename BTileDesc,
+          typename AMmaTileDesc,
+          typename BMmaTileDesc,
+          index_t ABlockTransferSrcScalarPerVector,
+          index_t BBlockTransferSrcScalarPerVector,
+          index_t MPerBlock,
+          index_t NPerBlock,
+          index_t KPerBlock,
+          index_t MPerXDL,
+          index_t NPerXDL,
+          index_t MRepeat,
+          index_t NRepeat,
+          index_t KPacks>
+struct BlockwiseGemmXdlops_pipeline_v5
+{
+};
+
+template <index_t BlockSize,
+          typename ADataType,
+          typename BDataType,
+          typename ComputeDataType,
+          typename AccDataType,
+          typename ATileDesc,
+          typename BTileDesc,
+          typename AMmaTileDesc,
+          typename BMmaTileDesc,
+          index_t ABlockTransferSrcScalarPerVector,
+          index_t BBlockTransferSrcScalarPerVector,
+          index_t MPerBlock,
+          index_t NPerBlock,
+          index_t KPerBlock,
+          index_t MPerXDL,
+          index_t NPerXDL,
+          index_t MRepeat,
+          index_t NRepeat,
+          index_t KPack
+          // ,bool TransposeC //disable transposec right now...
+          >
+struct BlockwiseGemmXdlops_pipeline_v5<BlockGemmPipelineScheduler::Intrawave,
+                                       BlockSize,
+                                       ADataType,
+                                       BDataType,
+                                       ComputeDataType,
+                                       AccDataType,
+                                       ATileDesc,
+                                       BTileDesc,
+                                       AMmaTileDesc,
+                                       BMmaTileDesc,
+                                       ABlockTransferSrcScalarPerVector,
+                                       BBlockTransferSrcScalarPerVector,
+                                       MPerBlock,
+                                       NPerBlock,
+                                       KPerBlock,
+                                       MPerXDL,
+                                       NPerXDL,
+                                       MRepeat,
+                                       NRepeat,
+                                       KPack>
+    : BlockwiseGemmXdlops_pipeline_base<BlockSize,
+                                        ADataType,
+                                        BDataType,
+                                        ComputeDataType,
+                                        AccDataType,
+                                        ATileDesc,
+                                        BTileDesc,
+                                        AMmaTileDesc,
+                                        BMmaTileDesc,
+                                        ABlockTransferSrcScalarPerVector,
+                                        BBlockTransferSrcScalarPerVector,
+                                        MPerBlock,
+                                        NPerBlock,
+                                        KPerBlock,
+                                        MPerXDL,
+                                        NPerXDL,
+                                        MRepeat,
+                                        NRepeat,
+                                        KPack>
+
+{
+    using Base = BlockwiseGemmXdlops_pipeline_base<BlockSize,
+                                                   ADataType,
+                                                   BDataType,
+                                                   ComputeDataType,
+                                                   AccDataType,
+                                                   ATileDesc,
+                                                   BTileDesc,
+                                                   AMmaTileDesc,
+                                                   BMmaTileDesc,
+                                                   ABlockTransferSrcScalarPerVector,
+                                                   BBlockTransferSrcScalarPerVector,
+                                                   MPerBlock,
+                                                   NPerBlock,
+                                                   KPerBlock,
+                                                   MPerXDL,
+                                                   NPerXDL,
+                                                   MRepeat,
+                                                   NRepeat,
+                                                   KPack>;
+    using Base::A_K1;
+    using Base::B_K1;
+    using Base::I0;
+    using Base::I1;
+    using Base::KRepeat;
+    using Base::xdlops_gemm;
+    using typename Base::HotLoopInstList;
+
+    using Base::CalculateCThreadOriginDataIndex;
+    using Base::CalculateCThreadOriginDataIndex8D;
+    using Base::GetCBlockDescriptor_G_M0_N0_M1_N1_M2_M3_M4_N2;
+    using Base::GetCBlockDescriptor_M0_N0_M1_N1_M2_M3_M4_N2;
+    using Base::GetCBlockDescriptor_M0_N0_M1_N1_M2_N2_N3_N4;
+    using Base::GetCThreadBuffer;
+    using Base::GetCThreadDescriptor_G_M0_N0_M1_N1_M2_M3_M4_N2;
+    using Base::GetCThreadDescriptor_M0_N0_M1_N1_M2_M3_M4_N2;
+    using Base::GetCThreadDescriptor_M0_N0_M1_N1_M2_N2_N3_N4;
+    using Base::MakeCGridDescriptor_G_M0_N0_M1_N1_M2_M3_M4_N2;
+    using Base::MakeCGridDescriptor_M0_N0_M1_N1_M2_M3_M4_N2;
+
+    using Base::a_block_desc_m0_m1_m2_k;
+    using Base::b_block_desc_n0_n1_n2_k;
+
+    using Base::AMmaKStride;
+    using Base::BMmaKStride;
+
+    static constexpr index_t PrefetchStages  = 3;
+    static constexpr index_t PrefillStages   = 1;
+    static constexpr index_t GlobalBufferNum = 2;
+    static constexpr index_t HotloopUnroll   = 2;
+
+    __host__ static constexpr bool BlockHasHotloop(index_t num_loop)
+    {
+        return num_loop > PrefetchStages;
+    }
+
+    __host__ static constexpr TailNumber BlockLoopTailNum(index_t num_loop)
+    {
+        if(num_loop % HotloopUnroll == 1)
+        {
+            return TailNumber::Odd;
+        }
+        else
+        {
+            return TailNumber::Even;
+        }
+    }
+
+    __device__ static constexpr auto HotLoopScheduler()
+    {
+        // TODO: Take data type into consideration as pipe ver 3
+        // A/B split schedule
+        // compiler is likely to use ds_read2 when instruction width smaller than 16bytes
+        constexpr auto num_ds_read_inst_a =
+            HotLoopInstList::A_LDS_Read_Width * sizeof(ADataType) == 16
+                ? HotLoopInstList::A_LDS_Read_Inst_Num
+                : HotLoopInstList::A_LDS_Read_Inst_Num / 2;
+        constexpr auto num_ds_read_inst_b =
+            HotLoopInstList::B_LDS_Read_Width * sizeof(BDataType) == 16
+                ? HotLoopInstList::B_LDS_Read_Inst_Num
+                : HotLoopInstList::B_LDS_Read_Inst_Num / 2;
+
+        constexpr auto num_ds_write_inst_a = HotLoopInstList::A_LDS_Write_Inst_Num;
+        constexpr auto num_ds_write_inst_b = HotLoopInstList::B_LDS_Write_Inst_Num;
+
+        constexpr auto num_buffer_load_inst_a = HotLoopInstList::A_Buffer_Load_Inst_Num;
+        constexpr auto num_buffer_load_inst_b = HotLoopInstList::B_Buffer_Load_Inst_Num;
+
+        constexpr auto num_mfma_inst = HotLoopInstList::C_MFMA_Inst_Num;
+
+        constexpr auto mfma_cycle = NPerXDL == 16 ? 16 : 32;
+        constexpr auto ds_read_a_issue_cycle =
+            HotLoopInstList::A_LDS_Read_Width * sizeof(ADataType) == 16 ? 8 : 4;
+        constexpr auto ds_read_b_issue_cycle =
+            HotLoopInstList::B_LDS_Read_Width * sizeof(BDataType) == 16 ? 8 : 4;
+        constexpr auto ds_read_a_mfma_rate =
+            (mfma_cycle - 8 + ds_read_a_issue_cycle - 1) / (2 * ds_read_a_issue_cycle);
+        constexpr auto ds_read_b_mfma_rate =
+            (mfma_cycle - 8 + ds_read_b_issue_cycle - 1) / (2 * ds_read_b_issue_cycle);
+
+        constexpr auto num_dsread_stage1_a = num_ds_read_inst_a / KRepeat * (KRepeat - 1);
+        constexpr auto num_dsread_stage1_b = num_ds_read_inst_b / KRepeat * (KRepeat - 1);
+        constexpr auto num_dsread_stage3_a = num_ds_read_inst_a / KRepeat;
+        constexpr auto num_dsread_stage3_b = num_ds_read_inst_b / KRepeat;
+
+        constexpr auto num_dsread_stage1_a_mfma =
+            (num_dsread_stage1_a + ds_read_a_mfma_rate - 1) / ds_read_a_mfma_rate;
+        constexpr auto num_dsread_stage1_b_mfma =
+            (num_dsread_stage1_b + ds_read_b_mfma_rate - 1) / ds_read_b_mfma_rate;
+        constexpr auto num_dsread_stage3_a_mfma =
+            (num_dsread_stage3_a + ds_read_a_mfma_rate - 1) / ds_read_a_mfma_rate;
+        constexpr auto num_dsread_stage3_b_mfma =
+            (num_dsread_stage3_b + ds_read_b_mfma_rate - 1) / ds_read_b_mfma_rate;
+
+        constexpr auto num_mfma_stage2 = num_mfma_inst - num_ds_read_inst_a / ds_read_a_mfma_rate -
+                                         num_ds_read_inst_b / ds_read_b_mfma_rate;
+        constexpr auto num_mfma_per_issue =
+            num_mfma_stage2 / (num_buffer_load_inst_a + num_buffer_load_inst_b);
+        constexpr auto num_dswrite_per_issue_a = num_ds_write_inst_a / num_buffer_load_inst_a;
+        constexpr auto num_dswrite_per_issue_b = num_ds_write_inst_b / num_buffer_load_inst_b;
+
+        // stage 1
+        static_for<0, num_dsread_stage1_a_mfma, 1>{}([&](auto i) {
+            ignore = i;
+            if constexpr((num_dsread_stage1_a - (i + 1) * ds_read_a_mfma_rate) >=
+                         ds_read_a_mfma_rate)
+            {
+                __builtin_amdgcn_sched_group_barrier(0x100, ds_read_a_mfma_rate, 0); // DS read
+            }
+            else
+            {
+                __builtin_amdgcn_sched_group_barrier(
+                    0x100,
+                    num_dsread_stage1_a - (num_dsread_stage1_a_mfma - 1) * ds_read_a_mfma_rate,
+                    0); // DS read
+            }
+            __builtin_amdgcn_sched_group_barrier(0x008, 1, 0); // MFMA
+        });
+        static_for<0, num_dsread_stage1_b_mfma, 1>{}([&](auto i) {
+            ignore = i;
+            if constexpr((num_dsread_stage1_b - (i + 1) * ds_read_b_mfma_rate) >=
+                         ds_read_b_mfma_rate)
+            {
+                __builtin_amdgcn_sched_group_barrier(0x100, ds_read_b_mfma_rate, 0); // DS read
+            }
+            else
+            {
+                __builtin_amdgcn_sched_group_barrier(
+                    0x100,
+                    num_dsread_stage1_b - (num_dsread_stage1_b_mfma - 1) * ds_read_b_mfma_rate,
+                    0); // DS read
+            }
+            __builtin_amdgcn_sched_group_barrier(0x008, 1, 0); // MFMA
+        });
+
+        // stage 2
+        static_for<0, num_buffer_load_inst_a, 1>{}([&](auto i) {
+            ignore = i;
+            static_for<0, num_dswrite_per_issue_a, 1>{}([&](auto idswrite) {
+                ignore = idswrite;
+                __builtin_amdgcn_sched_group_barrier(0x200, 1, 0); // DS write
+                __builtin_amdgcn_sched_group_barrier(0x008, 1, 0); // MFMA
+            });
+            __builtin_amdgcn_sched_group_barrier(0x020, 1, 0); // VMEM read
+            __builtin_amdgcn_sched_group_barrier(
+                0x008, num_mfma_per_issue - num_dswrite_per_issue_a, 0); // MFMA
+        });
+        static_for<0, num_buffer_load_inst_b, 1>{}([&](auto i) {
+            ignore = i;
+            static_for<0, num_dswrite_per_issue_b, 1>{}([&](auto idswrite) {
+                ignore = idswrite;
+                __builtin_amdgcn_sched_group_barrier(0x200, 1, 0); // DS write
+                __builtin_amdgcn_sched_group_barrier(0x008, 1, 0); // MFMA
+            });
+            __builtin_amdgcn_sched_group_barrier(0x020, 1, 0); // VMEM read
+            __builtin_amdgcn_sched_group_barrier(
+                0x008, num_mfma_per_issue - num_dswrite_per_issue_b, 0); // MFMA
+        });
+
+        // stage 3
+        static_for<0, num_dsread_stage3_a_mfma, 1>{}([&](auto i) {
+            ignore = i;
+            if constexpr((num_dsread_stage3_a - (i + 1) * ds_read_a_mfma_rate) >=
+                         ds_read_a_mfma_rate)
+            {
+                __builtin_amdgcn_sched_group_barrier(0x100, ds_read_a_mfma_rate, 0); // DS read
+            }
+            else
+            {
+                __builtin_amdgcn_sched_group_barrier(
+                    0x100,
+                    num_dsread_stage3_a - (num_dsread_stage3_a_mfma - 1) * ds_read_a_mfma_rate,
+                    0); // DS read
+            }
+            __builtin_amdgcn_sched_group_barrier(0x008, 1, 0); // MFMA
+        });
+        static_for<0, num_dsread_stage3_b_mfma, 1>{}([&](auto i) {
+            ignore = i;
+            if constexpr((num_dsread_stage3_b - (i + 1) * ds_read_b_mfma_rate) >=
+                         ds_read_b_mfma_rate)
+            {
+                __builtin_amdgcn_sched_group_barrier(0x100, ds_read_b_mfma_rate, 0); // DS read
+            }
+            else
+            {
+                __builtin_amdgcn_sched_group_barrier(
+                    0x100,
+                    num_dsread_stage3_b - (num_dsread_stage3_b_mfma - 1) * ds_read_b_mfma_rate,
+                    0); // DS read
+            }
+            __builtin_amdgcn_sched_group_barrier(0x008, 1, 0); // MFMA
+        });
+
+        // IGLP COMPILER BUG:
+        // If comment out following scheduler barrier would cause sanity fail.
+        __builtin_amdgcn_sched_barrier(0);
+    }
+
+    template <bool HasMainLoop,
+              TailNumber TailNum,
+              typename AGridDesc,
+              typename ABlockDesc,
+              typename ABlockTransfer,
+              typename AGridBuffer,
+              typename ABlockBuffer,
+              typename ABlockTransferStep,
+              typename BGridDesc,
+              typename BBlockDesc,
+              typename BBlockTransfer,
+              typename BGridBuffer,
+              typename BBlockBuffer,
+              typename BBlockTransferStep,
+              typename CThreadBuffer>
+    __device__ void Run(const AGridDesc& a_grid_desc,
+                        const ABlockDesc& a_block_desc,
+                        ABlockTransfer& a_blockwise_copy,
+                        const AGridBuffer& a_grid_buf,
+                        ABlockBuffer& a_block_buf,
+                        const ABlockTransferStep& a_block_copy_step,
+                        const BGridDesc& b_grid_desc,
+                        const BBlockDesc& b_block_desc,
+                        BBlockTransfer& b_blockwise_copy,
+                        const BGridBuffer& b_grid_buf,
+                        BBlockBuffer& b_block_buf,
+                        const BBlockTransferStep& b_block_copy_step,
+                        CThreadBuffer& c_thread_buf,
+                        index_t num_loop) const
+    {
+        auto a_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataType>(
+            a_thread_desc_.GetElementSpaceSize());
