Merge pull request #9 from ROCmSoftwarePlatform/mergedown-from-public

Merge down from public repo

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

 // SPDX-License-Identifier: MIT
-// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
+// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
 
 #pragma once
 
@@ -940,7 +940,15 @@ struct DeviceGroupedConvFwdMultipleD_Xdl_CShuffle
             << MPerBlock << ", "
             << NPerBlock << ", "
             << KPerBlock << ", "
-            << getConvForwardSpecializationString(ConvForwardSpecialization)
+            << getConvForwardSpecializationString(ConvForwardSpecialization) << ", "
+            << MPerXDL << ", "
+            << NPerXDL << ", "
+            << MXdlPerWave << ", "
+            << NXdlPerWave << ", "
+            << ABlockTransferSrcScalarPerVector << ", "
+            << BBlockTransferSrcScalarPerVector << ", "
+            << CShuffleMXdlPerWavePerShuffle << ", "
+            << CShuffleNXdlPerWavePerShuffle
             << ">";
         // clang-format on
 

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

+#pragma once
+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include <iostream>
+#include <sstream>
+
+#include "ck/utility/common_header.hpp"
+#include "ck/tensor_description/tensor_descriptor.hpp"
+#include "ck/tensor_description/tensor_descriptor_helper.hpp"
+#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
+#include "ck/tensor_operation/gpu/device/device_grouped_gemm.hpp"
+#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
+#include "ck/tensor_operation/gpu/device/matrix_padder.hpp"
+#include "ck/tensor_operation/gpu/grid/gridwise_gemm_dl_multiple_d.hpp"
+#include "ck/host_utility/device_prop.hpp"
+#include "ck/host_utility/kernel_launch.hpp"
+
+namespace ck {
+namespace tensor_operation {
+namespace device {
+
+template <typename GridwiseGemm,
+          typename GemmDesc,
+          typename AElementwiseOperation,
+          typename BElementwiseOperation,
+          typename CDEElementwiseOperation,
+          bool HasMainKBlockLoop,
+          bool HasDoubleTailKBlockLoop>
+__global__ void
+#if CK_USE_LAUNCH_BOUNDS
+    __launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
+#endif
+        kernel_grouped_gemm_multiple_d_dl(const void CK_CONSTANT_ADDRESS_SPACE* gemm_descs_const,
+                                          const index_t group_count,
+                                          const AElementwiseOperation a_element_op,
+                                          const BElementwiseOperation b_element_op,
+                                          const CDEElementwiseOperation cde_element_op)
+{
+#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx906__) || defined(__gfx908__) || \
+    defined(__gfx90a__) || defined(__gfx1030__))
+    __shared__ char p_shared[GridwiseGemm::GetSharedMemoryNumberOfByte()];
+
+    const index_t block_id = get_block_1d_id();
+
+    const auto gemm_desc_ptr =
+        reinterpret_cast<const GemmDesc*>(cast_pointer_to_generic_address_space(gemm_descs_const));
+
+    index_t left     = 0;
+    index_t right    = group_count;
+    index_t group_id = index_t((left + right) / 2);
+    while((!(block_id >= gemm_desc_ptr[group_id].BlockStart_ &&
+             block_id < gemm_desc_ptr[group_id].BlockEnd_)) &&
+          left <= right)
+    {
+        if(block_id < gemm_desc_ptr[group_id].BlockStart_)
+        {
+            right = group_id;
+        }
+        else
+        {
+            left = group_id;
+        }
+        group_id = index_t((left + right) / 2);
+    }
+
+    GridwiseGemm::Run(gemm_desc_ptr[group_id].a_ptr_,
+                      gemm_desc_ptr[group_id].b_ptr_,
+                      gemm_desc_ptr[group_id].ds_ptr_,
+                      gemm_desc_ptr[group_id].e_ptr_,
+                      p_shared,
+                      a_element_op,
+                      b_element_op,
+                      cde_element_op,
+                      gemm_desc_ptr[group_id].a_grid_desc_k0_m0_m1_k1_,
+                      gemm_desc_ptr[group_id].b_grid_desc_k0_n0_n1_k1_,
+                      gemm_desc_ptr[group_id].ds_grid_desc_m0_m10_m11_n0_n10_n11_,
+                      gemm_desc_ptr[group_id].e_grid_desc_m0_m10_m11_n0_n10_n11_,
+                      gemm_desc_ptr[group_id].block_2_etile_map_,
+                      integral_constant<bool, HasMainKBlockLoop>{},
+                      integral_constant<bool, HasDoubleTailKBlockLoop>{});
+#else
+    ignore = gemm_descs_const;
+    ignore = group_count;
+    ignore = a_element_op;
+    ignore = b_element_op;
+    ignore = cde_element_op;
+#endif
+}
+
+template <typename ALayout,
+          typename BLayout,
+          typename DsLayout,
+          typename ELayout,
+          typename ADataType,
+          typename BDataType,
+          typename AccDataType,
+          typename DsDataType,
+          typename EDataType,
+          typename AElementwiseOperation,
+          typename BElementwiseOperation,
+          typename CDEElementwiseOperation,
+          GemmSpecialization GemmSpec,
+          index_t BlockSize,
+          index_t MPerBlock,
+          index_t NPerBlock,
+          index_t K0PerBlock,
+          index_t K1,
+          index_t M1PerThread,
+          index_t N1PerThread,
+          index_t KPerThread,
+          typename M1N1ThreadClusterM1Xs,
+          typename M1N1ThreadClusterN1Xs,
+          typename ABlockTransferThreadSliceLengths_K0_M0_M1_K1,
+          typename ABlockTransferThreadClusterLengths_K0_M0_M1_K1,
+          typename ABlockTransferThreadClusterArrangeOrder,
+          typename ABlockTransferSrcAccessOrder,
+          typename ABlockTransferSrcVectorTensorLengths_K0_M0_M1_K1,
+          typename ABlockTransferSrcVectorTensorContiguousDimOrder,
+          typename ABlockTransferDstVectorTensorLengths_K0_M0_M1_K1,
+          typename BBlockTransferThreadSliceLengths_K0_N0_N1_K1,
+          typename BBlockTransferThreadClusterLengths_K0_N0_N1_K1,
+          typename BBlockTransferThreadClusterArrangeOrder,
+          typename BBlockTransferSrcAccessOrder,
+          typename BBlockTransferSrcVectorTensorLengths_K0_N0_N1_K1,
+          typename BBlockTransferSrcVectorTensorContiguousDimOrder,
+          typename BBlockTransferDstVectorTensorLengths_K0_N0_N1_K1,
+          typename CThreadTransferSrcDstAccessOrder,
+          index_t CThreadTransferSrcDstVectorDim,
+          index_t CThreadTransferDstScalarPerVector,
+          enable_if_t<
+              is_same_v<AElementwiseOperation, ck::tensor_operation::element_wise::PassThrough> &&
+                  is_same_v<BElementwiseOperation, ck::tensor_operation::element_wise::PassThrough>,
+              bool> = false>
+struct DeviceGroupedGemmMultipleD_Dl : public DeviceGroupedGemm<ALayout,
+                                                                BLayout,
+                                                                DsLayout,
+                                                                ELayout,
+                                                                ADataType,
+                                                                BDataType,
+                                                                DsDataType,
+                                                                EDataType,
+                                                                AElementwiseOperation,
+                                                                BElementwiseOperation,
+                                                                CDEElementwiseOperation>
+{
+    using DeviceOp                      = DeviceGroupedGemmMultipleD_Dl;
+    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 K1Number = Number<K1>{};
+
+    static auto MakeAGridDescriptor_K0_M_K1(index_t M, index_t K, index_t StrideA)
+    {
+        assert(K % K1 == 0);
+
+        const index_t K0 = K / K1;
+
+        const auto a_grid_desc_m_k = [&]() {
+            if constexpr(is_same<tensor_layout::gemm::RowMajor, ALayout>::value)
+            {
+                return make_naive_tensor_descriptor(make_tuple(M, K), make_tuple(StrideA, I1));
+            }
+            else if constexpr(is_same<tensor_layout::gemm::ColumnMajor, ALayout>::value)
+            {
+                return make_naive_tensor_descriptor(make_tuple(M, K), make_tuple(I1, StrideA));
+            }
+        }();
+
+        if constexpr(GemmSpec == GemmSpecialization::MNPadding)
+        {
+            const auto PadM = (MPerBlock - M % MPerBlock) % MPerBlock;
+
+            return transform_tensor_descriptor(
+                a_grid_desc_m_k,
+                make_tuple(make_unmerge_transform(make_tuple(K0, K1Number)),
+                           make_right_pad_transform(M, PadM)),
+                make_tuple(Sequence<1>{}, Sequence<0>{}),
+                make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
+        }
+        else
+        {
+            return transform_tensor_descriptor(
+                a_grid_desc_m_k,
+                make_tuple(make_unmerge_transform(make_tuple(K0, K1Number)),
+                           make_pass_through_transform(M)),
+                make_tuple(Sequence<1>{}, Sequence<0>{}),
+                make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
+        }
+    }
+
+    static auto MakeBGridDescriptor_K0_N_K1(index_t K, index_t N, index_t StrideB)
+    {
+        assert(K % K1 == 0);
+
+        const index_t K0 = K / K1;
+
+        const auto b_grid_desc_k_n = [&]() {
+            if constexpr(is_same<tensor_layout::gemm::RowMajor, BLayout>::value)
+            {
+                return make_naive_tensor_descriptor(make_tuple(K, N), make_tuple(StrideB, I1));
+            }
+            else if constexpr(is_same<tensor_layout::gemm::ColumnMajor, BLayout>::value)
+            {
+                return make_naive_tensor_descriptor(make_tuple(K, N), make_tuple(I1, StrideB));
+            }
+        }();
+
+        if constexpr(GemmSpec == GemmSpecialization::MNPadding)
+        {
+            const auto PadN = (NPerBlock - N % NPerBlock) % NPerBlock;
+
+            return transform_tensor_descriptor(
+                b_grid_desc_k_n,
+                make_tuple(make_unmerge_transform(make_tuple(K0, K1Number)),
+                           make_right_pad_transform(N, PadN)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}),
+                make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
+        }
+        else
+        {
+            return transform_tensor_descriptor(
+                b_grid_desc_k_n,
+                make_tuple(make_unmerge_transform(make_tuple(K0, K1Number)),
+                           make_pass_through_transform(N)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}),
+                make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
+        }
+    }
+
+    template <typename ELay>
+    static auto MakeEGridDescriptor_M_N(index_t M, index_t N, index_t StrideE)
+    {
+        const auto c_grid_desc_m_n = [&]() {
+            if constexpr(is_same<tensor_layout::gemm::RowMajor, ELay>::value)
+            {
+                return make_naive_tensor_descriptor(make_tuple(M, N), make_tuple(StrideE, I1));
+            }
+            else if constexpr(is_same<tensor_layout::gemm::ColumnMajor, ELay>::value)
+            {
+                return make_naive_tensor_descriptor(make_tuple(M, N), make_tuple(I1, StrideE));
+            }
+        }();
+
+        if constexpr(GemmSpec == GemmSpecialization::MNPadding)
+        {
+            const auto PadM = (MPerBlock - M % MPerBlock) % MPerBlock;
+            const auto PadN = (NPerBlock - N % NPerBlock) % NPerBlock;
+
+            return transform_tensor_descriptor(
+                c_grid_desc_m_n,
+                make_tuple(make_right_pad_transform(M, PadM), make_right_pad_transform(N, PadN)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}),
+                make_tuple(Sequence<0>{}, Sequence<1>{}));
+        }
+        else
+        {
+
+            return transform_tensor_descriptor(
+                c_grid_desc_m_n,
+                make_tuple(make_pass_through_transform(M), make_pass_through_transform(N)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}),
+                make_tuple(Sequence<0>{}, Sequence<1>{}));
+        }
+    }
+
+    static auto MakeDsGridDescriptor_M_N(const std::array<index_t, NumDTensor>& MRaws,
+                                         const std::array<index_t, NumDTensor>& NRaws,
+                                         const std::array<index_t, NumDTensor>& DsStride)
+    {
+        return generate_tuple(
+            [&](auto i) {
+                using DLayout = remove_cvref_t<tuple_element_t<i.value, DsLayout>>;
+
+                return DeviceOp::MakeEGridDescriptor_M_N<DLayout>(MRaws[i], NRaws[i], DsStride[i]);
+            },
+            Number<NumDTensor>{});
+    }
+
+    using AGridDesc_K0_M_K1 = decltype(MakeAGridDescriptor_K0_M_K1(1, 1, 1));
+    using BGridDesc_K0_N_K1 = decltype(MakeBGridDescriptor_K0_N_K1(1, 1, 1));
+    using DsGridDesc_M_N    = decltype(MakeDsGridDescriptor_M_N({}, {}, {}));
+    using EGridDesc_M_N     = decltype(MakeEGridDescriptor_M_N<ELayout>(1, 1, 1));
+
+    // GridwiseGemm
+    using GridwiseGemm =
+        GridwiseGemmDlMultipleD_km_kn_mn<BlockSize,
+                                         ADataType,
+                                         AccDataType,
+                                         DsDataType,
+                                         EDataType,
+                                         AElementwiseOperation,
+                                         BElementwiseOperation,
+                                         CDEElementwiseOperation,
+                                         InMemoryDataOperationEnum::Set,
+                                         AGridDesc_K0_M_K1,
+                                         BGridDesc_K0_N_K1,
+                                         EGridDesc_M_N,
+                                         MPerBlock,
+                                         NPerBlock,
+                                         K0PerBlock,
+                                         K1,
+                                         M1PerThread,
+                                         N1PerThread,
+                                         KPerThread,
+                                         M1N1ThreadClusterM1Xs,
+                                         M1N1ThreadClusterN1Xs,
+                                         ABlockTransferThreadSliceLengths_K0_M0_M1_K1,
+                                         ABlockTransferThreadClusterLengths_K0_M0_M1_K1,
+                                         ABlockTransferThreadClusterArrangeOrder,
+                                         ABlockTransferSrcAccessOrder,
+                                         ABlockTransferSrcVectorTensorLengths_K0_M0_M1_K1,
+                                         ABlockTransferSrcVectorTensorContiguousDimOrder,
+                                         ABlockTransferDstVectorTensorLengths_K0_M0_M1_K1,
+                                         BBlockTransferThreadSliceLengths_K0_N0_N1_K1,
+                                         BBlockTransferThreadClusterLengths_K0_N0_N1_K1,
+                                         BBlockTransferThreadClusterArrangeOrder,
+                                         BBlockTransferSrcAccessOrder,
+                                         BBlockTransferSrcVectorTensorLengths_K0_N0_N1_K1,
+                                         BBlockTransferSrcVectorTensorContiguousDimOrder,
+                                         BBlockTransferDstVectorTensorLengths_K0_N0_N1_K1,
+                                         CThreadTransferSrcDstAccessOrder,
+                                         CThreadTransferSrcDstVectorDim,
+                                         CThreadTransferDstScalarPerVector>;
+
+    using AGridDesc_K0_M0_M1_K1 =
+        decltype(GridwiseGemm::MakeAGridDescriptor_K0_M0_M1_K1(AGridDesc_K0_M_K1{}));
+    using BGridDesc_K0_N0_N1_K1 =
+        decltype(GridwiseGemm::MakeBGridDescriptor_K0_N0_N1_K1(BGridDesc_K0_N_K1{}));
+    using DsGridDesc_M0_M10_M11_N0_N10_N11 =
+        decltype(GridwiseGemm::MakeDsGridDescriptor_M0_M10_M11_N0_N10_N11(DsGridDesc_M_N{}));
+    using EGridDesc_M0_M10_M11_N0_N10_N11 =
+        decltype(GridwiseGemm::MakeCGridDescriptor_M0_M10_M11_N0_N10_N11(EGridDesc_M_N{}));
+
+    struct GroupedGemmBlock2ETileMap
+    {
+        using Block2ETileMap =
+            remove_cvref_t<decltype(GridwiseGemm::MakeDefaultBlock2CTileMap(EGridDesc_M_N{}))>;
+
+        GroupedGemmBlock2ETileMap()
+        {
+            block_2_etile_map_ = GridwiseGemm::MakeDefaultBlock2CTileMap(EGridDesc_M_N{});
+            BlockStart_        = -1;
+        }
+
+        GroupedGemmBlock2ETileMap(const EGridDesc_M_N& e_grid_desc_m_n, ck::index_t BlockStart)
+        {
+            block_2_etile_map_ = GridwiseGemm::MakeDefaultBlock2CTileMap(e_grid_desc_m_n);
+            BlockStart_        = BlockStart;
+        }
+
+        template <typename TopIdx>
+        __host__ __device__ constexpr auto CalculateBottomIndex(const TopIdx& idx_top) const
+        {
+            return block_2_etile_map_.CalculateBottomIndex(
+                make_multi_index(idx_top[I0] - BlockStart_));
