Update tuning method

deps/cget/pkg/ROCmSoftwarePlatform__composable_kernel/install/include/ck/tensor_operation/gpu/device/device_batchnorm_forward.hpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include <array>
+#include <memory>
+
+#include "ck/ck.hpp"
+#include "ck/tensor_operation/gpu/device/device_base.hpp"
+
+namespace ck {
+namespace tensor_operation {
+namespace device {
+
+template <index_t Rank, index_t NumBatchNormReduceDim>
+struct DeviceBatchNormFwd : public BaseOperator
+{
+    virtual std::unique_ptr<BaseArgument> MakeArgumentPointer(
+        const std::array<index_t, Rank> xyLengths,
+        const std::array<index_t, Rank> xStrides,
+        const std::array<index_t, Rank> yStrides,
+        const std::array<index_t, Rank - NumBatchNormReduceDim> bnScaleBiasMeanVarLengths,
+        const std::array<index_t, Rank - NumBatchNormReduceDim> bnScaleBiasMeanVarStrides,
+        const void* p_x,
+        const void* bnScale,
+        const void* bnBias,
+        void* p_y,
+        double exponentialAverageFactor,
+        void* resultRunningMean,
+        void* resultRunningVariance,
+        double epsilon,
+        void* resultSaveMean,
+        void* resultSaveInvVariance) = 0;
+
+    virtual std::unique_ptr<BaseInvoker> MakeInvokerPointer() = 0;
+};
+
+template <index_t Rank, index_t NumBatchNormReduceDim>
+using DeviceBatchNormFwdPtr = std::unique_ptr<DeviceBatchNormFwd<Rank, NumBatchNormReduceDim>>;
+
+} // namespace device
+} // namespace tensor_operation
+} // namespace ck

deps/cget/pkg/ROCmSoftwarePlatform__composable_kernel/install/include/ck/tensor_operation/gpu/device/device_batchnorm_infer.hpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include <array>
+#include <memory>
+
+#include "ck/ck.hpp"
+#include "ck/tensor_operation/gpu/device/device_base.hpp"
+
+namespace ck {
+namespace tensor_operation {
+namespace device {
+
+template <index_t Rank, index_t NumBatchNormReduceDim>
+struct DeviceBatchNormInfer : public BaseOperator
+{
+    virtual std::unique_ptr<BaseArgument> MakeArgumentPointer(
+        const std::array<index_t, Rank> xyLengths,
+        const std::array<index_t, Rank> xStrides,
+        const std::array<index_t, Rank> yStrides,
+        const std::array<index_t, Rank - NumBatchNormReduceDim> bnScaleBiasMeanVarLengths,
+        const std::array<index_t, Rank - NumBatchNormReduceDim> bnScaleBiasMeanVarStrides,
+        const void* p_x,
+        const void* bnScale,
+        const void* bnBias,
+        double epsilon,
+        const void* estimatedMean,
+        const void* estimatedInvVariance,
+        void* p_y) = 0;
+
+    virtual std::unique_ptr<BaseInvoker> MakeInvokerPointer() = 0;
+};
+
+template <index_t Rank, index_t NumBatchNormReduceDim>
+using DeviceBatchNormInferPtr = std::unique_ptr<DeviceBatchNormInfer<Rank, NumBatchNormReduceDim>>;
+
+} // namespace device
+} // namespace tensor_operation
+} // namespace ck

deps/cget/pkg/ROCmSoftwarePlatform__composable_kernel/install/include/ck/tensor_operation/gpu/device/device_cgemm.hpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+#include "device_base.hpp"
+
+namespace ck {
+namespace tensor_operation {
+namespace device {
+
+template <typename AElementwiseOperation,
+          typename BElementwiseOperation,
+          typename CElementwiseOperation>
+struct DeviceCGemm : public BaseOperator
+{
+    virtual std::unique_ptr<BaseArgument> MakeArgumentPointer(const void* p_a_real,
+                                                              const void* p_a_imag,
+                                                              const void* p_b_real,
+                                                              const void* p_b_imag,
+                                                              void* p_c_real,
+                                                              void* p_c_imag,
+                                                              void* p_workspace,
+                                                              ck::index_t M,
+                                                              ck::index_t N,
+                                                              ck::index_t K,
+                                                              ck::index_t StrideA,
+                                                              ck::index_t StrideB,
+                                                              ck::index_t StrideC,
+                                                              AElementwiseOperation a_element_op,
+                                                              BElementwiseOperation b_element_op,
+                                                              CElementwiseOperation c_element_op,
+                                                              ck::index_t KBatch = 1) = 0;
+
+    virtual std::unique_ptr<BaseInvoker> MakeInvokerPointer() = 0;
+    virtual std::size_t GetWorkspaceSize(index_t MRaw,
+                                         index_t NRaw,
+                                         index_t KRaw,
+                                         index_t StrideA,
+                                         index_t StrideB,
+                                         index_t StrideC)     = 0;
+};
+
+template <typename AElementwiseOperation,
+          typename BElementwiseOperation,
+          typename CElementwiseOperation>
+using DeviceCGemmPtr = std::unique_ptr<
+    DeviceCGemm<AElementwiseOperation, BElementwiseOperation, CElementwiseOperation>>;
+
+} // namespace device
+} // namespace tensor_operation
+} // namespace ck

deps/cget/pkg/ROCmSoftwarePlatform__composable_kernel/install/include/ck/tensor_operation/gpu/device/device_cgemm_4gemm_xdl_cshuffle.hpp

+// 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_gemm.hpp"
+#include "ck/tensor_operation/gpu/device/device_cgemm.hpp"
+#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
+#include "ck/tensor_operation/gpu/grid/gridwise_gemm_xdl_cshuffle_v1.hpp"
+#include "ck/tensor_operation/gpu/grid/gridwise_elementwise_1d.hpp"
+#include "ck/tensor_operation/gpu/element/binary_element_wise_operation.hpp"
+#include "ck/host_utility/device_prop.hpp"
+#include "ck/host_utility/kernel_launch.hpp"
+
+namespace ck {
+namespace tensor_operation {
+namespace device {
+
+template <
+    typename ALayout,
+    typename BLayout,
+    typename CLayout,
+    typename ADataType,
+    typename BDataType,
+    typename CDataType,
+    typename GemmAccDataType,
+    typename CShuffleDataType,
+    typename AElementwiseOperation,
+    typename BElementwiseOperation,
+    typename CElementwiseOperation,
+    GemmSpecialization GemmSpec,
+    index_t NumGemmKPrefetchStage,
+    index_t BlockSize,
+    index_t MPerBlock,
+    index_t NPerBlock,
+    index_t KPerBlock,
+    index_t AK1,
+    index_t BK1,
+    index_t MPerXDL,
+    index_t NPerXDL,
+    index_t MXdlPerWave,
+    index_t NXdlPerWave,
+    typename ABlockTransferThreadClusterLengths_AK0_M_AK1,
+    typename ABlockTransferThreadClusterArrangeOrder,
+    typename ABlockTransferSrcAccessOrder,
+    index_t ABlockTransferSrcVectorDim,
+    index_t ABlockTransferSrcScalarPerVector,
+    index_t ABlockTransferDstScalarPerVector_AK1,
+    bool ABlockLdsExtraM,
+    typename BBlockTransferThreadClusterLengths_BK0_N_BK1,
+    typename BBlockTransferThreadClusterArrangeOrder,
+    typename BBlockTransferSrcAccessOrder,
+    index_t BBlockTransferSrcVectorDim,
+    index_t BBlockTransferSrcScalarPerVector,
+    index_t BBlockTransferDstScalarPerVector_BK1,
+    bool BBlockLdsExtraN,
+    index_t CShuffleMXdlPerWavePerShuffle,
+    index_t CShuffleNXdlPerWavePerShuffle,
+    typename CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
+    index_t CShuffleBlockTransferScalarPerVector_NPerBlock,
+    LoopScheduler LoopSched = make_default_loop_scheduler(),
+    enable_if_t<
+        is_same_v<AElementwiseOperation, ck::tensor_operation::element_wise::PassThrough> &&
+            is_same_v<BElementwiseOperation, ck::tensor_operation::element_wise::PassThrough> &&
+            is_same_v<CElementwiseOperation, ck::tensor_operation::element_wise::PassThrough>,
+        bool> = false>
+struct DeviceCGemm_4Gemm_Xdl_CShuffle
+    : public DeviceCGemm<AElementwiseOperation, BElementwiseOperation, CElementwiseOperation>
+{
+    using DeviceOp = DeviceCGemm_4Gemm_Xdl_CShuffle;
+
+    static constexpr auto I0 = Number<0>{};
+    static constexpr auto I1 = Number<1>{};
+    static constexpr auto I2 = Number<2>{};
+
+    static constexpr auto MPerThread       = Number<4>{};
+    static constexpr auto AScalarPerVector = Number<4>{};
+    static constexpr auto BScalarPerVector = Number<4>{};
+    static constexpr auto CScalarPerVector = Number<4>{};
+
+    template <typename Desc_M>
+    static auto PadDescriptor_M_1d(Desc_M desc_m, index_t gridSize, index_t blockSize)
+    {
+        const auto M            = desc_m.GetLength(I0);
+        const index_t loop_step = gridSize * blockSize * MPerThread;
+        const auto pad          = math::integer_least_multiple(M, loop_step) - M;
+        const auto desc_m_pad =
+            transform_tensor_descriptor(desc_m,
+                                        make_tuple(make_right_pad_transform(M, pad)),
+                                        make_tuple(Sequence<0>{}),
+                                        make_tuple(Sequence<0>{}));
+        return desc_m_pad;
+    }
+
+    static auto MakeDescriptor_M(const std::vector<index_t>& lengths,
+                                 const std::vector<index_t>& strides,
+                                 index_t gridSize,
+                                 index_t blockSize)
+    {
+        auto tupleOfShape  = generate_tuple([&](auto I) { return lengths[I]; }, Number<2>{});
+        auto tupleOfStride = generate_tuple([&](auto I) { return strides[I]; }, Number<2>{});
+
+        // nd desc - [s0, s1, s2, ...]
+        const auto desc   = make_naive_tensor_descriptor(tupleOfShape, tupleOfStride);
+        const auto desc_m = transform_tensor_descriptor(
+            desc,
+            make_tuple(make_merge_transform(tupleOfShape)),
+            make_tuple(generate_sequence_v2([&](auto I) { return I; }, Number<2>{})),
+            make_tuple(Sequence<0>{}));
+
+        return PadDescriptor_M_1d(desc_m, gridSize, blockSize);
+    }
+
+    static auto MakeAGridDescriptor_AK0_M_AK1(index_t MRaw, index_t KRaw, index_t StrideA)
+    {
+        const auto a_grid_desc_mraw_kraw = [&]() {
+            if constexpr(is_same_v<tensor_layout::gemm::RowMajor, ALayout>)
+            {
+                return make_naive_tensor_descriptor(make_tuple(MRaw, KRaw),
+                                                    make_tuple(StrideA, I1));
+            }
+            else if constexpr(is_same_v<tensor_layout::gemm::ColumnMajor, ALayout>)
+            {
+                return make_naive_tensor_descriptor(make_tuple(MRaw, KRaw),
+                                                    make_tuple(I1, StrideA));
+            }
+        }();
+
+        const auto M = math::integer_divide_ceil(MRaw, MPerBlock) * MPerBlock;
+        const auto K = math::integer_divide_ceil(KRaw, KPerBlock) * KPerBlock;
+
+        const auto MPad = M - MRaw;
+        const auto KPad = K - KRaw;
+
+        if constexpr(GemmSpec == GemmSpecialization::MKPadding ||
+                     GemmSpec == GemmSpecialization::MNKPadding)
+        {
+            // pad both M and K
+            assert(K % AK1 == 0);
+
+            const auto AK0 = K / AK1;
+
+            const auto a_grid_desc_m_k =
+                transform_tensor_descriptor(a_grid_desc_mraw_kraw,
+                                            make_tuple(make_right_pad_transform(MRaw, MPad),
+                                                       make_right_pad_transform(KRaw, KPad)),
+                                            make_tuple(Sequence<0>{}, Sequence<1>{}),
+                                            make_tuple(Sequence<0>{}, Sequence<1>{}));
+
+            const auto a_grid_desc_ak0_m_ak1 =
+                transform_tensor_descriptor(a_grid_desc_m_k,
+                                            make_tuple(make_unmerge_transform(make_tuple(AK0, AK1)),
+                                                       make_pass_through_transform(M)),
+                                            make_tuple(Sequence<1>{}, Sequence<0>{}),
+                                            make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
+
+            return a_grid_desc_ak0_m_ak1;
+        }
+        else if constexpr(GemmSpec == GemmSpecialization::MPadding ||
+                          GemmSpec == GemmSpecialization::MNPadding)
+        {
+            // pad M, but not K
+            assert(KRaw % AK1 == 0);
+
+            const auto AK0 = KRaw / AK1;
+
+            const auto a_grid_desc_ak0_m_ak1 =
+                transform_tensor_descriptor(a_grid_desc_mraw_kraw,
+                                            make_tuple(make_unmerge_transform(make_tuple(AK0, AK1)),
+                                                       make_right_pad_transform(MRaw, MPad)),
+                                            make_tuple(Sequence<1>{}, Sequence<0>{}),
+                                            make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
+
+            return a_grid_desc_ak0_m_ak1;
+        }
+        else if constexpr(GemmSpec == GemmSpecialization::KPadding ||
+                          GemmSpec == GemmSpecialization::NKPadding)
+        {
+            // pad K, but not M
+            assert(K % AK1 == 0);
+
+            const auto AK0 = K / AK1;
+
+            const auto a_grid_desc_m_k = transform_tensor_descriptor(
+                a_grid_desc_mraw_kraw,
+                make_tuple(make_pass_through_transform(MRaw), make_right_pad_transform(KRaw, KPad)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}),
+                make_tuple(Sequence<0>{}, Sequence<1>{}));
+
+            const auto a_grid_desc_ak0_m_ak1 =
+                transform_tensor_descriptor(a_grid_desc_m_k,
+                                            make_tuple(make_unmerge_transform(make_tuple(AK0, AK1)),
+                                                       make_pass_through_transform(MRaw)),
+                                            make_tuple(Sequence<1>{}, Sequence<0>{}),
+                                            make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
+
+            return a_grid_desc_ak0_m_ak1;
+        }
+        else
+        {
+            // not pad M or K
+            assert(KRaw % AK1 == 0);
+
+            const auto AK0 = KRaw / AK1;
+
+            const auto a_grid_desc_ak0_m_ak1 =
+                transform_tensor_descriptor(a_grid_desc_mraw_kraw,
+                                            make_tuple(make_unmerge_transform(make_tuple(AK0, AK1)),
+                                                       make_pass_through_transform(MRaw)),
+                                            make_tuple(Sequence<1>{}, Sequence<0>{}),
+                                            make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
+
+            return a_grid_desc_ak0_m_ak1;
+        }
+    }
+
+    static auto MakeBGridDescriptor_BK0_N_BK1(index_t KRaw, index_t NRaw, index_t StrideB)
+    {
+        const auto b_grid_desc_nraw_kraw = [&]() {
+            if constexpr(is_same<tensor_layout::gemm::RowMajor, BLayout>::value)
+            {
+                return make_naive_tensor_descriptor(make_tuple(NRaw, KRaw),
+                                                    make_tuple(I1, StrideB));
+            }
+            else if constexpr(is_same<tensor_layout::gemm::ColumnMajor, BLayout>::value)
+            {
+                return make_naive_tensor_descriptor(make_tuple(NRaw, KRaw),
+                                                    make_tuple(StrideB, I1));
+            }
+        }();
+
+        const auto N = math::integer_divide_ceil(NRaw, NPerBlock) * NPerBlock;
+        const auto K = math::integer_divide_ceil(KRaw, KPerBlock) * KPerBlock;
+
+        const auto NPad = N - NRaw;
+        const auto KPad = K - KRaw;
+
+        if constexpr(GemmSpec == GemmSpecialization::NKPadding ||
+                     GemmSpec == GemmSpecialization::MNKPadding)
+        {
+            // pad both N and K
+            assert(K % BK1 == 0);
+
+            const auto BK0 = K / BK1;
+
+            const auto b_grid_desc_n_k =
+                transform_tensor_descriptor(b_grid_desc_nraw_kraw,
+                                            make_tuple(make_right_pad_transform(NRaw, NPad),
+                                                       make_right_pad_transform(KRaw, KPad)),
+                                            make_tuple(Sequence<0>{}, Sequence<1>{}),
+                                            make_tuple(Sequence<0>{}, Sequence<1>{}));
+
+            const auto b_grid_desc_bk0_n_bk1 =
+                transform_tensor_descriptor(b_grid_desc_n_k,
+                                            make_tuple(make_unmerge_transform(make_tuple(BK0, BK1)),
+                                                       make_pass_through_transform(N)),
+                                            make_tuple(Sequence<1>{}, Sequence<0>{}),
+                                            make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
+
+            return b_grid_desc_bk0_n_bk1;
+        }
+        else if constexpr(GemmSpec == GemmSpecialization::NPadding ||
+                          GemmSpec == GemmSpecialization::MNPadding)
+        {
+            // pad N, but not K
+            assert(KRaw % BK1 == 0);
+
+            const auto BK0 = KRaw / BK1;
+
+            const auto b_grid_desc_bk0_n_bk1 =
+                transform_tensor_descriptor(b_grid_desc_nraw_kraw,
+                                            make_tuple(make_unmerge_transform(make_tuple(BK0, BK1)),
+                                                       make_right_pad_transform(NRaw, NPad)),
+                                            make_tuple(Sequence<1>{}, Sequence<0>{}),
+                                            make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
+
+            return b_grid_desc_bk0_n_bk1;
+        }
+        else if constexpr(GemmSpec == GemmSpecialization::KPadding ||
+                          GemmSpec == GemmSpecialization::MKPadding)
+        {
+            // pad K, but not N
+            assert(K % BK1 == 0);
+
+            const auto BK0 = K / BK1;
+
+            const auto b_grid_desc_n_k = transform_tensor_descriptor(
+                b_grid_desc_nraw_kraw,
+                make_tuple(make_pass_through_transform(NRaw), make_right_pad_transform(KRaw, KPad)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}),
+                make_tuple(Sequence<0>{}, Sequence<1>{}));
+
+            const auto b_grid_desc_bk0_n_bk1 =
+                transform_tensor_descriptor(b_grid_desc_n_k,
+                                            make_tuple(make_unmerge_transform(make_tuple(BK0, BK1)),
+                                                       make_pass_through_transform(NRaw)),
+                                            make_tuple(Sequence<1>{}, Sequence<0>{}),
+                                            make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
+
+            return b_grid_desc_bk0_n_bk1;
+        }
+        else
+        {
+            // not pad N or K
+            assert(KRaw % BK1 == 0);
+
+            const auto BK0 = KRaw / BK1;
+
+            const auto b_grid_desc_bk0_n_bk1 =
+                transform_tensor_descriptor(b_grid_desc_nraw_kraw,
+                                            make_tuple(make_unmerge_transform(make_tuple(BK0, BK1)),
+                                                       make_pass_through_transform(NRaw)),
+                                            make_tuple(Sequence<1>{}, Sequence<0>{}),
+                                            make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
+
+            return b_grid_desc_bk0_n_bk1;
+        }
+    }
+
+    static auto MakeCGridDescriptor_M_N(index_t MRaw, index_t NRaw, index_t StrideC)
+    {
+        const auto c_grid_desc_mraw_nraw = [&]() {
+            if constexpr(is_same<tensor_layout::gemm::RowMajor, CLayout>::value)
+            {
+                return make_naive_tensor_descriptor(make_tuple(MRaw, NRaw),
+                                                    make_tuple(StrideC, I1));
+            }
+            else if constexpr(is_same<tensor_layout::gemm::ColumnMajor, CLayout>::value)
+            {
+                return make_naive_tensor_descriptor(make_tuple(MRaw, NRaw),
+                                                    make_tuple(I1, StrideC));
+            }
+        }();
+
+        const auto M = math::integer_divide_ceil(MRaw, MPerBlock) * MPerBlock;
+        const auto N = math::integer_divide_ceil(NRaw, NPerBlock) * NPerBlock;
+
+        const auto MPad = M - MRaw;
+        const auto NPad = N - NRaw;
+
+        if constexpr(GemmSpec == GemmSpecialization::MNPadding ||
+                     GemmSpec == GemmSpecialization::MNKPadding)
+        {
+            // pad M and N
+            return transform_tensor_descriptor(c_grid_desc_mraw_nraw,
+                                               make_tuple(make_right_pad_transform(MRaw, MPad),
+                                                          make_right_pad_transform(NRaw, NPad)),
+                                               make_tuple(Sequence<0>{}, Sequence<1>{}),
+                                               make_tuple(Sequence<0>{}, Sequence<1>{}));
+        }
+        else if constexpr(GemmSpec == GemmSpecialization::MPadding ||
+                          GemmSpec == GemmSpecialization::MKPadding)
+        {
+            // pad M, but not N
+            return transform_tensor_descriptor(
+                c_grid_desc_mraw_nraw,
+                make_tuple(make_right_pad_transform(MRaw, MPad), make_pass_through_transform(NRaw)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}),
+                make_tuple(Sequence<0>{}, Sequence<1>{}));
+        }
+        else if constexpr(GemmSpec == GemmSpecialization::NPadding ||
+                          GemmSpec == GemmSpecialization::NKPadding)
+        {
+            // pad N, but not M
+            return transform_tensor_descriptor(
+                c_grid_desc_mraw_nraw,
+                make_tuple(make_pass_through_transform(MRaw), make_right_pad_transform(NRaw, NPad)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}),
+                make_tuple(Sequence<0>{}, Sequence<1>{}));
+        }
+        else
+        {
+            // not pad M or N
+            return c_grid_desc_mraw_nraw;
+        }
+    }
+
+    using AGridDesc_AK0_M_AK1 = decltype(MakeAGridDescriptor_AK0_M_AK1(1, 1, 1));
+    using BGridDesc_BK0_N_BK1 = decltype(MakeBGridDescriptor_BK0_N_BK1(1, 1, 1));
+    using CGridDesc_M_N       = decltype(MakeCGridDescriptor_M_N(1, 1, 1));
+    using CGridDesc_M         = decltype(MakeDescriptor_M({1, 1}, {1, 1}, 1, 1));
+
+    // GridwiseGemm
+    using GridwiseGemm = GridwiseGemm_k0mk1_k0nk1_mn_xdl_cshuffle_v1<
+        ADataType, // TODO: distinguish A/B datatype
+        GemmAccDataType,
+        CShuffleDataType,
+        CDataType,
+        AElementwiseOperation,
+        BElementwiseOperation,
+        CElementwiseOperation,
+        InMemoryDataOperationEnum::Set,
+        AGridDesc_AK0_M_AK1,
+        BGridDesc_BK0_N_BK1,
+        CGridDesc_M_N,
+        NumGemmKPrefetchStage,
+        BlockSize,
+        MPerBlock,
+        NPerBlock,
+        KPerBlock,
+        AK1,
+        BK1,
+        MPerXDL,
+        NPerXDL,
+        MXdlPerWave,
+        NXdlPerWave,
+        ABlockTransferThreadClusterLengths_AK0_M_AK1,
+        ABlockTransferThreadClusterArrangeOrder,
+        ABlockTransferSrcAccessOrder,
+        ABlockTransferSrcVectorDim,
+        ABlockTransferSrcScalarPerVector,
+        ABlockTransferDstScalarPerVector_AK1,
+        false,
+        ABlockLdsExtraM,
+        BBlockTransferThreadClusterLengths_BK0_N_BK1,
+        BBlockTransferThreadClusterArrangeOrder,
+        BBlockTransferSrcAccessOrder,
+        BBlockTransferSrcVectorDim,
+        BBlockTransferSrcScalarPerVector,
+        BBlockTransferDstScalarPerVector_BK1,
+        false,
+        BBlockLdsExtraN,
+        CShuffleMXdlPerWavePerShuffle,
+        CShuffleNXdlPerWavePerShuffle,
+        CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
+        CShuffleBlockTransferScalarPerVector_NPerBlock,
+        LoopSched>;
+
+    // Argument
+    struct Argument : public BaseArgument
+    {
+        Argument(const ADataType* p_a_grid_real,
+                 const ADataType* p_a_grid_imag,
+                 const BDataType* p_b_grid_real,
+                 const BDataType* p_b_grid_imag,
+                 CDataType* p_c_grid_real,
+                 CDataType* p_c_grid_imag,
+                 CDataType* p_workspace,
+                 index_t MRaw,
+                 index_t NRaw,
+                 index_t KRaw,
+                 index_t StrideA,
+                 index_t StrideB,
+                 index_t StrideC,
+                 AElementwiseOperation a_element_op,
+                 BElementwiseOperation b_element_op,
+                 CElementwiseOperation c_element_op)
+            : p_a_grid_real_{p_a_grid_real},
+              p_a_grid_imag_{p_a_grid_imag},
+              p_b_grid_real_{p_b_grid_real},
+              p_b_grid_imag_{p_b_grid_imag},
+              p_c_grid_real_{p_c_grid_real},
+              p_c_grid_imag_{p_c_grid_imag},
+              p_aux_grid_{p_workspace},
+              a_grid_desc_ak0_m_ak1_{DeviceOp::MakeAGridDescriptor_AK0_M_AK1(MRaw, KRaw, StrideA)},
+              b_grid_desc_bk0_n_bk1_{DeviceOp::MakeBGridDescriptor_BK0_N_BK1(KRaw, NRaw, StrideB)},
+              c_grid_desc_m_n_{DeviceOp::MakeCGridDescriptor_M_N(MRaw, NRaw, StrideC)},
+              c_grid_desc_mblock_mperblock_nblock_nperblock_{},
+              block_2_ctile_map_{GridwiseGemm::MakeDefaultBlock2CTileMap(c_grid_desc_m_n_)},
+              a_element_op_{a_element_op},
+              b_element_op_{b_element_op},
+              c_element_op_{c_element_op}
+        {
+            if(GridwiseGemm::CheckValidity(a_grid_desc_ak0_m_ak1_,
+                                           b_grid_desc_bk0_n_bk1_,
+                                           c_grid_desc_m_n_,
+                                           block_2_ctile_map_))
+            {
+                c_grid_desc_mblock_mperblock_nblock_nperblock_ =
+                    GridwiseGemm::MakeCGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(
+                        c_grid_desc_m_n_);
+            }
+
+            const index_t grid_size = block_2_ctile_map_.CalculateGridSize(c_grid_desc_m_n_);
+
+            if constexpr(is_same<tensor_layout::gemm::RowMajor, CLayout>::value)
+            {
+                c_grid_desc_m_ =
+                    DeviceOp::MakeDescriptor_M({MRaw, NRaw}, {StrideC, I1}, grid_size, BlockSize);
+            }
+            else if constexpr(is_same<tensor_layout::gemm::ColumnMajor, CLayout>::value)
+            {
+                c_grid_desc_m_ =
+                    DeviceOp::MakeDescriptor_M({MRaw, NRaw}, {I1, StrideC}, grid_size, BlockSize);
+            }
+
+            p_aux_2_grid_ = p_workspace + c_grid_desc_m_n_.GetElementSpaceSize();
+        }
+
+        //  private:
+        const ADataType* p_a_grid_real_;
+        const ADataType* p_a_grid_imag_;
+        const BDataType* p_b_grid_real_;
+        const BDataType* p_b_grid_imag_;
+        CDataType* p_c_grid_real_;
+        CDataType* p_c_grid_imag_;
+        CDataType* p_aux_grid_;
+        CDataType* p_aux_2_grid_;
+        AGridDesc_AK0_M_AK1 a_grid_desc_ak0_m_ak1_;
+        BGridDesc_BK0_N_BK1 b_grid_desc_bk0_n_bk1_;
+        CGridDesc_M_N c_grid_desc_m_n_;
+        CGridDesc_M c_grid_desc_m_;
+        typename GridwiseGemm::CGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock
+            c_grid_desc_mblock_mperblock_nblock_nperblock_;
+        typename GridwiseGemm::DefaultBlock2CTileMap block_2_ctile_map_;
+        AElementwiseOperation a_element_op_;
+        BElementwiseOperation b_element_op_;
+        CElementwiseOperation c_element_op_;
+    };
+
+    // Invoker
+    struct Invoker : public BaseInvoker
+    {
+        using Argument = DeviceOp::Argument;
+
+        float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
+        {
+            if(!GridwiseGemm::CheckValidity(arg.a_grid_desc_ak0_m_ak1_,
+                                            arg.b_grid_desc_bk0_n_bk1_,
+                                            arg.c_grid_desc_m_n_,
+                                            arg.block_2_ctile_map_))
+            {
+                throw std::runtime_error("wrong! GridwiseGemm has invalid setting");
+            }
+
+            const index_t grid_size =
+                arg.block_2_ctile_map_.CalculateGridSize(arg.c_grid_desc_m_n_);
+
+            const auto K =
+                arg.a_grid_desc_ak0_m_ak1_.GetLength(I0) * arg.a_grid_desc_ak0_m_ak1_.GetLength(I2);
+
+            float ave_time = 0;
+
+            using Add      = ck::tensor_operation::element_wise::Add;
+            using Subtract = ck::tensor_operation::element_wise::Subtract;
+
+            using GridwiseBinAdd =
+                GridwiseElementwise_1D<Tuple<CGridDesc_M, CGridDesc_M>,
+                                       Tuple<CGridDesc_M>,
+                                       Tuple<const CDataType*, const CDataType*>,
+                                       Tuple<CDataType*>,
+                                       Add,
+                                       MPerThread,
+                                       Sequence<AScalarPerVector, BScalarPerVector>,
+                                       Sequence<CScalarPerVector>>;
+
+            using GridwiseBinSubtract =
+                GridwiseElementwise_1D<Tuple<CGridDesc_M, CGridDesc_M>,
+                                       Tuple<CGridDesc_M>,
+                                       Tuple<const CDataType*, const CDataType*>,
+                                       Tuple<CDataType*>,
+                                       Subtract,
+                                       MPerThread,
+                                       Sequence<AScalarPerVector, BScalarPerVector>,
+                                       Sequence<CScalarPerVector>>;
+
+            const auto add_kernel = kernel_elementwise_1d<GridwiseBinAdd,
+                                                          Tuple<CGridDesc_M, CGridDesc_M>,
+                                                          Tuple<CGridDesc_M>,
+                                                          Tuple<const CDataType*, const CDataType*>,
+                                                          Tuple<CDataType*>,
+                                                          Add>;
+
+            const auto subtract_kernel =
+                kernel_elementwise_1d<GridwiseBinSubtract,
+                                      Tuple<CGridDesc_M, CGridDesc_M>,
+                                      Tuple<CGridDesc_M>,
+                                      Tuple<const CDataType*, const CDataType*>,
+                                      Tuple<CDataType*>,
+                                      Subtract>;
+
+            if(GridwiseGemm::CalculateHasMainKBlockLoop(K))
+            {
+                const auto kernel = kernel_gemm_xdl_cshuffle_v1<
+                    GridwiseGemm,
+                    ADataType, // TODO: distiguish A/B datatype
+                    CDataType,
+                    AElementwiseOperation,
+                    BElementwiseOperation,
+                    CElementwiseOperation,
+                    DeviceOp::AGridDesc_AK0_M_AK1,
+                    DeviceOp::BGridDesc_BK0_N_BK1,
+                    typename GridwiseGemm::CGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
+                    typename GridwiseGemm::DefaultBlock2CTileMap,
+                    true>;
+
+                ave_time +=
+                    launch_and_time_kernel(stream_config,
+                                           kernel,
+                                           dim3(grid_size),
+                                           dim3(BlockSize),
+                                           0,
+                                           arg.p_a_grid_real_,
+                                           arg.p_b_grid_real_,
+                                           arg.p_aux_grid_,
+                                           arg.a_element_op_,
+                                           arg.b_element_op_,
+                                           arg.c_element_op_,
+                                           arg.a_grid_desc_ak0_m_ak1_,
+                                           arg.b_grid_desc_bk0_n_bk1_,
+                                           arg.c_grid_desc_mblock_mperblock_nblock_nperblock_,
+                                           arg.block_2_ctile_map_);
+
+                ave_time +=
+                    launch_and_time_kernel(stream_config,
+                                           kernel,
+                                           dim3(grid_size),
+                                           dim3(BlockSize),
+                                           0,
+                                           arg.p_a_grid_imag_,
+                                           arg.p_b_grid_imag_,
+                                           arg.p_aux_2_grid_,
+                                           arg.a_element_op_,
+                                           arg.b_element_op_,
+                                           arg.c_element_op_,
+                                           arg.a_grid_desc_ak0_m_ak1_,
+                                           arg.b_grid_desc_bk0_n_bk1_,
+                                           arg.c_grid_desc_mblock_mperblock_nblock_nperblock_,
+                                           arg.block_2_ctile_map_);
+
+                // c_real = aux - aux_2
+                ave_time += launch_and_time_kernel(
+                    stream_config,
+                    subtract_kernel,
+                    dim3(grid_size),
+                    dim3(BlockSize),
+                    0,
+                    make_tuple(arg.c_grid_desc_m_, arg.c_grid_desc_m_),
+                    make_tuple(arg.c_grid_desc_m_),
+                    make_tuple(const_cast<const CDataType*>(arg.p_aux_grid_),
+                               const_cast<const CDataType*>(arg.p_aux_2_grid_)),
+                    make_tuple(arg.p_c_grid_real_),
+                    Subtract{});
+
+                ave_time +=
+                    launch_and_time_kernel(stream_config,
+                                           kernel,
+                                           dim3(grid_size),
+                                           dim3(BlockSize),
+                                           0,
+                                           arg.p_a_grid_real_,
+                                           arg.p_b_grid_imag_,
+                                           arg.p_aux_grid_,
+                                           arg.a_element_op_,
+                                           arg.b_element_op_,
+                                           arg.c_element_op_,
+                                           arg.a_grid_desc_ak0_m_ak1_,
+                                           arg.b_grid_desc_bk0_n_bk1_,
+                                           arg.c_grid_desc_mblock_mperblock_nblock_nperblock_,
+                                           arg.block_2_ctile_map_);
+
+                ave_time +=
+                    launch_and_time_kernel(stream_config,
+                                           kernel,
+                                           dim3(grid_size),
+                                           dim3(BlockSize),
+                                           0,
+                                           arg.p_a_grid_imag_,
+                                           arg.p_b_grid_real_,
+                                           arg.p_aux_2_grid_,
+                                           arg.a_element_op_,
+                                           arg.b_element_op_,
+                                           arg.c_element_op_,
+                                           arg.a_grid_desc_ak0_m_ak1_,
+                                           arg.b_grid_desc_bk0_n_bk1_,
+                                           arg.c_grid_desc_mblock_mperblock_nblock_nperblock_,
+                                           arg.block_2_ctile_map_);
+
+                // c_imag = aux + aux_2
+                ave_time += launch_and_time_kernel(
+                    stream_config,
+                    add_kernel,
+                    dim3(grid_size),
+                    dim3(BlockSize),
+                    0,
+                    make_tuple(arg.c_grid_desc_m_, arg.c_grid_desc_m_),
+                    make_tuple(arg.c_grid_desc_m_),
+                    make_tuple(const_cast<const CDataType*>(arg.p_aux_grid_),
+                               const_cast<const CDataType*>(arg.p_aux_2_grid_)),
+                    make_tuple(arg.p_c_grid_imag_),
+                    Add{});
+            }
+            else
+            {
+                const auto kernel = kernel_gemm_xdl_cshuffle_v1<
+                    GridwiseGemm,
+                    ADataType, // TODO: distiguish A/B datatype
+                    CDataType,
+                    AElementwiseOperation,
+                    BElementwiseOperation,
+                    CElementwiseOperation,
+                    DeviceOp::AGridDesc_AK0_M_AK1,
+                    DeviceOp::BGridDesc_BK0_N_BK1,
+                    typename GridwiseGemm::CGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
+                    typename GridwiseGemm::DefaultBlock2CTileMap,
+                    false>;
+
+                ave_time +=
+                    launch_and_time_kernel(stream_config,
+                                           kernel,
+                                           dim3(grid_size),
+                                           dim3(BlockSize),
+                                           0,
+                                           arg.p_a_grid_real_,
+                                           arg.p_b_grid_real_,
+                                           arg.p_aux_grid_,
+                                           arg.a_element_op_,
+                                           arg.b_element_op_,
+                                           arg.c_element_op_,
+                                           arg.a_grid_desc_ak0_m_ak1_,
+                                           arg.b_grid_desc_bk0_n_bk1_,
+                                           arg.c_grid_desc_mblock_mperblock_nblock_nperblock_,
+                                           arg.block_2_ctile_map_);
+
+                ave_time +=
+                    launch_and_time_kernel(stream_config,
+                                           kernel,
+                                           dim3(grid_size),
+                                           dim3(BlockSize),
+                                           0,
+                                           arg.p_a_grid_imag_,
+                                           arg.p_b_grid_imag_,
+                                           arg.p_aux_2_grid_,
+                                           arg.a_element_op_,
+                                           arg.b_element_op_,
+                                           arg.c_element_op_,
+                                           arg.a_grid_desc_ak0_m_ak1_,
+                                           arg.b_grid_desc_bk0_n_bk1_,
+                                           arg.c_grid_desc_mblock_mperblock_nblock_nperblock_,
+                                           arg.block_2_ctile_map_);
+
+                // c_real = aux - aux_2
+                ave_time += launch_and_time_kernel(
+                    stream_config,
+                    subtract_kernel,
+                    dim3(grid_size),
+                    dim3(BlockSize),
+                    0,
+                    make_tuple(arg.c_grid_desc_m_, arg.c_grid_desc_m_),
+                    make_tuple(arg.c_grid_desc_m_),
+                    make_tuple(const_cast<const CDataType*>(arg.p_aux_grid_),
+                               const_cast<const CDataType*>(arg.p_aux_2_grid_)),
+                    make_tuple(arg.p_c_grid_real_),
+                    Subtract{});
+
+                ave_time +=
+                    launch_and_time_kernel(stream_config,
+                                           kernel,
+                                           dim3(grid_size),
+                                           dim3(BlockSize),
+                                           0,
+                                           arg.p_a_grid_real_,
+                                           arg.p_b_grid_imag_,
+                                           arg.p_aux_grid_,
+                                           arg.a_element_op_,
+                                           arg.b_element_op_,
+                                           arg.c_element_op_,
+                                           arg.a_grid_desc_ak0_m_ak1_,
+                                           arg.b_grid_desc_bk0_n_bk1_,
+                                           arg.c_grid_desc_mblock_mperblock_nblock_nperblock_,
+                                           arg.block_2_ctile_map_);
+
+                ave_time +=
+                    launch_and_time_kernel(stream_config,
+                                           kernel,
+                                           dim3(grid_size),
+                                           dim3(BlockSize),
+                                           0,
+                                           arg.p_a_grid_imag_,
+                                           arg.p_b_grid_real_,
+                                           arg.p_aux_2_grid_,
+                                           arg.a_element_op_,
+                                           arg.b_element_op_,
+                                           arg.c_element_op_,
+                                           arg.a_grid_desc_ak0_m_ak1_,
+                                           arg.b_grid_desc_bk0_n_bk1_,
+                                           arg.c_grid_desc_mblock_mperblock_nblock_nperblock_,
+                                           arg.block_2_ctile_map_);
+
+                // c_imag = aux + aux_2
+                ave_time += launch_and_time_kernel(
+                    stream_config,
+                    add_kernel,
+                    dim3(grid_size),
+                    dim3(BlockSize),
+                    0,
+                    make_tuple(arg.c_grid_desc_m_, arg.c_grid_desc_m_),
+                    make_tuple(arg.c_grid_desc_m_),
+                    make_tuple(const_cast<const CDataType*>(arg.p_aux_grid_),
+                               const_cast<const CDataType*>(arg.p_aux_2_grid_)),
+                    make_tuple(arg.p_c_grid_imag_),
+                    Add{});
+            }
+
+            return ave_time;
+        }
+
+        // polymorphic
+        float Run(const BaseArgument* p_arg,
+                  const StreamConfig& stream_config = StreamConfig{}) override
+        {
+            return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
+        }
+    };
+
+    static constexpr bool IsValidCompilationParameter()
+    {
+        // TODO: properly implement this check
+        return true;
+    }
+
+    static bool IsSupportedArgument(const Argument& arg)
+    {
+        return GridwiseGemm::CheckValidity(arg.a_grid_desc_ak0_m_ak1_,
+                                           arg.b_grid_desc_bk0_n_bk1_,
+                                           arg.c_grid_desc_m_n_,
+                                           arg.block_2_ctile_map_);
+    }
+
+    // polymorphic
+    bool IsSupportedArgument(const BaseArgument* p_arg) override
+    {
+        return IsSupportedArgument(*dynamic_cast<const Argument*>(p_arg));
+    }
+
+    static auto MakeArgument(const ADataType* p_a_real,
+                             const ADataType* p_a_imag,
+                             const BDataType* p_b_real,
+                             const BDataType* p_b_imag,
+                             CDataType* p_c_real,
+                             CDataType* p_c_imag,
+                             CDataType* p_workspace,
+                             index_t MRaw,
+                             index_t NRaw,
+                             index_t KRaw,
+                             index_t StrideA,
+                             index_t StrideB,
+                             index_t StrideC,
+                             AElementwiseOperation a_element_op,
+                             BElementwiseOperation b_element_op,
+                             CElementwiseOperation c_element_op)
+    {
+        return Argument{p_a_real,
+                        p_a_imag,
+                        p_b_real,
+                        p_b_imag,
+                        p_c_real,
+                        p_c_imag,
+                        p_workspace,
+                        MRaw,
+                        NRaw,
+                        KRaw,
+                        StrideA,
+                        StrideB,
+                        StrideC,
+                        a_element_op,
+                        b_element_op,
+                        c_element_op};
+    }
+
+    static auto MakeInvoker() { return Invoker{}; }
+
+    // polymorphic
+    std::unique_ptr<BaseArgument> MakeArgumentPointer(const void* p_a_real,
+                                                      const void* p_a_imag,
+                                                      const void* p_b_real,
+                                                      const void* p_b_imag,
+                                                      void* p_c_real,
+                                                      void* p_c_imag,
+                                                      void* p_workspace,
+                                                      index_t MRaw,
+                                                      index_t NRaw,
+                                                      index_t KRaw,
+                                                      index_t StrideA,
+                                                      index_t StrideB,
+                                                      index_t StrideC,
+                                                      AElementwiseOperation a_element_op,
+                                                      BElementwiseOperation b_element_op,
+                                                      CElementwiseOperation c_element_op,
+                                                      index_t /* KBatch */ = 1) override
+    {
+        return std::make_unique<Argument>(static_cast<const ADataType*>(p_a_real),
+                                          static_cast<const ADataType*>(p_a_imag),
+                                          static_cast<const BDataType*>(p_b_real),
+                                          static_cast<const BDataType*>(p_b_imag),
+                                          static_cast<CDataType*>(p_c_real),
+                                          static_cast<CDataType*>(p_c_imag),
+                                          static_cast<CDataType*>(p_workspace),
+                                          MRaw,
+                                          NRaw,
+                                          KRaw,
+                                          StrideA,
+                                          StrideB,
+                                          StrideC,
+                                          a_element_op,
+                                          b_element_op,
+                                          c_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 << "DeviceCGemm_4Gemm_Xdl_CShuffle"
+            << "<"
+            << BlockSize << ", "
+            << MPerBlock << ", "
+            << NPerBlock << ", "
+            << KPerBlock << ", "
+            << AK1 << ", "
+            << BK1
+            << ">";
+        // clang-format on
+
+        return str.str();
+    }
+
+    std::size_t GetWorkspaceSize(index_t MRaw,
+                                 index_t NRaw,
+                                 [[maybe_unused]] index_t KRaw,
+                                 [[maybe_unused]] index_t StrideA,
+                                 [[maybe_unused]] index_t StrideB,
+                                 index_t StrideC) override
+    {
+        const auto c_grid_desc_m_n = MakeCGridDescriptor_M_N(MRaw, NRaw, StrideC);
+
+        return 2 * sizeof(CDataType) * c_grid_desc_m_n.GetElementSpaceSize();
+    }
+};
+
+} // namespace device
+} // namespace tensor_operation
+} // namespace ck

deps/cget/pkg/ROCmSoftwarePlatform__composable_kernel/install/include/ck/tensor_operation/gpu/device/device_contraction_multiple_d.hpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include <iostream>
+#include <vector>
+
+#include "ck/tensor_operation/gpu/device/device_base.hpp"
+
+namespace ck {
+namespace tensor_operation {
+namespace device {
+
+// Tensor Contraction:
+//   input : A
+//   input : B
+//   input : D0, D1, ...
+//   output : E
+//   C = a_op(A) * b_op(B)
+//   E = cde_op(C, D0, D1, ...)
+// Assume:
+//   A[M0, M1, M2, ..., K0, K1, K2, ...]
+//   B[N0, N1, N2, ..., K0, K1, K2, ...]
+//   D[M0, M1, M2, ..., N0, N1, N2, ...]
+//   E[M0, M1, M2, ..., N0, N1, N2, ...]
+template <index_t NumDimM,
+          index_t NumDimN,
+          index_t NumDimK,
+          typename ADataType,
+          typename BDataType,
+          typename DsDataType,
+          typename EDataType,
+          typename AElementwiseOperation,
+          typename BElementwiseOperation,
+          typename CDEElementwiseOperation>
+struct DeviceContractionMultipleD : public BaseOperator
+{
+    static constexpr index_t NumDTensor = DsDataType::Size();
+
+    virtual std::unique_ptr<BaseArgument>
+    MakeArgumentPointer(const void* p_a,
+                        const void* p_b,
+                        std::array<const void*, NumDTensor> p_ds,
+                        void* p_e,
+                        const std::vector<index_t>& a_ms_ns_lengths,
+                        const std::vector<index_t>& a_ms_ks_strides,
+                        const std::vector<index_t>& b_ns_ks_lengths,
+                        const std::vector<index_t>& b_ns_ks_strides,
+                        const std::array<std::vector<index_t>, NumDTensor>& ds_ms_ns_lengths,
+                        const std::array<std::vector<index_t>, NumDTensor>& ds_ms_ns_strides,
+                        const std::vector<index_t>& e_ms_ns_lengths,
+                        const std::vector<index_t>& e_ms_ns_strides,
+                        AElementwiseOperation a_element_op,
+                        BElementwiseOperation b_element_op,
+                        CDEElementwiseOperation cde_element_op) = 0;
+
+    virtual std::unique_ptr<BaseInvoker> MakeInvokerPointer() = 0;
+};
+
+} // namespace device
+} // namespace tensor_operation
+} // namespace ck

deps/cget/pkg/ROCmSoftwarePlatform__composable_kernel/install/include/ck/tensor_operation/gpu/device/device_contraction_multiple_d_xdl_cshuffle.hpp

