Rebased the hipTENSOR development branch with the contraction branch

include/ck/tensor_operation/gpu/grid/gridwise_gemm_dlops_v2.hpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
+
 #ifndef CK_GRIDWISE_GEMM_V2_HPP
 #define CK_GRIDWISE_GEMM_V2_HPP
 

include/ck/tensor_operation/gpu/grid/gridwise_gemm_dlops_v3.hpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
+
 #ifndef CK_GRIDWISE_GEMM_V3_HPP
 #define CK_GRIDWISE_GEMM_V3_HPP
 

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

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include "ck/utility/common_header.hpp"
+#include "ck/tensor_description/multi_index_transform_helper.hpp"
+#include "ck/tensor_description/tensor_descriptor.hpp"
+#include "ck/tensor_description/tensor_descriptor_helper.hpp"
+#include "ck/tensor_operation/gpu/grid/block_to_ctile_map.hpp"
+#include "ck/tensor_operation/gpu/grid/gridwise_gemm_pipeline_v1.hpp"
+#include "ck/tensor_operation/gpu/block/blockwise_gemm_xdlops.hpp"
+#include "ck/tensor_operation/gpu/block/thread_group_tensor_slice_transfer_v4r1.hpp"
+#include "ck/tensor_operation/gpu/block/thread_group_tensor_slice_transfer_v7.hpp"
+#include "ck/tensor_operation/gpu/thread/threadwise_tensor_slice_transfer.hpp"
+#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
+
+namespace ck {
+
+// GEMM:
+//   input : A[AK0, M, AK1]
+//   input : B[AK0, N, AK1]
+//   input : D0[M, N], D1[M, N], ...
+//   output : E[M, N]
+//   C = a_op(A) * b_op(B)
+//   E = cde_op(C, D0, D1, ...)
+// Assume:
+//   D0, D1, ... and E have the same layout
+template <typename FloatAB,
+          typename FloatGemmAcc,
+          typename FloatCShuffle,
+          typename DsDataType,
+          typename FloatE,
+          typename AElementwiseOperation,
+          typename BElementwiseOperation,
+          typename CDEElementwiseOperation,
+          InMemoryDataOperationEnum EGlobalMemoryDataOperation,
+          typename AGridDesc_AK0_M_AK1,
+          typename BGridDesc_BK0_N_BK1,
+          typename EGridDesc_M_N,
+          index_t NumGemmKPrefetchStage,
+          index_t BlockSize,
+          index_t MPerBlock,
+          index_t NPerBlock,
+          index_t KPerBlock,
+          index_t AK1Value,
+          index_t BK1Value,
+          index_t MPerXdl,
+          index_t NPerXdl,
+          index_t MXdlPerWave,
+          index_t NXdlPerWave,
+          typename ABlockTransferThreadClusterLengths_AK0_M_AK1,
+          typename ABlockTransferThreadClusterArrangeOrder,
+          typename ABlockTransferSrcAccessOrder,
+          index_t ABlockTransferSrcVectorDim,
+          index_t ABlockTransferSrcScalarPerVector,
+          index_t ABlockTransferDstScalarPerVector_AK1,
+          bool AThreadTransferSrcResetCoordinateAfterRun,
+          index_t ABlockLdsExtraM,
+          typename BBlockTransferThreadClusterLengths_BK0_N_BK1,
+          typename BBlockTransferThreadClusterArrangeOrder,
+          typename BBlockTransferSrcAccessOrder,
+          index_t BBlockTransferSrcVectorDim,
+          index_t BBlockTransferSrcScalarPerVector,
+          index_t BBlockTransferDstScalarPerVector_BK1,
+          bool BThreadTransferSrcResetCoordinateAfterRun,
+          index_t BBlockLdsExtraN,
+          index_t CShuffleMXdlPerWavePerShuffle,
+          index_t CShuffleNXdlPerWavePerShuffle,
+          typename CDEBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
+          index_t CDEShuffleBlockTransferScalarPerVector_NPerBlock,
+          LoopScheduler LoopSched>
+struct GridwiseGemmMultipleD_k0mk1_k0nk1_mn_xdl_cshuffle
+{
+    static constexpr index_t NumDTensor = DsDataType::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 constexpr auto I5 = Number<5>{};
+    static constexpr auto I6 = Number<6>{};
+    static constexpr auto I7 = Number<7>{};
+
+    // K1 should be Number<...>
+    static constexpr auto AK0 = Number<KPerBlock / AK1Value>{};
+    static constexpr auto BK0 = Number<KPerBlock / BK1Value>{};
+    static constexpr auto AK1 = Number<AK1Value>{};
+    static constexpr auto BK1 = Number<BK1Value>{};
+
+    using ThisThreadBlock = ThisThreadBlock<BlockSize>;
+
+    using GridwiseGemmPipe = GridwiseGemmPipeline_v1<NumGemmKPrefetchStage>;
+
+    __host__ __device__ static constexpr auto GetABlockDescriptor_AK0PerBlock_MPerBlock_AK1()
+    {
+        // A matrix in LDS memory, dst of blockwise copy
+        return make_naive_tensor_descriptor(
+            make_tuple(AK0, Number<MPerBlock>{}, AK1),
+            make_tuple(Number<MPerBlock + ABlockLdsExtraM>{} * AK1, AK1, I1));
+    }
+
+    __host__ __device__ static constexpr auto GetBBlockDescriptor_BK0PerBlock_NPerBlock_BK1()
+    {
+        // B matrix in LDS memory, dst of blockwise copy
+        return make_naive_tensor_descriptor(
+            make_tuple(BK0, Number<NPerBlock>{}, BK1),
+            make_tuple(Number<NPerBlock + BBlockLdsExtraN>{} * BK1, BK1, I1));
+    }
+
+    __host__ __device__ static constexpr auto
+    GetCShuffleBlockDescriptor_MBlock_MPerBlock_NBlock_NPerBlock()
+    {
+        constexpr index_t MWave = MPerBlock / (MXdlPerWave * MPerXdl);
+        constexpr index_t NWave = NPerBlock / (NXdlPerWave * NPerXdl);
+
+        constexpr auto c_shuffle_block_desc_mblock_mperblock_nblock_nperblock =
+            make_naive_tensor_descriptor_packed(
+                make_tuple(I1,
+                           Number<CShuffleMXdlPerWavePerShuffle * MWave * MPerXdl>{},
+                           I1,
+                           Number<CShuffleNXdlPerWavePerShuffle * NWave * NPerXdl>{}));
+
+        return c_shuffle_block_desc_mblock_mperblock_nblock_nperblock;
+    }
+
+    // ck::Tuple<const D0DataType*, const D1DataType*, ...>
+    static constexpr auto MakeDsGridPointer()
+    {
+        return generate_tuple(
+            [&](auto i) {
+                using DDataType = remove_cvref_t<tuple_element_t<i.value, DsDataType>>;
+
+                return static_cast<const DDataType*>(nullptr);
+            },
+            Number<NumDTensor>{});
+    }
+
+    __host__ __device__ static constexpr index_t GetSharedMemoryNumberOfByte()
+    {
+        // LDS allocation for A and B: be careful of alignment
+        constexpr auto a_block_desc_ak0_m_ak1 = GetABlockDescriptor_AK0PerBlock_MPerBlock_AK1();
+        constexpr auto b_block_desc_bk0_n_bk1 = GetBBlockDescriptor_BK0PerBlock_NPerBlock_BK1();
+
+        // lds max alignment
+        constexpr auto max_lds_align = math::lcm(AK1, BK1);
+
+        constexpr auto a_block_space_size_aligned = math::integer_least_multiple(
+            a_block_desc_ak0_m_ak1.GetElementSpaceSize(), max_lds_align);
+
+        constexpr auto b_block_space_size_aligned = math::integer_least_multiple(
+            b_block_desc_bk0_n_bk1.GetElementSpaceSize(), max_lds_align);
+
+        // LDS allocation for C shuffle in LDS
+        constexpr auto c_shuffle_block_desc_mblock_mperblock_nblock_nperblock =
+            GetCShuffleBlockDescriptor_MBlock_MPerBlock_NBlock_NPerBlock();
+
+        constexpr auto c_block_size =
+            c_shuffle_block_desc_mblock_mperblock_nblock_nperblock.GetElementSpaceSize();
+
+        return math::max((a_block_space_size_aligned + b_block_space_size_aligned) *
+                             sizeof(FloatAB),
+                         c_block_size * sizeof(FloatCShuffle));
+    }
+
+    // block_id to matrix tile idx (m0, n0) mapping are controlled by {M01, N01}
+    template <typename Block2ETileMap>
+    __host__ __device__ static constexpr bool
+    CheckValidity(const AGridDesc_AK0_M_AK1& a_grid_desc_ak0_m_ak1,
+                  const BGridDesc_BK0_N_BK1& b_grid_desc_bk0_n_bk1,
+                  const EGridDesc_M_N& e_grid_desc_m_n,
+                  const Block2ETileMap& block_2_etile_map)
+    {
+        static_assert((MPerBlock % (MPerXdl * MXdlPerWave) == 0) &&
+                          (NPerBlock % (NXdlPerWave * NPerXdl)) == 0,
+                      "Invalid tuning param!");
+
+        const auto M = a_grid_desc_ak0_m_ak1.GetLength(I1);
+        const auto N = b_grid_desc_bk0_n_bk1.GetLength(I1);
+        const auto K = a_grid_desc_ak0_m_ak1.GetLength(I0) * a_grid_desc_ak0_m_ak1.GetLength(I2);
+
+        if(!(M == e_grid_desc_m_n.GetLength(I0) && N == e_grid_desc_m_n.GetLength(I1)))
+            return false;
+
+        if(!(M % MPerBlock == 0 && N % NPerBlock == 0 && K % KPerBlock == 0))
+            return false;
+
+        // check gridwise gemm pipeline
+        const auto num_k_loop = K / KPerBlock;
+
+        if(!GridwiseGemmPipe::IsSupported(num_k_loop))
+        {
+            return false;
+        }
+
+        if(!block_2_etile_map.CheckValidity(e_grid_desc_m_n))
+        {
+            return false;
+        }
+
+        // TODO: also check validity of all components (blockwise-copy, threadwise-copy, etc)
+        return true;
+    }
+
+    __host__ __device__ static constexpr bool CalculateHasMainKBlockLoop(index_t K)
+    {
+        const index_t num_loop = K / KPerBlock;
+
+        return GridwiseGemmPipe::CalculateHasMainLoop(num_loop);
+    }
+
+    __host__ __device__ static constexpr auto
+    MakeEGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(const EGridDesc_M_N& e_grid_desc_m_n)
+    {
+        const auto M = e_grid_desc_m_n.GetLength(I0);
+        const auto N = e_grid_desc_m_n.GetLength(I1);
+
+        const auto MBlock = M / MPerBlock;
+        const auto NBlock = N / NPerBlock;
+
+        const auto e_grid_desc_mblock_mperblock_nblock_nperblock = transform_tensor_descriptor(
+            e_grid_desc_m_n,
+            make_tuple(make_unmerge_transform(make_tuple(MBlock, Number<MPerBlock>{})),
+                       make_unmerge_transform(make_tuple(NBlock, Number<NPerBlock>{}))),
+            make_tuple(Sequence<0>{}, Sequence<1>{}),
+            make_tuple(Sequence<0, 1>{}, Sequence<2, 3>{}));
+
+        return e_grid_desc_mblock_mperblock_nblock_nperblock;
+    }
+
+    // return block_id to E matrix tile idx (m0, n0) mapping
+    __host__ __device__ static constexpr auto
+    MakeDefaultBlock2ETileMap(const EGridDesc_M_N& e_grid_desc_m_n)
+    {
+        return BlockToCTileMap_M00_N0_M01Adapt<MPerBlock, NPerBlock, EGridDesc_M_N>(
+            e_grid_desc_m_n);
+    }
+
+    using EGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock = remove_cvref_t<decltype(
+        MakeEGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(EGridDesc_M_N{}))>;
+
+    using DefaultBlock2ETileMap =
+        remove_cvref_t<decltype(MakeDefaultBlock2ETileMap(EGridDesc_M_N{}))>;
+
+    using DsGridPointer = decltype(MakeDsGridPointer());
+
+    template <bool HasMainKBlockLoop, typename Block2ETileMap>
+    __device__ static void
+    Run(const FloatAB* __restrict__ p_a_grid,
+        const FloatAB* __restrict__ p_b_grid,
+        DsGridPointer p_ds_grid,
+        FloatE* __restrict__ p_e_grid,
+        void* __restrict__ p_shared,
+        const AElementwiseOperation& a_element_op,
+        const BElementwiseOperation& b_element_op,
+        const CDEElementwiseOperation& cde_element_op,
+        const AGridDesc_AK0_M_AK1& a_grid_desc_ak0_m_ak1,
+        const BGridDesc_BK0_N_BK1& b_grid_desc_bk0_n_bk1,
+        const StaticallyIndexedArray<EGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
+                                     NumDTensor>&
+            ds_grid_desc_mblock_mperblock_nblock_nperblock, // FIXME: Ds desc may be of different
+                                                            // type from E
+        const EGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock&
+            e_grid_desc_mblock_mperblock_nblock_nperblock,
+        const Block2ETileMap& block_2_etile_map)
+    {
+        const auto a_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
+            p_a_grid, a_grid_desc_ak0_m_ak1.GetElementSpaceSize());
+
+        const auto b_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
+            p_b_grid, b_grid_desc_bk0_n_bk1.GetElementSpaceSize());
+
+        const auto ds_grid_buf = generate_tuple(
+            [&](auto i) {
+                return make_dynamic_buffer<AddressSpaceEnum::Global>(
+                    p_ds_grid[i],
+                    ds_grid_desc_mblock_mperblock_nblock_nperblock[i].GetElementSpaceSize());
+            },
+            Number<NumDTensor>{});
+
+        auto e_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
+            p_e_grid, e_grid_desc_mblock_mperblock_nblock_nperblock.GetElementSpaceSize());
+
+        // divide block work by [M, N]
+        const auto block_work_idx =
+            block_2_etile_map.CalculateBottomIndex(make_multi_index(get_block_1d_id()));
+
+        if(!block_2_etile_map.ValidCTileIndex(
+               block_work_idx,
+               make_tuple(e_grid_desc_mblock_mperblock_nblock_nperblock.GetLength(I0),
+                          e_grid_desc_mblock_mperblock_nblock_nperblock.GetLength(I2))))
+        {
+            return;
+        }
+
+        // HACK: this force m/n_block_data_idx_on_grid into SGPR
+        const index_t m_block_data_idx_on_grid =
+            __builtin_amdgcn_readfirstlane(block_work_idx[I0] * MPerBlock);
+
+        const index_t n_block_data_idx_on_grid =
+            __builtin_amdgcn_readfirstlane(block_work_idx[I1] * NPerBlock);
+
+        // lds max alignment
+        constexpr auto max_lds_align = math::lcm(AK1, BK1);
+
+        // A matrix in LDS memory, dst of blockwise copy
+        constexpr auto a_block_desc_ak0_m_ak1 = GetABlockDescriptor_AK0PerBlock_MPerBlock_AK1();
+
+        // B matrix in LDS memory, dst of blockwise copy
+        constexpr auto b_block_desc_bk0_n_bk1 = GetBBlockDescriptor_BK0PerBlock_NPerBlock_BK1();
+
+        // A matrix blockwise copy
+        auto a_blockwise_copy =
+            ThreadGroupTensorSliceTransfer_v4r1<ThisThreadBlock,
+                                                AElementwiseOperation,
+                                                ck::tensor_operation::element_wise::PassThrough,
+                                                InMemoryDataOperationEnum::Set,
+                                                Sequence<AK0, MPerBlock, AK1>,
+                                                ABlockTransferThreadClusterLengths_AK0_M_AK1,
+                                                ABlockTransferThreadClusterArrangeOrder,
+                                                FloatAB,
+                                                FloatAB,
+                                                decltype(a_grid_desc_ak0_m_ak1),
+                                                decltype(a_block_desc_ak0_m_ak1),
+                                                ABlockTransferSrcAccessOrder,
+                                                Sequence<1, 0, 2>,
+                                                ABlockTransferSrcVectorDim,
+                                                2,
+                                                ABlockTransferSrcScalarPerVector,
+                                                ABlockTransferDstScalarPerVector_AK1,
+                                                1,
+                                                1,
+                                                AThreadTransferSrcResetCoordinateAfterRun,
+                                                true,
+                                                NumGemmKPrefetchStage>(
+                a_grid_desc_ak0_m_ak1,
+                make_multi_index(0, m_block_data_idx_on_grid, 0),
+                a_element_op,
+                a_block_desc_ak0_m_ak1,
+                make_multi_index(0, 0, 0),
+                ck::tensor_operation::element_wise::PassThrough{});
+
+        // B matrix blockwise copy
+        auto b_blockwise_copy =
+            ThreadGroupTensorSliceTransfer_v4r1<ThisThreadBlock,
+                                                BElementwiseOperation,
+                                                ck::tensor_operation::element_wise::PassThrough,
+                                                InMemoryDataOperationEnum::Set,
+                                                Sequence<BK0, NPerBlock, BK1>,
+                                                BBlockTransferThreadClusterLengths_BK0_N_BK1,
+                                                BBlockTransferThreadClusterArrangeOrder,
+                                                FloatAB,
+                                                FloatAB,
+                                                decltype(b_grid_desc_bk0_n_bk1),
+                                                decltype(b_block_desc_bk0_n_bk1),
+                                                BBlockTransferSrcAccessOrder,
+                                                Sequence<1, 0, 2>,
+                                                BBlockTransferSrcVectorDim,
+                                                2,
+                                                BBlockTransferSrcScalarPerVector,
+                                                BBlockTransferDstScalarPerVector_BK1,
+                                                1,
+                                                1,
+                                                BThreadTransferSrcResetCoordinateAfterRun,
+                                                true,
+                                                NumGemmKPrefetchStage>(
+                b_grid_desc_bk0_n_bk1,
+                make_multi_index(0, n_block_data_idx_on_grid, 0),
+                b_element_op,
+                b_block_desc_bk0_n_bk1,
+                make_multi_index(0, 0, 0),
+                ck::tensor_operation::element_wise::PassThrough{});
+
+        // GEMM definition
+        //   c_mtx += transpose(a_mtx) * b_mtx
+        //     a_mtx[K0PerBlock, MPerBlock] is in LDS
+        //     b_mtx[K0PerBlock, NPerBlock] is in LDS
+        //     c_mtx[MPerBlock, NPerBlock] is distributed among threads, and saved in
+        //       register
+        // sanity check
+        constexpr index_t KPack = math::max(
+            math::lcm(AK1, BK1), MfmaSelector<FloatAB, MPerXdl, NPerXdl>::selected_mfma.k_per_blk);
+
+        auto blockwise_gemm = BlockwiseGemmXdlops_k0mk1_k0nk1_m0n0m1n1m2m3m4n2_Selector<
+            BlockSize,
+            FloatAB,
+            FloatGemmAcc,
+            decltype(a_block_desc_ak0_m_ak1),
+            decltype(b_block_desc_bk0_n_bk1),
+            MPerXdl,
+            NPerXdl,
+            MXdlPerWave,
+            NXdlPerWave,
+            KPack,
+            LoopSched>();
+
+        auto c_thread_buf = blockwise_gemm.GetCThreadBuffer();
+
+        // LDS allocation for A and B: be careful of alignment
+        constexpr auto a_block_space_size_aligned = math::integer_least_multiple(
+            a_block_desc_ak0_m_ak1.GetElementSpaceSize(), max_lds_align);
+
+        auto a_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
+            static_cast<FloatAB*>(p_shared), a_block_desc_ak0_m_ak1.GetElementSpaceSize());
+
+        auto b_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
+            static_cast<FloatAB*>(p_shared) + a_block_space_size_aligned,
+            b_block_desc_bk0_n_bk1.GetElementSpaceSize());
+
+        constexpr auto a_block_slice_copy_step = make_multi_index(KPerBlock / AK1, 0, 0);
+        constexpr auto b_block_slice_copy_step = make_multi_index(KPerBlock / BK1, 0, 0);
+
+        // gridwise GEMM pipeline
+        const auto gridwise_gemm_pipeline =
+            GridwiseGemmPipeline_v1_Selector<NumGemmKPrefetchStage, LoopSched>();
+
+        const index_t num_k_block_main_loop = __builtin_amdgcn_readfirstlane(
+            (a_grid_desc_ak0_m_ak1.GetLength(I0) * a_grid_desc_ak0_m_ak1.GetLength(I2)) /
+            KPerBlock);
+
+        gridwise_gemm_pipeline.template Run<HasMainKBlockLoop>(a_grid_desc_ak0_m_ak1,
+                                                               a_block_desc_ak0_m_ak1,
+                                                               a_blockwise_copy,
+                                                               a_grid_buf,
+                                                               a_block_buf,
+                                                               a_block_slice_copy_step,
+                                                               b_grid_desc_bk0_n_bk1,
+                                                               b_block_desc_bk0_n_bk1,
+                                                               b_blockwise_copy,
+                                                               b_grid_buf,
+                                                               b_block_buf,
+                                                               b_block_slice_copy_step,
+                                                               blockwise_gemm,
+                                                               c_thread_buf,
+                                                               num_k_block_main_loop);
+
+        // shuffle C and write out
+        {
+            static_assert(MXdlPerWave % CShuffleMXdlPerWavePerShuffle == 0 &&
+                              NXdlPerWave % CShuffleNXdlPerWavePerShuffle == 0,
+                          "wrong!");
+
+            constexpr index_t MWave = MPerBlock / (MXdlPerWave * MPerXdl);
+            constexpr index_t NWave = NPerBlock / (NXdlPerWave * NPerXdl);
+
+            // TODO: hacky, fix it!
+            constexpr auto c_thread_desc_m0_n0_m1_n1_m2_m3_m4_n2 =
+                blockwise_gemm.GetCThreadDescriptor_M0_N0_M1_N1_M2_M3_M4_N2();
+
+            // TODO: hacky, fix it!
+            // c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp is only used to get lengths
+            constexpr auto c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp =
+                blockwise_gemm.GetCBlockDescriptor_M0_N0_M1_N1_M2_M3_M4_N2();
+
+            constexpr auto M0 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I0);
+            constexpr auto N0 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I1);
+            constexpr auto M1 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I2);
+            constexpr auto N1 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I3);
+            constexpr auto M2 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I4);
+            constexpr auto M3 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I5);
+            constexpr auto M4 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I6);
+            constexpr auto N2 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I7);
+
+            constexpr auto c_shuffle_block_desc_mblock_mperblock_nblock_nperblock =
+                GetCShuffleBlockDescriptor_MBlock_MPerBlock_NBlock_NPerBlock();
+
+            auto c_shuffle_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
+                static_cast<FloatCShuffle*>(p_shared),
+                c_shuffle_block_desc_mblock_mperblock_nblock_nperblock.GetElementSpaceSize());
+
+            constexpr auto c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2 = transform_tensor_descriptor(
+                c_shuffle_block_desc_mblock_mperblock_nblock_nperblock,
+                make_tuple(
+                    make_freeze_transform(I0),
+                    make_unmerge_transform(make_tuple(
+                        Number<CShuffleMXdlPerWavePerShuffle>{}, // M0 (MXdlPerWave) per shuffle
+                        M1,                                      // M1 = MWave
+                        M2,                                      // M2 * M3 * M4 = MPerXdl
+                        M3,
+                        M4)),
+                    make_freeze_transform(I0),
+                    make_unmerge_transform(make_tuple(
+                        Number<CShuffleNXdlPerWavePerShuffle>{}, // N0 (NXdlPerWave) per shuffle
+                        N1,                                      // N1 = NWave
+                        N2))),                                   // N2 = NPerXdl
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
+                make_tuple(
+                    Sequence<>{}, Sequence<0, 2, 4, 5, 6>{}, Sequence<>{}, Sequence<1, 3, 7>{}));
+
+            // calculate origin of thread output tensor on global memory
+            //     blockwise GEMM c matrix starting index
+            const auto c_thread_mtx_on_block =
+                blockwise_gemm.CalculateCThreadOriginDataIndex(I0, I0, I0, I0);
+
+            const index_t m_thread_data_on_block = c_thread_mtx_on_block[I0];
+            const index_t n_thread_data_on_block = c_thread_mtx_on_block[I1];
+
+            const auto m_thread_data_on_block_to_m0_m1_m2_m3_m4_adaptor =
+                make_single_stage_tensor_adaptor(
+                    make_tuple(make_merge_transform(make_tuple(M0, M1, M2, M3, M4))),
+                    make_tuple(Sequence<0, 1, 2, 3, 4>{}),
+                    make_tuple(Sequence<0>{}));
+
+            const auto m_thread_data_on_block_idx =
+                m_thread_data_on_block_to_m0_m1_m2_m3_m4_adaptor.CalculateBottomIndex(
+                    make_multi_index(m_thread_data_on_block));
+
+            const auto n_thread_data_on_block_to_n0_n1_n2_adaptor =
+                make_single_stage_tensor_adaptor(
+                    make_tuple(make_merge_transform(make_tuple(N0, N1, N2))),
+                    make_tuple(Sequence<0, 1, 2>{}),
+                    make_tuple(Sequence<0>{}));
+
+            const auto n_thread_data_on_block_idx =
+                n_thread_data_on_block_to_n0_n1_n2_adaptor.CalculateBottomIndex(
+                    make_multi_index(n_thread_data_on_block));
+
+            // shuffle: threadwise copy C from VGPR to LDS
+            auto c_thread_copy_vgpr_to_lds =
+                ThreadwiseTensorSliceTransfer_v1r3<FloatGemmAcc,
+                                                   FloatCShuffle,
+                                                   decltype(c_thread_desc_m0_n0_m1_n1_m2_m3_m4_n2),
+                                                   decltype(c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2),
+                                                   ck::tensor_operation::element_wise::PassThrough,
+                                                   Sequence<CShuffleMXdlPerWavePerShuffle,
+                                                            CShuffleNXdlPerWavePerShuffle,
+                                                            I1,
+                                                            I1,
+                                                            M2,
+                                                            I1,
+                                                            M4,
+                                                            I1>,
+                                                   Sequence<0, 1, 2, 3, 4, 5, 6, 7>,
+                                                   7,
+                                                   1,
+                                                   InMemoryDataOperationEnum::Set,
+                                                   1,
+                                                   true>{
+                    c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2,
+                    make_multi_index(0,
+                                     0,
+                                     m_thread_data_on_block_idx[I1],
+                                     n_thread_data_on_block_idx[I1],
+                                     m_thread_data_on_block_idx[I2],
+                                     m_thread_data_on_block_idx[I3],
+                                     m_thread_data_on_block_idx[I4],
+                                     n_thread_data_on_block_idx[I2]),
+                    ck::tensor_operation::element_wise::PassThrough{}};
+
+            // tuple of reference to C/Ds tensor descriptors
+            const auto c_ds_desc_refs = concat_tuple_of_reference(
+                tie(c_shuffle_block_desc_mblock_mperblock_nblock_nperblock),
+                generate_tie(
+                    [&](auto i) -> const auto& // return type should be reference
+                    { return ds_grid_desc_mblock_mperblock_nblock_nperblock[i]; },
+                    Number<NumDTensor>{}));
+
+            // tuple of reference to C/Ds tensor descriptors
+            const auto c_ds_buf_refs = concat_tuple_of_reference(
+                tie(c_shuffle_block_buf),
+                generate_tie(
+                    [&](auto i) -> const auto& // return type should be reference
+                    { return ds_grid_buf[i]; },
+                    Number<NumDTensor>{}));
+
+            // tuple of starting index of C/Ds blockwise copy
+            const auto idx_c_ds_block_begin = container_concat(
+                make_tuple(make_multi_index(0, 0, 0, 0)),
+                generate_tuple(
+                    [&](auto) {
+                        return make_multi_index(block_work_idx[I0], 0, block_work_idx[I1], 0);
+                    },
+                    Number<NumDTensor>{}));
+
+            // blockwise copy C/D/E between LDS and global
+            auto cde_block_copy_lds_and_global = ThreadGroupTensorSliceTransfer_v7<
+                ThisThreadBlock,
+                decltype(container_concat(make_tuple(FloatCShuffle{}), DsDataType{})),
+                Tuple<FloatE>,
+                decltype(c_ds_desc_refs),
+                decltype(tie(e_grid_desc_mblock_mperblock_nblock_nperblock)),
+                CDEElementwiseOperation,
+                Sequence<static_cast<index_t>(EGlobalMemoryDataOperation)>, // FIXME: make Sequence
+                                                                            // support arbitray type
+                Sequence<1,
+                         CShuffleMXdlPerWavePerShuffle * MWave * MPerXdl,
+                         1,
+                         CShuffleNXdlPerWavePerShuffle * NWave * NPerXdl>, // BlockSliceLengths,
+                CDEBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
+                Sequence<0, 1, 2, 3>, // typename ThreadClusterArrangeOrder,
+                Sequence<0, 1, 2, 3>, // typename DimAccessOrder,
+                3,                    // index_t VectorDim,
+                CDEShuffleBlockTransferScalarPerVector_NPerBlock,
+                sequence_merge_t<
+                    Sequence<true>,
+                    uniform_sequence_gen_t<NumDTensor,
+                                           false>>, // ThreadTransferSrcResetCoordinateAfterRunFlags
+                Sequence<false>>                    // ThreadTransferDstResetCoordinateAfterRunFlags
+                {c_ds_desc_refs,
+                 idx_c_ds_block_begin,
+                 tie(e_grid_desc_mblock_mperblock_nblock_nperblock),
+                 make_tuple(make_multi_index(block_work_idx[I0], 0, block_work_idx[I1], 0)),
+                 cde_element_op};
+
+            // space filling curve for threadwise C in VGPR before shuffle
+            constexpr auto sfc_c_vgpr =
+                SpaceFillingCurve<Sequence<MXdlPerWave, NXdlPerWave, 1, 1, M2, 1, M4, 1>,
+                                  Sequence<0, 1, 2, 3, 4, 5, 6, 7>,
+                                  Sequence<CShuffleMXdlPerWavePerShuffle,
+                                           CShuffleNXdlPerWavePerShuffle,
+                                           1,
+                                           1,
+                                           M2,
+                                           1,
+                                           M4,
+                                           1>>{};
+
+            // space filling curve for shuffled blockwise C/D/E
+            constexpr auto sfc_cde_block =
+                SpaceFillingCurve<Sequence<1, MPerBlock, 1, NPerBlock>,
+                                  Sequence<0, 2, 1, 3>,
+                                  Sequence<1,
+                                           CShuffleMXdlPerWavePerShuffle * MWave * MPerXdl,
+                                           1,
+                                           CShuffleNXdlPerWavePerShuffle * NWave * NPerXdl>>{};
+
+            constexpr index_t num_access = sfc_c_vgpr.GetNumOfAccess();
+
+            static_assert(num_access == sfc_cde_block.GetNumOfAccess(), "wrong!");
+
+            static_for<0, num_access, 1>{}([&](auto access_id) {
+                // make sure it's safe to write to LDS
+                block_sync_lds();
+
+                // each thread write its data from VGPR to LDS
+                c_thread_copy_vgpr_to_lds.Run(c_thread_desc_m0_n0_m1_n1_m2_m3_m4_n2,
+                                              sfc_c_vgpr.GetIndexTupleOfNumber(access_id),
+                                              c_thread_buf,
+                                              c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2,
+                                              c_shuffle_block_buf);
+
+                // make sure it's safe to read from LDS
+                block_sync_lds();
+
+                // each block copy its data from LDS to global
+                cde_block_copy_lds_and_global.Run(
+                    c_ds_desc_refs,
+                    c_ds_buf_refs,
+                    tie(e_grid_desc_mblock_mperblock_nblock_nperblock),
+                    tie(e_grid_buf));
+
+                if constexpr(access_id < num_access - 1)
+                {
+                    constexpr auto cde_lds_and_global_step =
+                        sfc_cde_block.GetForwardStep(access_id);
+
+                    // move on Ds
+                    static_for<0, NumDTensor, 1>{}([&](auto i) {
+                        cde_block_copy_lds_and_global.MoveSrcSliceWindow(
+                            c_ds_desc_refs, i + I1, cde_lds_and_global_step);
+                    });
+
+                    // move on E
+                    cde_block_copy_lds_and_global.MoveDstSliceWindow(
+                        tie(e_grid_desc_mblock_mperblock_nblock_nperblock),
+                        I0,
+                        cde_lds_and_global_step);
+                }
+            });
+        }
+    }
+};
+
+} // namespace ck