+        auto b_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ComputeDataType>(
+            b_thread_desc_.GetElementSpaceSize());
+
+        // Global prefetch 1
+        a_blockwise_copy.RunRead(a_grid_desc, a_grid_buf, I0);
+        b_blockwise_copy.RunRead(b_grid_desc, b_grid_buf, I0);
+
+        a_blockwise_copy.MoveSrcSliceWindow(a_grid_desc, a_block_copy_step);
+        b_blockwise_copy.MoveSrcSliceWindow(b_grid_desc, b_block_copy_step);
+
+        // Local prefill 1
+        a_blockwise_copy.RunWrite(a_block_desc, a_block_buf, I0);
+        b_blockwise_copy.RunWrite(b_block_desc, b_block_buf, I0);
+
+        // Global prefetch 2
+        a_blockwise_copy.RunRead(a_grid_desc, a_grid_buf, I0);
+        b_blockwise_copy.RunRead(b_grid_desc, b_grid_buf, I0);
+
+        a_blockwise_copy.MoveSrcSliceWindow(a_grid_desc, a_block_copy_step);
+        b_blockwise_copy.MoveSrcSliceWindow(b_grid_desc, b_block_copy_step);
+
+        // Global prefetch 3
+        a_blockwise_copy.RunRead(a_grid_desc, a_grid_buf, I1);
+        b_blockwise_copy.RunRead(b_grid_desc, b_grid_buf, I1);
+
+        a_blockwise_copy.MoveSrcSliceWindow(a_grid_desc, a_block_copy_step);
+        b_blockwise_copy.MoveSrcSliceWindow(b_grid_desc, b_block_copy_step);
+
+        // Initialize C
+        c_thread_buf.Clear();
+
+        // Local prefetch 1
+        block_sync_lds();
+        static_for<0, MRepeat, 1>{}([&](auto m0) {
+            a_thread_copy_.Run(a_block_desc_m0_m1_m2_k,
+                               make_tuple(m0, I0, I0, I0),
+                               a_block_buf,
+                               a_thread_desc_,
+                               make_tuple(m0, I0, I0, I0),
+                               a_thread_buf);
+        });
+        static_for<0, NRepeat, 1>{}([&](auto n0) {
+            b_thread_copy_.Run(b_block_desc_n0_n1_n2_k,
+                               make_tuple(n0, I0, I0, I0),
+                               b_block_buf,
+                               b_thread_desc_,
+                               make_tuple(n0, I0, I0, I0),
+                               b_thread_buf);
+        });
+
+        // main body
+        if constexpr(HasMainLoop)
+        {
+            index_t i = 0;
+            do
+            {
+                auto LoopFunc = [&](auto vmem_buf) {
+                    vector_type<ComputeDataType, KPack> a_thread_vec;
+                    vector_type<ComputeDataType, KPack> b_thread_vec;
+
+                    static_for<0, KRepeat, 1>{}([&](auto k0) {
+                        if constexpr(k0 == (KRepeat - 1))
+                        {
+                            block_sync_lds();
+
+                            a_blockwise_copy.RunWrite(a_block_desc, a_block_buf, vmem_buf);
+                            b_blockwise_copy.RunWrite(b_block_desc, b_block_buf, vmem_buf);
+
+                            a_blockwise_copy.RunRead(a_grid_desc, a_grid_buf, vmem_buf);
+                            b_blockwise_copy.RunRead(b_grid_desc, b_grid_buf, vmem_buf);
+
+                            a_blockwise_copy.MoveSrcSliceWindow(a_grid_desc, a_block_copy_step);
+                            b_blockwise_copy.MoveSrcSliceWindow(b_grid_desc, b_block_copy_step);
+
+                            block_sync_lds();
+                        }
+                        static_for<0, MRepeat, 1>{}([&](auto m0) {
+                            static_for<0, NRepeat, 1>{}([&](auto n0) {
+                                static_for<0, KPack, 1>{}([&](auto ik) {
+                                    a_thread_vec.template AsType<ComputeDataType>()(ik) =
+                                        a_thread_buf[Number<a_thread_desc_.CalculateOffset(
+                                            make_tuple(m0, I0, I0, ik))>{}];
+                                });
+                                static_for<0, KPack, 1>{}([&](auto ik) {
+                                    b_thread_vec.template AsType<ComputeDataType>()(ik) =
+                                        b_thread_buf[Number<b_thread_desc_.CalculateOffset(
+                                            make_tuple(n0, I0, I0, ik))>{}];
+                                });
+
+                                using mfma_input_type =
+                                    typename vector_type<ComputeDataType,
+                                                         xdlops_gemm.K1PerXdlops>::type;
+
+                                constexpr index_t c_offset =
+                                    c_thread_desc_.CalculateOffset(make_tuple(m0, n0, 0));
+
+                                xdlops_gemm.template Run(
+                                    a_thread_vec.template AsType<mfma_input_type>(),
+                                    b_thread_vec.template AsType<mfma_input_type>(),
+                                    c_thread_buf.GetVectorTypeReference(Number<c_offset>{}));
+                            });
+
+                            a_thread_copy_.Run(
+                                a_block_desc_m0_m1_m2_k,
+                                make_tuple(m0, I0, I0, Number<(k0 + 1) % KRepeat * AMmaKStride>{}),
+                                a_block_buf,
+                                a_thread_desc_,
+                                make_tuple(m0, I0, I0, I0),
+                                a_thread_buf);
+                        });
+
+                        static_for<0, NRepeat, 1>{}([&](auto n0) {
+                            b_thread_copy_.Run(
+                                b_block_desc_n0_n1_n2_k,
+                                make_tuple(n0, I0, I0, Number<(k0 + 1) % KRepeat * BMmaKStride>{}),
+                                b_block_buf,
+                                b_thread_desc_,
+                                make_tuple(n0, I0, I0, I0),
+                                b_thread_buf);
+                        });
+                    });
+
+                    HotLoopScheduler();
+                };
+
+                LoopFunc(I0);
+                LoopFunc(I1);
+
+                i += HotloopUnroll;
+            } while(i < (num_loop - PrefetchStages));
+        }
+        // tail
+        auto ReadWriteCompFunc = [&](auto vmem_buf) {
+            vector_type<ComputeDataType, KPack> a_thread_vec;
+            vector_type<ComputeDataType, KPack> b_thread_vec;
+
+            static_for<0, KRepeat, 1>{}([&](auto k0) {
+                if constexpr(k0 == (KRepeat - 1))
+                {
+                    block_sync_lds();
+
+                    a_blockwise_copy.RunWrite(a_block_desc, a_block_buf, vmem_buf);
+                    b_blockwise_copy.RunWrite(b_block_desc, b_block_buf, vmem_buf);
+
+                    block_sync_lds();
+                }
+                static_for<0, MRepeat, 1>{}([&](auto m0) {
+                    static_for<0, NRepeat, 1>{}([&](auto n0) {
+                        static_for<0, KPack, 1>{}([&](auto ik) {
+                            a_thread_vec.template AsType<ComputeDataType>()(ik) =
+                                a_thread_buf[Number<a_thread_desc_.CalculateOffset(
+                                    make_tuple(m0, I0, I0, ik))>{}];
+                        });
+                        static_for<0, KPack, 1>{}([&](auto ik) {
+                            b_thread_vec.template AsType<ComputeDataType>()(ik) =
+                                b_thread_buf[Number<b_thread_desc_.CalculateOffset(
+                                    make_tuple(n0, I0, I0, ik))>{}];
+                        });
+
+                        using mfma_input_type =
+                            typename vector_type<ComputeDataType, xdlops_gemm.K1PerXdlops>::type;
+
+                        constexpr index_t c_offset =
+                            c_thread_desc_.CalculateOffset(make_tuple(m0, n0, 0));
+
+                        xdlops_gemm.template Run(
+                            a_thread_vec.template AsType<mfma_input_type>(),
+                            b_thread_vec.template AsType<mfma_input_type>(),
+                            c_thread_buf.GetVectorTypeReference(Number<c_offset>{}));
+                    });
+                    a_thread_copy_.Run(
+                        a_block_desc_m0_m1_m2_k,
+                        make_tuple(m0, I0, I0, Number<(k0 + 1) % KRepeat * AMmaKStride>{}),
+                        a_block_buf,
+                        a_thread_desc_,
+                        make_tuple(m0, I0, I0, I0),
+                        a_thread_buf);
+                });
+
+                static_for<0, NRepeat, 1>{}([&](auto n0) {
+                    b_thread_copy_.Run(
+                        b_block_desc_n0_n1_n2_k,
+                        make_tuple(n0, I0, I0, Number<(k0 + 1) % KRepeat * BMmaKStride>{}),
+                        b_block_buf,
+                        b_thread_desc_,
+                        make_tuple(n0, I0, I0, I0),
+                        b_thread_buf);
+                });
+            });
+
+            HotLoopScheduler();
+        };
+        auto ReadCompFunc = [&]() {
+            vector_type<ComputeDataType, KPack> a_thread_vec;
+            vector_type<ComputeDataType, KPack> b_thread_vec;
+
+            static_for<0, KRepeat - 1, 1>{}([&](auto k0) {
+                static_for<0, MRepeat, 1>{}([&](auto m0) {
+                    static_for<0, NRepeat, 1>{}([&](auto n0) {
+                        static_for<0, KPack, 1>{}([&](auto ik) {
+                            a_thread_vec.template AsType<ComputeDataType>()(ik) =
+                                a_thread_buf[Number<a_thread_desc_.CalculateOffset(
+                                    make_tuple(m0, I0, I0, ik))>{}];
+                        });
+                        static_for<0, KPack, 1>{}([&](auto ik) {
+                            b_thread_vec.template AsType<ComputeDataType>()(ik) =
+                                b_thread_buf[Number<b_thread_desc_.CalculateOffset(
+                                    make_tuple(n0, I0, I0, ik))>{}];
+                        });
+
+                        using mfma_input_type =
+                            typename vector_type<ComputeDataType, xdlops_gemm.K1PerXdlops>::type;
+
+                        constexpr index_t c_offset =
+                            c_thread_desc_.CalculateOffset(make_tuple(m0, n0, 0));
+
+                        xdlops_gemm.template Run(
+                            a_thread_vec.template AsType<mfma_input_type>(),
+                            b_thread_vec.template AsType<mfma_input_type>(),
+                            c_thread_buf.GetVectorTypeReference(Number<c_offset>{}));
+                    });
+
+                    a_thread_copy_.Run(
+                        a_block_desc_m0_m1_m2_k,
+                        make_tuple(m0, I0, I0, Number<(k0 + 1) % KRepeat * AMmaKStride>{}),
+                        a_block_buf,
+                        a_thread_desc_,
+                        make_tuple(m0, I0, I0, I0),
+                        a_thread_buf);
+                });
+
+                static_for<0, NRepeat, 1>{}([&](auto n0) {
+                    b_thread_copy_.Run(
+                        b_block_desc_n0_n1_n2_k,
+                        make_tuple(n0, I0, I0, Number<(k0 + 1) % KRepeat * BMmaKStride>{}),
+                        b_block_buf,
+                        b_thread_desc_,
+                        make_tuple(n0, I0, I0, I0),
+                        b_thread_buf);
+                });
+            });
+
+            static_for<0, MRepeat, 1>{}([&](auto m0) {
+                static_for<0, NRepeat, 1>{}([&](auto n0) {
+                    static_for<0, KPack, 1>{}([&](auto ik) {
+                        a_thread_vec.template AsType<ComputeDataType>()(ik) = a_thread_buf
+                            [Number<a_thread_desc_.CalculateOffset(make_tuple(m0, I0, I0, ik))>{}];
+                    });
+                    static_for<0, KPack, 1>{}([&](auto ik) {
+                        b_thread_vec.template AsType<ComputeDataType>()(ik) = b_thread_buf
+                            [Number<b_thread_desc_.CalculateOffset(make_tuple(n0, I0, I0, ik))>{}];
+                    });
+
+                    using mfma_input_type =
+                        typename vector_type<ComputeDataType, xdlops_gemm.K1PerXdlops>::type;
+
+                    constexpr index_t c_offset =
+                        c_thread_desc_.CalculateOffset(make_tuple(m0, n0, 0));
+
+                    xdlops_gemm.template Run(
+                        a_thread_vec.template AsType<mfma_input_type>(),
+                        b_thread_vec.template AsType<mfma_input_type>(),
+                        c_thread_buf.GetVectorTypeReference(Number<c_offset>{}));
+                });
+            });
+
+            HotLoopScheduler();
+        };
+
+        if constexpr(TailNum == TailNumber::Odd)
+        {
+            ReadWriteCompFunc(I0);
+            ReadWriteCompFunc(I1);
+            ReadCompFunc();
+        }
+        else if constexpr(TailNum == TailNumber::Even)
+        {
+            ReadWriteCompFunc(I0);
+            ReadCompFunc();
+        }
+    }
+
+    protected:
+    // A[MRepeat, I1, I1, KPack]
+    static constexpr auto a_thread_desc_ =
+        make_naive_tensor_descriptor_packed(make_tuple(Number<MRepeat>{}, I1, I1, Number<KPack>{}));
+
+    // B[NRepeat, N1, N2, KPack]
+    static constexpr auto b_thread_desc_ =
+        make_naive_tensor_descriptor_packed(make_tuple(Number<NRepeat>{}, I1, I1, Number<KPack>{}));
+
+    using AThreadCopy = ThreadwiseTensorSliceTransfer_v4<ADataType,
+                                                         ComputeDataType,
+                                                         decltype(a_block_desc_m0_m1_m2_k),
+                                                         decltype(a_thread_desc_),
+                                                         Sequence<1, 1, 1, KPack>,
+                                                         Sequence<0, 1, 2, 3>,
+                                                         3,
+                                                         A_K1,
+                                                         A_K1>;
+
+    using BThreadCopy = ThreadwiseTensorSliceTransfer_v4<BDataType,
+                                                         ComputeDataType,
+                                                         decltype(b_block_desc_n0_n1_n2_k),
+                                                         decltype(b_thread_desc_),
+                                                         Sequence<1, 1, 1, KPack>,
+                                                         Sequence<0, 1, 2, 3>,
+                                                         3,
+                                                         B_K1,
+                                                         B_K1>;
+
+    AThreadCopy a_thread_copy_{Base::CalculateAThreadOriginDataIndex()};
+    BThreadCopy b_thread_copy_{Base::CalculateBThreadOriginDataIndex()};
+    using Base::c_thread_desc_;
+};
+
+} // namespace ck