+// 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_contraction_multiple_d.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_multiple_d_xdl_cshuffle.hpp"
+#include "ck/host_utility/device_prop.hpp"
+#include "ck/host_utility/kernel_launch.hpp"
+
+namespace ck {
+
+template <typename GridwiseGemm,
+          typename FloatAB,
+          typename FloatDsPointer,
+          typename FloatE,
+          typename AElementwiseOperation,
+          typename BElementwiseOperation,
+          typename CDEElementwiseOperation,
+          typename AGridDesc_AK0_M_AK1,
+          typename BGridDesc_BK0_N_BK1,
+          typename DsGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
+          typename EGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
+          typename Block2ETileMap,
+          bool HasMainKBlockLoop>
+__global__ void
+#if CK_USE_LAUNCH_BOUNDS
+    __launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
+#endif
+        kernel_contraction_multiple_d_xdl_cshuffle(
+            const FloatAB* __restrict__ p_a_grid,
+            const FloatAB* __restrict__ p_b_grid,
+            FloatDsPointer p_ds_grid,
+            FloatE* __restrict__ p_e_grid,
+            const AElementwiseOperation a_element_op,
+            const BElementwiseOperation b_element_op,
+            const CDEElementwiseOperation cde_element_op,
+            const AGridDesc_AK0_M_AK1 a_grid_desc_ak0_m_ak1,
+            const BGridDesc_BK0_N_BK1 b_grid_desc_bk0_n_bk1,
+            const DsGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock
+                ds_grid_desc_mblock_mperblock_nblock_nperblock,
+            const EGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock
+                e_grid_desc_mblock_mperblock_nblock_nperblock,
+            const Block2ETileMap block_2_etile_map)
+{
+#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx908__) || defined(__gfx90a__))
+    __shared__ char p_shared[GridwiseGemm::GetSharedMemoryNumberOfByte()];
+
+    GridwiseGemm::template Run<HasMainKBlockLoop>(p_a_grid,
+                                                  p_b_grid,
+                                                  p_ds_grid,
+                                                  p_e_grid,
+                                                  p_shared,
+                                                  a_element_op,
+                                                  b_element_op,
+                                                  cde_element_op,
+                                                  a_grid_desc_ak0_m_ak1,
+                                                  b_grid_desc_bk0_n_bk1,
+                                                  ds_grid_desc_mblock_mperblock_nblock_nperblock,
+                                                  e_grid_desc_mblock_mperblock_nblock_nperblock,
+                                                  block_2_etile_map);
+#else
+    ignore = p_a_grid;
+    ignore = p_b_grid;
+    ignore = p_ds_grid;
+    ignore = p_e_grid;
+    ignore = a_element_op;
+    ignore = b_element_op;
+    ignore = cde_element_op;
+    ignore = a_grid_desc_ak0_m_ak1;
+    ignore = b_grid_desc_bk0_n_bk1;
+    ignore = ds_grid_desc_mblock_mperblock_nblock_nperblock;
+    ignore = e_grid_desc_mblock_mperblock_nblock_nperblock;
+    ignore = block_2_etile_map;
+#endif
+}
+
+} // namespace ck
+
+namespace ck {
+namespace tensor_operation {
+namespace device {
+
+// Tensor Contraction:
+//   input : A
+//   input : B
+//   input : D0, D1, ...
+//   output : E
+//   C = a_op(A) * b_op(B)
+//   E = cde_op(C, D0, D1, ...)
+// Assume:
+//   A[M0, M1, M2, ..., K0, K1, K2, ...]
+//   B[N0, N1, N2, ..., K0, K1, K2, ...]
+//   D[M0, M1, M2, ..., N0, N1, N2, ...]
+//   E[M0, M1, M2, ..., N0, N1, N2, ...]
+template <index_t NumDimM,
+          index_t NumDimN,
+          index_t NumDimK,
+          typename ADataType,
+          typename BDataType,
+          typename AccDataType,
+          typename CShuffleDataType,
+          typename DsDataType,
+          typename EDataType,
+          typename AElementwiseOperation,
+          typename BElementwiseOperation,
+          typename CDEElementwiseOperation,
+          GemmSpecialization GemmSpec,
+          index_t NumGemmKPrefetchStage,
+          index_t BlockSize,
+          index_t MPerBlock,
+          index_t NPerBlock,
+          index_t KPerBlock,
+          index_t AK1,
+          index_t BK1,
+          index_t MPerXDL,
+          index_t NPerXDL,
+          index_t MXdlPerWave,
+          index_t NXdlPerWave,
+          typename ABlockTransferThreadClusterLengths_AK0_M_AK1,
+          typename ABlockTransferThreadClusterArrangeOrder,
+          typename ABlockTransferSrcAccessOrder,
+          index_t ABlockTransferSrcVectorDim,
+          index_t ABlockTransferSrcScalarPerVector,
+          index_t ABlockTransferDstScalarPerVector_AK1,
+          bool ABlockLdsExtraM,
+          typename BBlockTransferThreadClusterLengths_BK0_N_BK1,
+          typename BBlockTransferThreadClusterArrangeOrder,
+          typename BBlockTransferSrcAccessOrder,
+          index_t BBlockTransferSrcVectorDim,
+          index_t BBlockTransferSrcScalarPerVector,
+          index_t BBlockTransferDstScalarPerVector_BK1,
+          bool BBlockLdsExtraN,
+          index_t CShuffleMXdlPerWavePerShuffle,
+          index_t CShuffleNXdlPerWavePerShuffle,
+          typename CDEBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
+          index_t CDEBlockTransferScalarPerVector_NPerBlock,
+          LoopScheduler LoopSched = make_default_loop_scheduler()>
+struct DeviceContractionMultipleD_Xdl_CShuffle
+    : public DeviceContractionMultipleD<NumDimM,
+                                        NumDimN,
+                                        NumDimK,
+                                        ADataType,
+                                        BDataType,
+                                        DsDataType,
+                                        EDataType,
+                                        AElementwiseOperation,
+                                        BElementwiseOperation,
+                                        CDEElementwiseOperation>
+{
+    using DeviceOp = DeviceContractionMultipleD_Xdl_CShuffle;
+
+    static constexpr index_t NumDTensor = DsDataType::Size();
+
+    static constexpr auto I0 = Number<0>{};
+    static constexpr auto I1 = Number<1>{};
+    static constexpr auto I2 = Number<2>{};
+    static constexpr auto I3 = Number<3>{};
+
+    static constexpr auto matrix_padder =
+        MatrixPadder<GemmSpec, index_t, index_t, index_t>{MPerBlock, NPerBlock, KPerBlock};
+
+    // Assume: A[M0, M1, M2, ..., K0, K1, K2, ...]
+    static auto MakeAGridDescriptor_M_K(const std::vector<index_t>& a_ms_ks_lengths_vec,
+                                        const std::vector<index_t>& a_ms_ks_strides_vec)
+    {
+        assert(a_ms_ks_lengths_vec.size() == NumDimM + NumDimK &&
+               a_ms_ks_strides_vec.size() == NumDimM + NumDimK);
+
+        const auto to_tuple = [&](auto& vec, auto num) {
+            return generate_tuple([&](auto i) { return vec[i]; }, num);
+        };
+
+        const auto a_ms_ns_lengths = to_tuple(a_ms_ks_lengths_vec, Number<NumDimM + NumDimK>{});
+        const auto a_ms_ks_strides = to_tuple(a_ms_ks_strides_vec, Number<NumDimM + NumDimK>{});
+
+        // dimension Ids for M0, M1, ...
+        constexpr auto mDimIds = typename arithmetic_sequence_gen<0, NumDimM, 1>::type{};
+
+        // dimension Ids for K0, K1, ...
+        constexpr auto kDimIds =
+            typename arithmetic_sequence_gen<NumDimM, NumDimM + NumDimK, 1>::type{};
+
+        // lengths for M0, M1, ...
+        const auto mLengths = get_container_subset(a_ms_ns_lengths, mDimIds);
+
+        // lengths for K0, K1, ...
+        const auto kLengths = get_container_subset(a_ms_ns_lengths, kDimIds);
+
+        // naive tensor A[M0, M1, M2, ..., K0, K1, K2...]
+        const auto a_grid_desc_ms_ks =
+            make_naive_tensor_descriptor(a_ms_ns_lengths, a_ms_ks_strides);
+
+        // transformed tensor A[MRaw = M0 * M1 * M2 * ... , KRaw = K0 * K1 * K2 * ...]
+        const auto a_grid_desc_mraw_kraw = transform_tensor_descriptor(
+            a_grid_desc_ms_ks,
+            make_tuple(make_merge_transform(mLengths), make_merge_transform(kLengths)),
+            make_tuple(mDimIds, kDimIds),
+            make_tuple(Sequence<0>{}, Sequence<1>{}));
+
+        return matrix_padder.PadADescriptor_M_K(a_grid_desc_mraw_kraw);
+    }
+
+    // Assume: B[N0, N1, N2, ..., K0, K1, K2, ...]
+    static auto MakeBGridDescriptor_N_K(const std::vector<index_t>& b_ns_ks_lengths_vec,
+                                        const std::vector<index_t>& b_ns_ks_strides_vec)
+    {
+        assert(b_ns_ks_lengths_vec.size() == NumDimN + NumDimK &&
+               b_ns_ks_strides_vec.size() == NumDimN + NumDimK);
+
+        const auto to_tuple = [&](auto& vec, auto num) {
+            return generate_tuple([&](auto i) { return vec[i]; }, num);
+        };
+
+        const auto b_ns_ks_lengths = to_tuple(b_ns_ks_lengths_vec, Number<NumDimN + NumDimK>{});
+        const auto b_ns_ks_strides = to_tuple(b_ns_ks_strides_vec, Number<NumDimN + NumDimK>{});
+
+        // dimension Ids for N0, N1, ...
+        constexpr auto nDimIds = typename arithmetic_sequence_gen<0, NumDimN, 1>::type{};
+
+        // dimension Ids for K0, K1, ...
+        constexpr auto kDimIds =
+            typename arithmetic_sequence_gen<NumDimN, NumDimN + NumDimK, 1>::type{};
+
+        // lengths for K0, K1, ...
+        const auto kLengths = get_container_subset(b_ns_ks_lengths, kDimIds);
+
+        // lengths for N0, N1, ...
+        const auto nLengths = get_container_subset(b_ns_ks_lengths, nDimIds);
+
+        // naive tensor B[N0, N1, N2, ..., K0, K1, K2, ...]
+        const auto b_grid_desc_ns_ks =
+            make_naive_tensor_descriptor(b_ns_ks_lengths, b_ns_ks_strides);
+
+        // transformed tensor B[NRaw = N0 * N1 * N2 * ..., KRaw = K0 * K1 * K2 * ...]
+        const auto b_grid_desc_nraw_kraw = transform_tensor_descriptor(
+            b_grid_desc_ns_ks,
+            make_tuple(make_merge_transform(nLengths), make_merge_transform(kLengths)),
+            make_tuple(nDimIds, kDimIds),
+            make_tuple(Sequence<0>{}, Sequence<1>{}));
+
+        return matrix_padder.PadBDescriptor_N_K(b_grid_desc_nraw_kraw);
+    }
+
+    // assume E[M0, M1, M2, ..., N0, N1, N2...]
+    static auto MakeEGridDescriptor_M_N(const std::vector<index_t>& e_ms_ns_lengths_vec,
+                                        const std::vector<index_t>& e_ms_ns_strides_vec)
+    {
+        assert(e_ms_ns_lengths_vec.size() == NumDimM + NumDimN &&
+               e_ms_ns_strides_vec.size() == NumDimM + NumDimN);
+
+        const auto to_tuple = [&](auto& vec, auto num) {
+            return generate_tuple([&](auto i) { return vec[i]; }, num);
+        };
+
+        const auto e_ms_ns_lengths = to_tuple(e_ms_ns_lengths_vec, Number<NumDimM + NumDimN>{});
+        const auto e_ms_ns_strides = to_tuple(e_ms_ns_strides_vec, Number<NumDimM + NumDimN>{});
+
+        // dimension Ids for M0, M1, ...
+        constexpr auto mDimIds = typename arithmetic_sequence_gen<0, NumDimM, 1>::type{};
+
+        // dimension Ids for N0, N1, ...
+        constexpr auto nDimIds =
+            typename arithmetic_sequence_gen<NumDimM, NumDimM + NumDimN, 1>::type{};
+
+        // lengths for M0, M1, ...
+        const auto mLengths = get_container_subset(e_ms_ns_lengths, mDimIds);
+
+        // lengths for K0, K1, ...
+        const auto nLengths = get_container_subset(e_ms_ns_lengths, nDimIds);
+
+        // naive tensor E[M0, M1, M2, ..., N0, N1, N2...]
+        const auto e_grid_desc_ms_ns =
+            make_naive_tensor_descriptor(e_ms_ns_lengths, e_ms_ns_strides);
+
+        // transformed tensor E[MRaw = M0 * M1 * M2 * ... , NRaw = N0 * N1 * N2 * ...]
+        const auto e_grid_desc_mraw_nraw = transform_tensor_descriptor(
+            e_grid_desc_ms_ns,
+            make_tuple(make_merge_transform(mLengths), make_merge_transform(nLengths)),
+            make_tuple(mDimIds, nDimIds),
+            make_tuple(Sequence<0>{}, Sequence<1>{}));
+
+        return matrix_padder.PadCDescriptor_M_N(e_grid_desc_mraw_nraw);
+    }
+
+    static auto MakeDsGridDescriptor_M_N(
+        const std::array<std::vector<index_t>, NumDTensor>& ds_ms_ns_lengths_vec,
+        const std::array<std::vector<index_t>, NumDTensor>& ds_ms_ns_strides_vec)
+    {
+        return generate_tuple(
+            [&](auto i) {
+                return DeviceOp::MakeEGridDescriptor_M_N(ds_ms_ns_lengths_vec[i],
+                                                         ds_ms_ns_strides_vec[i]);
+            },
+            Number<NumDTensor>{});
+    }
+
+    using AGridDesc_M_K  = decltype(MakeAGridDescriptor_M_K({}, {}));
+    using BGridDesc_N_K  = decltype(MakeBGridDescriptor_N_K({}, {}));
+    using DsGridDesc_M_N = remove_cvref_t<decltype(MakeDsGridDescriptor_M_N({{}}, {{}}))>;
+    using EGridDesc_M_N  = decltype(MakeEGridDescriptor_M_N({}, {}));
+
+    // GridwiseGemm
+    using GridwiseGemm = GridwiseGemmMultipleD_xdl_cshuffle<
+        ADataType, // TODO: distinguish A/B datatype
+        AccDataType,
+        CShuffleDataType,
+        DsDataType,
+        EDataType,
+        AElementwiseOperation,
+        BElementwiseOperation,
+        CDEElementwiseOperation,
+        InMemoryDataOperationEnum::Set,
+        NumGemmKPrefetchStage,
+        BlockSize,
+        MPerBlock,
+        NPerBlock,
+        KPerBlock,
+        AK1,
+        BK1,
+        MPerXDL,
+        NPerXDL,
+        MXdlPerWave,
+        NXdlPerWave,
+        ABlockTransferThreadClusterLengths_AK0_M_AK1,
+        ABlockTransferThreadClusterArrangeOrder,
+        ABlockTransferSrcAccessOrder,
+        ABlockTransferSrcVectorDim,
+        ABlockTransferSrcScalarPerVector,
+        ABlockTransferDstScalarPerVector_AK1,
+        false,
+        ABlockLdsExtraM,
+        BBlockTransferThreadClusterLengths_BK0_N_BK1,
+        BBlockTransferThreadClusterArrangeOrder,
+        BBlockTransferSrcAccessOrder,
+        BBlockTransferSrcVectorDim,
+        BBlockTransferSrcScalarPerVector,
+        BBlockTransferDstScalarPerVector_BK1,
+        false,
+        BBlockLdsExtraN,
+        CShuffleMXdlPerWavePerShuffle,
+        CShuffleNXdlPerWavePerShuffle,
+        CDEBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
+        CDEBlockTransferScalarPerVector_NPerBlock,
+        LoopSched>;
+
+    // desc for blockwise copy
+    using AGridDesc_AK0_M_AK1                          = remove_cvref_t<decltype(
+        GridwiseGemm::MakeDefaultAGridDescriptor_AK0_M_AK1(AGridDesc_M_K{}))>;
+    using BGridDesc_BK0_N_BK1                          = remove_cvref_t<decltype(
+        GridwiseGemm::MakeDefaultBGridDescriptor_BK0_N_BK1(BGridDesc_N_K{}))>;
+    using DsGridDesc_MBlock_MPerBlock_NBlock_NPerBlock = remove_cvref_t<decltype(
+        GridwiseGemm::MakeDsGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(DsGridDesc_M_N{}))>;
+    using EGridDesc_MBlock_MPerBlock_NBlock_NPerBlock  = remove_cvref_t<decltype(
+        GridwiseGemm::MakeEGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(EGridDesc_M_N{}))>;
+
+    // block-to-e-tile map
+    using Block2ETileMap =
+        remove_cvref_t<decltype(GridwiseGemm::MakeDefaultBlock2ETileMap(EGridDesc_M_N{}))>;
+
+    // Argument
+    struct Argument : public BaseArgument
+    {
+        Argument(const void* p_a_grid,
+                 const void* p_b_grid,
+                 std::array<const void*, NumDTensor> p_ds_grid,
+                 void* p_e_grid,
+                 const std::vector<index_t>& a_ms_ns_lengths,
+                 const std::vector<index_t>& a_ms_ks_strides,
+                 const std::vector<index_t>& b_ns_ks_lengths,
+                 const std::vector<index_t>& b_ns_ks_strides,
+                 const std::array<std::vector<index_t>, NumDTensor>& ds_ms_ns_lengths,
+                 const std::array<std::vector<index_t>, NumDTensor>& ds_ms_ns_strides,
+                 const std::vector<index_t>& e_ms_ns_lengths,
+                 const std::vector<index_t>& e_ms_ns_strides,
+                 AElementwiseOperation a_element_op,
+                 BElementwiseOperation b_element_op,
+                 CDEElementwiseOperation cde_element_op)
+            : p_a_grid_{static_cast<const ADataType*>(p_a_grid)},
+              p_b_grid_{static_cast<const BDataType*>(p_b_grid)},
+              p_ds_grid_{},
+              p_e_grid_{static_cast<EDataType*>(p_e_grid)},
+              a_grid_desc_m_k_{DeviceOp::MakeAGridDescriptor_M_K(a_ms_ns_lengths, a_ms_ks_strides)},
+              b_grid_desc_n_k_{DeviceOp::MakeBGridDescriptor_N_K(b_ns_ks_lengths, b_ns_ks_strides)},
+              ds_grid_desc_m_n_{},
+              e_grid_desc_m_n_{DeviceOp::MakeEGridDescriptor_M_N(e_ms_ns_lengths, e_ms_ns_strides)},
+              a_grid_desc_ak0_m_ak1_{
+                  GridwiseGemm::MakeDefaultAGridDescriptor_AK0_M_AK1(a_grid_desc_m_k_)},
+              b_grid_desc_bk0_n_bk1_{
+                  GridwiseGemm::MakeDefaultBGridDescriptor_BK0_N_BK1(b_grid_desc_n_k_)},
+              ds_grid_desc_mblock_mperblock_nblock_nperblock_{},
+              e_grid_desc_mblock_mperblock_nblock_nperblock_{},
+              block_2_etile_map_{GridwiseGemm::MakeDefaultBlock2ETileMap(e_grid_desc_m_n_)},
+              a_element_op_{a_element_op},
+              b_element_op_{b_element_op},
+              cde_element_op_{cde_element_op},
+              a_mz_stride_{},
+              a_kz_stride_{},
+              b_nz_stride_{},
+              b_kz_stride_{},
+              ds_nz_stride_{},
+              e_nz_stride_{}
+        {
+            // populate pointer, batch stride, desc for Ds
+            static_for<0, NumDTensor, 1>{}([&](auto i) {
+                using DDataType = remove_cvref_t<tuple_element_t<i.value, DsDataType>>;
+
+                // D pointer
+                p_ds_grid_(i) = static_cast<const DDataType*>(p_ds_grid[i]);
+
+                // D desc
+                ds_grid_desc_m_n_(i) =
+                    DeviceOp::MakeEGridDescriptor_M_N(ds_ms_ns_lengths[i], ds_ms_ns_strides[i]);
+            });
+
+            // populate desc for Ds/E
+            if(GridwiseGemm::CheckValidity(a_grid_desc_m_k_,
+                                           b_grid_desc_n_k_,
+                                           ds_grid_desc_m_n_,
+                                           e_grid_desc_m_n_,
+                                           block_2_etile_map_))
+            {
+                e_grid_desc_mblock_mperblock_nblock_nperblock_ =
+                    GridwiseGemm::MakeEGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(
+                        e_grid_desc_m_n_);
+
+                ds_grid_desc_mblock_mperblock_nblock_nperblock_ =
+                    GridwiseGemm::MakeDsGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(
+                        ds_grid_desc_m_n_);
+            }
+
+            // for sanity check of vector memory access
+            a_mz_stride_ = a_ms_ks_strides[NumDimM - 1];
+            a_kz_stride_ = a_ms_ks_strides[NumDimM + NumDimK - 1];
+
+            b_nz_stride_ = b_ns_ks_strides[NumDimN - 1];
+            b_kz_stride_ = b_ns_ks_strides[NumDimN + NumDimK - 1];
+
+            for(index_t i = 0; i < NumDTensor; ++i)
+            {
+                ds_nz_stride_[i] = ds_ms_ns_strides[i][NumDimM + NumDimN - 1];
+            }
+
+            e_nz_stride_ = e_ms_ns_strides[NumDimM + NumDimN - 1];
+        }
+
+        void Print() const
+        {
+            std::cout << "A[M, K]: " << a_grid_desc_m_k_ << std::endl;
+            std::cout << "B[N, K]: " << b_grid_desc_n_k_ << std::endl;
+            static_for<0, NumDTensor, 1>{}(
+                [&](auto i) { std::cout << "Ds[M, N]: " << ds_grid_desc_m_n_[i] << std::endl; });
+            std::cout << "E[M, N]: " << e_grid_desc_m_n_ << std::endl;
+        }
+
+        //  private:
+        // pointers
+        const ADataType* p_a_grid_;
+        const BDataType* p_b_grid_;
+        typename GridwiseGemm::DsGridPointer p_ds_grid_;
+        EDataType* p_e_grid_;
+
+        // tensor descriptors for problem definiton
+        AGridDesc_M_K a_grid_desc_m_k_;
+        BGridDesc_N_K b_grid_desc_n_k_;
+        DsGridDesc_M_N ds_grid_desc_m_n_;
+        EGridDesc_M_N e_grid_desc_m_n_;
+
+        // tensor descriptors for block/thread-wise copy
+        AGridDesc_AK0_M_AK1 a_grid_desc_ak0_m_ak1_;
+        BGridDesc_BK0_N_BK1 b_grid_desc_bk0_n_bk1_;
+        DsGridDesc_MBlock_MPerBlock_NBlock_NPerBlock
+            ds_grid_desc_mblock_mperblock_nblock_nperblock_;
+        EGridDesc_MBlock_MPerBlock_NBlock_NPerBlock e_grid_desc_mblock_mperblock_nblock_nperblock_;
+
+        // block-to-e-tile map
+        Block2ETileMap block_2_etile_map_;
+
+        // element-wise op
+        AElementwiseOperation a_element_op_;
+        BElementwiseOperation b_element_op_;
+        CDEElementwiseOperation cde_element_op_;
+
+        // Strides for the last M/N/K dimensions of A/B/Ds/E
+        //   for sanity check of vector load/store
+        index_t a_mz_stride_;
+        index_t a_kz_stride_;
+        index_t b_nz_stride_;
+        index_t b_kz_stride_;
+        std::array<index_t, NumDTensor> ds_nz_stride_;
+        index_t e_mz_stride_;
+        index_t e_nz_stride_;
+    };
+
+    // Invoker
+    struct Invoker : public BaseInvoker
+    {
+        using Argument = DeviceOp::Argument;
+
+        float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
+        {
+            if(!GridwiseGemm::CheckValidity(arg.a_grid_desc_m_k_,
+                                            arg.b_grid_desc_n_k_,
+                                            arg.ds_grid_desc_m_n_,
+                                            arg.e_grid_desc_m_n_,
+                                            arg.block_2_etile_map_))
+            {
+                throw std::runtime_error(
+                    "wrong! GridwiseGemmMultipleD_xdl_cshuffle has invalid setting");
+            }
+
+            const index_t grid_size =
+                arg.block_2_etile_map_.CalculateGridSize(arg.e_grid_desc_m_n_);
+
+            const auto K =
+                arg.a_grid_desc_ak0_m_ak1_.GetLength(I0) * arg.a_grid_desc_ak0_m_ak1_.GetLength(I2);
+
+            auto launch_kernel = [&](auto has_main_k_block_loop) {
+                constexpr bool has_main_loop = has_main_k_block_loop.value;
+
+                const auto kernel = kernel_contraction_multiple_d_xdl_cshuffle<
+                    GridwiseGemm,
+                    ADataType, // TODO: distiguish A/B datatype
+                    typename GridwiseGemm::DsGridPointer,
+                    EDataType,
+                    AElementwiseOperation,
+                    BElementwiseOperation,
+                    CDEElementwiseOperation,
+                    DeviceOp::AGridDesc_AK0_M_AK1,
+                    DeviceOp::BGridDesc_BK0_N_BK1,
+                    DeviceOp::DsGridDesc_MBlock_MPerBlock_NBlock_NPerBlock,
+                    DeviceOp::EGridDesc_MBlock_MPerBlock_NBlock_NPerBlock,
+                    DeviceOp::Block2ETileMap,
+                    has_main_loop>;
+
+                return launch_and_time_kernel(stream_config,
+                                              kernel,
+                                              dim3(grid_size),
+                                              dim3(BlockSize),
+                                              0,
+                                              arg.p_a_grid_,
+                                              arg.p_b_grid_,
+                                              arg.p_ds_grid_,
+                                              arg.p_e_grid_,
+                                              arg.a_element_op_,
+                                              arg.b_element_op_,
+                                              arg.cde_element_op_,
+                                              arg.a_grid_desc_ak0_m_ak1_,
+                                              arg.b_grid_desc_bk0_n_bk1_,
+                                              arg.ds_grid_desc_mblock_mperblock_nblock_nperblock_,
+                                              arg.e_grid_desc_mblock_mperblock_nblock_nperblock_,
+                                              arg.block_2_etile_map_);
+            };
+
+            if(GridwiseGemm::CalculateHasMainKBlockLoop(K))
+            {
+                return launch_kernel(integral_constant<bool, true>{});
+            }
+            else
+            {
+                return launch_kernel(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::get_device_name() == "gfx908" || ck::get_device_name() == "gfx90a"))
+        {
+            return false;
+        }
+
+        if(!GridwiseGemm::CheckValidity(arg.a_grid_desc_m_k_,
+                                        arg.b_grid_desc_n_k_,
+                                        arg.ds_grid_desc_m_n_,
+                                        arg.e_grid_desc_m_n_,
+                                        arg.block_2_etile_map_))
+        {
+            return false;
+        }
+
+        // check vector access
+        static_assert((ABlockTransferSrcVectorDim == 1 || ABlockTransferSrcVectorDim == 2) &&
+                          (BBlockTransferSrcVectorDim == 1 || BBlockTransferSrcVectorDim == 2),
+                      "wrong!");
+
+        // vector memory access of A: could be on M or AK1 dimension
+        if constexpr(ABlockTransferSrcVectorDim == 1)
+        {
+            if(!(arg.a_mz_stride_ == 1 &&
+                 arg.a_grid_desc_ak0_m_ak1_.GetLength(I1) % ABlockTransferSrcScalarPerVector == 0))
+            {
+                return false;
+            }
+        }
+        else
+        {
+            if(!(arg.a_kz_stride_ == 1 &&
+                 arg.a_grid_desc_ak0_m_ak1_.GetLength(I2) % ABlockTransferSrcScalarPerVector == 0))
+            {
+                return false;
+            }
+        }
+
+        // vector memory access of B: could be on N or BK1 dimension
+        if constexpr(BBlockTransferSrcVectorDim == 1)
+        {
+            if(!(arg.b_nz_stride_ == 1 &&
+                 arg.b_grid_desc_bk0_n_bk1_.GetLength(I1) % BBlockTransferSrcScalarPerVector == 0))
+            {
+                return false;
+            }
+        }
+        else
+        {
+            if(!(arg.b_kz_stride_ == 1 &&
+                 arg.b_grid_desc_bk0_n_bk1_.GetLength(I2) % BBlockTransferSrcScalarPerVector == 0))
+            {
+                return false;
+            }
+        }
+
+        // vector memory access of Ds: always on NPerBlock dimension
+        bool valid_d_access = true;
+
+        static_for<0, NumDTensor, 1>{}([&](auto i) {
+            if(!(arg.ds_nz_stride_[i] == 1 &&
+                 arg.ds_grid_desc_mblock_mperblock_nblock_nperblock_[i].GetLength(I3) %
+                         CDEBlockTransferScalarPerVector_NPerBlock ==
+                     0))
+            {
+                valid_d_access = false;
+            }
+        });
+
+        if(valid_d_access == false)
+        {
+            return false;
+        }
+
+        // vector memory access of E: always on NPerBlock dimension
+        if(!(arg.e_nz_stride_ == 1 &&
+             arg.e_grid_desc_mblock_mperblock_nblock_nperblock_.GetLength(I3) %
+                     CDEBlockTransferScalarPerVector_NPerBlock ==
+                 0))
+        {
+            return false;
+        }
+
+        return true;
+    }
+
+    // polymorphic
+    bool IsSupportedArgument(const BaseArgument* p_arg) override
+    {
+        return IsSupportedArgument(*dynamic_cast<const Argument*>(p_arg));
+    }
+
+    static auto MakeArgument(const void* p_a,
+                             const void* p_b,
+                             std::array<const void*, NumDTensor> p_ds,
+                             void* p_e,
+                             const std::vector<index_t>& a_ms_ns_lengths,
+                             const std::vector<index_t>& a_ms_ks_strides,
+                             const std::vector<index_t>& b_ns_ks_lengths,
+                             const std::vector<index_t>& b_ns_ks_strides,
+                             const std::array<std::vector<index_t>, NumDTensor>& ds_ms_ns_lengths,
+                             const std::array<std::vector<index_t>, NumDTensor>& ds_ms_ns_strides,
+                             const std::vector<index_t>& e_ms_ns_lengths,
+                             const std::vector<index_t>& e_ms_ns_strides,
+                             AElementwiseOperation a_element_op,
+                             BElementwiseOperation b_element_op,
+                             CDEElementwiseOperation cde_element_op)
+    {
+        return Argument{p_a,
+                        p_b,
+                        p_ds,
+                        p_e,
+                        a_ms_ns_lengths,
+                        a_ms_ks_strides,
+                        b_ns_ks_lengths,
+                        b_ns_ks_strides,
+                        ds_ms_ns_lengths,
+                        ds_ms_ns_strides,
+                        e_ms_ns_lengths,
+                        e_ms_ns_strides,
+                        a_element_op,
+                        b_element_op,
+                        cde_element_op};
+    }
+
+    static auto MakeInvoker() { return Invoker{}; }
+
+    // polymorphic
+    std::unique_ptr<BaseArgument>
+    MakeArgumentPointer(const void* p_a,
+                        const void* p_b,
+                        std::array<const void*, NumDTensor> p_ds,
+                        void* p_e,
+                        const std::vector<index_t>& a_ms_ns_lengths,
+                        const std::vector<index_t>& a_ms_ks_strides,
+                        const std::vector<index_t>& b_ns_ks_lengths,
+                        const std::vector<index_t>& b_ns_ks_strides,
+                        const std::array<std::vector<index_t>, NumDTensor>& ds_ms_ns_lengths,
+                        const std::array<std::vector<index_t>, NumDTensor>& ds_ms_ns_strides,
+                        const std::vector<index_t>& e_ms_ns_lengths,
+                        const std::vector<index_t>& e_ms_ns_strides,
+                        AElementwiseOperation a_element_op,
+                        BElementwiseOperation b_element_op,
+                        CDEElementwiseOperation cde_element_op) override
+    {
+        return std::make_unique<Argument>(p_a,
+                                          p_b,
+                                          p_ds,
+                                          p_e,
+                                          a_ms_ns_lengths,
+                                          a_ms_ks_strides,
+                                          b_ns_ks_lengths,
+                                          b_ns_ks_strides,
+                                          ds_ms_ns_lengths,
+                                          ds_ms_ns_strides,
+                                          e_ms_ns_lengths,
+                                          e_ms_ns_strides,
+                                          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 << "DeviceContractionMultipleD_Xdl_CShuffle"
+            << "<"
+            << NumDimM << ", "
+            << NumDimN << ", "
+            << NumDimK << ", "
+            << BlockSize << ", "
+            << MPerBlock << ", "
+            << NPerBlock << ", "
+            << KPerBlock << ", "
+            << AK1 << ", "
+            << BK1 << ", "
+            << ABlockTransferSrcVectorDim << ", "
+            << BBlockTransferSrcVectorDim
+            << ">";
+        // clang-format on
+
+        return str.str();
+    }
+};
+
+} // namespace device
+} // namespace tensor_operation
+} // namespace ck

deps/cget/pkg/ROCmSoftwarePlatform__composable_kernel/install/include/ck/tensor_operation/gpu/device/device_conv2d_backward_weight_xdl_c_shuffle_nhwc_kyxc_nhwk.hpp