+        }
+
+        // it's actually E-Tile
+        template <typename CTileIdx, typename CTileDim>
+        __host__ __device__ bool ValidCTileIndex(const CTileIdx& c_tile_idx,
+                                                 const CTileDim& c_tile_dim) const
+        {
+            return block_2_etile_map_.ValidCTileIndex(c_tile_idx, c_tile_dim);
+        }
+
+        __host__ bool CheckValidity(const EGridDesc_M_N& e_grid_desc_m_n) const
+        {
+            return block_2_etile_map_.CheckValidity(e_grid_desc_m_n);
+        }
+
+        Block2ETileMap block_2_etile_map_;
+        ck::index_t BlockStart_;
+    };
+
+    struct GemmKernelArg
+    {
+        // pointers
+        const ADataType* a_ptr_;
+        const BDataType* b_ptr_;
+        typename GridwiseGemm::DsGridPointer ds_ptr_;
+        EDataType* e_ptr_;
+
+        // tensor descriptors for problem definiton
+        AGridDesc_K0_M_K1 a_grid_desc_k0_m_k1_;
+        BGridDesc_K0_N_K1 b_grid_desc_k0_n_k1_;
+        DsGridDesc_M_N ds_grid_desc_m_n_;
+        EGridDesc_M_N e_grid_desc_m_n_;
+
+        // tensor descriptors for block/thread-wise copy
+        AGridDesc_K0_M0_M1_K1 a_grid_desc_k0_m0_m1_k1_;
+        BGridDesc_K0_N0_N1_K1 b_grid_desc_k0_n0_n1_k1_;
+        DsGridDesc_M0_M10_M11_N0_N10_N11 ds_grid_desc_m0_m10_m11_n0_n10_n11_;
+        EGridDesc_M0_M10_M11_N0_N10_N11 e_grid_desc_m0_m10_m11_n0_n10_n11_;
+
+        // block-to-e-tile map
+        GroupedGemmBlock2ETileMap block_2_etile_map_;
+        ck::index_t BlockStart_, BlockEnd_;
+    };
+
+    // Argument
+    struct Argument : public BaseArgument
+    {
+        Argument(std::vector<const void*>& p_As,
+                 std::vector<const void*>& p_Bs,
+                 std::vector<std::array<const void*, NumDTensor>>& p_Ds,
+                 std::vector<void*>& p_Es,
+                 std::vector<GemmDesc>& gemm_descs,
+                 AElementwiseOperation a_element_op,
+                 BElementwiseOperation b_element_op,
+                 CDEElementwiseOperation cde_element_op)
+            : a_element_op_{a_element_op},
+              b_element_op_{b_element_op},
+              cde_element_op_{cde_element_op}
+        {
+            grid_size_ = 0;
+
+            group_count_ = ck::type_convert<ck::index_t>(gemm_descs.size());
+
+            if(!(group_count_ == ck::type_convert<ck::index_t>(p_As.size()) &&
+                 group_count_ == ck::type_convert<ck::index_t>(p_Bs.size()) &&
+                 group_count_ == ck::type_convert<ck::index_t>(p_Es.size())))
+            {
+                throw std::runtime_error("wrong! group_count_ != p_As/b/c.size");
+            }
+
+            gemm_desc_kernel_arg_.reserve(group_count_);
+
+            skipped_group_count_ = 0;
+
+            for(std::size_t i = 0; i < gemm_descs.size(); i++)
+            {
+                const index_t M = gemm_descs[i].M_;
+                const index_t N = gemm_descs[i].N_;
+                const index_t K = gemm_descs[i].K_;
+
+                a_mtx_mraw_kraw_.emplace_back(M, K);
+                b_mtx_nraw_kraw_.emplace_back(N, K);
+
+                if(M == 0)
+                {
+                    skipped_group_count_++;
+                    continue;
+                }
+
+                const index_t StrideA = gemm_descs[i].stride_A_;
+                const index_t StrideB = gemm_descs[i].stride_B_;
+                const index_t StrideE = gemm_descs[i].stride_C_;
+
+                typename GridwiseGemm::DsGridPointer p_ds_grid{};
+                DsGridDesc_M_N ds_grid_desc_m_n;
+
+                static_for<0, NumDTensor, 1>{}([&](auto j) {
+                    using DLayout   = remove_cvref_t<tuple_element_t<j.value, DsLayout>>;
+                    using DDataType = remove_cvref_t<tuple_element_t<j.value, DsDataType>>;
+
+                    p_ds_grid(j)        = static_cast<const DDataType*>(p_Ds[i][j]);
+                    ds_grid_desc_m_n(j) = DeviceOp::MakeEGridDescriptor_M_N<DLayout>(
+                        M, N, gemm_descs[i].stride_Ds_[j]);
+                });
+
+                // tensor descriptors for problem definiton
+                const auto a_grid_desc_k0_m_k1 =
+                    DeviceOp::MakeAGridDescriptor_K0_M_K1(M, K, StrideA);
+                const auto b_grid_desc_k0_n_k1 =
+                    DeviceOp::MakeBGridDescriptor_K0_N_K1(K, N, StrideB);
+                const auto e_grid_desc_m_n =
+                    DeviceOp::MakeEGridDescriptor_M_N<ELayout>(M, N, StrideE);
+
+                if(GridwiseGemm::CheckValidity(
+                       a_grid_desc_k0_m_k1, b_grid_desc_k0_n_k1, e_grid_desc_m_n))
+                {
+
+                    const index_t grid_size_grp =
+                        GroupedGemmBlock2ETileMap(e_grid_desc_m_n, 0)
+                            .block_2_etile_map_.CalculateGridSize(e_grid_desc_m_n);
+
+                    const index_t BlockStart = grid_size_;
+                    const index_t BlockEnd   = grid_size_ + grid_size_grp;
+
+                    grid_size_ += grid_size_grp;
+
+                    // block-to-e-tile map
+                    const auto block_2_etile_map =
+                        GroupedGemmBlock2ETileMap(e_grid_desc_m_n, BlockStart);
+
+                    // tensor descriptors for block/thread-wise copy
+                    const auto a_grid_desc_k0_m0_m1_k1 =
+                        GridwiseGemm::MakeAGridDescriptor_K0_M0_M1_K1(a_grid_desc_k0_m_k1);
+                    const auto b_grid_desc_k0_n0_n1_k1 =
+                        GridwiseGemm::MakeBGridDescriptor_K0_N0_N1_K1(b_grid_desc_k0_n_k1);
+                    const auto ds_grid_desc_m0_m10_m11_n0_n10_n11 =
+                        GridwiseGemm::MakeDsGridDescriptor_M0_M10_M11_N0_N10_N11(ds_grid_desc_m_n);
+                    const auto e_grid_desc_m0_m10_m11_n0_n10_n11 =
+                        GridwiseGemm::MakeCGridDescriptor_M0_M10_M11_N0_N10_N11(e_grid_desc_m_n);
+
+                    gemm_desc_kernel_arg_.push_back(
+                        GemmKernelArg{static_cast<const ADataType*>(p_As[i]),
+                                      static_cast<const BDataType*>(p_Bs[i]),
+                                      p_ds_grid,
+                                      static_cast<EDataType*>(p_Es[i]),
+                                      a_grid_desc_k0_m_k1,
+                                      b_grid_desc_k0_n_k1,
+                                      ds_grid_desc_m_n,
+                                      e_grid_desc_m_n,
+                                      a_grid_desc_k0_m0_m1_k1,
+                                      b_grid_desc_k0_n0_n1_k1,
+                                      ds_grid_desc_m0_m10_m11_n0_n10_n11,
+                                      e_grid_desc_m0_m10_m11_n0_n10_n11,
+                                      block_2_etile_map,
+                                      BlockStart,
+                                      BlockEnd});
+                }
+            }
+        }
+
+        //  private:
+        index_t group_count_;
+        index_t skipped_group_count_;
+
+        // TODO: A,B element op is unused since gridwise_gemm_dl_v1r3 does NOT support prologue
+        //       for the time being.
+        AElementwiseOperation a_element_op_;
+        BElementwiseOperation b_element_op_;
+        CDEElementwiseOperation cde_element_op_;
+
+        std::vector<GemmKernelArg> gemm_desc_kernel_arg_;
+        std::vector<Tuple<index_t, index_t>> a_mtx_mraw_kraw_;
+        std::vector<Tuple<index_t, index_t>> b_mtx_nraw_kraw_;
+
+        index_t grid_size_;
+    };
+
+    // Invoker
+    struct Invoker : public BaseInvoker
+    {
+        using Argument = DeviceOp::Argument;
+
+        float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
+        {
+            auto K0 = arg.gemm_desc_kernel_arg_[0].a_grid_desc_k0_m_k1_.GetLength(I0);
+            bool all_has_main_k_block_loop = GridwiseGemm::CalculateHasMainKBlockLoop(K0);
+            bool all_has_double_tail_k_block_loop =
+                GridwiseGemm::CalculateHasDoubleTailKBlockLoop(K0);
+
+            for(std::size_t i = 0; i < arg.gemm_desc_kernel_arg_.size(); i++)
+            {
+#if DEBUG_LOG
+                std::cout << "group: " << i << " arg.a_grid_desc_k0_m_k1_{"
+                          << arg.gemm_desc_kernel_arg_[i].a_grid_desc_k0_m_k1_.GetLength(I0) << ", "
+                          << arg.gemm_desc_kernel_arg_[i].a_grid_desc_k0_m_k1_.GetLength(I1) << ", "
+                          << arg.gemm_desc_kernel_arg_[i].a_grid_desc_k0_m_k1_.GetLength(I2) << "}"
+                          << std::endl;
+
+                std::cout << ", arg.b_grid_desc_k0_n_k1_{"
+                          << arg.gemm_desc_kernel_arg_[i].b_grid_desc_k0_n_k1_.GetLength(I0) << ", "
+                          << arg.gemm_desc_kernel_arg_[i].b_grid_desc_k0_n_k1_.GetLength(I1) << ", "
+                          << arg.gemm_desc_kernel_arg_[i].b_grid_desc_k0_n_k1_.GetLength(I2) << "}"
+                          << std::endl;
+
+                std::cout << ", arg.e_grid_desc_m_n_{ "
+                          << arg.gemm_desc_kernel_arg_[i].e_grid_desc_m_n_.GetLength(I0) << ", "
+                          << arg.gemm_desc_kernel_arg_[i].e_grid_desc_m_n_.GetLength(I1) << "}"
+                          << std::endl;
+#endif
+
+                if(!GridwiseGemm::CheckValidity(arg.gemm_desc_kernel_arg_[i].a_grid_desc_k0_m_k1_,
+                                                arg.gemm_desc_kernel_arg_[i].b_grid_desc_k0_n_k1_,
+                                                arg.gemm_desc_kernel_arg_[i].e_grid_desc_m_n_))
+                {
+                    throw std::runtime_error(
+                        "wrong! GridwiseGemmDlMultipleD_km_kn_mn has invalid setting");
+                }
+
+                K0 = arg.gemm_desc_kernel_arg_[i].a_grid_desc_k0_m0_m1_k1_.GetLength(I0);
+                bool not_all_has_main_k_block_loop_same =
+                    all_has_main_k_block_loop xor GridwiseGemm::CalculateHasMainKBlockLoop(K0);
+                bool not_all_has_double_tail_k_block_loop_same =
+                    all_has_double_tail_k_block_loop xor
+                    GridwiseGemm::CalculateHasDoubleTailKBlockLoop(K0);
+
+                if(not_all_has_main_k_block_loop_same or not_all_has_double_tail_k_block_loop_same)
+                {
+                    std::ostringstream err;
+                    err << "Not all gemms have same value for [main|double_tail]_k_block_loop! in "
+                        << __FILE__ << ":" << __LINE__ << ", in function: " << __func__;
+                    throw std::runtime_error(err.str());
+                }
+            }
+
+            hipGetErrorString(hipMemcpy(arg.p_workspace_,
+                                        arg.gemm_desc_kernel_arg_.data(),
+                                        arg.gemm_desc_kernel_arg_.size() * sizeof(GemmKernelArg),
+                                        hipMemcpyHostToDevice));
+
+            auto launch_kernel = [&](auto has_main_k_block_loop,
+                                     auto has_double_tail_k_block_loop) {
+                constexpr bool has_main_loop   = has_main_k_block_loop.value;
+                constexpr bool has_double_loop = has_double_tail_k_block_loop.value;
+
+                const auto kernel = kernel_grouped_gemm_multiple_d_dl<GridwiseGemm,
+                                                                      GemmKernelArg,
+                                                                      AElementwiseOperation,
+                                                                      BElementwiseOperation,
+                                                                      CDEElementwiseOperation,
+                                                                      has_main_loop,
+                                                                      has_double_loop>;
+
+                return launch_and_time_kernel(
+                    stream_config,
+                    kernel,
+                    dim3(arg.grid_size_),
+                    dim3(BlockSize),
+                    0,
+                    cast_pointer_to_constant_address_space(arg.p_workspace_),
+                    arg.gemm_desc_kernel_arg_.size(),
+                    arg.a_element_op_,
+                    arg.b_element_op_,
+                    arg.cde_element_op_);
+            };
+
+            if(all_has_main_k_block_loop && all_has_double_tail_k_block_loop)
+            {
+                return launch_kernel(integral_constant<bool, true>{},
+                                     integral_constant<bool, true>{});
+            }
+            else if(all_has_main_k_block_loop && !all_has_double_tail_k_block_loop)
+            {
+                return launch_kernel(integral_constant<bool, true>{},
+                                     integral_constant<bool, false>{});
+            }
+            else if(!all_has_main_k_block_loop && all_has_double_tail_k_block_loop)
+            {
+                return launch_kernel(integral_constant<bool, false>{},
+                                     integral_constant<bool, true>{});
+            }
+            else
+            {
+                return launch_kernel(integral_constant<bool, false>{},
+                                     integral_constant<bool, false>{});
+            }
+        }
+
+        // polymorphic
+        float Run(const BaseArgument* p_arg,
+                  const StreamConfig& stream_config = StreamConfig{}) override
+        {
+            return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
+        }
+    };
+
+    static bool IsSupportedArgument(const Argument& arg)
+    {
+        if((ck::type_convert<ck::index_t>(arg.gemm_desc_kernel_arg_.size()) +
+            arg.skipped_group_count_) != arg.group_count_)
+        {
+            return false;
+        }
+
+        const std::string device_name = ck::get_device_name();
+
+        //  TODO add newer Navi arch
+        if(device_name != "gfx906" and device_name != "gfx908" and device_name != "gfx90a" and
+           device_name != "gfx1030")
+        {
+            return false;
+        }
+
+        for(std::size_t i = 0; i < arg.gemm_desc_kernel_arg_.size(); i++)
+        {
+            if(!GridwiseGemm::CheckValidity(arg.gemm_desc_kernel_arg_[i].a_grid_desc_k0_m_k1_,
+                                            arg.gemm_desc_kernel_arg_[i].b_grid_desc_k0_n_k1_,
+                                            arg.gemm_desc_kernel_arg_[i].e_grid_desc_m_n_))
+            {
+                return false;
+            }
+        }
+
+        return true;
+    }
+
+    // polymorphic
+    bool IsSupportedArgument(const BaseArgument* p_arg) override
+    {
+        return IsSupportedArgument(*dynamic_cast<const Argument*>(p_arg));
+    }
+
+    static auto MakeArgument(std::vector<const void*>& p_As,
+                             std::vector<const void*>& p_Bs,
+                             std::vector<std::array<const void*, NumDTensor>>& p_Ds,
+                             std::vector<void*>& p_Es,
+                             std::vector<GemmDesc> gemm_descs,
+                             AElementwiseOperation a_element_op,
+                             BElementwiseOperation b_element_op,
+                             CDEElementwiseOperation cde_element_op)
+    {
+        return Argument{
+            p_As, p_Bs, p_Ds, p_Es, gemm_descs, a_element_op, b_element_op, cde_element_op};
+    }
+
+    static auto MakeInvoker() { return Invoker{}; }
+
+    // polymorphic
+    std::unique_ptr<BaseArgument>
+    MakeArgumentPointer(std::vector<const void*>& p_As,
+                        std::vector<const void*>& p_Bs,
+                        std::vector<std::array<const void*, NumDTensor>>& p_Ds,
+                        std::vector<void*>& p_Es,
+                        std::vector<GemmDesc>& gemm_descs,
+                        AElementwiseOperation a_element_op,
+                        BElementwiseOperation b_element_op,
+                        CDEElementwiseOperation cde_element_op) override
+    {
+        return std::make_unique<Argument>(
+            p_As, p_Bs, p_Ds, p_Es, gemm_descs, a_element_op, b_element_op, cde_element_op);
+    }
+
+    // polymorphic
+    std::unique_ptr<BaseInvoker> MakeInvokerPointer() override
+    {
+        return std::make_unique<Invoker>(Invoker{});
+    }
+
+    // polymorphic
+    std::string GetTypeString() const override
+    {
+        auto str = std::stringstream();
+
+        // clang-format off
+        str << "DeviceGroupedGemmMultipleD_Dl"
+            << "<"
+            << BlockSize << ", "
+            << MPerBlock << ", "
+            << NPerBlock << ", "
+            << K0PerBlock << ", "
+            << K1 << ", "
+            << M1PerThread << ", "
+            << N1PerThread << ", "
+            << KPerThread
+            << getGemmSpecializationString(GemmSpec)
+            << ">";
+        // clang-format on
+
+        return str.str();
+    }
+
+    size_t GetWorkSpaceSize(const BaseArgument* p_arg) const override
+    {
+        return dynamic_cast<const Argument*>(p_arg)->group_count_ * sizeof(GemmKernelArg);
+    }
+};
+
+} // namespace device
+} // namespace tensor_operation
+} // namespace ck