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

 // SPDX-License-Identifier: MIT
-// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
 
 #pragma once
 
@@ -13,6 +13,10 @@ namespace ck {
 namespace tensor_operation {
 namespace device {
 
+/**
+ * \note This structure is deprecated (left for backwards compatibility). Please use
+ *       DeviceElementwise from device_elementwise.hpp.
+ */
 template <typename InDataTypeTuple,
           typename OutDataTypeTuple,
           typename ElementwiseOperation,

include/ck/tensor_operation/gpu/device/device_gemm_v2.hpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include "ck/tensor_operation/gpu/device/device_base.hpp"
+
+namespace ck {
+namespace tensor_operation {
+namespace device {
+
+template <typename ALayout,
+          typename BLayout,
+          typename CLayout,
+          typename ADataType,
+          typename BDataType,
+          typename CDataType,
+          typename AElementwiseOperation,
+          typename BElementwiseOperation,
+          typename CElementwiseOperation>
+struct DeviceGemmV2 : public BaseOperator
+{
+    virtual std::unique_ptr<BaseArgument>
+    MakeArgumentPointer(const void* p_a,
+                        const void* p_b,
+                        void* p_c,
+                        ck::index_t M,
+                        ck::index_t N,
+                        ck::index_t K,
+                        ck::index_t StrideA,
+                        ck::index_t StrideB,
+                        ck::index_t StrideC,
+                        ck::index_t KSplit,
+                        AElementwiseOperation a_element_op,
+                        BElementwiseOperation b_element_op,
+                        CElementwiseOperation c_element_op) = 0;
+
+    virtual std::unique_ptr<BaseInvoker> MakeInvokerPointer() = 0;
+};
+
+} // namespace device
+} // namespace tensor_operation
+} // namespace ck