+// 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_conv_bwd_weight.hpp"
+#include "ck/tensor_operation/gpu/device/convolution_backward_weight_specialization.hpp"
+#include "ck/tensor_operation/gpu/grid/gridwise_gemm_xdlops_bwd_weight.hpp"
+#include "ck/host_utility/device_prop.hpp"
+#include "ck/host_utility/kernel_launch.hpp"
+
+namespace ck {
+namespace tensor_operation {
+namespace device {
+
+// out[N, Ho, Wo, K] = in[N, Hi, Wi, C] * wei[K, Y, X, C]
+template <typename InDataType,
+          typename WeiDataType,
+          typename OutDataType,
+          typename AccDataType,
+          typename InElementwiseOperation,
+          typename WeiElementwiseOperation,
+          typename OutElementwiseOperation,
+          ck::index_t BlockSize,
+          ck::index_t MPerBlock,
+          ck::index_t NPerBlock,
+          ck::index_t K0PerBlock,
+          ck::index_t K1,
+          ck::index_t MPerXdl,
+          ck::index_t NPerXdl,
+          ck::index_t MXdlPerWave,
+          ck::index_t NXdlPerWave,
+          typename ABlockTransferThreadClusterLengths_K0_M_K1,
+          typename ABlockTransferThreadClusterArrangeOrder,
+          typename ABlockTransferSrcAccessOrder,
+          ck::index_t ABlockTransferSrcVectorDim,
+          ck::index_t ABlockTransferSrcScalarPerVector,
+          ck::index_t ABlockTransferDstScalarPerVector_K1,
+          bool ABlockLdsAddExtraM,
+          typename BBlockTransferThreadClusterLengths_K0_N_K1,
+          typename BBlockTransferThreadClusterArrangeOrder,
+          typename BBlockTransferSrcAccessOrder,
+          ck::index_t BBlockTransferSrcVectorDim,
+          ck::index_t BBlockTransferSrcScalarPerVector,
+          ck::index_t BBlockTransferDstScalarPerVector_K1,
+          bool BBlockLdsAddExtraN,
+          index_t CShuffleMXdlPerWavePerShuffle,
+          index_t CShuffleNXdlPerWavePerShuffle,
+          typename CBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
+          index_t CBlockTransferScalarPerVector_NWaveNPerXdl>
+struct DeviceConv2dBwdWeightXdl_C_Shuffle_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K
+    : public DeviceConvBwdWeight<2,
+                                 ck::tensor_layout::convolution::NHWC,
+                                 ck::tensor_layout::convolution::KYXC,
+                                 ck::tensor_layout::convolution::NHWK,
+                                 InDataType,
+                                 WeiDataType,
+                                 OutDataType,
+                                 InElementwiseOperation,
+                                 WeiElementwiseOperation,
+                                 OutElementwiseOperation>
+{
+    using DeviceOp =
+        DeviceConv2dBwdWeightXdl_C_Shuffle_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K;
+
+    using ADataType = OutDataType;
+    using BDataType = InDataType;
+    using CDataType = WeiDataType;
+
+    using AElementwiseOperation = OutElementwiseOperation;
+    using BElementwiseOperation = InElementwiseOperation;
+    using CElementwiseOperation = WeiElementwiseOperation;
+
+    // TODO make A/B datatype different
+    using ABDataType = InDataType;
+
+    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 constexpr auto GemmK1Number = K1Number;
+
+    static constexpr auto N1Number = K1Number;
+
+    // Bytes per 32 lds bank: 32 * 4 bytes
+    static constexpr auto BankLength = 128;
+    static constexpr auto ElePerBank = BankLength / sizeof(ADataType);
+
+    // M1 & M0
+    static constexpr auto ABlockLdsM1PerBlock = ElePerBank / K1;
+    static constexpr auto ABlockLdsM0PerBlock = MPerBlock / ABlockLdsM1PerBlock;
+    static constexpr auto ABlockLdsM1Padding  = 4;
+
+    // N1 & N0
+    static constexpr auto BBlockLdsN1PerBlock = ElePerBank / K1;
+    static constexpr auto BBlockLdsN0PerBlock = NPerBlock / BBlockLdsN1PerBlock;
+    static constexpr auto BBlockLdsN1Padding  = 4;
+
+    static auto
+    MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N(ck::index_t N,
+                                                    ck::index_t K,
+                                                    ck::index_t C,
+                                                    std::vector<ck::index_t> input_spatial_lengths,
+                                                    std::vector<ck::index_t> filter_spatial_lengths,
+                                                    std::vector<ck::index_t> output_spatial_lengths,
+                                                    std::vector<ck::index_t> conv_filter_strides,
+                                                    std::vector<ck::index_t> conv_filter_dilations,
+                                                    std::vector<ck::index_t> input_left_pads,
+                                                    std::vector<ck::index_t> input_right_pads,
+                                                    ck::index_t batch_k)
+    {
+        using namespace ck;
+
+        const index_t Hi = input_spatial_lengths[0];
+        const index_t Wi = input_spatial_lengths[1];
+
+        const index_t Ho = output_spatial_lengths[0];
+        const index_t Wo = output_spatial_lengths[1];
+
+        const index_t Y = filter_spatial_lengths[0];
+        const index_t X = filter_spatial_lengths[1];
+
+        const index_t ConvStrideH = conv_filter_strides[0];
+        const index_t ConvStrideW = conv_filter_strides[1];
+
+        const index_t ConvDilationH = conv_filter_dilations[0];
+        const index_t ConvDilationW = conv_filter_dilations[1];
+
+        const index_t InLeftPadH = input_left_pads[0];
+        const index_t InLeftPadW = input_left_pads[1];
+
+        const index_t InRightPadH = input_right_pads[0];
+        const index_t InRightPadW = input_right_pads[1];
+
+        const index_t GemmKTotal = N * Ho * Wo;
+        const index_t GemmM      = K;
+        const index_t GemmN      = C * X * Y;
+
+        const index_t GemmKBatch = batch_k;
+        const index_t GemmK0 =
+            math::integer_divide_ceil(GemmKTotal, GemmK1Number * K0PerBlock * GemmKBatch) *
+            K0PerBlock;
+
+        const auto in_n_hi_wi_c_grid_desc =
+            make_naive_tensor_descriptor_packed(make_tuple(N, Hi, Wi, C));
+
+        // A: output tensor
+        const index_t N0          = N / N1Number;
+        const index_t GemmK0Total = N0 * Ho * Wo;
+
+        const index_t GemmK0S =
+            math::integer_divide_ceil(GemmK0Total, K0PerBlock * GemmKBatch) * K0PerBlock;
+        const index_t GemmK0Pad = GemmKBatch * GemmK0S;
+        const auto out_n_ho_wo_k_grid_desc =
+            make_naive_tensor_descriptor_packed(make_tuple(N, Ho * Wo, K));
+
+        const auto out_n0_ho_wo_k_n1_grid_desc =
+            transform_tensor_descriptor(out_n_ho_wo_k_grid_desc,
+                                        make_tuple(make_unmerge_transform(make_tuple(N0, N1Number)),
+                                                   make_pass_through_transform(Ho * Wo),
+                                                   make_pass_through_transform(K)),
+                                        make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
+                                        make_tuple(Sequence<0, 3>{}, Sequence<1>{}, Sequence<2>{}));
+
+        const auto out_gemmk0total_gemmm_gemmk1_grid_desc =
+            transform_tensor_descriptor(out_n0_ho_wo_k_n1_grid_desc,
+                                        make_tuple(make_merge_transform(make_tuple(N0, Ho * Wo)),
+                                                   make_pass_through_transform(K),
+                                                   make_pass_through_transform(N1Number)),
+                                        make_tuple(Sequence<0, 1>{}, Sequence<2>{}, Sequence<3>{}),
+                                        make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
+
+        const auto out_gemmk0pad_gemmm_gemmk1_grid_desc = transform_tensor_descriptor(
+            out_gemmk0total_gemmm_gemmk1_grid_desc,
+            make_tuple(make_right_pad_transform(GemmK0Total, GemmK0Pad - GemmK0Total),
+                       make_pass_through_transform(GemmM),
+                       make_pass_through_transform(N1Number)),
+            make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
+            make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
+
+        const auto out_gemmkbatch_gemmk0_gemmm_gemmk1_grid_desc = transform_tensor_descriptor(
+            out_gemmk0pad_gemmm_gemmk1_grid_desc,
+            make_tuple(make_unmerge_transform(make_tuple(GemmKBatch, GemmK0)),
+                       make_pass_through_transform(GemmM),
+                       make_pass_through_transform(N1Number)),
+            make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
+            make_tuple(Sequence<0, 1>{}, Sequence<2>{}, Sequence<3>{}));
+
+        // B: input tensor
+        const auto in_n_hip_wip_c_grid_desc = transform_tensor_descriptor(
+            in_n_hi_wi_c_grid_desc,
+            make_tuple(make_pass_through_transform(N),
+                       make_pad_transform(Hi, InLeftPadH, InRightPadH),
+                       make_pad_transform(Wi, InLeftPadW, InRightPadW),
+                       make_pass_through_transform(C)),
+            make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
+            make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
+
+        const auto in_n_y_ho_x_wo_c_grid_desc = transform_tensor_descriptor(
+            in_n_hip_wip_c_grid_desc,
+            make_tuple(
+                make_pass_through_transform(N),
+                make_embed_transform(make_tuple(Y, Ho), make_tuple(ConvDilationH, ConvStrideH)),
+                make_embed_transform(make_tuple(X, Wo), make_tuple(ConvDilationW, ConvStrideW)),
+                make_pass_through_transform(C)),
+            make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
+            make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}));
+
+        const auto in_n0_y_ho_x_wo_c_n1_grid_desc =
+            transform_tensor_descriptor(in_n_y_ho_x_wo_c_grid_desc,
+                                        make_tuple(make_unmerge_transform(make_tuple(N0, N1Number)),
+                                                   make_pass_through_transform(Y),
+                                                   make_pass_through_transform(Ho),
+                                                   make_pass_through_transform(X),
+                                                   make_pass_through_transform(Wo),
+                                                   make_pass_through_transform(C)),
+                                        make_tuple(Sequence<0>{},
+                                                   Sequence<1>{},
+                                                   Sequence<2>{},
+                                                   Sequence<3>{},
+                                                   Sequence<4>{},
+                                                   Sequence<5>{}),
+                                        make_tuple(Sequence<0, 6>{},
+                                                   Sequence<1>{},
+                                                   Sequence<2>{},
+                                                   Sequence<3>{},
+                                                   Sequence<4>{},
+                                                   Sequence<5>{}));
+
+        const auto in_gemmk0total_gemmn_gemmk1_grid_desc = transform_tensor_descriptor(
+            in_n0_y_ho_x_wo_c_n1_grid_desc,
+            make_tuple(make_merge_transform(make_tuple(N0, Ho, Wo)),
+                       make_merge_transform(make_tuple(Y, X, C)),
+                       make_pass_through_transform(N1Number)),
+            make_tuple(Sequence<0, 2, 4>{}, Sequence<1, 3, 5>{}, Sequence<6>{}),
+            make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
+
+        const auto in_gemmk0pad_gemmn_gemmk1_grid_desc = transform_tensor_descriptor(
+            in_gemmk0total_gemmn_gemmk1_grid_desc,
+            make_tuple(make_right_pad_transform(GemmK0Total, GemmK0Pad - GemmK0Total),
+                       make_pass_through_transform(GemmN),
+                       make_pass_through_transform(N1Number)),
+            make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
+            make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
+
+        const auto in_gemmkbatch_gemmk0_gemmn_gemmk1_grid_desc = transform_tensor_descriptor(
+            in_gemmk0pad_gemmn_gemmk1_grid_desc,
+            make_tuple(make_unmerge_transform(make_tuple(GemmKBatch, GemmK0)),
+                       make_pass_through_transform(GemmN),
+                       make_pass_through_transform(N1Number)),
+            make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
+            make_tuple(Sequence<0, 1>{}, Sequence<2>{}, Sequence<3>{}));
+
+        // C: weight tensor
+        const auto wei_gemmm_gemmn_grid_desc =
+            make_naive_tensor_descriptor_packed(make_tuple(K, Y * X * C));
+
+        return make_tuple(out_gemmkbatch_gemmk0_gemmm_gemmk1_grid_desc,
+                          in_gemmkbatch_gemmk0_gemmn_gemmk1_grid_desc,
+                          wei_gemmm_gemmn_grid_desc);
+    }
+
+    using ABCGridDescs = decltype(MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N(
+        1, 1, 1, {1, 1}, {1, 1}, {1, 1}, {1, 1}, {1, 1}, {1, 1}, {1, 1}, 1));
+
+    using AGridDesc_K0_M_K1 = remove_cvref_t<decltype(ABCGridDescs{}[I0])>;
+    using BGridDesc_K0_N_K1 = remove_cvref_t<decltype(ABCGridDescs{}[I1])>;
+    using CGridDesc_M_N     = remove_cvref_t<decltype(ABCGridDescs{}[I2])>;
+
+    // GridwiseGemm
+    using GridwiseGemm = GridwiseGemm_bk0mk1_bk0nk1_mn_xdlops_bwd_weight<
+        BlockSize,
+        ADataType, // TODO: distinguish A/B datatype
+        AccDataType,
+        CDataType,
+        InMemoryDataOperationEnum::Set,
+        AGridDesc_K0_M_K1,
+        BGridDesc_K0_N_K1,
+        CGridDesc_M_N,
+        AElementwiseOperation,
+        BElementwiseOperation,
+        CElementwiseOperation,
+        MPerBlock,
+        NPerBlock,
+        K0PerBlock,
+        MPerXdl,
+        NPerXdl,
+        K1,
+        MXdlPerWave,
+        NXdlPerWave,
+        ABlockTransferThreadClusterLengths_K0_M_K1,
+        ABlockTransferThreadClusterArrangeOrder,
+        ABlockTransferSrcAccessOrder,
+        ABlockTransferSrcVectorDim,
+        ABlockTransferSrcScalarPerVector,
+        ABlockTransferDstScalarPerVector_K1,
+        false, // AThreadTransferSrcResetCoordinateAfterRun,
+        ABlockLdsAddExtraM,
+        ABlockLdsM1PerBlock,
+        ABlockLdsM0PerBlock,
+        ABlockLdsM1Padding,
+        BBlockTransferThreadClusterLengths_K0_N_K1,
+        BBlockTransferThreadClusterArrangeOrder,
+        BBlockTransferSrcAccessOrder,
+        BBlockTransferSrcVectorDim,
+        BBlockTransferSrcScalarPerVector,
+        BBlockTransferDstScalarPerVector_K1,
+        false, // BThreadTransferSrcResetCoordinateAfterRun,
+        BBlockLdsAddExtraN,
+        BBlockLdsN1PerBlock,
+        BBlockLdsN0PerBlock,
+        BBlockLdsN1Padding,
+        CShuffleMXdlPerWavePerShuffle,
+        CShuffleNXdlPerWavePerShuffle,
+        CBlockTransferScalarPerVector_NWaveNPerXdl,
+        CBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
+        true,
+        true>;
+
+    using GridwiseGemmAtomicAdd = GridwiseGemm_bk0mk1_bk0nk1_mn_xdlops_bwd_weight<
+        BlockSize,
+        ADataType, // TODO: distinguish A/B datatype
+        AccDataType,
+        CDataType,
+        InMemoryDataOperationEnum::AtomicAdd,
+        AGridDesc_K0_M_K1,
+        BGridDesc_K0_N_K1,
+        CGridDesc_M_N,
+        AElementwiseOperation,
+        BElementwiseOperation,
+        CElementwiseOperation,
+        MPerBlock,
+        NPerBlock,
+        K0PerBlock,
+        MPerXdl,
+        NPerXdl,
+        K1,
+        MXdlPerWave,
+        NXdlPerWave,
+        ABlockTransferThreadClusterLengths_K0_M_K1,
+        ABlockTransferThreadClusterArrangeOrder,
+        ABlockTransferSrcAccessOrder,
+        ABlockTransferSrcVectorDim,
+        ABlockTransferSrcScalarPerVector,
+        ABlockTransferDstScalarPerVector_K1,
+        false, // AThreadTransferSrcResetCoordinateAfterRun,
+        ABlockLdsAddExtraM,
+        ABlockLdsM1PerBlock,
+        ABlockLdsM0PerBlock,
+        ABlockLdsM1Padding,
+        BBlockTransferThreadClusterLengths_K0_N_K1,
+        BBlockTransferThreadClusterArrangeOrder,
+        BBlockTransferSrcAccessOrder,
+        BBlockTransferSrcVectorDim,
+        BBlockTransferSrcScalarPerVector,
+        BBlockTransferDstScalarPerVector_K1,
+        false, // BThreadTransferSrcResetCoordinateAfterRun,
+        BBlockLdsAddExtraN,
+        BBlockLdsN1PerBlock,
+        BBlockLdsN0PerBlock,
+        BBlockLdsN1Padding,
+        CShuffleMXdlPerWavePerShuffle,
+        CShuffleNXdlPerWavePerShuffle,
+        CBlockTransferScalarPerVector_NWaveNPerXdl,
+        CBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
+        true,
+        true>;
+    // Argument
+    using CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock =
+        decltype(GridwiseGemm::MakeCGridDesc_MBlock_MPerBlock_NBlock_NPerBlock(CGridDesc_M_N{}));
+
+    using Block2CTileMap =
+        decltype(GridwiseGemm::MakeCBlockClusterAdaptor(CGridDesc_M_N{}, 1, 1, 1));
+    struct Argument : public BaseArgument
+    {
+        Argument(const InDataType* p_in_grid,
+                 WeiDataType* p_wei_grid,
+                 const OutDataType* p_out_grid,
+                 ck::index_t N,
+                 ck::index_t K,
+                 ck::index_t C,
+                 std::vector<ck::index_t> input_spatial_lengths,
+                 std::vector<ck::index_t> filter_spatial_lengths,
+                 std::vector<ck::index_t> output_spatial_lengths,
+                 std::vector<ck::index_t> conv_filter_strides,
+                 std::vector<ck::index_t> conv_filter_dilations,
+                 std::vector<ck::index_t> input_left_pads,
+                 std::vector<ck::index_t> input_right_pads,
+                 ck::index_t M01,
+                 ck::index_t N01,
+                 InElementwiseOperation in_element_op,
+                 WeiElementwiseOperation wei_element_op,
+                 OutElementwiseOperation out_element_op,
+                 ck::index_t split_k)
+            : p_a_grid_{p_out_grid},
+              p_b_grid_{p_in_grid},
+              p_c_grid_{p_wei_grid},
+              a_grid_desc_kbatch_k0_m_k1_{},
+              b_grid_desc_kbatch_k0_n_k1_{},
+              c_grid_desc_m_n_{},
+              c_grid_desc_mblock_mperblock_nblock_nperblock_{},
+              block_2_ctile_map_{},
+              M01_{M01},
+              N01_{N01},
+              a_element_op_{out_element_op},
+              b_element_op_{in_element_op},
+              c_element_op_{wei_element_op},
+              Conv_N_{N},
+              Conv_K_{K},
+              Conv_C_{C},
+              output_spatial_lengths_{output_spatial_lengths},
+              filter_spatial_lengths_{filter_spatial_lengths},
+              conv_filter_strides_{conv_filter_strides},
+              input_left_pads_{input_left_pads},
+              input_right_pads_{input_right_pads},
+              k_batch_{split_k}
+        {
+            const auto descs =
+                DeviceOp::MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N(N,
+                                                                          K,
+                                                                          C,
+                                                                          input_spatial_lengths,
+                                                                          filter_spatial_lengths,
+                                                                          output_spatial_lengths,
+                                                                          conv_filter_strides,
+                                                                          conv_filter_dilations,
+                                                                          input_left_pads,
+                                                                          input_right_pads,
+                                                                          k_batch_);
+
+            a_grid_desc_kbatch_k0_m_k1_ = descs[I0];
+            b_grid_desc_kbatch_k0_n_k1_ = descs[I1];
+            c_grid_desc_m_n_            = descs[I2];
+
+            block_2_ctile_map_ =
+                GridwiseGemm::MakeCBlockClusterAdaptor(c_grid_desc_m_n_, M01, N01, k_batch_);
+
+            if(GridwiseGemm::CheckValidity(a_grid_desc_kbatch_k0_m_k1_,
+                                           b_grid_desc_kbatch_k0_n_k1_,
+                                           c_grid_desc_m_n_,
+                                           block_2_ctile_map_))
+            {
+                c_grid_desc_mblock_mperblock_nblock_nperblock_ =
+                    GridwiseGemm::MakeCGridDesc_MBlock_MPerBlock_NBlock_NPerBlock(c_grid_desc_m_n_);
+            }
+        }
+
+        const ADataType* p_a_grid_;
+        const BDataType* p_b_grid_;
+        CDataType* p_c_grid_;
+        AGridDesc_K0_M_K1 a_grid_desc_kbatch_k0_m_k1_;
+        BGridDesc_K0_N_K1 b_grid_desc_kbatch_k0_n_k1_;
+        CGridDesc_M_N c_grid_desc_m_n_;
+        CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock c_grid_desc_mblock_mperblock_nblock_nperblock_;
+        Block2CTileMap block_2_ctile_map_;
+        index_t M01_;
+        index_t N01_;
+        InElementwiseOperation a_element_op_;
+        OutElementwiseOperation b_element_op_;
+        WeiElementwiseOperation c_element_op_;
+        // for checking IsSupportedArgument()
+        index_t Conv_N_;
+        index_t Conv_K_;
+        index_t Conv_C_;
+        std::vector<index_t> output_spatial_lengths_;
+        std::vector<index_t> filter_spatial_lengths_;
+        std::vector<index_t> conv_filter_strides_;
+        std::vector<index_t> input_left_pads_;
+        std::vector<index_t> input_right_pads_;
+        index_t k_batch_;
+    };
+
+    // Invoker
+    struct Invoker : public BaseInvoker
+    {
+        using Argument = DeviceOp::Argument;
+
+        void ShowInfo(const Argument& arg)
+        {
+            std::cout << "arg.a_grid_desc_kbatch_k0_m_k1_{"
+                      << arg.a_grid_desc_kbatch_k0_m_k1_.GetLength(I0) << ", "
+                      << arg.a_grid_desc_kbatch_k0_m_k1_.GetLength(I1) << ", "
+                      << arg.a_grid_desc_kbatch_k0_m_k1_.GetLength(I2) << ", "
+                      << arg.a_grid_desc_kbatch_k0_m_k1_.GetLength(I3) << "}" << std::endl;
+
+            std::cout << "arg.b_grid_desc_kbatch_k0_n_k1_{"
+                      << arg.b_grid_desc_kbatch_k0_n_k1_.GetLength(I0) << ", "
+                      << arg.b_grid_desc_kbatch_k0_n_k1_.GetLength(I1) << ", "
+                      << arg.b_grid_desc_kbatch_k0_n_k1_.GetLength(I2) << ", "
+                      << arg.b_grid_desc_kbatch_k0_n_k1_.GetLength(I3) << "}" << std::endl;
+
+            std::cout << "arg.c_grid_desc_m_n_{ " << arg.c_grid_desc_m_n_.GetLength(I0) << ", "
+                      << arg.c_grid_desc_m_n_.GetLength(I1) << "}" << std::endl;
+        }
+
+        float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
+        {
+            ShowInfo(arg);
+
+            if(!GridwiseGemm::CheckValidity(arg.a_grid_desc_kbatch_k0_m_k1_,
+                                            arg.b_grid_desc_kbatch_k0_n_k1_,
+                                            arg.c_grid_desc_m_n_,
+                                            arg.block_2_ctile_map_))
+            {
+                throw std::runtime_error(
+                    "wrong! GridwiseGemm_bk0mk1_bk0nk1_mn_xdlops_bwd_weight has invalid setting");
+            }
+            const auto kbatch = arg.a_grid_desc_kbatch_k0_m_k1_.GetLength(I0);
+            const index_t grid_size =
+                arg.block_2_ctile_map_.CalculateGridSize(arg.c_grid_desc_m_n_);
+
+            const auto K0 = arg.a_grid_desc_kbatch_k0_m_k1_.GetLength(I1);
+
+            const bool has_main_k0_block_loop = GridwiseGemm::CalculateHasMainK0BlockLoop(K0);
+
+            float ave_time = 0;
+
+            const auto Run = [&](const auto& kernel) {
+                hipGetErrorString(hipMemset(
+                    arg.p_c_grid_,
+                    0,
+                    arg.c_grid_desc_mblock_mperblock_nblock_nperblock_.GetElementSpaceSize() *
+                        sizeof(CDataType)));
+
+                ave_time =
+                    launch_and_time_kernel(stream_config,
+                                           kernel,
+                                           dim3(grid_size),
+                                           dim3(BlockSize),
+                                           0,
+                                           arg.p_a_grid_,
+                                           arg.p_b_grid_,
+                                           arg.p_c_grid_,
+                                           arg.a_grid_desc_kbatch_k0_m_k1_,
+                                           arg.b_grid_desc_kbatch_k0_n_k1_,
+                                           arg.c_grid_desc_mblock_mperblock_nblock_nperblock_,
+                                           arg.a_element_op_,
+                                           arg.b_element_op_,
+                                           arg.c_element_op_,
+                                           arg.block_2_ctile_map_);
+            };
+
+            if(has_main_k0_block_loop)
+            {
+                if(kbatch == 1)
+                {
+                    const auto kernel = kernel_gemm_xdlops_bwd_weight<
+                        GridwiseGemm,
+                        ADataType, // TODO: distiguish A/B datatype
+                        CDataType,
+                        remove_reference_t<DeviceOp::AGridDesc_K0_M_K1>,
+                        remove_reference_t<DeviceOp::BGridDesc_K0_N_K1>,
+                        remove_reference_t<DeviceOp::CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock>,
+                        OutElementwiseOperation,
+                        InElementwiseOperation,
+                        WeiElementwiseOperation,
+                        remove_reference_t<DeviceOp::Block2CTileMap>,
+                        true>;
+
+                    Run(kernel);
+                }
+                else
+                {
+                    const auto kernel = kernel_gemm_xdlops_bwd_weight<
+                        GridwiseGemmAtomicAdd,
+                        ADataType, // TODO: distiguish A/B datatype
+                        CDataType,
+                        remove_reference_t<DeviceOp::AGridDesc_K0_M_K1>,
+                        remove_reference_t<DeviceOp::BGridDesc_K0_N_K1>,
+                        remove_reference_t<DeviceOp::CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock>,
+                        OutElementwiseOperation,
+                        InElementwiseOperation,
+                        WeiElementwiseOperation,
+                        remove_reference_t<DeviceOp::Block2CTileMap>,
+                        true>;
+
+                    Run(kernel);
+                }
+            }
+            else
+            {
+                if(kbatch == 1)
+                {
+                    const auto kernel = kernel_gemm_xdlops_bwd_weight<
+                        GridwiseGemm,
+                        ADataType, // TODO: distiguish A/B datatype
+                        CDataType,
+                        remove_reference_t<DeviceOp::AGridDesc_K0_M_K1>,
+                        remove_reference_t<DeviceOp::BGridDesc_K0_N_K1>,
+                        remove_reference_t<DeviceOp::CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock>,
+                        OutElementwiseOperation,
+                        InElementwiseOperation,
+                        WeiElementwiseOperation,
+                        remove_reference_t<DeviceOp::Block2CTileMap>,
+                        false>;
+
+                    Run(kernel);
+                }
+                else
+                {
+                    const auto kernel = kernel_gemm_xdlops_bwd_weight<
+                        GridwiseGemmAtomicAdd,
+                        ADataType, // TODO: distiguish A/B datatype
+                        CDataType,
+                        remove_reference_t<DeviceOp::AGridDesc_K0_M_K1>,
+                        remove_reference_t<DeviceOp::BGridDesc_K0_N_K1>,
+                        remove_reference_t<DeviceOp::CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock>,
+                        OutElementwiseOperation,
+                        InElementwiseOperation,
+                        WeiElementwiseOperation,
+                        remove_reference_t<DeviceOp::Block2CTileMap>,
+                        false>;
+
+                    Run(kernel);
+                }
+            }
+
+            return ave_time;
+        }
+
+        float Run(const BaseArgument* p_arg,
+                  const StreamConfig& stream_config = StreamConfig{}) override
+        {
+            return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
+        }
+    };
+
+    static constexpr bool IsValidCompilationParameter()
+    {
+        // TODO: properly implement this check
+        return true;
+    }
+
+    static bool IsSupportedArgument(const Argument& arg)
+    {
+        // vector load A/B matrix from global memory
+        if(!(ABlockTransferSrcVectorDim == 2 && BBlockTransferSrcVectorDim == 2 &&
+             arg.Conv_K_ % ABlockTransferSrcScalarPerVector == 0 &&
+             arg.Conv_C_ % BBlockTransferSrcScalarPerVector == 0))
+        {
+            return false;
+        }
+
+        // unmerge N to N0 and N1, where N1 equals to K1
+        if(!(arg.Conv_N_ % K1 == 0))
+        {
+            return false;
+        }
+
+        // vector store C matrix into global memory
+        if(!(arg.Conv_C_ % CBlockTransferScalarPerVector_NWaveNPerXdl == 0))
+        {
+            return false;
+        }
+
+        // Gridwise GEMM size
+        return GridwiseGemm::CheckValidity(arg.a_grid_desc_kbatch_k0_m_k1_,
+                                           arg.b_grid_desc_kbatch_k0_n_k1_,
+                                           arg.c_grid_desc_m_n_,
+                                           arg.block_2_ctile_map_);
+    }
+
+    bool IsSupportedArgument(const BaseArgument* p_arg) override
+    {
+        return IsSupportedArgument(*dynamic_cast<const Argument*>(p_arg));
+    }
+
+    static auto MakeArgument(const InDataType* p_in_grid,
+                             WeiDataType* p_wei_grid,
+                             const OutDataType* p_out_grid,
+                             ck::index_t N,
+                             ck::index_t K,
+                             ck::index_t C,
+                             std::vector<ck::index_t> input_spatial_lengths,
+                             std::vector<ck::index_t> filter_spatial_lengths,
+                             std::vector<ck::index_t> output_spatial_lengths,
+                             std::vector<ck::index_t> conv_filter_strides,
+                             std::vector<ck::index_t> conv_filter_dilations,
+                             std::vector<ck::index_t> input_left_pads,
+                             std::vector<ck::index_t> input_right_pads,
+                             InElementwiseOperation in_element_op,
+                             WeiElementwiseOperation wei_element_op,
+                             OutElementwiseOperation out_element_op,
+                             ck::index_t split_k)
+    {
+        return Argument{p_in_grid,
+                        p_wei_grid,
+                        p_out_grid,
+                        N,
+                        K,
+                        C,
+                        input_spatial_lengths,
+                        filter_spatial_lengths,
+                        output_spatial_lengths,
+                        conv_filter_strides,
+                        conv_filter_dilations,
+                        input_left_pads,
+                        input_right_pads,
+                        1,
+                        1,
+                        in_element_op,
+                        wei_element_op,
+                        out_element_op,
+                        split_k};
+    }
+
+    static auto MakeInvoker() { return Invoker{}; }
+
+    std::unique_ptr<BaseArgument>
+    MakeArgumentPointer(const void* p_in_grid,
+                        void* p_wei_grid,
+                        const void* p_out_grid,
+                        ck::index_t N,
+                        ck::index_t K,
+                        ck::index_t C,
+                        std::vector<ck::index_t> input_spatial_lengths,
+                        std::vector<ck::index_t> filter_spatial_lengths,
+                        std::vector<ck::index_t> output_spatial_lengths,
+                        std::vector<ck::index_t> conv_filter_strides,
+                        std::vector<ck::index_t> conv_filter_dilations,
+                        std::vector<ck::index_t> input_left_pads,
+                        std::vector<ck::index_t> input_right_pads,
+                        InElementwiseOperation in_element_op,
+                        WeiElementwiseOperation wei_element_op,
+                        OutElementwiseOperation out_element_op,
+                        ck::index_t split_k) override
+    {
+        return std::make_unique<Argument>(static_cast<const InDataType*>(p_in_grid),
+                                          static_cast<WeiDataType*>(p_wei_grid),
+                                          static_cast<const OutDataType*>(p_out_grid),
+                                          N,
+                                          K,
+                                          C,
+                                          input_spatial_lengths,
+                                          filter_spatial_lengths,
+                                          output_spatial_lengths,
+                                          conv_filter_strides,
+                                          conv_filter_dilations,
+                                          input_left_pads,
+                                          input_right_pads,
+                                          1,
+                                          1,
+                                          in_element_op,
+                                          wei_element_op,
+                                          out_element_op,
+                                          split_k);
+    }
+
+    std::unique_ptr<BaseInvoker> MakeInvokerPointer() override
+    {
+        return std::make_unique<Invoker>(Invoker{});
+    }
+
+    std::string GetTypeString() const override
+    {
+        auto str = std::stringstream();
+
+        // clang-format off
+        str << "DeviceConv2dBwdWeightXdl_C_Shuffle_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K"
+            << "<"
+            << BlockSize << ", "
+            << MPerBlock << ", "
+            << NPerBlock << ", "
+            << K0PerBlock
+            << ">";
+        // clang-format on
+
+        return str.str();
+    }
+};
+
+} // namespace device
+} // namespace tensor_operation
+} // namespace ck

deps/cget/pkg/ROCmSoftwarePlatform__composable_kernel/install/include/ck/tensor_operation/gpu/device/device_conv2d_bwd_data_xdl_nhwc_kyxc_nhwk.hpp