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

@@ -382,6 +382,9 @@ struct DeviceGroupedGemm_Xdl : public DeviceGroupedGemm<ALayout,
                 const index_t N = gemm_descs[i].N_;
                 const index_t K = gemm_descs[i].K_;
 
+                a_mtx_mraw_kraw_.emplace_back(M, K);
+                b_mtx_nraw_kraw_.emplace_back(N, K);
+
                 if(M == 0)
                 {
                     skipped_group_count_++;
@@ -486,6 +489,8 @@ struct DeviceGroupedGemm_Xdl : public DeviceGroupedGemm<ALayout,
         CDEElementwiseOperation c_element_op_;
 
         std::vector<GemmBiasTransKernelArg> gemm_desc_kernel_arg_;
+        std::vector<Tuple<index_t, index_t>> a_mtx_mraw_kraw_;
+        std::vector<Tuple<index_t, index_t>> b_mtx_nraw_kraw_;
 
         index_t grid_size_;
     };
@@ -600,7 +605,28 @@ struct DeviceGroupedGemm_Xdl : public DeviceGroupedGemm<ALayout,
             return false;
         }
 
-        return true;
+        bool supported = true;
+
+        // If we use padding we do not support vector loads for dimensions not divisible by vector
+        // load size.
+        if constexpr(GemmSpec != GemmSpecialization::Default)
+        {
+            // [A|B]BlockTransferSrcVectorDim value define dimension in the block {K0,M,K1} layout,
+            // thus we have to adapt it to the {M,K} or {N,K} layout.
+            const auto a_raw_vector_dim = ABlockTransferSrcVectorDim != 1 ? 1 : 0;
+            const auto b_raw_vector_dim = BBlockTransferSrcVectorDim != 1 ? 1 : 0;
+
+            for(index_t i = 0; i < arg.group_count_; ++i)
+            {
+                const auto a_vector_dim = arg.a_mtx_mraw_kraw_[i].At(Number<a_raw_vector_dim>{});
+                const auto b_vector_dim = arg.b_mtx_nraw_kraw_[i].At(Number<b_raw_vector_dim>{});
+
+                supported = supported & (a_vector_dim % ABlockTransferSrcScalarPerVector == 0);
+                supported = supported & (b_vector_dim % BBlockTransferSrcScalarPerVector == 0);
+            }
+        }
+
+        return supported;
     }
 
     // polymorphic
@@ -662,7 +688,12 @@ struct DeviceGroupedGemm_Xdl : public DeviceGroupedGemm<ALayout,
             << MPerXDL << ", "
             << NPerXDL << ", "
             << MXdlPerWave << ", "
-            << NXdlPerWave
+            << NXdlPerWave << ", "
+            << ABlockTransferSrcScalarPerVector << ", "
+            << BBlockTransferSrcScalarPerVector << ", "
+            << CShuffleMXdlPerWavePerShuffle << ", "
+            << CShuffleNXdlPerWavePerShuffle << ", "
+            << getGemmSpecializationString(GemmSpec)
             << ">";
         // clang-format on
 

include/ck/tensor_operation/gpu/element/binary_element_wise_operation.hpp

@@ -4,6 +4,7 @@
 #pragma once
 
 #include "ck/utility/data_type.hpp"
+#include "ck/tensor_operation/gpu/element/unary_element_wise_operation.hpp"
 
 namespace ck {
 namespace tensor_operation {
@@ -280,43 +281,42 @@ struct AddHardswish
     };
 };
 
-// C = A * B
 // E = FastGelu(C + D)
 struct AddFastGelu
 {
-    // Fast GeLU
-    // https://paperswithcode.com/method/gelu
-    // y = 0.5*x*(1+tanh(sqrt(2/pi)*(x+0.044715*x^3)))
-    __host__ __device__ static constexpr float GetFastGeLU(float x)
-    {
-        const float u   = 2.f * x * (0.035677f * x * x + 0.797885f);
-        const float emu = exp(-u);
-        const float cdf = 0.5f + 0.5f * (2.f / (1.f + emu) - 1.f);
-        return x * cdf;
-    }
-
-    template <typename T>
-    static inline constexpr bool is_valid_param_type_v =
-        std::is_same_v<T, float> || std::is_same_v<T, half_t> || std::is_same_v<T, bhalf_t> ||
-        std::is_same_v<T, int32_t> || std::is_same_v<T, int8_t>;
-
     template <typename E, typename C, typename D>
-    __host__ __device__ constexpr void operator()(E& e, const C& c, const D& d) const
+    __host__ __device__ constexpr void operator()(E& e, const C& c, const D& d) const;
+
+    template <>
+    __host__ __device__ constexpr void
+    operator()<float, float, float>(float& e, const float& c, const float& d) const
     {
-        static_assert(is_valid_param_type_v<E> && is_valid_param_type_v<C> &&
-                      is_valid_param_type_v<D>);
+        const float x = c + d;
+
+        FastGelu{}.template operator()<float, float>(e, x);
+    }
 
-        const float y = GetFastGeLU(type_convert<float>(c) + type_convert<float>(d));
+    template <>
+    __host__ __device__ constexpr void
+    operator()<half_t, half_t, half_t>(half_t& e, const half_t& c, const half_t& d) const
+    {
+        const half_t x = c + d;
 
-        e = type_convert<E>(y);
+        ck::tensor_operation::element_wise::FastGelu{}.template operator()<half_t, half_t>(e, x);
     }
 
-    template <typename D>
-    __host__ __device__ constexpr void operator()(float& e, const float& c, const D& d) const
+    template <>
+    __host__ __device__ constexpr void
+    operator()<half_t, float, half_t>(half_t& e, const float& c, const half_t& d) const
     {
-        static_assert(is_valid_param_type_v<D>);
+        const float x0_f = c + d;
+
+        float x1_f = 0;
+
+        ck::tensor_operation::element_wise::FastGelu{}.template operator()<float, float>(x1_f,
+                                                                                         x0_f);
 
-        e = GetFastGeLU(c + type_convert<float>(d));
+        e = type_convert<half_t>(x1_f);
     }
 };
 

include/ck/tensor_operation/gpu/element/element_wise_operation.hpp

@@ -16,7 +16,7 @@ namespace element_wise {
 // Need to ensure compiler will fail if there is no matching candidate, instead of compiler
 // siliently do implicit type conversion
 //
-// Method 1:
+// Example:
 //
 // struct ExampleElementwiseOp
 // {
@@ -30,19 +30,6 @@ namespace element_wise {
 //     {
 //     }
 // };
-//
-// Method 2:
-//
-// template <typename Y, typename X>
-// struct ExampleElementwiseOp;
-//
-// template <>
-// struct ExampleElementwiseOp<float, ck::bhalf_t>
-// {
-//     __host__ __device__ void operator()(float& y, ck::bhalf_t& x) const
-//     {
-//     }
-// };
 
 struct AddReluAdd
 {
@@ -208,41 +195,74 @@ struct AddMultiply
     }
 };
 
-// C = A * B
 // E = FastGelu(C + D0 + D1)
 struct AddAddFastGelu
 {
-    // Fast GeLU
-    // https://paperswithcode.com/method/gelu
-    // y = 0.5*x*(1+tanh(sqrt(2/pi)*(x+0.044715*x^3)))
-    __host__ __device__ static constexpr float GetFastGeLU(float x)
+    template <typename E, typename C, typename D0, typename D1>
+    __host__ __device__ constexpr void
+    operator()(E& e, const C& c, const D0& d0, const D1& d1) const;
+
+    template <>
+    __host__ __device__ constexpr void operator()<float, float, float, float>(float& e,
+                                                                              const float& c,
+                                                                              const float& d0,
+                                                                              const float& d1) const
     {
-        const float u   = 2.f * x * (0.035677f * x * x + 0.797885f);
-        const float emu = exp(-u);
-        const float cdf = 0.5f + 0.5f * (2.f / (1.f + emu) - 1.f);
-        return x * cdf;
+        const float x = c + d0 + d1;
+
+        FastGelu{}.template operator()<float, float>(e, x);
     }
 
-    template <typename T>
-    static inline constexpr bool is_valid_param_type_v =
-        std::is_same_v<T, float> || std::is_same_v<T, half_t> || std::is_same_v<T, bhalf_t> ||
-        std::is_same_v<T, int32_t> || std::is_same_v<T, int8_t>
-#ifdef CK_EXPERIMENTAL_BIT_INT_EXTENSION_INT4
-        || std::is_same_v<T, ck::int4_t>
-#endif
-        ;
+    template <>
+    __host__ __device__ constexpr void operator()<half_t, half_t, half_t, half_t>(
+        half_t& e, const half_t& c, const half_t& d0, const half_t& d1) const
+    {
+        const half_t x = c + d0 + d1;
 
-    template <typename E, typename C, typename D0, typename D1>
-    __host__ __device__ constexpr void
-    operator()(E& e, const C& c, const D0& d0, const D1& d1) const
+        ck::tensor_operation::element_wise::FastGelu{}.template operator()<half_t, half_t>(e, x);
+    }
+
+    template <>
+    __host__ __device__ constexpr void operator()<half_t, float, half_t, half_t>(
+        half_t& e, const float& c, const half_t& d0, const half_t& d1) const
     {
-        static_assert(is_valid_param_type_v<E> && is_valid_param_type_v<C> &&
-                      is_valid_param_type_v<D0> && is_valid_param_type_v<D1>);
+        const float x0_f = c + d0 + d1;
+
+        float x1_f = 0;
+
+        ck::tensor_operation::element_wise::FastGelu{}.template operator()<float, float>(x1_f,
+                                                                                         x0_f);
+
+        e = type_convert<half_t>(x1_f);
+    }
+
+    template <>
+    __host__ __device__ constexpr void operator()<bhalf_t, float, bhalf_t, bhalf_t>(
+        bhalf_t& e, const float& c, const bhalf_t& d0, const bhalf_t& d1) const
+    {
+        const float x0_f = c + type_convert<float>(d0) + type_convert<float>(d1);
+
+        float x1_f = 0;
+
+        ck::tensor_operation::element_wise::FastGelu{}.template operator()<float, float>(x1_f,
+                                                                                         x0_f);
+
+        e = type_convert<bhalf_t>(x1_f);
+    }
+
+    template <>
+    __host__ __device__ constexpr void operator()<int8_t, int32_t, int8_t, int8_t>(
+        int8_t& e, const int32_t& c, const int8_t& d0, const int8_t& d1) const
+    {
+        const float x0_f =
+            type_convert<float>(c) + type_convert<float>(d0) + type_convert<float>(d1);
+
+        float x1_f = 0;
 