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

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include <array>
+
+#include "ck/tensor_operation/gpu/device/device_base.hpp"
+
+namespace ck {
+namespace tensor_operation {
+namespace device {
+
+template <ck::index_t NDimSpatial,
+          typename InLayout,
+          typename WeiLayout,
+          typename OutLayout,
+          typename DsLayout,
+          typename InDataType,
+          typename WeiDataType,
+          typename OutDataType,
+          typename DsDataType,
+          typename InElementwiseOperation,
+          typename WeiElementwiseOperation,
+          typename OutElementwiseOperation,
+          typename ComputeTypeA = InDataType,
+          typename ComputeTypeB = ComputeTypeA>
+struct DeviceGroupedConvBwdWeightMultipleD : public BaseOperator
+{
+    static constexpr index_t NumDTensor = DsLayout::Size();
+
+    virtual std::unique_ptr<BaseArgument> MakeArgumentPointer(
+        const void* p_in_grid,
+        void* p_wei_grid,
+        const void* p_out_grid,
+        const std::array<const void*, NumDTensor>& p_ds,
+        const std::array<index_t, NDimSpatial + 3>& b_g_n_c_wis_lengths, // input
+        const std::array<index_t, NDimSpatial + 3>& b_g_n_c_wis_strides,
+        const std::array<index_t, NDimSpatial + 3>& e_g_k_c_xs_lengths, // weight
+        const std::array<index_t, NDimSpatial + 3>& e_g_k_c_xs_strides,
+        const std::array<index_t, NDimSpatial + 3>& a_g_n_k_wos_lengths, // output
+        const std::array<index_t, NDimSpatial + 3>& a_g_n_k_wos_strides,
+        const std::array<std::array<index_t, NDimSpatial + 3>, NumDTensor>& ds_g_k_c_xs_lengths,
+        const std::array<std::array<index_t, NDimSpatial + 3>, NumDTensor>& ds_g_k_c_xs_strides,
+        const std::array<ck::index_t, NDimSpatial>& conv_filter_strides,
+        const std::array<ck::index_t, NDimSpatial>& conv_filter_dilations,
+        const std::array<ck::index_t, NDimSpatial>& input_left_pads,
+        const std::array<ck::index_t, NDimSpatial>& input_right_pads,
+        InElementwiseOperation in_element_op,
+        WeiElementwiseOperation wei_element_op,
+        OutElementwiseOperation out_element_op,
+        const ck::index_t split_k) = 0;
+
+    virtual std::unique_ptr<BaseInvoker> MakeInvokerPointer() = 0;
+};
+
+} // namespace device
+} // namespace tensor_operation
+} // namespace ck

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

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include <iostream>
+#include <vector>
+
+#include "device_base.hpp"
+
+namespace ck {
+namespace tensor_operation {
+namespace device {
+
+struct GemmMultiABDDesc
+{
+    ck::index_t M_, N_, K_;
+
+    std::vector<ck::index_t> stride_As_;
+    std::vector<ck::index_t> stride_Bs_;
+    std::vector<ck::index_t> stride_Ds_;
+
+    ck::index_t stride_C_;
+};
+
+/*
+ * \brief Grouped Gemm Multi ABD
+ *
+ * C = a_op(A, A1...) * b_op(B, B1...)
+ * E = cde_op(C, D0, D1, ...)
+ *
+ * \tparam AsLayout A layouts (tuple).
+ * \tparam BsLayout B layouts (tuple).
+ * \tparam DsLayout Ds layouts (tuple).
+ * \tparam ELayout Output layout.
+ * \tparam AsDataType A data types (tuple).
+ * \tparam BsDataType B data types (tuple).
+ * \tparam DsDataType D data types (tuple).
+ * \tparam EDataType Output data type.
+ * \tparam AElementwiseOperation A elementwise operation.
+ * \tparam BElementwiseOperation B elementwise operation.
+ * \tparam CDEElementwiseOperation C elementwise operation.
+ */
+template <typename AsLayout,
+          typename BsLayout,
+          typename DsLayout,
+          typename ELayout,
+          typename AsDataType,
+          typename BsDataType,
+          typename DsDataType,
+          typename EDataType,
+          typename AElementwiseOperation,
+          typename BElementwiseOperation,
+          typename CDEElementwiseOperation>
+struct DeviceGroupedGemmMultiABD : public BaseOperator
+{
+    static constexpr index_t NumATensor = AsDataType::Size();
+    static constexpr index_t NumBTensor = BsDataType::Size();
+    static constexpr index_t NumDTensor = DsDataType::Size();
+
+    static_assert(AsLayout::Size() == AsDataType::Size(), "wrong! inconsistent NumATensor");
+    static_assert(BsLayout::Size() == BsDataType::Size(), "wrong! inconsistent NumBTensor");
+    static_assert(DsLayout::Size() == DsDataType::Size(), "wrong! inconsistent NumDTensor");
+
+    /*
+     * \brief Make argument pointer for grouped gemm multi abd.
+     *
+     * \param p_as A pointers to the A.
+     * \param p_bs A pointers to the B.
+     * \param p_ds A pointers to the Ds.
+     * \param p_e A pointers to the E.
+     * \param gemm_desc Gemm descriptors for each group.
+     * \param a_element_op A elementwise operation object.
+     * \param b_element_op B elementwise operation object.
+     * \param cde_element_op CDE elementwise operation object.
+     * \return Pointer to the argument.
+     */
+    virtual std::unique_ptr<BaseArgument>
+    MakeArgumentPointer(std::vector<std::array<const void*, NumATensor>>& p_as,
+                        std::vector<std::array<const void*, NumBTensor>>& p_bs,
+                        std::vector<std::array<const void*, NumDTensor>>& p_ds,
+                        std::vector<void*>& p_e,
+                        std::vector<GemmMultiABDDesc>& gemm_desc,
+                        AElementwiseOperation a_element_op   = AElementwiseOperation{},
+                        BElementwiseOperation b_element_op   = BElementwiseOperation{},
+                        CDEElementwiseOperation c_element_op = CDEElementwiseOperation{}) = 0;
+
+    virtual std::unique_ptr<BaseInvoker> MakeInvokerPointer() = 0;
+
+    virtual void SetElementwiseOps(BaseArgument* p_arg,
+                                   AElementwiseOperation a_element_op,
+                                   BElementwiseOperation b_element_op,
+                                   CDEElementwiseOperation cde_element_op) const = 0;
+};
+
+} // namespace device
+} // namespace tensor_operation
+} // namespace ck

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

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include <iostream>
+#include <array>
+
+#include "device_grouped_gemm_multi_abd.hpp"
+
+namespace ck {
+namespace tensor_operation {
+namespace device {
+
+template <index_t NumATensor = 1, index_t NumBTensor = 1, index_t NumDTensor = 0>
+struct GroupedGemmMultiABDKernelArgument
+{
+    std::array<const void*, NumATensor> p_as_grid;
+    std::array<const void*, NumBTensor> p_bs_grid;
+    std::array<const void*, NumDTensor> p_ds_grid;
+    void* p_e_grid;
+
+    index_t M;
+    index_t N;
+    index_t K;
+
+    std::array<index_t, NumATensor> StrideAs;
+    std::array<index_t, NumBTensor> StrideBs;
+    std::array<index_t, NumDTensor> StrideDs;
+    index_t StrideE;
+};
+
+/*
+ * \brief Grouped Gemm Multi ABD Fixed NK
+ *
+ * C = a_op(A, A1...) * b_op(B, B1...)
+ * E = cde_op(C, D0, D1, ...)
+ *
+ * \tparam AsLayout A layouts (tuple).
+ * \tparam BsLayout B layouts (tuple).
+ * \tparam DsLayout Ds layouts (tuple).
+ * \tparam ELayout Output layout.
+ * \tparam AsDataType A data types (tuple).
+ * \tparam BsDataType B data types (tuple).
+ * \tparam DsDataType D data types (tuple).
+ * \tparam EDataType Output data type.
+ * \tparam AElementwiseOperation A elementwise operation.
+ * \tparam BElementwiseOperation B elementwise operation.
+ * \tparam CDEElementwiseOperation C elementwise operation.
+ */
+template <typename AsLayout,
+          typename BsLayout,
+          typename DsLayout,
+          typename ELayout,
+          typename AsDataType,
+          typename BsDataType,
+          typename DsDataType,
+          typename EDataType,
+          typename AElementwiseOperation,
+          typename BElementwiseOperation,
+          typename CElementwiseOperation>
+struct DeviceGroupedGemmMultiABDFixedNK : DeviceGroupedGemmMultiABD<AsLayout,
+                                                                    BsLayout,
+                                                                    DsLayout,
+                                                                    ELayout,
+                                                                    AsDataType,
+                                                                    BsDataType,
+                                                                    DsDataType,
+                                                                    EDataType,
+                                                                    AElementwiseOperation,
+                                                                    BElementwiseOperation,
+                                                                    CElementwiseOperation>
+{
+    virtual void SetDeviceKernelArgs(BaseArgument* p_arg, const void* kernel_args) const = 0;
+    virtual size_t GetDeviceKernelArgSize(const BaseArgument* p_arg) const               = 0;
+    virtual void SetKBatch(BaseArgument* p_arg, index_t k_batch) const                   = 0;
+};
+
+} // namespace device
+} // namespace tensor_operation
+} // namespace ck

include/ck/tensor_operation/gpu/device/impl/device_cgemm_4gemm_xdl_cshuffle.hpp

 // SPDX-License-Identifier: MIT
-// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
 
 #pragma once
 
@@ -14,7 +14,7 @@
 #include "ck/tensor_operation/gpu/device/device_cgemm.hpp"
 #include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
 #include "ck/tensor_operation/gpu/grid/gridwise_gemm_xdl_cshuffle_v1.hpp"
-#include "ck/tensor_operation/gpu/grid/gridwise_elementwise_1d.hpp"
+#include "ck/tensor_operation/gpu/grid/gridwise_elementwise_2d.hpp"
 #include "ck/tensor_operation/gpu/element/binary_element_wise_operation.hpp"
 #include "ck/host_utility/device_prop.hpp"
 #include "ck/host_utility/kernel_launch.hpp"
@@ -80,42 +80,41 @@ struct DeviceCGemm_4Gemm_Xdl_CShuffle
     static constexpr auto I1 = Number<1>{};
     static constexpr auto I2 = Number<2>{};
 