include/ck/tensor_operation/gpu/grid/gridwise_gemm_pipeline_v1.hpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
+
 #pragma once
-#include "common_header.hpp"
-#include "tensor_operation/gpu/block/blockwise_gemm_xdlops.hpp"
+
+#include "ck/utility/common_header.hpp"
+#include "ck/tensor_operation/gpu/block/blockwise_gemm_xdlops.hpp"
 
 namespace ck {
 

include/ck/tensor_operation/gpu/grid/gridwise_gemm_reduce_xdl_cshuffle_v1.hpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
+
 #pragma once
-#include "common_header.hpp"
-#include "multi_index_transform_helper.hpp"
-#include "tensor_descriptor.hpp"
-#include "tensor_descriptor_helper.hpp"
-#include "tensor_operation/gpu/grid/block_to_ctile_map.hpp"
-#include "blockwise_gemm_xdlops.hpp"
-#include "thread_group_tensor_slice_transfer_v4r1.hpp"
-#include "thread_group_tensor_slice_transfer_v6r1.hpp"
-#include "threadwise_tensor_slice_transfer.hpp"
-#include "gridwise_gemm_pipeline_v1.hpp"
-#include "reduction_functions_threadwise.hpp"
+
+#include "ck/utility/common_header.hpp"
+#include "ck/tensor_description/multi_index_transform_helper.hpp"
+#include "ck/tensor_description/tensor_descriptor.hpp"
+#include "ck/tensor_description/tensor_descriptor_helper.hpp"
+#include "ck/tensor_operation/gpu/grid/block_to_ctile_map.hpp"
+#include "ck/tensor_operation/gpu/grid/gridwise_gemm_pipeline_v1.hpp"
+#include "ck/tensor_operation/gpu/block/blockwise_gemm_xdlops.hpp"
+#include "ck/tensor_operation/gpu/block/thread_group_tensor_slice_transfer_v4r1.hpp"
+#include "ck/tensor_operation/gpu/block/thread_group_tensor_slice_transfer_v6r1.hpp"
+#include "ck/tensor_operation/gpu/thread/threadwise_tensor_slice_transfer.hpp"
+#include "ck/tensor_operation/gpu/thread/reduction_functions_threadwise.hpp"
+#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
 