-        const float y =
-            GetFastGeLU(type_convert<float>(c) + type_convert<float>(d0) + type_convert<float>(d1));
+        ck::tensor_operation::element_wise::FastGelu{}.template operator()<float, float>(x1_f,
+                                                                                         x0_f);
 
-        e = type_convert<E>(y);
+        e = type_convert<int8_t>(x1_f);
     }
 };
 

include/ck/tensor_operation/gpu/element/quantization_operation.hpp

 #pragma once
 
 #include "ck/utility/data_type.hpp"
+// #include "ck/utility/get_id.hpp"
 
 namespace ck {
 namespace tensor_operation {
 namespace element_wise {
 
+// Y = Sy * Qy
+// W = Sw * Qw
+// X = Sx * Qx
+// B = Sb * Qb = Sw * Sx * Qb
+// Where X, W, Y are float32, Qx, Qw, Qy are int8
+// Sx, Sw, Sy are scale of x, w, y (float32), which is calculated from quantization range
+// Qb is int32, scale of B is Sw * Sx for convenient
+
+// Y = W @ X, where @ is convolution or matrix multiplication
+// Sy * Qy = Sw * Qw @ Sx * Qx
+// Qy = [(Sw*Sx)/Sy] * Qw @ Qx
+
 // For Activation function which is piecewise linear function, such as relu, leaky relu ...etc
+// Activation(Sy * Qy) = Sy * Activation(Qy)
 template <typename Activation>
 struct Activation_Mul_Clamp
 {
+    // Convolution + Activation (piecewise linear function)
+    // If an activation is piecewise linear function, then Activation(Sy * Qy) = Sy * Activation(Qy)
+    // Z = Activation(Y) = Activation(W @ X)
+    // Sz * Qz = Activation(Sy * Qy)
+    // Qz = Sy / Sz * Activation(Qy) = (Sw * Sx / Sz) * Activation(Qw @ Qx)
+
+    // requantScale_ = Sw * Sx / Sz
     Activation_Mul_Clamp(float requantScale, Activation activationOp)
         : requantScale_(requantScale), activationOp_(activationOp)
     {
@@ -17,26 +38,66 @@ struct Activation_Mul_Clamp
 
     __host__ __device__ constexpr void operator()(int8_t& y, const int32_t& x) const
     {
-        float x_fp32 = ck::type_convert<float>(x);
-        activationOp_(x_fp32, x_fp32);
-        float y_fp32 = math::clamp(requantScale_ * x_fp32, -128.f, 127.f);
-        y            = ck::type_convert<int8_t>(y_fp32);
+        float y_fp32 = ck::type_convert<float>(x);
+        activationOp_(y_fp32, y_fp32);
+        y_fp32 = math::clamp(requantScale_ * y_fp32, -128.f, 127.f);
+        y      = ck::type_convert<int8_t>(y_fp32);
+    }
+
+    __device__ constexpr void operator()(int32_t& y, const int32_t& x) const
+    {
+        // CAUSION - We might type_convert to int8 in threadwise copy
+        // eg. GridwiseGemmDlMultipleD_km_kn_mn
+        float y_fp32 = ck::type_convert<float>(x);
+        activationOp_(y_fp32, y_fp32);
+        y_fp32 = math::clamp(requantScale_ * y_fp32, -128.f, 127.f);
+        y      = ck::type_convert<int32_t>(y_fp32);
     }
 
-    __host__ __device__ constexpr void operator()(float& y, const int32_t& x) const
+    __host__ constexpr void operator()(float& y, const float& x) const
     {
-        // We might type_convert to int8 after lambda in someplace
-        float x_fp32 = ck::type_convert<float>(x);
-        activationOp_(x_fp32, x_fp32);
-        y = math::clamp(requantScale_ * x_fp32, -128.f, 127.f);
+        // CAUSION - We might float in & float out in reference code
+        activationOp_(y, x);
+        y = math::clamp(requantScale_ * y, -128.f, 127.f);
     }
 
     float requantScale_;
     Activation activationOp_;
 };
 
+// For Activation function which is non piecewise linear function, such as TanH, Sigmoid ...etc
+// If an activation is not piecewise linear function
+// then Activation(Sy * Qy) != Sy * Activation(Qy)
+template <typename Activation>
+struct Mul_Activation_Mul_Clamp
+{
+    // Convolution + Activation (non piecewise linear function)
+    // Z = Activation(Y) = Activation(W @ X)
+    // Sz * Qz = Activation(Sy * Qy)
+    // Qz = S1 * Activation[Sacc * (Qw @ Qx)]
+    // Where S1 = 1 / Sz, Sacc = Sw * Sx
+    Mul_Activation_Mul_Clamp(float scale_z_inv, float scaleAcc, Activation activationOp)
+        : scale_z_inv_(scale_z_inv), scaleAcc_(scaleAcc), activationOp_(activationOp)
+    {
+    }
+
+    __host__ __device__ constexpr void operator()(int8_t& y, const int32_t& x) const
+    {
+        float y_fp32 = ck::type_convert<float>(x);
+        y_fp32       = scaleAcc_ * y_fp32;
+        activationOp_(y_fp32, y_fp32);
+        y_fp32 = math::clamp(scale_z_inv_ * y_fp32, -128.f, 127.f);
+        y      = ck::type_convert<int8_t>(y_fp32);
+    }
+
+    float scale_z_inv_;
+    float scaleAcc_;
+    Activation activationOp_;
+};
+
 // Conv Perchannel quantization + Activation function which is piecewise linear function, such as
 // relu, leaky relu ...etc
+// Activation(Sy * Qy) = Sy * Activation(Qy)
 template <typename Activation>
 struct Activation_Mul2_Clamp
 {
@@ -51,13 +112,35 @@ struct Activation_Mul2_Clamp
         y      = ck::type_convert<int8_t>(y_fp32);
     }
 
+    __device__ constexpr void
+    operator()(int32_t& y, const int32_t& x, const float& requantScale) const
+    {
+        // CAUSION - We might type_convert to int8 in threadwise copy
+        // eg. GridwiseGemmDlMultipleD_km_kn_mn
+        float y_fp32 = ck::type_convert<float>(x);
+        activationOp_(y_fp32, y_fp32);
+        y_fp32 = math::clamp(requantScale * y_fp32, -128.f, 127.f);
+        y      = ck::type_convert<int32_t>(y_fp32);
+    }
+
     Activation activationOp_;
 };
 
 // For Activation function which is piecewise linear function, such as relu, leaky relu ...etc
+// Activation(Sy * Qy) = Sy * Activation(Qy)
 template <typename Activation>
 struct Add_Activation_Mul_Clamp
 {
+    // Convolution + bias
+    // Let Bias = B = Sw * Sx * Qb
+    // Where Qb is int32
+    // Y = W @ X + B
+    // Sy * Qy = Sw * Qw @ Sx * Qx + Sw * Sx * Qb
+    // Qy = [(Sw*Sx)/Sy] * (Qw @ Qx + Qb)
+
+    // For activation, Z = Activaiton(Y)
+    // Sz * Qz = Activation(Sy * Qy)
+    // Qz = Sy / Sz * Activation(Qy) = [(Sw*Sx)/Sz] * Activation(Qw @ Qx + Qb)
     Add_Activation_Mul_Clamp(float requantScale, Activation activationOp)
         : requantScale_(requantScale), activationOp_(activationOp)
     {
@@ -72,6 +155,17 @@ struct Add_Activation_Mul_Clamp
         y      = ck::type_convert<int8_t>(y_fp32);
     }
 
+    __host__ __device__ constexpr void
+    operator()(int32_t& y, const int32_t& x, const int32_t& bias) const
+    {
+        // CAUSION - We might type_convert to int8 in threadwise copy
+        // eg. GridwiseGemmDlMultipleD_km_kn_mn
+        float y_fp32 = ck::type_convert<float>(x + bias);
+        activationOp_(y_fp32, y_fp32);
+        y_fp32 = math::clamp(requantScale_ * y_fp32, -128.f, 127.f);
+        y      = ck::type_convert<int32_t>(y_fp32);
+    }
+
     float requantScale_;
     Activation activationOp_;
 };
@@ -92,15 +186,33 @@ struct Add_Activation_Mul2_Clamp
         y      = ck::type_convert<int8_t>(y_fp32);
     }
 
+    __host__ __device__ constexpr void
+    operator()(int32_t& y, const int32_t& x, const int32_t& bias, const float& requantScale) const
+    {
+        // CAUSION - We might type_convert to int8 in threadwise copy
+        // eg. GridwiseGemmDlMultipleD_km_kn_mn
+        float y_fp32 = ck::type_convert<float>(x + bias);
+        activationOp_(y_fp32, y_fp32);
+        y_fp32 = math::clamp(requantScale * y_fp32, -128.f, 127.f);
+        y      = ck::type_convert<int32_t>(y_fp32);
+    }
+
     Activation activationOp_;
 };
 
 // For Activation function which is non piecewise linear function, such as TanH, Sigmoid ...etc
+// If an activation is not piecewise linear function
+// then Activation(Sy * Qy) != Sy * Activation(Qy)
 template <typename Activation>
 struct Add_Mul_Activation_Mul_Clamp
 {
-    Add_Mul_Activation_Mul_Clamp(float requantScale1, float requantScale2, Activation activationOp)
-        : requantScale1_(requantScale1), requantScale2_(requantScale2), activationOp_(activationOp)
+    // Convolution + Activation (non piecewise linear function)
+    // Z = Activation(Y) = Activation(W @ X + B)
+    // Sz * Qz = Activation(Sy * Qy)
+    // Qz = S1 * Activation[Sacc * (Qw @ Qx + Qb)]
+    // Where S1 = 1 / Sz, Sacc = Sw * Sx
+    Add_Mul_Activation_Mul_Clamp(float scale_z_inv, float scaleAcc, Activation activationOp)
+        : scale_z_inv_(scale_z_inv), scaleAcc_(scaleAcc), activationOp_(activationOp)
     {
     }
 
@@ -108,14 +220,64 @@ struct Add_Mul_Activation_Mul_Clamp
     operator()(int8_t& y, const int32_t& x, const int32_t& bias) const
     {
         float y_fp32 = ck::type_convert<float>(x + bias);
-        y_fp32       = requantScale1_ * y_fp32;
+        y_fp32       = scaleAcc_ * y_fp32;
+        activationOp_(y_fp32, y_fp32);
+        y_fp32 = math::clamp(scale_z_inv_ * y_fp32, -128.f, 127.f);
+        y      = ck::type_convert<int8_t>(y_fp32);
+    }
+
+    __host__ __device__ constexpr void
+    operator()(int32_t& y, const int32_t& x, const int32_t& bias) const
+    {
+        // CAUSION - We might type_convert to int8 in threadwise copy
+        // eg. GridwiseGemmDlMultipleD_km_kn_mn
+        float y_fp32 = ck::type_convert<float>(x + bias);
+        y_fp32       = scaleAcc_ * y_fp32;
+        activationOp_(y_fp32, y_fp32);
+        y_fp32 = math::clamp(scale_z_inv_ * y_fp32, -128.f, 127.f);
+        y      = ck::type_convert<int32_t>(y_fp32);
+    }
+
+    float scale_z_inv_;
+    float scaleAcc_;
+    Activation activationOp_;
+};
+
+// Conv Perchannel quantization + Activation function which is non piecewise linear function,
+// such as TanH, Sigmoid ...etc
+// If an activation is not piecewise linear function
+// then Activation(Sy *Qy) != Sy * Activation(Qy)
+template <typename Activation>
+struct Add_Mul2_Activation_Mul_Clamp
+{
+    Add_Mul2_Activation_Mul_Clamp(float scale_z_inv, Activation activationOp)
+        : scale_z_inv_(scale_z_inv), activationOp_(activationOp)
+    {
+    }
+
+    __host__ __device__ constexpr void
+    operator()(int8_t& y, const int32_t& x, const int32_t& bias, const float& scaleAcc) const
+    {
+        float y_fp32 = ck::type_convert<float>(x + bias);
+        y_fp32       = scaleAcc * y_fp32;
         activationOp_(y_fp32, y_fp32);
-        y_fp32 = math::clamp(requantScale2_ * y_fp32, -128.f, 127.f);
+        y_fp32 = math::clamp(scale_z_inv_ * y_fp32, -128.f, 127.f);
         y      = ck::type_convert<int8_t>(y_fp32);
     }
 
-    float requantScale1_;
-    float requantScale2_;
+    __host__ __device__ constexpr void
+    operator()(int32_t& y, const int32_t& x, const int32_t& bias, const float& scaleAcc) const
+    {
+        // CAUSION - We might type_convert to int8 in threadwise copy
+        // eg. GridwiseGemmDlMultipleD_km_kn_mn
+        float y_fp32 = ck::type_convert<float>(x + bias);
+        y_fp32       = scaleAcc * y_fp32;
+        activationOp_(y_fp32, y_fp32);
+        y_fp32 = math::clamp(scale_z_inv_ * y_fp32, -128.f, 127.f);
+        y      = ck::type_convert<int32_t>(y_fp32);
+    }
+
+    float scale_z_inv_;
     Activation activationOp_;
 };
 

include/ck/tensor_operation/gpu/element/unary_element_wise_operation.hpp

@@ -11,6 +11,10 @@ namespace ck {
 namespace tensor_operation {
 namespace element_wise {
 
+#if CK_WORKAROUND_SWDEV_383542
+extern "C" __device__ float __ocml_native_recip_f32(float);
+#endif
+
 struct PassThrough
 {
     template <typename Y, typename X>
@@ -200,36 +204,83 @@ struct Relu
     }
 };
 
-// Y = FastGelu(X)
+// Fast GeLU
+// https://paperswithcode.com/method/gelu
+// y = 0.5*x*(1+tanh(sqrt(2/pi)*(x+0.044715*x^3)))
+// host code use higher accuracy "exp" and "div"
+// gpu code use lower accuracy "__expf" and "rcp" function
 struct FastGelu
 {
-    // Fast GeLU
-    // https://paperswithcode.com/method/gelu
-    // y = 0.5*x*(1+tanh(sqrt(2/pi)*(x+0.044715*x^3)))
-    __host__ __device__ static constexpr float GetFastGeLU(float x)
+    template <typename Y, typename X>
+    __host__ void operator()(Y& y, const X& x) const;
+
+    template <typename Y, typename X>
+    __device__ void operator()(Y& y, const X& x) const;
+
+    template <>
+    __host__ void operator()<float, float>(float& y, const float& x) const
     {
         const float u   = 2.f * x * (0.035677f * x * x + 0.797885f);
         const float emu = exp(-u);
         const float cdf = 0.5f + 0.5f * (2.f / (1.f + emu) - 1.f);
-        return x * cdf;
+
+        y = x * cdf;
     }
 