+// 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_conv_bwd_data.hpp"
+#include "ck/tensor_operation/gpu/device/convolution_backward_data_specialization.hpp"
+#include "ck/tensor_operation/gpu/grid/gridwise_gemm_xdlops_v2r3.hpp"
+#include "ck/host_utility/device_prop.hpp"
+#include "ck/host_utility/kernel_launch.hpp"
+
+namespace ck {
+namespace tensor_operation {
+namespace device {
+
+// out[N, Ho, Wo, K] = in[N, Hi, Wi, C] * wei[K, Y, X, C]
+template <typename InDataType,
+          typename WeiDataType,
+          typename OutDataType,
+          typename AccDataType,
+          typename InElementwiseOperation,
+          typename WeiElementwiseOperation,
+          typename OutElementwiseOperation,
+          ConvolutionBackwardDataSpecialization ConvBackwardDataSpecialization,
+          ck::index_t BlockSize,
+          ck::index_t MPerBlock,
+          ck::index_t NPerBlock,
+          ck::index_t K0PerBlock,
+          ck::index_t K1,
+          ck::index_t MPerXdl,
+          ck::index_t NPerXdl,
+          ck::index_t MXdlPerWave,
+          ck::index_t NXdlPerWave,
+          typename ABlockTransferThreadClusterLengths_K0_M_K1,
+          typename ABlockTransferThreadClusterArrangeOrder,
+          typename ABlockTransferSrcAccessOrder,
+          ck::index_t ABlockTransferSrcVectorDim,
+          ck::index_t ABlockTransferSrcScalarPerVector,
+          ck::index_t ABlockTransferDstScalarPerVector_K1,
+          bool ABlockLdsAddExtraM,
+          typename BBlockTransferThreadClusterLengths_K0_N_K1,
+          typename BBlockTransferThreadClusterArrangeOrder,
+          typename BBlockTransferSrcAccessOrder,
+          ck::index_t BBlockTransferSrcVectorDim,
+          ck::index_t BBlockTransferSrcScalarPerVector,
+          ck::index_t BBlockTransferDstScalarPerVector_K1,
+          bool BBlockLdsAddExtraN,
+          ck::index_t CThreadTransferSrcDstVectorDim,
+          ck::index_t CThreadTransferDstScalarPerVector>
+struct DeviceConv2dBwdDataXdl_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K
+    : public DeviceConvBwdData<2,
+                               ck::tensor_layout::convolution::NHWC,
+                               ck::tensor_layout::convolution::KYXC,
+                               ck::tensor_layout::convolution::NHWK,
+                               InDataType,
+                               WeiDataType,
+                               OutDataType,
+                               InElementwiseOperation,
+                               WeiElementwiseOperation,
+                               OutElementwiseOperation>
+{
+    using DeviceOp = DeviceConv2dBwdDataXdl_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K;
+
+    using ADataType = OutDataType;
+    using BDataType = WeiDataType;
+    using CDataType = InDataType;
+
+    // TODO make A/B datatype different
+    using ABDataType = InDataType;
+
+    static constexpr index_t NDimSpatial = 2;
+
+    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_assert((K1 % ABlockTransferThreadClusterLengths_K0_M_K1{}[I2]) %
+                      ABlockTransferSrcScalarPerVector ==
+                  0);
+    static_assert((NPerBlock / BBlockTransferThreadClusterLengths_K0_N_K1{}[I1]) %
+                      BBlockTransferSrcScalarPerVector ==
+                  0);
+
+    static constexpr auto K1Number     = Number<K1>{};
+    static constexpr auto GemmK1Number = K1Number;
+
+    static auto
+    MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N(ck::index_t N,
+                                                    ck::index_t K,
+                                                    ck::index_t C,
+                                                    std::vector<ck::index_t> input_spatial_lengths,
+                                                    std::vector<ck::index_t> filter_spatial_lengths,
+                                                    std::vector<ck::index_t> output_spatial_lengths,
+                                                    std::vector<ck::index_t> conv_filter_strides,
+                                                    std::vector<ck::index_t> conv_filter_dilations,
+                                                    std::vector<ck::index_t> input_left_pads,
+                                                    std::vector<ck::index_t> input_right_pads,
+                                                    index_t i_ytilde,
+                                                    index_t i_xtilde)
+    {
+        using namespace ck;
+
+        const index_t Hi = input_spatial_lengths[0];
+        const index_t Wi = input_spatial_lengths[1];
+
+        const index_t Ho = output_spatial_lengths[0];
+        const index_t Wo = output_spatial_lengths[1];
+
+        const index_t Y = filter_spatial_lengths[0];
+        const index_t X = filter_spatial_lengths[1];
+
+        const index_t InLeftPadH = input_left_pads[0];
+        const index_t InLeftPadW = input_left_pads[1];
+
+        const index_t InRightPadH = input_right_pads[0];
+        const index_t InRightPadW = input_right_pads[1];
+
+        const index_t ConvStrideH = conv_filter_strides[0];
+        const index_t ConvStrideW = conv_filter_strides[1];
+
+        const index_t ConvDilationH = conv_filter_dilations[0];
+        const index_t ConvDilationW = conv_filter_dilations[1];
+
+        const auto K0 = K / K1;
+
+        const auto out_n_ho_wo_k_grid_desc =
+            make_naive_tensor_descriptor_packed(make_tuple(N, Ho, Wo, K));
+        const auto wei_k_y_x_c_grid_desc =
+            make_naive_tensor_descriptor_packed(make_tuple(K, Y, X, C));
+        const auto in_n_hi_wi_c_grid_desc =
+            make_naive_tensor_descriptor_packed(make_tuple(N, Hi, Wi, C));
+
+        if constexpr(ConvBackwardDataSpecialization ==
+                     ConvolutionBackwardDataSpecialization::Filter1x1Stride1Pad0)
+        {
+            // A: output tensor
+            const auto out_gemmk0_gemmm_gemmk1_grid_desc = transform_tensor_descriptor(
+                make_naive_tensor_descriptor_packed(make_tuple(N * Ho * Wo, K)),
+                make_tuple(make_pass_through_transform(N * Ho * Wo),
+                           make_unmerge_transform(make_tuple(K0, K1))),
+                make_tuple(Sequence<0>{}, Sequence<1>{}),
+                make_tuple(Sequence<1>{}, Sequence<0, 2>{}));
+
+            // B: weight tensor
+            const auto wei_gemmk0_gemmn_gemmk1_grid_desc =
+                transform_tensor_descriptor(make_naive_tensor_descriptor_packed(make_tuple(K, C)),
+                                            make_tuple(make_unmerge_transform(make_tuple(K0, K1)),
+                                                       make_pass_through_transform(C)),
+                                            make_tuple(Sequence<0>{}, Sequence<1>{}),
+                                            make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
+
+            // C: input tensor
+            const auto in_n_y_ho_x_wo_c_grid_desc = transform_tensor_descriptor(
+                in_n_hi_wi_c_grid_desc,
+                make_tuple(make_pass_through_transform(N),
+                           make_embed_transform(make_tuple(I1, Ho), make_tuple(I1, ConvStrideH)),
+                           make_embed_transform(make_tuple(I1, Wo), make_tuple(I1, ConvStrideW)),
+                           make_pass_through_transform(C)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
+                make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}));
+
+            const auto in_gemmm_gemmn_grid_desc = transform_tensor_descriptor(
+                in_n_y_ho_x_wo_c_grid_desc,
+                make_tuple(make_freeze_transform(I0),
+                           make_freeze_transform(I0),
+                           make_merge_transform(make_tuple(N, Ho, Wo)),
+                           make_pass_through_transform(C)),
+                make_tuple(Sequence<1>{}, Sequence<3>{}, Sequence<0, 2, 4>{}, Sequence<5>{}),
+                make_tuple(Sequence<>{}, Sequence<>{}, Sequence<0>{}, Sequence<1>{}));
+
+            return make_tuple(out_gemmk0_gemmm_gemmk1_grid_desc,
+                              wei_gemmk0_gemmn_gemmk1_grid_desc,
+                              in_gemmm_gemmn_grid_desc);
+        }
+        else
+        {
+            const auto GcdStrideDilationH = math::gcd(ConvStrideH, ConvDilationH);
+            const auto GcdStrideDilationW = math::gcd(ConvStrideW, ConvDilationW);
+
+            const auto YTilde = ConvStrideH / GcdStrideDilationH;
+            const auto XTilde = ConvStrideW / GcdStrideDilationW;
+
+            const auto YDot = math::integer_divide_ceil(Y, YTilde);
+            const auto XDot = math::integer_divide_ceil(X, XTilde);
+
+            const auto HTilde =
+                Ho + math::integer_divide_ceil(ConvDilationH * (Y - I1), ConvStrideH);
+            const auto WTilde =
+                Wo + math::integer_divide_ceil(ConvDilationW * (X - I1), ConvStrideW);
+
+            // only work on HTilde and WTilde that contribute to non-padding area of input tensor
+            const auto IHTildeSliceBegin = math::integer_divide_floor(
+                math::max(I0, InLeftPadH - ConvDilationH * (YTilde - I1)), ConvStrideH);
+            const auto IWTildeSliceBegin = math::integer_divide_floor(
+                math::max(I0, InLeftPadW - ConvDilationW * (XTilde - I1)), ConvStrideW);
+
+            const auto IHTildeSliceEnd = math::min(
+                HTilde, math::integer_divide_ceil(InLeftPadH + Hi - I1, ConvStrideH) + I1);
+            const auto IWTildeSliceEnd = math::min(
+                WTilde, math::integer_divide_ceil(InLeftPadW + Wi - I1, ConvStrideW) + I1);
+
+            const auto HTildeSlice = IHTildeSliceEnd - IHTildeSliceBegin;
+            const auto WTildeSlice = IWTildeSliceEnd - IWTildeSliceBegin;
+
+            // GemmK is different for each GEMM
+            const auto YDotSlice = math::integer_divide_ceil(Y - i_ytilde, YTilde);
+            const auto XDotSlice = math::integer_divide_ceil(X - i_xtilde, XTilde);
+
+            // A: output tensor
+            const auto out_n_hop_wop_k_grid_desc = transform_tensor_descriptor(
+                out_n_ho_wo_k_grid_desc,
+                make_tuple(make_pass_through_transform(N),
+                           make_pad_transform(Ho, I0, I0),
+                           make_pad_transform(Wo, I0, I0),
+                           make_pass_through_transform(K)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
+
+            const auto out_n_ydot_htilde_xdot_wtilde_k_grid_desc = transform_tensor_descriptor(
+                out_n_hop_wop_k_grid_desc,
+                make_tuple(
+                    make_pass_through_transform(N),
+                    make_embed_transform(make_tuple(YDot, HTilde),
+                                         make_tuple(-ConvDilationH / GcdStrideDilationH, I1)),
+                    make_embed_transform(make_tuple(XDot, WTilde),
+                                         make_tuple(-ConvDilationW / GcdStrideDilationW, I1)),
+                    make_pass_through_transform(K)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
+                make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}));
+
+            const auto out_n_ydotslice_htildeslice_xdotslice_wtildeslice_k0_k1_grid_desc =
+                transform_tensor_descriptor(
+                    out_n_ydot_htilde_xdot_wtilde_k_grid_desc,
+                    make_tuple(make_pass_through_transform(N),
+                               make_slice_transform(YDot, I0, YDotSlice),
+                               make_slice_transform(HTilde, IHTildeSliceBegin, HTildeSlice),
+                               make_slice_transform(XDot, I0, XDotSlice),
+                               make_slice_transform(WTilde, IWTildeSliceBegin, WTildeSlice),
+                               make_unmerge_transform(make_tuple(K0, K1))),
+                    make_tuple(Sequence<0>{},
+                               Sequence<1>{},
+                               Sequence<2>{},
+                               Sequence<3>{},
+                               Sequence<4>{},
+                               Sequence<5>{}),
+                    make_tuple(Sequence<0>{},
+                               Sequence<1>{},
+                               Sequence<2>{},
+                               Sequence<3>{},
+                               Sequence<4>{},
+                               Sequence<5, 6>{}));
+
+            const auto out_gemmk0_gemmm_gemmk1_grid_desc = transform_tensor_descriptor(
+                out_n_ydotslice_htildeslice_xdotslice_wtildeslice_k0_k1_grid_desc,
+                make_tuple(make_merge_transform(make_tuple(YDotSlice, XDotSlice, K0)),
+                           make_merge_transform(make_tuple(N, HTildeSlice, WTildeSlice)),
+                           make_pass_through_transform(K1)),
+                make_tuple(Sequence<1, 3, 5>{}, Sequence<0, 2, 4>{}, Sequence<6>{}),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
+
+            // B weight tensor
+            const auto wei_k_ydot_ytilde_xdot_xtilde_c_grid_desc = transform_tensor_descriptor(
+                wei_k_y_x_c_grid_desc,
+                make_tuple(make_pass_through_transform(K),
+                           make_embed_transform(make_tuple(YDot, YTilde),
+                                                make_tuple(ConvStrideH / GcdStrideDilationH, I1)),
+                           make_embed_transform(make_tuple(XDot, XTilde),
+                                                make_tuple(ConvStrideW / GcdStrideDilationW, I1)),
+                           make_pass_through_transform(C)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
+                make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}));
+
+            const auto wei_k0_k1_ydotslice_xdotslice_c_grid_desc =
+                transform_tensor_descriptor(wei_k_ydot_ytilde_xdot_xtilde_c_grid_desc,
+                                            make_tuple(make_unmerge_transform(make_tuple(K0, K1)),
+                                                       make_slice_transform(YDot, I0, YDotSlice),
+                                                       make_slice_transform(XDot, I0, XDotSlice),
+                                                       make_freeze_transform(i_ytilde),
+                                                       make_freeze_transform(i_xtilde),
+                                                       make_pass_through_transform(C)),
+                                            make_tuple(Sequence<0>{},
+                                                       Sequence<1>{},
+                                                       Sequence<3>{},
+                                                       Sequence<2>{},
+                                                       Sequence<4>{},
+                                                       Sequence<5>{}),
+                                            make_tuple(Sequence<0, 1>{},
+                                                       Sequence<2>{},
+                                                       Sequence<3>{},
+                                                       Sequence<>{},
+                                                       Sequence<>{},
+                                                       Sequence<4>{}));
+
+            const auto wei_gemmk0_gemmn_gemmk1_grid_desc = transform_tensor_descriptor(
+                wei_k0_k1_ydotslice_xdotslice_c_grid_desc,
+                make_tuple(make_merge_transform(make_tuple(YDotSlice, XDotSlice, K0)),
+                           make_pass_through_transform(C),
+                           make_pass_through_transform(K1)),
+                make_tuple(Sequence<2, 3, 0>{}, Sequence<4>{}, Sequence<1>{}),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
+
+            // C: input tensor
+            const auto in_n_hip_wip_c_grid_desc = transform_tensor_descriptor(
+                in_n_hi_wi_c_grid_desc,
+                make_tuple(make_pass_through_transform(N),
+                           make_pad_transform(Hi, InLeftPadH, InRightPadH),
+                           make_pad_transform(Wi, InLeftPadW, InRightPadW),
+                           make_pass_through_transform(C)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
+
+            const auto in_n_ytilde_htilde_xtilde_wtilde_c_grid_desc = transform_tensor_descriptor(
+                in_n_hip_wip_c_grid_desc,
+                make_tuple(make_pass_through_transform(N),
+                           make_embed_transform(make_tuple(YTilde, HTilde),
+                                                make_tuple(ConvDilationH, ConvStrideH)),
+                           make_embed_transform(make_tuple(XTilde, WTilde),
+                                                make_tuple(ConvDilationW, ConvStrideW)),
+                           make_pass_through_transform(C)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
+                make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}));
+
+            const auto in_n_htildeslice_wtildeslice_c_grid_desc = transform_tensor_descriptor(
+                in_n_ytilde_htilde_xtilde_wtilde_c_grid_desc,
+                make_tuple(make_pass_through_transform(N),
+                           make_freeze_transform(i_ytilde),
+                           make_slice_transform(HTilde, IHTildeSliceBegin, HTildeSlice),
+                           make_freeze_transform(i_xtilde),
+                           make_slice_transform(WTilde, IWTildeSliceBegin, WTildeSlice),
+                           make_pass_through_transform(C)),
+                make_tuple(Sequence<0>{},
+                           Sequence<1>{},
+                           Sequence<2>{},
+                           Sequence<3>{},
+                           Sequence<4>{},
+                           Sequence<5>{}),
+                make_tuple(Sequence<0>{},
+                           Sequence<>{},
+                           Sequence<1>{},
+                           Sequence<>{},
+                           Sequence<2>{},
+                           Sequence<3>{}));
+
+            const auto in_gemmm_gemmn_grid_desc = transform_tensor_descriptor(
+                in_n_htildeslice_wtildeslice_c_grid_desc,
+                make_tuple(make_merge_transform(make_tuple(N, HTildeSlice, WTildeSlice)),
+                           make_pass_through_transform(C)),
+                make_tuple(Sequence<0, 1, 2>{}, Sequence<3>{}),
+                make_tuple(Sequence<0>{}, Sequence<1>{}));
+
+            return make_tuple(out_gemmk0_gemmm_gemmk1_grid_desc,
+                              wei_gemmk0_gemmn_gemmk1_grid_desc,
+                              in_gemmm_gemmn_grid_desc);
+        }
+
+    } // function end
+
+    using ABCGridDescs = decltype(MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N(
+        1, 1, 1, {1, 1}, {1, 1}, {1, 1}, {1, 1}, {1, 1}, {1, 1}, {1, 1}, 0, 0));
+
+    using AGridDesc_K0_M_K1 = remove_cvref_t<decltype(ABCGridDescs{}[I0])>;
+    using BGridDesc_K0_N_K1 = remove_cvref_t<decltype(ABCGridDescs{}[I1])>;
+    using CGridDesc_M_N     = remove_cvref_t<decltype(ABCGridDescs{}[I2])>;
+
+    // GridwiseGemm
+    using GridwiseGemm = GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v2r3<
+        BlockSize,
+        ABDataType, // TODO: distinguish A/B datatype
+        AccDataType,
+        CDataType,
+        InMemoryDataOperationEnum::Set,
+        AGridDesc_K0_M_K1,
+        BGridDesc_K0_N_K1,
+        CGridDesc_M_N,
+        InElementwiseOperation,
+        WeiElementwiseOperation,
+        OutElementwiseOperation,
+        MPerBlock,
+        NPerBlock,
+        K0PerBlock,
+        MPerXdl,
+        NPerXdl,
+        K1,
+        MXdlPerWave,
+        NXdlPerWave,
+        ABlockTransferThreadClusterLengths_K0_M_K1,
+        ABlockTransferThreadClusterArrangeOrder,
+        ABlockTransferSrcAccessOrder,
+        ABlockTransferSrcVectorDim,
+        ABlockTransferSrcScalarPerVector,
+        ABlockTransferDstScalarPerVector_K1,
+        false, // AThreadTransferSrcResetCoordinateAfterRun,
+        ABlockLdsAddExtraM,
+        BBlockTransferThreadClusterLengths_K0_N_K1,
+        BBlockTransferThreadClusterArrangeOrder,
+        BBlockTransferSrcAccessOrder,
+        BBlockTransferSrcVectorDim,
+        BBlockTransferSrcScalarPerVector,
+        BBlockTransferDstScalarPerVector_K1,
+        false, // BThreadTransferSrcResetCoordinateAfterRun,
+        BBlockLdsAddExtraN,
+        Sequence<2, 3, 0, 1, 7, 5, 4, 6>, // CThreadTransferSrcDstAccessOrder,
+        7,                                // CThreadTransferSrcDstVectorDim,
+        CThreadTransferDstScalarPerVector>;
+
+    // Argument
+    struct Argument : public BaseArgument
+    {
+        Argument(InDataType* p_in_grid,
+                 const WeiDataType* p_wei_grid,
+                 const OutDataType* p_out_grid,
+                 ck::index_t N,
+                 ck::index_t K,
+                 ck::index_t C,
+                 std::vector<ck::index_t> input_spatial_lengths,
+                 std::vector<ck::index_t> filter_spatial_lengths,
+                 std::vector<ck::index_t> output_spatial_lengths,
+                 std::vector<ck::index_t> conv_filter_strides,
+                 std::vector<ck::index_t> conv_filter_dilations,
+                 std::vector<ck::index_t> input_left_pads,
+                 std::vector<ck::index_t> input_right_pads,
+                 ck::index_t M01,
+                 ck::index_t N01,
+                 InElementwiseOperation in_element_op,
+                 WeiElementwiseOperation wei_element_op,
+                 OutElementwiseOperation out_element_op)
+            : p_a_grid_{p_out_grid},
+              p_b_grid_{p_wei_grid},
+              p_c_grid_{p_in_grid},
+              M01_{M01},
+              N01_{N01},
+              a_element_op_{out_element_op},
+              b_element_op_{wei_element_op},
+              c_element_op_{in_element_op},
+              Conv_N_{N},
+              Conv_K_{K},
+              Conv_C_{C},
+              input_spatial_lengths_{input_spatial_lengths},
+              filter_spatial_lengths_{filter_spatial_lengths},
+              output_spatial_lengths_{output_spatial_lengths},
+              conv_filter_strides_{conv_filter_strides},
+              conv_filter_dilations_{conv_filter_dilations},
+              input_left_pads_{input_left_pads},
+              input_right_pads_{input_right_pads}
+        {
+            const index_t ConvStrideH = conv_filter_strides[0];
+            const index_t ConvStrideW = conv_filter_strides[1];
+
+            const index_t ConvDilationH = conv_filter_dilations[0];
+            const index_t ConvDilationW = conv_filter_dilations[1];
+
+            const auto GcdStrideDilationH = math::gcd(ConvStrideH, ConvDilationH);
+            const auto GcdStrideDilationW = math::gcd(ConvStrideW, ConvDilationW);
+
+            const auto YTilde = ConvStrideH / GcdStrideDilationH;
+            const auto XTilde = ConvStrideW / GcdStrideDilationW;
+
+            for(index_t i_ytilde = 0; i_ytilde < YTilde; ++i_ytilde)
+            {
+                for(index_t i_xtilde = 0; i_xtilde < XTilde; ++i_xtilde)
+                {
+                    // check slice is valid
+                    const index_t Y      = filter_spatial_lengths_[0];
+                    const index_t X      = filter_spatial_lengths_[1];
+                    const auto YDotSlice = math::integer_divide_ceil(Y - i_ytilde, YTilde);
+                    const auto XDotSlice = math::integer_divide_ceil(X - i_xtilde, XTilde);
+                    if(YDotSlice * XDotSlice <= 0)
+                    {
+                        continue;
+                    }
+
+                    const auto descs = DeviceOp::MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N(
+                        N,
+                        K,
+                        C,
+                        input_spatial_lengths,
+                        filter_spatial_lengths,
+                        output_spatial_lengths,
+                        conv_filter_strides,
+                        conv_filter_dilations,
+                        input_left_pads,
+                        input_right_pads,
+                        i_ytilde,
+                        i_xtilde);
+                    a_grid_desc_k0_m_k1_container_.push_back(descs[I0]);
+                    b_grid_desc_k0_n_k1_container_.push_back(descs[I1]);
+                    c_grid_desc_m_n_container_.push_back(descs[I2]);
+
+                    auto block_2_ctile_map =
+                        GridwiseGemm::MakeDefaultBlock2CTileMap(descs[I2], M01, N01);
+
+                    if(GridwiseGemm::CheckValidity(
+                           descs[I0], descs[I1], descs[I2], block_2_ctile_map))
+                    {
+                        c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_container_.push_back(
+                            GridwiseGemm::MakeCGridDescriptor_M0_N0_M1_N1_M2_M3_M4_N2(descs[I2]));
+
+                        block_2_ctile_map_container_.push_back(block_2_ctile_map);
+                    }
+                }
+            }
+        }
+
+        const ADataType* p_a_grid_;
+        const BDataType* p_b_grid_;
+        CDataType* p_c_grid_;
+        std::vector<AGridDesc_K0_M_K1> a_grid_desc_k0_m_k1_container_;
+        std::vector<BGridDesc_K0_N_K1> b_grid_desc_k0_n_k1_container_;
+        std::vector<CGridDesc_M_N> c_grid_desc_m_n_container_;
+        std::vector<typename GridwiseGemm::CGridDesc_M0_N0_M1_N1_M2_M3_M4_N2>
+            c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_container_;
+        std::vector<typename GridwiseGemm::DefaultBlock2CTileMap> block_2_ctile_map_container_;
+        index_t M01_;
+        index_t N01_;
+        OutElementwiseOperation a_element_op_;
+        WeiElementwiseOperation b_element_op_;
+        InElementwiseOperation c_element_op_;
+        // for checking IsSupportedArgument()
+        index_t Conv_N_;
+        index_t Conv_K_;
+        index_t Conv_C_;
+
+        std::vector<ck::index_t> input_spatial_lengths_;
+        std::vector<ck::index_t> filter_spatial_lengths_;
+        std::vector<ck::index_t> output_spatial_lengths_;
+        std::vector<ck::index_t> conv_filter_strides_;
+        std::vector<ck::index_t> conv_filter_dilations_;
+        std::vector<ck::index_t> input_left_pads_;
+        std::vector<ck::index_t> input_right_pads_;
+    };
+
+    // Invoker
+    struct Invoker : public BaseInvoker
+    {
+        using Argument = DeviceOp::Argument;
+
+        float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
+        {
+            float ave_time = 0;
+            for(size_t i = 0; i < arg.a_grid_desc_k0_m_k1_container_.size(); i++)
+            {
+                {
+                    std::cout << "arg.a_grid_desc_k0_m_k1_container_{"
+                              << arg.a_grid_desc_k0_m_k1_container_[i].GetLength(I0) << ", "
+                              << arg.a_grid_desc_k0_m_k1_container_[i].GetLength(I1) << ", "
+                              << arg.a_grid_desc_k0_m_k1_container_[i].GetLength(I2) << "}"
+                              << std::endl;
+
+                    std::cout << "arg.b_grid_desc_k0_n_k1_container_{"
+                              << arg.b_grid_desc_k0_n_k1_container_[i].GetLength(I0) << ", "
+                              << arg.b_grid_desc_k0_n_k1_container_[i].GetLength(I1) << ", "
+                              << arg.b_grid_desc_k0_n_k1_container_[i].GetLength(I2) << "}"
+                              << std::endl;
+
+                    std::cout << "arg.c_grid_desc_m_n_container_{ "
+                              << arg.c_grid_desc_m_n_container_[i].GetLength(I0) << ", "
+                              << arg.c_grid_desc_m_n_container_[i].GetLength(I1) << "}"
+                              << std::endl;
+
+                    std::cout << "arg.c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_container_( "
+                              << arg.c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_container_[i].GetLength(I0)
+                              << ", "
+                              << arg.c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_container_[i].GetLength(I1)
+                              << ", "
+                              << arg.c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_container_[i].GetLength(I2)
+                              << ", "
+                              << arg.c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_container_[i].GetLength(I3)
+                              << ", "
+                              << arg.c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_container_[i].GetLength(I4)
+                              << ", "
+                              << arg.c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_container_[i].GetLength(I5)
+                              << " ) " << std::endl;
+                }
+
+                if(!GridwiseGemm::CheckValidity(arg.a_grid_desc_k0_m_k1_container_[i],
+                                                arg.b_grid_desc_k0_n_k1_container_[i],
+                                                arg.c_grid_desc_m_n_container_[i],
+                                                arg.block_2_ctile_map_container_[i]))
+                {
+                    throw std::runtime_error(
+                        "wrong! GridwiseGemm_km_kn_m0m1n0n1_xdlops_v3r1 has invalid setting");
+                }
+
+                const index_t grid_size = arg.block_2_ctile_map_container_[i].CalculateGridSize(
+                    arg.c_grid_desc_m_n_container_[i]);
+
+                const auto K = arg.a_grid_desc_k0_m_k1_container_[i].GetLength(I0) *
+                               arg.a_grid_desc_k0_m_k1_container_[i].GetLength(I2);
+
+                if(GridwiseGemm::CalculateHasMainKBlockLoop(K))
+                {
+                    const auto kernel = kernel_gemm_xdlops_v2r3<
+                        GridwiseGemm,
+                        ADataType, // TODO: distiguish A/B datatype
+                        CDataType,
+                        remove_reference_t<DeviceOp::AGridDesc_K0_M_K1>,
+                        remove_reference_t<DeviceOp::BGridDesc_K0_N_K1>,
+                        remove_reference_t<
+                            typename GridwiseGemm::CGridDesc_M0_N0_M1_N1_M2_M3_M4_N2>,
+                        OutElementwiseOperation,
+                        WeiElementwiseOperation,
+                        InElementwiseOperation,
+                        remove_reference_t<typename GridwiseGemm::DefaultBlock2CTileMap>,
+                        true>;
+
+                    ave_time += launch_and_time_kernel(
+                        stream_config,
+                        kernel,
+                        dim3(grid_size),
+                        dim3(BlockSize),
+                        0,
+                        arg.p_a_grid_,
+                        arg.p_b_grid_,
+                        arg.p_c_grid_,
+                        arg.a_grid_desc_k0_m_k1_container_[i],
+                        arg.b_grid_desc_k0_n_k1_container_[i],
+                        arg.c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_container_[i],
+                        arg.a_element_op_,
+                        arg.b_element_op_,
+                        arg.c_element_op_,
+                        arg.block_2_ctile_map_container_[i]);
+                }
+                else
+                {
+                    const auto kernel = kernel_gemm_xdlops_v2r3<
+                        GridwiseGemm,
+                        ADataType, // TODO: distiguish A/B datatype
+                        CDataType,
+                        remove_reference_t<DeviceOp::AGridDesc_K0_M_K1>,
+                        remove_reference_t<DeviceOp::BGridDesc_K0_N_K1>,
+                        remove_reference_t<
+                            typename GridwiseGemm::CGridDesc_M0_N0_M1_N1_M2_M3_M4_N2>,
+                        OutElementwiseOperation,
+                        WeiElementwiseOperation,
+                        InElementwiseOperation,
+                        remove_reference_t<typename GridwiseGemm::DefaultBlock2CTileMap>,
+                        false>;
+
+                    ave_time += launch_and_time_kernel(
+                        stream_config,
+                        kernel,
+                        dim3(grid_size),
+                        dim3(BlockSize),
+                        0,
+                        arg.p_a_grid_,
+                        arg.p_b_grid_,
+                        arg.p_c_grid_,
+                        arg.a_grid_desc_k0_m_k1_container_[i],
+                        arg.b_grid_desc_k0_n_k1_container_[i],
+                        arg.c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_container_[i],
+                        arg.a_element_op_,
+                        arg.b_element_op_,
+                        arg.c_element_op_,
+                        arg.block_2_ctile_map_container_[i]);
+                }
+            }
+            return ave_time;
+        }
+
+        float Run(const BaseArgument* p_arg,
+                  const StreamConfig& stream_config = StreamConfig{}) override
+        {
+            return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
+        }
+    };
+
+    static constexpr bool IsValidCompilationParameter()
+    {
+        // TODO: properly implement this check
+        return true;
+    }
+
+    static bool IsSupportedArgument(const Argument& arg)
+    {
+        if constexpr(ConvBackwardDataSpecialization ==
+                     ConvolutionBackwardDataSpecialization::Filter1x1Stride1Pad0)
+        {
+            // check if it's 1x1, stride=1 pad = 0 conv
+            if(!(arg.filter_spatial_lengths_[0] == 1 && arg.filter_spatial_lengths_[1] == 1 &&
+                 arg.conv_filter_strides_[0] == 1 && arg.conv_filter_strides_[1] == 1 &&
+                 arg.input_left_pads_[0] == 0 && arg.input_left_pads_[1] == 0 &&
+                 arg.input_right_pads_[0] == 0 && arg.input_right_pads_[1] == 0))
+            {
+                return false;
+            }
+        }
+
+        // vector load A/B matrix from global memory
+        if(!(ABlockTransferSrcVectorDim == 2 && BBlockTransferSrcVectorDim == 1 &&
+             arg.Conv_K_ % ABlockTransferSrcScalarPerVector == 0 &&
+             arg.Conv_C_ % BBlockTransferSrcScalarPerVector == 0))
+        {
+            return false;
+        }
+
+        // vector store C matrix into global memory
+        if(!(arg.Conv_C_ % CThreadTransferDstScalarPerVector == 0))
+        {
+            return false;
+        }
+
+        // Gridwise GEMM size
+        for(std::size_t i = 0; i < arg.a_grid_desc_k0_m_k1_container_.size(); i++)
+        {
+            if(!GridwiseGemm::CheckValidity(arg.a_grid_desc_k0_m_k1_container_[i],
+                                            arg.b_grid_desc_k0_n_k1_container_[i],
+                                            arg.c_grid_desc_m_n_container_[i],
+                                            arg.block_2_ctile_map_container_[i]))
+            {
+                return false;
+            }
+        }
+        return true;
+    }
+
+    bool IsSupportedArgument(const BaseArgument* p_arg) override
+    {
+        return IsSupportedArgument(*dynamic_cast<const Argument*>(p_arg));
+    }
+
+    static auto MakeArgument(InDataType* p_in_grid,
+                             const WeiDataType* p_wei_grid,
+                             const OutDataType* p_out_grid,
+                             ck::index_t N,
+                             ck::index_t K,
+                             ck::index_t C,
+                             std::vector<ck::index_t> input_spatial_lengths,
+                             std::vector<ck::index_t> filter_spatial_lengths,
+                             std::vector<ck::index_t> output_spatial_lengths,
+                             std::vector<ck::index_t> conv_filter_strides,
+                             std::vector<ck::index_t> conv_filter_dilations,
+                             std::vector<ck::index_t> input_left_pads,
+                             std::vector<ck::index_t> input_right_pads,
+                             InElementwiseOperation in_element_op,
+                             WeiElementwiseOperation wei_element_op,
+                             OutElementwiseOperation out_element_op)
+    {
+        return Argument{p_in_grid,
+                        p_wei_grid,
+                        p_out_grid,
+                        N,
+                        K,
+                        C,
+                        input_spatial_lengths,
+                        filter_spatial_lengths,
+                        output_spatial_lengths,
+                        conv_filter_strides,
+                        conv_filter_dilations,
+                        input_left_pads,
+                        input_right_pads,
+                        1,
+                        1,
+                        in_element_op,
+                        wei_element_op,
+                        out_element_op};
+    }
+
+    static auto MakeInvoker() { return Invoker{}; }
+
+    std::unique_ptr<BaseArgument>
+    MakeArgumentPointer(void* p_in_grid,
+                        const void* p_wei_grid,
+                        const void* p_out_grid,
+                        ck::index_t N,
+                        ck::index_t K,
+                        ck::index_t C,
+                        std::vector<ck::index_t> input_spatial_lengths,
+                        std::vector<ck::index_t> filter_spatial_lengths,
+                        std::vector<ck::index_t> output_spatial_lengths,
+                        std::vector<ck::index_t> conv_filter_strides,
+                        std::vector<ck::index_t> conv_filter_dilations,
+                        std::vector<ck::index_t> input_left_pads,
+                        std::vector<ck::index_t> input_right_pads,
+                        InElementwiseOperation in_element_op,
+                        WeiElementwiseOperation wei_element_op,
+                        OutElementwiseOperation out_element_op) override
+    {
+        return std::make_unique<Argument>(static_cast<InDataType*>(p_in_grid),
+                                          static_cast<const WeiDataType*>(p_wei_grid),
+                                          static_cast<const OutDataType*>(p_out_grid),
+                                          N,
+                                          K,
+                                          C,
+                                          input_spatial_lengths,
+                                          filter_spatial_lengths,
+                                          output_spatial_lengths,
+                                          conv_filter_strides,
+                                          conv_filter_dilations,
+                                          input_left_pads,
+                                          input_right_pads,
+                                          1,
+                                          1,
+                                          in_element_op,
+                                          wei_element_op,
+                                          out_element_op);
+    }
+
+    std::unique_ptr<BaseInvoker> MakeInvokerPointer() override
+    {
+        return std::make_unique<Invoker>(Invoker{});
+    }
+
+    std::string GetTypeString() const override
+    {
+        auto str = std::stringstream();
+
+        // clang-format off
+        str << "DeviceConv2dBwdDataXdl_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K"
+            << "<"
+            << BlockSize << ", "
+            << MPerBlock << ", "
+            << NPerBlock << ", "
+            << K0PerBlock
+            << ">";
+        // clang-format on
+
+        return str.str();
+    }
+};
+
+} // namespace device
+} // namespace tensor_operation
+} // namespace ck

deps/cget/pkg/ROCmSoftwarePlatform__composable_kernel/install/include/ck/tensor_operation/gpu/device/device_conv2d_fwd_xdl_c_shuffle_bias_activation_add_nhwc_kyxc_nhwk.hpp