 namespace ck {
 
 template <typename GridwiseGemm,
           typename FloatAB,
           typename FloatC,
-          typename DPtrsGlobal,
+          typename ReducePtrsGlobal,
           typename AElementwiseOperation,
           typename BElementwiseOperation,
           typename CElementwiseOperation,
-          typename DxsInElementwiseOperation,
-          typename DxsAccElementwiseOperation,
+          typename ReduceInElementwiseOperations,
+          typename ReduceAccElementwiseOperations,
           typename AGridDesc_AK0_M_AK1,
           typename BGridDesc_BK0_N_BK1,
           typename CGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
-          typename DGridDescriptor_MBlock_MPerBlock,
+          typename ReduceGridDescriptor_MBlock_MPerBlock,
           typename Block2CTileMap,
           bool HasMainKBlockLoop>
 __global__ void
@@ -36,17 +41,17 @@ __global__ void
             const FloatAB* __restrict__ p_a_grid,
             const FloatAB* __restrict__ p_b_grid,
             FloatC* __restrict__ p_c_grid,
-            DPtrsGlobal p_ds_grid,
+            ReducePtrsGlobal p_reduces_grid,
             const AElementwiseOperation a_element_op,
             const BElementwiseOperation b_element_op,
             const CElementwiseOperation c_element_op,
-            const DxsInElementwiseOperation dxs_in_element_op,
-            const DxsAccElementwiseOperation dxs_out_element_op,
+            const ReduceInElementwiseOperations reduce_in_element_ops,
+            const ReduceAccElementwiseOperations reduce_out_element_ops,
             const AGridDesc_AK0_M_AK1 a_grid_desc_ak0_m_ak1,
             const BGridDesc_BK0_N_BK1 b_grid_desc_bk0_n_bk1,
             const CGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock
                 c_grid_desc_mblock_mperblock_nblock_nperblock,
-            const DGridDescriptor_MBlock_MPerBlock d_grid_desc_mblock_mperblock,
+            const ReduceGridDescriptor_MBlock_MPerBlock reduce_grid_desc_mblock_mperblock,
             const Block2CTileMap block_2_ctile_map)
 {
 #if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx908__) || defined(__gfx90a__))
@@ -55,32 +60,32 @@ __global__ void
     GridwiseGemm::template Run<HasMainKBlockLoop>(p_a_grid,
                                                   p_b_grid,
                                                   p_c_grid,
-                                                  p_ds_grid,
+                                                  p_reduces_grid,
                                                   p_shared,
                                                   a_element_op,
                                                   b_element_op,
                                                   c_element_op,
-                                                  dxs_in_element_op,
-                                                  dxs_out_element_op,
+                                                  reduce_in_element_ops,
+                                                  reduce_out_element_ops,
                                                   a_grid_desc_ak0_m_ak1,
                                                   b_grid_desc_bk0_n_bk1,
                                                   c_grid_desc_mblock_mperblock_nblock_nperblock,
-                                                  d_grid_desc_mblock_mperblock,
+                                                  reduce_grid_desc_mblock_mperblock,
                                                   block_2_ctile_map);
 #else
     ignore = p_a_grid;
     ignore = p_b_grid;
     ignore = p_c_grid;
-    ignore = p_ds_grid;
+    ignore = p_reduces_grid;
     ignore = a_element_op;
     ignore = b_element_op;
     ignore = c_element_op;
-    ignore = dxs_in_element_op;
-    ignore = dxs_out_element_op;
+    ignore = reduce_in_element_ops;
+    ignore = reduce_out_element_ops;
     ignore = a_grid_desc_ak0_m_ak1;
     ignore = b_grid_desc_bk0_n_bk1;
     ignore = c_grid_desc_mblock_mperblock_nblock_nperblock;
-    ignore = d_grid_desc_mblock_mperblock;
+    ignore = reduce_grid_desc_mblock_mperblock;
     ignore = block_2_ctile_map;
 #endif // end of if (defined(__gfx908__) || defined(__gfx90a__))
 }
@@ -90,19 +95,19 @@ template <typename FloatAB,
           typename FloatCShuffle,
           typename FloatC,
           typename FloatReduceAcc,
-          typename DPtrsGlobal,
+          typename ReducePtrsGlobal,
           typename AElementwiseOperation,
           typename BElementwiseOperation,
           typename CElementwiseOperation,
-          typename DxsReduceOperation,
-          typename DxsInElementwiseOperation,
-          typename DxsAccElementwiseOperation,
+          typename ReduceOperations,
+          typename ReduceInElementwiseOperations,
+          typename ReduceAccElementwiseOperations,
           InMemoryDataOperationEnum CGlobalMemoryDataOperation,
-          typename DGlobalMemoryDataOperation,
+          typename ReduceGlobalMemoryDataOperation,
           typename AGridDesc_AK0_M_AK1,
           typename BGridDesc_BK0_N_BK1,
           typename CGridDesc_M_N,
-          typename DGridDesc_M,
+          typename ReduceGridDesc_M,
           index_t NumGemmKPrefetchStage,
           index_t BlockSize,
           index_t MPerBlock,
@@ -288,18 +293,18 @@ struct GridwiseGemmReduce_k0mk1_k0nk1_mn_xdl_cshuffle_v1
     }
 
     __host__ __device__ static constexpr auto
-    MakeDGridDescriptor_MBlock_MPerBlock(const DGridDesc_M& d_grid_desc_m)
+    MakeReduceGridDescriptor_MBlock_MPerBlock(const ReduceGridDesc_M& d_grid_desc_m)
     {
         const auto M      = d_grid_desc_m.GetLength(I0);
         const auto MBlock = M / MPerBlock;
 
-        const auto d_grid_desc_mblock_mperblock = transform_tensor_descriptor(
+        const auto reduce_grid_desc_mblock_mperblock = transform_tensor_descriptor(
             d_grid_desc_m,
             make_tuple(make_unmerge_transform(make_tuple(MBlock, Number<MPerBlock>{}))),
             make_tuple(Sequence<0>{}),
             make_tuple(Sequence<0, 1>{}));
 
-        return d_grid_desc_mblock_mperblock;
+        return reduce_grid_desc_mblock_mperblock;
     }
 
     // return block_id to C matrix tile idx (m0, n0) mapping
@@ -313,28 +318,29 @@ struct GridwiseGemmReduce_k0mk1_k0nk1_mn_xdl_cshuffle_v1
     using CGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock = remove_cvref_t<decltype(
         MakeCGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(CGridDesc_M_N{}))>;
 
-    using DGridDescriptor_MBlock_MPerBlock =
-        remove_cvref_t<decltype(MakeDGridDescriptor_MBlock_MPerBlock(DGridDesc_M{}))>;
+    using ReduceGridDescriptor_MBlock_MPerBlock =
+        remove_cvref_t<decltype(MakeReduceGridDescriptor_MBlock_MPerBlock(ReduceGridDesc_M{}))>;
 
     using DefaultBlock2CTileMap =
         remove_cvref_t<decltype(MakeDefaultBlock2CTileMap(CGridDesc_M_N{}))>;
 
     template <bool HasMainKBlockLoop, typename Block2CTileMap>
-    __device__ static void Run(const FloatAB* __restrict__ p_a_grid,
+    __device__ static void
+    Run(const FloatAB* __restrict__ p_a_grid,
         const FloatAB* __restrict__ p_b_grid,
         FloatC* __restrict__ p_c_grid,
-                               DPtrsGlobal p_ds_grid,
+        ReducePtrsGlobal p_reduces_grid,
         void* __restrict__ p_shared,
         const AElementwiseOperation& a_element_op,
         const BElementwiseOperation& b_element_op,
         const CElementwiseOperation& c_element_op,
-                               const DxsInElementwiseOperation& dxs_in_element_op,
-                               const DxsAccElementwiseOperation& dxs_out_element_op,
+        const ReduceInElementwiseOperations& reduce_in_element_ops,
+        const ReduceAccElementwiseOperations& reduce_out_element_ops,
         const AGridDesc_AK0_M_AK1& a_grid_desc_ak0_m_ak1,
         const BGridDesc_BK0_N_BK1& b_grid_desc_bk0_n_bk1,
         const CGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock&
             c_grid_desc_mblock_mperblock_nblock_nperblock,
-                               const DGridDescriptor_MBlock_MPerBlock& d_grid_desc_mblock_mperblock,
+        const ReduceGridDescriptor_MBlock_MPerBlock& reduce_grid_desc_mblock_mperblock,
         const Block2CTileMap& block_2_ctile_map)
     {
         const auto a_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
@@ -701,12 +707,12 @@ struct GridwiseGemmReduce_k0mk1_k0nk1_mn_xdl_cshuffle_v1
                 make_naive_tensor_descriptor_packed(
                     make_tuple(Number<mreduce_per_thread>{}, Number<nreduce_per_thread>{}));
 
-            // VGPR d_reduce_thread_desc_mperblock
-            constexpr auto d_reduce_thread_desc_mperblock =
+            // VGPR reduce_thread_desc_mperblock
+            constexpr auto reduce_thread_desc_mperblock =
                 make_naive_tensor_descriptor_packed(make_tuple(Number<mreduce_per_thread>{}));
 
-            // VGPR d_reduce_thread_desc_mblock_mperblock
-            constexpr auto d_reduce_thread_desc_mblock_mperblock =
+            // VGPR reduce_thread_desc_mblock_mperblock
+            constexpr auto reduce_thread_desc_mblock_mperblock =
                 make_naive_tensor_descriptor_packed(make_tuple(I1, Number<mreduce_per_thread>{}));
 
             auto c_reduce_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, FloatReduceAcc>(
@@ -735,29 +741,29 @@ struct GridwiseGemmReduce_k0mk1_k0nk1_mn_xdl_cshuffle_v1
                 1,
                 true>{c_reduce_block_desc_mperblock_nperblock, c_reduce_thread_data_idx_begin};
 
-            auto dxs_reduce_thread_copy_vgpr_to_global = generate_tuple(
+            auto reduce_tuple_thread_copy_vgpr_to_global = generate_tuple(
                 [&](auto I) {
-                    auto p_d_grid         = p_ds_grid[I];
-                    auto d_out_element_op = dxs_out_element_op[I];
+                    auto p_reduce_grid         = p_reduces_grid[I];
+                    auto reduce_acc_element_op = reduce_out_element_ops[I];
 
                     return ThreadwiseTensorSliceTransfer_v1r3<
                         FloatReduceAcc,
-                        remove_pointer_t<decltype(p_d_grid)>,
-                        decltype(d_reduce_thread_desc_mblock_mperblock),
-                        decltype(d_grid_desc_mblock_mperblock),
-                        decltype(d_out_element_op),
+                        remove_pointer_t<decltype(p_reduce_grid)>,
+                        decltype(reduce_thread_desc_mblock_mperblock),
+                        decltype(reduce_grid_desc_mblock_mperblock),
+                        decltype(reduce_acc_element_op),
                         Sequence<1, mreduce_per_thread>,
                         Sequence<0, 1>,
                         1,
                         CReduceThreadVgpr2GlobalCopySrcDstScalarPerVector_MPerBlock,
-                        DGlobalMemoryDataOperation::At(I),
+                        ReduceGlobalMemoryDataOperation::At(I),
                         1,
-                        false>{d_grid_desc_mblock_mperblock,
+                        false>{reduce_grid_desc_mblock_mperblock,
                                make_multi_index(block_work_idx[I0],                  // mblock
                                                 c_reduce_thread_data_idx_begin[I0]), // mperblock
-                               d_out_element_op};
+                               reduce_acc_element_op};
                 },
-                Number<p_ds_grid.Size()>{});
+                Number<p_reduces_grid.Size()>{});
 
             constexpr index_t num_access = sfc_c_vgpr.GetNumOfAccess();
 
@@ -792,34 +798,35 @@ struct GridwiseGemmReduce_k0mk1_k0nk1_mn_xdl_cshuffle_v1
                                                          make_tuple(I0, I0),
                                                          c_reduce_thread_buf);
 
-                    static_for<0, p_ds_grid.Size(), 1>{}([&](auto In) {
-                        auto& p_d_grid = p_ds_grid[In];
+                    static_for<0, p_reduces_grid.Size(), 1>{}([&](auto In) {
+                        auto& p_reduce_grid = p_reduces_grid[In];
 
-                        auto d_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
-                            p_d_grid, d_grid_desc_mblock_mperblock.GetElementSpaceSize());
+                        auto reduce_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
+                            p_reduce_grid, reduce_grid_desc_mblock_mperblock.GetElementSpaceSize());
 
-                        auto d_thread_buf =
+                        auto reduce_thread_buf =
                             make_static_buffer<AddressSpaceEnum::Vgpr, FloatReduceAcc>(
-                                d_reduce_thread_desc_mperblock.GetElementSpaceSize());
+                                reduce_thread_desc_mperblock.GetElementSpaceSize());
 
-                        auto& d_in_element_op = dxs_in_element_op[In];
+                        auto& reduce_in_element_op = reduce_in_element_ops[In];
 
-                        auto& d_reduce_thread_copy_vgpr_to_global =
-                            dxs_reduce_thread_copy_vgpr_to_global(In);
+                        auto& reduce_thread_copy_vgpr_to_global =
+                            reduce_tuple_thread_copy_vgpr_to_global(In);
 
-                        using DReduceOperation = remove_cvref_t<decltype(DxsReduceOperation{}[In])>;
+                        using ReduceOperation = remove_cvref_t<decltype(ReduceOperations{}[In])>;
                         using ThreadwiseReduce =
                             ThreadwiseReduction<FloatReduceAcc,
                                                 decltype(c_reduce_thread_desc_mperblock_nperblock),
-                                                decltype(d_reduce_thread_desc_mperblock),
-                                                DReduceOperation,
+                                                decltype(reduce_thread_desc_mperblock),
+                                                ReduceOperation,
                                                 false>;
 
                         // Global write Gemm shuffle + reduction
-                        const auto d_zeroVal = DReduceOperation::GetReductionZeroVal();
+                        const auto reduce_identityVal =
+                            ReduceOperation::template GetIdentityValue<FloatReduceAcc>();
 
                         static_for<0, mreduce_per_thread, 1>{}(
-                            [&](auto I) { d_thread_buf(I) = d_zeroVal; });
+                            [&](auto I) { reduce_thread_buf(I) = reduce_identityVal; });
 
                         // reduce in VGPR
                         static_for<0, mreduce_per_thread, 1>{}([&](auto im) {
@@ -828,26 +835,25 @@ struct GridwiseGemmReduce_k0mk1_k0nk1_mn_xdl_cshuffle_v1
                                     Number<c_reduce_thread_desc_mperblock_nperblock.CalculateOffset(
                                         make_tuple(im, in))>{};
 
-                                d_in_element_op(c_reduce_thread_buf(offset),
+                                reduce_in_element_op(c_reduce_thread_buf(offset),
                                                      c_reduce_thread_buf(offset));
                             });
                         });
 
-                        ThreadwiseReduce::Reduce(c_reduce_thread_buf, d_thread_buf);
+                        ThreadwiseReduce::Reduce(c_reduce_thread_buf, reduce_thread_buf);
 
                         // copy from VGPR to Global
-                        d_reduce_thread_copy_vgpr_to_global.Run(
-                            d_reduce_thread_desc_mblock_mperblock,
+                        reduce_thread_copy_vgpr_to_global.Run(reduce_thread_desc_mblock_mperblock,
                                                               make_tuple(I0, I0),
-                            d_thread_buf,
-                            d_grid_desc_mblock_mperblock,
-                            d_grid_buf);
+                                                              reduce_thread_buf,
+                                                              reduce_grid_desc_mblock_mperblock,
+                                                              reduce_grid_buf);
 
                         if constexpr(access_id < num_access - 1)
                         {
                             constexpr auto c_global_step = sfc_c_global.GetForwardStep(access_id);
-                            d_reduce_thread_copy_vgpr_to_global.MoveDstSliceWindow(
-                                d_grid_desc_mblock_mperblock,
+                            reduce_thread_copy_vgpr_to_global.MoveDstSliceWindow(
+                                reduce_grid_desc_mblock_mperblock,
                                 make_tuple(c_global_step[I0], c_global_step[I1]));
                         }
                     });

include/ck/tensor_operation/gpu/grid/gridwise_gemm_xdl_cshuffle_v1.hpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
+
 #pragma once
 
-#include "common_header.hpp"
-#include "multi_index_transform_helper.hpp"
-#include "tensor_descriptor.hpp"
-#include "tensor_descriptor_helper.hpp"
-#include "tensor_operation/gpu/grid/block_to_ctile_map.hpp"
-#include "blockwise_gemm_xdlops.hpp"
-#include "thread_group_tensor_slice_transfer_v4r1.hpp"
-#include "thread_group_tensor_slice_transfer_v6r1.hpp"
-#include "threadwise_tensor_slice_transfer.hpp"
-#include "gridwise_gemm_pipeline_v1.hpp"
+#include "ck/utility/common_header.hpp"
+#include "ck/tensor_description/multi_index_transform_helper.hpp"
+#include "ck/tensor_description/tensor_descriptor.hpp"
+#include "ck/tensor_description/tensor_descriptor_helper.hpp"
+#include "ck/tensor_operation/gpu/grid/block_to_ctile_map.hpp"
+#include "ck/tensor_operation/gpu/grid/gridwise_gemm_pipeline_v1.hpp"
+#include "ck/tensor_operation/gpu/block/blockwise_gemm_xdlops.hpp"
+#include "ck/tensor_operation/gpu/block/thread_group_tensor_slice_transfer_v4r1.hpp"
+#include "ck/tensor_operation/gpu/block/thread_group_tensor_slice_transfer_v6r1.hpp"
+#include "ck/tensor_operation/gpu/thread/threadwise_tensor_slice_transfer.hpp"
+#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
 