-    template <typename T>
-    static inline constexpr bool is_valid_param_type_v =
-        std::is_same_v<T, float> || std::is_same_v<T, half_t> || std::is_same_v<T, bhalf_t> ||
-        std::is_same_v<T, int32_t> || std::is_same_v<T, int8_t>
-#ifdef CK_EXPERIMENTAL_BIT_INT_EXTENSION_INT4
-        || std::is_same_v<T, ck::int4_t>
+    // device code, use lower precision "__expf" and "rcp"
+    template <>
+    __device__ void operator()<float, float>(float& y, const float& x) const
+    {
+        const float u   = 2.f * x * (0.035677f * x * x + 0.797885f);
+        const float emu = __expf(-u);
+
+#if !CK_WORKAROUND_SWDEV_383542
+        const float cdf = 0.5f + 0.5f * (2.f * __frcp_rn(1.f + emu) - 1.f);
+#else
+        const float cdf = 0.5f + 0.5f * (2.f * __ocml_native_recip_f32(1.f + emu) - 1.f);
 #endif
-        ;
 
-    template <typename Y, typename X>
-    __host__ __device__ void operator()(Y& y, const X& x) const
+        y = x * cdf;
+    }
+
+    template <>
+    __host__ void operator()<half_t, half_t>(half_t& y, const half_t& x) const
+    {
+        float y_f;
+
+        this->operator()<float, float>(y_f, type_convert<float>(x));
+
+        y = type_convert<half_t>(y_f);
+    }
+
+    template <>
+    __device__ void operator()<half_t, half_t>(half_t& y, const half_t& x) const
     {
-        static_assert(is_valid_param_type_v<Y> && is_valid_param_type_v<X>);
+        float y_f;
+
+        this->operator()<float, float>(y_f, type_convert<float>(x));
 
-        const float tmp_y = GetFastGeLU(type_convert<float>(x));
-        y                 = type_convert<Y>(tmp_y);
+        y = type_convert<half_t>(y_f);
+    }
+
+    template <>
+    __host__ void operator()<half_t, float>(half_t& y, const float& x) const
+    {
+        float y_f;
+
+        this->operator()<float, float>(y_f, x);
+
+        y = type_convert<half_t>(y_f);
+    }
+
+    template <>
+    __device__ void operator()<half_t, float>(half_t& y, const float& x) const
+    {
+        float y_f;
+
+        this->operator()<float, float>(y_f, x);
+
+        y = type_convert<half_t>(y_f);
     }
 };
 
@@ -269,6 +320,19 @@ struct Sigmoid
     int32_t divider_ = 1;
 };
 
+struct TanH
+{
+    template <typename T>
+    __host__ __device__ void operator()(T& y, const T& x) const
+    {
+        static_assert(is_same<T, float>::value || is_same<T, double>::value ||
+                          is_same<T, ck::half_t>::value,
+                      "Data type is not supported by this operation!");
+
+        y = ck::math::tanh(x);
+    };
+};
+
 } // namespace element_wise
 } // namespace tensor_operation
 } // namespace ck

include/ck/tensor_operation/gpu/grid/gridwise_batched_gemm_softmax_gemm_xdl_cshuffle_v1.hpp

@@ -18,6 +18,10 @@
 
 namespace ck {
 
+/**
+ * @brief Gridwise gemm + softmax + gemm fusion
+ *
+ */
 template <typename FloatAB,
           typename FloatGemmAcc,
           typename FloatCShuffle,

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

@@ -185,8 +185,10 @@ struct GridwiseGemmDlMultipleD_km_kn_mn
         return b_grid_desc_k0_n0_n1_k1;
     }
 
+    // E desc for destination in blockwise copy
+    template <typename CGridDesc_M_N_>
     __host__ __device__ static constexpr auto
-    MakeCGridDescriptor_M0_M10_M11_N0_N10_N11(const CGridDesc_M_N& c_grid_desc_m_n)
+    MakeCGridDescriptor_M0_M10_M11_N0_N10_N11(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);
@@ -238,19 +240,19 @@ struct GridwiseGemmDlMultipleD_km_kn_mn
     using BGridDesc_K0_N0_N1_K1 = decltype(MakeBGridDescriptor_K0_N0_N1_K1(BGridDesc_K0_N_K1{}));
     using CGridDesc_M0_M10_M11_N0_N10_N11 =
         decltype(MakeCGridDescriptor_M0_M10_M11_N0_N10_N11(CGridDesc_M_N{}));
-    using Block2CTileMap = decltype(MakeDefaultBlock2CTileMap(CGridDesc_M_N{}));
 
     using DsGridPointer = decltype(MakeDsGridPointer());
 
     template <typename DsGridDesc_M0_M10_M11_N0_N10_N11,
               bool HasMainKBlockLoop,
-              bool HasDoubleTailKBlockLoop>
+              bool HasDoubleTailKBlockLoop,
+              typename Block2CTileMap>
     __device__ static void
     Run(const FloatAB* __restrict__ p_a_grid,
         const FloatAB* __restrict__ p_b_grid,
         DsGridPointer p_ds_grid,
         FloatC* __restrict__ p_c_grid,
-        FloatAB* __restrict__ p_shared_block,
+        void* __restrict__ p_shared_block,
         const AElementwiseOperation&,
         const BElementwiseOperation&,
         const CDEElementwiseOperation& cde_element_op,
@@ -399,8 +401,9 @@ struct GridwiseGemmDlMultipleD_km_kn_mn
         constexpr auto b_block_aligned_space_size = math::integer_least_multiple(
             b_block_desc_k0_n0_n1_k1.GetElementSpaceSize(), max_lds_align);
 
-        FloatAB* p_a_block_double = p_shared_block;
-        FloatAB* p_b_block_double = p_shared_block + 2 * a_block_aligned_space_size;
+        FloatAB* p_a_block_double = static_cast<FloatAB*>(p_shared_block);
+        FloatAB* p_b_block_double =
+            static_cast<FloatAB*>(p_shared_block) + 2 * a_block_aligned_space_size;
 
         // register allocation for output
         auto c_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, FloatAcc>(

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

@@ -54,7 +54,8 @@ __global__ void
             const Block2CTileMap block_2_ctile_map,
             const ComputePtrOffsetOfBatch compute_ptr_offset_of_batch)
 {
-#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx1100__))
+#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx1100__) || defined(__gfx1101__) || \
+    defined(__gfx1102__))
     // offset base pointer for each work-group
     const index_t num_blocks_per_batch =
         __builtin_amdgcn_readfirstlane(get_grid_size() / batch_count);
@@ -147,7 +148,8 @@ __global__ void
             const ComputePtrOffsetOfBatch compute_ptr_offset_of_batch,
             const Block2CTileMap block_2_etile_map)
 {
-#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx1100__))
+#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx1100__) || defined(__gfx1101__) || \
+    defined(__gfx1102__))
     // printf("entry kernel launch");
     __shared__ char p_shared[GridwiseOp::GetSharedMemoryNumberOfByte()];
 
@@ -242,7 +244,8 @@ __global__ void
             const CDEElementwiseOperation cde_element_op,
             const Block2CTileMap block_2_ctile_map)
 {
-#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx1100__))
+#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx1100__) || defined(__gfx1101__) || \
+    defined(__gfx1102__))
     __shared__ char p_shared[GridwiseOp::GetSharedMemoryNumberOfByte()];
 
     GridwiseOp::template Run<HasMainKBlockLoop>(p_a_grid,
@@ -271,7 +274,7 @@ __global__ void
     ignore = b_element_op;
     ignore = cde_element_op;
     ignore = block_2_ctile_map;
-#endif // end of if (defined(__gfx1100__))
+#endif // end of if (defined(__gfx1100__ ))
 }
 
 template < // DataType Family
@@ -428,6 +431,9 @@ struct GridwiseGemmMultipleD_k0mk1_k0nk1_mn_wmma_cshuffle
         constexpr auto b_block_desc_k0perblock_nperblock_k1 =
             GetBBlockDescriptor_K0PerBlock_NPerBlock_K1();
 
+        constexpr auto cshuffle_block_desc_mshrepeat_mpershrepeat_nshrepeat_npershrepeat =
+            GetCShuffleBlockDescriptor_MShRepeat_MPerShRepeat_NShRepeat_NPerShRepeat();
+
         constexpr auto max_lds_align = K1;
 
         constexpr auto a_block_space_size_aligned = math::integer_least_multiple(
@@ -436,8 +442,13 @@ struct GridwiseGemmMultipleD_k0mk1_k0nk1_mn_wmma_cshuffle
         constexpr auto b_block_space_size_aligned = math::integer_least_multiple(
             b_block_desc_k0perblock_nperblock_k1.GetElementSpaceSize(), max_lds_align);
 
-        return (a_block_space_size_aligned * sizeof(ADataType) +
-                b_block_space_size_aligned * sizeof(BDataType));
+        constexpr auto c_block_space_size_aligned = math::integer_least_multiple(
+            cshuffle_block_desc_mshrepeat_mpershrepeat_nshrepeat_npershrepeat.GetElementSpaceSize(),
+            max_lds_align);
+
+        return math::max((a_block_space_size_aligned * sizeof(ADataType) +
+                          b_block_space_size_aligned * sizeof(BDataType)),
+                         c_block_space_size_aligned * sizeof(CShuffleDataType));
     }
 
     // block_id to matrix tile idx (m0, n0) mapping are controlled by {M01, N01}
@@ -673,7 +684,7 @@ struct GridwiseGemmMultipleD_k0mk1_k0nk1_mn_wmma_cshuffle
         constexpr auto KPack = math::integer_least_multiple(K1, WmmaK);
 
         auto blockwise_gemm =
-            BlockwiseGemmWMMA_k0mk1_k0nk1_m0m1m2n0n1n2m3_CShuffle_FIFO<BlockSize,
+            BlockwiseGemmWMMA_k0mk1_k0nk1_m0m1m2n0n1n2m3_CShuffle<BlockSize,
                                                          ADataType,
                                                          BDataType,
                                                          AccDataType,
@@ -716,7 +727,6 @@ struct GridwiseGemmMultipleD_k0mk1_k0nk1_mn_wmma_cshuffle
                                                           c_thread_buf,
                                                           K0BlockMainLoop);
 /*******************************************************************************/
-        //printf("safe 1");
         // write out to C, implement shuffle
         {
             constexpr auto c_thread_desc_mrepeat_mwave_msubgroup_nrepeat_nwave_nthreadpersubgroup_maccvgprs =  

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.
+// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
 
 #pragma once
 
@@ -92,6 +92,17 @@ struct GridwiseGemmMultipleD_xdl_cshuffle
     using GridwiseGemmPipe = remove_cvref_t<decltype(
         GridwiseGemmPipeline_Selector<PipelineVer, NumGemmKPrefetchStage, LoopSched>())>;
 
+    // denorm test fix, required to work around fp16 mfma issue
+    // we convert fp16->fp32->bf16 and execute bf16 mfma instruction
+    // when mfma if fixed, remove this section and update
+    // ABDataTypeAdjusted -> ABDataType throughout this file
+#if defined(__gfx90a__)
+    using ABDataTypeAdjusted =
+        conditional_t<is_same_v<ABDataType, ck::half_t>, ck::bhalf_t, ABDataType>;
+#else
+    using ABDataTypeAdjusted = ABDataType;
+#endif
+
     __host__ __device__ static constexpr auto GetABlockDescriptor_AK0PerBlock_MPerBlock_AK1()
     {
         // A matrix in LDS memory, dst of blockwise copy
@@ -397,7 +408,7 @@ struct GridwiseGemmMultipleD_xdl_cshuffle
                                                 ABlockTransferThreadClusterLengths_AK0_M_AK1,
                                                 ABlockTransferThreadClusterArrangeOrder,
                                                 ABDataType,
-                                                ABDataType,
+                                                ABDataTypeAdjusted,
                                                 decltype(a_grid_desc_ak0_m_ak1),
                                                 decltype(a_block_desc_ak0_m_ak1),
                                                 ABlockTransferSrcAccessOrder,
@@ -428,7 +439,7 @@ struct GridwiseGemmMultipleD_xdl_cshuffle
                                                 BBlockTransferThreadClusterLengths_BK0_N_BK1,
                                                 BBlockTransferThreadClusterArrangeOrder,
                                                 ABDataType,
-                                                ABDataType,
+                                                ABDataTypeAdjusted,
                                                 decltype(b_grid_desc_bk0_n_bk1),
                                                 decltype(b_block_desc_bk0_n_bk1),
                                                 BBlockTransferSrcAccessOrder,
@@ -458,11 +469,11 @@ struct GridwiseGemmMultipleD_xdl_cshuffle
         // sanity check
         constexpr index_t KPack =
             math::max(math::lcm(AK1, BK1),
-                      MfmaSelector<ABDataType, MPerXdl, NPerXdl>::selected_mfma.k_per_blk);
+                      MfmaSelector<ABDataTypeAdjusted, MPerXdl, NPerXdl>::selected_mfma.k_per_blk);
 
         auto blockwise_gemm = BlockwiseGemmXdlops_k0mk1_k0nk1_m0n0m1n1m2m3m4n2_Selector<
             BlockSize,
-            ABDataType,
+            ABDataTypeAdjusted,
             AccDataType,
             decltype(a_block_desc_ak0_m_ak1),
             decltype(b_block_desc_bk0_n_bk1),
@@ -480,10 +491,11 @@ struct GridwiseGemmMultipleD_xdl_cshuffle
             a_block_desc_ak0_m_ak1.GetElementSpaceSize(), max_lds_align);
 
         auto a_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
-            static_cast<ABDataType*>(p_shared), a_block_desc_ak0_m_ak1.GetElementSpaceSize());
+            static_cast<ABDataTypeAdjusted*>(p_shared),
+            a_block_desc_ak0_m_ak1.GetElementSpaceSize());
 
         auto b_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
-            static_cast<ABDataType*>(p_shared) + a_block_space_size_aligned,
+            static_cast<ABDataTypeAdjusted*>(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);

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.
+// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
 
 #pragma once
 

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

@@ -49,7 +49,8 @@ __global__ void
             const CElementwiseOperation c_element_op,
             const Block2CTileMap block_2_ctile_map)
 {
-#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx1100__))
+#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx1100__) || defined(__gfx1101__) || \
+    defined(__gfx1102__))
     __shared__ char p_shared[GridwiseGemm::GetSharedMemoryNumberOfByte()];
 
     GridwiseGemm::template Run<HasMainKBlockLoop>(p_a_grid,
@@ -414,7 +415,7 @@ struct GridwiseGemm_k0mk1_k0nk1_mn_wmma
         constexpr auto KPack = math::integer_least_multiple(K1, WmmaK);
 
         auto blockwise_gemm =
-            BlockwiseGemmWMMA_k0mk1_k0nk1_m0m1m2n0n1n2m3_CShuffle_FIFO<BlockSize,
+            BlockwiseGemmWMMA_k0mk1_k0nk1_m0m1m2n0n1n2m3_CShuffle<BlockSize,
                                                          FloatA,
                                                          FloatB,
                                                          FloatAcc,

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.
+// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
 