+// 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_conv_fwd_bias_activation_add.hpp"
+#include "ck/tensor_operation/gpu/device/convolution_forward_specialization.hpp"
+#include "ck/tensor_operation/gpu/grid/gridwise_gemm_xdlops_v3r3.hpp"
+#include "ck/host_utility/device_prop.hpp"
+#include "ck/host_utility/kernel_launch.hpp"
+
+namespace ck {
+namespace tensor_operation {
+namespace device {
+
+// out[N, Ho, Wo, K] =
+//     activate(in[N, Hi, Wi, C] * wei[K, Y, X, C] + bias[K]) + residual[N, Ho, Wo, K]
+template <
+    typename InDataType,
+    typename WeiDataType,
+    typename OutDataType,
+    typename AccDataType,
+    typename InElementwiseOperation,
+    typename WeiElementwiseOperation,
+    typename OutElementwiseOperation,
+    ConvolutionForwardSpecialization ConvForwardSpecialization,
+    ck::index_t BlockSize,
+    ck::index_t MPerBlock,
+    ck::index_t NPerBlock,
+    ck::index_t K0PerBlock,
+    ck::index_t K1,
+    ck::index_t MPerXDL,
+    ck::index_t NPerXDL,
+    ck::index_t MXdlPerWave,
+    ck::index_t NXdlPerWave,
+    typename ABlockTransferThreadClusterLengths_K0_M_K1,
+    typename ABlockTransferThreadClusterArrangeOrder,
+    typename ABlockTransferSrcAccessOrder,
+    ck::index_t ABlockTransferSrcVectorDim,
+    ck::index_t ABlockTransferSrcScalarPerVector,
+    ck::index_t ABlockTransferDstScalarPerVector_K1,
+    bool ABlockLdsAddExtraM,
+    typename BBlockTransferThreadClusterLengths_K0_N_K1,
+    typename BBlockTransferThreadClusterArrangeOrder,
+    typename BBlockTransferSrcAccessOrder,
+    ck::index_t BBlockTransferSrcVectorDim,
+    ck::index_t BBlockTransferSrcScalarPerVector,
+    ck::index_t BBlockTransferDstScalarPerVector_K1,
+    bool BBlockLdsAddExtraN,
+    index_t CShuffleMXdlPerWavePerShuffle,
+    index_t CShuffleNXdlPerWavePerShuffle,
+    typename CBlockTransferClusterLengths_MBlock_MXdlPerWave_MWaveMPerXdl_NBlock_NXdlPerWave_NWaveNPerXdl,
+    index_t CBlockTransferScalarPerVector_NWaveNPerXdl>
+struct
+    DeviceConv2dFwdXdl_C_Shuffle_Bias_Activation_Add_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K
+    : public DeviceConvFwdBiasActivationAdd<InElementwiseOperation,
+                                            WeiElementwiseOperation,
+                                            OutElementwiseOperation>
+{
+    using DeviceOp =
+        DeviceConv2dFwdXdl_C_Shuffle_Bias_Activation_Add_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K;
+
+    using ADataType = InDataType;
+    using BDataType = WeiDataType;
+    using CDataType = OutDataType;
+
+    // TODO make A/B datatype different
+    using ABDataType = InDataType;
+
+    // TODO make it support any # of spatial dimensions
+    static constexpr index_t NDimSpatial = 2;
+
+    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 K1Number     = Number<K1>{};
+    static constexpr auto GemmK1Number = K1Number;
+
+    static auto
+    MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N(ck::index_t N,
+                                                    ck::index_t K,
+                                                    ck::index_t C,
+                                                    std::vector<ck::index_t> input_spatial_lengths,
+                                                    std::vector<ck::index_t> filter_spatial_lengths,
+                                                    std::vector<ck::index_t> output_spatial_lengths,
+                                                    std::vector<ck::index_t> conv_filter_strides,
+                                                    std::vector<ck::index_t> conv_filter_dilations,
+                                                    std::vector<ck::index_t> input_left_pads,
+                                                    std::vector<ck::index_t> input_right_pads)
+    {
+        using namespace ck;
+
+        const index_t Hi = input_spatial_lengths[0];
+        const index_t Wi = input_spatial_lengths[1];
+
+        const index_t Ho = output_spatial_lengths[0];
+        const index_t Wo = output_spatial_lengths[1];
+
+        const index_t Y = filter_spatial_lengths[0];
+        const index_t X = filter_spatial_lengths[1];
+
+        const index_t ConvStrideH = conv_filter_strides[0];
+        const index_t ConvStrideW = conv_filter_strides[1];
+
+        const index_t ConvDilationH = conv_filter_dilations[0];
+        const index_t ConvDilationW = conv_filter_dilations[1];
+
+        const index_t InLeftPadH = input_left_pads[0];
+        const index_t InLeftPadW = input_left_pads[1];
+
+        const index_t InRightPadH = input_right_pads[0];
+        const index_t InRightPadW = input_right_pads[1];
+
+        const index_t GemmMRaw = N * Ho * Wo;
+        const index_t GemmN    = K;
+
+        const auto GemmM    = math::integer_least_multiple(GemmMRaw, MPerBlock);
+        const auto GemmMPad = GemmM - GemmMRaw;
+
+        if constexpr(ConvForwardSpecialization ==
+                     ConvolutionForwardSpecialization::Filter1x1Stride1Pad0)
+        { // 1x1, stride=1, pad=0
+            const index_t GemmK = Y * X * C;
+            assert(GemmK % GemmK1Number == 0);
+
+            const index_t GemmK0 = GemmK / GemmK1Number;
+
+            // A: input tensor
+            const auto in_gemmmraw_gemmk_grid_desc =
+                make_naive_tensor_descriptor_packed(make_tuple(N * Ho * Wo, C));
+
+            const auto in_gemmk0_gemmm_gemmk1_grid_desc = transform_tensor_descriptor(
+                in_gemmmraw_gemmk_grid_desc,
+                make_tuple(make_unmerge_transform(make_tuple(GemmK0, GemmK1Number)),
+                           make_right_pad_transform(GemmMRaw, GemmMPad)),
+                make_tuple(Sequence<1>{}, Sequence<0>{}),
+                make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
+
+            // B: weight tensor
+            const auto wei_gemmn_gemmk_grid_desc =
+                make_naive_tensor_descriptor_packed(make_tuple(K, C));
+
+            const auto wei_gemmk0_gemmn_gemmk1_grid_desc = transform_tensor_descriptor(
+                wei_gemmn_gemmk_grid_desc,
+                make_tuple(make_unmerge_transform(make_tuple(GemmK0, GemmK1Number)),
+                           make_pass_through_transform(GemmN)),
+                make_tuple(Sequence<1>{}, Sequence<0>{}),
+                make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
+
+            // C: output tensor
+            const auto out_gemmmraw_gemmn_grid_desc =
+                make_naive_tensor_descriptor_packed(make_tuple(N * Ho * Wo, K));
+
+            const auto out_gemmm_gemmn_grid_desc =
+                transform_tensor_descriptor(out_gemmmraw_gemmn_grid_desc,
+                                            make_tuple(make_right_pad_transform(GemmMRaw, GemmMPad),
+                                                       make_pass_through_transform(GemmN)),
+                                            make_tuple(Sequence<0>{}, Sequence<1>{}),
+                                            make_tuple(Sequence<0>{}, Sequence<1>{}));
+
+            // C0: bias tensor: assume a contiguous vector
+            const auto bias_grid_desc_gemmm_gemmn =
+                make_naive_tensor_descriptor(make_tuple(GemmM, GemmN), make_tuple(I0, I1));
+
+            // C1: residual tensor: assume same layout as output tensor
+            const auto resi_grid_desc_gemmm_gemmn = out_gemmm_gemmn_grid_desc;
+
+            return make_tuple(in_gemmk0_gemmm_gemmk1_grid_desc,
+                              wei_gemmk0_gemmn_gemmk1_grid_desc,
+                              out_gemmm_gemmn_grid_desc,
+                              bias_grid_desc_gemmm_gemmn,
+                              resi_grid_desc_gemmm_gemmn);
+        }
+        else if constexpr(ConvForwardSpecialization ==
+                          ConvolutionForwardSpecialization::Filter1x1Pad0)
+        { // 1x1, pad=0
+            const index_t GemmK = Y * X * C;
+            assert(GemmK % GemmK1Number == 0);
+
+            const index_t GemmK0 = GemmK / GemmK1Number;
+
+            // A: input tensor
+            const auto in_n_hi_wi_c_grid_desc =
+                make_naive_tensor_descriptor_packed(make_tuple(N, Hi, Wi, C));
+
+            const auto in_n_ho_wo_c_grid_desc = transform_tensor_descriptor(
+                in_n_hi_wi_c_grid_desc,
+                make_tuple(make_pass_through_transform(N),
+                           make_embed_transform(make_tuple(Ho), make_tuple(ConvStrideH)),
+                           make_embed_transform(make_tuple(Wo), make_tuple(ConvStrideW)),
+                           make_pass_through_transform(C)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
+
+            const auto in_gemmk0_gemmmraw_gemmk1_grid_desc = transform_tensor_descriptor(
+                in_n_ho_wo_c_grid_desc,
+                make_tuple(make_unmerge_transform(make_tuple(GemmK0, GemmK1Number)),
+                           make_merge_transform(make_tuple(N, Ho, Wo))),
+                make_tuple(Sequence<3>{}, Sequence<0, 1, 2>{}),
+                make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
+
+            const auto in_gemmk0_gemmm_gemmk1_grid_desc = transform_tensor_descriptor(
+                in_gemmk0_gemmmraw_gemmk1_grid_desc,
+                make_tuple(make_pass_through_transform(GemmK0),
+                           make_right_pad_transform(GemmMRaw, GemmMPad),
+                           make_pass_through_transform(GemmK1Number)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
+
+            // B: weight tensor
+            const auto wei_gemmn_gemmk_grid_desc =
+                make_naive_tensor_descriptor_packed(make_tuple(K, C));
+
+            const auto wei_gemmk0_gemmn_gemmk1_grid_desc = transform_tensor_descriptor(
+                wei_gemmn_gemmk_grid_desc,
+                make_tuple(make_unmerge_transform(make_tuple(GemmK0, GemmK1Number)),
+                           make_pass_through_transform(GemmN)),
+                make_tuple(Sequence<1>{}, Sequence<0>{}),
+                make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
+
+            // C: output tensor
+            const auto out_gemmmraw_gemmn_grid_desc =
+                make_naive_tensor_descriptor_packed(make_tuple(N * Ho * Wo, K));
+
+            const auto out_gemmm_gemmn_grid_desc =
+                transform_tensor_descriptor(out_gemmmraw_gemmn_grid_desc,
+                                            make_tuple(make_right_pad_transform(GemmMRaw, GemmMPad),
+                                                       make_pass_through_transform(GemmN)),
+                                            make_tuple(Sequence<0>{}, Sequence<1>{}),
+                                            make_tuple(Sequence<0>{}, Sequence<1>{}));
+
+            // C0: bias tensor: assume a contiguous vector
+            const auto bias_grid_desc_gemmm_gemmn =
+                make_naive_tensor_descriptor(make_tuple(GemmM, GemmN), make_tuple(I0, I1));
+
+            // C1: residual tensor: assume same layout as output tensor
+            const auto resi_grid_desc_gemmm_gemmn = out_gemmm_gemmn_grid_desc;
+
+            return make_tuple(in_gemmk0_gemmm_gemmk1_grid_desc,
+                              wei_gemmk0_gemmn_gemmk1_grid_desc,
+                              out_gemmm_gemmn_grid_desc,
+                              bias_grid_desc_gemmm_gemmn,
+                              resi_grid_desc_gemmm_gemmn);
+        }
+        else if constexpr(ConvForwardSpecialization == ConvolutionForwardSpecialization::OddC)
+        { // C = odd value
+            const index_t GemmKRaw = Y * X * C;
+            const index_t GemmK = math::integer_least_multiple(GemmKRaw, K0PerBlock * GemmK1Number);
+            const index_t GemmKPad = GemmK - GemmKRaw;
+            const index_t GemmK0   = GemmK / GemmK1Number;
+
+            // A: input tensor
+            const auto in_n_hi_wi_c_grid_desc =
+                make_naive_tensor_descriptor_packed(make_tuple(N, Hi, Wi, C));
+
+            const auto in_n_hip_wip_c_grid_desc = transform_tensor_descriptor(
+                in_n_hi_wi_c_grid_desc,
+                make_tuple(make_pass_through_transform(N),
+                           make_pad_transform(Hi, InLeftPadH, InRightPadH),
+                           make_pad_transform(Wi, InLeftPadW, InRightPadW),
+                           make_pass_through_transform(C)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
+
+            const auto in_n_y_ho_x_wo_c_grid_desc = transform_tensor_descriptor(
+                in_n_hip_wip_c_grid_desc,
+                make_tuple(
+                    make_pass_through_transform(N),
+                    make_embed_transform(make_tuple(Y, Ho), make_tuple(ConvDilationH, ConvStrideH)),
+                    make_embed_transform(make_tuple(X, Wo), make_tuple(ConvDilationW, ConvStrideW)),
+                    make_pass_through_transform(C)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
+                make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}));
+
+            const auto in_gemmkraw_gemmmraw_grid_desc =
+                transform_tensor_descriptor(in_n_y_ho_x_wo_c_grid_desc,
+                                            make_tuple(make_merge_transform(make_tuple(Y, X, C)),
+                                                       make_merge_transform(make_tuple(N, Ho, Wo))),
+                                            make_tuple(Sequence<1, 3, 5>{}, Sequence<0, 2, 4>{}),
+                                            make_tuple(Sequence<0>{}, Sequence<1>{}));
+
+            const auto in_gemmk_gemmm_grid_desc = transform_tensor_descriptor(
+                in_gemmkraw_gemmmraw_grid_desc,
+                make_tuple(make_right_pad_transform(GemmKRaw, GemmKPad),
+                           make_right_pad_transform(GemmMRaw, GemmMPad)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}),
+                make_tuple(Sequence<0>{}, Sequence<1>{}));
+
+            const auto in_gemmk0_gemmm_gemmk1_grid_desc = transform_tensor_descriptor(
+                in_gemmk_gemmm_grid_desc,
+                make_tuple(make_unmerge_transform(make_tuple(GemmK0, GemmK1Number)),
+                           make_pass_through_transform(GemmM)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}),
+                make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
+
+            // B: weight tensor
+            const auto wei_k_yxc_grid_desc =
+                make_naive_tensor_descriptor_packed(make_tuple(K, Y * X * C));
+
+            const auto wei_gemmk_gemmn_grid_desc = transform_tensor_descriptor(
+                wei_k_yxc_grid_desc,
+                make_tuple(make_pass_through_transform(K),
+                           make_right_pad_transform(GemmKRaw, GemmKPad)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}),
+                make_tuple(Sequence<1>{}, Sequence<0>{}));
+
+            const auto wei_gemmk0_gemmn_gemmk1_grid_desc = transform_tensor_descriptor(
+                wei_gemmk_gemmn_grid_desc,
+                make_tuple(make_unmerge_transform(make_tuple(GemmK0, GemmK1Number)),
+                           make_pass_through_transform(GemmN)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}),
+                make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
+
+            // C: output tensor
+            const auto out_nhowo_k_grid_desc =
+                make_naive_tensor_descriptor_packed(make_tuple(N * Ho * Wo, K));
+
+            const auto out_gemmmraw_gemmn_grid_desc =
+                transform_tensor_descriptor(out_nhowo_k_grid_desc,
+                                            make_tuple(make_pass_through_transform(N * Ho * Wo),
+                                                       make_pass_through_transform(K)),
+                                            make_tuple(Sequence<0>{}, Sequence<1>{}),
+                                            make_tuple(Sequence<0>{}, Sequence<1>{}));
+
+            const auto out_gemmm_gemmn_grid_desc =
+                transform_tensor_descriptor(out_gemmmraw_gemmn_grid_desc,
+                                            make_tuple(make_right_pad_transform(GemmMRaw, GemmMPad),
+                                                       make_pass_through_transform(GemmN)),
+                                            make_tuple(Sequence<0>{}, Sequence<1>{}),
+                                            make_tuple(Sequence<0>{}, Sequence<1>{}));
+
+            // C0: bias tensor: assume a contiguous vector
+            const auto bias_grid_desc_gemmm_gemmn =
+                make_naive_tensor_descriptor(make_tuple(GemmM, GemmN), make_tuple(I0, I1));
+
+            // C1: residual tensor: assume same layout as output tensor
+            const auto resi_grid_desc_gemmm_gemmn = out_gemmm_gemmn_grid_desc;
+
+            return make_tuple(in_gemmk0_gemmm_gemmk1_grid_desc,
+                              wei_gemmk0_gemmn_gemmk1_grid_desc,
+                              out_gemmm_gemmn_grid_desc,
+                              bias_grid_desc_gemmm_gemmn,
+                              resi_grid_desc_gemmm_gemmn);
+        }
+        else
+        {
+            const index_t GemmK = Y * X * C;
+            assert(GemmK % GemmK1Number == 0);
+
+            const index_t GemmK0 = GemmK / GemmK1Number;
+
+            // A: input tensor
+            const auto in_n_hi_wi_c_grid_desc =
+                make_naive_tensor_descriptor_packed(make_tuple(N, Hi, Wi, C));
+
+            const auto in_n_hip_wip_c_grid_desc = transform_tensor_descriptor(
+                in_n_hi_wi_c_grid_desc,
+                make_tuple(make_pass_through_transform(N),
+                           make_pad_transform(Hi, InLeftPadH, InRightPadH),
+                           make_pad_transform(Wi, InLeftPadW, InRightPadW),
+                           make_pass_through_transform(C)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
+
+            const auto in_n_y_ho_x_wo_c_grid_desc = transform_tensor_descriptor(
+                in_n_hip_wip_c_grid_desc,
+                make_tuple(
+                    make_pass_through_transform(N),
+                    make_embed_transform(make_tuple(Y, Ho), make_tuple(ConvDilationH, ConvStrideH)),
+                    make_embed_transform(make_tuple(X, Wo), make_tuple(ConvDilationW, ConvStrideW)),
+                    make_pass_through_transform(C)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
+                make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}));
+
+            const auto in_gemmk_gemmmraw_grid_desc =
+                transform_tensor_descriptor(in_n_y_ho_x_wo_c_grid_desc,
+                                            make_tuple(make_merge_transform(make_tuple(Y, X, C)),
+                                                       make_merge_transform(make_tuple(N, Ho, Wo))),
+                                            make_tuple(Sequence<1, 3, 5>{}, Sequence<0, 2, 4>{}),
+                                            make_tuple(Sequence<0>{}, Sequence<1>{}));
+
+            const auto in_gemmk0_gemmmraw_gemmk1_grid_desc = transform_tensor_descriptor(
+                in_gemmk_gemmmraw_grid_desc,
+                make_tuple(make_unmerge_transform(make_tuple(GemmK0, GemmK1Number)),
+                           make_pass_through_transform(GemmMRaw)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}),
+                make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
+
+            const auto in_gemmk0_gemmm_gemmk1_grid_desc = transform_tensor_descriptor(
+                in_gemmk0_gemmmraw_gemmk1_grid_desc,
+                make_tuple(make_pass_through_transform(GemmK0),
+                           make_right_pad_transform(GemmMRaw, GemmMPad),
+                           make_pass_through_transform(GemmK1Number)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
+
+            // B: weight tensor
+            const auto wei_k_yxc_grid_desc =
+                make_naive_tensor_descriptor_packed(make_tuple(K, Y * X * C));
+
+            const auto wei_gemmk_gemmn_grid_desc = transform_tensor_descriptor(
+                wei_k_yxc_grid_desc,
+                make_tuple(make_pass_through_transform(K), make_pass_through_transform(Y * X * C)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}),
+                make_tuple(Sequence<1>{}, Sequence<0>{}));
+
+            const auto wei_gemmk0_gemmn_gemmk1_grid_desc = transform_tensor_descriptor(
+                wei_gemmk_gemmn_grid_desc,
+                make_tuple(make_unmerge_transform(make_tuple(GemmK0, GemmK1Number)),
+                           make_pass_through_transform(GemmN)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}),
+                make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
+
+            // C: output tensor
+            const auto out_nhowo_k_grid_desc =
+                make_naive_tensor_descriptor_packed(make_tuple(N * Ho * Wo, K));
+
+            const auto out_gemmmraw_gemmn_grid_desc =
+                transform_tensor_descriptor(out_nhowo_k_grid_desc,
+                                            make_tuple(make_pass_through_transform(N * Ho * Wo),
+                                                       make_pass_through_transform(K)),
+                                            make_tuple(Sequence<0>{}, Sequence<1>{}),
+                                            make_tuple(Sequence<0>{}, Sequence<1>{}));
+
+            const auto out_gemmm_gemmn_grid_desc =
+                transform_tensor_descriptor(out_gemmmraw_gemmn_grid_desc,
+                                            make_tuple(make_right_pad_transform(GemmMRaw, GemmMPad),
+                                                       make_pass_through_transform(GemmN)),
+                                            make_tuple(Sequence<0>{}, Sequence<1>{}),
+                                            make_tuple(Sequence<0>{}, Sequence<1>{}));
+
+            // C0: bias tensor: assume a contiguous vector
+            const auto bias_grid_desc_gemmm_gemmn =
+                make_naive_tensor_descriptor(make_tuple(GemmM, GemmN), make_tuple(I0, I1));
+
+            // C1: residual tensor: assume same layout as output tensor
+            const auto resi_grid_desc_gemmm_gemmn = out_gemmm_gemmn_grid_desc;
+
+            return make_tuple(in_gemmk0_gemmm_gemmk1_grid_desc,
+                              wei_gemmk0_gemmn_gemmk1_grid_desc,
+                              out_gemmm_gemmn_grid_desc,
+                              bias_grid_desc_gemmm_gemmn,
+                              resi_grid_desc_gemmm_gemmn);
+        }
+    }
+
+    using GridDescs = decltype(MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N(
+        1, 1, 1, {1, 1}, {1, 1}, {1, 1}, {1, 1}, {1, 1}, {1, 1}, {1, 1}));
+
+    using AGridDesc_K0_M_K1 = remove_cvref_t<decltype(GridDescs{}[I0])>;
+    using BGridDesc_K0_N_K1 = remove_cvref_t<decltype(GridDescs{}[I1])>;
+    using CGridDesc_M_N     = remove_cvref_t<decltype(GridDescs{}[I2])>;
+    using C0GridDesc_M_N    = remove_cvref_t<decltype(GridDescs{}[I3])>;
+    using C1GridDesc_M_N    = remove_cvref_t<decltype(GridDescs{}[I4])>;
+
+    using Block2CTileMap = BlockToCTileMap_M00_N0_M01<MPerBlock, NPerBlock, CGridDesc_M_N>;
+
+    // GridwiseGemm
+    using GridwiseGemm = GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v3r3<
+        BlockSize,
+        ABDataType, // TODO: distinguish A/B datatype
+        AccDataType,
+        CDataType,
+        InMemoryDataOperationEnum::Set,
+        AGridDesc_K0_M_K1,
+        BGridDesc_K0_N_K1,
+        CGridDesc_M_N,
+        C0GridDesc_M_N,
+        C1GridDesc_M_N,
+        InElementwiseOperation,
+        WeiElementwiseOperation,
+        OutElementwiseOperation,
+        MPerBlock,
+        NPerBlock,
+        K0PerBlock,
+        MPerXDL,
+        NPerXDL,
+        K1,
+        MXdlPerWave,
+        NXdlPerWave,
+        ABlockTransferThreadClusterLengths_K0_M_K1,
+        Sequence<1, 0, 2>, // ABlockTransferThreadClusterArrangeOrder,
+        Sequence<1, 0, 2>, // ABlockTransferSrcAccessOrder,
+        2,                 // ABlockTransferSrcVectorDim,
+        ABlockTransferSrcScalarPerVector,
+        ABlockTransferDstScalarPerVector_K1,
+        false, // AThreadTransferSrcResetCoordinateAfterRun,
+        ABlockLdsAddExtraM,
+        BBlockTransferThreadClusterLengths_K0_N_K1,
+        Sequence<1, 0, 2>, // BBlockTransferThreadClusterArrangeOrder,
+        Sequence<1, 0, 2>, // BBlockTransferSrcAccessOrder,
+        2,                 // BBlockTransferSrcVectorDim,
+        BBlockTransferSrcScalarPerVector,
+        BBlockTransferDstScalarPerVector_K1,
+        false, // BThreadTransferSrcResetCoordinateAfterRun,
+        BBlockLdsAddExtraN,
+        CShuffleMXdlPerWavePerShuffle,
+        CShuffleNXdlPerWavePerShuffle,
+        CBlockTransferClusterLengths_MBlock_MXdlPerWave_MWaveMPerXdl_NBlock_NXdlPerWave_NWaveNPerXdl,
+        CBlockTransferScalarPerVector_NWaveNPerXdl>;
+
+    // Argument
+    struct Argument : public BaseArgument
+    {
+        Argument(const InDataType* p_in_grid,
+                 const WeiDataType* p_wei_grid,
+                 OutDataType* p_out_grid,
+                 const OutDataType* p_bias_grid,
+                 const OutDataType* p_resi_grid,
+                 ck::index_t N,
+                 ck::index_t K,
+                 ck::index_t C,
+                 std::vector<ck::index_t> input_spatial_lengths,
+                 std::vector<ck::index_t> filter_spatial_lengths,
+                 std::vector<ck::index_t> output_spatial_lengths,
+                 std::vector<ck::index_t> conv_filter_strides,
+                 std::vector<ck::index_t> conv_filter_dilations,
+                 std::vector<ck::index_t> input_left_pads,
+                 std::vector<ck::index_t> input_right_pads,
+                 InElementwiseOperation in_element_op,
+                 WeiElementwiseOperation wei_element_op,
+                 OutElementwiseOperation out_element_op)
+            : p_a_grid_{p_in_grid},
+              p_b_grid_{p_wei_grid},
+              p_c_grid_{p_out_grid},
+              p_c0_grid_{p_bias_grid},
+              p_c1_grid_{p_resi_grid},
+              a_grid_desc_k0_m_k1_{},
+              b_grid_desc_k0_n_k1_{},
+              c_grid_desc_m_n_{},
+              c0_grid_desc_m_n_{},
+              c1_grid_desc_m_n_{},
+              c_grid_desc_mblock_mxdlperwave_mwavemperxdl_nblock_nxdlperwave_nwavenperxdl_{},
+              c0_grid_desc_mblock_mxdlperwave_mwavemperxdl_nblock_nxdlperwave_nwavenperxdl_{},
+              c1_grid_desc_mblock_mxdlperwave_mwavemperxdl_nblock_nxdlperwave_nwavenperxdl_{},
+              block_2_ctile_map_{},
+              in_element_op_{in_element_op},
+              wei_element_op_{wei_element_op},
+              out_element_op_{out_element_op},
+              Conv_N_{N},
+              Conv_K_{K},
+              Conv_C_{C},
+              input_spatial_lengths_{input_spatial_lengths},
+              filter_spatial_lengths_{filter_spatial_lengths},
+              output_spatial_lengths_{output_spatial_lengths},
+              conv_filter_strides_{conv_filter_strides},
+              conv_filter_dilations_{conv_filter_dilations},
+              input_left_pads_{input_left_pads},
+              input_right_pads_{input_right_pads}
+        {
+            const auto descs =
+                DeviceOp::MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N(N,
+                                                                          K,
+                                                                          C,
+                                                                          input_spatial_lengths,
+                                                                          filter_spatial_lengths,
+                                                                          output_spatial_lengths,
+                                                                          conv_filter_strides,
+                                                                          conv_filter_dilations,
+                                                                          input_left_pads,
+                                                                          input_right_pads);
+
+            a_grid_desc_k0_m_k1_ = descs[I0];
+            b_grid_desc_k0_n_k1_ = descs[I1];
+            c_grid_desc_m_n_     = descs[I2];
+            c0_grid_desc_m_n_    = descs[I3];
+            c1_grid_desc_m_n_    = descs[I4];
+
+            block_2_ctile_map_ = Block2CTileMap{c_grid_desc_m_n_};
+
+            if(GridwiseGemm::CheckValidity(a_grid_desc_k0_m_k1_,
+                                           b_grid_desc_k0_n_k1_,
+                                           c_grid_desc_m_n_,
+                                           block_2_ctile_map_))
+            {
+                c_grid_desc_mblock_mxdlperwave_mwavemperxdl_nblock_nxdlperwave_nwavenperxdl_ =
+                    GridwiseGemm::
+                        MakeCGridDescriptor_MBlock_MXdlPerWave_MWaveMPerXdl_NBlock_NXdlPerWave_NWaveNPerXdl(
+                            c_grid_desc_m_n_);
+
+                c0_grid_desc_mblock_mxdlperwave_mwavemperxdl_nblock_nxdlperwave_nwavenperxdl_ =
+                    GridwiseGemm::
+                        MakeCGridDescriptor_MBlock_MXdlPerWave_MWaveMPerXdl_NBlock_NXdlPerWave_NWaveNPerXdl(
+                            c0_grid_desc_m_n_);
+
+                c1_grid_desc_mblock_mxdlperwave_mwavemperxdl_nblock_nxdlperwave_nwavenperxdl_ =
+                    GridwiseGemm::
+                        MakeCGridDescriptor_MBlock_MXdlPerWave_MWaveMPerXdl_NBlock_NXdlPerWave_NWaveNPerXdl(
+                            c1_grid_desc_m_n_);
+            }
+        }
+
+        //  private:
+        const ADataType* p_a_grid_;
+        const BDataType* p_b_grid_;
+        CDataType* p_c_grid_;
+        const CDataType* p_c0_grid_;
+        const CDataType* p_c1_grid_;
+        AGridDesc_K0_M_K1 a_grid_desc_k0_m_k1_;
+        BGridDesc_K0_N_K1 b_grid_desc_k0_n_k1_;
+        CGridDesc_M_N c_grid_desc_m_n_;
+        C0GridDesc_M_N c0_grid_desc_m_n_;
+        C1GridDesc_M_N c1_grid_desc_m_n_;
+        typename GridwiseGemm::
+            CGridDescriptor_MBlock_MXdlPerWave_MWaveMPerXdl_NBlock_NXdlPerWave_NWaveNPerXdl
+                c_grid_desc_mblock_mxdlperwave_mwavemperxdl_nblock_nxdlperwave_nwavenperxdl_;
+        typename GridwiseGemm::
+            C0GridDescriptor_MBlock_MXdlPerWave_MWaveMPerXdl_NBlock_NXdlPerWave_NWaveNPerXdl
+                c0_grid_desc_mblock_mxdlperwave_mwavemperxdl_nblock_nxdlperwave_nwavenperxdl_;
+        typename GridwiseGemm::
+            C1GridDescriptor_MBlock_MXdlPerWave_MWaveMPerXdl_NBlock_NXdlPerWave_NWaveNPerXdl
+                c1_grid_desc_mblock_mxdlperwave_mwavemperxdl_nblock_nxdlperwave_nwavenperxdl_;
+        Block2CTileMap block_2_ctile_map_;
+        InElementwiseOperation in_element_op_;
+        WeiElementwiseOperation wei_element_op_;
+        OutElementwiseOperation out_element_op_;
+        // for checking IsSupportedArgument()
+        index_t Conv_N_;
+        index_t Conv_K_;
+        index_t Conv_C_;
+        std::vector<index_t> input_spatial_lengths_;
+        std::vector<index_t> filter_spatial_lengths_;
+        std::vector<index_t> output_spatial_lengths_;
+        std::vector<index_t> conv_filter_strides_;
+        std::vector<index_t> conv_filter_dilations_;
+        std::vector<index_t> input_left_pads_;
+        std::vector<index_t> input_right_pads_;
+    };
+
+    // Invoker
+    struct Invoker : public BaseInvoker
+    {
+        using Argument = DeviceOp::Argument;
+
+        float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
+        {
+#if 0
+            {
+                std::cout << DeviceOp{}.GetTypeString() << std::endl;
+                std::cout << "N " << arg.Conv_N_ << ", "
+                          << "K " << arg.Conv_K_ << ", "
+                          << "C " << arg.Conv_C_ << ", " << std::endl;
+                std::cout << "Y X " << arg.filter_spatial_lengths_[0] << ", "
+                          << arg.filter_spatial_lengths_[1] << ", " << std::endl;
+                std::cout << "Hi Wi " << arg.input_spatial_lengths_[0] << ", "
+                          << arg.input_spatial_lengths_[1] << ", " << std::endl;
+                std::cout << "Ho Wo " << arg.output_spatial_lengths_[0] << ", "
+                          << arg.output_spatial_lengths_[1] << ", " << std::endl;
+                std::cout << "Strides " << arg.conv_filter_strides_[0] << ", "
+                          << arg.conv_filter_strides_[1] << ", " << std::endl;
+                std::cout << "Dilations " << arg.conv_filter_dilations_[0] << ", "
+                          << arg.conv_filter_dilations_[1] << ", " << std::endl;
+                std::cout << "InLeftPads " << arg.input_left_pads_[0] << ", "
+                          << arg.input_left_pads_[1] << ", " << std::endl;
+                std::cout << "InLeftPads " << arg.input_right_pads_[0] << ", "
+                          << arg.input_right_pads_[1] << ", " << std::endl;
+            }
+
+            {
+                std::cout << "arg.a_grid_desc_k0_m_k1_{" << arg.a_grid_desc_k0_m_k1_.GetLength(I0)
+                          << ", " << arg.a_grid_desc_k0_m_k1_.GetLength(I1) << ", "
+                          << arg.a_grid_desc_k0_m_k1_.GetLength(I2) << "}" << std::endl;
+
+                std::cout << "arg.b_grid_desc_k0_n_k1_{" << arg.b_grid_desc_k0_n_k1_.GetLength(I0)
+                          << ", " << arg.b_grid_desc_k0_n_k1_.GetLength(I1) << ", "
+                          << arg.b_grid_desc_k0_n_k1_.GetLength(I2) << "}" << std::endl;
+
+                std::cout << "arg.c_grid_desc_m_n_{ " << arg.c_grid_desc_m_n_.GetLength(I0) << ", "
+                          << arg.c_grid_desc_m_n_.GetLength(I1) << "}" << std::endl;
+
+                std::cout << "arg.c0_grid_desc_m_n_{ " << arg.c0_grid_desc_m_n_.GetLength(I0)
+                          << ", " << arg.c0_grid_desc_m_n_.GetLength(I1) << "}" << std::endl;
+
+                std::cout << "arg.c1_grid_desc_m_n_{ " << arg.c1_grid_desc_m_n_.GetLength(I0)
+                          << ", " << arg.c1_grid_desc_m_n_.GetLength(I1) << "}" << std::endl;
+            }
+#endif
+
+            if(!GridwiseGemm::CheckValidity(arg.a_grid_desc_k0_m_k1_,
+                                            arg.b_grid_desc_k0_n_k1_,
+                                            arg.c_grid_desc_m_n_,
+                                            arg.block_2_ctile_map_))
+            {
+                throw std::runtime_error(
+                    "wrong! GridwiseGemm_km_kn_m0m1n0n1_xdlops_v3r3 has invalid setting");
+            }
+
+            const index_t grid_size =
+                arg.block_2_ctile_map_.CalculateGridSize(arg.c_grid_desc_m_n_);
+
+            const auto K =
+                arg.a_grid_desc_k0_m_k1_.GetLength(I0) * arg.a_grid_desc_k0_m_k1_.GetLength(I2);
+
+            float ave_time = 0;
+
+            if(GridwiseGemm::CalculateHasMainKBlockLoop(K))
+            {
+                const auto kernel = kernel_gemm_xdlops_v3r3<
+                    GridwiseGemm,
+                    ADataType, // TODO: distiguish A/B datatype
+                    CDataType,
+                    remove_reference_t<DeviceOp::AGridDesc_K0_M_K1>,
+                    remove_reference_t<DeviceOp::BGridDesc_K0_N_K1>,
+                    remove_reference_t<
+                        typename GridwiseGemm::
+                            CGridDescriptor_MBlock_MXdlPerWave_MWaveMPerXdl_NBlock_NXdlPerWave_NWaveNPerXdl>,
+                    remove_reference_t<
+                        typename GridwiseGemm::
+                            C0GridDescriptor_MBlock_MXdlPerWave_MWaveMPerXdl_NBlock_NXdlPerWave_NWaveNPerXdl>,
+                    remove_reference_t<
+                        typename GridwiseGemm::
+                            C1GridDescriptor_MBlock_MXdlPerWave_MWaveMPerXdl_NBlock_NXdlPerWave_NWaveNPerXdl>,
+                    InElementwiseOperation,
+                    WeiElementwiseOperation,
+                    OutElementwiseOperation,
+                    Block2CTileMap,
+                    true>;
+
+                ave_time = launch_and_time_kernel(
+                    stream_config,
+                    kernel,
+                    dim3(grid_size),
+                    dim3(BlockSize),
+                    0,
+                    arg.p_a_grid_,
+                    arg.p_b_grid_,
+                    arg.p_c_grid_,
+                    arg.p_c0_grid_,
+                    arg.p_c1_grid_,
+                    arg.a_grid_desc_k0_m_k1_,
+                    arg.b_grid_desc_k0_n_k1_,
+                    arg.c_grid_desc_mblock_mxdlperwave_mwavemperxdl_nblock_nxdlperwave_nwavenperxdl_,
+                    arg.c0_grid_desc_mblock_mxdlperwave_mwavemperxdl_nblock_nxdlperwave_nwavenperxdl_,
+                    arg.c1_grid_desc_mblock_mxdlperwave_mwavemperxdl_nblock_nxdlperwave_nwavenperxdl_,
+                    arg.in_element_op_,
+                    arg.wei_element_op_,
+                    arg.out_element_op_,
+                    arg.block_2_ctile_map_);
+            }
+            else
+            {
+                const auto kernel = kernel_gemm_xdlops_v3r3<
+                    GridwiseGemm,
+                    ADataType, // TODO: distiguish A/B datatype
+                    CDataType,
+                    remove_reference_t<DeviceOp::AGridDesc_K0_M_K1>,
+                    remove_reference_t<DeviceOp::BGridDesc_K0_N_K1>,
+                    remove_reference_t<
+                        typename GridwiseGemm::
+                            CGridDescriptor_MBlock_MXdlPerWave_MWaveMPerXdl_NBlock_NXdlPerWave_NWaveNPerXdl>,
+                    remove_reference_t<
+                        typename GridwiseGemm::
+                            C0GridDescriptor_MBlock_MXdlPerWave_MWaveMPerXdl_NBlock_NXdlPerWave_NWaveNPerXdl>,
+                    remove_reference_t<
+                        typename GridwiseGemm::
+                            C1GridDescriptor_MBlock_MXdlPerWave_MWaveMPerXdl_NBlock_NXdlPerWave_NWaveNPerXdl>,
+                    InElementwiseOperation,
+                    WeiElementwiseOperation,
+                    OutElementwiseOperation,
+                    Block2CTileMap,
+                    false>;
+
+                ave_time = launch_and_time_kernel(
+                    stream_config,
+                    kernel,
+                    dim3(grid_size),
+                    dim3(BlockSize),
+                    0,
+                    arg.p_a_grid_,
+                    arg.p_b_grid_,
+                    arg.p_c_grid_,
+                    arg.p_c0_grid_,
+                    arg.p_c1_grid_,
+                    arg.a_grid_desc_k0_m_k1_,
+                    arg.b_grid_desc_k0_n_k1_,
+                    arg.c_grid_desc_mblock_mxdlperwave_mwavemperxdl_nblock_nxdlperwave_nwavenperxdl_,
+                    arg.c0_grid_desc_mblock_mxdlperwave_mwavemperxdl_nblock_nxdlperwave_nwavenperxdl_,
+                    arg.c1_grid_desc_mblock_mxdlperwave_mwavemperxdl_nblock_nxdlperwave_nwavenperxdl_,
+                    arg.in_element_op_,
+                    arg.wei_element_op_,
+                    arg.out_element_op_,
+                    arg.block_2_ctile_map_);
+            }
+
+            return ave_time;
+        }
+
+        float Run(const BaseArgument* p_arg,
+                  const StreamConfig& stream_config = StreamConfig{}) override
+        {
+            return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
+        }
+    };
+
+    static constexpr bool IsValidCompilationParameter()
+    {
+        // TODO: properly implement this check
+        return true;
+    }
+
+    static bool IsSupportedArgument(const Argument& arg)
+    {
+        if constexpr(ConvForwardSpecialization ==
+                     ConvolutionForwardSpecialization::Filter1x1Stride1Pad0)
+        {
+            // check if it's 1x1, stride=1 conv
+            if(!(arg.filter_spatial_lengths_[0] == 1 && arg.filter_spatial_lengths_[1] == 1 &&
+                 arg.conv_filter_strides_[0] == 1 && arg.conv_filter_strides_[1] == 1 &&
+                 arg.input_left_pads_[0] == 0 && arg.input_left_pads_[1] == 0 &&
+                 arg.input_right_pads_[0] == 0 && arg.input_right_pads_[1] == 0))
+            {
+                return false;
+            }
+        }
+        else if constexpr(ConvForwardSpecialization ==
+                          ConvolutionForwardSpecialization::Filter1x1Pad0)
+        {
+            // check if it's 1x1 conv
+            if(!(arg.filter_spatial_lengths_[0] == 1 && arg.filter_spatial_lengths_[1] == 1 &&
+                 arg.input_left_pads_[0] == 0 && arg.input_left_pads_[1] == 0 &&
+                 arg.input_right_pads_[0] == 0 && arg.input_right_pads_[1] == 0))
+            {
+                return false;
+            }
+        }
+
+        // vector load A/B matrix from global memory
+        if(!(ABlockTransferSrcVectorDim == 2 && BBlockTransferSrcVectorDim == 2 &&
+             arg.Conv_C_ % ABlockTransferSrcScalarPerVector == 0 &&
+             arg.Conv_C_ % BBlockTransferSrcScalarPerVector == 0))
+        {
+            return false;
+        }
+
+        // vector store C matrix into global memory
+        if(!(arg.Conv_K_ % CBlockTransferScalarPerVector_NWaveNPerXdl == 0))
+        {
+            return false;
+        }
+
+        // Gridwise GEMM size
+        return GridwiseGemm::CheckValidity(arg.a_grid_desc_k0_m_k1_,
+                                           arg.b_grid_desc_k0_n_k1_,
+                                           arg.c_grid_desc_m_n_,
+                                           arg.block_2_ctile_map_);
+    }
+
+    bool IsSupportedArgument(const BaseArgument* p_arg) override
+    {
+        return IsSupportedArgument(*dynamic_cast<const Argument*>(p_arg));
+    }
+
+    static auto MakeArgument(const InDataType* p_in_grid,
+                             const WeiDataType* p_wei_grid,
+                             OutDataType* p_out_grid,
+                             const OutDataType* p_bias_grid,
+                             const OutDataType* p_resi_grid,
+                             ck::index_t N,
+                             ck::index_t K,
+                             ck::index_t C,
+                             std::vector<ck::index_t> input_spatial_lengths,
+                             std::vector<ck::index_t> filter_spatial_lengths,
+                             std::vector<ck::index_t> output_spatial_lengths,
+                             std::vector<ck::index_t> conv_filter_strides,
+                             std::vector<ck::index_t> conv_filter_dilations,
+                             std::vector<ck::index_t> input_left_pads,
+                             std::vector<ck::index_t> input_right_pads,
+                             InElementwiseOperation in_element_op,
+                             WeiElementwiseOperation wei_element_op,
+                             OutElementwiseOperation out_element_op)
+    {
+        return Argument{p_in_grid,
+                        p_wei_grid,
+                        p_out_grid,
+                        p_bias_grid,
+                        p_resi_grid,
+                        N,
+                        K,
+                        C,
+                        input_spatial_lengths,
+                        filter_spatial_lengths,
+                        output_spatial_lengths,
+                        conv_filter_strides,
+                        conv_filter_dilations,
+                        input_left_pads,
+                        input_right_pads,
+                        in_element_op,
+                        wei_element_op,
+                        out_element_op};
+    }
+
+    static auto MakeInvoker() { return Invoker{}; }
+
+    std::unique_ptr<BaseArgument>
+    MakeArgumentPointer(const void* p_in_grid,
+                        const void* p_wei_grid,
+                        void* p_out_grid,
+                        const void* p_bias_grid,
+                        const void* p_resi_grid,
+                        ck::index_t N,
+                        ck::index_t K,
+                        ck::index_t C,
+                        std::vector<ck::index_t> input_spatial_lengths,
+                        std::vector<ck::index_t> filter_spatial_lengths,
+                        std::vector<ck::index_t> output_spatial_lengths,
+                        std::vector<ck::index_t> conv_filter_strides,
+                        std::vector<ck::index_t> conv_filter_dilations,
+                        std::vector<ck::index_t> input_left_pads,
+                        std::vector<ck::index_t> input_right_pads,
+                        InElementwiseOperation in_element_op,
+                        WeiElementwiseOperation wei_element_op,
+                        OutElementwiseOperation out_element_op) override
+    {
+        return std::make_unique<Argument>(static_cast<const InDataType*>(p_in_grid),
+                                          static_cast<const WeiDataType*>(p_wei_grid),
+                                          static_cast<OutDataType*>(p_out_grid),
+                                          static_cast<const OutDataType*>(p_bias_grid),
+                                          static_cast<const OutDataType*>(p_resi_grid),
+                                          N,
+                                          K,
+                                          C,
+                                          input_spatial_lengths,
+                                          filter_spatial_lengths,
+                                          output_spatial_lengths,
+                                          conv_filter_strides,
+                                          conv_filter_dilations,
+                                          input_left_pads,
+                                          input_right_pads,
+                                          in_element_op,
+                                          wei_element_op,
+                                          out_element_op);
+    }
+
+    std::unique_ptr<BaseInvoker> MakeInvokerPointer() override
+    {
+        return std::make_unique<Invoker>(Invoker{});
+    }
+
+    std::string GetTypeString() const override
+    {
+        auto str = std::stringstream();
+
+        // clang-format off
+        str << "DeviceConv2dFwdXdl_C_Shuffle_Bias_Activation_Add_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K"
+            << "<"
+            << BlockSize << ", "
+            << MPerBlock << ", "
+            << NPerBlock << ", "
+            << K0PerBlock
+            << ">";
+        // clang-format on
+
+        return str.str();
+    }
+};
+} // namespace device
+} // namespace tensor_operation
+} // namespace ck