 namespace ck {
 

include/ck/tensor_operation/gpu/grid/gridwise_gemm_xdl_layernorm_cshuffle_v1.hpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include "ck/utility/common_header.hpp"
+#include "ck/tensor_description/multi_index_transform_helper.hpp"
+#include "ck/tensor_description/tensor_descriptor.hpp"
+#include "ck/tensor_description/tensor_descriptor_helper.hpp"
+#include "ck/tensor_operation/gpu/grid/block_to_ctile_map.hpp"
+#include "ck/tensor_operation/gpu/grid/gridwise_gemm_pipeline_v1.hpp"
+#include "ck/tensor_operation/gpu/block/blockwise_gemm_xdlops.hpp"
+#include "ck/tensor_operation/gpu/block/thread_group_tensor_slice_transfer_v4r1.hpp"
+#include "ck/tensor_operation/gpu/block/thread_group_tensor_slice_transfer_v6r1.hpp"
+#include "ck/tensor_operation/gpu/thread/threadwise_tensor_slice_transfer.hpp"
+#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
+#include "ck/tensor_operation/gpu/thread/reduction_functions_threadwise.hpp"
+#include "ck/tensor_operation/gpu/block/reduction_functions_blockwise.hpp"
+
+namespace ck {
+
+// D = Layernorm(acc_element_op(A * B + broadcast(bias)) + add) * broadcast(gamma) + broadcast(beta)
+template <typename GridwiseGemm,
+          typename FloatAB,
+          typename FloatC,
+          typename FloatC0,
+          typename AElementwiseOperation,
+          typename BElementwiseOperation,
+          typename AccElementwiseOperation,
+          typename CElementwiseOperation,
+          typename AGridDesc_AK0_M_AK1,
+          typename BGridDesc_BK0_N_BK1,
+          typename CGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
+          typename C0GridDescriptor_NBlock_NPerBlock,
+          typename Block2CTileMap,
+          bool HasMainKBlockLoop>
+__global__ void
+#if CK_USE_LAUNCH_BOUNDS
+    __launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
+#endif
+        kernel_gemm_layernorm_xdl_cshuffle_v1(
+            const FloatAB* __restrict__ p_a_grid,
+            const FloatAB* __restrict__ p_b_grid,
+            FloatC* __restrict__ p_c_grid,               // MxN
+            const FloatC0* __restrict__ p_c0_bias_grid,  // 1xN
+            const FloatC0* __restrict__ p_c0_add_grid,   // MxN
+            const FloatC0* __restrict__ p_c0_gamma_grid, // 1xN
+            const FloatC0* __restrict__ p_c0_beta_grid,  // 1xN
+            const AElementwiseOperation a_element_op,
+            const BElementwiseOperation b_element_op,
+            const AccElementwiseOperation acc_element_op,
+            const CElementwiseOperation c_element_op,
+            const AGridDesc_AK0_M_AK1 a_grid_desc_ak0_m_ak1,
+            const BGridDesc_BK0_N_BK1 b_grid_desc_bk0_n_bk1,
+            const CGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock
+                c_grid_desc_mblock_mperblock_nblock_nperblock,
+            const C0GridDescriptor_NBlock_NPerBlock c0_grid_desc_nblock_nperblock,
+            const Block2CTileMap block_2_ctile_map)
+{
+#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx908__) || defined(__gfx90a__))
+    __shared__ char p_shared[GridwiseGemm::GetSharedMemoryNumberOfByte()];
+
+    // TODO ANT: separate into MMA + Epilogue
+    GridwiseGemm::template Run<HasMainKBlockLoop>(p_a_grid,
+                                                  p_b_grid,
+                                                  p_c_grid,
+                                                  p_c0_bias_grid,
+                                                  p_c0_add_grid,
+                                                  p_c0_gamma_grid,
+                                                  p_c0_beta_grid,
+                                                  p_shared,
+                                                  a_element_op,
+                                                  b_element_op,
+                                                  acc_element_op,
+                                                  c_element_op,
+                                                  a_grid_desc_ak0_m_ak1,
+                                                  b_grid_desc_bk0_n_bk1,
+                                                  c_grid_desc_mblock_mperblock_nblock_nperblock,
+                                                  c0_grid_desc_nblock_nperblock,
+                                                  block_2_ctile_map);
+
+    // TODO ANT: Run layernorm epilogue here
+#else
+    ignore = p_a_grid;
+    ignore = p_b_grid;
+    ignore = p_c_grid;
+    ignore = p_c0_bias_grid;
+    ignore = p_c0_add_grid;
+    ignore = p_c0_gamma_grid;
+    ignore = p_c0_beta_grid;
+    ignore = a_element_op;
+    ignore = b_element_op;
+    ignore = acc_element_op;
+    ignore = c_element_op;
+    ignore = a_grid_desc_ak0_m_ak1;
+    ignore = b_grid_desc_bk0_n_bk1;
+    ignore = c_grid_desc_mblock_mperblock_nblock_nperblock;
+    ignore = c0_grid_desc_nblock_nperblock;
+    ignore = block_2_ctile_map;
+#endif // end of if (defined(__gfx908__) || defined(__gfx90a__))
+}
+
+// The GEMM + Layernorm implementation is a specialized kernel which allows fusing both layers
+// together given the condition GEMM extents N of MNK is spanned by a single workgroup. For example,
+// a kernel configured with NPerBlock = 128 allows to operate on all GEMM sizes if N <= 128
+template <typename FloatAB,
+          typename FloatGemmAcc,
+          typename FloatCShuffle,
+          typename FloatC,
+          typename FloatC0,
+          typename FloatReduceAcc, // Data type after shuffle
+          typename AElementwiseOperation,
+          typename BElementwiseOperation,
+          typename AccElementwiseOperation,
+          typename CElementwiseOperation,
+          InMemoryDataOperationEnum CGlobalMemoryDataOperation,
+          typename AGridDesc_AK0_M_AK1,
+          typename BGridDesc_BK0_N_BK1,
+          typename CGridDesc_M_N,
+          typename C0GridDesc_N,
+          index_t NumGemmKPrefetchStage,
+          index_t BlockSize,
+          index_t MPerBlock,
+          index_t NPerBlock,
+          index_t KPerBlock,
+          index_t AK1Value,
+          index_t BK1Value,
+          index_t MPerXdl,
+          index_t NPerXdl,
+          index_t MXdlPerWave,
+          index_t NXdlPerWave,
+          typename ABlockTransferThreadClusterLengths_AK0_M_AK1,
+          typename ABlockTransferThreadClusterArrangeOrder,
+          typename ABlockTransferSrcAccessOrder,
+          index_t ABlockTransferSrcVectorDim,
+          index_t ABlockTransferSrcScalarPerVector,
+          index_t ABlockTransferDstScalarPerVector_AK1,
+          bool AThreadTransferSrcResetCoordinateAfterRun,
+          index_t ABlockLdsExtraM,
+          typename BBlockTransferThreadClusterLengths_BK0_N_BK1,
+          typename BBlockTransferThreadClusterArrangeOrder,
+          typename BBlockTransferSrcAccessOrder,
+          index_t BBlockTransferSrcVectorDim,
+          index_t BBlockTransferSrcScalarPerVector,
+          index_t BBlockTransferDstScalarPerVector_BK1,
+          bool BThreadTransferSrcResetCoordinateAfterRun,
+          index_t BBlockLdsExtraN,
+          index_t CShuffleMXdlPerWavePerShuffle,
+          index_t CShuffleNXdlPerWavePerShuffle,
+          typename CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
+          index_t CShuffleBlockTransferScalarPerVector_NPerBlock,
+          typename CReduceThreadClusterLengths_MPerBlock_NPerBlock,
+          index_t CReduceThreadCopySrcDstScalarPerVector_NPerBlock,
+          LoopScheduler LoopSched>
+struct GridwiseGemmLayernorm_k0mk1_k0nk1_mn_xdl_cshuffle_v1
+{
+    static constexpr auto I0 = Number<0>{};
+    static constexpr auto I1 = Number<1>{};
+    static constexpr auto I2 = Number<2>{};
+    static constexpr auto I3 = Number<3>{};
+    static constexpr auto I4 = Number<4>{};
+    static constexpr auto I5 = Number<5>{};
+    static constexpr auto I6 = Number<6>{};
+    static constexpr auto I7 = Number<7>{};
+
+    // K1 should be Number<...>
+    static constexpr auto AK0 = Number<KPerBlock / AK1Value>{};
+    static constexpr auto BK0 = Number<KPerBlock / BK1Value>{};
+    static constexpr auto AK1 = Number<AK1Value>{};
+    static constexpr auto BK1 = Number<BK1Value>{};
+
+    using ThisThreadBlock = ThisThreadBlock<BlockSize>;
+
+    using GridwiseGemmPipe = GridwiseGemmPipeline_v1<NumGemmKPrefetchStage>;
+
+    __host__ __device__ static constexpr auto GetABlockDescriptor_AK0PerBlock_MPerBlock_AK1()
+    {
+        // A matrix in LDS memory, dst of blockwise copy
+        return make_naive_tensor_descriptor(
+            make_tuple(AK0, Number<MPerBlock>{}, AK1),
+            make_tuple(Number<MPerBlock + ABlockLdsExtraM>{} * AK1, AK1, I1));
+    }
+
+    __host__ __device__ static constexpr auto GetBBlockDescriptor_BK0PerBlock_NPerBlock_BK1()
+    {
+        // B matrix in LDS memory, dst of blockwise copy
+        return make_naive_tensor_descriptor(
+            make_tuple(BK0, Number<NPerBlock>{}, BK1),
+            make_tuple(Number<NPerBlock + BBlockLdsExtraN>{} * BK1, BK1, I1));
+    }
+
+    __host__ __device__ static constexpr auto
+    GetCShuffleBlockDescriptor_MBlock_MPerBlock_NBlock_NPerBlock()
+    {
+        constexpr index_t MWave = MPerBlock / (MXdlPerWave * MPerXdl);
+        constexpr index_t NWave = NPerBlock / (NXdlPerWave * NPerXdl);
+
+        constexpr auto c_shuffle_block_desc_mblock_mperblock_nblock_nperblock =
+            make_naive_tensor_descriptor_packed(
+                make_tuple(I1,
+                           Number<CShuffleMXdlPerWavePerShuffle * MWave * MPerXdl>{},
+                           I1,
+                           Number<CShuffleNXdlPerWavePerShuffle * NWave * NPerXdl>{}));
+
+        return c_shuffle_block_desc_mblock_mperblock_nblock_nperblock;
+    }
+
+    __host__ __device__ static constexpr index_t GetSharedMemoryNumberOfByte()
+    {
+        // LDS allocation for A and B: be careful of alignment
+        constexpr auto a_block_desc_ak0_m_ak1 = GetABlockDescriptor_AK0PerBlock_MPerBlock_AK1();
+        constexpr auto b_block_desc_bk0_n_bk1 = GetBBlockDescriptor_BK0PerBlock_NPerBlock_BK1();
+
+        // lds max alignment
+        constexpr auto max_lds_align = math::lcm(AK1, BK1);
+
+        constexpr auto a_block_space_size_aligned = math::integer_least_multiple(
+            a_block_desc_ak0_m_ak1.GetElementSpaceSize(), max_lds_align);
+
+        constexpr auto b_block_space_size_aligned = math::integer_least_multiple(
+            b_block_desc_bk0_n_bk1.GetElementSpaceSize(), max_lds_align);
+
+        // LDS allocation for C shuffle in LDS
+        constexpr auto c_shuffle_block_desc_mblock_mperblock_nblock_nperblock =
+            GetCShuffleBlockDescriptor_MBlock_MPerBlock_NBlock_NPerBlock();
+
+        // Align 16 bytes (maximum LDS read/write width)
+        constexpr auto c_block_size_aligned =
+            math::integer_least_multiple(
+                c_shuffle_block_desc_mblock_mperblock_nblock_nperblock.GetElementSpaceSize() *
+                    sizeof(FloatCShuffle),
+                16) /
+            sizeof(FloatCShuffle);
+
+        // LDS allocation for reduction workspace
+        constexpr index_t c_lds_workspace_size = BlockSize;
+
+        return math::max((a_block_space_size_aligned + b_block_space_size_aligned) *
+                             sizeof(FloatAB),
+                         c_block_size_aligned * sizeof(FloatCShuffle) +
+                             c_lds_workspace_size * sizeof(FloatReduceAcc));
+    }
+
+    // block_id to matrix tile idx (m0, n0) mapping are controlled by {M01, N01}
+    template <typename Block2CTileMap>
+    __host__ __device__ static constexpr bool
+    CheckValidity(const AGridDesc_AK0_M_AK1& a_grid_desc_ak0_m_ak1,
+                  const BGridDesc_BK0_N_BK1& b_grid_desc_bk0_n_bk1,
+                  const CGridDesc_M_N& c_grid_desc_m_n,
+                  const Block2CTileMap& block_2_ctile_map)
+    {
+        static_assert((MPerBlock % (MPerXdl * MXdlPerWave) == 0) &&
+                          (NPerBlock % (NXdlPerWave * NPerXdl)) == 0,
+                      "Invalid tuning param!");
+
+        const auto M = a_grid_desc_ak0_m_ak1.GetLength(I1);
+        const auto N = b_grid_desc_bk0_n_bk1.GetLength(I1);
+        const auto K = a_grid_desc_ak0_m_ak1.GetLength(I0) * a_grid_desc_ak0_m_ak1.GetLength(I2);
+
+        if(!(M == c_grid_desc_m_n.GetLength(I0) && N == c_grid_desc_m_n.GetLength(I1)))
+            return false;
+
+        if(!(M % MPerBlock == 0 && N % NPerBlock == 0 && K % KPerBlock == 0))
+            return false;
+
+        // in order to reduce N dim without elaborate sync across CUs in single kernel, one
+        // workgroup must span the entire N extent
+        if(math::integer_divide_ceil(N, NPerBlock) > 1)
+        {
+            return false;
+        }
+
+        // static check: all waves in the workgroups combined must cover whole N extent in order
+        // to have efficient N-dim reduction
+        static_assert(CShuffleNXdlPerWavePerShuffle == NXdlPerWave,
+                      "condition not met for efficient layernorm");
+
+        // check gridwise gemm pipeline
+        const auto num_k_loop = K / KPerBlock;
+
+        if(!GridwiseGemmPipe::IsSupported(num_k_loop))
+        {
+            return false;
+        }
+
+        if(!block_2_ctile_map.CheckValidity(c_grid_desc_m_n))
+        {
+            return false;
+        }
+
+        // TODO: also check validity of all components (blockwise-copy, threadwise-copy, etc)
+        return true;
+    }
+
+    __host__ __device__ static constexpr bool CalculateHasMainKBlockLoop(index_t K)
+    {
+        const index_t num_loop = K / KPerBlock;
+
+        return GridwiseGemmPipe::CalculateHasMainLoop(num_loop);
+    }
+
+    __host__ __device__ static constexpr auto
+    MakeCGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(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 MBlock = M / MPerBlock;
+        const auto NBlock = N / NPerBlock;
+
+        const auto c_grid_desc_mblock_mperblock_nblock_nperblock = transform_tensor_descriptor(
+            c_grid_desc_m_n,
+            make_tuple(make_unmerge_transform(make_tuple(MBlock, Number<MPerBlock>{})),
+                       make_unmerge_transform(make_tuple(NBlock, Number<NPerBlock>{}))),
+            make_tuple(Sequence<0>{}, Sequence<1>{}),
+            make_tuple(Sequence<0, 1>{}, Sequence<2, 3>{}));
+
+        return c_grid_desc_mblock_mperblock_nblock_nperblock;
+    }
+
+    // for bias, beta, gamma
+    __host__ __device__ static constexpr auto
+    MakeC0GridDescriptor_NBlock_NPerBlock(const C0GridDesc_N& c0_grid_desc_n)
+    {
+        const auto N      = c0_grid_desc_n.GetLength(I0);
+        const auto NBlock = N / NPerBlock;
+
+        const auto c0_grid_desc_nblock_nperblock = transform_tensor_descriptor(
+            c0_grid_desc_n,
+            make_tuple(make_unmerge_transform(make_tuple(NBlock, Number<NPerBlock>{}))),
+            make_tuple(Sequence<0>{}),
+            make_tuple(Sequence<0, 1>{}));
+
+        return c0_grid_desc_nblock_nperblock;
+    }
+
+    // return block_id to C matrix tile idx (m0, n0) mapping
+    __host__ __device__ static constexpr auto
+    MakeDefaultBlock2CTileMap(const CGridDesc_M_N& c_grid_desc_m_n)
+    {
+        return BlockToCTileMap_M00_N0_M01Adapt<MPerBlock, NPerBlock, CGridDesc_M_N>(
+            c_grid_desc_m_n);
+    }
+
+    using CGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock = remove_cvref_t<decltype(
+        MakeCGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(CGridDesc_M_N{}))>;
+
+    using C0GridDescriptor_NBlock_NPerBlock =
+        remove_cvref_t<decltype(MakeC0GridDescriptor_NBlock_NPerBlock(C0GridDesc_N{}))>;
+
+    using DefaultBlock2CTileMap =
+        remove_cvref_t<decltype(MakeDefaultBlock2CTileMap(CGridDesc_M_N{}))>;
+
+    template <bool HasMainKBlockLoop, typename Block2CTileMap>
+    __device__ static void
+    Run(const FloatAB* __restrict__ p_a_grid,
+        const FloatAB* __restrict__ p_b_grid,
+        FloatC* __restrict__ p_c_grid,
+        const FloatC0* __restrict__ p_c0_bias_grid,  // 1xN
+        const FloatC0* __restrict__ p_c0_add_grid,   // MxN
+        const FloatC0* __restrict__ p_c0_gamma_grid, // 1xN
+        const FloatC0* __restrict__ p_c0_beta_grid,  // 1xN
+        void* __restrict__ p_shared,
+        const AElementwiseOperation& a_element_op,
+        const BElementwiseOperation& b_element_op,
+        const AccElementwiseOperation& acc_element_op,
+        const CElementwiseOperation& c_element_op,
+        const AGridDesc_AK0_M_AK1& a_grid_desc_ak0_m_ak1,
+        const BGridDesc_BK0_N_BK1& b_grid_desc_bk0_n_bk1,
+        const CGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock&
+            c_grid_desc_mblock_mperblock_nblock_nperblock,
+        const C0GridDescriptor_NBlock_NPerBlock& c0_grid_desc_nblock_nperblock,
+        const Block2CTileMap& block_2_ctile_map)
+    {
+        const auto a_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
+            p_a_grid, a_grid_desc_ak0_m_ak1.GetElementSpaceSize());
+        const auto b_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
+            p_b_grid, b_grid_desc_bk0_n_bk1.GetElementSpaceSize());
+        auto c_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
+            p_c_grid, c_grid_desc_mblock_mperblock_nblock_nperblock.GetElementSpaceSize());
+        auto c0_bias_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
+            p_c0_bias_grid, c0_grid_desc_nblock_nperblock.GetElementSpaceSize());
+        // Note: c0_add is of same layout as c so we don't declare new c0_add_desc here
+        auto c0_add_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
+            p_c0_add_grid, c_grid_desc_mblock_mperblock_nblock_nperblock.GetElementSpaceSize());
+        auto c0_gamma_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
+            p_c0_gamma_grid, c0_grid_desc_nblock_nperblock.GetElementSpaceSize());
+        auto c0_beta_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
+            p_c0_beta_grid, c0_grid_desc_nblock_nperblock.GetElementSpaceSize());
+
+        // divide block work by [M, N]
+        const auto block_work_idx =
+            block_2_ctile_map.CalculateBottomIndex(make_multi_index(get_block_1d_id()));
+
+        if(!block_2_ctile_map.ValidCTileIndex(
+               block_work_idx,
+               make_tuple(c_grid_desc_mblock_mperblock_nblock_nperblock.GetLength(I0),
+                          c_grid_desc_mblock_mperblock_nblock_nperblock.GetLength(I2))))
+        {
+            return;
+        }
+
+        // HACK: this force m/n_block_data_idx_on_grid into SGPR
+        const index_t m_block_data_idx_on_grid =
+            __builtin_amdgcn_readfirstlane(block_work_idx[I0] * MPerBlock);
+
+        const index_t n_block_data_idx_on_grid =
+            __builtin_amdgcn_readfirstlane(block_work_idx[I1] * NPerBlock);
+
+        // lds max alignment
+        constexpr auto max_lds_align = math::lcm(AK1, BK1);
+
+        // A matrix in LDS memory, dst of blockwise copy
+        constexpr auto a_block_desc_ak0_m_ak1 = GetABlockDescriptor_AK0PerBlock_MPerBlock_AK1();
+
+        // B matrix in LDS memory, dst of blockwise copy
+        constexpr auto b_block_desc_bk0_n_bk1 = GetBBlockDescriptor_BK0PerBlock_NPerBlock_BK1();
+
+        // A matrix blockwise copy
+        auto a_blockwise_copy =
+            ThreadGroupTensorSliceTransfer_v4r1<ThisThreadBlock,
+                                                AElementwiseOperation,
+                                                ck::tensor_operation::element_wise::PassThrough,
+                                                InMemoryDataOperationEnum::Set,
+                                                Sequence<AK0, MPerBlock, AK1>,
+                                                ABlockTransferThreadClusterLengths_AK0_M_AK1,
+                                                ABlockTransferThreadClusterArrangeOrder,
+                                                FloatAB,
+                                                FloatAB,
+                                                decltype(a_grid_desc_ak0_m_ak1),
+                                                decltype(a_block_desc_ak0_m_ak1),
+                                                ABlockTransferSrcAccessOrder,
+                                                Sequence<1, 0, 2>,
+                                                ABlockTransferSrcVectorDim,
+                                                2,
+                                                ABlockTransferSrcScalarPerVector,
+                                                ABlockTransferDstScalarPerVector_AK1,
+                                                1,
+                                                1,
+                                                AThreadTransferSrcResetCoordinateAfterRun,
+                                                true,
+                                                NumGemmKPrefetchStage>(
+                a_grid_desc_ak0_m_ak1,
+                make_multi_index(0, m_block_data_idx_on_grid, 0),
+                a_element_op,
+                a_block_desc_ak0_m_ak1,
+                make_multi_index(0, 0, 0),
+                ck::tensor_operation::element_wise::PassThrough{});
+
+        // B matrix blockwise copy
+        auto b_blockwise_copy =
+            ThreadGroupTensorSliceTransfer_v4r1<ThisThreadBlock,
+                                                BElementwiseOperation,
+                                                ck::tensor_operation::element_wise::PassThrough,
+                                                InMemoryDataOperationEnum::Set,
+                                                Sequence<BK0, NPerBlock, BK1>,
+                                                BBlockTransferThreadClusterLengths_BK0_N_BK1,
+                                                BBlockTransferThreadClusterArrangeOrder,
+                                                FloatAB,
+                                                FloatAB,
+                                                decltype(b_grid_desc_bk0_n_bk1),
+                                                decltype(b_block_desc_bk0_n_bk1),
+                                                BBlockTransferSrcAccessOrder,
+                                                Sequence<1, 0, 2>,
+                                                BBlockTransferSrcVectorDim,
+                                                2,
+                                                BBlockTransferSrcScalarPerVector,
+                                                BBlockTransferDstScalarPerVector_BK1,
+                                                1,
+                                                1,
+                                                BThreadTransferSrcResetCoordinateAfterRun,
+                                                true,
+                                                NumGemmKPrefetchStage>(
+                b_grid_desc_bk0_n_bk1,
+                make_multi_index(0, n_block_data_idx_on_grid, 0),
+                b_element_op,
+                b_block_desc_bk0_n_bk1,
+                make_multi_index(0, 0, 0),
+                ck::tensor_operation::element_wise::PassThrough{});
+
+        // GEMM definition
+        //   c_mtx += transpose(a_mtx) * b_mtx
+        //     a_mtx[K0PerBlock, MPerBlock] is in LDS
+        //     b_mtx[K0PerBlock, NPerBlock] is in LDS
+        //     c_mtx[MPerBlock, NPerBlock] is distributed among threads, and saved in
+        //       register
+        // sanity check
+        constexpr index_t KPack = math::max(
+            math::lcm(AK1, BK1), MfmaSelector<FloatAB, MPerXdl, NPerXdl>::selected_mfma.k_per_blk);
+
+        auto blockwise_gemm = BlockwiseGemmXdlops_k0mk1_k0nk1_m0n0m1n1m2m3m4n2_Selector<
+            BlockSize,
+            FloatAB,
+            FloatGemmAcc,
+            decltype(a_block_desc_ak0_m_ak1),
+            decltype(b_block_desc_bk0_n_bk1),
+            MPerXdl,
+            NPerXdl,
+            MXdlPerWave,
+            NXdlPerWave,
+            KPack,
+            LoopSched>();
+
+        auto c_thread_buf = blockwise_gemm.GetCThreadBuffer();
+
+        // LDS allocation for A and B: be careful of alignment
+        constexpr auto a_block_space_size_aligned = math::integer_least_multiple(
+            a_block_desc_ak0_m_ak1.GetElementSpaceSize(), max_lds_align);
+
+        auto a_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
+            static_cast<FloatAB*>(p_shared), a_block_desc_ak0_m_ak1.GetElementSpaceSize());
+
+        auto b_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
+            static_cast<FloatAB*>(p_shared) + a_block_space_size_aligned,
+            b_block_desc_bk0_n_bk1.GetElementSpaceSize());
+
+        constexpr auto a_block_slice_copy_step = make_multi_index(KPerBlock / AK1, 0, 0);
+        constexpr auto b_block_slice_copy_step = make_multi_index(KPerBlock / BK1, 0, 0);
+
+        // gridwise GEMM pipeline
+        const auto gridwise_gemm_pipeline =
+            GridwiseGemmPipeline_v1_Selector<NumGemmKPrefetchStage, LoopSched>();
+
+        const index_t num_k_block_main_loop = __builtin_amdgcn_readfirstlane(
+            (a_grid_desc_ak0_m_ak1.GetLength(I0) * a_grid_desc_ak0_m_ak1.GetLength(I2)) /
+            KPerBlock);
+
+        gridwise_gemm_pipeline.template Run<HasMainKBlockLoop>(a_grid_desc_ak0_m_ak1,
+                                                               a_block_desc_ak0_m_ak1,
+                                                               a_blockwise_copy,
+                                                               a_grid_buf,
+                                                               a_block_buf,
+                                                               a_block_slice_copy_step,
+                                                               b_grid_desc_bk0_n_bk1,
+                                                               b_block_desc_bk0_n_bk1,
+                                                               b_blockwise_copy,
+                                                               b_grid_buf,
+                                                               b_block_buf,
+                                                               b_block_slice_copy_step,
+                                                               blockwise_gemm,
+                                                               c_thread_buf,
+                                                               num_k_block_main_loop);
+
+        // shuffle C and write out
+        {
+            static_assert(MXdlPerWave % CShuffleMXdlPerWavePerShuffle == 0 &&
+                              NXdlPerWave % CShuffleNXdlPerWavePerShuffle == 0,
+                          "wrong!");
+
+            constexpr index_t MWave = MPerBlock / (MXdlPerWave * MPerXdl);
+            constexpr index_t NWave = NPerBlock / (NXdlPerWave * NPerXdl);
+
+            // TODO: hacky, fix it!
+            constexpr auto c_thread_desc_m0_n0_m1_n1_m2_m3_m4_n2 =
+                blockwise_gemm.GetCThreadDescriptor_M0_N0_M1_N1_M2_M3_M4_N2();
+
+            // TODO: hacky, fix it!
+            // c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp is only used to get lengths
+            constexpr auto c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp =
+                blockwise_gemm.GetCBlockDescriptor_M0_N0_M1_N1_M2_M3_M4_N2();
+
+            constexpr auto M0 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I0);
+            constexpr auto N0 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I1);