-    static constexpr auto MPerThread       = Number<4>{};
+    static constexpr index_t MPerThread =
+        MPerBlock / CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock::At(1);
+    static constexpr index_t NPerThread =
+        NPerBlock / CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock::At(3);
+
     static constexpr auto AScalarPerVector = Number<4>{};
     static constexpr auto BScalarPerVector = Number<4>{};
     static constexpr auto CScalarPerVector = Number<4>{};
 
-    template <typename Desc_M>
-    static auto PadDescriptor_M_1d(Desc_M desc_m, index_t gridSize, index_t blockSize)
+    template <typename Desc_M_N>
+    static auto PadDescriptor_M_N(Desc_M_N desc)
     {
-        const auto M            = desc_m.GetLength(I0);
-        const index_t loop_step = gridSize * blockSize * MPerThread;
-        const auto pad          = math::integer_least_multiple(M, loop_step) - M;
-        const auto desc_m_pad =
-            transform_tensor_descriptor(desc_m,
-                                        make_tuple(make_right_pad_transform(M, pad)),
-                                        make_tuple(Sequence<0>{}),
-                                        make_tuple(Sequence<0>{}));
-        return desc_m_pad;
+        const auto M     = desc.GetLength(I0);
+        const auto N     = desc.GetLength(I1);
+        const auto pad_M = math::integer_divide_ceil(M, MPerThread) * MPerThread - M;
+        const auto pad_N = math::integer_divide_ceil(N, NPerThread) * NPerThread - N;
+
+        const auto padded_desc = transform_tensor_descriptor(
+            desc,
+            make_tuple(make_right_pad_transform(M, pad_M), make_right_pad_transform(N, pad_N)),
+            make_tuple(Sequence<0>{}, Sequence<1>{}),
+            make_tuple(Sequence<0>{}, Sequence<1>{}));
+
+        return padded_desc;
     }
 
-    static auto MakeDescriptor_M(const std::vector<index_t>& lengths,
-                                 const std::vector<index_t>& strides,
-                                 index_t gridSize,
-                                 index_t blockSize)
+    static auto MakeDescriptor_M_N(const std::vector<index_t>& lengths,
+                                   const std::vector<index_t>& strides)
     {
         auto tupleOfShape  = generate_tuple([&](auto I) { return lengths[I]; }, Number<2>{});
         auto tupleOfStride = generate_tuple([&](auto I) { return strides[I]; }, Number<2>{});
 
         // nd desc - [s0, s1, s2, ...]
         const auto desc = make_naive_tensor_descriptor(tupleOfShape, tupleOfStride);
-        const auto desc_m = transform_tensor_descriptor(
-            desc,
-            make_tuple(make_merge_transform(tupleOfShape)),
-            make_tuple(generate_sequence_v2([&](auto I) { return I; }, Number<2>{})),
-            make_tuple(Sequence<0>{}));
-
-        return PadDescriptor_M_1d(desc_m, gridSize, blockSize);
+        return PadDescriptor_M_N(desc);
     }
 
     // GridwiseGemm
@@ -166,7 +165,7 @@ struct DeviceCGemm_4Gemm_Xdl_CShuffle
         CShuffleBlockTransferScalarPerVector_NPerBlock,
         LoopSched>;
 
-    using CGridDesc_M = decltype(MakeDescriptor_M({1, 1}, {1, 1}, 1, 1));
+    using CGridDesc_M_N = decltype(MakeDescriptor_M_N({1, 1}, {1, 1}));
 
     // Argument
     struct Argument : public tensor_operation::device::BaseArgument, public GridwiseGemm::Problem
@@ -195,17 +194,13 @@ struct DeviceCGemm_4Gemm_Xdl_CShuffle
               p_c_grid_imag{p_c_grid_imag_},
               p_aux_grid{p_workspace}
         {
-            const index_t grid_size = std::get<1>(GridwiseGemm::CalculateGridSize(M_, N_));
-
             if constexpr(is_same<tensor_layout::gemm::RowMajor, CLayout>::value)
             {
-                c_grid_desc_m =
-                    DeviceOp::MakeDescriptor_M({M_, N_}, {StrideC_, I1}, grid_size, BlockSize);
+                c_grid_desc_m_n = DeviceOp::MakeDescriptor_M_N({M_, N_}, {StrideC_, I1});
             }
             else if constexpr(is_same<tensor_layout::gemm::ColumnMajor, CLayout>::value)
             {
-                c_grid_desc_m =
-                    DeviceOp::MakeDescriptor_M({M_, N_}, {I1, StrideC_}, grid_size, BlockSize);
+                c_grid_desc_m_n = DeviceOp::MakeDescriptor_M_N({M_, N_}, {I1, StrideC_});
             }
 
             p_aux_2_grid = p_workspace + GetCElementSpaceSize(M_, N_, StrideC_);
@@ -220,7 +215,7 @@ struct DeviceCGemm_4Gemm_Xdl_CShuffle
         CDataType* p_c_grid_imag;
         CDataType* p_aux_grid;
         CDataType* p_aux_2_grid;
-        CGridDesc_M c_grid_desc_m;
+        CGridDesc_M_N c_grid_desc_m_n;
     };
 
     // Invoker
@@ -248,39 +243,62 @@ struct DeviceCGemm_4Gemm_Xdl_CShuffle
             using Add      = ck::tensor_operation::element_wise::Add;
             using Subtract = ck::tensor_operation::element_wise::Subtract;
 
-            using GridwiseBinAdd =
-                GridwiseElementwise_1D<Tuple<CGridDesc_M, CGridDesc_M>,
-                                       Tuple<CGridDesc_M>,
+            using Block2TileMap = BlockToCTileMap_M00_N0_M01Adapt<MPerBlock, NPerBlock>;
+
+            using GridwiseBinAdd = GridwiseElementwise<Tuple<CGridDesc_M_N, CGridDesc_M_N>,
+                                                       Tuple<CGridDesc_M_N>,
                                                        Tuple<const CDataType*, const CDataType*>,
                                                        Tuple<CDataType*>,
+                                                       Block2TileMap,
                                                        Add,
+                                                       BlockSize,
+                                                       MPerBlock,
+                                                       NPerBlock,
                                                        MPerThread,
+                                                       NPerThread,
+                                                       Sequence<0, 1>,
                                                        Sequence<AScalarPerVector, BScalarPerVector>,
-                                       Sequence<CScalarPerVector>>;
+                                                       Sequence<CScalarPerVector>,
+                                                       I1,
+                                                       I1>;
 
             using GridwiseBinSubtract =
-                GridwiseElementwise_1D<Tuple<CGridDesc_M, CGridDesc_M>,
-                                       Tuple<CGridDesc_M>,
+                GridwiseElementwise<Tuple<CGridDesc_M_N, CGridDesc_M_N>,
+                                    Tuple<CGridDesc_M_N>,
                                     Tuple<const CDataType*, const CDataType*>,
                                     Tuple<CDataType*>,
+                                    Block2TileMap,
                                     Subtract,
+                                    BlockSize,
+                                    MPerBlock,
+                                    NPerBlock,
                                     MPerThread,
+                                    NPerThread,
+                                    Sequence<0, 1>,
                                     Sequence<AScalarPerVector, BScalarPerVector>,
-                                       Sequence<CScalarPerVector>>;
+                                    Sequence<CScalarPerVector>,
+                                    I1,
+                                    I1>;
+
+            const index_t M             = arg.c_grid_desc_m_n.GetLength(I0);
+            const index_t N             = arg.c_grid_desc_m_n.GetLength(I1);
+            const auto block_2_tile_map = Block2TileMap(M, N);
 
-            const auto add_kernel = kernel_elementwise_1d<GridwiseBinAdd,
-                                                          Tuple<CGridDesc_M, CGridDesc_M>,
-                                                          Tuple<CGridDesc_M>,
+            const auto add_kernel = kernel_elementwise<GridwiseBinAdd,
+                                                       Tuple<CGridDesc_M_N, CGridDesc_M_N>,
+                                                       Tuple<CGridDesc_M_N>,
                                                        Tuple<const CDataType*, const CDataType*>,
                                                        Tuple<CDataType*>,
+                                                       Block2TileMap,
                                                        Add>;
 
             const auto subtract_kernel =
-                kernel_elementwise_1d<GridwiseBinSubtract,
-                                      Tuple<CGridDesc_M, CGridDesc_M>,
-                                      Tuple<CGridDesc_M>,
+                kernel_elementwise<GridwiseBinSubtract,
+                                   Tuple<CGridDesc_M_N, CGridDesc_M_N>,
+                                   Tuple<CGridDesc_M_N>,
                                    Tuple<const CDataType*, const CDataType*>,
                                    Tuple<CDataType*>,
+                                   Block2TileMap,
                                    Subtract>;
 
             if(GridwiseGemm::CalculateHasMainKBlockLoop(K))
@@ -318,11 +336,12 @@ struct DeviceCGemm_4Gemm_Xdl_CShuffle
                     dim3(gdx, gdy, gdz),
                     dim3(BlockSize),
                     0,
-                    make_tuple(arg.c_grid_desc_m, arg.c_grid_desc_m),
-                    make_tuple(arg.c_grid_desc_m),
+                    make_tuple(arg.c_grid_desc_m_n, arg.c_grid_desc_m_n),
+                    make_tuple(arg.c_grid_desc_m_n),
                     make_tuple(const_cast<const CDataType*>(arg.p_aux_grid),
                                const_cast<const CDataType*>(arg.p_aux_2_grid)),
                     make_tuple(arg.p_c_grid_real),
+                    block_2_tile_map,
                     Subtract{});
 
                 ave_time += launch_and_time_kernel(stream_config,
@@ -352,11 +371,12 @@ struct DeviceCGemm_4Gemm_Xdl_CShuffle
                     dim3(gdx, gdy, gdz),
                     dim3(BlockSize),
                     0,
-                    make_tuple(arg.c_grid_desc_m, arg.c_grid_desc_m),
-                    make_tuple(arg.c_grid_desc_m),
+                    make_tuple(arg.c_grid_desc_m_n, arg.c_grid_desc_m_n),
+                    make_tuple(arg.c_grid_desc_m_n),
                     make_tuple(const_cast<const CDataType*>(arg.p_aux_grid),
                                const_cast<const CDataType*>(arg.p_aux_2_grid)),
                     make_tuple(arg.p_c_grid_imag),
+                    block_2_tile_map,
                     Add{});
             }
             else
@@ -394,11 +414,12 @@ struct DeviceCGemm_4Gemm_Xdl_CShuffle
                     dim3(gdx, gdy, gdz),
                     dim3(BlockSize),
                     0,
-                    make_tuple(arg.c_grid_desc_m, arg.c_grid_desc_m),
-                    make_tuple(arg.c_grid_desc_m),
+                    make_tuple(arg.c_grid_desc_m_n, arg.c_grid_desc_m_n),
+                    make_tuple(arg.c_grid_desc_m_n),
                     make_tuple(const_cast<const CDataType*>(arg.p_aux_grid),
                                const_cast<const CDataType*>(arg.p_aux_2_grid)),
                     make_tuple(arg.p_c_grid_real),
+                    block_2_tile_map,
                     Subtract{});
 
                 ave_time += launch_and_time_kernel(stream_config,
@@ -428,11 +449,12 @@ struct DeviceCGemm_4Gemm_Xdl_CShuffle
                     dim3(gdx, gdy, gdz),
                     dim3(BlockSize),
                     0,
-                    make_tuple(arg.c_grid_desc_m, arg.c_grid_desc_m),
-                    make_tuple(arg.c_grid_desc_m),
+                    make_tuple(arg.c_grid_desc_m_n, arg.c_grid_desc_m_n),
+                    make_tuple(arg.c_grid_desc_m_n),
                     make_tuple(const_cast<const CDataType*>(arg.p_aux_grid),
                                const_cast<const CDataType*>(arg.p_aux_2_grid)),
                     make_tuple(arg.p_c_grid_imag),
+                    block_2_tile_map,
                     Add{});
             }
 

include/ck/tensor_operation/gpu/device/impl/device_contraction_multiple_abd_xdl_cshuffle.hpp