 #pragma once
 
@@ -167,15 +167,12 @@ __global__ void
 {
 #if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx908__) || defined(__gfx90a__) || \
     defined(__gfx940__))
-    constexpr index_t shared_block_size =
-        GridwiseGemm::GetSharedMemoryNumberOfByte() / sizeof(FloatAB);
-
-    __shared__ FloatAB p_shared_block[shared_block_size];
+    __shared__ char p_shared[GridwiseGemm::GetSharedMemoryNumberOfByte()];
 
     GridwiseGemm::template Run<HasMainKBlockLoop>(p_a_grid,
                                                   p_b_grid,
                                                   p_c_grid,
-                                                  p_shared_block,
+                                                  p_shared,
                                                   a_b_k0_m_k1_grid_desc,
                                                   b_b_k0_n_k1_grid_desc,
                                                   c_grid_desc_mblock_mperblock_nblock_nperblock,
@@ -184,16 +181,16 @@ __global__ void
                                                   c_element_op,
                                                   c_block_cluster_adaptor);
 #else
-    ignore = p_a_grid;
-    ignore = p_b_grid;
-    ignore = p_c_grid;
-    ignore = a_b_k0_m_k1_grid_desc;
-    ignore = b_b_k0_n_k1_grid_desc;
-    ignore = c_grid_desc_mblock_mperblock_nblock_nperblock;
-    ignore = a_element_op;
-    ignore = b_element_op;
-    ignore = c_element_op;
-    ignore = c_block_cluster_adaptor;
+    ignore                = p_a_grid;
+    ignore                = p_b_grid;
+    ignore                = p_c_grid;
+    ignore                = a_b_k0_m_k1_grid_desc;
+    ignore                = b_b_k0_n_k1_grid_desc;
+    ignore                = c_grid_desc_mblock_mperblock_nblock_nperblock;
+    ignore                = a_element_op;
+    ignore                = b_element_op;
+    ignore                = c_element_op;
+    ignore                = c_block_cluster_adaptor;
 #endif // end of if (defined(__gfx908__) || defined(__gfx90a__))
 }
 
@@ -265,6 +262,16 @@ struct GridwiseGemm_bk0mk1_bk0nk1_mn_xdlops_bwd_weight
     using GridwiseGemmPipe = remove_cvref_t<decltype(
         GridwiseGemmPipeline_Selector<PipelineVer, NumGemmKPrefetchStage>())>;
 
+    // denorm test fix, required to work around fp16 mfma issue
+    // we convert fp16->fp32->bf16 and execute bf16 mfma instruction
+    // when mfma if fixed, remove this section and update
+    // FloatABAdjusted -> FloatAB throughout this file
+#if defined(__gfx90a__)
+    using FloatABAdjusted = conditional_t<is_same_v<FloatAB, ck::half_t>, ck::bhalf_t, FloatAB>;
+#else
+    using FloatABAdjusted = FloatAB;
+#endif
+
     // M0/M1/M1Padding
     static constexpr auto M1PerBlock = Number<ABlockLdsM1PerBlock>{};
     static constexpr auto M0PerBlock = Number<ABlockLdsM0PerBlock>{};
@@ -606,7 +613,7 @@ struct GridwiseGemm_bk0mk1_bk0nk1_mn_xdlops_bwd_weight
     __device__ static void Run(const FloatAB* __restrict__ p_a_grid,
                                const FloatAB* __restrict__ p_b_grid,
                                FloatC* __restrict__ p_c_grid,
-                               FloatAB* __restrict__ p_shared_block,
+                               void* __restrict__ p_shared,
                                const AGridDesc_B_K0_M_K1& a_b_k0_m_k1_grid_desc,
                                const BGridDesc_B_K0_N_K1& b_b_k0_n_k1_grid_desc,
                                const CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock&
@@ -667,7 +674,7 @@ struct GridwiseGemm_bk0mk1_bk0nk1_mn_xdlops_bwd_weight
                                                 ABlockTransferThreadClusterLengths_K0_M_K1,
                                                 ABlockTransferThreadClusterArrangeOrder,
                                                 FloatAB,
-                                                FloatAB,
+                                                FloatABAdjusted,
                                                 decltype(a_b_k0_m_k1_grid_desc),
                                                 decltype(a_b_k0_m_k1_block_desc),
                                                 ABlockTransferSrcAccessOrder,
@@ -697,7 +704,7 @@ struct GridwiseGemm_bk0mk1_bk0nk1_mn_xdlops_bwd_weight
                                                 BBlockTransferThreadClusterLengths_K0_N_K1,
                                                 BBlockTransferThreadClusterArrangeOrder,
                                                 FloatAB,
-                                                FloatAB,
+                                                FloatABAdjusted,
                                                 decltype(b_b_k0_n_k1_grid_desc),
                                                 decltype(b_b_k0_n_k1_block_desc),
                                                 BBlockTransferSrcAccessOrder,
@@ -726,11 +733,11 @@ struct GridwiseGemm_bk0mk1_bk0nk1_mn_xdlops_bwd_weight
         // sanity check
 
         constexpr index_t KPack =
-            math::max(K1, MfmaSelector<FloatAB, MPerXDL, NPerXDL>::selected_mfma.k_per_blk);
+            math::max(K1, MfmaSelector<FloatABAdjusted, MPerXDL, NPerXDL>::selected_mfma.k_per_blk);
 
         auto blockwise_gemm =
             BlockwiseGemmXdlops_k0mk1_k0nk1_m0n0m1n1m2m3m4n2_v1<BlockSize,
-                                                                FloatAB,
+                                                                FloatABAdjusted,
                                                                 FloatAcc,
                                                                 decltype(a_k0_m_k1_block_desc),
                                                                 decltype(b_k0_n_k1_block_desc),
@@ -746,16 +753,15 @@ struct GridwiseGemm_bk0mk1_bk0nk1_mn_xdlops_bwd_weight
         constexpr auto a_block_space_size =
             math::integer_least_multiple(a_k0_m_k1_block_desc.GetElementSpaceSize(), max_lds_align);
 
-        FloatAB* p_a_block = p_shared_block;
-        FloatAB* p_b_block = p_shared_block + a_block_space_size;
-
         constexpr auto a_block_slice_copy_step = make_multi_index(0, K0PerBlock, 0, 0);
         constexpr auto b_block_slice_copy_step = make_multi_index(0, K0PerBlock, 0, 0);
 
         auto a_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
-            p_a_block, a_k0_m_k1_block_desc.GetElementSpaceSize());
+            static_cast<FloatABAdjusted*>(p_shared), a_k0_m_k1_block_desc.GetElementSpaceSize());
+
         auto b_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
-            p_b_block, b_k0_n_k1_block_desc.GetElementSpaceSize());
+            static_cast<FloatABAdjusted*>(p_shared) + a_block_space_size,
+            b_k0_n_k1_block_desc.GetElementSpaceSize());
 
         // gridwise GEMM pipeline
         const index_t K0BlockMainLoop = __builtin_amdgcn_readfirstlane(K0 / K0PerBlock);
@@ -799,8 +805,6 @@ 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),
                 c_block_desc_mblock_mperblock_nblock_nperblock.GetElementSpaceSize());

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.
+// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
 
 #pragma once
 
@@ -59,16 +59,16 @@ __global__ void
                                                   c_element_op,
                                                   block_2_ctile_map);
 #else
-    ignore = p_a_grid;
-    ignore = p_b_grid;
-    ignore = p_c_grid;
-    ignore = a_grid_desc_k0_m_k1;
-    ignore = b_grid_desc_k0_n_k1;
-    ignore = c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2;
-    ignore = a_element_op;
-    ignore = b_element_op;
-    ignore = c_element_op;
-    ignore = block_2_ctile_map;
+    ignore                = p_a_grid;
+    ignore                = p_b_grid;
+    ignore                = p_c_grid;
+    ignore                = a_grid_desc_k0_m_k1;
+    ignore                = b_grid_desc_k0_n_k1;
+    ignore                = c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2;
+    ignore                = a_element_op;
+    ignore                = b_element_op;
+    ignore                = c_element_op;
+    ignore                = block_2_ctile_map;
 #endif // end of if (defined(__gfx908__) || defined(__gfx90a__))
 }
 
@@ -132,6 +132,16 @@ struct GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v2r3
     using GridwiseGemmPipe = remove_cvref_t<decltype(
         GridwiseGemmPipeline_Selector<PipelineVer, NumGemmKPrefetchStage, LoopSched>())>;
 
+    // denorm test fix, required to work around fp16 mfma issue
+    // we convert fp16->fp32->bf16 and execute bf16 mfma instruction
+    // when mfma if fixed, remove this section and update
+    // FloatABAdjusted -> FloatAB throughout this file
+#if defined(__gfx90a__)
+    using FloatABAdjusted = conditional_t<is_same_v<FloatAB, ck::half_t>, ck::bhalf_t, FloatAB>;
+#else
+    using FloatABAdjusted = FloatAB;
+#endif
+
     __host__ __device__ static constexpr auto GetABlockDescriptor_K0PerBlock_MPerBlock_K1()
     {
         constexpr auto max_lds_align = K1;
@@ -282,7 +292,7 @@ struct GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v2r3
 
         using BlockwiseGemm =
             BlockwiseGemmXdlops_k0mk1_k0nk1_m0n0m1n1m2m3m4n2_v1<BlockSize,
-                                                                FloatAB,
+                                                                FloatABAdjusted,
                                                                 FloatAcc,
                                                                 decltype(a_block_desc_k0_m_k1),
                                                                 decltype(b_block_desc_k0_n_k1),
@@ -368,7 +378,7 @@ struct GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v2r3
                                                 ABlockTransferThreadClusterLengths_K0_M_K1,
                                                 ABlockTransferThreadClusterArrangeOrder,
                                                 FloatAB,
-                                                FloatAB,
+                                                FloatABAdjusted,
                                                 decltype(a_grid_desc_k0_m_k1),
                                                 decltype(a_block_desc_k0_m_k1),
                                                 ABlockTransferSrcAccessOrder,
@@ -399,7 +409,7 @@ struct GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v2r3
                                                 BBlockTransferThreadClusterLengths_K0_N_K1,
                                                 BBlockTransferThreadClusterArrangeOrder,
                                                 FloatAB,
-                                                FloatAB,
+                                                FloatABAdjusted,
                                                 decltype(b_grid_desc_k0_n_k1),
                                                 decltype(b_block_desc_k0_n_k1),
                                                 BBlockTransferSrcAccessOrder,
@@ -429,7 +439,7 @@ struct GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v2r3
         // sanity check
         auto blockwise_gemm = BlockwiseGemmXdlops_k0mk1_k0nk1_m0n0m1n1m2m3m4n2_Selector<
             BlockSize,
-            FloatAB,
+            FloatABAdjusted,
             FloatAcc,
             decltype(a_block_desc_k0_m_k1),
             decltype(b_block_desc_k0_n_k1),
@@ -447,10 +457,10 @@ struct GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v2r3
             math::integer_least_multiple(a_block_desc_k0_m_k1.GetElementSpaceSize(), max_lds_align);
 
         auto a_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
-            static_cast<FloatAB*>(p_shared), a_block_desc_k0_m_k1.GetElementSpaceSize());
+            static_cast<FloatABAdjusted*>(p_shared), a_block_desc_k0_m_k1.GetElementSpaceSize());
 
         auto b_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
-            static_cast<FloatAB*>(p_shared) + a_block_space_size_aligned,
+            static_cast<FloatABAdjusted*>(p_shared) + a_block_space_size_aligned,
             b_block_desc_k0_n_k1.GetElementSpaceSize());
 
         constexpr auto a_block_slice_copy_step = make_multi_index(K0PerBlock, 0, 0);

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

@@ -18,61 +18,24 @@
 namespace ck {
 
 template <typename GridwiseGemm,
-          typename FloatAB,
-          typename FloatC,
-          typename AGridDesc_B_K0_M_K1,
-          typename BGridDesc_B_K0_N_K1,
-          typename CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock,
-          typename AElementwiseOperation,
-          typename BElementwiseOperation,
-          typename CElementwiseOperation,
-          typename CBlockClusterAdaptor,
-          bool HasMainKBlockLoop>
+          bool HasMainKBlockLoop,
+          InMemoryDataOperationEnum CGlobalMemoryDataOperation>
 __global__ void
 #if CK_USE_LAUNCH_BOUNDS
     __launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
 #endif
-        kernel_gemm_xdlops_v2r4r2(const FloatAB* __restrict__ p_a_grid,
-                                  const FloatAB* __restrict__ p_b_grid,
-                                  FloatC* __restrict__ p_c_grid,
-                                  const AGridDesc_B_K0_M_K1 a_b_k0_m_k1_grid_desc,
-                                  const BGridDesc_B_K0_N_K1 b_b_k0_n_k1_grid_desc,
-                                  const CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock
-                                      c_grid_desc_mblock_mperblock_nblock_nperblock,
-                                  const AElementwiseOperation a_element_op,
-                                  const BElementwiseOperation b_element_op,
-                                  const CElementwiseOperation c_element_op,
-                                  const CBlockClusterAdaptor c_block_cluster_adaptor)
+        kernel_gemm_xdlops_v2r4r2_simplified(typename GridwiseGemm::Argument karg)
 {
 #if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx908__) || defined(__gfx90a__) || \
     defined(__gfx940__))
-    constexpr index_t shared_block_size =
-        GridwiseGemm::GetSharedMemoryNumberOfByte() / sizeof(FloatAB);
-
-    __shared__ FloatAB p_shared_block[shared_block_size];
-
-    GridwiseGemm::template Run<HasMainKBlockLoop>(p_a_grid,
-                                                  p_b_grid,
-                                                  p_c_grid,
-                                                  static_cast<void*>(p_shared_block),
-                                                  a_b_k0_m_k1_grid_desc,
-                                                  b_b_k0_n_k1_grid_desc,
-                                                  c_grid_desc_mblock_mperblock_nblock_nperblock,
-                                                  a_element_op,
-                                                  b_element_op,
-                                                  c_element_op,
-                                                  c_block_cluster_adaptor);
+    constexpr index_t shared_size = GridwiseGemm::GetSharedMemoryNumberOfByte();
+
+    __shared__ uint8_t p_shared[shared_size];
+
+    GridwiseGemm::template Run<HasMainKBlockLoop, CGlobalMemoryDataOperation>(
+        karg, static_cast<void*>(p_shared));
 #else
-    ignore = p_a_grid;
-    ignore = p_b_grid;
-    ignore = p_c_grid;
-    ignore = a_b_k0_m_k1_grid_desc;
-    ignore = b_b_k0_n_k1_grid_desc;
-    ignore = c_grid_desc_mblock_mperblock_nblock_nperblock;
-    ignore = a_element_op;
-    ignore = b_element_op;
-    ignore = c_element_op;
-    ignore = c_block_cluster_adaptor;
+    ignore = karg;
 #endif // end of if (defined(__gfx908__) || defined(__gfx90a__))
 }
 