+            constexpr auto M1 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I2);
+            constexpr auto N1 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I3);
+            constexpr auto M2 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I4);
+            constexpr auto M3 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I5);
+            constexpr auto M4 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I6);
+            constexpr auto N2 = c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2_tmp.GetLength(I7);
+
+            constexpr auto c_shuffle_block_desc_mblock_mperblock_nblock_nperblock =
+                GetCShuffleBlockDescriptor_MBlock_MPerBlock_NBlock_NPerBlock();
+
+            auto c_shuffle_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
+                static_cast<FloatCShuffle*>(p_shared),
+                c_shuffle_block_desc_mblock_mperblock_nblock_nperblock.GetElementSpaceSize());
+
+            constexpr auto c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2 = transform_tensor_descriptor(
+                c_shuffle_block_desc_mblock_mperblock_nblock_nperblock,
+                make_tuple(
+                    make_freeze_transform(I0),
+                    make_unmerge_transform(make_tuple(
+                        Number<CShuffleMXdlPerWavePerShuffle>{}, // M0 (MXdlPerWave) per shuffle
+                        M1,                                      // M1 = MWave
+                        M2,                                      // M2 * M3 * M4 = MPerXdl
+                        M3,
+                        M4)),
+                    make_freeze_transform(I0),
+                    make_unmerge_transform(make_tuple(
+                        Number<CShuffleNXdlPerWavePerShuffle>{}, // N0 (NXdlPerWave) per shuffle
+                        N1,                                      // N1 = NWave
+                        N2))),                                   // N2 = NPerXdl
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
+                make_tuple(
+                    Sequence<>{}, Sequence<0, 2, 4, 5, 6>{}, Sequence<>{}, Sequence<1, 3, 7>{}));
+
+            // calculate origin of thread output tensor on global memory
+            //     blockwise GEMM c matrix starting index
+            const auto c_thread_mtx_on_block =
+                blockwise_gemm.CalculateCThreadOriginDataIndex(I0, I0, I0, I0);
+
+            const index_t m_thread_data_on_block = c_thread_mtx_on_block[I0];
+            const index_t n_thread_data_on_block = c_thread_mtx_on_block[I1];
+
+            const auto m_thread_data_on_block_to_m0_m1_m2_m3_m4_adaptor =
+                make_single_stage_tensor_adaptor(
+                    make_tuple(make_merge_transform(make_tuple(M0, M1, M2, M3, M4))),
+                    make_tuple(Sequence<0, 1, 2, 3, 4>{}),
+                    make_tuple(Sequence<0>{}));
+
+            const auto m_thread_data_on_block_idx =
+                m_thread_data_on_block_to_m0_m1_m2_m3_m4_adaptor.CalculateBottomIndex(
+                    make_multi_index(m_thread_data_on_block));
+
+            const auto n_thread_data_on_block_to_n0_n1_n2_adaptor =
+                make_single_stage_tensor_adaptor(
+                    make_tuple(make_merge_transform(make_tuple(N0, N1, N2))),
+                    make_tuple(Sequence<0, 1, 2>{}),
+                    make_tuple(Sequence<0>{}));
+
+            const auto n_thread_data_on_block_idx =
+                n_thread_data_on_block_to_n0_n1_n2_adaptor.CalculateBottomIndex(
+                    make_multi_index(n_thread_data_on_block));
+
+            // shuffle: threadwise copy C from VGPR to LDS
+            auto c_thread_copy_vgpr_to_lds =
+                ThreadwiseTensorSliceTransfer_v1r3<FloatGemmAcc,
+                                                   FloatCShuffle,
+                                                   decltype(c_thread_desc_m0_n0_m1_n1_m2_m3_m4_n2),
+                                                   decltype(c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2),
+                                                   tensor_operation::element_wise::PassThrough,
+                                                   Sequence<CShuffleMXdlPerWavePerShuffle,
+                                                            CShuffleNXdlPerWavePerShuffle,
+                                                            I1,
+                                                            I1,
+                                                            M2,
+                                                            I1,
+                                                            M4,
+                                                            I1>,
+                                                   Sequence<0, 1, 2, 3, 4, 5, 6, 7>,
+                                                   7,
+                                                   1,
+                                                   InMemoryDataOperationEnum::Set,
+                                                   1,
+                                                   true>{
+                    c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2,
+                    make_multi_index(0,
+                                     0,
+                                     m_thread_data_on_block_idx[I1],
+                                     n_thread_data_on_block_idx[I1],
+                                     m_thread_data_on_block_idx[I2],
+                                     m_thread_data_on_block_idx[I3],
+                                     m_thread_data_on_block_idx[I4],
+                                     n_thread_data_on_block_idx[I2]),
+                    tensor_operation::element_wise::PassThrough{}};
+
+            // shuffle: blockwise copy C from LDS to global
+            auto c_shuffle_block_copy_lds_to_global = ThreadGroupTensorSliceTransfer_v6r1<
+                ThisThreadBlock,            // ThreadGroup
+                CElementwiseOperation,      // ElementwiseOperation,
+                CGlobalMemoryDataOperation, // DstInMemOp,
+                Sequence<1,
+                         CShuffleMXdlPerWavePerShuffle * MWave * MPerXdl,
+                         1,
+                         CShuffleNXdlPerWavePerShuffle * NWave * NPerXdl>, // BlockSliceLengths,
+                CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
+                Sequence<0, 1, 2, 3>, // typename ThreadClusterArrangeOrder,
+                FloatCShuffle,        // typename SrcData,
+                FloatC,               // typename DstData,
+                decltype(c_shuffle_block_desc_mblock_mperblock_nblock_nperblock),
+                decltype(c_grid_desc_mblock_mperblock_nblock_nperblock),
+                Sequence<0, 1, 2, 3>,                           // typename DimAccessOrder,
+                3,                                              // index_t VectorDim,
+                CShuffleBlockTransferScalarPerVector_NPerBlock, // index_t ScalarPerVector,
+                true,  // bool ThreadTransferSrcResetCoordinateAfterRun,
+                false> // bool ThreadTransferDstResetCoordinateAfterRun>
+                {c_shuffle_block_desc_mblock_mperblock_nblock_nperblock,
+                 make_multi_index(0, 0, 0, 0),
+                 c_grid_desc_mblock_mperblock_nblock_nperblock,
+                 make_multi_index(block_work_idx[I0], 0, block_work_idx[I1], 0),
+                 c_element_op};
+
+            const auto NBlock = c0_grid_desc_nblock_nperblock.GetLength(I0);
+
+            // for broadcasting bias, beta, gamma
+            const auto c0_grid_desc_mblock_mperblock_nblock_nperblock = transform_tensor_descriptor(
+                c0_grid_desc_nblock_nperblock,
+                make_tuple(make_insert_transform(I1),
+                           make_insert_transform(I1),
+                           make_pass_through_transform(NBlock),
+                           make_pass_through_transform(NPerBlock)),
+                make_tuple(Sequence<>{}, Sequence<>{}, Sequence<0>{}, Sequence<1>{}),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
+
+            // LDS c_reduce_block_desc_mperblock_nperblock
+            constexpr auto c_reduce_block_desc_mperblock_nperblock = transform_tensor_descriptor(
+                c_shuffle_block_desc_mblock_mperblock_nblock_nperblock,
+                make_tuple(
+                    make_freeze_transform(I0),
+                    make_pass_through_transform(
+                        c_shuffle_block_desc_mblock_mperblock_nblock_nperblock.GetLength(I1)),
+                    make_freeze_transform(I0),
+                    make_pass_through_transform(
+                        c_shuffle_block_desc_mblock_mperblock_nblock_nperblock.GetLength(I3))),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
+                make_tuple(Sequence<>{}, Sequence<0>{}, Sequence<>{}, Sequence<1>{}));
+
+            static_assert(CReduceThreadClusterLengths_MPerBlock_NPerBlock::At(I0) *
+                                  CReduceThreadClusterLengths_MPerBlock_NPerBlock::At(I1) ==
+                              BlockSize,
+                          "wrong!");
+
+            static_assert((CShuffleMXdlPerWavePerShuffle * MWave * MPerXdl) %
+                                      CReduceThreadClusterLengths_MPerBlock_NPerBlock::At(I0) ==
+                                  0 &&
+                              (CShuffleNXdlPerWavePerShuffle * NWave * NPerXdl) %
+                                      CReduceThreadClusterLengths_MPerBlock_NPerBlock::At(I1) ==
+                                  0,
+                          "wrong!");
+
+            constexpr index_t mreduce_per_thread =
+                (CShuffleMXdlPerWavePerShuffle * MWave * MPerXdl) /
+                CReduceThreadClusterLengths_MPerBlock_NPerBlock::At(I0);
+
+            constexpr index_t nreduce_per_thread =
+                (CShuffleNXdlPerWavePerShuffle * NWave * NPerXdl) /
+                CReduceThreadClusterLengths_MPerBlock_NPerBlock::At(I1);
+
+            constexpr auto c_reduce_thread_lengths_mperblock_nperblock =
+                Sequence<mreduce_per_thread, nreduce_per_thread>{};
+
+            // pytorch default
+            // https://pytorch.org/docs/stable/generated/torch.nn.LayerNorm.html
+            static constexpr FloatReduceAcc epsilon = 1e-5;
+
+            // VGPR c_reduce_thread_desc_mperblock_nperblock
+            constexpr auto c_reduce_thread_desc_mperblock_nperblock =
+                make_naive_tensor_descriptor_packed(
+                    make_tuple(Number<mreduce_per_thread>{}, Number<nreduce_per_thread>{}));
+
+            constexpr auto c_reduce_thread_desc_mblock_mperblock_nblock_nperblock =
+                make_naive_tensor_descriptor_packed(
+                    make_tuple(I1, Number<mreduce_per_thread>{}, I1, Number<nreduce_per_thread>{}));
+
+            // VGPR d_reduce_thread_desc_mperblock
+            constexpr auto d_reduce_thread_desc_mperblock =
+                make_naive_tensor_descriptor_packed(make_tuple(Number<mreduce_per_thread>{}));
+
+            // TODO: this should be implemented as a blockwise reduction
+            auto c_reduce_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, FloatReduceAcc>(
+                c_reduce_thread_desc_mperblock_nperblock.GetElementSpaceSize());
+
+            auto c0_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, FloatC0>(
+                c_reduce_thread_desc_mperblock_nperblock.GetElementSpaceSize());
+
+            // Align 16 bytes (maximum LDS read/write width)
+            constexpr auto c_block_size_aligned =
+                math::integer_least_multiple(
+                    c_shuffle_block_desc_mblock_mperblock_nblock_nperblock.GetElementSpaceSize() *
+                        sizeof(FloatCShuffle),
+                    16) /
+                sizeof(FloatCShuffle);
+
+            auto d_reduce_work_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
+                reinterpret_cast<FloatReduceAcc*>(static_cast<FloatCShuffle*>(p_shared) +
+                                                  c_block_size_aligned),
+                BlockSize);
+
+            // Sum thread workspace
+            auto d0_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, FloatReduceAcc>(
+                d_reduce_thread_desc_mperblock.GetElementSpaceSize());
+
+            // Squared sum thread workspace
+            auto d1_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, FloatReduceAcc>(
+                d_reduce_thread_desc_mperblock.GetElementSpaceSize());
+
+            // reduce: threadwise copy from LDS to VGPR
+            constexpr auto c_reduce_thread_cluster_desc = make_cluster_descriptor(
+                CReduceThreadClusterLengths_MPerBlock_NPerBlock{}, Sequence<1, 0>{});
+
+            const auto c_reduce_thread_cluster_idx =
+                c_reduce_thread_cluster_desc.CalculateBottomIndex(
+                    make_multi_index(get_thread_local_1d_id()));
+
+            const auto c_reduce_thread_data_idx_begin =
+                c_reduce_thread_cluster_idx * c_reduce_thread_lengths_mperblock_nperblock;
+
+            auto c_reduce_thread_copy_lds_to_vgpr = ThreadwiseTensorSliceTransfer_v2<
+                FloatCShuffle,
+                FloatReduceAcc,
+                decltype(c_reduce_block_desc_mperblock_nperblock),
+                decltype(c_reduce_thread_desc_mperblock_nperblock),
+                decltype(c_reduce_thread_lengths_mperblock_nperblock),
+                Sequence<0, 1>,
+                1,
+                CReduceThreadCopySrcDstScalarPerVector_NPerBlock,
+                1,
+                true>{c_reduce_block_desc_mperblock_nperblock, c_reduce_thread_data_idx_begin};
+
+            auto c_reduce_thread_copy_vgpr_to_lds = ThreadwiseTensorSliceTransfer_v1r3<
+                FloatReduceAcc,
+                FloatCShuffle,
+                decltype(c_reduce_thread_desc_mperblock_nperblock),
+                decltype(c_reduce_block_desc_mperblock_nperblock),
+                tensor_operation::element_wise::PassThrough,
+                decltype(c_reduce_thread_lengths_mperblock_nperblock),
+                Sequence<0, 1>,
+                1,
+                CReduceThreadCopySrcDstScalarPerVector_NPerBlock,
+                InMemoryDataOperationEnum::Set,
+                1,
+                true>{c_reduce_block_desc_mperblock_nperblock,
+                      c_reduce_thread_data_idx_begin,
+                      tensor_operation::element_wise::PassThrough{}};
+
+            auto c0_thread_copy_global_to_vgpr = ThreadwiseTensorSliceTransfer_v2<
+                FloatC0,
+                FloatC0,
+                decltype(c0_grid_desc_mblock_mperblock_nblock_nperblock),
+                decltype(c_reduce_thread_desc_mblock_mperblock_nblock_nperblock),
+                Sequence<I1, mreduce_per_thread, I1, nreduce_per_thread>,
+                Sequence<0, 1, 2, 3>,
+                3,
+                CReduceThreadCopySrcDstScalarPerVector_NPerBlock,
+                1,
+                true>(c0_grid_desc_mblock_mperblock_nblock_nperblock,
+                      make_multi_index(block_work_idx[I0],
+                                       c_reduce_thread_data_idx_begin[I0],
+                                       block_work_idx[I1],
+                                       c_reduce_thread_data_idx_begin[I1]));
+
+            // Note: c0_add is of same layout as c so we don't declare new c0_add_desc here
+            auto c0_add_thread_copy_global_to_vgpr = ThreadwiseTensorSliceTransfer_v2<
+                FloatC0,
+                FloatC0,
+                decltype(c_grid_desc_mblock_mperblock_nblock_nperblock),
+                decltype(c_reduce_thread_desc_mblock_mperblock_nblock_nperblock),
+                Sequence<I1, mreduce_per_thread, I1, nreduce_per_thread>,
+                Sequence<0, 1, 2, 3>,
+                3,
+                CReduceThreadCopySrcDstScalarPerVector_NPerBlock,
+                1,
+                true>(c_grid_desc_mblock_mperblock_nblock_nperblock,
+                      make_multi_index(block_work_idx[I0],
+                                       c_reduce_thread_data_idx_begin[I0],
+                                       block_work_idx[I1],
+                                       c_reduce_thread_data_idx_begin[I1]));
+
+            // space filling curve for threadwise C in VGPR
+            constexpr auto sfc_c_vgpr =
+                SpaceFillingCurve<Sequence<MXdlPerWave, NXdlPerWave, 1, 1, M2, 1, M4, 1>,
+                                  Sequence<0, 1, 2, 3, 4, 5, 6, 7>,
+                                  Sequence<CShuffleMXdlPerWavePerShuffle,
+                                           CShuffleNXdlPerWavePerShuffle,
+                                           1,
+                                           1,
+                                           M2,
+                                           1,
+                                           M4,
+                                           1>>{};
+
+            // space filling curve for shuffled blockwise C in global mem
+            constexpr auto sfc_c_global =
+                SpaceFillingCurve<Sequence<1, MPerBlock, 1, NPerBlock>,
+                                  Sequence<0, 2, 1, 3>,
+                                  Sequence<1,
+                                           CShuffleMXdlPerWavePerShuffle * MWave * MPerXdl,
+                                           1,
+                                           CShuffleNXdlPerWavePerShuffle * NWave * NPerXdl>>{};
+
+            constexpr index_t num_access = sfc_c_vgpr.GetNumOfAccess();
+
+            static_assert(num_access == sfc_c_global.GetNumOfAccess(), "wrong!");
+
+            static_for<0, num_access, 1>{}([&](auto access_id) {
+                // make sure it's safe to write to LDS
+                block_sync_lds();
+
+                // each thread write its data from VGPR to LDS
+                c_thread_copy_vgpr_to_lds.Run(c_thread_desc_m0_n0_m1_n1_m2_m3_m4_n2,
+                                              sfc_c_vgpr.GetIndexTupleOfNumber(access_id),
+                                              c_thread_buf,
+                                              c_block_desc_m0_n0_m1_n1_m2_m3_m4_n2,
+                                              c_shuffle_block_buf);
+
+                block_sync_lds();
+
+                // load from LDS and global, add bias
+                c_reduce_thread_copy_lds_to_vgpr.Run(c_reduce_block_desc_mperblock_nperblock,
+                                                     c_shuffle_block_buf,
+                                                     c_reduce_thread_desc_mperblock_nperblock,
+                                                     make_tuple(I0, I0),
+                                                     c_reduce_thread_buf);
+
+                c0_thread_copy_global_to_vgpr.Run(
+                    c0_grid_desc_mblock_mperblock_nblock_nperblock,
+                    c0_bias_grid_buf,
+                    c_reduce_thread_desc_mblock_mperblock_nblock_nperblock,
+                    make_tuple(I0, I0, I0, I0),
+                    c0_thread_buf);
+
+                static_for<0, c_reduce_thread_desc_mperblock_nperblock.GetElementSize(), 1>{}(
+                    [&](auto i) {
+                        FloatReduceAcc out;
+                        acc_element_op(out,
+                                       c_reduce_thread_buf(i) +
+                                           static_cast<FloatReduceAcc>(c0_thread_buf(i)));
+                        c_reduce_thread_buf(i) = out; // acc_element_op(acc + bias)
+                    });
+
+                c0_add_thread_copy_global_to_vgpr.Run(
+                    c_grid_desc_mblock_mperblock_nblock_nperblock,
+                    c0_add_grid_buf,
+                    c_reduce_thread_desc_mblock_mperblock_nblock_nperblock,
+                    make_tuple(I0, I0, I0, I0),
+                    c0_thread_buf);
+
+                static_for<0, c_reduce_thread_desc_mperblock_nperblock.GetElementSize(), 1>{}(
+                    [&](auto i) {
+                        c_reduce_thread_buf(i) +=
+                            static_cast<FloatReduceAcc>(c0_thread_buf(i)); // add
+                    });
+
+                // layernorm
+                {
+                    using ThreadwiseReduceD0 =
+                        ThreadwiseReduction<FloatReduceAcc,
+                                            decltype(c_reduce_thread_desc_mperblock_nperblock),
+                                            decltype(d_reduce_thread_desc_mperblock),
+                                            reduce::Add,
+                                            false>;
+                    using ThreadwiseReduceD1 =
+                        ThreadwiseReduction<FloatReduceAcc,
+                                            decltype(c_reduce_thread_desc_mperblock_nperblock),
+                                            decltype(d_reduce_thread_desc_mperblock),
+                                            reduce::SquaredAdd,
+                                            false>;
+
+                    const auto d0_zeroVal =
+                        ThreadwiseReduceD0::Op::template GetIdentityValue<FloatReduceAcc>();
+                    const auto d1_zeroVal =
+                        ThreadwiseReduceD1::Op::template GetIdentityValue<FloatReduceAcc>();
+                    static_for<0, mreduce_per_thread, 1>{}(
+                        [&](auto i) { d0_thread_buf(i) = d0_zeroVal; });
+                    static_for<0, mreduce_per_thread, 1>{}(
+                        [&](auto i) { d1_thread_buf(i) = d1_zeroVal; });
+
+                    // reduce sum in VGPR
+                    ThreadwiseReduceD0::Reduce(c_reduce_thread_buf, d0_thread_buf);
+
+                    // reduce squared sum in VGPR
+                    ThreadwiseReduceD1::Reduce(c_reduce_thread_buf, d1_thread_buf);
+
+                    // reduce within workgroup
+                    using BlockwiseReduce = PartitionedBlockwiseReduction<
+                        FloatReduceAcc,
+                        BlockSize,
+                        CReduceThreadClusterLengths_MPerBlock_NPerBlock, // ThreadClusterLengths_M_K
+                        Sequence<1, 0>, // ThreadClusterArrangeOrder
+                        reduce::Add,
+                        false>;
+
+                    static_for<0, mreduce_per_thread, 1>{}([&](auto i) {
+                        block_sync_lds();
+                        BlockwiseReduce::Reduce(d_reduce_work_buf,
+                                                d0_thread_buf(i)); // blockwise reduced sum
+                        block_sync_lds();
+                        BlockwiseReduce::Reduce(d_reduce_work_buf,
+                                                d1_thread_buf(i)); // blockwise reduced squared sum
+                    });
+
+                    // normalize
+                    const index_t NRaw =
+                        c_grid_desc_mblock_mperblock_nblock_nperblock.GetTransforms()[I0]
+                            .GetUpperLengths()[I1]; // TODO: proper handle
+
+                    static_for<0, mreduce_per_thread, 1>{}([&](auto im) {
+                        static_for<0, nreduce_per_thread, 1>{}([&](auto in) {
+                            constexpr auto dst_offset =
+                                Number<c_reduce_thread_desc_mperblock_nperblock.CalculateOffset(
+                                    make_tuple(im, in))>{};
+
+                            constexpr auto src_offset =
+                                Number<d_reduce_thread_desc_mperblock.CalculateOffset(
+                                    make_tuple(im))>{};
+
+                            FloatReduceAcc avg_sum         = d0_thread_buf(src_offset) / NRaw;
+                            FloatReduceAcc avg_squared_sum = d1_thread_buf(src_offset) / NRaw;
+
+                            FloatReduceAcc numerator = c_reduce_thread_buf(dst_offset) - avg_sum;
+                            FloatReduceAcc divisor = epsilon + avg_squared_sum - avg_sum * avg_sum;
+                            FloatReduceAcc divisor_sqrt;
+                            tensor_operation::element_wise::UnarySqrt{}(divisor_sqrt, divisor);
+
+                            c_reduce_thread_buf(dst_offset) = numerator / divisor_sqrt;
+                        });
+                    });
+
+                    // scaling
+                    c0_thread_copy_global_to_vgpr.Run(
+                        c0_grid_desc_mblock_mperblock_nblock_nperblock,
+                        c0_gamma_grid_buf,
+                        c_reduce_thread_desc_mblock_mperblock_nblock_nperblock,
+                        make_tuple(I0, I0, I0, I0),
+                        c0_thread_buf);
+
+                    static_for<0, c_reduce_thread_desc_mperblock_nperblock.GetElementSize(), 1>{}(
+                        [&](auto i) {
+                            c_reduce_thread_buf(i) *=
+                                static_cast<FloatReduceAcc>(c0_thread_buf(i)); // * gamma
+                        });
+
+                    c0_thread_copy_global_to_vgpr.Run(
+                        c0_grid_desc_mblock_mperblock_nblock_nperblock,
+                        c0_beta_grid_buf,
+                        c_reduce_thread_desc_mblock_mperblock_nblock_nperblock,
+                        make_tuple(I0, I0, I0, I0),
+                        c0_thread_buf);
+
+                    static_for<0, c_reduce_thread_desc_mperblock_nperblock.GetElementSize(), 1>{}(
+                        [&](auto i) {
+                            c_reduce_thread_buf(i) +=
+                                static_cast<FloatReduceAcc>(c0_thread_buf(i)); // + beta
+                        });
+
+                    block_sync_lds();
+
+                    c_reduce_thread_copy_vgpr_to_lds.Run(c_reduce_thread_desc_mperblock_nperblock,
+                                                         make_tuple(I0, I0),
+                                                         c_reduce_thread_buf,
+                                                         c_reduce_block_desc_mperblock_nperblock,
+                                                         c_shuffle_block_buf);
+
+                } // end layernorm
+
+                block_sync_lds();
+
+                // each block copy its data from LDS to global
+                c_shuffle_block_copy_lds_to_global.Run(
+                    c_shuffle_block_desc_mblock_mperblock_nblock_nperblock,
+                    c_shuffle_block_buf,
+                    c_grid_desc_mblock_mperblock_nblock_nperblock,
+                    c_grid_buf);
+
+                if constexpr(access_id < num_access - 1)
+                {
+                    constexpr auto c_global_step = sfc_c_global.GetForwardStep(access_id);
+
+                    // move on C
+                    c_shuffle_block_copy_lds_to_global.MoveDstSliceWindow(
+                        c_grid_desc_mblock_mperblock_nblock_nperblock, c_global_step);
+
+                    // move on C0
+                    c0_thread_copy_global_to_vgpr.MoveSrcSliceWindow(
+                        c0_grid_desc_mblock_mperblock_nblock_nperblock, c_global_step);
+
+                    // move on C0_add
+                    c0_add_thread_copy_global_to_vgpr.MoveSrcSliceWindow(
+                        c_grid_desc_mblock_mperblock_nblock_nperblock, c_global_step);
+                }
+            });
+        }
+    }
+};
+
+} // namespace ck