@@ -663,7 +663,8 @@ struct DeviceContractionMultipleABD_Xdl_CShuffle
                 const bool valid_a_access_dim_k =
                     ABlockTransferSrcVectorDim == 2 && arg.as_kz_consecutive_[i];
                 const bool valid_a_access_dim = valid_a_access_dim_m || valid_a_access_dim_k;
-                if(!(valid_a_vector_size && valid_a_access_dim))
+                if(!((valid_a_vector_size && valid_a_access_dim) ||
+                     ABlockTransferSrcScalarPerVector == 1))
                 {
                     valid_as_access = false;
                 }
@@ -682,7 +683,8 @@ struct DeviceContractionMultipleABD_Xdl_CShuffle
                 const bool valid_b_access_dim_k =
                     BBlockTransferSrcVectorDim == 2 && arg.bs_kz_consecutive_[i];
                 const bool valid_b_access_dim = valid_b_access_dim_n || valid_b_access_dim_k;
-                if(!(valid_b_vector_size && valid_b_access_dim))
+                if(!((valid_b_vector_size && valid_b_access_dim) ||
+                     BBlockTransferSrcScalarPerVector == 1))
                 {
                     valid_bs_access = false;
                 }
@@ -698,7 +700,8 @@ struct DeviceContractionMultipleABD_Xdl_CShuffle
                     arg.ds_max_read_elems_[i] % CDEBlockTransferScalarPerVector_NPerBlock == 0;
                 // Vector read of Ds is always on N dimension.
                 const bool valid_d_access_dim = arg.ds_nz_consecutive_[i];
-                if(!(valid_d_vector_size && valid_d_access_dim))
+                if(!((valid_d_vector_size && valid_d_access_dim) ||
+                     CDEBlockTransferScalarPerVector_NPerBlock == 1))
                 {
                     valid_ds_access = false;
                 }
@@ -712,7 +715,8 @@ struct DeviceContractionMultipleABD_Xdl_CShuffle
                 arg.e_max_write_elems_ % CDEBlockTransferScalarPerVector_NPerBlock == 0;
             // Vector write of E is always on N dimension.
             const bool valid_e_access_dim = arg.e_nz_consecutive_;
-            if(!(valid_e_vector_size && valid_e_access_dim))
+            if(!((valid_e_vector_size && valid_e_access_dim) ||
+                 CDEBlockTransferScalarPerVector_NPerBlock == 1))
             {
                 return false;
             }