@@ -80,13 +43,13 @@ template <index_t BlockSize,
           typename FloatAB,
           typename FloatAcc,
           typename FloatC,
-          InMemoryDataOperationEnum CGlobalMemoryDataOperation,
-          typename AGridDesc_B_K0_M_K1,
-          typename BGridDesc_B_K0_N_K1,
-          typename CMNGridDesc,
+          typename ALayout,
+          typename BLayout,
+          typename CLayout,
           typename AElementwiseOperation,
           typename BElementwiseOperation,
           typename CElementwiseOperation,
+          tensor_operation::device::GemmSpecialization GemmSpec,
           index_t MPerBlock,
           index_t NPerBlock,
           index_t K0PerBlock,
@@ -127,10 +90,238 @@ struct GridwiseGemm_bk0mk1_bk0nk1_mn_xdlops_v2r4r2
     static constexpr auto I7 = Number<7>{};
 
     // K1 should be Number<...>
-    static constexpr auto K1 = Number<K1Value>{};
+    static constexpr auto K1  = Number<K1Value>{};
+    static constexpr auto M01 = 1;
+    static constexpr auto N01 = 1;
 
     using ThisThreadBlock = ThisThreadBlock<BlockSize>;
 
+    struct Argument : public ck::tensor_operation::device::BaseArgument
+    {
+        const FloatAB* p_a_grid;
+        const FloatAB* p_b_grid;
+        FloatC* p_c_grid;
+        index_t M;
+        index_t N;
+        index_t K;
+        index_t StrideA;
+        index_t StrideB;
+        index_t StrideC;
+        index_t MPadded;
+        index_t NPadded;
+        index_t KPadded;
+        index_t K0;
+        index_t k_batch;
+
+        Argument(const FloatAB* p_a_grid_,
+                 const FloatAB* p_b_grid_,
+                 FloatC* p_c_grid_,
+                 index_t M_,
+                 index_t N_,
+                 index_t K_,
+                 index_t StrideA_,
+                 index_t StrideB_,
+                 index_t StrideC_,
+                 index_t MPadded_,
+                 index_t NPadded_,
+                 index_t KPadded_,
+                 index_t K0_,
+                 index_t k_batch_)
+            : p_a_grid(p_a_grid_),
+              p_b_grid(p_b_grid_),
+              p_c_grid(p_c_grid_),
+              M(M_),
+              N(N_),
+              K(K_),
+              StrideA(StrideA_),
+              StrideB(StrideB_),
+              StrideC(StrideC_),
+              MPadded(MPadded_),
+              NPadded(NPadded_),
+              KPadded(KPadded_),
+              K0(K0_),
+              k_batch(k_batch_)
+        {
+        }
+
+        void Print() const
+        {
+            std::cout << "arg {"
+                      << "M:" << M << ", "
+                      << "N:" << N << ", "
+                      << "K:" << K << ", "
+                      << "SA:" << StrideA << ", "
+                      << "SB:" << StrideB << ", "
+                      << "SC:" << StrideC << ", "
+                      << "MP:" << MPadded << ", "
+                      << "NP:" << NPadded << ", "
+                      << "KP:" << KPadded << ", "
+                      << "K0:" << K0 << ", "
+                      << "KB:" << k_batch << "}" << std::endl;
+        }
+    };
+
+    __host__ __device__ static auto CalculateGridSize(const Argument& karg)
+    {
+        return std::make_tuple(math::integer_divide_ceil(karg.N, NPerBlock),
+                               math::integer_divide_ceil(karg.M, MPerBlock),
+                               karg.k_batch);
+    }
+
+    // prefer this to be called on host
+    __host__ __device__ static auto CalculateMPadded(index_t M)
+    {
+        return (M + MPerBlock - 1) / MPerBlock * MPerBlock;
+    }
+
+    __host__ __device__ static auto CalculateNPadded(index_t N)
+    {
+        return (N + NPerBlock - 1) / NPerBlock * NPerBlock;
+    }
+
+    __host__ __device__ static auto CalculateK0(index_t K, index_t K_Batch = 1)
+    {
+        // k_batch * k0 * k0_per_block * k1
+        auto K_t = K_Batch * K0PerBlock * K1;
+        return (K + K_t - 1) / K_t * K0PerBlock;
+    }
+
+    __host__ __device__ static auto CalculateKPadded(index_t K, index_t K_Batch = 1)
+    {
+        auto K0 = CalculateK0(K, K_Batch);
+        return K_Batch * K0 * K1;
+    }
+
+    __host__ __device__ static auto MakeAGridDescriptor_KBatch_K0_M_K1(index_t M,
+                                                                       index_t MPad,
+                                                                       index_t K,
+                                                                       index_t StrideA,
+                                                                       index_t KBatch,
+                                                                       index_t K0,
+                                                                       index_t KPad)
+    {
+        const auto a_grid_desc_m_k = [&]() {
+            if constexpr(is_same<tensor_layout::gemm::RowMajor, ALayout>::value)
+            {
+                return make_naive_tensor_descriptor(make_tuple(M, K), make_tuple(StrideA, I1));
+            }
+            else if constexpr(is_same<tensor_layout::gemm::ColumnMajor, ALayout>::value)
+            {
+                return make_naive_tensor_descriptor(make_tuple(M, K), make_tuple(I1, StrideA));
+            }
+        }();
+
+        const auto a_grid_desc_m_kpad = transform_tensor_descriptor(
+            a_grid_desc_m_k,
+            make_tuple(make_pass_through_transform(M), make_right_pad_transform(K, KPad - K)),
+            make_tuple(Sequence<0>{}, Sequence<1>{}),
+            make_tuple(Sequence<0>{}, Sequence<1>{}));
+
+        if constexpr(GemmSpec == tensor_operation::device::GemmSpecialization::MPadding ||
+                     GemmSpec == tensor_operation::device::GemmSpecialization::MNPadding ||
+                     GemmSpec == tensor_operation::device::GemmSpecialization::MKPadding ||
+                     GemmSpec == tensor_operation::device::GemmSpecialization::MNKPadding)
+        {
+            // const auto PadM = (MPerBlock - M % MPerBlock) % MPerBlock;
+            return transform_tensor_descriptor(
+                a_grid_desc_m_kpad,
+                make_tuple(make_unmerge_transform(make_tuple(KBatch, K0, K1)),
+                           make_right_pad_transform(M, MPad - M)),
+                make_tuple(Sequence<1>{}, Sequence<0>{}),
+                make_tuple(Sequence<0, 1, 3>{}, Sequence<2>{}));
+        }
+        else
+        {
+            return transform_tensor_descriptor(
+                a_grid_desc_m_kpad,
+                make_tuple(make_unmerge_transform(make_tuple(KBatch, K0, K1)),
+                           make_pass_through_transform(M)),
+                make_tuple(Sequence<1>{}, Sequence<0>{}),
+                make_tuple(Sequence<0, 1, 3>{}, Sequence<2>{}));
+        }
+    }
+
+    __host__ __device__ static auto MakeBGridDescriptor_KBatch_K0_N_K1(index_t K,
+                                                                       index_t NPad,
+                                                                       index_t N,
+                                                                       index_t StrideB,
+                                                                       index_t KBatch,
+                                                                       index_t K0,
+                                                                       index_t KPad)
+    {
+        const auto b_grid_desc_k_n = [&]() {
+            if constexpr(is_same<tensor_layout::gemm::RowMajor, BLayout>::value)
+            {
+                return make_naive_tensor_descriptor(make_tuple(K, N), make_tuple(StrideB, I1));
+            }
+            else if constexpr(is_same<tensor_layout::gemm::ColumnMajor, BLayout>::value)
+            {
+                return make_naive_tensor_descriptor(make_tuple(K, N), make_tuple(I1, StrideB));
+            }
+        }();
+
+        const auto b_grid_desc_kpad_n = transform_tensor_descriptor(
+            b_grid_desc_k_n,
+            make_tuple(make_right_pad_transform(K, KPad - K), make_pass_through_transform(N)),
+            make_tuple(Sequence<0>{}, Sequence<1>{}),
+            make_tuple(Sequence<0>{}, Sequence<1>{}));
+
+        if constexpr(GemmSpec == tensor_operation::device::GemmSpecialization::NPadding ||
+                     GemmSpec == tensor_operation::device::GemmSpecialization::MNPadding ||
+                     GemmSpec == tensor_operation::device::GemmSpecialization::NKPadding ||
+                     GemmSpec == tensor_operation::device::GemmSpecialization::MNKPadding)
+        {
+            // const auto PadN = (NPerBlock - N % NPerBlock) % NPerBlock;
+            return transform_tensor_descriptor(
+                b_grid_desc_kpad_n,
+                make_tuple(make_unmerge_transform(make_tuple(KBatch, K0, K1)),
+                           make_right_pad_transform(N, NPad - N)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}),
+                make_tuple(Sequence<0, 1, 3>{}, Sequence<2>{}));
+        }
+        else
+        {
+            return transform_tensor_descriptor(
+                b_grid_desc_kpad_n,
+                make_tuple(make_unmerge_transform(make_tuple(KBatch, K0, K1)),
+                           make_pass_through_transform(N)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}),
+                make_tuple(Sequence<0, 1, 3>{}, Sequence<2>{}));
+        }
+    }
+
+    __host__ __device__ static auto
+    MakeCGridDescriptor_M_N(index_t M, index_t N, index_t MPad, index_t NPad, index_t StrideC)
+    {
+        const auto c_grid_desc_m_n = [&]() {
+            if constexpr(is_same<tensor_layout::gemm::RowMajor, CLayout>::value)
+            {
+                return make_naive_tensor_descriptor(make_tuple(M, N), make_tuple(StrideC, I1));
+            }
+            else if constexpr(is_same<tensor_layout::gemm::ColumnMajor, CLayout>::value)
+            {
+                return make_naive_tensor_descriptor(make_tuple(M, N), make_tuple(I1, StrideC));
+            }
+        }();
+
+        if constexpr(GemmSpec == tensor_operation::device::GemmSpecialization::MNPadding)
+        {
+            return transform_tensor_descriptor(c_grid_desc_m_n,
+                                               make_tuple(make_right_pad_transform(M, MPad - M),
+                                                          make_right_pad_transform(N, NPad - N)),
+                                               make_tuple(Sequence<0>{}, Sequence<1>{}),
+                                               make_tuple(Sequence<0>{}, Sequence<1>{}));
+        }
+        else
+        {
+            return transform_tensor_descriptor(
+                c_grid_desc_m_n,
+                make_tuple(make_pass_through_transform(M), make_pass_through_transform(N)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}),
+                make_tuple(Sequence<0>{}, Sequence<1>{}));
+        }
+    }
+
     __host__ __device__ static constexpr index_t GetSharedMemoryNumberOfByte()
     {
         constexpr auto max_lds_align = K1;
@@ -179,45 +370,68 @@ struct GridwiseGemm_bk0mk1_bk0nk1_mn_xdlops_v2r4r2
                          c_block_size * sizeof(FloatC));
     }
 
-    // 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_B_K0_M_K1& a_b_k0_m_k1_grid_desc,
-                  const BGridDesc_B_K0_N_K1& b_b_k0_n_k1_grid_desc,
-                  const CMNGridDesc& c_m_n_grid_desc,
-                  const Block2CTileMap& block_2_ctile_map)
+    __host__ __device__ static constexpr bool CheckValidity(const Argument& karg)
     {
-        static_assert(is_known_at_compile_time<remove_cv_t<decltype(K1)>>::value,
-                      "wrong! K1 need to be known at compile-time");
-
-        static_assert((MPerBlock % (MPerXDL * MRepeat) == 0) &&
-                          (NPerBlock % (NRepeat * NPerXDL)) == 0,
-                      "Invalid tuning param!");
-
-        const auto M      = a_b_k0_m_k1_grid_desc.GetLength(I2);
-        const auto N      = b_b_k0_n_k1_grid_desc.GetLength(I2);
-        const auto K0     = a_b_k0_m_k1_grid_desc.GetLength(I1);
-        const auto KBatch = a_b_k0_m_k1_grid_desc.GetLength(I0);
+        if constexpr(!(GemmSpec == tensor_operation::device::GemmSpecialization::MPadding ||
+                       GemmSpec == tensor_operation::device::GemmSpecialization::MNPadding ||
+                       GemmSpec == tensor_operation::device::GemmSpecialization::MKPadding ||
+                       GemmSpec == tensor_operation::device::GemmSpecialization::MNKPadding))
+        {
+            if(!(karg.M % MPerBlock == 0))
+                return false;
+        }
+        if constexpr(!(GemmSpec == tensor_operation::device::GemmSpecialization::NPadding ||
+                       GemmSpec == tensor_operation::device::GemmSpecialization::MNPadding ||
+                       GemmSpec == tensor_operation::device::GemmSpecialization::NKPadding ||
+                       GemmSpec == tensor_operation::device::GemmSpecialization::MNKPadding))
+        {
+            if(!(karg.N % NPerBlock == 0))
+                return false;
+        }
 
-        if(!(M == c_m_n_grid_desc.GetLength(I0) && N == c_m_n_grid_desc.GetLength(I1) &&
-             K0 == b_b_k0_n_k1_grid_desc.GetLength(I1) &&
-             K1 == a_b_k0_m_k1_grid_desc.GetLength(I3) &&
-             K1 == b_b_k0_n_k1_grid_desc.GetLength(I3) &&
-             KBatch == b_b_k0_n_k1_grid_desc.GetLength(I0)))
-            return false;
+        if constexpr(is_same<tensor_layout::gemm::RowMajor, ALayout>::value)
+        {
+            if(karg.K % ABlockTransferSrcScalarPerVector != 0)
+                return false;
+        }
+        else
+        {
+            if(karg.M % ABlockTransferSrcScalarPerVector != 0)
+                return false;
+        }
 
-        if(!(M % MPerBlock == 0 && N % NPerBlock == 0 && K0 % K0PerBlock == 0))
-            return false;
+        if constexpr(is_same<tensor_layout::gemm::RowMajor, BLayout>::value)
+        {
+            if(karg.N % BBlockTransferSrcScalarPerVector != 0)
+                return false;
+        }
+        else
+        {
+            if(karg.K % BBlockTransferSrcScalarPerVector != 0)
+                return false;
+        }
 
-        if(!block_2_ctile_map.CheckValidity(c_m_n_grid_desc))
+        if constexpr(is_same<tensor_layout::gemm::RowMajor, CLayout>::value)
         {
-            return false;
+            if(karg.N % CBlockTransferScalarPerVector_NWaveNPerXDL != 0)
+                return false;
+        }
+        else
+        {
+            if(karg.M % CBlockTransferScalarPerVector_NWaveNPerXDL != 0)
+                return false;
         }
 
-        // TODO: also check validity of all components (blockwise-copy, threadwise-copy, etc)
         return true;
     }
 
+    __host__ __device__ static auto GetKPad(index_t K, index_t KBatch)
+    {
+        const index_t K0   = math::integer_divide_ceil(K, K1 * K0PerBlock * KBatch) * K0PerBlock;
+        const index_t KPad = KBatch * K0 * K1;
+        return KPad;
+    }
+
     __host__ __device__ static constexpr bool CalculateHasMainK0BlockLoop(index_t K0)
     {
         const bool has_main_k0_block_loop = K0 > K0PerBlock;
@@ -225,8 +439,9 @@ struct GridwiseGemm_bk0mk1_bk0nk1_mn_xdlops_v2r4r2
         return has_main_k0_block_loop;
     }
 