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

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
+
 #pragma once
 
-#include "common_header.hpp"
-#include "multi_index_transform_helper.hpp"
-#include "tensor_descriptor.hpp"
-#include "tensor_descriptor_helper.hpp"
-#include "tensor_operation/gpu/grid/block_to_ctile_map.hpp"
-#include "blockwise_gemm_xdlops.hpp"
-#include "thread_group_tensor_slice_transfer_v4r1.hpp"
-#include "thread_group_tensor_slice_transfer_v6r1.hpp"
-#include "threadwise_tensor_slice_transfer.hpp"
-#include "gridwise_gemm_pipeline_v1.hpp"
+#include "ck/utility/common_header.hpp"
+#include "ck/tensor_description/multi_index_transform_helper.hpp"
+#include "ck/tensor_description/tensor_descriptor.hpp"
+#include "ck/tensor_description/tensor_descriptor_helper.hpp"
+#include "ck/tensor_operation/gpu/grid/block_to_ctile_map.hpp"
+#include "ck/tensor_operation/gpu/grid/gridwise_gemm_pipeline_v1.hpp"
+#include "ck/tensor_operation/gpu/block/blockwise_gemm_xdlops.hpp"
+#include "ck/tensor_operation/gpu/block/thread_group_tensor_slice_transfer_v4r1.hpp"
+#include "ck/tensor_operation/gpu/block/thread_group_tensor_slice_transfer_v6r1.hpp"
+#include "ck/tensor_operation/gpu/thread/threadwise_tensor_slice_transfer.hpp"
+#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
 
 namespace ck {
 
@@ -791,8 +795,10 @@ struct GridwiseGemm_bk0mk1_bk0nk1_mn_xdlops_bwd_weight
             constexpr auto c_block_desc_mblock_mperblock_nblock_nperblock =
                 GetCBlockDescriptor_MBlock_MPerBlock_NBlock_NPerBlock();
 
+            void* p_shared = static_cast<void*>(p_shared_block);
+
             auto c_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
-                static_cast<FloatC*>(p_shared_block),
+                static_cast<FloatC*>(p_shared),
                 c_block_desc_mblock_mperblock_nblock_nperblock.GetElementSpaceSize());
 
             static_assert(M1 == MWave, "");

include/ck/tensor_operation/gpu/grid/gridwise_gemm_xdlops_v2r3.hpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
+
 #pragma once
 
-#include "common_header.hpp"
-#include "multi_index_transform_helper.hpp"
-#include "tensor_descriptor.hpp"
-#include "tensor_descriptor_helper.hpp"
-#include "tensor_operation/gpu/grid/block_to_ctile_map.hpp"
-#include "blockwise_gemm_xdlops.hpp"
-#include "thread_group_tensor_slice_transfer_v4r1.hpp"
-#include "threadwise_tensor_slice_transfer.hpp"
-#include "gridwise_gemm_pipeline_v1.hpp"
+#include "ck/utility/common_header.hpp"
+#include "ck/tensor_description/multi_index_transform_helper.hpp"
+#include "ck/tensor_description/tensor_descriptor.hpp"
+#include "ck/tensor_description/tensor_descriptor_helper.hpp"
+#include "ck/tensor_operation/gpu/grid/block_to_ctile_map.hpp"
+#include "ck/tensor_operation/gpu/grid/gridwise_gemm_pipeline_v1.hpp"
+#include "ck/tensor_operation/gpu/block/blockwise_gemm_xdlops.hpp"
+#include "ck/tensor_operation/gpu/block/thread_group_tensor_slice_transfer_v4r1.hpp"
+#include "ck/tensor_operation/gpu/thread/threadwise_tensor_slice_transfer.hpp"
+#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
 
 namespace ck {
 

include/ck/tensor_operation/gpu/grid/gridwise_gemm_xdlops_v2r4.hpp

-#ifndef CK_GRIDWISE_GEMM_XDLOPS_V2R4_HPP
-#define CK_GRIDWISE_GEMM_XDLOPS_V2R4_HPP
-
-#include "common_header.hpp"
-#include "multi_index_transform_helper.hpp"
-#include "tensor_descriptor.hpp"
-#include "tensor_descriptor_helper.hpp"
-#include "tensor_operation/gpu/grid/block_to_ctile_map.hpp"
-#include "blockwise_gemm_xdlops.hpp"
-#include "thread_group_tensor_slice_transfer_v4r1.hpp"
-#include "threadwise_tensor_slice_transfer.hpp"
+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include "ck/utility/common_header.hpp"
+#include "ck/tensor_description/multi_index_transform_helper.hpp"
+#include "ck/tensor_description/tensor_descriptor.hpp"
+#include "ck/tensor_description/tensor_descriptor_helper.hpp"
+#include "ck/tensor_operation/gpu/grid/block_to_ctile_map.hpp"
+#include "ck/tensor_operation/gpu/grid/gridwise_gemm_pipeline_v1.hpp"
+#include "ck/tensor_operation/gpu/block/blockwise_gemm_xdlops.hpp"
+#include "ck/tensor_operation/gpu/block/thread_group_tensor_slice_transfer_v4r1.hpp"
+#include "ck/tensor_operation/gpu/thread/threadwise_tensor_slice_transfer.hpp"
+#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
 
 namespace ck {
 
@@ -607,7 +611,6 @@ struct GridwiseGemm_bk0mk1_bk0nk1_mn_xdlops_v2r4
                               c_grid_buf);
         }
     }
-}; // namespace ck
+};
 