deps/cget/pkg/ROCmSoftwarePlatform__composable_kernel/install/include/ck/tensor_operation/gpu/device/device_conv2d_fwd_xdl_c_shuffle_bias_activation_nhwc_kyxc_nhwk.hpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include <iostream>
+#include <sstream>
+#include <vector>
+
+#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_conv_fwd_bias_activation.hpp"
+#include "ck/tensor_operation/gpu/device/convolution_forward_specialization.hpp"
+#include "ck/tensor_operation/gpu/grid/gridwise_gemm_xdlops_v3r2.hpp"
+#include "ck/host_utility/device_prop.hpp"
+#include "ck/host_utility/kernel_launch.hpp"
+
+namespace ck {
+namespace tensor_operation {
+namespace device {
+
+// out[N, Ho, Wo, K] =
+//     activate(in[N, Hi, Wi, C] * wei[K, Y, X, C] + bias[K])
+template <
+    typename InDataType,
+    typename WeiDataType,
+    typename OutDataType,
+    typename AccDataType,
+    typename InElementwiseOperation,
+    typename WeiElementwiseOperation,
+    typename OutElementwiseOperation,
+    InMemoryDataOperationEnum OutGlobalMemoryDataOperation,
+    ConvolutionForwardSpecialization ConvForwardSpecialization,
+    ck::index_t BlockSize,
+    ck::index_t MPerBlock,
+    ck::index_t NPerBlock,
+    ck::index_t K0PerBlock,
+    ck::index_t K1,
+    ck::index_t MPerXDL,
+    ck::index_t NPerXDL,
+    ck::index_t MXdlPerWave,
+    ck::index_t NXdlPerWave,
+    typename ABlockTransferThreadClusterLengths_K0_M_K1,
+    typename ABlockTransferThreadClusterArrangeOrder,
+    typename ABlockTransferSrcAccessOrder,
+    ck::index_t ABlockTransferSrcVectorDim,
+    ck::index_t ABlockTransferSrcScalarPerVector,
+    ck::index_t ABlockTransferDstScalarPerVector_K1,
+    bool ABlockLdsAddExtraM,
+    typename BBlockTransferThreadClusterLengths_K0_N_K1,
+    typename BBlockTransferThreadClusterArrangeOrder,
+    typename BBlockTransferSrcAccessOrder,
+    ck::index_t BBlockTransferSrcVectorDim,
+    ck::index_t BBlockTransferSrcScalarPerVector,
+    ck::index_t BBlockTransferDstScalarPerVector_K1,
+    bool BBlockLdsAddExtraN,
+    index_t CShuffleMXdlPerWavePerShuffle,
+    index_t CShuffleNXdlPerWavePerShuffle,
+    typename CBlockTransferClusterLengths_MBlock_MXdlPerWave_MWaveMPerXdl_NBlock_NXdlPerWave_NWaveNPerXdl,
+    index_t CBlockTransferScalarPerVector_NWaveNPerXdl>
+struct DeviceConv2dFwdXdl_C_Shuffle_Bias_Activation_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K
+    : public DeviceConvFwdBiasActivation<InElementwiseOperation,
+                                         WeiElementwiseOperation,
+                                         OutElementwiseOperation>
+{
+    using DeviceOp =
+        DeviceConv2dFwdXdl_C_Shuffle_Bias_Activation_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K;
+
+    using ADataType = InDataType;
+    using BDataType = WeiDataType;
+    using CDataType = OutDataType;
+
+    // TODO make A/B datatype different
+    using ABDataType = InDataType;
+
+    // TODO make it support any # of spatial dimensions
+    static constexpr index_t NDimSpatial = 2;
+
+    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 K1Number     = Number<K1>{};
+    static constexpr auto GemmK1Number = K1Number;
+
+    static auto
+    MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N(ck::index_t N,
+                                                    ck::index_t K,
+                                                    ck::index_t C,
+                                                    std::vector<ck::index_t> input_spatial_lengths,
+                                                    std::vector<ck::index_t> filter_spatial_lengths,
+                                                    std::vector<ck::index_t> output_spatial_lengths,
+                                                    std::vector<ck::index_t> conv_filter_strides,
+                                                    std::vector<ck::index_t> conv_filter_dilations,
+                                                    std::vector<ck::index_t> input_left_pads,
+                                                    std::vector<ck::index_t> input_right_pads)
+    {
+        using namespace ck;
+
+        const index_t Hi = input_spatial_lengths[0];
+        const index_t Wi = input_spatial_lengths[1];
+
+        const index_t Ho = output_spatial_lengths[0];
+        const index_t Wo = output_spatial_lengths[1];
+
+        const index_t Y = filter_spatial_lengths[0];
+        const index_t X = filter_spatial_lengths[1];
+
+        const index_t ConvStrideH = conv_filter_strides[0];
+        const index_t ConvStrideW = conv_filter_strides[1];
+
+        const index_t ConvDilationH = conv_filter_dilations[0];
+        const index_t ConvDilationW = conv_filter_dilations[1];
+
+        const index_t InLeftPadH = input_left_pads[0];
+        const index_t InLeftPadW = input_left_pads[1];
+
+        const index_t InRightPadH = input_right_pads[0];
+        const index_t InRightPadW = input_right_pads[1];
+
+        const index_t GemmMRaw = N * Ho * Wo;
+        const index_t GemmN    = K;
+
+        const auto GemmM    = math::integer_least_multiple(GemmMRaw, MPerBlock);
+        const auto GemmMPad = GemmM - GemmMRaw;
+
+        if constexpr(ConvForwardSpecialization ==
+                     ConvolutionForwardSpecialization::Filter1x1Stride1Pad0)
+        { // 1x1, stride=1, pad=0
+            const index_t GemmK = Y * X * C;
+            assert(GemmK % GemmK1Number == 0);
+
+            const index_t GemmK0 = GemmK / GemmK1Number;
+
+            // A: input tensor
+            const auto in_gemmmraw_gemmk_grid_desc =
+                make_naive_tensor_descriptor_packed(make_tuple(N * Ho * Wo, C));
+
+            const auto in_gemmk0_gemmm_gemmk1_grid_desc = transform_tensor_descriptor(
+                in_gemmmraw_gemmk_grid_desc,
+                make_tuple(make_unmerge_transform(make_tuple(GemmK0, GemmK1Number)),
+                           make_right_pad_transform(GemmMRaw, GemmMPad)),
+                make_tuple(Sequence<1>{}, Sequence<0>{}),
+                make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
+
+            // B: weight tensor
+            const auto wei_gemmn_gemmk_grid_desc =
+                make_naive_tensor_descriptor_packed(make_tuple(K, C));
+
+            const auto wei_gemmk0_gemmn_gemmk1_grid_desc = transform_tensor_descriptor(
+                wei_gemmn_gemmk_grid_desc,
+                make_tuple(make_unmerge_transform(make_tuple(GemmK0, GemmK1Number)),
+                           make_pass_through_transform(GemmN)),
+                make_tuple(Sequence<1>{}, Sequence<0>{}),
+                make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
+
+            // C: output tensor
+            const auto out_gemmmraw_gemmn_grid_desc =
+                make_naive_tensor_descriptor_packed(make_tuple(N * Ho * Wo, K));
+
+            const auto out_gemmm_gemmn_grid_desc =
+                transform_tensor_descriptor(out_gemmmraw_gemmn_grid_desc,
+                                            make_tuple(make_right_pad_transform(GemmMRaw, GemmMPad),
+                                                       make_pass_through_transform(GemmN)),
+                                            make_tuple(Sequence<0>{}, Sequence<1>{}),
+                                            make_tuple(Sequence<0>{}, Sequence<1>{}));
+
+            // C0: bias tensor: assume a contiguous vector
+            const auto bias_grid_desc_gemmm_gemmn =
+                make_naive_tensor_descriptor(make_tuple(GemmM, GemmN), make_tuple(I0, I1));
+
+            return make_tuple(in_gemmk0_gemmm_gemmk1_grid_desc,
+                              wei_gemmk0_gemmn_gemmk1_grid_desc,
+                              out_gemmm_gemmn_grid_desc,
+                              bias_grid_desc_gemmm_gemmn);
+        }
+        else if constexpr(ConvForwardSpecialization ==
+                          ConvolutionForwardSpecialization::Filter1x1Pad0)
+        { // 1x1, pad=0
+            const index_t GemmK = Y * X * C;
+            assert(GemmK % GemmK1Number == 0);
+
+            const index_t GemmK0 = GemmK / GemmK1Number;
+
+            // A: input tensor
+            const auto in_n_hi_wi_c_grid_desc =
+                make_naive_tensor_descriptor_packed(make_tuple(N, Hi, Wi, C));
+
+            const auto in_n_ho_wo_c_grid_desc = transform_tensor_descriptor(
+                in_n_hi_wi_c_grid_desc,
+                make_tuple(make_pass_through_transform(N),
+                           make_embed_transform(make_tuple(Ho), make_tuple(ConvStrideH)),
+                           make_embed_transform(make_tuple(Wo), make_tuple(ConvStrideW)),
+                           make_pass_through_transform(C)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
+
+            const auto in_gemmk0_gemmmraw_gemmk1_grid_desc = transform_tensor_descriptor(
+                in_n_ho_wo_c_grid_desc,
+                make_tuple(make_unmerge_transform(make_tuple(GemmK0, GemmK1Number)),
+                           make_merge_transform(make_tuple(N, Ho, Wo))),
+                make_tuple(Sequence<3>{}, Sequence<0, 1, 2>{}),
+                make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
+
+            const auto in_gemmk0_gemmm_gemmk1_grid_desc = transform_tensor_descriptor(
+                in_gemmk0_gemmmraw_gemmk1_grid_desc,
+                make_tuple(make_pass_through_transform(GemmK0),
+                           make_right_pad_transform(GemmMRaw, GemmMPad),
+                           make_pass_through_transform(GemmK1Number)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
+
+            // B: weight tensor
+            const auto wei_gemmn_gemmk_grid_desc =
+                make_naive_tensor_descriptor_packed(make_tuple(K, C));
+
+            const auto wei_gemmk0_gemmn_gemmk1_grid_desc = transform_tensor_descriptor(
+                wei_gemmn_gemmk_grid_desc,
+                make_tuple(make_unmerge_transform(make_tuple(GemmK0, GemmK1Number)),
+                           make_pass_through_transform(GemmN)),
+                make_tuple(Sequence<1>{}, Sequence<0>{}),
+                make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
+
+            // C: output tensor
+            const auto out_gemmmraw_gemmn_grid_desc =
+                make_naive_tensor_descriptor_packed(make_tuple(N * Ho * Wo, K));
+
+            const auto out_gemmm_gemmn_grid_desc =
+                transform_tensor_descriptor(out_gemmmraw_gemmn_grid_desc,
+                                            make_tuple(make_right_pad_transform(GemmMRaw, GemmMPad),
+                                                       make_pass_through_transform(GemmN)),
+                                            make_tuple(Sequence<0>{}, Sequence<1>{}),
+                                            make_tuple(Sequence<0>{}, Sequence<1>{}));
+
+            // C0: bias tensor: assume a contiguous vector
+            const auto bias_grid_desc_gemmm_gemmn =
+                make_naive_tensor_descriptor(make_tuple(GemmM, GemmN), make_tuple(I0, I1));
+
+            return make_tuple(in_gemmk0_gemmm_gemmk1_grid_desc,
+                              wei_gemmk0_gemmn_gemmk1_grid_desc,
+                              out_gemmm_gemmn_grid_desc,
+                              bias_grid_desc_gemmm_gemmn);
+        }
+        else if constexpr(ConvForwardSpecialization == ConvolutionForwardSpecialization::OddC)
+        { // C = odd value
+            const index_t GemmKRaw = Y * X * C;
+            const index_t GemmK = math::integer_least_multiple(GemmKRaw, K0PerBlock * GemmK1Number);
+            const index_t GemmKPad = GemmK - GemmKRaw;
+            const index_t GemmK0   = GemmK / GemmK1Number;
+
+            // A: input tensor
+            const auto in_n_hi_wi_c_grid_desc =
+                make_naive_tensor_descriptor_packed(make_tuple(N, Hi, Wi, C));
+
+            const auto in_n_hip_wip_c_grid_desc = transform_tensor_descriptor(
+                in_n_hi_wi_c_grid_desc,
+                make_tuple(make_pass_through_transform(N),
+                           make_pad_transform(Hi, InLeftPadH, InRightPadH),
+                           make_pad_transform(Wi, InLeftPadW, InRightPadW),
+                           make_pass_through_transform(C)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
+
+            const auto in_n_y_ho_x_wo_c_grid_desc = transform_tensor_descriptor(
+                in_n_hip_wip_c_grid_desc,
+                make_tuple(
+                    make_pass_through_transform(N),
+                    make_embed_transform(make_tuple(Y, Ho), make_tuple(ConvDilationH, ConvStrideH)),
+                    make_embed_transform(make_tuple(X, Wo), make_tuple(ConvDilationW, ConvStrideW)),
+                    make_pass_through_transform(C)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
+                make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}));
+
+            const auto in_gemmkraw_gemmmraw_grid_desc =
+                transform_tensor_descriptor(in_n_y_ho_x_wo_c_grid_desc,
+                                            make_tuple(make_merge_transform(make_tuple(Y, X, C)),
+                                                       make_merge_transform(make_tuple(N, Ho, Wo))),
+                                            make_tuple(Sequence<1, 3, 5>{}, Sequence<0, 2, 4>{}),
+                                            make_tuple(Sequence<0>{}, Sequence<1>{}));
+
+            const auto in_gemmk_gemmm_grid_desc = transform_tensor_descriptor(
+                in_gemmkraw_gemmmraw_grid_desc,
+                make_tuple(make_right_pad_transform(GemmKRaw, GemmKPad),
+                           make_right_pad_transform(GemmMRaw, GemmMPad)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}),
+                make_tuple(Sequence<0>{}, Sequence<1>{}));
+
+            const auto in_gemmk0_gemmm_gemmk1_grid_desc = transform_tensor_descriptor(
+                in_gemmk_gemmm_grid_desc,
+                make_tuple(make_unmerge_transform(make_tuple(GemmK0, GemmK1Number)),
+                           make_pass_through_transform(GemmM)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}),
+                make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
+
+            // B: weight tensor
+            const auto wei_k_yxc_grid_desc =
+                make_naive_tensor_descriptor_packed(make_tuple(K, Y * X * C));
+
+            const auto wei_gemmk_gemmn_grid_desc = transform_tensor_descriptor(
+                wei_k_yxc_grid_desc,
+                make_tuple(make_pass_through_transform(K),
+                           make_right_pad_transform(GemmKRaw, GemmKPad)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}),
+                make_tuple(Sequence<1>{}, Sequence<0>{}));
+
+            const auto wei_gemmk0_gemmn_gemmk1_grid_desc = transform_tensor_descriptor(
+                wei_gemmk_gemmn_grid_desc,
+                make_tuple(make_unmerge_transform(make_tuple(GemmK0, GemmK1Number)),
+                           make_pass_through_transform(GemmN)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}),
+                make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
+
+            // C: output tensor
+            const auto out_nhowo_k_grid_desc =
+                make_naive_tensor_descriptor_packed(make_tuple(N * Ho * Wo, K));
+
+            const auto out_gemmmraw_gemmn_grid_desc =
+                transform_tensor_descriptor(out_nhowo_k_grid_desc,
+                                            make_tuple(make_pass_through_transform(N * Ho * Wo),
+                                                       make_pass_through_transform(K)),
+                                            make_tuple(Sequence<0>{}, Sequence<1>{}),
+                                            make_tuple(Sequence<0>{}, Sequence<1>{}));
+
+            const auto out_gemmm_gemmn_grid_desc =
+                transform_tensor_descriptor(out_gemmmraw_gemmn_grid_desc,
+                                            make_tuple(make_right_pad_transform(GemmMRaw, GemmMPad),
+                                                       make_pass_through_transform(GemmN)),
+                                            make_tuple(Sequence<0>{}, Sequence<1>{}),
+                                            make_tuple(Sequence<0>{}, Sequence<1>{}));
+
+            // C0: bias tensor: assume a contiguous vector
+            const auto bias_grid_desc_gemmm_gemmn =
+                make_naive_tensor_descriptor(make_tuple(GemmM, GemmN), make_tuple(I0, I1));
+
+            return make_tuple(in_gemmk0_gemmm_gemmk1_grid_desc,
+                              wei_gemmk0_gemmn_gemmk1_grid_desc,
+                              out_gemmm_gemmn_grid_desc,
+                              bias_grid_desc_gemmm_gemmn);
+        }
+        else
+        {
+            const index_t GemmK = Y * X * C;
+            assert(GemmK % GemmK1Number == 0);
+
+            const index_t GemmK0 = GemmK / GemmK1Number;
+
+            // A: input tensor
+            const auto in_n_hi_wi_c_grid_desc =
+                make_naive_tensor_descriptor_packed(make_tuple(N, Hi, Wi, C));
+
+            const auto in_n_hip_wip_c_grid_desc = transform_tensor_descriptor(
+                in_n_hi_wi_c_grid_desc,
+                make_tuple(make_pass_through_transform(N),
+                           make_pad_transform(Hi, InLeftPadH, InRightPadH),
+                           make_pad_transform(Wi, InLeftPadW, InRightPadW),
+                           make_pass_through_transform(C)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
+
+            const auto in_n_y_ho_x_wo_c_grid_desc = transform_tensor_descriptor(
+                in_n_hip_wip_c_grid_desc,
+                make_tuple(
+                    make_pass_through_transform(N),
+                    make_embed_transform(make_tuple(Y, Ho), make_tuple(ConvDilationH, ConvStrideH)),
+                    make_embed_transform(make_tuple(X, Wo), make_tuple(ConvDilationW, ConvStrideW)),
+                    make_pass_through_transform(C)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
+                make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}));
+
+            const auto in_gemmk_gemmmraw_grid_desc =
+                transform_tensor_descriptor(in_n_y_ho_x_wo_c_grid_desc,
+                                            make_tuple(make_merge_transform(make_tuple(Y, X, C)),
+                                                       make_merge_transform(make_tuple(N, Ho, Wo))),
+                                            make_tuple(Sequence<1, 3, 5>{}, Sequence<0, 2, 4>{}),
+                                            make_tuple(Sequence<0>{}, Sequence<1>{}));
+
+            const auto in_gemmk0_gemmmraw_gemmk1_grid_desc = transform_tensor_descriptor(
+                in_gemmk_gemmmraw_grid_desc,
+                make_tuple(make_unmerge_transform(make_tuple(GemmK0, GemmK1Number)),
+                           make_pass_through_transform(GemmMRaw)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}),
+                make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
+
+            const auto in_gemmk0_gemmm_gemmk1_grid_desc = transform_tensor_descriptor(
+                in_gemmk0_gemmmraw_gemmk1_grid_desc,
+                make_tuple(make_pass_through_transform(GemmK0),
+                           make_right_pad_transform(GemmMRaw, GemmMPad),
+                           make_pass_through_transform(GemmK1Number)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
+
+            // B: weight tensor
+            const auto wei_k_yxc_grid_desc =
+                make_naive_tensor_descriptor_packed(make_tuple(K, Y * X * C));
+
+            const auto wei_gemmk_gemmn_grid_desc = transform_tensor_descriptor(
+                wei_k_yxc_grid_desc,
+                make_tuple(make_pass_through_transform(K), make_pass_through_transform(Y * X * C)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}),
+                make_tuple(Sequence<1>{}, Sequence<0>{}));
+
+            const auto wei_gemmk0_gemmn_gemmk1_grid_desc = transform_tensor_descriptor(
+                wei_gemmk_gemmn_grid_desc,
+                make_tuple(make_unmerge_transform(make_tuple(GemmK0, GemmK1Number)),
+                           make_pass_through_transform(GemmN)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}),
+                make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
+
+            // C: output tensor
+            const auto out_nhowo_k_grid_desc =
+                make_naive_tensor_descriptor_packed(make_tuple(N * Ho * Wo, K));
+
+            const auto out_gemmmraw_gemmn_grid_desc =
+                transform_tensor_descriptor(out_nhowo_k_grid_desc,
+                                            make_tuple(make_pass_through_transform(N * Ho * Wo),
+                                                       make_pass_through_transform(K)),
+                                            make_tuple(Sequence<0>{}, Sequence<1>{}),
+                                            make_tuple(Sequence<0>{}, Sequence<1>{}));
+
+            const auto out_gemmm_gemmn_grid_desc =
+                transform_tensor_descriptor(out_gemmmraw_gemmn_grid_desc,
+                                            make_tuple(make_right_pad_transform(GemmMRaw, GemmMPad),
+                                                       make_pass_through_transform(GemmN)),
+                                            make_tuple(Sequence<0>{}, Sequence<1>{}),
+                                            make_tuple(Sequence<0>{}, Sequence<1>{}));
+
+            // C0: bias tensor: assume a contiguous vector
+            const auto bias_grid_desc_gemmm_gemmn =
+                make_naive_tensor_descriptor(make_tuple(GemmM, GemmN), make_tuple(I0, I1));
+
+            return make_tuple(in_gemmk0_gemmm_gemmk1_grid_desc,
+                              wei_gemmk0_gemmn_gemmk1_grid_desc,
+                              out_gemmm_gemmn_grid_desc,
+                              bias_grid_desc_gemmm_gemmn);
+        }
+    }
+
+    using ABCGridDescs = decltype(MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N(
+        1, 1, 1, {1, 1}, {1, 1}, {1, 1}, {1, 1}, {1, 1}, {1, 1}, {1, 1}));
+
+    using AGridDesc_K0_M_K1 = remove_cvref_t<decltype(ABCGridDescs{}[I0])>;
+    using BGridDesc_K0_N_K1 = remove_cvref_t<decltype(ABCGridDescs{}[I1])>;
+    using CGridDesc_M_N     = remove_cvref_t<decltype(ABCGridDescs{}[I2])>;
+    using C0GridDesc_M_N    = remove_cvref_t<decltype(ABCGridDescs{}[I3])>;
+
+    // GridwiseGemm
+    using GridwiseGemm = GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v3r2<
+        BlockSize,
+        ABDataType, // TODO: distinguish A/B datatype
+        AccDataType,
+        CDataType,
+        OutGlobalMemoryDataOperation,
+        AGridDesc_K0_M_K1,
+        BGridDesc_K0_N_K1,
+        CGridDesc_M_N,
+        C0GridDesc_M_N,
+        InElementwiseOperation,
+        WeiElementwiseOperation,
+        OutElementwiseOperation,
+        MPerBlock,
+        NPerBlock,
+        K0PerBlock,
+        MPerXDL,
+        NPerXDL,
+        K1,
+        MXdlPerWave,
+        NXdlPerWave,
+        ABlockTransferThreadClusterLengths_K0_M_K1,
+        Sequence<1, 0, 2>, // ABlockTransferThreadClusterArrangeOrder,
+        Sequence<1, 0, 2>, // ABlockTransferSrcAccessOrder,
+        2,                 // ABlockTransferSrcVectorDim,
+        ABlockTransferSrcScalarPerVector,
+        ABlockTransferDstScalarPerVector_K1,
+        false, // AThreadTransferSrcResetCoordinateAfterRun,
+        ABlockLdsAddExtraM,
+        BBlockTransferThreadClusterLengths_K0_N_K1,
+        Sequence<1, 0, 2>, // BBlockTransferThreadClusterArrangeOrder,
+        Sequence<1, 0, 2>, // BBlockTransferSrcAccessOrder,
+        2,                 // BBlockTransferSrcVectorDim,
+        BBlockTransferSrcScalarPerVector,
+        BBlockTransferDstScalarPerVector_K1,
+        false, // BThreadTransferSrcResetCoordinateAfterRun,
+        BBlockLdsAddExtraN,
+        CShuffleMXdlPerWavePerShuffle,
+        CShuffleNXdlPerWavePerShuffle,
+        CBlockTransferClusterLengths_MBlock_MXdlPerWave_MWaveMPerXdl_NBlock_NXdlPerWave_NWaveNPerXdl,
+        CBlockTransferScalarPerVector_NWaveNPerXdl>;
+
+    // Argument
+    struct Argument : public BaseArgument
+    {
+        Argument(const InDataType* p_in_grid,
+                 const WeiDataType* p_wei_grid,
+                 OutDataType* p_out_grid,
+                 const OutDataType* p_bias_grid,
+                 ck::index_t N,
+                 ck::index_t K,
+                 ck::index_t C,
+                 std::vector<ck::index_t> input_spatial_lengths,
+                 std::vector<ck::index_t> filter_spatial_lengths,
+                 std::vector<ck::index_t> output_spatial_lengths,
+                 std::vector<ck::index_t> conv_filter_strides,
+                 std::vector<ck::index_t> conv_filter_dilations,
+                 std::vector<ck::index_t> input_left_pads,
+                 std::vector<ck::index_t> input_right_pads,
+                 ck::index_t M01,
+                 ck::index_t N01,
+                 InElementwiseOperation in_element_op,
+                 WeiElementwiseOperation wei_element_op,
+                 OutElementwiseOperation out_element_op)
+            : p_a_grid_{p_in_grid},
+              p_b_grid_{p_wei_grid},
+              p_c_grid_{p_out_grid},
+              p_c0_grid_{p_bias_grid},
+              a_grid_desc_k0_m_k1_{},
+              b_grid_desc_k0_n_k1_{},
+              c_grid_desc_m_n_{},
+              c0_grid_desc_m_n_{},
+              c_grid_desc_mblock_mxdlperwave_mwavemperxdl_nblock_nxdlperwave_nwavenperxdl_{},
+              c0_grid_desc_mblock_mxdlperwave_mwavemperxdl_nblock_nxdlperwave_nwavenperxdl_{},
+              block_2_ctile_map_{},
+              M01_{M01},
+              N01_{N01},
+              in_element_op_{in_element_op},
+              wei_element_op_{wei_element_op},
+              out_element_op_{out_element_op},
+              Conv_N_{N},
+              Conv_K_{K},
+              Conv_C_{C},
+              input_spatial_lengths_{input_spatial_lengths},
+              filter_spatial_lengths_{filter_spatial_lengths},
+              output_spatial_lengths_{output_spatial_lengths},
+              conv_filter_strides_{conv_filter_strides},
+              conv_filter_dilations_{conv_filter_dilations},
+              input_left_pads_{input_left_pads},
+              input_right_pads_{input_right_pads}
+        {
+            const auto descs =
+                DeviceOp::MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N(N,
+                                                                          K,
+                                                                          C,
+                                                                          input_spatial_lengths,
+                                                                          filter_spatial_lengths,
+                                                                          output_spatial_lengths,
+                                                                          conv_filter_strides,
+                                                                          conv_filter_dilations,
+                                                                          input_left_pads,
+                                                                          input_right_pads);
+
+            a_grid_desc_k0_m_k1_ = descs[I0];
+            b_grid_desc_k0_n_k1_ = descs[I1];
+            c_grid_desc_m_n_     = descs[I2];
+            c0_grid_desc_m_n_    = descs[I3];
+            block_2_ctile_map_ =
+                GridwiseGemm::MakeDefaultBlock2CTileMap(c_grid_desc_m_n_, M01, N01);
+
+            if(GridwiseGemm::CheckValidity(a_grid_desc_k0_m_k1_,
+                                           b_grid_desc_k0_n_k1_,
+                                           c_grid_desc_m_n_,
+                                           block_2_ctile_map_))
+            {
+                c_grid_desc_mblock_mxdlperwave_mwavemperxdl_nblock_nxdlperwave_nwavenperxdl_ =
+                    GridwiseGemm::
+                        MakeCGridDescriptor_MBlock_MXdlPerWave_MWaveMPerXdl_NBlock_NXdlPerWave_NWaveNPerXdl(
+                            c_grid_desc_m_n_);
+
+                c0_grid_desc_mblock_mxdlperwave_mwavemperxdl_nblock_nxdlperwave_nwavenperxdl_ =
+                    GridwiseGemm::
+                        MakeCGridDescriptor_MBlock_MXdlPerWave_MWaveMPerXdl_NBlock_NXdlPerWave_NWaveNPerXdl(
+                            c0_grid_desc_m_n_);
+            }
+        }
+
+        const ADataType* p_a_grid_;
+        const BDataType* p_b_grid_;
+        CDataType* p_c_grid_;
+        const CDataType* p_c0_grid_;
+        AGridDesc_K0_M_K1 a_grid_desc_k0_m_k1_;
+        BGridDesc_K0_N_K1 b_grid_desc_k0_n_k1_;
+        CGridDesc_M_N c_grid_desc_m_n_;
+        C0GridDesc_M_N c0_grid_desc_m_n_;
+        typename GridwiseGemm::
+            CGridDescriptor_MBlock_MXdlPerWave_MWaveMPerXdl_NBlock_NXdlPerWave_NWaveNPerXdl
+                c_grid_desc_mblock_mxdlperwave_mwavemperxdl_nblock_nxdlperwave_nwavenperxdl_;
+        typename GridwiseGemm::
+            C0GridDescriptor_MBlock_MXdlPerWave_MWaveMPerXdl_NBlock_NXdlPerWave_NWaveNPerXdl
+                c0_grid_desc_mblock_mxdlperwave_mwavemperxdl_nblock_nxdlperwave_nwavenperxdl_;
+        typename GridwiseGemm::DefaultBlock2CTileMap block_2_ctile_map_;
+        index_t M01_;
+        index_t N01_;
+        InElementwiseOperation in_element_op_;
+        WeiElementwiseOperation wei_element_op_;
+        OutElementwiseOperation out_element_op_;
+        // for checking IsSupportedArgument()
+        index_t Conv_N_;
+        index_t Conv_K_;
+        index_t Conv_C_;
+        std::vector<index_t> input_spatial_lengths_;
+        std::vector<index_t> filter_spatial_lengths_;
+        std::vector<index_t> output_spatial_lengths_;
+        std::vector<index_t> conv_filter_strides_;
+        std::vector<index_t> conv_filter_dilations_;
+        std::vector<index_t> input_left_pads_;
+        std::vector<index_t> input_right_pads_;
+    };
+
+    // Invoker
+    struct Invoker : public BaseInvoker
+    {
+        using Argument = DeviceOp::Argument;
+
+        float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
+        {
+#if 0
+            {
+                std::cout << DeviceOp{}.GetTypeString() << std::endl;
+                std::cout << "N " << arg.Conv_N_ << ", "
+                          << "K " << arg.Conv_K_ << ", "
+                          << "C " << arg.Conv_C_ << ", " << std::endl;
+                std::cout << "Y X " << arg.filter_spatial_lengths_[0] << ", "
+                          << arg.filter_spatial_lengths_[1] << ", " << std::endl;
+                std::cout << "Hi Wi " << arg.input_spatial_lengths_[0] << ", "
+                          << arg.input_spatial_lengths_[1] << ", " << std::endl;
+                std::cout << "Ho Wo " << arg.output_spatial_lengths_[0] << ", "
+                          << arg.output_spatial_lengths_[1] << ", " << std::endl;
+                std::cout << "Strides " << arg.conv_filter_strides_[0] << ", "
+                          << arg.conv_filter_strides_[1] << ", " << std::endl;
+                std::cout << "Dilations " << arg.conv_filter_dilations_[0] << ", "
+                          << arg.conv_filter_dilations_[1] << ", " << std::endl;
+                std::cout << "InLeftPads " << arg.input_left_pads_[0] << ", "
+                          << arg.input_left_pads_[1] << ", " << std::endl;
+                std::cout << "InLeftPads " << arg.input_right_pads_[0] << ", "
+                          << arg.input_right_pads_[1] << ", " << std::endl;
+            }
+
+            {
+                std::cout << "arg.a_grid_desc_k0_m_k1_{" << arg.a_grid_desc_k0_m_k1_.GetLength(I0)
+                          << ", " << arg.a_grid_desc_k0_m_k1_.GetLength(I1) << ", "
+                          << arg.a_grid_desc_k0_m_k1_.GetLength(I2) << "}" << std::endl;
+
+                std::cout << "arg.b_grid_desc_k0_n_k1_{" << arg.b_grid_desc_k0_n_k1_.GetLength(I0)
+                          << ", " << arg.b_grid_desc_k0_n_k1_.GetLength(I1) << ", "
+                          << arg.b_grid_desc_k0_n_k1_.GetLength(I2) << "}" << std::endl;
+
+                std::cout << "arg.c_grid_desc_m_n_{ " << arg.c_grid_desc_m_n_.GetLength(I0) << ", "
+                          << arg.c_grid_desc_m_n_.GetLength(I1) << "}" << std::endl;
+
+                std::cout << "arg.c0_grid_desc_m_n_{ " << arg.c0_grid_desc_m_n_.GetLength(I0)
+                          << ", " << arg.c0_grid_desc_m_n_.GetLength(I1) << "}" << std::endl;
+            }
+#endif
+
+            if(!GridwiseGemm::CheckValidity(arg.a_grid_desc_k0_m_k1_,
+                                            arg.b_grid_desc_k0_n_k1_,
+                                            arg.c_grid_desc_m_n_,
+                                            arg.block_2_ctile_map_))
+            {
+                throw std::runtime_error(
+                    "wrong! GridwiseGemm_km_kn_m0m1n0n1_xdlops_v3r2 has invalid setting");
+            }
+
+            const index_t grid_size =
+                arg.block_2_ctile_map_.CalculateGridSize(arg.c_grid_desc_m_n_);
+
+            const auto K =
+                arg.a_grid_desc_k0_m_k1_.GetLength(I0) * arg.a_grid_desc_k0_m_k1_.GetLength(I2);
+
+            float ave_time = 0;
+
+            if(GridwiseGemm::CalculateHasMainKBlockLoop(K))
+            {
+                const auto kernel = kernel_gemm_xdlops_v3r2<
+                    GridwiseGemm,
+                    ADataType, // TODO: distiguish A/B datatype
+                    CDataType,
+                    remove_reference_t<DeviceOp::AGridDesc_K0_M_K1>,
+                    remove_reference_t<DeviceOp::BGridDesc_K0_N_K1>,
+                    remove_reference_t<
+                        typename GridwiseGemm::
+                            CGridDescriptor_MBlock_MXdlPerWave_MWaveMPerXdl_NBlock_NXdlPerWave_NWaveNPerXdl>,
+                    remove_reference_t<
+                        typename GridwiseGemm::
+                            C0GridDescriptor_MBlock_MXdlPerWave_MWaveMPerXdl_NBlock_NXdlPerWave_NWaveNPerXdl>,
+                    InElementwiseOperation,
+                    WeiElementwiseOperation,
+                    OutElementwiseOperation,
+                    remove_reference_t<typename GridwiseGemm::DefaultBlock2CTileMap>,
+                    true>;
+
+                ave_time = launch_and_time_kernel(
+                    stream_config,
+                    kernel,
+                    dim3(grid_size),
+                    dim3(BlockSize),
+                    0,
+                    arg.p_a_grid_,
+                    arg.p_b_grid_,
+                    arg.p_c_grid_,
+                    arg.p_c0_grid_,
+                    arg.a_grid_desc_k0_m_k1_,
+                    arg.b_grid_desc_k0_n_k1_,
+                    arg.c_grid_desc_mblock_mxdlperwave_mwavemperxdl_nblock_nxdlperwave_nwavenperxdl_,
+                    arg.c0_grid_desc_mblock_mxdlperwave_mwavemperxdl_nblock_nxdlperwave_nwavenperxdl_,
+                    arg.in_element_op_,
+                    arg.wei_element_op_,
+                    arg.out_element_op_,
+                    arg.block_2_ctile_map_);
+            }
+            else
+            {
+                const auto kernel = kernel_gemm_xdlops_v3r2<
+                    GridwiseGemm,
+                    ADataType, // TODO: distiguish A/B datatype
+                    CDataType,
+                    remove_reference_t<DeviceOp::AGridDesc_K0_M_K1>,
+                    remove_reference_t<DeviceOp::BGridDesc_K0_N_K1>,
+                    remove_reference_t<
+                        typename GridwiseGemm::
+                            CGridDescriptor_MBlock_MXdlPerWave_MWaveMPerXdl_NBlock_NXdlPerWave_NWaveNPerXdl>,
+                    remove_reference_t<
+                        typename GridwiseGemm::
+                            C0GridDescriptor_MBlock_MXdlPerWave_MWaveMPerXdl_NBlock_NXdlPerWave_NWaveNPerXdl>,
+                    InElementwiseOperation,
+                    WeiElementwiseOperation,
+                    OutElementwiseOperation,
+                    remove_reference_t<typename GridwiseGemm::DefaultBlock2CTileMap>,
+                    false>;
+
+                ave_time = launch_and_time_kernel(
+                    stream_config,
+                    kernel,
+                    dim3(grid_size),
+                    dim3(BlockSize),
+                    0,
+                    arg.p_a_grid_,
+                    arg.p_b_grid_,
+                    arg.p_c_grid_,
+                    arg.p_c0_grid_,
+                    arg.a_grid_desc_k0_m_k1_,
+                    arg.b_grid_desc_k0_n_k1_,
+                    arg.c_grid_desc_mblock_mxdlperwave_mwavemperxdl_nblock_nxdlperwave_nwavenperxdl_,
+                    arg.c0_grid_desc_mblock_mxdlperwave_mwavemperxdl_nblock_nxdlperwave_nwavenperxdl_,
+                    arg.in_element_op_,
+                    arg.wei_element_op_,
+                    arg.out_element_op_,
+                    arg.block_2_ctile_map_);
+            }
+
+            return ave_time;
+        }
+
+        float Run(const BaseArgument* p_arg,
+                  const StreamConfig& stream_config = StreamConfig{}) override
+        {
+            return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
+        }
+    };
+
+    static constexpr bool IsValidCompilationParameter()
+    {
+        // TODO: properly implement this check
+        return true;
+    }
+
+    static bool IsSupportedArgument(const Argument& arg)
+    {
+        if constexpr(ConvForwardSpecialization ==
+                     ConvolutionForwardSpecialization::Filter1x1Stride1Pad0)
+        {
+            // check if it's 1x1, stride=1 conv
+            if(!(arg.filter_spatial_lengths_[0] == 1 && arg.filter_spatial_lengths_[1] == 1 &&
+                 arg.conv_filter_strides_[0] == 1 && arg.conv_filter_strides_[1] == 1 &&
+                 arg.input_left_pads_[0] == 0 && arg.input_left_pads_[1] == 0 &&
+                 arg.input_right_pads_[0] == 0 && arg.input_right_pads_[1] == 0))
+            {
+                return false;
+            }
+        }
+        else if constexpr(ConvForwardSpecialization ==
+                          ConvolutionForwardSpecialization::Filter1x1Pad0)
+        {
+            // check if it's 1x1 conv
+            if(!(arg.filter_spatial_lengths_[0] == 1 && arg.filter_spatial_lengths_[1] == 1 &&
+                 arg.input_left_pads_[0] == 0 && arg.input_left_pads_[1] == 0 &&
+                 arg.input_right_pads_[0] == 0 && arg.input_right_pads_[1] == 0))
+            {
+                return false;
+            }
+        }
+
+        // vector load A/B matrix from global memory
+        if(!(ABlockTransferSrcVectorDim == 2 && BBlockTransferSrcVectorDim == 2 &&
+             arg.Conv_C_ % ABlockTransferSrcScalarPerVector == 0 &&
+             arg.Conv_C_ % BBlockTransferSrcScalarPerVector == 0))
+        {
+            return false;
+        }
+
+        // vector store C matrix into global memory
+        if(!(arg.Conv_K_ % CBlockTransferScalarPerVector_NWaveNPerXdl == 0))
+        {
+            return false;
+        }
+
+        // Gridwise GEMM size
+        return GridwiseGemm::CheckValidity(arg.a_grid_desc_k0_m_k1_,
+                                           arg.b_grid_desc_k0_n_k1_,
+                                           arg.c_grid_desc_m_n_,
+                                           arg.block_2_ctile_map_);
+    }
+
+    bool IsSupportedArgument(const BaseArgument* p_arg) override
+    {
+        return IsSupportedArgument(*dynamic_cast<const Argument*>(p_arg));
+    }
+
+    static auto MakeArgument(const InDataType* p_in_grid,
+                             const WeiDataType* p_wei_grid,
+                             OutDataType* p_out_grid,
+                             const OutDataType* p_bias_grid,
+                             ck::index_t N,
+                             ck::index_t K,
+                             ck::index_t C,
+                             std::vector<ck::index_t> input_spatial_lengths,
+                             std::vector<ck::index_t> filter_spatial_lengths,
+                             std::vector<ck::index_t> output_spatial_lengths,
+                             std::vector<ck::index_t> conv_filter_strides,
+                             std::vector<ck::index_t> conv_filter_dilations,
+                             std::vector<ck::index_t> input_left_pads,
+                             std::vector<ck::index_t> input_right_pads,
+                             InElementwiseOperation in_element_op,
+                             WeiElementwiseOperation wei_element_op,
+                             OutElementwiseOperation out_element_op)
+    {
+        return Argument{p_in_grid,
+                        p_wei_grid,
+                        p_out_grid,
+                        p_bias_grid,
+                        N,
+                        K,
+                        C,
+                        input_spatial_lengths,
+                        filter_spatial_lengths,
+                        output_spatial_lengths,
+                        conv_filter_strides,
+                        conv_filter_dilations,
+                        input_left_pads,
+                        input_right_pads,
+                        1,
+                        1,
+                        in_element_op,
+                        wei_element_op,
+                        out_element_op};
+    }
+
+    static auto MakeInvoker() { return Invoker{}; }
+
+    std::unique_ptr<BaseArgument>
+    MakeArgumentPointer(const void* p_in_grid,
+                        const void* p_wei_grid,
+                        void* p_out_grid,
+                        const void* p_bias_grid,
+                        ck::index_t N,
+                        ck::index_t K,
+                        ck::index_t C,
+                        std::vector<ck::index_t> input_spatial_lengths,
+                        std::vector<ck::index_t> filter_spatial_lengths,
+                        std::vector<ck::index_t> output_spatial_lengths,
+                        std::vector<ck::index_t> conv_filter_strides,
+                        std::vector<ck::index_t> conv_filter_dilations,
+                        std::vector<ck::index_t> input_left_pads,
+                        std::vector<ck::index_t> input_right_pads,
+                        InElementwiseOperation in_element_op,
+                        WeiElementwiseOperation wei_element_op,
+                        OutElementwiseOperation out_element_op) override
+    {
+        return std::make_unique<Argument>(static_cast<const InDataType*>(p_in_grid),
+                                          static_cast<const WeiDataType*>(p_wei_grid),
+                                          static_cast<OutDataType*>(p_out_grid),
+                                          static_cast<const OutDataType*>(p_bias_grid),
+                                          N,
+                                          K,
+                                          C,
+                                          input_spatial_lengths,
+                                          filter_spatial_lengths,
+                                          output_spatial_lengths,
+                                          conv_filter_strides,
+                                          conv_filter_dilations,
+                                          input_left_pads,
+                                          input_right_pads,
+                                          1,
+                                          1,
+                                          in_element_op,
+                                          wei_element_op,
+                                          out_element_op);
+    }
+
+    std::unique_ptr<BaseInvoker> MakeInvokerPointer() override
+    {
+        return std::make_unique<Invoker>(Invoker{});
+    }
+
+    std::string GetTypeString() const override
+    {
+        auto str = std::stringstream();
+
+        // clang-format off
+        str << "DeviceConv2dFwdXdl_C_Shuffle_Bias_Activation_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K"
+            << "<"
+            << BlockSize << ", "
+            << MPerBlock << ", "
+            << NPerBlock << ", "
+            << K0PerBlock
+            << ">";
+        // clang-format on
+
+        return str.str();
+    }
+};
+} // namespace device
+} // namespace tensor_operation
+} // namespace ck

deps/cget/pkg/ROCmSoftwarePlatform__composable_kernel/install/include/ck/tensor_operation/gpu/device/device_conv2d_fwd_xdl_c_shuffle_nhwc_kyxc_nhwk.hpp