+    template <typename CGridDesc>
     __host__ __device__ static constexpr auto
-    MakeCGridDesc_MBlock_MPerBlock_NBlock_NPerBlock(const CMNGridDesc& c_m_n_grid_desc)
+    MakeCGridDesc_MBlock_MPerBlock_NBlock_NPerBlock(const CGridDesc& c_m_n_grid_desc)
     {
         const auto M = c_m_n_grid_desc.GetLength(I0);
         const auto N = c_m_n_grid_desc.GetLength(I1);
@@ -243,10 +458,11 @@ struct GridwiseGemm_bk0mk1_bk0nk1_mn_xdlops_v2r4r2
     }
 
     // return block_id to C matrix tile idx (m0, n0) mapping
+    template <typename CGridDesc>
     __host__ __device__ static constexpr auto MakeCBlockClusterAdaptor(
-        const CMNGridDesc& c_m_n_grid_desc, index_t /* M01 */, index_t /* N01 */, index_t KBatch)
+        const CGridDesc& c_m_n_grid_desc, index_t /* M01 */, index_t /* N01 */, index_t KBatch)
     {
-        return BlockToCTileMap_KSplit_M00_N0_M01Adapt<MPerBlock, NPerBlock, CMNGridDesc>(
+        return BlockToCTileMap_KSplit_M00_N0_M01Adapt<MPerBlock, NPerBlock, CGridDesc>(
             c_m_n_grid_desc, 8, KBatch);
     }
 
@@ -263,24 +479,25 @@ struct GridwiseGemm_bk0mk1_bk0nk1_mn_xdlops_v2r4r2
                        Number<CShuffleNRepeatPerShuffle * NWave * NPerXDL>{}));
     }
 
-    using CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock =
-        decltype(MakeCGridDesc_MBlock_MPerBlock_NBlock_NPerBlock(CMNGridDesc{}));
-    using CBlockClusterAdaptor = decltype(MakeCBlockClusterAdaptor(CMNGridDesc{}, 1, 1, 1));
-
-    template <bool HasMainKBlockLoop>
-    __device__ static void Run(const FloatAB* __restrict__ p_a_grid,
-                               const FloatAB* __restrict__ p_b_grid,
-                               FloatC* __restrict__ p_c_grid,
-                               void* __restrict__ p_shared_block,
-                               const AGridDesc_B_K0_M_K1& a_b_k0_m_k1_grid_desc,
-                               const BGridDesc_B_K0_N_K1& b_b_k0_n_k1_grid_desc,
-                               const CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock&
-                                   c_grid_desc_mblock_mperblock_nblock_nperblock,
-                               const AElementwiseOperation& a_element_op,
-                               const BElementwiseOperation& b_element_op,
-                               const CElementwiseOperation& c_element_op,
-                               const CBlockClusterAdaptor& c_block_cluster_adaptor)
+    template <bool HasMainKBlockLoop, InMemoryDataOperationEnum CGlobalMemoryDataOperation>
+    __device__ static void Run(const Argument& karg, void* __restrict__ p_shared_block)
     {
+        const FloatAB* p_a_grid          = karg.p_a_grid;
+        const FloatAB* p_b_grid          = karg.p_b_grid;
+        FloatC* p_c_grid                 = karg.p_c_grid;
+        const auto a_b_k0_m_k1_grid_desc = MakeAGridDescriptor_KBatch_K0_M_K1(
+            karg.M, karg.MPadded, karg.K, karg.StrideA, karg.k_batch, karg.K0, karg.KPadded);
+        const auto b_b_k0_n_k1_grid_desc = MakeBGridDescriptor_KBatch_K0_N_K1(
+            karg.K, karg.NPadded, karg.N, karg.StrideB, karg.k_batch, karg.K0, karg.KPadded);
+        const auto c_grid_desc_m_n =
+            MakeCGridDescriptor_M_N(karg.M, karg.N, karg.MPadded, karg.NPadded, karg.StrideC);
+
+        const auto c_grid_desc_mblock_mperblock_nblock_nperblock =
+            MakeCGridDesc_MBlock_MPerBlock_NBlock_NPerBlock(c_grid_desc_m_n);
+        const AElementwiseOperation a_element_op = AElementwiseOperation{};
+        const BElementwiseOperation b_element_op = BElementwiseOperation{};
+        const CElementwiseOperation c_element_op = CElementwiseOperation{};
+
         const auto a_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
             p_a_grid, a_b_k0_m_k1_grid_desc.GetElementSpaceSize());
         const auto b_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
@@ -290,26 +507,16 @@ struct GridwiseGemm_bk0mk1_bk0nk1_mn_xdlops_v2r4r2
 
         const auto K0 = a_b_k0_m_k1_grid_desc.GetLength(I1);
 
-        // divide block work by [M, N]
-        const auto block_work_idx =
-            c_block_cluster_adaptor.CalculateBottomIndex(make_multi_index(get_block_1d_id()));
-
-        if(!c_block_cluster_adaptor.ValidCTileIndex(
-               make_tuple(block_work_idx[I1], block_work_idx[I2]),
-               make_tuple(c_grid_desc_mblock_mperblock_nblock_nperblock.GetLength(I0),
-                          c_grid_desc_mblock_mperblock_nblock_nperblock.GetLength(I2))))
-        {
-            return;
-        }
-
-        const index_t k_batch_id = block_work_idx[I0];
+        const index_t block_m_id = __builtin_amdgcn_readfirstlane(blockIdx.y);
+        const index_t block_n_id = __builtin_amdgcn_readfirstlane(blockIdx.x);
+        const index_t k_batch_id = __builtin_amdgcn_readfirstlane(blockIdx.z);
 
         // HACK: this force m/n_block_data_idx_on_grid into SGPR
         const index_t m_block_data_idx_on_grid =
-            __builtin_amdgcn_readfirstlane(block_work_idx[I1] * MPerBlock);
+            __builtin_amdgcn_readfirstlane(block_m_id * MPerBlock);
 
         const index_t n_block_data_idx_on_grid =
-            __builtin_amdgcn_readfirstlane(block_work_idx[I2] * NPerBlock);
+            __builtin_amdgcn_readfirstlane(block_n_id * NPerBlock);
 
         // lds max alignment
         constexpr auto max_lds_align = K1;
@@ -445,7 +652,6 @@ struct GridwiseGemm_bk0mk1_bk0nk1_mn_xdlops_v2r4r2
         //     c_mtx[MPerBlock, NPerBlock] is distributed among threads, and saved in
         //       register
         // sanity check
-
         auto blockwise_gemm =
             BlockwiseGemmXdlops_k0mk1_k0nk1_m0n0m1n1m2m3m4n2_v1<BlockSize,
                                                                 FloatAB,
@@ -648,7 +854,7 @@ struct GridwiseGemm_bk0mk1_bk0nk1_mn_xdlops_v2r4r2
                 {c_block_desc_mblock_mperblock_nblock_nperblock,
                  make_multi_index(0, 0, 0, 0),
                  c_grid_desc_mblock_mperblock_nblock_nperblock,
-                 make_multi_index(block_work_idx[I1], 0, block_work_idx[I2], 0),
+                 make_multi_index(block_m_id, 0, block_n_id, 0),
                  c_element_op};
 
             constexpr auto mxdlperwave_forward_step =
@@ -717,6 +923,48 @@ struct GridwiseGemm_bk0mk1_bk0nk1_mn_xdlops_v2r4r2
             });
         }
     }
+
+    template <typename Layout>
+    struct LStr
+    {
+        static std::string Get() { return ""; }
+    };
+
+    template <>
+    struct LStr<ck::tensor_layout::gemm::RowMajor>
+    {
+        static std::string Get() { return "R"; }
+    };
+
+    template <>
+    struct LStr<ck::tensor_layout::gemm::ColumnMajor>
+    {
+        static std::string Get() { return "C"; }
+    };
+
+    static std::string GetTypeString()
+    {
+        auto str = std::stringstream();
+
+        // clang-format off
+        str << "GemmXdlSplitKCShuffle_"
+            << getGemmSpecializationString(GemmSpec) << "_"
+            << std::string(ALayout::name)[0]
+            << std::string(BLayout::name)[0]
+            << std::string(CLayout::name)[0]
+            << "_"
+            << "B" << BlockSize << "_"
+            << "Vec" << ABlockTransferSrcScalarPerVector << "x"
+            << BBlockTransferSrcScalarPerVector << "x"
+            << CBlockTransferScalarPerVector_NWaveNPerXDL << "_"
+            << MPerBlock << "x"
+            << NPerBlock << "x"
+            << K0PerBlock << "x"
+            << K1 ;
+        // clang-format on
+
+        return str.str();
+    }
 };
 
 } // namespace ck

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

@@ -1201,7 +1201,12 @@ struct ThreadwiseTensorSliceTransfer_v4
     SrcCoord src_ref_coord_;
 };
 
-// Do NOT involve any tensor coordinates with StaticBuffer
+/**
+ * @brief Threadwise data transfer
+ *
+ * Do NOT involve any tensor coordinates with StaticBuffer
+ *
+ */
 template <typename SrcData,
           typename DstData,
           typename SrcDesc,

include/ck/tensor_operation/gpu/thread/threadwise_tensor_slice_transfer_v3r1.hpp

 // SPDX-License-Identifier: MIT
-// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
+// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
 
 #pragma once
 

include/ck/utility/amd_buffer_addressing.hpp

@@ -1030,7 +1030,7 @@ amd_buffer_load_invalid_element_return_zero(const T* p_src_wave,
     constexpr index_t vector_size = scalar_type<vector_t>::vector_size;
 
 #if CK_EXPERIMENTAL_USE_BUFFER_LOAD_OOB_CHECK_OFFSET_TRICK
-    uint32_t src_addr_shift = src_thread_element_valid ? 0 : 0x7fffffff;
+    uint32_t src_addr_shift = src_thread_element_valid ? 0 : 0x80000000;
 
     return amd_buffer_load_impl<scalar_t, vector_size>(
         src_wave_buffer_resource, src_addr_shift + src_thread_addr_offset, 0);
@@ -1091,7 +1091,7 @@ __device__ void amd_buffer_store(const typename vector_type_maker<T, N>::type::t
     constexpr index_t vector_size = scalar_type<vector_t>::vector_size;
 
 #if CK_EXPERIMENTAL_USE_BUFFER_STORE_OOB_CHECK_OFFSET_TRICK
-    uint32_t dst_addr_shift = dst_thread_element_valid ? 0 : 0x7fffffff;
+    uint32_t dst_addr_shift = dst_thread_element_valid ? 0 : 0x80000000;
 
     amd_buffer_store_impl<scalar_t, vector_size>(
         src_thread_data, dst_wave_buffer_resource, dst_addr_shift + dst_thread_addr_offset, 0);
@@ -1126,7 +1126,7 @@ amd_buffer_atomic_add(const typename vector_type_maker<T, N>::type::type src_thr
     constexpr index_t vector_size = scalar_type<vector_t>::vector_size;
 
 #if CK_EXPERIMENTAL_USE_BUFFER_ATOMIC_ADD_OOB_CHECK_OFFSET_TRICK
-    uint32_t dst_addr_shift = dst_thread_element_valid ? 0 : 0x7fffffff;
+    uint32_t dst_addr_shift = dst_thread_element_valid ? 0 : 0x80000000;
 
     amd_buffer_atomic_add_impl<scalar_t, vector_size>(
         src_thread_data, dst_wave_buffer_resource, dst_addr_shift + dst_thread_addr_offset, 0);
@@ -1161,7 +1161,7 @@ amd_buffer_atomic_max(const typename vector_type_maker<T, N>::type::type src_thr
     constexpr index_t vector_size = scalar_type<vector_t>::vector_size;
 
 #if CK_EXPERIMENTAL_USE_BUFFER_ATOMIC_MAX_OOB_CHECK_OFFSET_TRICK
-    uint32_t dst_addr_shift = dst_thread_element_valid ? 0 : 0x7fffffff;
+    uint32_t dst_addr_shift = dst_thread_element_valid ? 0 : 0x80000000;
 
     amd_buffer_atomic_max_impl<scalar_t, vector_size>(
         src_thread_data, dst_wave_buffer_resource, dst_addr_shift + dst_thread_addr_offset, 0);

include/ck/utility/amd_inline_asm.hpp

@@ -220,8 +220,8 @@ amd_assembly_outer_product_1x2(int8x4_t a, int8x4_t b0, int8x4_t b1, int32_t& c0
                    "0"(c0),
                    "1"(c1));
 #else
-    c0 = __builtin_amdgcn_sdot4(bit_cast<int32_t>(a), bit_cast<int32_t>(b0), c0, false);
-    c1 = __builtin_amdgcn_sdot4(bit_cast<int32_t>(a), bit_cast<int32_t>(b1), c1, false);
+    c0     = __builtin_amdgcn_sdot4(bit_cast<int32_t>(a), bit_cast<int32_t>(b0), c0, false);
+    c1     = __builtin_amdgcn_sdot4(bit_cast<int32_t>(a), bit_cast<int32_t>(b1), c1, false);
 #endif
 }
 
@@ -257,10 +257,10 @@ __device__ void amd_assembly_outer_product_1x4(int8x4_t a,
                    "2"(c2),
                    "3"(c3));
 #else
-    c0 = __builtin_amdgcn_sdot4(bit_cast<int32_t>(a), bit_cast<int32_t>(b0), c0, false);
-    c1 = __builtin_amdgcn_sdot4(bit_cast<int32_t>(a), bit_cast<int32_t>(b1), c1, false);
-    c2 = __builtin_amdgcn_sdot4(bit_cast<int32_t>(a), bit_cast<int32_t>(b2), c2, false);
-    c3 = __builtin_amdgcn_sdot4(bit_cast<int32_t>(a), bit_cast<int32_t>(b3), c3, false);
+    c0     = __builtin_amdgcn_sdot4(bit_cast<int32_t>(a), bit_cast<int32_t>(b0), c0, false);
+    c1     = __builtin_amdgcn_sdot4(bit_cast<int32_t>(a), bit_cast<int32_t>(b1), c1, false);
+    c2     = __builtin_amdgcn_sdot4(bit_cast<int32_t>(a), bit_cast<int32_t>(b2), c2, false);
+    c3     = __builtin_amdgcn_sdot4(bit_cast<int32_t>(a), bit_cast<int32_t>(b3), c3, false);
 #endif
 }
 
@@ -358,7 +358,13 @@ __device__ void amd_assembly_outer_product_1x4(int8x16_t a,
 // Ranged input operand
 __device__ void amd_assembly_wmma_f32_16x16x16_f16_w32(half16_t a, half16_t b, float8_t& c)
 {
+#if defined(__gfx11__)
     asm volatile("v_wmma_f32_16x16x16_f16 %0, %1, %2, %0" : "=v"(c) : "v"(a), "v"(b), "0"(c));
+#else
+    ignore = a;
+    ignore = b;
+    ignore = c;
+#endif
 }
 
 } // namespace ck