include/ck/tensor_operation/gpu/device/impl/device_elementwise_2d_impl.hpp

-// SPDX-License-Identifier: MIT
-// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
-
-#pragma once
-
-#include <iostream>
-#include <sstream>
-
-#include "ck/utility/math.hpp"
-#include "ck/utility/sequence.hpp"
-#include "ck/tensor_operation/gpu/device/device_elementwise.hpp"
-#include "ck/tensor_operation/gpu/grid/gridwise_elementwise_2d.hpp"
-#include "ck/tensor_description/tensor_descriptor_helper.hpp"
-
-#include "ck/host_utility/kernel_launch.hpp"
-#include "ck/host_utility/stream_utility.hpp"
-
-namespace ck {
-namespace tensor_operation {
-namespace device {
-template <typename InDataTypeTuple,
-          typename OutDataTypeTuple,
-          typename ElementwiseOperation,
-          index_t NumDim_m,
-          index_t NumDim_n,
-          index_t MPerThread,
-          index_t NPerThread,
-          typename InScalarPerVectorSeq,
-          typename OutScalarPerVectorSeq>
-struct DeviceElementwise2dImpl : public DeviceElementwise<InDataTypeTuple,
-                                                          OutDataTypeTuple,
-                                                          ElementwiseOperation,
-                                                          NumDim_m + NumDim_n>
-{
-    static constexpr index_t NumDim = NumDim_m + NumDim_n;
-
-    static constexpr int NumInput  = InDataTypeTuple::Size();
-    static constexpr int NumOutput = OutDataTypeTuple::Size();
-
-    static constexpr auto I0 = Number<0>{};
-    static constexpr auto I1 = Number<1>{};
-
-    static_assert(NumInput == InScalarPerVectorSeq::Size() &&
-                      NumOutput == OutScalarPerVectorSeq::Size(),
-                  "Tuple size is inconsistent with the number of in/out!");
-
-    static auto GenerateInDataTypePointerTuple()
-    {
-        return generate_tuple(
-            [&](auto I) {
-                using DataType = remove_cvref_t<decltype(InDataTypeTuple{}[I])>;
-
-                return static_cast<const DataType*>(nullptr);
-            },
-            Number<NumInput>{});
-    };
-
-    static auto GenerateOutDataTypePointerTuple()
-    {
-        return generate_tuple(
-            [&](auto I) {
-                using DataType = remove_cvref_t<decltype(OutDataTypeTuple{}[I])>;
-
-                return static_cast<DataType*>(nullptr);
-            },
-            Number<NumOutput>{});
-    };
-
-    using InDataTypePointerTuple  = decltype(GenerateInDataTypePointerTuple());
-    using OutDataTypePointerTuple = decltype(GenerateOutDataTypePointerTuple());
-
-    template <typename Desc_MN>
-    static auto PadDescriptor_MN_2d(Desc_MN desc_mn,
-                                    index_t gridSize,
-                                    index_t blockSize,
-                                    index_t num_threads_m,
-                                    index_t num_threads_n)
-    {
-        std::ignore               = blockSize;
-        std::ignore               = gridSize;
-        const auto m              = desc_mn.GetLength(I0);
-        const auto n              = desc_mn.GetLength(I1);
-        const index_t loop_step_m = num_threads_m * MPerThread;
-        const index_t loop_step_n = num_threads_n * NPerThread;
-        const auto pad_m          = math::integer_least_multiple(m, loop_step_m) - m;
-        const auto pad_n          = math::integer_least_multiple(n, loop_step_n) - n;
-
-        const auto desc_mn_pad = transform_tensor_descriptor(
-            desc_mn,
-            make_tuple(make_right_pad_transform(m, pad_m), make_right_pad_transform(n, pad_n)),
-            make_tuple(Sequence<0>{}, Sequence<1>{}),
-            make_tuple(Sequence<0>{}, Sequence<1>{}));
-        return desc_mn_pad;
-    }
-
-    static auto MakeDescriptor_MN(const std::array<index_t, NumDim>& lengths,
-                                  const std::array<index_t, NumDim>& stride,
-                                  index_t gridSize,
-                                  index_t blockSize,
-                                  index_t num_threads_m,
-                                  index_t num_threads_n)
-    {
-        auto tupleOfShape  = generate_tuple([&](auto I) { return lengths[I]; }, Number<NumDim>{});
-        auto tupleOfStride = generate_tuple([&](auto I) { return stride[I]; }, Number<NumDim>{});
-
-        // nd desc - [s0, s1, s2, ...]
-        const auto desc = make_naive_tensor_descriptor(tupleOfShape, tupleOfStride);
-
-        constexpr auto mDimIds = typename arithmetic_sequence_gen<0, NumDim_m, 1>::type();
-        constexpr auto nDimIds =
-            typename arithmetic_sequence_gen<NumDim_m, NumDim_m + NumDim_n, 1>::type();
-
-        const auto mLengths = get_container_subset(tupleOfShape, mDimIds);
-        const auto nLengths = get_container_subset(tupleOfShape, nDimIds);
-
-        // merge nd to 2d desc - [s0 * s1 * ...]
-
-        if constexpr(NumDim > 2)
-        {
-            const auto desc_mn = transform_tensor_descriptor(
-                desc,
-                make_tuple(make_merge_transform(mLengths), make_merge_transform(nLengths)),
-                make_tuple(mDimIds, nDimIds),
-                make_tuple(Sequence<0>{}, Sequence<1>{}));
-
-            return PadDescriptor_MN_2d(desc_mn, gridSize, blockSize, num_threads_m, num_threads_n);
-        }
-        else
-            return PadDescriptor_MN_2d(desc, gridSize, blockSize, num_threads_m, num_threads_n);
-    }
-
-    template <index_t TupleSize>
-    static auto GenerateInOutGrid2dDescTuple(Number<TupleSize>)
-    {
-        return generate_tuple(
-            [&](auto) {
-                if constexpr(NumDim > 2)
-                {
-                    return MakeDescriptor_MN({1, 1}, {1, 1}, 1, 1, 1, 1);
-                }
-                else
-                {
-                    return MakeDescriptor_MN({1}, {1}, 1, 1, 1, 1);
-                };
-            },
-            Number<TupleSize>{});
-    };
-
-    using OutGrid2dDescTuple = decltype(GenerateInOutGrid2dDescTuple(Number<NumOutput>{}));
-    using InGrid2dDescTuple  = decltype(GenerateInOutGrid2dDescTuple(Number<NumInput>{}));
-
-    using GridwiseElementwise = GridwiseElementwise_2D<InGrid2dDescTuple,
-                                                       OutGrid2dDescTuple,
-                                                       InDataTypePointerTuple,
-                                                       OutDataTypePointerTuple,
-                                                       ElementwiseOperation,
-                                                       MPerThread,
-                                                       NPerThread,
-                                                       InScalarPerVectorSeq,
-                                                       OutScalarPerVectorSeq>;
-
-    struct Argument : public BaseArgument
-    {
-        Argument(const std::array<index_t, NumDim> lengths,
-                 const std::array<std::array<index_t, NumDim>, NumInput> inStridesArray,
-                 const std::array<std::array<index_t, NumDim>, NumOutput> outStridesArray,
-                 const std::array<const void*, NumInput> in_dev_buffers,
-                 const std::array<void*, NumOutput> out_dev_buffers,
-                 ElementwiseOperation elementwise_op)
-
-            : lengths_(lengths),
-              inStridesArray_(inStridesArray),
-              outStridesArray_(outStridesArray),
-              elementwise_op_(elementwise_op),
-              blockSize_(256)
-        {
-            static_assert(NumDim_m > 0, "");
-            static_assert(NumDim_n > 0, "");
-
-            in_dev_buffers_ = generate_tuple(
-                [&](auto I) {
-                    using DataType = remove_cvref_t<decltype(InDataTypeTuple{}[I])>;
-                    return static_cast<const DataType*>(in_dev_buffers[I.value]);
-                },
-                Number<NumInput>{});
-
-            out_dev_buffers_ = generate_tuple(
-                [&](auto I) {
-                    using DataType = remove_cvref_t<decltype(OutDataTypeTuple{}[I])>;
-                    return static_cast<DataType*>(out_dev_buffers[I.value]);
-                },
-                Number<NumOutput>{});
-        }
-
-        InDataTypePointerTuple in_dev_buffers_;
-        OutDataTypePointerTuple out_dev_buffers_;
-
-        std::array<index_t, NumDim> lengths_;
-        std::array<std::array<index_t, NumDim>, NumInput> inStridesArray_;
-        std::array<std::array<index_t, NumDim>, NumOutput> outStridesArray_;
-
-        ElementwiseOperation elementwise_op_;
-        index_t blockSize_;
-    };
-
-    struct Invoker : public BaseInvoker
-    {
-        float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
-        {
-            index_t gridSize      = getAvailableComputeUnitCount(stream_config);
-            index_t num_threads_m = (gridSize * arg.blockSize_) / 16;
-            index_t num_threads_n = 16;
-
-            auto in_grid_2d_desc_tuple = generate_tuple(
-                [&](auto I) {
-                    return MakeDescriptor_MN(arg.lengths_,
-                                             arg.inStridesArray_[I.value],
-                                             gridSize,
-                                             arg.blockSize_,
-                                             num_threads_m,
-                                             num_threads_n);
-                },
-                Number<NumInput>{});
-
-            auto out_grid_2d_desc_tuple = generate_tuple(
-                [&](auto I) {
-                    return MakeDescriptor_MN(arg.lengths_,
-                                             arg.outStridesArray_[I.value],
-                                             gridSize,
-                                             arg.blockSize_,
-                                             num_threads_m,
-                                             num_threads_n);
-                },
-                Number<NumOutput>{});
-
-            const auto kernel = kernel_elementwise_2d<GridwiseElementwise,
-                                                      InGrid2dDescTuple,
-                                                      OutGrid2dDescTuple,
-                                                      InDataTypePointerTuple,
-                                                      OutDataTypePointerTuple,
-                                                      ElementwiseOperation>;
-
-            float elapsed_time = launch_and_time_kernel(stream_config,
-                                                        kernel,
-                                                        dim3(gridSize),
-                                                        dim3(arg.blockSize_),
-                                                        0,
-                                                        in_grid_2d_desc_tuple,
-                                                        out_grid_2d_desc_tuple,
-                                                        arg.in_dev_buffers_,
-                                                        arg.out_dev_buffers_,
-                                                        arg.elementwise_op_,
-                                                        num_threads_m,
-                                                        num_threads_n);
-            return elapsed_time;
-        }
-
-        // polymorphic
-        float Run(const BaseArgument* p_arg,
-                  const StreamConfig& stream_config = StreamConfig{}) override
-        {
-            return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
-        }
-    };
-
-    bool IsSupportedArgument(const BaseArgument* p_arg) override
-    {
-        const Argument* pArg = dynamic_cast<const Argument*>(p_arg);
-
-        if(pArg == nullptr)
-            return false;
-
-        if(pArg->lengths_.back() % MPerThread != 0)
-            return false;
-
-        auto IsScalarPerVectorValid = [&](const std::array<index_t, NumDim>& lengths,
-                                          const std::array<index_t, NumDim>& strides,
-                                          index_t scalarPerVector,
-                                          index_t vectorDim) {
-            if(strides[vectorDim] == 1 &&
-               (lengths[vectorDim] % scalarPerVector == 0 ||
-                lengths[vectorDim] % scalarPerVector == lengths[vectorDim]))
-            {
-                return true;
-            }
-            if(strides[vectorDim] != 1 && scalarPerVector == strides[vectorDim])
-            {
-                return true;
-            }
-            return false;
-        };
-
-        bool valid = true;
-        static_for<0, NumInput, 1>{}([&](auto I) {
-            if(!IsScalarPerVectorValid(pArg->lengths_,
-                                       pArg->inStridesArray_[I.value],
-                                       InScalarPerVectorSeq::At(I),
-                                       NumDim_m - 1))
-                valid = false;
-        });
-
-        static_for<0, NumOutput, 1>{}([&](auto I) {
-            if(!IsScalarPerVectorValid(pArg->lengths_,
-                                       pArg->outStridesArray_[I.value],
-                                       OutScalarPerVectorSeq::At(I),
-                                       NumDim - 1))
-                valid = false;
-        });
-
-        return valid;
-    };
-
-    std::unique_ptr<BaseArgument>
-    MakeArgumentPointer(const std::array<index_t, NumDim> lengths,
-                        const std::array<std::array<index_t, NumDim>, NumInput> inStridesArray,
-                        const std::array<std::array<index_t, NumDim>, NumOutput> outStridesArray,
-                        const std::array<const void*, NumInput> in_dev_buffers,
-                        const std::array<void*, NumOutput> out_dev_buffers,
-                        ElementwiseOperation elementwise_op) override
-    {
-        return std::make_unique<Argument>(lengths,
-                                          inStridesArray,
-                                          outStridesArray,
-                                          in_dev_buffers,
-                                          out_dev_buffers,
-                                          elementwise_op);
-    }
-
-    static auto MakeInvoker() { return Invoker{}; }
-    std::unique_ptr<BaseInvoker> MakeInvokerPointer() override
-    {
-        return std::make_unique<Invoker>();
-    };
-}; // namespace device
-
-} // namespace device
-} // namespace tensor_operation
-} // namespace ck