 } // namespace ck
-#endif

include/ck/tensor_operation/gpu/grid/gridwise_gemm_xdlops_v2r4r2.hpp

-#ifndef CK_GRIDWISE_GEMM_XDLOPS_V2R4R2_HPP
-#define CK_GRIDWISE_GEMM_XDLOPS_V2R4R2_HPP
-
-#include "common_header.hpp"
-#include "multi_index_transform_helper.hpp"
-#include "tensor_descriptor.hpp"
-#include "tensor_descriptor_helper.hpp"
-#include "tensor_operation/gpu/grid/block_to_ctile_map.hpp"
-#include "blockwise_gemm_xdlops.hpp"
-#include "thread_group_tensor_slice_transfer_v4r1.hpp"
-#include "thread_group_tensor_slice_transfer_v6r1.hpp"
-#include "threadwise_tensor_slice_transfer.hpp"
+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include "ck/utility/common_header.hpp"
+#include "ck/tensor_description/multi_index_transform_helper.hpp"
+#include "ck/tensor_description/tensor_descriptor.hpp"
+#include "ck/tensor_description/tensor_descriptor_helper.hpp"
+#include "ck/tensor_operation/gpu/grid/block_to_ctile_map.hpp"
+#include "ck/tensor_operation/gpu/grid/gridwise_gemm_pipeline_v1.hpp"
+#include "ck/tensor_operation/gpu/block/blockwise_gemm_xdlops.hpp"
+#include "ck/tensor_operation/gpu/block/thread_group_tensor_slice_transfer_v4r1.hpp"
+#include "ck/tensor_operation/gpu/block/thread_group_tensor_slice_transfer_v6r1.hpp"
+#include "ck/tensor_operation/gpu/thread/threadwise_tensor_slice_transfer.hpp"
+#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
 
 namespace ck {
 
@@ -717,7 +721,6 @@ struct GridwiseGemm_bk0mk1_bk0nk1_mn_xdlops_v2r4r2
             });
         }
     }
-}; // namespace ck
+};
 
 } // namespace ck
-#endif

include/ck/tensor_operation/gpu/grid/gridwise_gemm_xdlops_v3r1.hpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
+
 #pragma once
-#include "common_header.hpp"
-#include "multi_index_transform_helper.hpp"
-#include "tensor_descriptor.hpp"
-#include "tensor_descriptor_helper.hpp"
-#include "tensor_operation/gpu/grid/block_to_ctile_map.hpp"
-#include "blockwise_gemm_xdlops.hpp"
-#include "thread_group_tensor_slice_transfer_v4r1.hpp"
-#include "thread_group_tensor_slice_transfer_v6r1.hpp"
-#include "threadwise_tensor_slice_transfer.hpp"
-#include "gridwise_gemm_pipeline_v1.hpp"
-#include "tensor_space_filling_curve.hpp"
+
+#include "ck/utility/common_header.hpp"
+#include "ck/tensor_description/multi_index_transform_helper.hpp"
+#include "ck/tensor_description/tensor_space_filling_curve.hpp"
+#include "ck/tensor_description/tensor_descriptor.hpp"
+#include "ck/tensor_description/tensor_descriptor_helper.hpp"
+#include "ck/tensor_operation/gpu/grid/block_to_ctile_map.hpp"
+#include "ck/tensor_operation/gpu/grid/gridwise_gemm_pipeline_v1.hpp"
+#include "ck/tensor_operation/gpu/block/blockwise_gemm_xdlops.hpp"
+#include "ck/tensor_operation/gpu/block/thread_group_tensor_slice_transfer_v4r1.hpp"
+#include "ck/tensor_operation/gpu/block/thread_group_tensor_slice_transfer_v6r1.hpp"
+#include "ck/tensor_operation/gpu/thread/threadwise_tensor_slice_transfer.hpp"
+#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
 
 namespace ck {
 

include/ck/tensor_operation/gpu/grid/gridwise_gemm_xdlops_v3r2.hpp

-#ifndef CK_GRIDWISE_GEMM_XDLOPS_V3R2_HPP
-#define CK_GRIDWISE_GEMM_XDLOPS_V3R2_HPP
-
-#include "common_header.hpp"
-#include "multi_index_transform_helper.hpp"
-#include "tensor_descriptor.hpp"
-#include "tensor_descriptor_helper.hpp"
-#include "tensor_operation/gpu/grid/block_to_ctile_map.hpp"
-#include "blockwise_gemm_xdlops.hpp"
-#include "thread_group_tensor_slice_transfer_v4r1.hpp"
-#include "thread_group_tensor_slice_transfer_v6r2.hpp"
-#include "threadwise_tensor_slice_transfer.hpp"
-#include "gridwise_gemm_pipeline_v1.hpp"
+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include "ck/utility/common_header.hpp"
+#include "ck/tensor_description/multi_index_transform_helper.hpp"
+#include "ck/tensor_description/tensor_descriptor.hpp"
+#include "ck/tensor_description/tensor_descriptor_helper.hpp"
+#include "ck/tensor_operation/gpu/grid/block_to_ctile_map.hpp"
+#include "ck/tensor_operation/gpu/grid/gridwise_gemm_pipeline_v1.hpp"
+#include "ck/tensor_operation/gpu/block/blockwise_gemm_xdlops.hpp"
+#include "ck/tensor_operation/gpu/block/thread_group_tensor_slice_transfer_v4r1.hpp"
+#include "ck/tensor_operation/gpu/block/thread_group_tensor_slice_transfer_v6r2.hpp"
+#include "ck/tensor_operation/gpu/thread/threadwise_tensor_slice_transfer.hpp"
+#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
 
 namespace ck {
 
@@ -755,4 +758,3 @@ struct GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v3r2
 };
 
 } // namespace ck
-#endif

include/ck/tensor_operation/gpu/grid/gridwise_gemm_xdlops_v3r3.hpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
+
 #pragma once
-#include "common_header.hpp"
-#include "multi_index_transform_helper.hpp"
-#include "tensor_descriptor.hpp"
-#include "tensor_descriptor_helper.hpp"
-#include "tensor_operation/gpu/grid/block_to_ctile_map.hpp"
-#include "blockwise_gemm_xdlops.hpp"
-#include "thread_group_tensor_slice_transfer_v4r1.hpp"
-#include "thread_group_tensor_slice_transfer_v6r3.hpp"
-#include "threadwise_tensor_slice_transfer.hpp"
-#include "gridwise_gemm_pipeline_v1.hpp"
+
+#include "ck/utility/common_header.hpp"
+#include "ck/tensor_description/multi_index_transform_helper.hpp"
+#include "ck/tensor_description/tensor_descriptor.hpp"
+#include "ck/tensor_description/tensor_descriptor_helper.hpp"
+#include "ck/tensor_operation/gpu/grid/block_to_ctile_map.hpp"
+#include "ck/tensor_operation/gpu/grid/gridwise_gemm_pipeline_v1.hpp"
+#include "ck/tensor_operation/gpu/block/blockwise_gemm_xdlops.hpp"
+#include "ck/tensor_operation/gpu/block/thread_group_tensor_slice_transfer_v4r1.hpp"
+#include "ck/tensor_operation/gpu/block/thread_group_tensor_slice_transfer_v6r3.hpp"
+#include "ck/tensor_operation/gpu/thread/threadwise_tensor_slice_transfer.hpp"
+#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
 
 namespace ck {
 
@@ -340,7 +345,7 @@ struct GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v3r3
     using DefaultBlock2CTileMap =
         remove_cvref_t<decltype(MakeDefaultBlock2CTileMap(CGridDesc_M_N{}, 1, 1))>;
 
-    template <bool HasMainKBlockLoop, typename Block2CTileMap = DefaultBlock2CTileMap>
+    template <bool HasMainKBlockLoop, typename Block2CTileMap>
     __device__ static void
     Run(const FloatAB* __restrict__ p_a_grid,
         const FloatAB* __restrict__ p_b_grid,

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

-/*******************************************************************************
- *
- * MIT License
- *
- * Copyright (c) 2020 Advanced Micro Devices, Inc.
- *
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to deal
- * in the Software without restriction, including without limitation the rights
- * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
- * copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in all
- * copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
- * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
- * SOFTWARE.
- *
- *******************************************************************************/
-#ifndef CK_GRIDWISE_SET_BUFFER_VALUE_HPP
-#define CK_GRIDWISE_SET_BUFFER_VALUE_HPP
-
-#include "threadwise_tensor_slice_transfer.hpp"
+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include "ck/tensor_operation/gpu/thread/threadwise_tensor_slice_transfer.hpp"
 
 namespace ck {
 
@@ -37,7 +14,7 @@ __global__ void kernel_buffer_set_value(const Grid1dBufferDescType grid_1d_buffe
 
 {
 
-    using PassThroughOp = tensor_operation::element_wise::UnaryIdentic<DataType, DataType>;
+    using PassThroughOp = tensor_operation::element_wise::PassThrough;
 
     constexpr auto I0 = Number<0>{};
 
@@ -77,4 +54,3 @@ __global__ void kernel_buffer_set_value(const Grid1dBufferDescType grid_1d_buffe
 };
 