+// 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_conv_fwd.hpp"
+#include "ck/tensor_operation/gpu/device/convolution_forward_specialization.hpp"
+#include "ck/tensor_operation/gpu/grid/gridwise_gemm_xdlops_v3r1.hpp"
+#include "ck/host_utility/device_prop.hpp"
+#include "ck/host_utility/kernel_launch.hpp"
+
+namespace ck {
+namespace tensor_operation {
+namespace device {
+
+// out[N, Ho, Wo, K] = in[N, Hi, Wi, C] * wei[K, Y, X, C]
+template <
+    typename InDataType,
+    typename WeiDataType,
+    typename OutDataType,
+    typename AccDataType,
+    typename InElementwiseOperation,
+    typename WeiElementwiseOperation,
+    typename OutElementwiseOperation,
+    ConvolutionForwardSpecialization ConvForwardSpecialization,
+    ck::index_t BlockSize,
+    ck::index_t MPerBlock,
+    ck::index_t NPerBlock,
+    ck::index_t K0PerBlock,
+    ck::index_t K1,
+    ck::index_t MPerXdl,
+    ck::index_t NPerXdl,
+    ck::index_t MXdlPerWave,
+    ck::index_t NXdlPerWave,
+    typename ABlockTransferThreadClusterLengths_K0_M_K1,
+    typename ABlockTransferThreadClusterArrangeOrder,
+    typename ABlockTransferSrcAccessOrder,
+    ck::index_t ABlockTransferSrcVectorDim,
+    ck::index_t ABlockTransferSrcScalarPerVector,
+    ck::index_t ABlockTransferDstScalarPerVector_K1,
+    bool ABlockLdsAddExtraM,
+    typename BBlockTransferThreadClusterLengths_K0_N_K1,
+    typename BBlockTransferThreadClusterArrangeOrder,
+    typename BBlockTransferSrcAccessOrder,
+    ck::index_t BBlockTransferSrcVectorDim,
+    ck::index_t BBlockTransferSrcScalarPerVector,
+    ck::index_t BBlockTransferDstScalarPerVector_K1,
+    bool BBlockLdsAddExtraN,
+    index_t CShuffleMXdlPerWavePerShuffle,
+    index_t CShuffleNXdlPerWavePerShuffle,
+    typename CBlockTransferClusterLengths_MBlock_MXdlPerWave_MWaveMPerXdl_NBlock_NXdlPerWave_NWaveNPerXdl,
+    index_t CBlockTransferScalarPerVector_NWaveNPerXdl>
+struct DeviceConv2dFwdXdl_C_Shuffle_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K
+    : public DeviceConvFwd<2,
+                           ck::tensor_layout::convolution::NHWC,
+                           ck::tensor_layout::convolution::KYXC,
+                           ck::tensor_layout::convolution::NHWK,
+                           InDataType,
+                           WeiDataType,
+                           OutDataType,
+                           InElementwiseOperation,
+                           WeiElementwiseOperation,
+                           OutElementwiseOperation>
+{
+    using DeviceOp = DeviceConv2dFwdXdl_C_Shuffle_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K;
+
+    using ADataType = InDataType;
+    using BDataType = WeiDataType;
+    using CDataType = OutDataType;
+
+    // TODO make A/B datatype different
+    using ABDataType = InDataType;
+
+    static constexpr index_t NDimSpatial = 2;
+
+    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 constexpr auto GemmK1Number = K1Number;
+
+    static auto
+    MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N(ck::index_t N,
+                                                    ck::index_t K,
+                                                    ck::index_t C,
+                                                    std::vector<ck::index_t> input_spatial_lengths,
+                                                    std::vector<ck::index_t> filter_spatial_lengths,
+                                                    std::vector<ck::index_t> output_spatial_lengths,
+                                                    std::vector<ck::index_t> conv_filter_strides,
+                                                    std::vector<ck::index_t> conv_filter_dilations,
+                                                    std::vector<ck::index_t> input_left_pads,
+                                                    std::vector<ck::index_t> input_right_pads)
+    {
+        using namespace ck;
+
+        const index_t Hi = input_spatial_lengths[0];
+        const index_t Wi = input_spatial_lengths[1];
+
+        const index_t Ho = output_spatial_lengths[0];
+        const index_t Wo = output_spatial_lengths[1];
+
+        const index_t Y = filter_spatial_lengths[0];
+        const index_t X = filter_spatial_lengths[1];
+
+        const index_t ConvStrideH = conv_filter_strides[0];
+        const index_t ConvStrideW = conv_filter_strides[1];
+
+        const index_t ConvDilationH = conv_filter_dilations[0];
+        const index_t ConvDilationW = conv_filter_dilations[1];
+
+        const index_t InLeftPadH = input_left_pads[0];
+        const index_t InLeftPadW = input_left_pads[1];
+
+        const index_t InRightPadH = input_right_pads[0];
+        const index_t InRightPadW = input_right_pads[1];
+
+        const index_t GemmMRaw = N * Ho * Wo;
+        const index_t GemmN    = K;
+
+        const auto GemmM    = math::integer_least_multiple(GemmMRaw, MPerBlock);
+        const auto GemmMPad = GemmM - GemmMRaw;
+
+        if constexpr(ConvForwardSpecialization ==
+                     ConvolutionForwardSpecialization::Filter1x1Stride1Pad0)
+        { // 1x1, stride=1, pad=0
+            const index_t GemmK = Y * X * C;
+            assert(GemmK % GemmK1Number == 0);
+
+            const index_t GemmK0 = GemmK / GemmK1Number;
+
+            // A: input tensor
+            const auto in_gemmmraw_gemmk_grid_desc =
+                make_naive_tensor_descriptor_packed(make_tuple(N * Ho * Wo, C));
+
+            const auto in_gemmk0_gemmm_gemmk1_grid_desc = transform_tensor_descriptor(
+                in_gemmmraw_gemmk_grid_desc,
+                make_tuple(make_unmerge_transform(make_tuple(GemmK0, GemmK1Number)),
+                           make_right_pad_transform(GemmMRaw, GemmMPad)),
+                make_tuple(Sequence<1>{}, Sequence<0>{}),
+                make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
+
+            // B: weight tensor
+            const auto wei_gemmn_gemmk_grid_desc =
+                make_naive_tensor_descriptor_packed(make_tuple(K, C));
+
+            const auto wei_gemmk0_gemmn_gemmk1_grid_desc = transform_tensor_descriptor(
+                wei_gemmn_gemmk_grid_desc,
+                make_tuple(make_unmerge_transform(make_tuple(GemmK0, GemmK1Number)),
+                           make_pass_through_transform(GemmN)),
+                make_tuple(Sequence<1>{}, Sequence<0>{}),
+                make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
+
+            // C: output tensor
+            const auto out_gemmmraw_gemmn_grid_desc =
+                make_naive_tensor_descriptor_packed(make_tuple(N * Ho * Wo, K));
+
+            const auto out_gemmm_gemmn_grid_desc =
+                transform_tensor_descriptor(out_gemmmraw_gemmn_grid_desc,
+                                            make_tuple(make_right_pad_transform(GemmMRaw, GemmMPad),
+                                                       make_pass_through_transform(GemmN)),
+                                            make_tuple(Sequence<0>{}, Sequence<1>{}),
+                                            make_tuple(Sequence<0>{}, Sequence<1>{}));
+
+            return make_tuple(in_gemmk0_gemmm_gemmk1_grid_desc,
+                              wei_gemmk0_gemmn_gemmk1_grid_desc,
+                              out_gemmm_gemmn_grid_desc);
+        }
+        else if constexpr(ConvForwardSpecialization ==
+                          ConvolutionForwardSpecialization::Filter1x1Pad0)
+        { // 1x1, pad=0
+            const index_t GemmK = Y * X * C;
+            assert(GemmK % GemmK1Number == 0);
+
+            const index_t GemmK0 = GemmK / GemmK1Number;
+
+            // A: input tensor
+            const auto in_n_hi_wi_c_grid_desc =
+                make_naive_tensor_descriptor_packed(make_tuple(N, Hi, Wi, C));
+
+            const auto in_n_ho_wo_c_grid_desc = transform_tensor_descriptor(
+                in_n_hi_wi_c_grid_desc,
+                make_tuple(make_pass_through_transform(N),
+                           make_embed_transform(make_tuple(Ho), make_tuple(ConvStrideH)),
+                           make_embed_transform(make_tuple(Wo), make_tuple(ConvStrideW)),
+                           make_pass_through_transform(C)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
+
+            const auto in_gemmk0_gemmmraw_gemmk1_grid_desc = transform_tensor_descriptor(
+                in_n_ho_wo_c_grid_desc,
+                make_tuple(make_unmerge_transform(make_tuple(GemmK0, GemmK1Number)),
+                           make_merge_transform(make_tuple(N, Ho, Wo))),
+                make_tuple(Sequence<3>{}, Sequence<0, 1, 2>{}),
+                make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
+
+            const auto in_gemmk0_gemmm_gemmk1_grid_desc = transform_tensor_descriptor(
+                in_gemmk0_gemmmraw_gemmk1_grid_desc,
+                make_tuple(make_pass_through_transform(GemmK0),
+                           make_right_pad_transform(GemmMRaw, GemmMPad),
+                           make_pass_through_transform(GemmK1Number)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
+
+            // B: weight tensor
+            const auto wei_gemmn_gemmk_grid_desc =
+                make_naive_tensor_descriptor_packed(make_tuple(K, C));
+
+            const auto wei_gemmk0_gemmn_gemmk1_grid_desc = transform_tensor_descriptor(
+                wei_gemmn_gemmk_grid_desc,
+                make_tuple(make_unmerge_transform(make_tuple(GemmK0, GemmK1Number)),
+                           make_pass_through_transform(GemmN)),
+                make_tuple(Sequence<1>{}, Sequence<0>{}),
+                make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
+
+            // C: output tensor
+            const auto out_gemmmraw_gemmn_grid_desc =
+                make_naive_tensor_descriptor_packed(make_tuple(N * Ho * Wo, K));
+
+            const auto out_gemmm_gemmn_grid_desc =
+                transform_tensor_descriptor(out_gemmmraw_gemmn_grid_desc,
+                                            make_tuple(make_right_pad_transform(GemmMRaw, GemmMPad),
+                                                       make_pass_through_transform(GemmN)),
+                                            make_tuple(Sequence<0>{}, Sequence<1>{}),
+                                            make_tuple(Sequence<0>{}, Sequence<1>{}));
+
+            return make_tuple(in_gemmk0_gemmm_gemmk1_grid_desc,
+                              wei_gemmk0_gemmn_gemmk1_grid_desc,
+                              out_gemmm_gemmn_grid_desc);
+        }
+        else if constexpr(ConvForwardSpecialization == ConvolutionForwardSpecialization::OddC)
+        { // C = odd value
+            const index_t GemmKRaw = Y * X * C;
+            const index_t GemmK = math::integer_least_multiple(GemmKRaw, K0PerBlock * GemmK1Number);
+            const index_t GemmKPad = GemmK - GemmKRaw;
+            const index_t GemmK0   = GemmK / GemmK1Number;
+
+            // A: input tensor
+            const auto in_n_hi_wi_c_grid_desc =
+                make_naive_tensor_descriptor_packed(make_tuple(N, Hi, Wi, C));
+
+            const auto in_n_hip_wip_c_grid_desc = transform_tensor_descriptor(
+                in_n_hi_wi_c_grid_desc,
+                make_tuple(make_pass_through_transform(N),
+                           make_pad_transform(Hi, InLeftPadH, InRightPadH),
+                           make_pad_transform(Wi, InLeftPadW, InRightPadW),
+                           make_pass_through_transform(C)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
+
+            const auto in_n_y_ho_x_wo_c_grid_desc = transform_tensor_descriptor(
+                in_n_hip_wip_c_grid_desc,
+                make_tuple(
+                    make_pass_through_transform(N),
+                    make_embed_transform(make_tuple(Y, Ho), make_tuple(ConvDilationH, ConvStrideH)),
+                    make_embed_transform(make_tuple(X, Wo), make_tuple(ConvDilationW, ConvStrideW)),
+                    make_pass_through_transform(C)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
+                make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}));
+
+            const auto in_gemmkraw_gemmmraw_grid_desc =
+                transform_tensor_descriptor(in_n_y_ho_x_wo_c_grid_desc,
+                                            make_tuple(make_merge_transform(make_tuple(Y, X, C)),
+                                                       make_merge_transform(make_tuple(N, Ho, Wo))),
+                                            make_tuple(Sequence<1, 3, 5>{}, Sequence<0, 2, 4>{}),
+                                            make_tuple(Sequence<0>{}, Sequence<1>{}));
+
+            const auto in_gemmk_gemmm_grid_desc = transform_tensor_descriptor(
+                in_gemmkraw_gemmmraw_grid_desc,
+                make_tuple(make_right_pad_transform(GemmKRaw, GemmKPad),
+                           make_right_pad_transform(GemmMRaw, GemmMPad)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}),
+                make_tuple(Sequence<0>{}, Sequence<1>{}));
+
+            const auto in_gemmk0_gemmm_gemmk1_grid_desc = transform_tensor_descriptor(
+                in_gemmk_gemmm_grid_desc,
+                make_tuple(make_unmerge_transform(make_tuple(GemmK0, GemmK1Number)),
+                           make_pass_through_transform(GemmM)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}),
+                make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
+
+            // B: weight tensor
+            const auto wei_k_yxc_grid_desc =
+                make_naive_tensor_descriptor_packed(make_tuple(K, Y * X * C));
+
+            const auto wei_gemmk_gemmn_grid_desc = transform_tensor_descriptor(
+                wei_k_yxc_grid_desc,
+                make_tuple(make_pass_through_transform(K),
+                           make_right_pad_transform(GemmKRaw, GemmKPad)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}),
+                make_tuple(Sequence<1>{}, Sequence<0>{}));
+
+            const auto wei_gemmk0_gemmn_gemmk1_grid_desc = transform_tensor_descriptor(
+                wei_gemmk_gemmn_grid_desc,
+                make_tuple(make_unmerge_transform(make_tuple(GemmK0, GemmK1Number)),
+                           make_pass_through_transform(GemmN)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}),
+                make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
+
+            // C: output tensor
+            const auto out_nhowo_k_grid_desc =
+                make_naive_tensor_descriptor_packed(make_tuple(N * Ho * Wo, K));
+
+            const auto out_gemmmraw_gemmn_grid_desc =
+                transform_tensor_descriptor(out_nhowo_k_grid_desc,
+                                            make_tuple(make_pass_through_transform(N * Ho * Wo),
+                                                       make_pass_through_transform(K)),
+                                            make_tuple(Sequence<0>{}, Sequence<1>{}),
+                                            make_tuple(Sequence<0>{}, Sequence<1>{}));
+
+            const auto out_gemmm_gemmn_grid_desc =
+                transform_tensor_descriptor(out_gemmmraw_gemmn_grid_desc,
+                                            make_tuple(make_right_pad_transform(GemmMRaw, GemmMPad),
+                                                       make_pass_through_transform(GemmN)),
+                                            make_tuple(Sequence<0>{}, Sequence<1>{}),
+                                            make_tuple(Sequence<0>{}, Sequence<1>{}));
+
+            return make_tuple(in_gemmk0_gemmm_gemmk1_grid_desc,
+                              wei_gemmk0_gemmn_gemmk1_grid_desc,
+                              out_gemmm_gemmn_grid_desc);
+        }
+        else
+        {
+            const index_t GemmK = Y * X * C;
+            assert(GemmK % GemmK1Number == 0);
+
+            const index_t GemmK0 = GemmK / GemmK1Number;
+
+            // A: input tensor
+            const auto in_n_hi_wi_c_grid_desc =
+                make_naive_tensor_descriptor_packed(make_tuple(N, Hi, Wi, C));
+
+            const auto in_n_hip_wip_c_grid_desc = transform_tensor_descriptor(
+                in_n_hi_wi_c_grid_desc,
+                make_tuple(make_pass_through_transform(N),
+                           make_pad_transform(Hi, InLeftPadH, InRightPadH),
+                           make_pad_transform(Wi, InLeftPadW, InRightPadW),
+                           make_pass_through_transform(C)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
+
+            const auto in_n_y_ho_x_wo_c_grid_desc = transform_tensor_descriptor(
+                in_n_hip_wip_c_grid_desc,
+                make_tuple(
+                    make_pass_through_transform(N),
+                    make_embed_transform(make_tuple(Y, Ho), make_tuple(ConvDilationH, ConvStrideH)),
+                    make_embed_transform(make_tuple(X, Wo), make_tuple(ConvDilationW, ConvStrideW)),
+                    make_pass_through_transform(C)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
+                make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}));
+
+            const auto in_gemmk_gemmmraw_grid_desc =
+                transform_tensor_descriptor(in_n_y_ho_x_wo_c_grid_desc,
+                                            make_tuple(make_merge_transform(make_tuple(Y, X, C)),
+                                                       make_merge_transform(make_tuple(N, Ho, Wo))),
+                                            make_tuple(Sequence<1, 3, 5>{}, Sequence<0, 2, 4>{}),
+                                            make_tuple(Sequence<0>{}, Sequence<1>{}));
+
+            const auto in_gemmk0_gemmmraw_gemmk1_grid_desc = transform_tensor_descriptor(
+                in_gemmk_gemmmraw_grid_desc,
+                make_tuple(make_unmerge_transform(make_tuple(GemmK0, GemmK1Number)),
+                           make_pass_through_transform(GemmMRaw)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}),
+                make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
+
+            const auto in_gemmk0_gemmm_gemmk1_grid_desc = transform_tensor_descriptor(
+                in_gemmk0_gemmmraw_gemmk1_grid_desc,
+                make_tuple(make_pass_through_transform(GemmK0),
+                           make_right_pad_transform(GemmMRaw, GemmMPad),
+                           make_pass_through_transform(GemmK1Number)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
+
+            // B: weight tensor
+            const auto wei_k_yxc_grid_desc =
+                make_naive_tensor_descriptor_packed(make_tuple(K, Y * X * C));
+
+            const auto wei_gemmk_gemmn_grid_desc = transform_tensor_descriptor(
+                wei_k_yxc_grid_desc,
+                make_tuple(make_pass_through_transform(K), make_pass_through_transform(Y * X * C)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}),
+                make_tuple(Sequence<1>{}, Sequence<0>{}));
+
+            const auto wei_gemmk0_gemmn_gemmk1_grid_desc = transform_tensor_descriptor(
+                wei_gemmk_gemmn_grid_desc,
+                make_tuple(make_unmerge_transform(make_tuple(GemmK0, GemmK1Number)),
+                           make_pass_through_transform(GemmN)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}),
+                make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
+
+            // C: output tensor
+            const auto out_nhowo_k_grid_desc =
+                make_naive_tensor_descriptor_packed(make_tuple(N * Ho * Wo, K));
+
+            const auto out_gemmmraw_gemmn_grid_desc =
+                transform_tensor_descriptor(out_nhowo_k_grid_desc,
+                                            make_tuple(make_pass_through_transform(N * Ho * Wo),
+                                                       make_pass_through_transform(K)),
+                                            make_tuple(Sequence<0>{}, Sequence<1>{}),
+                                            make_tuple(Sequence<0>{}, Sequence<1>{}));
+
+            const auto out_gemmm_gemmn_grid_desc =
+                transform_tensor_descriptor(out_gemmmraw_gemmn_grid_desc,
+                                            make_tuple(make_right_pad_transform(GemmMRaw, GemmMPad),
+                                                       make_pass_through_transform(GemmN)),
+                                            make_tuple(Sequence<0>{}, Sequence<1>{}),
+                                            make_tuple(Sequence<0>{}, Sequence<1>{}));
+
+            return make_tuple(in_gemmk0_gemmm_gemmk1_grid_desc,
+                              wei_gemmk0_gemmn_gemmk1_grid_desc,
+                              out_gemmm_gemmn_grid_desc);
+        }
+    }
+
+    using ABCGridDescs = decltype(MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N(
+        1, 1, 1, {1, 1}, {1, 1}, {1, 1}, {1, 1}, {1, 1}, {1, 1}, {1, 1}));
+
+    using AGridDesc_K0_M_K1 = remove_cvref_t<decltype(ABCGridDescs{}[I0])>;
+    using BGridDesc_K0_N_K1 = remove_cvref_t<decltype(ABCGridDescs{}[I1])>;
+    using CGridDesc_M_N     = remove_cvref_t<decltype(ABCGridDescs{}[I2])>;
+
+    using Block2CTileMap = BlockToCTileMap_M00_N0_M01<MPerBlock, NPerBlock, CGridDesc_M_N>;
+
+    // GridwiseGemm
+    using GridwiseGemm = GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v3r1<
+        BlockSize,
+        ABDataType, // TODO: distinguish A/B datatype
+        AccDataType,
+        CDataType, // TODO: Add ShuffleType for DeviceConv2d
+        CDataType,
+        InMemoryDataOperationEnum::Set,
+        AGridDesc_K0_M_K1,
+        BGridDesc_K0_N_K1,
+        CGridDesc_M_N,
+        InElementwiseOperation,
+        WeiElementwiseOperation,
+        OutElementwiseOperation,
+        MPerBlock,
+        NPerBlock,
+        K0PerBlock * K1,
+        K1, // AK1
+        K1, // BK1
+        MPerXdl,
+        NPerXdl,
+        MXdlPerWave,
+        NXdlPerWave,
+        ABlockTransferThreadClusterLengths_K0_M_K1,
+        Sequence<1, 0, 2>, // ABlockTransferThreadClusterArrangeOrder,
+        Sequence<1, 0, 2>, // ABlockTransferSrcAccessOrder,
+        2,                 // ABlockTransferSrcVectorDim,
+        ABlockTransferSrcScalarPerVector,
+        ABlockTransferDstScalarPerVector_K1,
+        false, // AThreadTransferSrcResetCoordinateAfterRun,
+        ABlockLdsAddExtraM,
+        BBlockTransferThreadClusterLengths_K0_N_K1,
+        Sequence<1, 0, 2>, // BBlockTransferThreadClusterArrangeOrder,
+        Sequence<1, 0, 2>, // BBlockTransferSrcAccessOrder,
+        2,                 // BBlockTransferSrcVectorDim,
+        BBlockTransferSrcScalarPerVector,
+        BBlockTransferDstScalarPerVector_K1,
+        false, // BThreadTransferSrcResetCoordinateAfterRun,
+        BBlockLdsAddExtraN,
+        CShuffleMXdlPerWavePerShuffle,
+        CShuffleNXdlPerWavePerShuffle,
+        CBlockTransferClusterLengths_MBlock_MXdlPerWave_MWaveMPerXdl_NBlock_NXdlPerWave_NWaveNPerXdl,
+        CBlockTransferScalarPerVector_NWaveNPerXdl>;
+
+    // Argument
+    struct Argument : public BaseArgument
+    {
+        Argument(const InDataType* p_in_grid,
+                 const WeiDataType* p_wei_grid,
+                 OutDataType* p_out_grid,
+                 ck::index_t N,
+                 ck::index_t K,
+                 ck::index_t C,
+                 std::vector<ck::index_t> input_spatial_lengths,
+                 std::vector<ck::index_t> filter_spatial_lengths,
+                 std::vector<ck::index_t> output_spatial_lengths,
+                 std::vector<ck::index_t> conv_filter_strides,
+                 std::vector<ck::index_t> conv_filter_dilations,
+                 std::vector<ck::index_t> input_left_pads,
+                 std::vector<ck::index_t> input_right_pads,
+                 InElementwiseOperation in_element_op,
+                 WeiElementwiseOperation wei_element_op,
+                 OutElementwiseOperation out_element_op)
+            : p_a_grid_{p_in_grid},
+              p_b_grid_{p_wei_grid},
+              p_c_grid_{p_out_grid},
+              a_grid_desc_k0_m_k1_{},
+              b_grid_desc_k0_n_k1_{},
+              c_grid_desc_m_n_{},
+              c_grid_desc_mblock_mxdlperwave_mwavemperxdl_nblock_nxdlperwave_nwavenperxdl_{},
+              block_2_ctile_map_{},
+              in_element_op_{in_element_op},
+              wei_element_op_{wei_element_op},
+              out_element_op_{out_element_op},
+              Conv_N_{N},
+              Conv_K_{K},
+              Conv_C_{C},
+              input_spatial_lengths_{input_spatial_lengths},
+              filter_spatial_lengths_{filter_spatial_lengths},
+              output_spatial_lengths_{output_spatial_lengths},
+              conv_filter_strides_{conv_filter_strides},
+              conv_filter_dilations_{conv_filter_dilations},
+              input_left_pads_{input_left_pads},
+              input_right_pads_{input_right_pads}
+        {
+            const auto descs =
+                DeviceOp::MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N(N,
+                                                                          K,
+                                                                          C,
+                                                                          input_spatial_lengths,
+                                                                          filter_spatial_lengths,
+                                                                          output_spatial_lengths,
+                                                                          conv_filter_strides,
+                                                                          conv_filter_dilations,
+                                                                          input_left_pads,
+                                                                          input_right_pads);
+
+            a_grid_desc_k0_m_k1_ = descs[I0];
+            b_grid_desc_k0_n_k1_ = descs[I1];
+            c_grid_desc_m_n_     = descs[I2];
+
+            block_2_ctile_map_ = Block2CTileMap{c_grid_desc_m_n_};
+
+            if(GridwiseGemm::CheckValidity(a_grid_desc_k0_m_k1_,
+                                           b_grid_desc_k0_n_k1_,
+                                           c_grid_desc_m_n_,
+                                           block_2_ctile_map_))
+            {
+                c_grid_desc_mblock_mxdlperwave_mwavemperxdl_nblock_nxdlperwave_nwavenperxdl_ =
+                    GridwiseGemm::
+                        MakeCGridDescriptor_MBlock_MXdlPerWave_MWaveMPerXdl_NBlock_NXdlPerWave_NWaveNPerXdl(
+                            c_grid_desc_m_n_);
+            }
+        }
+
+        const ADataType* p_a_grid_;
+        const BDataType* p_b_grid_;
+        CDataType* p_c_grid_;
+        AGridDesc_K0_M_K1 a_grid_desc_k0_m_k1_;
+        BGridDesc_K0_N_K1 b_grid_desc_k0_n_k1_;
+        CGridDesc_M_N c_grid_desc_m_n_;
+        typename GridwiseGemm::
+            CGridDescriptor_MBlock_MXdlPerWave_MWaveMPerXdl_NBlock_NXdlPerWave_NWaveNPerXdl
+                c_grid_desc_mblock_mxdlperwave_mwavemperxdl_nblock_nxdlperwave_nwavenperxdl_;
+        Block2CTileMap block_2_ctile_map_;
+        InElementwiseOperation in_element_op_;
+        WeiElementwiseOperation wei_element_op_;
+        OutElementwiseOperation out_element_op_;
+        // for checking IsSupportedArgument()
+        index_t Conv_N_;
+        index_t Conv_K_;
+        index_t Conv_C_;
+        std::vector<index_t> input_spatial_lengths_;
+        std::vector<index_t> filter_spatial_lengths_;
+        std::vector<index_t> output_spatial_lengths_;
+        std::vector<index_t> conv_filter_strides_;
+        std::vector<index_t> conv_filter_dilations_;
+        std::vector<index_t> input_left_pads_;
+        std::vector<index_t> input_right_pads_;
+    };
+
+    // Invoker
+    struct Invoker : public BaseInvoker
+    {
+        using Argument = DeviceOp::Argument;
+
+        float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
+        {
+#if 0
+            {
+                std::cout << DeviceOp{}.GetTypeString() << std::endl;
+                std::cout << "N " << arg.Conv_N_ << ", "
+                          << "K " << arg.Conv_K_ << ", "
+                          << "C " << arg.Conv_C_ << ", " << std::endl;
+                std::cout << "Y X " << arg.filter_spatial_lengths_[0] << ", "
+                          << arg.filter_spatial_lengths_[1] << ", " << std::endl;
+                std::cout << "Hi Wi " << arg.input_spatial_lengths_[0] << ", "
+                          << arg.input_spatial_lengths_[1] << ", " << std::endl;
+                std::cout << "Ho Wo " << arg.output_spatial_lengths_[0] << ", "
+                          << arg.output_spatial_lengths_[1] << ", " << std::endl;
+                std::cout << "Strides " << arg.conv_filter_strides_[0] << ", "
+                          << arg.conv_filter_strides_[1] << ", " << std::endl;
+                std::cout << "Dilations " << arg.conv_filter_dilations_[0] << ", "
+                          << arg.conv_filter_dilations_[1] << ", " << std::endl;
+                std::cout << "InLeftPads " << arg.input_left_pads_[0] << ", "
+                          << arg.input_left_pads_[1] << ", " << std::endl;
+                std::cout << "InLeftPads " << arg.input_right_pads_[0] << ", "
+                          << arg.input_right_pads_[1] << ", " << std::endl;
+            }
+
+            {
+                std::cout << "arg.a_grid_desc_k0_m_k1_{" << arg.a_grid_desc_k0_m_k1_.GetLength(I0)
+                          << ", " << arg.a_grid_desc_k0_m_k1_.GetLength(I1) << ", "
+                          << arg.a_grid_desc_k0_m_k1_.GetLength(I2) << "}" << std::endl;
+
+                std::cout << "arg.b_grid_desc_k0_n_k1_{" << arg.b_grid_desc_k0_n_k1_.GetLength(I0)
+                          << ", " << arg.b_grid_desc_k0_n_k1_.GetLength(I1) << ", "
+                          << arg.b_grid_desc_k0_n_k1_.GetLength(I2) << "}" << std::endl;
+
+                std::cout << "arg.c_grid_desc_m_n_{ " << arg.c_grid_desc_m_n_.GetLength(I0) << ", "
+                          << arg.c_grid_desc_m_n_.GetLength(I1) << "}" << std::endl;
+
+                std::cout
+                    << "arg.c_grid_desc_mblock_mxdlperwave_mwavemperxdl_nblock_nxdlperwave_"
+                       "nwavenperxdl_{ "
+                    << arg.c_grid_desc_mblock_mxdlperwave_mwavemperxdl_nblock_nxdlperwave_nwavenperxdl_
+                           .GetLength(I0)
+                    << ", "
+                    << arg.c_grid_desc_mblock_mxdlperwave_mwavemperxdl_nblock_nxdlperwave_nwavenperxdl_
+                           .GetLength(I1)
+                    << ", "
+                    << arg.c_grid_desc_mblock_mxdlperwave_mwavemperxdl_nblock_nxdlperwave_nwavenperxdl_
+                           .GetLength(I2)
+                    << ", "
+                    << arg.c_grid_desc_mblock_mxdlperwave_mwavemperxdl_nblock_nxdlperwave_nwavenperxdl_
+                           .GetLength(I3)
+                    << ", "
+                    << arg.c_grid_desc_mblock_mxdlperwave_mwavemperxdl_nblock_nxdlperwave_nwavenperxdl_
+                           .GetLength(I4)
+                    << ", "
+                    << arg.c_grid_desc_mblock_mxdlperwave_mwavemperxdl_nblock_nxdlperwave_nwavenperxdl_
+                           .GetLength(I5)
+                    << "}" << std::endl;
+            }
+#endif
+
+            if(!GridwiseGemm::CheckValidity(arg.a_grid_desc_k0_m_k1_,
+                                            arg.b_grid_desc_k0_n_k1_,
+                                            arg.c_grid_desc_m_n_,
+                                            arg.block_2_ctile_map_))
+            {
+                throw std::runtime_error(
+                    "wrong! GridwiseGemm_km_kn_m0m1n0n1_xdlops_v3r1 has invalid setting");
+            }
+
+            const index_t grid_size =
+                arg.block_2_ctile_map_.CalculateGridSize(arg.c_grid_desc_m_n_);
+
+            const auto K =
+                arg.a_grid_desc_k0_m_k1_.GetLength(I0) * arg.a_grid_desc_k0_m_k1_.GetLength(I2);
+
+            float ave_time = 0;
+
+            if(GridwiseGemm::CalculateHasMainKBlockLoop(K))
+            {
+                const auto kernel = kernel_gemm_xdlops_v3r1<
+                    GridwiseGemm,
+                    ADataType, // TODO: distiguish A/B datatype
+                    CDataType,
+                    remove_reference_t<DeviceOp::AGridDesc_K0_M_K1>,
+                    remove_reference_t<DeviceOp::BGridDesc_K0_N_K1>,
+                    remove_reference_t<
+                        typename GridwiseGemm::
+                            CGridDescriptor_MBlock_MXdlPerWave_MWaveMPerXdl_NBlock_NXdlPerWave_NWaveNPerXdl>,
+                    InElementwiseOperation,
+                    WeiElementwiseOperation,
+                    OutElementwiseOperation,
+                    Block2CTileMap,
+                    true>;
+
+                ave_time = launch_and_time_kernel(
+                    stream_config,
+                    kernel,
+                    dim3(grid_size),
+                    dim3(BlockSize),
+                    0,
+                    arg.p_a_grid_,
+                    arg.p_b_grid_,
+                    arg.p_c_grid_,
+                    arg.a_grid_desc_k0_m_k1_,
+                    arg.b_grid_desc_k0_n_k1_,
+                    arg.c_grid_desc_mblock_mxdlperwave_mwavemperxdl_nblock_nxdlperwave_nwavenperxdl_,
+                    arg.in_element_op_,
+                    arg.wei_element_op_,
+                    arg.out_element_op_,
+                    arg.block_2_ctile_map_);
+            }
+            else
+            {
+                const auto kernel = kernel_gemm_xdlops_v3r1<
+                    GridwiseGemm,
+                    ADataType, // TODO: distiguish A/B datatype
+                    CDataType,
+                    remove_reference_t<DeviceOp::AGridDesc_K0_M_K1>,
+                    remove_reference_t<DeviceOp::BGridDesc_K0_N_K1>,
+                    remove_reference_t<
+                        typename GridwiseGemm::
+                            CGridDescriptor_MBlock_MXdlPerWave_MWaveMPerXdl_NBlock_NXdlPerWave_NWaveNPerXdl>,
+                    InElementwiseOperation,
+                    WeiElementwiseOperation,
+                    OutElementwiseOperation,
+                    Block2CTileMap,
+                    false>;
+
+                ave_time = launch_and_time_kernel(
+                    stream_config,
+                    kernel,
+                    dim3(grid_size),
+                    dim3(BlockSize),
+                    0,
+                    arg.p_a_grid_,
+                    arg.p_b_grid_,
+                    arg.p_c_grid_,
+                    arg.a_grid_desc_k0_m_k1_,
+                    arg.b_grid_desc_k0_n_k1_,
+                    arg.c_grid_desc_mblock_mxdlperwave_mwavemperxdl_nblock_nxdlperwave_nwavenperxdl_,
+                    arg.in_element_op_,
+                    arg.wei_element_op_,
+                    arg.out_element_op_,
+                    arg.block_2_ctile_map_);
+            }
+
+            return ave_time;
+        }
+
+        float Run(const BaseArgument* p_arg,
+                  const StreamConfig& stream_config = StreamConfig{}) override
+        {
+            return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
+        }
+    };
+
+    static constexpr bool IsValidCompilationParameter()
+    {
+        // TODO: properly implement this check
+        return true;
+    }
+
+    static bool IsSupportedArgument(const Argument& arg)
+    {
+        if constexpr(ConvForwardSpecialization ==
+                     ConvolutionForwardSpecialization::Filter1x1Stride1Pad0)
+        {
+            // check if it's 1x1, stride=1 conv
+            if(!(arg.filter_spatial_lengths_[0] == 1 && arg.filter_spatial_lengths_[1] == 1 &&
+                 arg.conv_filter_strides_[0] == 1 && arg.conv_filter_strides_[1] == 1 &&
+                 arg.input_left_pads_[0] == 0 && arg.input_left_pads_[1] == 0 &&
+                 arg.input_right_pads_[0] == 0 && arg.input_right_pads_[1] == 0))
+            {
+                return false;
+            }
+        }
+        else if constexpr(ConvForwardSpecialization ==
+                          ConvolutionForwardSpecialization::Filter1x1Pad0)
+        {
+            // check if it's 1x1 conv
+            if(!(arg.filter_spatial_lengths_[0] == 1 && arg.filter_spatial_lengths_[1] == 1 &&
+                 arg.input_left_pads_[0] == 0 && arg.input_left_pads_[1] == 0 &&
+                 arg.input_right_pads_[0] == 0 && arg.input_right_pads_[1] == 0))
+            {
+                return false;
+            }
+        }
+
+        // vector load A/B matrix from global memory
+        if(!(ABlockTransferSrcVectorDim == 2 && BBlockTransferSrcVectorDim == 2 &&
+             arg.Conv_C_ % ABlockTransferSrcScalarPerVector == 0 &&
+             arg.Conv_C_ % BBlockTransferSrcScalarPerVector == 0))
+        {
+            return false;
+        }
+
+        // vector store C matrix into global memory
+        if(!(arg.Conv_K_ % CBlockTransferScalarPerVector_NWaveNPerXdl == 0))
+        {
+            return false;
+        }
+
+        // Gridwise GEMM size
+        return GridwiseGemm::CheckValidity(arg.a_grid_desc_k0_m_k1_,
+                                           arg.b_grid_desc_k0_n_k1_,
+                                           arg.c_grid_desc_m_n_,
+                                           arg.block_2_ctile_map_);
+    }
+
+    bool IsSupportedArgument(const BaseArgument* p_arg) override
+    {
+        return IsSupportedArgument(*dynamic_cast<const Argument*>(p_arg));
+    }
+
+    static auto MakeArgument(const InDataType* p_in_grid,
+                             const WeiDataType* p_wei_grid,
+                             OutDataType* p_out_grid,
+                             ck::index_t N,
+                             ck::index_t K,
+                             ck::index_t C,
+                             std::vector<ck::index_t> input_spatial_lengths,
+                             std::vector<ck::index_t> filter_spatial_lengths,
+                             std::vector<ck::index_t> output_spatial_lengths,
+                             std::vector<ck::index_t> conv_filter_strides,
+                             std::vector<ck::index_t> conv_filter_dilations,
+                             std::vector<ck::index_t> input_left_pads,
+                             std::vector<ck::index_t> input_right_pads,
+                             InElementwiseOperation in_element_op,
+                             WeiElementwiseOperation wei_element_op,
+                             OutElementwiseOperation out_element_op)
+    {
+        return Argument{p_in_grid,
+                        p_wei_grid,
+                        p_out_grid,
+                        N,
+                        K,
+                        C,
+                        input_spatial_lengths,
+                        filter_spatial_lengths,
+                        output_spatial_lengths,
+                        conv_filter_strides,
+                        conv_filter_dilations,
+                        input_left_pads,
+                        input_right_pads,
+                        in_element_op,
+                        wei_element_op,
+                        out_element_op};
+    }
+
+    static auto MakeInvoker() { return Invoker{}; }
+
+    std::unique_ptr<BaseArgument>
+    MakeArgumentPointer(const void* p_in_grid,
+                        const void* p_wei_grid,
+                        void* p_out_grid,
+                        ck::index_t N,
+                        ck::index_t K,
+                        ck::index_t C,
+                        std::vector<ck::index_t> input_spatial_lengths,
+                        std::vector<ck::index_t> filter_spatial_lengths,
+                        std::vector<ck::index_t> output_spatial_lengths,
+                        std::vector<ck::index_t> conv_filter_strides,
+                        std::vector<ck::index_t> conv_filter_dilations,
+                        std::vector<ck::index_t> input_left_pads,
+                        std::vector<ck::index_t> input_right_pads,
+                        InElementwiseOperation in_element_op,
+                        WeiElementwiseOperation wei_element_op,
+                        OutElementwiseOperation out_element_op) override
+    {
+        return std::make_unique<Argument>(static_cast<const InDataType*>(p_in_grid),
+                                          static_cast<const WeiDataType*>(p_wei_grid),
+                                          static_cast<OutDataType*>(p_out_grid),
+                                          N,
+                                          K,
+                                          C,
+                                          input_spatial_lengths,
+                                          filter_spatial_lengths,
+                                          output_spatial_lengths,
+                                          conv_filter_strides,
+                                          conv_filter_dilations,
+                                          input_left_pads,
+                                          input_right_pads,
+                                          in_element_op,
+                                          wei_element_op,
+                                          out_element_op);
+    }
+
+    std::unique_ptr<BaseInvoker> MakeInvokerPointer() override
+    {
+        return std::make_unique<Invoker>(Invoker{});
+    }
+
+    std::string GetTypeString() const override
+    {
+        auto str = std::stringstream();
+
+        // clang-format off
+        str << "DeviceConv2dFwdXdl_C_Shuffle_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K"
+            << "<"
+            << BlockSize << ", "
+            << MPerBlock << ", "
+            << NPerBlock << ", "
+            << K0PerBlock << ", "
+            << getConvForwardSpecializationString(ConvForwardSpecialization)
+            << ">";
+        // clang-format on
+
+        return str.str();
+    }
+};
+
+} // namespace device
+} // namespace tensor_operation
+} // namespace ck

deps/cget/pkg/ROCmSoftwarePlatform__composable_kernel/install/include/ck/tensor_operation/gpu/device/device_conv2d_fwd_xdl_nhwc_kyxc_nhwk.hpp