include/ck/tensor_operation/gpu/device/impl/device_elementwise_3d_impl.hpp

-// SPDX-License-Identifier: MIT
-// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
-
-#pragma once
-
-#include <iostream>
-#include <sstream>
-
-#include "ck/utility/math.hpp"
-#include "ck/utility/sequence.hpp"
-#include "ck/tensor_operation/gpu/device/device_elementwise.hpp"
-#include "ck/tensor_operation/gpu/grid/gridwise_elementwise_3d.hpp"
-#include "ck/tensor_description/tensor_descriptor_helper.hpp"
-
-#include "ck/host_utility/kernel_launch.hpp"
-#include "ck/host_utility/device_prop.hpp"
-#include "ck/host_utility/stream_utility.hpp"
-
-namespace ck {
-namespace tensor_operation {
-namespace device {
-template <typename InDataTypeTuple,
-          typename OutDataTypeTuple,
-          typename ElementwiseOperation,
-          index_t NumDim_m, // choose how to set dims
-          index_t NumDim_n,
-          index_t NumDim_k,
-          index_t MPerThread,
-          index_t NPerThread,
-          index_t KPerThread,
-          typename InScalarPerVectorSeq,
-          typename OutScalarPerVectorSeq>
-struct DeviceElementwise3dImpl : public DeviceElementwise<InDataTypeTuple,
-                                                          OutDataTypeTuple,
-                                                          ElementwiseOperation,
-                                                          NumDim_m + NumDim_n + NumDim_k>
-{
-    static constexpr index_t NumDim = NumDim_m + NumDim_n + NumDim_k;
-
-    static constexpr int NumInput  = InDataTypeTuple::Size();
-    static constexpr int NumOutput = OutDataTypeTuple::Size();
-
-    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_assert(NumInput == InScalarPerVectorSeq::Size() &&
-                      NumOutput == OutScalarPerVectorSeq::Size(),
-                  "Tuple size is inconsistent with the number of in/out!");
-
-    static auto GenerateInDataTypePointerTuple()
-    {
-        return generate_tuple(
-            [&](auto I) {
-                using DataType = remove_cvref_t<decltype(InDataTypeTuple{}[I])>;
-
-                return static_cast<const DataType*>(nullptr);
-            },
-            Number<NumInput>{});
-    }
-
-    static auto GenerateOutDataTypePointerTuple()
-    {
-        return generate_tuple(
-            [&](auto I) {
-                using DataType = remove_cvref_t<decltype(OutDataTypeTuple{}[I])>;
-
-                return static_cast<DataType*>(nullptr);
-            },
-            Number<NumOutput>{});
-    }
-
-    using InDataTypePointerTuple  = decltype(GenerateInDataTypePointerTuple());
-    using OutDataTypePointerTuple = decltype(GenerateOutDataTypePointerTuple());
-
-    template <typename Desc_MNK>
-    static auto PadDescriptor_MNK(Desc_MNK desc_mnk,
-                                  index_t gridSize,
-                                  index_t blockSize,
-                                  index_t num_threads_m,
-                                  index_t num_threads_n,
-                                  index_t num_threads_k)
-    {
-        std::ignore = blockSize;
-        std::ignore = gridSize;
-
-        const auto m = desc_mnk.GetLength(I0);
-        const auto n = desc_mnk.GetLength(I1);
-        const auto k = desc_mnk.GetLength(I2);
-
-        const index_t loop_step_m = num_threads_m * MPerThread;
-        const index_t loop_step_n = num_threads_n * NPerThread;
-        const index_t loop_step_k = num_threads_k * KPerThread;
-
-        const auto pad_m = math::integer_least_multiple(m, loop_step_m) - m;
-        const auto pad_n = math::integer_least_multiple(n, loop_step_n) - n;
-        const auto pad_k = math::integer_least_multiple(k, loop_step_k) - k;
-
-        const auto desc_mnk_pad =
-            transform_tensor_descriptor(desc_mnk,
-                                        make_tuple(make_right_pad_transform(m, pad_m),
-                                                   make_right_pad_transform(n, pad_n),
-                                                   make_right_pad_transform(k, pad_k)),
-                                        make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
-                                        make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
-        return desc_mnk_pad;
-    }
-
-    static auto MakeDescriptor_MNK(const std::array<index_t, NumDim>& lengths,
-                                   const std::array<index_t, NumDim>& stride,
-                                   index_t gridSize,
-                                   index_t blockSize,
-                                   index_t num_threads_m,
-                                   index_t num_threads_n,
-                                   index_t num_threads_k)
-    {
-        auto tupleOfShape  = generate_tuple([&](auto I) { return lengths[I]; }, Number<NumDim>{});
-        auto tupleOfStride = generate_tuple([&](auto I) { return stride[I]; }, Number<NumDim>{});
-
-        // nd desc - [s0, s1, s2, ...]
-        const auto desc = make_naive_tensor_descriptor(tupleOfShape, tupleOfStride);
-
-        constexpr auto mDimIds = typename arithmetic_sequence_gen<0, NumDim_m, 1>::type();
-        constexpr auto nDimIds =
-            typename arithmetic_sequence_gen<NumDim_m, NumDim_m + NumDim_n, 1>::type();
-        constexpr auto kDimIds =
-            typename arithmetic_sequence_gen<NumDim_m + NumDim_n, NumDim, 1>::type();
-
-        const auto mLengths = get_container_subset(tupleOfShape, mDimIds);
-        const auto nLengths = get_container_subset(tupleOfShape, nDimIds);
-        const auto kLengths = get_container_subset(tupleOfShape, kDimIds);
-
-        // merge nd to 3d desc - [s0 * s1 * ...]
-        if constexpr(NumDim > 3)
-        {
-            const auto desc_mnk = transform_tensor_descriptor(
-                desc,
-                make_tuple(make_merge_transform(mLengths),
-                           make_merge_transform(nLengths),
-                           make_merge_transform(kLengths)),
-                make_tuple(mDimIds, nDimIds, kDimIds),
-                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
-
-            return PadDescriptor_MNK(
-                desc_mnk, gridSize, blockSize, num_threads_m, num_threads_n, num_threads_k);
-        }
-        else
-            return PadDescriptor_MNK(
-                desc, gridSize, blockSize, num_threads_m, num_threads_n, num_threads_k);
-    }
-
-    template <index_t TupleSize>
-    static auto GenerateInOutGrid3dDescTuple(Number<TupleSize>)
-    {
-        return generate_tuple(
-            [&](auto) {
-                if constexpr(NumDim > 3)
-                {
-                    return MakeDescriptor_MNK({1, 1, 1}, {1, 1, 1}, 1, 1, 1, 1, 1);
-                }
-                else
-                {
-                    return MakeDescriptor_MNK({1}, {1}, 1, 1, 1, 1, 1);
-                };
-            },
-            Number<TupleSize>{});
-    }
-
-    using OutGrid3dDescTuple = decltype(GenerateInOutGrid3dDescTuple(Number<NumOutput>{}));
-    using InGrid3dDescTuple  = decltype(GenerateInOutGrid3dDescTuple(Number<NumInput>{}));
-
-    using GridwiseElementwise = GridwiseElementwise_3D<InGrid3dDescTuple,
-                                                       OutGrid3dDescTuple,
-                                                       InDataTypePointerTuple,
-                                                       OutDataTypePointerTuple,
-                                                       ElementwiseOperation,
-                                                       MPerThread,
-                                                       NPerThread,
-                                                       KPerThread,
-                                                       InScalarPerVectorSeq,
-                                                       OutScalarPerVectorSeq>;
-
-    struct Argument : public BaseArgument
-    {
-        Argument(const std::array<index_t, NumDim> lengths,
-                 const std::array<std::array<index_t, NumDim>, NumInput> inStridesArray,
-                 const std::array<std::array<index_t, NumDim>, NumOutput> outStridesArray,
-                 const std::array<const void*, NumInput> in_dev_buffers,
-                 const std::array<void*, NumOutput> out_dev_buffers,
-                 ElementwiseOperation elementwise_op)
-
-            : lengths_(lengths),
-              inStridesArray_(inStridesArray),
-              outStridesArray_(outStridesArray),
-              elementwise_op_(elementwise_op),
-              blockSize_(256)
-        {
-            static_assert(NumDim_m > 0, "");
-            static_assert(NumDim_n > 0, "");
-            static_assert(NumDim_k > 0, "");
-
-            in_dev_buffers_ = generate_tuple(
-                [&](auto I) {
-                    using DataType = remove_cvref_t<decltype(InDataTypeTuple{}[I])>;
-                    return static_cast<const DataType*>(in_dev_buffers[I.value]);
-                },
-                Number<NumInput>{});
-
-            out_dev_buffers_ = generate_tuple(
-                [&](auto I) {
-                    using DataType = remove_cvref_t<decltype(OutDataTypeTuple{}[I])>;
-                    return static_cast<DataType*>(out_dev_buffers[I.value]);
-                },
-                Number<NumOutput>{});
-        }
-
-        InDataTypePointerTuple in_dev_buffers_;
-        OutDataTypePointerTuple out_dev_buffers_;
-
-        std::array<index_t, NumDim> lengths_;
-        std::array<std::array<index_t, NumDim>, NumInput> inStridesArray_;
-        std::array<std::array<index_t, NumDim>, NumOutput> outStridesArray_;
-
-        ElementwiseOperation elementwise_op_;
-        index_t blockSize_;
-    };
-
-    struct Invoker : public BaseInvoker
-    {
-        float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
-        {
-            index_t gridSize      = getAvailableComputeUnitCount(stream_config) * arg.blockSize_;
-            index_t num_threads_m = gridSize / (16 * 16);
-            index_t num_threads_n = 16;
-            index_t num_threads_k = 16;
-
-            auto in_grid_3d_desc_tuple = generate_tuple(
-                [&](auto I) {
-                    return MakeDescriptor_MNK(arg.lengths_,
-                                              arg.inStridesArray_[I.value],
-                                              gridSize,
-                                              arg.blockSize_,
-                                              num_threads_m,
-                                              num_threads_n,
-                                              num_threads_k);
-                },
-                Number<NumInput>{});
-
-            auto out_grid_3d_desc_tuple = generate_tuple(
-                [&](auto I) {
-                    return MakeDescriptor_MNK(arg.lengths_,
-                                              arg.outStridesArray_[I.value],
-                                              gridSize,
-                                              arg.blockSize_,
-                                              num_threads_m,
-                                              num_threads_n,
-                                              num_threads_k);
-                },
-                Number<NumOutput>{});
-
-            const auto kernel = kernel_elementwise_3d<GridwiseElementwise,
-                                                      InGrid3dDescTuple,
-                                                      OutGrid3dDescTuple,
-                                                      InDataTypePointerTuple,
-                                                      OutDataTypePointerTuple,
-                                                      ElementwiseOperation>;
-
-            float elapsed_time = launch_and_time_kernel(stream_config,
-                                                        kernel,
-                                                        dim3(gridSize),
-                                                        dim3(arg.blockSize_),
-                                                        0,
-                                                        in_grid_3d_desc_tuple,
-                                                        out_grid_3d_desc_tuple,
-                                                        arg.in_dev_buffers_,
-                                                        arg.out_dev_buffers_,
-                                                        arg.elementwise_op_,
-                                                        num_threads_m,
-                                                        num_threads_n,
-                                                        num_threads_k);
-            return elapsed_time;
-        }
-
-        // polymorphic
-        float Run(const BaseArgument* p_arg,
-                  const StreamConfig& stream_config = StreamConfig{}) override
-        {
-            return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
-        }
-    };
-
-    bool IsSupportedArgument(const BaseArgument* p_arg) override
-    {
-        if((ck::get_device_name() == "gfx940" || ck::get_device_name() == "gfx941" ||
-            ck::get_device_name() == "gfx942"))
-        {
-            return false;
-        }
-
-        const Argument* pArg = dynamic_cast<const Argument*>(p_arg);
-
-        if(pArg == nullptr)
-            return false;
-
-        if(pArg->lengths_.back() % MPerThread != 0)
-            return false;
-
-        auto IsScalarPerVectorValid = [&](const std::array<index_t, NumDim>& lengths,
-                                          const std::array<index_t, NumDim>& strides,
-                                          index_t scalarPerVector,
-                                          index_t vectorDim) {
-            if(strides[vectorDim] == 1 &&
-               (lengths[vectorDim] % scalarPerVector == 0 ||
-                lengths[vectorDim] % scalarPerVector == lengths[vectorDim]))
-            {
-                return true;
-            }
-
-            if(strides[vectorDim] >= scalarPerVector)
-            {
-                return true;
-            }
-            return false;
-        };
-
-        bool valid = true;
-        static_for<0, NumInput, 1>{}([&](auto I) {
-            valid = valid && IsScalarPerVectorValid(pArg->lengths_,
-                                                    pArg->inStridesArray_[I.value],
-                                                    InScalarPerVectorSeq::At(I),
-                                                    NumDim_m - 1);
-        });
-
-        static_for<0, NumOutput, 1>{}([&](auto I) {
-            valid = valid && IsScalarPerVectorValid(pArg->lengths_,
-                                                    pArg->outStridesArray_[I.value],
-                                                    OutScalarPerVectorSeq::At(I),
-                                                    NumDim - 1);
-        });
-
-        return valid;
-    }
-
-    std::unique_ptr<BaseArgument>
-    MakeArgumentPointer(const std::array<index_t, NumDim> lengths,
-                        const std::array<std::array<index_t, NumDim>, NumInput> inStridesArray,
-                        const std::array<std::array<index_t, NumDim>, NumOutput> outStridesArray,
-                        const std::array<const void*, NumInput> in_dev_buffers,
-                        const std::array<void*, NumOutput> out_dev_buffers,
-                        ElementwiseOperation elementwise_op) override
-    {
-        return std::make_unique<Argument>(lengths,
-                                          inStridesArray,
-                                          outStridesArray,
-                                          in_dev_buffers,
-                                          out_dev_buffers,
-                                          elementwise_op);
-    }
-
-    static auto MakeInvoker() { return Invoker{}; }
-    std::unique_ptr<BaseInvoker> MakeInvokerPointer() override
-    {
-        return std::make_unique<Invoker>();
-    }
-}; // namespace device
-
-} // namespace device
-} // namespace tensor_operation
-} // namespace ck