 } // namespace ck
-#endif

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

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include "ck/utility/data_type.hpp"
+#include "ck/utility/reduction_common.hpp"
+#include "ck/utility/reduction_operator.hpp"
+#include "ck/utility/reduction_functions_accumulate.hpp"
+#include "ck/tensor_operation/gpu/block/reduction_functions_blockwise.hpp"
+#include "ck/tensor_operation/gpu/thread/reduction_functions_threadwise.hpp"
+#include "ck/tensor_operation/gpu/thread/threadwise_tensor_slice_transfer.hpp"
+#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
+
+namespace ck {
+
+template <typename GridwiseReduction,
+          typename InDataType,
+          typename OutDataType,
+          typename AccDataType,
+          typename GridDesc_M_K>
+__global__ void kernel_softmax(const GridDesc_M_K in_grid_desc_m_k,
+                               const GridDesc_M_K out_grid_desc_m_k,
+                               index_t block_group_size,
+                               index_t num_k_block_tile_iteration,
+                               AccDataType alpha,
+                               const InDataType* const __restrict__ p_in_value_global,
+                               AccDataType beta,
+                               OutDataType* const __restrict__ p_out_value_global)
+{
+    GridwiseReduction::Run(in_grid_desc_m_k,
+                           out_grid_desc_m_k,
+                           block_group_size,
+                           num_k_block_tile_iteration,
+                           alpha,
+                           p_in_value_global,
+                           beta,
+                           p_out_value_global);
+};
+
+template <typename InDataType,
+          typename OutDataType,
+          typename AccDataType,
+          typename GridDesc_M_K,
+          index_t BlockSize,
+          index_t MThreadClusterSize,
+          index_t KThreadClusterSize,
+          index_t MThreadSliceSize,
+          index_t KThreadSliceSize,
+          index_t InSrcVectorDim,
+          index_t InSrcVectorSize,
+          index_t OutDstVectorSize,
+          bool SweepOnce>
+struct GridwiseSoftmax_mk_to_mk
+{
+    static_assert(((InSrcVectorDim == 0 && MThreadSliceSize % InSrcVectorSize == 0) ||
+                   (InSrcVectorDim == 1 && KThreadSliceSize % InSrcVectorSize == 0)) &&
+                      (KThreadSliceSize % OutDstVectorSize == 0),
+                  "Invalid thread slice sizes and/or vector sizes configuration, please check!");
+
+    static constexpr bool reorder_thread_cluster = (InSrcVectorDim == 0);
+
+    using ThreadClusterLengths_M_K = Sequence<MThreadClusterSize, KThreadClusterSize>;
+
+    using ThreadBufferDimAccessOrder =
+        typename conditional<reorder_thread_cluster, Sequence<1, 0>, Sequence<0, 1>>::type;
+
+    using ThreadClusterArrangeOrder =
+        typename conditional<reorder_thread_cluster, Sequence<1, 0>, Sequence<0, 1>>::type;
+
+    static constexpr auto thread_cluster_desc =
+        make_cluster_descriptor(ThreadClusterLengths_M_K{}, ThreadClusterArrangeOrder{});
+
+    using ThreadReduceSrcDesc_M_K = decltype(make_naive_tensor_descriptor_packed(
+        make_tuple(Number<MThreadSliceSize>{}, Number<KThreadSliceSize>{})));
+    using ThreadReduceDstDesc_M =
+        decltype(make_naive_tensor_descriptor_packed(make_tuple(Number<MThreadSliceSize>{})));
+
+    using PassThroughOp = tensor_operation::element_wise::PassThrough;
+
+    static constexpr auto I0 = Number<0>{};
+    static constexpr auto I1 = Number<1>{};
+
+    static constexpr index_t M_BlockTileSize = MThreadClusterSize * MThreadSliceSize;
+    static constexpr index_t K_BlockTileSize = KThreadClusterSize * KThreadSliceSize;
+
+    __device__ static void Run(const GridDesc_M_K& in_grid_desc_m_k,
+                               const GridDesc_M_K& out_grid_desc_m_k,
+                               index_t block_group_size,
+                               index_t num_k_block_tile_iteration,
+                               AccDataType alpha,
+                               const InDataType* const __restrict__ p_in_value_global,
+                               AccDataType beta,
+                               OutDataType* const __restrict__ p_out_value_global)
+    {
+        if constexpr(SweepOnce)
+        {
+            num_k_block_tile_iteration = 1;
+        }
+
+        // LDS
+        __shared__ AccDataType p_reduce_work_buffer[BlockSize];
+
+        auto out_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
+            p_out_value_global, out_grid_desc_m_k.GetElementSpaceSize());
+
+        auto reduce_work_buf =
+            make_dynamic_buffer<AddressSpaceEnum::Lds>(p_reduce_work_buffer, BlockSize);
+
+        StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, MThreadSliceSize * KThreadSliceSize, true>
+            in_thread_buf;
+
+        StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, MThreadSliceSize * KThreadSliceSize, true>
+            out_thread_buf;
+
+        StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, MThreadSliceSize, true> max_value_buf;
+
+        static_for<0, MThreadSliceSize, 1>{}([&](auto I) {
+            max_value_buf(I) = reduce::Max::template GetIdentityValue<AccDataType>();
+        });
+
+        StaticBuffer<AddressSpaceEnum::Vgpr, AccDataType, MThreadSliceSize, true> accu_value_buf;
+
+        static_for<0, MThreadSliceSize, 1>{}([&](auto I) {
+            accu_value_buf(I) = reduce::Add::template GetIdentityValue<AccDataType>();
+        });
+
+        const index_t thread_local_id = get_thread_local_1d_id();
+        const index_t block_global_id = get_block_1d_id();
+        const index_t blkgroup_id     = block_global_id / block_group_size;
+        const index_t block_local_id  = block_global_id % block_group_size;
+
+        const auto thread_cluster_idx =
+            thread_cluster_desc.CalculateBottomIndex(make_multi_index(thread_local_id));
+
+        const auto thread_m_cluster_id = thread_cluster_idx[I0];
+        const auto thread_k_cluster_id = thread_cluster_idx[I1];
+
+        const index_t reduceSizePerBlock = K_BlockTileSize * num_k_block_tile_iteration;
+
+        using ThreadBufferLengths         = Sequence<MThreadSliceSize, KThreadSliceSize>;
+        constexpr auto thread_buffer_desc = make_naive_tensor_descriptor_packed(
+            make_tuple(Number<MThreadSliceSize>{}, Number<KThreadSliceSize>{}));
+
+        // Normally, 0 as invalid element value is adequate since 0 makes no contribution to
+        // accumulated result. However, in stable softmax, all values 0s or not are subtracted by
+        // another value_max. As numbers become non-zero, effectively it allows invalid values to
+        // slip through and contribute to the accumulated result.
+        //
+        // The trick here is leveraging the fact that many math functions (add, sub, exp, ...)
+        // propagate NaNs when operands have NaNs involved. By initialiing invalid element value
+        // with NaN, an invalid value doing math manipulations is still NaN, which in turn can still
+        // be identified as an invalid value. We can then discard the invalid values which
+        // originally failed the bound check during accumulation. This allows to ignore values that
+        // failed bound check even after multiple math manipulations.
+        //
+        // NOTE: reset coordinate after every step because the same threadwise copy will sweep
+        // through global memory 3 times back and forth
+        auto threadwise_src_load = ThreadwiseTensorSliceTransfer_v2<InDataType,
+                                                                    AccDataType,
+                                                                    GridDesc_M_K,
+                                                                    decltype(thread_buffer_desc),
+                                                                    ThreadBufferLengths,
+                                                                    ThreadBufferDimAccessOrder,
+                                                                    InSrcVectorDim,
+                                                                    InSrcVectorSize,
+                                                                    1,
+                                                                    true /* ResetCoordAfterRun */,
+                                                                    true /* InvalidElementAsNaN */>(
+            in_grid_desc_m_k,
+            make_multi_index(blkgroup_id * M_BlockTileSize + thread_m_cluster_id * MThreadSliceSize,
+                             block_local_id * reduceSizePerBlock +
+                                 thread_k_cluster_id * KThreadSliceSize));
+
+        auto threadwise_dst_load = ThreadwiseTensorSliceTransfer_v2<OutDataType,
+                                                                    AccDataType,
+                                                                    GridDesc_M_K,
+                                                                    decltype(thread_buffer_desc),
+                                                                    ThreadBufferLengths,
+                                                                    ThreadBufferDimAccessOrder,
+                                                                    InSrcVectorDim,
+                                                                    InSrcVectorSize,
+                                                                    1,
+                                                                    false>(
+            out_grid_desc_m_k,
+            make_multi_index(blkgroup_id * M_BlockTileSize + thread_m_cluster_id * MThreadSliceSize,
+                             block_local_id * reduceSizePerBlock +
+                                 thread_k_cluster_id * KThreadSliceSize));
+
+        auto threadwise_dst_store =
+            ThreadwiseTensorSliceTransfer_v1r3<AccDataType,
+                                               OutDataType,
+                                               decltype(thread_buffer_desc),
+                                               GridDesc_M_K,
+                                               PassThroughOp,
+                                               ThreadBufferLengths,
+                                               ThreadBufferDimAccessOrder,
+                                               InSrcVectorDim,
+                                               OutDstVectorSize,
+                                               InMemoryDataOperationEnum::Set,
+                                               1,
+                                               true>(
+                out_grid_desc_m_k,
+                make_multi_index(
+                    blkgroup_id * M_BlockTileSize + thread_m_cluster_id * MThreadSliceSize,
+                    block_local_id * reduceSizePerBlock + thread_k_cluster_id * KThreadSliceSize),
+                PassThroughOp{});
+
+        constexpr auto in_thread_copy_fwd_step =
+            make_multi_index(0, SweepOnce ? 0 : K_BlockTileSize);
+        constexpr auto in_thread_copy_bwd_step =
+            make_multi_index(0, SweepOnce ? 0 : -K_BlockTileSize);
+
+        ///
+        /// max(x)
+        ///
+        using BlockwiseMaxReduce = PartitionedBlockwiseReduction<
+            AccDataType,
+            BlockSize,
+            ThreadClusterLengths_M_K,
+            ThreadClusterArrangeOrder,
+            reduce::Max,
+            false, // param ignored
+            detail::AccumulateWithNanIgnore<reduce::Max, AccDataType>>;
+
+        using ThreadwiseMaxReduce =
+            ThreadwiseReduction<AccDataType,
+                                ThreadReduceSrcDesc_M_K,
+                                ThreadReduceDstDesc_M,
+                                reduce::Max,
+                                false, // param ignored
+                                detail::AccumulateWithNanIgnore<reduce::Max, AccDataType>>;
+
+        const auto in_global_val_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
+            p_in_value_global, in_grid_desc_m_k.GetElementSpaceSize());
+
+        index_t reducedTiles = 0;
+        do
+        {
+            threadwise_src_load.Run(in_grid_desc_m_k,
+                                    in_global_val_buf,
+                                    thread_buffer_desc,
+                                    make_tuple(I0, I0),
+                                    in_thread_buf);
+
+            ThreadwiseMaxReduce::Reduce(in_thread_buf, max_value_buf);
+
+            threadwise_src_load.MoveSrcSliceWindow(in_grid_desc_m_k, in_thread_copy_fwd_step);
+
+            reducedTiles++;
+        } while(reducedTiles < num_k_block_tile_iteration);
+
+        static_for<0, MThreadSliceSize, 1>{}(
+            [&](auto I) { BlockwiseMaxReduce::Reduce(reduce_work_buf, max_value_buf(I)); });
+
+        threadwise_src_load.MoveSrcSliceWindow(in_grid_desc_m_k, in_thread_copy_bwd_step);
+
+        ///
+        /// sum(exp(x - max(x)))
+        ///
+        using BlockwiseSumReduce = PartitionedBlockwiseReduction<
+            AccDataType,
+            BlockSize,
+            ThreadClusterLengths_M_K,
+            ThreadClusterArrangeOrder,
+            reduce::Add,
+            false, // ignored
+            detail::AccumulateWithNanIgnore<reduce::Add, AccDataType>>;
+
+        using ThreadwiseSumReduce =
+            ThreadwiseReduction<AccDataType,
+                                ThreadReduceSrcDesc_M_K,
+                                ThreadReduceDstDesc_M,
+                                reduce::Add,
+                                false, // ignored
+                                detail::AccumulateWithNanIgnore<reduce::Add, AccDataType>>;
+
+        reducedTiles = 0;
+        do
+        {
+            if constexpr(!SweepOnce)
+            {
+                threadwise_src_load.Run(in_grid_desc_m_k,
+                                        in_global_val_buf,
+                                        thread_buffer_desc,
+                                        make_tuple(I0, I0),
+                                        in_thread_buf);
+            }
+
+            // do element-wise pre-reduction operation
+            static_for<0, MThreadSliceSize, 1>{}([&](auto iM) {
+                static_for<0, KThreadSliceSize, 1>{}([&](auto iK) {
+                    constexpr auto offset = thread_buffer_desc.CalculateOffset(make_tuple(iM, iK));
+                    out_thread_buf(Number<offset>{}) =
+                        math::exp(in_thread_buf(Number<offset>{}) - max_value_buf(iM));
+                });
+            });
+
+            ThreadwiseSumReduce::Reduce(out_thread_buf, accu_value_buf);
+
+            threadwise_src_load.MoveSrcSliceWindow(in_grid_desc_m_k, in_thread_copy_bwd_step);
+
+            reducedTiles++;
+        } while(reducedTiles < num_k_block_tile_iteration);
+
+        static_for<0, MThreadSliceSize, 1>{}([&](auto I) {
+            BlockwiseSumReduce::Reduce(reduce_work_buf, accu_value_buf(I));
+            // block_sync_lds();
+        });
+
+        threadwise_src_load.MoveSrcSliceWindow(in_grid_desc_m_k, in_thread_copy_fwd_step);
+
+        ///
+        /// softmax
+        ///
+        reducedTiles = 0;
+        if(float_equal_zero{}(beta))
+        {
+            do
+            {
+                if constexpr(!SweepOnce)
+                {
+                    threadwise_src_load.Run(in_grid_desc_m_k,
+                                            in_global_val_buf,
+                                            thread_buffer_desc,
+                                            make_tuple(I0, I0),
+                                            in_thread_buf);
+                }
+
+                static_for<0, MThreadSliceSize, 1>{}([&](auto iM) {
+                    // out = alpha * exp(x - max(x)) / sum(exp(x - max(x)))
+                    static_for<0, KThreadSliceSize, 1>{}([&](auto iK) {
+                        constexpr auto offset =
+                            thread_buffer_desc.CalculateOffset(make_tuple(iM, iK));
+                        out_thread_buf(Number<offset>{}) =
+                            alpha * math::exp(in_thread_buf(Number<offset>{}) - max_value_buf(iM)) /
+                            accu_value_buf(iM);
+                    });
+                });
+
+                threadwise_dst_store.Run(thread_buffer_desc,
+                                         make_tuple(I0, I0),
+                                         out_thread_buf,
+                                         out_grid_desc_m_k,
+                                         out_global_val_buf);
+
+                threadwise_src_load.MoveSrcSliceWindow(in_grid_desc_m_k, in_thread_copy_fwd_step);
+                threadwise_dst_store.MoveDstSliceWindow(out_grid_desc_m_k, in_thread_copy_fwd_step);
+
+                reducedTiles++;
+            } while(reducedTiles < num_k_block_tile_iteration);
+        }
+        else
+        {
+            StaticBuffer<AddressSpaceEnum::Vgpr,
+                         AccDataType,
+                         MThreadSliceSize * KThreadSliceSize,
+                         true>
+                in_prior_dst_buf;
+            do
+            {
+                if constexpr(!SweepOnce)
+                {
+                    threadwise_src_load.Run(in_grid_desc_m_k,
+                                            in_global_val_buf,
+                                            thread_buffer_desc,
+                                            make_tuple(I0, I0),
+                                            in_thread_buf);
+                }
+                threadwise_dst_load.Run(out_grid_desc_m_k,
+                                        out_global_val_buf,
+                                        thread_buffer_desc,
+                                        make_tuple(I0, I0),
+                                        in_prior_dst_buf);
+
+                static_for<0, MThreadSliceSize, 1>{}([&](auto iM) {
+                    // out = alpha * exp(x - max(x)) / sum(exp(x - max(x))) + beta * prior_out
+                    static_for<0, KThreadSliceSize, 1>{}([&](auto iK) {
+                        constexpr auto offset =
+                            thread_buffer_desc.CalculateOffset(make_tuple(iM, iK));
+                        out_thread_buf(Number<offset>{}) =
+                            alpha * math::exp(in_thread_buf(Number<offset>{}) - max_value_buf(iM)) /
+                                accu_value_buf(iM) +
+                            beta * in_prior_dst_buf(Number<offset>{});
+                    });
+                });
+
+                threadwise_dst_store.Run(thread_buffer_desc,
+                                         make_tuple(I0, I0),
+                                         out_thread_buf,
+                                         out_grid_desc_m_k,
+                                         out_global_val_buf);
+
+                threadwise_src_load.MoveSrcSliceWindow(in_grid_desc_m_k, in_thread_copy_fwd_step);
+                threadwise_dst_store.MoveDstSliceWindow(out_grid_desc_m_k, in_thread_copy_fwd_step);
+                threadwise_dst_load.MoveSrcSliceWindow(out_grid_desc_m_k, in_thread_copy_fwd_step);
+
+                reducedTiles++;
+            } while(reducedTiles < num_k_block_tile_iteration);
+        }
+    }
+};
+
+} // namespace ck

include/ck/tensor_operation/gpu/grid/gridwise_unary_elementwise_1d.hpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include "ck/utility/data_type.hpp"
+#include "ck/tensor_description/cluster_descriptor.hpp"
+#include "ck/tensor_operation/gpu/thread/threadwise_tensor_slice_transfer.hpp"
+#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
+
+namespace ck {
+
+template <typename GridwiseUEltwise,
+          typename ADataType,
+          typename BDataType,
+          typename GridDesc_M0,
+          typename ElementwiseFunctor>
+__global__ void kernel_unary_elementwise_1d(const ADataType* __restrict__ p_a_global,
+                                            BDataType* __restrict__ p_b_global,
+                                            const GridDesc_M0 a_grid_desc_m0,
+                                            const GridDesc_M0 b_grid_desc_m0,
+                                            const ElementwiseFunctor functor)
+{
+    GridwiseUEltwise::Run(p_a_global, p_b_global, a_grid_desc_m0, b_grid_desc_m0, functor);
+}
+
+template <typename ADataType,
+          typename BDataType,
+          typename GridDesc_M0,
+          typename ElementwiseFunctor,
+          index_t ScalarPerVector>
+struct GridwiseUnaryElementwise_1D
+{
+    static constexpr auto I0 = Number<0>{};
+    static constexpr auto thread_desc_m0 =
+        make_naive_tensor_descriptor_packed(make_tuple(Number<ScalarPerVector>{}));
+
+    using PassThrough = tensor_operation::element_wise::PassThrough;
+
+    static __device__ auto CalculateElementwiseIndex()
+    {
+        const index_t global_thread_id = get_thread_global_1d_id();
+        return make_multi_index(global_thread_id * ScalarPerVector);
+    }
+
+    __host__ __device__ static constexpr bool CheckValidity(const GridDesc_M0 a_grid_desc_m0,
+                                                            const GridDesc_M0 b_grid_desc_m0)
+    {
+        return a_grid_desc_m0.GetLength(I0) == b_grid_desc_m0.GetLength(I0);
+    }
+
+    __host__ __device__ static constexpr index_t CalculateGridSize(const index_t tensor_size)
+    {
+        const index_t grid_size = math::integer_divide_ceil(tensor_size, 256 * ScalarPerVector);
+
+        return grid_size;
+    }
+
+    __device__ static void Run(const ADataType* __restrict__ p_a_global,
+                               BDataType* __restrict__ p_b_global,
+                               const GridDesc_M0 a_grid_desc_m0,
+                               const GridDesc_M0 b_grid_desc_m0,
+                               const ElementwiseFunctor functor)
+    {
+        const auto a_global_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
+            p_a_global, a_grid_desc_m0.GetElementSpaceSize());
+        auto b_global_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
+            p_b_global, b_grid_desc_m0.GetElementSpaceSize());
+
+        StaticBuffer<AddressSpaceEnum::Vgpr, ADataType, ScalarPerVector, true> a_thread_buf;
+        StaticBuffer<AddressSpaceEnum::Vgpr, BDataType, ScalarPerVector, true> b_thread_buf;
+
+        const auto thread_store_global_offset = CalculateElementwiseIndex();
+
+        auto a_global_load =
+            ThreadwiseTensorSliceTransfer_v2<ADataType,
+                                             ADataType,
+                                             GridDesc_M0,
+                                             decltype(thread_desc_m0),
+                                             Sequence<ScalarPerVector>, // SliceLengths
+                                             Sequence<0>,               // DimAccessOrder
+                                             0,                         // SrcVectorDim
+                                             ScalarPerVector,
+                                             1, // SrcScalarStrideInVector
+                                             false>{a_grid_desc_m0, thread_store_global_offset};
+
+        auto b_global_write =
+            ThreadwiseTensorSliceTransfer_v1r3<BDataType,
+                                               BDataType,
+                                               decltype(thread_desc_m0),
+                                               GridDesc_M0,
+                                               PassThrough,
+                                               Sequence<ScalarPerVector>, // SliceLengths
+                                               Sequence<0>,               // DimAccessOrder
+                                               0,                         // DstVectorDim
+                                               ScalarPerVector,
+                                               InMemoryDataOperationEnum::Set,
+                                               1, // DstScalarStrideInVector
+                                               false>{
+                b_grid_desc_m0, thread_store_global_offset, PassThrough{}};
+
+        const index_t blockSize    = get_block_size();
+        const index_t blockPerGrid = get_grid_size();
+        const auto m0              = b_grid_desc_m0.GetLength(I0);
+        const index_t loop_step    = blockPerGrid * blockSize * ScalarPerVector;
+        const auto loop_step_index = make_multi_index(loop_step);
+
+        index_t num_iter = m0 / (loop_step);
+        do
+        {
+            // read and process ScalarPerVector elements
+            a_global_load.Run(
+                a_grid_desc_m0, a_global_buf, thread_desc_m0, make_tuple(I0), a_thread_buf);
+
+            static_for<0, ScalarPerVector, 1>{}([&](auto m) {
+                constexpr auto offset = thread_desc_m0.CalculateOffset(make_tuple(m));
+                functor(b_thread_buf(Number<offset>{}), a_thread_buf(Number<offset>{}));
+            });
+
+            b_global_write.Run(thread_desc_m0,
+                               make_tuple(I0), // SrcSliceOriginIdx
+                               b_thread_buf,
+                               b_grid_desc_m0,
+                               b_global_buf);
+
+            a_global_load.MoveSrcSliceWindow(a_grid_desc_m0, loop_step_index);
+            b_global_write.MoveDstSliceWindow(b_grid_desc_m0, loop_step_index);
+        } while(--num_iter);
+    }
+};
+
+} // namespace ck

include/ck/tensor_operation/gpu/thread/reduction_functions_threadwise.hpp

-/*******************************************************************************
- *
- * MIT License
- *
- * Copyright (c) 2020 Advanced Micro Devices, Inc.
- *
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to deal
- * in the Software without restriction, including without limitation the rights
- * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
- * copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in all
- * copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
- * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
- * SOFTWARE.
- *
- *******************************************************************************/
-#ifndef CK_REDUCTION_FUNCTIONS_THREADWISE_HPP
-#define CK_REDUCTION_FUNCTIONS_THREADWISE_HPP
-
-#include "reduction_functions_accumulate.hpp"
+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include "ck/utility/reduction_functions_accumulate.hpp"
 
 namespace ck {
 
@@ -39,7 +16,9 @@ template <typename AccDataType,
           typename SrcThreadDesc_M_K,
           typename DstThreadDesc_M,
           typename OpReduce,
-          bool PropagateNan>
+          bool PropagateNan,
+          typename Accumulation =
+              detail::AccumulateWithNanCheck<PropagateNan, OpReduce, AccDataType>>
 struct ThreadwiseReduction
 {
     static constexpr auto src_thread_desc_m_k = SrcThreadDesc_M_K{};
@@ -51,7 +30,7 @@ struct ThreadwiseReduction
 
     static_assert(src_length_m == dst_length_m, "lengths of source and dst buffer must match!");
 
-    using Accumulation = detail::AccumulateWithNanCheck<PropagateNan, OpReduce, AccDataType>;
+    using Op = OpReduce;
 
     template <typename SrcBufferType, typename DstBufferType>
     __device__ static void Reduce(const SrcBufferType& src_buf, DstBufferType& dst_buf)
@@ -73,12 +52,15 @@ struct ThreadwiseReduction
 //  2) DstDesc is known at compile-time
 //  3) SrcBuffer is static buffer
 //  4) DstBuffer is static buffer
-template <typename AccDataType,
+template <
+    typename AccDataType,
     typename IndexDataType,
     typename SrcThreadDesc_M_K,
     typename DstThreadDesc_M,
     typename OpReduce,
-          bool PropagateNan>
+    bool PropagateNan,
+    typename Accumulation =
+        detail::AccumulateWithIndexAndNanCheck<PropagateNan, OpReduce, AccDataType, IndexDataType>>
 struct ThreadwiseReductionWithIndex
 {
     static constexpr auto src_thread_desc_m_k = SrcThreadDesc_M_K{};
@@ -90,9 +72,6 @@ struct ThreadwiseReductionWithIndex
 
     static_assert(src_length_m == dst_length_m, "lengths of source and dst buffer must match!");
 
-    using Accumulation =
-        detail::AccumulateWithIndexAndNanCheck<PropagateNan, OpReduce, AccDataType, IndexDataType>;
-
     template <typename SrcValueBufferType,
               typename SrcIndexBufferType,
               typename DstValueBufferType,
@@ -117,6 +96,4 @@ struct ThreadwiseReductionWithIndex
     };
 };
 
-}; // end of namespace ck
-
-#endif
+} // namespace ck

include/ck/tensor_operation/gpu/thread/threadwise_contraction_dl.hpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
+
 #pragma once
-#include "common_header.hpp"
-#include "math.hpp"
+
+#include "ck/utility/common_header.hpp"
+#include "ck/utility/math.hpp"
 
 namespace ck {
 

include/ck/tensor_operation/gpu/thread/threadwise_gemm_dlops_v3.hpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
+
 #ifndef CK_THREADWISE_GEMM_DLOPS_V3_HPP
 #define CK_THREADWISE_GEMM_DLOPS_V3_HPP