+// 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_conv_fwd.hpp"
+#include "ck/tensor_operation/gpu/device/convolution_forward_specialization.hpp"
+#include "ck/tensor_operation/gpu/grid/gridwise_gemm_xdlops_v2r3.hpp"
+#include "ck/host_utility/device_prop.hpp"
+#include "ck/host_utility/kernel_launch.hpp"
+
+namespace ck {
+namespace tensor_operation {
+namespace device {
+
+// out[N, Ho, Wo, K] = in[N, Hi, Wi, C] * wei[K, Y, X, C]
+template <typename InDataType,
+          typename WeiDataType,
+          typename OutDataType,
+          typename AccDataType,
+          typename InElementwiseOperation,
+          typename WeiElementwiseOperation,
+          typename OutElementwiseOperation,
+          ConvolutionForwardSpecialization ConvForwardSpecialization,
+          ck::index_t BlockSize,
+          ck::index_t MPerBlock,
+          ck::index_t NPerBlock,
+          ck::index_t K0PerBlock,
+          ck::index_t K1,
+          ck::index_t MPerXDL,
+          ck::index_t NPerXDL,
+          ck::index_t MXdlPerWave,
+          ck::index_t NXdlPerWave,
+          typename ABlockTransferThreadClusterLengths_K0_M_K1,
+          typename ABlockTransferThreadClusterArrangeOrder,
+          typename ABlockTransferSrcAccessOrder,
+          ck::index_t ABlockTransferSrcVectorDim,
+          ck::index_t ABlockTransferSrcScalarPerVector,
+          ck::index_t ABlockTransferDstScalarPerVector_K1,
+          bool ABlockLdsAddExtraM,
+          typename BBlockTransferThreadClusterLengths_K0_N_K1,
+          typename BBlockTransferThreadClusterArrangeOrder,
+          typename BBlockTransferSrcAccessOrder,
+          ck::index_t BBlockTransferSrcVectorDim,
+          ck::index_t BBlockTransferSrcScalarPerVector,
+          ck::index_t BBlockTransferDstScalarPerVector_K1,
+          bool BBlockLdsAddExtraN,
+          ck::index_t CThreadTransferSrcDstVectorDim,
+          ck::index_t CThreadTransferDstScalarPerVector>
+struct DeviceConv2dFwdXdl_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K
+    : public DeviceConvFwd<2,
+                           ck::tensor_layout::convolution::NHWC,
+                           ck::tensor_layout::convolution::KYXC,
+                           ck::tensor_layout::convolution::NHWK,
+                           InDataType,
+                           WeiDataType,
+                           OutDataType,
+                           InElementwiseOperation,
+                           WeiElementwiseOperation,
+                           OutElementwiseOperation>
+{
+    using DeviceOp = DeviceConv2dFwdXdl_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K;
+
+    using ADataType = InDataType;
+    using BDataType = WeiDataType;
+    using CDataType = OutDataType;
+
+    // TODO make A/B datatype different
+    using ABDataType = InDataType;
+
+    static constexpr index_t NDimSpatial = 2;
+
+    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 K1Number     = Number<K1>{};
+    static constexpr auto GemmK1Number = K1Number;
+
+    static auto
+    MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N(ck::index_t N,
+                                                    ck::index_t K,
+                                                    ck::index_t C,
+                                                    std::vector<ck::index_t> input_spatial_lengths,
+                                                    std::vector<ck::index_t> filter_spatial_lengths,
+                                                    std::vector<ck::index_t> output_spatial_lengths,
+                                                    std::vector<ck::index_t> conv_filter_strides,
+                                                    std::vector<ck::index_t> conv_filter_dilations,
+                                                    std::vector<ck::index_t> input_left_pads,
+                                                    std::vector<ck::index_t> input_right_pads)
+    {
+        using namespace ck;
+
+        const index_t Hi = input_spatial_lengths[0];
+        const index_t Wi = input_spatial_lengths[1];
+
+        const index_t Ho = output_spatial_lengths[0];
+        const index_t Wo = output_spatial_lengths[1];
+
+        const index_t Y = filter_spatial_lengths[0];
+        const index_t X = filter_spatial_lengths[1];
+
+        const index_t ConvStrideH = conv_filter_strides[0];
+        const index_t ConvStrideW = conv_filter_strides[1];
+
+        const index_t ConvDilationH = conv_filter_dilations[0];
+        const index_t ConvDilationW = conv_filter_dilations[1];
+
+        const index_t InLeftPadH = input_left_pads[0];
+        const index_t InLeftPadW = input_left_pads[1];
+
+        const index_t InRightPadH = input_right_pads[0];
+        const index_t InRightPadW = input_right_pads[1];
+
+        const index_t GemmMRaw = N * Ho * Wo;
+        const index_t GemmN    = K;
+        const index_t GemmK    = Y * X * C;
+
+        const auto GemmMPad = math::integer_least_multiple(GemmMRaw, MPerBlock) - GemmMRaw;
+
+        assert(GemmK % GemmK1Number == 0);
+
+        const index_t GemmK0 = GemmK / GemmK1Number;
+
+        if constexpr(ConvForwardSpecialization ==
+                     ConvolutionForwardSpecialization::Filter1x1Stride1Pad0)
+        {
+            // A: input tensor
+            const auto in_gemmmraw_gemmk_grid_desc =
+                make_naive_tensor_descriptor_packed(make_tuple(N * Ho * Wo, C));
+
+            const auto in_gemmk0_gemmm_gemmk1_grid_desc = transform_tensor_descriptor(
+                in_gemmmraw_gemmk_grid_desc,
+                make_tuple(make_unmerge_transform(make_tuple(GemmK0, GemmK1Number)),
+                           make_right_pad_transform(GemmMRaw, GemmMPad)),
+                make_tuple(Sequence<1>{}, Sequence<0>{}),
+                make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
+
+            // B: weight tensor
+            const auto wei_gemmn_gemmk_grid_desc =
+                make_naive_tensor_descriptor_packed(make_tuple(K, C));
+
+            const auto wei_gemmk0_gemmn_gemmk1_grid_desc = transform_tensor_descriptor(
+                wei_gemmn_gemmk_grid_desc,
+                make_tuple(make_unmerge_transform(make_tuple(GemmK0, GemmK1Number)),
+                           make_pass_through_transform(GemmN)),
+                make_tuple(Sequence<1>{}, Sequence<0>{}),
+                make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
+
+            // C: output tensor
+            const auto out_gemmmraw_gemmn_grid_desc =
+                make_naive_tensor_descriptor_packed(make_tuple(N * Ho * Wo, K));
+
+            const auto out_gemmm_gemmn_grid_desc =
+                transform_tensor_descriptor(out_gemmmraw_gemmn_grid_desc,
+                                            make_tuple(make_right_pad_transform(GemmMRaw, GemmMPad),
+                                                       make_pass_through_transform(GemmN)),
+                                            make_tuple(Sequence<0>{}, Sequence<1>{}),
+                                            make_tuple(Sequence<0>{}, Sequence<1>{}));
+
+            return make_tuple(in_gemmk0_gemmm_gemmk1_grid_desc,
+                              wei_gemmk0_gemmn_gemmk1_grid_desc,
+                              out_gemmm_gemmn_grid_desc);
+        }
+        else if constexpr(ConvForwardSpecialization ==
+                          ConvolutionForwardSpecialization::Filter1x1Pad0)
+        {
+            // A: input tensor
+            const auto in_n_hi_wi_c_grid_desc =
+                make_naive_tensor_descriptor_packed(make_tuple(N, Hi, Wi, C));
+
+            const auto in_n_ho_wo_c_grid_desc = transform_tensor_descriptor(
+                in_n_hi_wi_c_grid_desc,
+                make_tuple(make_pass_through_transform(N),
+                           make_embed_transform(make_tuple(Ho), make_tuple(ConvStrideH)),
+                           make_embed_transform(make_tuple(Wo), make_tuple(ConvStrideW)),
+                           make_pass_through_transform(C)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
+
+            const auto in_gemmk0_gemmmraw_gemmk1_grid_desc = transform_tensor_descriptor(
+                in_n_ho_wo_c_grid_desc,
+                make_tuple(make_unmerge_transform(make_tuple(GemmK0, GemmK1Number)),
+                           make_merge_transform(make_tuple(N, Ho, Wo))),
+                make_tuple(Sequence<3>{}, Sequence<0, 1, 2>{}),
+                make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
+
+            const auto in_gemmk0_gemmm_gemmk1_grid_desc = transform_tensor_descriptor(
+                in_gemmk0_gemmmraw_gemmk1_grid_desc,
+                make_tuple(make_pass_through_transform(GemmK0),
+                           make_right_pad_transform(GemmMRaw, GemmMPad),
+                           make_pass_through_transform(GemmK1Number)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
+
+            // B: weight tensor
+            const auto wei_gemmn_gemmk_grid_desc =
+                make_naive_tensor_descriptor_packed(make_tuple(K, C));
+
+            const auto wei_gemmk0_gemmn_gemmk1_grid_desc = transform_tensor_descriptor(
+                wei_gemmn_gemmk_grid_desc,
+                make_tuple(make_unmerge_transform(make_tuple(GemmK0, GemmK1Number)),
+                           make_pass_through_transform(GemmN)),
+                make_tuple(Sequence<1>{}, Sequence<0>{}),
+                make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
+
+            // C: output tensor
+            const auto out_gemmmraw_gemmn_grid_desc =
+                make_naive_tensor_descriptor_packed(make_tuple(N * Ho * Wo, K));
+
+            const auto out_gemmm_gemmn_grid_desc =
+                transform_tensor_descriptor(out_gemmmraw_gemmn_grid_desc,
+                                            make_tuple(make_right_pad_transform(GemmMRaw, GemmMPad),
+                                                       make_pass_through_transform(GemmN)),
+                                            make_tuple(Sequence<0>{}, Sequence<1>{}),
+                                            make_tuple(Sequence<0>{}, Sequence<1>{}));
+
+            return make_tuple(in_gemmk0_gemmm_gemmk1_grid_desc,
+                              wei_gemmk0_gemmn_gemmk1_grid_desc,
+                              out_gemmm_gemmn_grid_desc);
+        }
+        else
+        {
+            // A: input tensor
+            const auto in_n_hi_wi_c_grid_desc =
+                make_naive_tensor_descriptor_packed(make_tuple(N, Hi, Wi, C));
+
+            const auto in_n_hip_wip_c_grid_desc = transform_tensor_descriptor(
+                in_n_hi_wi_c_grid_desc,
+                make_tuple(make_pass_through_transform(N),
+                           make_pad_transform(Hi, InLeftPadH, InRightPadH),
+                           make_pad_transform(Wi, InLeftPadW, InRightPadW),
+                           make_pass_through_transform(C)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
+
+            const auto in_n_y_ho_x_wo_c_grid_desc = transform_tensor_descriptor(
+                in_n_hip_wip_c_grid_desc,
+                make_tuple(
+                    make_pass_through_transform(N),
+                    make_embed_transform(make_tuple(Y, Ho), make_tuple(ConvDilationH, ConvStrideH)),
+                    make_embed_transform(make_tuple(X, Wo), make_tuple(ConvDilationW, ConvStrideW)),
+                    make_pass_through_transform(C)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
+                make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}));
+
+            const auto in_gemmk_gemmmraw_grid_desc =
+                transform_tensor_descriptor(in_n_y_ho_x_wo_c_grid_desc,
+                                            make_tuple(make_merge_transform(make_tuple(Y, X, C)),
+                                                       make_merge_transform(make_tuple(N, Ho, Wo))),
+                                            make_tuple(Sequence<1, 3, 5>{}, Sequence<0, 2, 4>{}),
+                                            make_tuple(Sequence<0>{}, Sequence<1>{}));
+
+            const auto in_gemmk0_gemmmraw_gemmk1_grid_desc = transform_tensor_descriptor(
+                in_gemmk_gemmmraw_grid_desc,
+                make_tuple(make_unmerge_transform(make_tuple(GemmK0, GemmK1Number)),
+                           make_pass_through_transform(GemmMRaw)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}),
+                make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
+
+            const auto in_gemmk0_gemmm_gemmk1_grid_desc = transform_tensor_descriptor(
+                in_gemmk0_gemmmraw_gemmk1_grid_desc,
+                make_tuple(make_pass_through_transform(GemmK0),
+                           make_right_pad_transform(GemmMRaw, GemmMPad),
+                           make_pass_through_transform(GemmK1Number)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
+
+            // B: weight tensor
+            const auto wei_k_yxc_grid_desc =
+                make_naive_tensor_descriptor_packed(make_tuple(K, Y * X * C));
+
+            const auto wei_gemmk_gemmn_grid_desc = transform_tensor_descriptor(
+                wei_k_yxc_grid_desc,
+                make_tuple(make_pass_through_transform(K), make_pass_through_transform(Y * X * C)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}),
+                make_tuple(Sequence<1>{}, Sequence<0>{}));
+
+            const auto wei_gemmk0_gemmn_gemmk1_grid_desc = transform_tensor_descriptor(
+                wei_gemmk_gemmn_grid_desc,
+                make_tuple(make_unmerge_transform(make_tuple(GemmK0, GemmK1Number)),
+                           make_pass_through_transform(GemmN)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}),
+                make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
+
+            // C: output tensor
+            const auto out_nhowo_k_grid_desc =
+                make_naive_tensor_descriptor_packed(make_tuple(N * Ho * Wo, K));
+
+            const auto out_gemmmraw_gemmn_grid_desc =
+                transform_tensor_descriptor(out_nhowo_k_grid_desc,
+                                            make_tuple(make_pass_through_transform(N * Ho * Wo),
+                                                       make_pass_through_transform(K)),
+                                            make_tuple(Sequence<0>{}, Sequence<1>{}),
+                                            make_tuple(Sequence<0>{}, Sequence<1>{}));
+
+            const auto out_gemmm_gemmn_grid_desc =
+                transform_tensor_descriptor(out_gemmmraw_gemmn_grid_desc,
+                                            make_tuple(make_right_pad_transform(GemmMRaw, GemmMPad),
+                                                       make_pass_through_transform(GemmN)),
+                                            make_tuple(Sequence<0>{}, Sequence<1>{}),
+                                            make_tuple(Sequence<0>{}, Sequence<1>{}));
+
+            return make_tuple(in_gemmk0_gemmm_gemmk1_grid_desc,
+                              wei_gemmk0_gemmn_gemmk1_grid_desc,
+                              out_gemmm_gemmn_grid_desc);
+        }
+    }
+
+    using ABCGridDescs = decltype(MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N(
+        1, 1, 1, {1, 1}, {1, 1}, {1, 1}, {1, 1}, {1, 1}, {1, 1}, {1, 1}));
+
+    using AGridDesc_K0_M_K1 = remove_cvref_t<decltype(ABCGridDescs{}[I0])>;
+    using BGridDesc_K0_N_K1 = remove_cvref_t<decltype(ABCGridDescs{}[I1])>;
+    using CGridDesc_M_N     = remove_cvref_t<decltype(ABCGridDescs{}[I2])>;
+
+    // GridwiseGemm
+    using GridwiseGemm = GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v2r3<
+        BlockSize,
+        ABDataType, // TODO: distinguish A/B datatype
+        AccDataType,
+        CDataType,
+        InMemoryDataOperationEnum::Set,
+        AGridDesc_K0_M_K1,
+        BGridDesc_K0_N_K1,
+        CGridDesc_M_N,
+        InElementwiseOperation,
+        WeiElementwiseOperation,
+        OutElementwiseOperation,
+        MPerBlock,
+        NPerBlock,
+        K0PerBlock,
+        MPerXDL,
+        NPerXDL,
+        K1,
+        MXdlPerWave,
+        NXdlPerWave,
+        ABlockTransferThreadClusterLengths_K0_M_K1,
+        Sequence<1, 0, 2>, // ABlockTransferThreadClusterArrangeOrder,
+        Sequence<1, 0, 2>, // ABlockTransferSrcAccessOrder,
+        2,                 // ABlockTransferSrcVectorDim,
+        ABlockTransferSrcScalarPerVector,
+        ABlockTransferDstScalarPerVector_K1,
+        false, // AThreadTransferSrcResetCoordinateAfterRun,
+        ABlockLdsAddExtraM,
+        BBlockTransferThreadClusterLengths_K0_N_K1,
+        Sequence<1, 0, 2>, // BBlockTransferThreadClusterArrangeOrder,
+        Sequence<1, 0, 2>, // BBlockTransferSrcAccessOrder,
+        2,                 // BBlockTransferSrcVectorDim,
+        BBlockTransferSrcScalarPerVector,
+        BBlockTransferDstScalarPerVector_K1,
+        false, // BThreadTransferSrcResetCoordinateAfterRun,
+        BBlockLdsAddExtraN,
+        Sequence<2, 3, 0, 1, 7, 5, 4, 6>, // CThreadTransferSrcDstAccessOrder,
+        7,                                // CThreadTransferSrcDstVectorDim,
+        CThreadTransferDstScalarPerVector>;
+
+    // Argument
+    struct Argument : public BaseArgument
+    {
+        Argument(const InDataType* p_in_grid,
+                 const WeiDataType* p_wei_grid,
+                 OutDataType* p_out_grid,
+                 ck::index_t N,
+                 ck::index_t K,
+                 ck::index_t C,
+                 std::vector<ck::index_t> input_spatial_lengths,
+                 std::vector<ck::index_t> filter_spatial_lengths,
+                 std::vector<ck::index_t> output_spatial_lengths,
+                 std::vector<ck::index_t> conv_filter_strides,
+                 std::vector<ck::index_t> conv_filter_dilations,
+                 std::vector<ck::index_t> input_left_pads,
+                 std::vector<ck::index_t> input_right_pads,
+                 ck::index_t M01,
+                 ck::index_t N01,
+                 InElementwiseOperation in_element_op,
+                 WeiElementwiseOperation wei_element_op,
+                 OutElementwiseOperation out_element_op)
+            : p_a_grid_{p_in_grid},
+              p_b_grid_{p_wei_grid},
+              p_c_grid_{p_out_grid},
+              a_grid_desc_k0_m_k1_{},
+              b_grid_desc_k0_n_k1_{},
+              c_grid_desc_m_n_{},
+              c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_{},
+              block_2_ctile_map_{},
+              M01_{M01},
+              N01_{N01},
+              in_element_op_{in_element_op},
+              wei_element_op_{wei_element_op},
+              out_element_op_{out_element_op},
+              Conv_N_{N},
+              Conv_K_{K},
+              Conv_C_{C},
+              filter_spatial_lengths_{filter_spatial_lengths},
+              conv_filter_strides_{conv_filter_strides},
+              input_left_pads_{input_left_pads},
+              input_right_pads_{input_right_pads}
+        {
+            const auto descs =
+                DeviceOp::MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N(N,
+                                                                          K,
+                                                                          C,
+                                                                          input_spatial_lengths,
+                                                                          filter_spatial_lengths,
+                                                                          output_spatial_lengths,
+                                                                          conv_filter_strides,
+                                                                          conv_filter_dilations,
+                                                                          input_left_pads,
+                                                                          input_right_pads);
+
+            a_grid_desc_k0_m_k1_ = descs[I0];
+            b_grid_desc_k0_n_k1_ = descs[I1];
+            c_grid_desc_m_n_     = descs[I2];
+            block_2_ctile_map_ =
+                GridwiseGemm::MakeDefaultBlock2CTileMap(c_grid_desc_m_n_, M01, N01);
+
+            if(GridwiseGemm::CheckValidity(a_grid_desc_k0_m_k1_,
+                                           b_grid_desc_k0_n_k1_,
+                                           c_grid_desc_m_n_,
+                                           block_2_ctile_map_))
+            {
+                c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_ =
+                    GridwiseGemm::MakeCGridDescriptor_M0_N0_M1_N1_M2_M3_M4_N2(c_grid_desc_m_n_);
+            }
+        }
+
+        //  private:
+        const ADataType* p_a_grid_;
+        const BDataType* p_b_grid_;
+        CDataType* p_c_grid_;
+        AGridDesc_K0_M_K1 a_grid_desc_k0_m_k1_;
+        BGridDesc_K0_N_K1 b_grid_desc_k0_n_k1_;
+        CGridDesc_M_N c_grid_desc_m_n_;
+        typename GridwiseGemm::CGridDesc_M0_N0_M1_N1_M2_M3_M4_N2
+            c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_;
+        typename GridwiseGemm::DefaultBlock2CTileMap block_2_ctile_map_;
+        index_t M01_;
+        index_t N01_;
+        InElementwiseOperation in_element_op_;
+        WeiElementwiseOperation wei_element_op_;
+        OutElementwiseOperation out_element_op_;
+        // for checking IsSupportedArgument()
+        index_t Conv_N_;
+        index_t Conv_K_;
+        index_t Conv_C_;
+        std::vector<index_t> filter_spatial_lengths_;
+        std::vector<index_t> conv_filter_strides_;
+        std::vector<index_t> input_left_pads_;
+        std::vector<index_t> input_right_pads_;
+    };
+
+    // Invoker
+    struct Invoker : public BaseInvoker
+    {
+        using Argument = DeviceOp::Argument;
+
+        float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
+        {
+#if 0
+            {
+                std::cout << "arg.a_grid_desc_k0_m_k1_{" << arg.a_grid_desc_k0_m_k1_.GetLength(I0)
+                          << ", " << arg.a_grid_desc_k0_m_k1_.GetLength(I1) << ", "
+                          << arg.a_grid_desc_k0_m_k1_.GetLength(I2) << "}" << std::endl;
+
+                std::cout << "arg.b_grid_desc_k0_n_k1_{" << arg.b_grid_desc_k0_n_k1_.GetLength(I0)
+                          << ", " << arg.b_grid_desc_k0_n_k1_.GetLength(I1) << ", "
+                          << arg.b_grid_desc_k0_n_k1_.GetLength(I2) << "}" << std::endl;
+
+                std::cout << "arg.c_grid_desc_m_n_{ " << arg.c_grid_desc_m_n_.GetLength(I0) << ", "
+                          << arg.c_grid_desc_m_n_.GetLength(I1) << "}" << std::endl;
+            }
+#endif
+            if(!GridwiseGemm::CheckValidity(arg.a_grid_desc_k0_m_k1_,
+                                            arg.b_grid_desc_k0_n_k1_,
+                                            arg.c_grid_desc_m_n_,
+                                            arg.block_2_ctile_map_))
+            {
+                throw std::runtime_error(
+                    "wrong! GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v2r3 has invalid setting");
+            }
+
+            const index_t grid_size =
+                arg.block_2_ctile_map_.CalculateGridSize(arg.c_grid_desc_m_n_);
+
+            const auto K =
+                arg.a_grid_desc_k0_m_k1_.GetLength(I0) * arg.a_grid_desc_k0_m_k1_.GetLength(I2);
+
+            float ave_time = 0;
+
+            if(GridwiseGemm::CalculateHasMainKBlockLoop(K))
+            {
+                const auto kernel = kernel_gemm_xdlops_v2r3<
+                    GridwiseGemm,
+                    ADataType, // TODO: distiguish A/B datatype
+                    CDataType,
+                    remove_reference_t<DeviceOp::AGridDesc_K0_M_K1>,
+                    remove_reference_t<DeviceOp::BGridDesc_K0_N_K1>,
+                    remove_reference_t<typename GridwiseGemm::CGridDesc_M0_N0_M1_N1_M2_M3_M4_N2>,
+                    InElementwiseOperation,
+                    WeiElementwiseOperation,
+                    OutElementwiseOperation,
+                    remove_reference_t<typename GridwiseGemm::DefaultBlock2CTileMap>,
+                    true>;
+
+                ave_time = launch_and_time_kernel(stream_config,
+                                                  kernel,
+                                                  dim3(grid_size),
+                                                  dim3(BlockSize),
+                                                  0,
+                                                  arg.p_a_grid_,
+                                                  arg.p_b_grid_,
+                                                  arg.p_c_grid_,
+                                                  arg.a_grid_desc_k0_m_k1_,
+                                                  arg.b_grid_desc_k0_n_k1_,
+                                                  arg.c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_,
+                                                  arg.in_element_op_,
+                                                  arg.wei_element_op_,
+                                                  arg.out_element_op_,
+                                                  arg.block_2_ctile_map_);
+            }
+            else
+            {
+                const auto kernel = kernel_gemm_xdlops_v2r3<
+                    GridwiseGemm,
+                    ADataType, // TODO: distiguish A/B datatype
+                    CDataType,
+                    remove_reference_t<DeviceOp::AGridDesc_K0_M_K1>,
+                    remove_reference_t<DeviceOp::BGridDesc_K0_N_K1>,
+                    remove_reference_t<typename GridwiseGemm::CGridDesc_M0_N0_M1_N1_M2_M3_M4_N2>,
+                    InElementwiseOperation,
+                    WeiElementwiseOperation,
+                    OutElementwiseOperation,
+                    remove_reference_t<typename GridwiseGemm::DefaultBlock2CTileMap>,
+                    false>;
+
+                ave_time = launch_and_time_kernel(stream_config,
+                                                  kernel,
+                                                  dim3(grid_size),
+                                                  dim3(BlockSize),
+                                                  0,
+                                                  arg.p_a_grid_,
+                                                  arg.p_b_grid_,
+                                                  arg.p_c_grid_,
+                                                  arg.a_grid_desc_k0_m_k1_,
+                                                  arg.b_grid_desc_k0_n_k1_,
+                                                  arg.c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_,
+                                                  arg.in_element_op_,
+                                                  arg.wei_element_op_,
+                                                  arg.out_element_op_,
+                                                  arg.block_2_ctile_map_);
+            }
+
+            return ave_time;
+        }
+
+        float Run(const BaseArgument* p_arg,
+                  const StreamConfig& stream_config = StreamConfig{}) override
+        {
+            return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
+        }
+    };
+
+    static constexpr bool IsValidCompilationParameter()
+    {
+        // TODO: properly implement this check
+        return true;
+    }
+
+    static bool IsSupportedArgument(const Argument& arg)
+    {
+        if constexpr(ConvForwardSpecialization ==
+                     ConvolutionForwardSpecialization::Filter1x1Stride1Pad0)
+        {
+            // check if it's 1x1, stride=1 conv
+            if(!(arg.filter_spatial_lengths_[0] == 1 && arg.filter_spatial_lengths_[1] == 1 &&
+                 arg.conv_filter_strides_[0] == 1 && arg.conv_filter_strides_[1] == 1 &&
+                 arg.input_left_pads_[0] == 0 && arg.input_left_pads_[1] == 0 &&
+                 arg.input_right_pads_[0] == 0 && arg.input_right_pads_[1] == 0))
+            {
+                return false;
+            }
+        }
+        else if constexpr(ConvForwardSpecialization ==
+                          ConvolutionForwardSpecialization::Filter1x1Pad0)
+        {
+            // check if it's 1x1 conv
+            if(!(arg.filter_spatial_lengths_[0] == 1 && arg.filter_spatial_lengths_[1] == 1 &&
+                 arg.input_left_pads_[0] == 0 && arg.input_left_pads_[1] == 0 &&
+                 arg.input_right_pads_[0] == 0 && arg.input_right_pads_[1] == 0))
+            {
+                return false;
+            }
+        }
+
+        // vector load A/B matrix from global memory
+        if(!(ABlockTransferSrcVectorDim == 2 && BBlockTransferSrcVectorDim == 2 &&
+             arg.Conv_C_ % ABlockTransferSrcScalarPerVector == 0 &&
+             arg.Conv_C_ % BBlockTransferSrcScalarPerVector == 0))
+        {
+            return false;
+        }
+
+        // vector store C matrix into global memory
+        if(!(arg.Conv_K_ % CThreadTransferDstScalarPerVector == 0))
+        {
+            return false;
+        }
+
+        // Gridwise GEMM size
+        return GridwiseGemm::CheckValidity(arg.a_grid_desc_k0_m_k1_,
+                                           arg.b_grid_desc_k0_n_k1_,
+                                           arg.c_grid_desc_m_n_,
+                                           arg.block_2_ctile_map_);
+    }
+
+    bool IsSupportedArgument(const BaseArgument* p_arg) override
+    {
+        return IsSupportedArgument(*dynamic_cast<const Argument*>(p_arg));
+    }
+
+    static auto MakeArgument(const InDataType* p_in_grid,
+                             const WeiDataType* p_wei_grid,
+                             OutDataType* p_out_grid,
+                             ck::index_t N,
+                             ck::index_t K,
+                             ck::index_t C,
+                             std::vector<ck::index_t> input_spatial_lengths,
+                             std::vector<ck::index_t> filter_spatial_lengths,
+                             std::vector<ck::index_t> output_spatial_lengths,
+                             std::vector<ck::index_t> conv_filter_strides,
+                             std::vector<ck::index_t> conv_filter_dilations,
+                             std::vector<ck::index_t> input_left_pads,
+                             std::vector<ck::index_t> input_right_pads,
+                             InElementwiseOperation in_element_op,
+                             WeiElementwiseOperation wei_element_op,
+                             OutElementwiseOperation out_element_op)
+    {
+        return Argument{p_in_grid,
+                        p_wei_grid,
+                        p_out_grid,
+                        N,
+                        K,
+                        C,
+                        input_spatial_lengths,
+                        filter_spatial_lengths,
+                        output_spatial_lengths,
+                        conv_filter_strides,
+                        conv_filter_dilations,
+                        input_left_pads,
+                        input_right_pads,
+                        1,
+                        1,
+                        in_element_op,
+                        wei_element_op,
+                        out_element_op};
+    }
+
+    static auto MakeInvoker() { return Invoker{}; }
+
+    std::unique_ptr<BaseArgument>
+    MakeArgumentPointer(const void* p_in_grid,
+                        const void* p_wei_grid,
+                        void* p_out_grid,
+                        ck::index_t N,
+                        ck::index_t K,
+                        ck::index_t C,
+                        std::vector<ck::index_t> input_spatial_lengths,
+                        std::vector<ck::index_t> filter_spatial_lengths,
+                        std::vector<ck::index_t> output_spatial_lengths,
+                        std::vector<ck::index_t> conv_filter_strides,
+                        std::vector<ck::index_t> conv_filter_dilations,
+                        std::vector<ck::index_t> input_left_pads,
+                        std::vector<ck::index_t> input_right_pads,
+                        InElementwiseOperation in_element_op,
+                        WeiElementwiseOperation wei_element_op,
+                        OutElementwiseOperation out_element_op) override
+    {
+        return std::make_unique<Argument>(static_cast<const InDataType*>(p_in_grid),
+                                          static_cast<const WeiDataType*>(p_wei_grid),
+                                          static_cast<OutDataType*>(p_out_grid),
+                                          N,
+                                          K,
+                                          C,
+                                          input_spatial_lengths,
+                                          filter_spatial_lengths,
+                                          output_spatial_lengths,
+                                          conv_filter_strides,
+                                          conv_filter_dilations,
+                                          input_left_pads,
+                                          input_right_pads,
+                                          1,
+                                          1,
+                                          in_element_op,
+                                          wei_element_op,
+                                          out_element_op);
+    }
+
+    std::unique_ptr<BaseInvoker> MakeInvokerPointer() override
+    {
+        return std::make_unique<Invoker>(Invoker{});
+    }
+
+    std::string GetTypeString() const override
+    {
+        auto str = std::stringstream();
+
+        // clang-format off
+        str << "DeviceConv2dFwdXdl_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K"
+            << "<"
+            << BlockSize << ", "
+            << MPerBlock << ", "
+            << NPerBlock << ", "
+            << K0PerBlock << ", "
+            << getConvForwardSpecializationString(ConvForwardSpecialization)
+            << ">";
+        // clang-format on
+
+        return str.str();
+    }
+};
+
+} // namespace device
+} // namespace tensor_operation
+} // namespace ck

deps/cget/pkg/ROCmSoftwarePlatform__composable_kernel/install/include/ck/tensor_operation/gpu/device/device_conv3d_fwd_naive_ndhwc_kzyxc_ndhwk.hpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
+
+#ifndef DEVICE_CONV3D_FWD_NAIVE_HPP
+#define DEVICE_CONV3D_FWD_NAIVE_HPP
+
+#include <iostream>
+#include <memory>
+#include <sstream>
+#include "conv_util.hpp"
+#include "device.hpp"
+#include "device_conv_fwd.hpp"
+#include "common_header.hpp"
+#include "naive_conv_fwd.hpp"
+
+namespace ck {
+namespace tensor_operation {
+namespace device {
+
+// specialization for #D conv: in[n, di, hi, wi, c] * wei[k, z, y, x, c] = out[n, do, ho, wo, k]
+template <typename InDataType,
+          typename WeiDataType, // WeiDataType must be the same as InDataType
+          typename OutDataType,
+          typename AccDataType,
+          typename InElementwiseOperation,
+          typename WeiElementwiseOperation,
+          typename OutElementwiseOperation>
+struct DeviceConv3dFwdNaive_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K
+    : public DeviceConvFwd<InElementwiseOperation, WeiElementwiseOperation, OutElementwiseOperation>
+
+{
+    using DeviceOp = DeviceConv3dFwdNaive_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K;
+
+    using ADataType = InDataType;
+    using BDataType = WeiDataType;
+    using CDataType = OutDataType;
+    // TODO make A/B datatype different
+    using ABDataType = InDataType;
+
+    // Argument
+    struct Argument : public BaseArgument
+    {
+        Argument(const InDataType* p_in,
+                 const WeiDataType* p_wei,
+                 OutDataType* p_out,
+                 const index_t N,
+                 const index_t K,
+                 const index_t C,
+                 std::vector<ck::index_t> input_spatial_lengths,
+                 std::vector<ck::index_t> filter_spatial_lengths,
+                 std::vector<ck::index_t> output_spatial_lengths,
+                 std::vector<ck::index_t> conv_filter_strides,
+                 std::vector<ck::index_t> conv_filter_dilations,
+                 std::vector<ck::index_t> input_left_pads,
+                 std::vector<ck::index_t> input_right_pads,
+                 InElementwiseOperation in_element_op,
+                 WeiElementwiseOperation wei_element_op,
+                 OutElementwiseOperation out_element_op)
+            : params_{3,
+                      N,
+                      K,
+                      C,
+                      filter_spatial_lengths,
+                      input_spatial_lengths,
+                      conv_filter_strides,
+                      conv_filter_dilations,
+                      input_left_pads,
+                      input_right_pads},
+              out_spatial_lengths_{output_spatial_lengths},
+              p_in_{p_in},
+              p_wei_{p_wei},
+              p_out_{p_out},
+              in_element_op_{in_element_op},
+              wei_element_op_{wei_element_op},
+              out_element_op_{out_element_op}
+
+        {
+        }
+
+        //  private:
+        utils::conv::ConvParams params_;
+        std::vector<index_t> out_spatial_lengths_;
+
+        const InDataType* p_in_;
+        const WeiDataType* p_wei_;
+        OutDataType* p_out_;
+
+        InElementwiseOperation in_element_op_;
+        WeiElementwiseOperation wei_element_op_;
+        OutElementwiseOperation out_element_op_;
+    };
+
+    // Invoker
+    struct Invoker : public BaseInvoker
+    {
+        using Argument = DeviceOp::Argument;
+
+        float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
+        {
+            const auto naive_conv3d_fwd =
+                ref::naive_conv_fwd_ndhwc_kzyxc_ndhwk<InDataType,
+                                                      WeiDataType,
+                                                      OutDataType,
+                                                      AccDataType,
+                                                      InElementwiseOperation,
+                                                      WeiElementwiseOperation,
+                                                      OutElementwiseOperation>;
+
+            float ave_time = launch_and_time_kernel(stream_config,
+                                                    naive_conv3d_fwd,
+                                                    dim3(256),
+                                                    dim3(256),
+                                                    0,
+                                                    arg.p_in_,
+                                                    arg.p_wei_,
+                                                    arg.p_out_,
+                                                    arg.N_,
+                                                    arg.K_,
+                                                    arg.C_,
+                                                    arg.in_spatial_lengths_[0],
+                                                    arg.in_spatial_lengths_[1],
+                                                    arg.in_spatial_lengths_[2],
+                                                    arg.filter_spatial_lengths_[0],
+                                                    arg.filter_spatial_lengths_[1],
+                                                    arg.filter_spatial_lengths_[2],
+                                                    arg.out_spatial_lengths_[0],
+                                                    arg.out_spatial_lengths_[1],
+                                                    arg.out_spatial_lengths_[2],
+                                                    arg.conv_filter_strides_[0],
+                                                    arg.conv_filter_strides_[1],
+                                                    arg.conv_filter_strides_[2],
+                                                    arg.conv_filter_dilations_[0],
+                                                    arg.conv_filter_dilations_[1],
+                                                    arg.conv_filter_dilations_[2],
+                                                    arg.in_left_pads_[0],
+                                                    arg.in_left_pads_[1],
+                                                    arg.in_left_pads_[2]);
+
+            return ave_time;
+        }
+
+        // polymorphic
+        float Run(const BaseArgument* p_arg,
+                  const StreamConfig& stream_config = StreamConfig{}) override
+        {
+            return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
+        }
+    };
+
+    static constexpr bool IsValidCompilationParameter()
+    {
+        // TODO: properly implement this check
+        return true;
+    }
+
+    static bool IsSupportedArgument(const Argument& arg)
+    {
+        std::vector<index_t> out_spatial_lengths = arg.params_.GetOutputSpatialLengths();
+
+        bool out_lengths_are_consistent = out_spatial_lengths[0] == arg.out_spatial_lengths_[0] &&
+                                          out_spatial_lengths[1] == arg.out_spatial_lengths_[1] &&
+                                          out_spatial_lengths[2] == arg.out_spatial_lengths_[2];
+        return out_lengths_are_consistent;
+    }
+
+    // polymorphic
+    bool IsSupportedArgument(const BaseArgument* p_arg) override
+    {
+        return IsSupportedArgument(*dynamic_cast<const Argument*>(p_arg));
+    }
+
+    static auto MakeArgument(const InDataType* p_in,
+                             const WeiDataType* p_wei,
+                             OutDataType* p_out,
+                             const index_t N,
+                             const index_t K,
+                             const index_t C,
+                             std::vector<ck::index_t> input_spatial_lengths,
+                             std::vector<ck::index_t> filter_spatial_lengths,
+                             std::vector<ck::index_t> output_spatial_lengths,
+                             std::vector<ck::index_t> conv_filter_strides,
+                             std::vector<ck::index_t> conv_filter_dilations,
+                             std::vector<ck::index_t> input_left_pads,
+                             std::vector<ck::index_t> input_right_pads,
+                             InElementwiseOperation in_element_op,
+                             WeiElementwiseOperation wei_element_op,
+                             OutElementwiseOperation out_element_op)
+    {
+        return Argument{p_in,
+                        p_wei,
+                        p_out,
+                        N,
+                        K,
+                        C,
+                        input_spatial_lengths,
+                        filter_spatial_lengths,
+                        output_spatial_lengths,
+                        conv_filter_strides,
+                        conv_filter_dilations,
+                        input_left_pads,
+                        input_right_pads,
+                        in_element_op,
+                        wei_element_op,
+                        out_element_op};
+    }
+
+    static auto MakeInvoker() { return Invoker{}; }
+
+    // polymorphic
+    std::unique_ptr<BaseArgument>
+    MakeArgumentPointer(const void* p_in,
+                        const void* p_wei,
+                        void* p_out,
+                        const index_t N,
+                        const index_t K,
+                        const index_t C,
+                        std::vector<ck::index_t> input_spatial_lengths,
+                        std::vector<ck::index_t> filter_spatial_lengths,
+                        std::vector<ck::index_t> output_spatial_lengths,
+                        std::vector<ck::index_t> conv_filter_strides,
+                        std::vector<ck::index_t> conv_filter_dilations,
+                        std::vector<ck::index_t> input_left_pads,
+                        std::vector<ck::index_t> input_right_pads,
+                        InElementwiseOperation in_element_op,
+                        WeiElementwiseOperation wei_element_op,
+                        OutElementwiseOperation out_element_op) override
+
+    {
+        return std::make_unique<Argument>(static_cast<const InDataType*>(p_in),
+                                          static_cast<const WeiDataType*>(p_wei),
+                                          static_cast<OutDataType*>(p_out),
+                                          N,
+                                          K,
+                                          C,
+                                          input_spatial_lengths,
+                                          filter_spatial_lengths,
+                                          output_spatial_lengths,
+                                          conv_filter_strides,
+                                          conv_filter_dilations,
+                                          input_left_pads,
+                                          input_right_pads,
+                                          in_element_op,
+                                          wei_element_op,
+                                          out_element_op);
+    }
+
+    // polymorphic
+    std::unique_ptr<BaseInvoker> MakeInvokerPointer() override
+    {
+        return std::make_unique<Invoker>(Invoker{});
+    }
+
+    std::string GetTypeString() const override
+    {
+        auto str = std::stringstream();
+
+        // clang-format off
+        str << "DeviceConv3dFwdNaive_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K<>";
+        // clang-format on
+
+        return str.str();
+    }
+};
+
+} // namespace device
+} // namespace tensor_operation
+} // namespace ck
+#endif

deps/cget/pkg/ROCmSoftwarePlatform__composable_kernel/install/include/ck/tensor_operation/gpu/device/device_conv3d_fwd_xdl_ndhwc_kzyxc_ndhwk.hpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
+
+#ifndef DEVICE_CONV3D_FWD_XDL_HPP
+#define DEVICE_CONV3D_FWD_XDL_HPP
+
+#include <iostream>
+#include <memory>
+#include <sstream>
+#include "device.hpp"
+#include "device_conv_fwd.hpp"
+#include "common_header.hpp"
+#include "tensor_layout.hpp"
+#include "convolution_forward_specialization.hpp"
+#include "tensor_descriptor.hpp"
+#include "tensor_descriptor_helper.hpp"
+#include "transform_forward_convolution3d_into_gemm_v4r4r4_ndhwc_kzyxc_ndhwk.hpp"
+#include "gridwise_gemm_xdlops_v2r3.hpp"
+
+namespace ck {
+namespace tensor_operation {
+namespace device {
+
+/*
+ * \see \link device_batched_gemm_xdl.hpp kernel_batched_gemm_xdlops_v2r3() \endlink.
+ */
+template <typename GridwiseGemm,
+          typename FloatAB,
+          typename FloatC,
+          typename AGridDesc_K0_M_K1,
+          typename BGridDesc_K0_N_K1,
+          typename CGridDesc_M0_N0_M1_N1_M2_M3_M4_N2,
+          typename AElementwiseOperation,
+          typename BElementwiseOperation,
+          typename CElementwiseOperation,
+          typename Block2CTileMap,
+          bool HasMainKBlockLoop>
+__global__ void
+#if CK_USE_LAUNCH_BOUNDS
+    __launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
+#endif
+        kernel_gemm_xdlops_v2r3_for_conv3d(
+            const FloatAB* __restrict__ p_a_grid,
+            const FloatAB* __restrict__ p_b_grid,
+            FloatC* __restrict__ p_c_grid,
+            const index_t num_batches,
+            const index_t a_batch_stride,
+            const index_t b_batch_stride,
+            const index_t c_batch_stride,
+            const AGridDesc_K0_M_K1 a_grid_desc_k0_m_k1,
+            const BGridDesc_K0_N_K1 b_grid_desc_k0_n_k1,
+            const CGridDesc_M0_N0_M1_N1_M2_M3_M4_N2 c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2,
+            const AElementwiseOperation a_element_op,
+            const BElementwiseOperation b_element_op,
+            const CElementwiseOperation c_element_op,
+            const Block2CTileMap block_2_ctile_map)
+{
+#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx908__) || defined(__gfx90a__))
+    const index_t num_blocks_per_batch =
+        __builtin_amdgcn_readfirstlane(get_grid_size() / num_batches);
+    const index_t g_idx = __builtin_amdgcn_readfirstlane(get_block_1d_id() / num_blocks_per_batch);
+
+    const long_index_t a_batch_offset =
+        __builtin_amdgcn_readfirstlane(static_cast<long_index_t>(a_batch_stride) * g_idx);
+    const long_index_t b_batch_offset =
+        __builtin_amdgcn_readfirstlane(static_cast<long_index_t>(b_batch_stride) * g_idx);
+    const long_index_t c_batch_offset =
+        __builtin_amdgcn_readfirstlane(static_cast<long_index_t>(c_batch_stride) * g_idx);
+
+    __shared__ char p_shared[GridwiseGemm::GetSharedMemoryNumberOfByte()];
+
+    GridwiseGemm::template Run<HasMainKBlockLoop>(p_a_grid + a_batch_offset,
+                                                  p_b_grid + b_batch_offset,
+                                                  p_c_grid + c_batch_offset,
+                                                  p_shared,
+                                                  a_grid_desc_k0_m_k1,
+                                                  b_grid_desc_k0_n_k1,
+                                                  c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2,
+                                                  a_element_op,
+                                                  b_element_op,
+                                                  c_element_op,
+                                                  block_2_ctile_map);
+
+#else
+    ignore = p_a_grid;
+    ignore = p_b_grid;
+    ignore = p_c_grid;
+    ignore = num_batches;
+    ignore = a_batch_stride;
+    ignore = b_batch_stride;
+    ignore = c_batch_stride;
+    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__))
+}
+
+// specialization for #D conv: in[n, di, hi, wi, c] * wei[k, z, y, x, c] = out[n, do, ho, wo, k]
+template <typename InDataType,
+          typename WeiDataType, // WeiDataType must be the same as InDataType
+          typename OutDataType,
+          typename AccDataType,
+          typename InElementwiseOperation,
+          typename WeiElementwiseOperation,
+          typename OutElementwiseOperation,
+          ConvolutionForwardSpecialization ConvForwardSpecialization,
+          ck::index_t BlockSize,
+          ck::index_t MPerBlock,
+          ck::index_t NPerBlock,
+          ck::index_t K0PerBlock,
+          ck::index_t K1,
+          ck::index_t MPerXDL,
+          ck::index_t NPerXDL,
+          ck::index_t MXdlPerWave,
+          ck::index_t NXdlPerWave,
+          typename ABlockTransferThreadClusterLengths_K0_M_K1,
+          typename ABlockTransferThreadClusterArrangeOrder,
+          typename ABlockTransferSrcAccessOrder,
+          ck::index_t ABlockTransferSrcVectorDim,
+          ck::index_t ABlockTransferSrcScalarPerVector,
+          ck::index_t ABlockTransferDstScalarPerVector_K1,
+          bool ABlockLdsAddExtraM,
+          typename BBlockTransferThreadClusterLengths_K0_N_K1,
+          typename BBlockTransferThreadClusterArrangeOrder,
+          typename BBlockTransferSrcAccessOrder,
+          ck::index_t BBlockTransferSrcVectorDim,
+          ck::index_t BBlockTransferSrcScalarPerVector,
+          ck::index_t BBlockTransferDstScalarPerVector_K1,
+          bool BBlockLdsAddExtraN,
+          ck::index_t CThreadTransferSrcDstVectorDim,
+          ck::index_t CThreadTransferDstScalarPerVector>
+struct DeviceConv3dFwdXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K
+    : public DeviceConvFwd<InElementwiseOperation, WeiElementwiseOperation, OutElementwiseOperation>
+
+{
+    using DeviceOp = DeviceConv3dFwdXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K;
+
+    using ADataType = InDataType;
+    using BDataType = WeiDataType;
+    using CDataType = OutDataType;
+    // TODO make A/B datatype different
+    using ABDataType = InDataType;
+
+    static constexpr auto I0 = Number<0>{};
+    static constexpr auto I1 = Number<1>{};
+    static constexpr auto I2 = Number<2>{};
+    static constexpr auto I3 = Number<3>{};
+
+    /*
+     * \brief Split the number of batches, \p N, into N = B * N1, such that the memory
+     * space of input and output tensors stays with the value range of index_t, and each subbatch
+     * can be dealed with GridwiseGemm.
+     */
+    static index_t GetMaxAllowableSubBatchSize(const index_t N,
+                                               const index_t K,
+                                               const index_t C,
+                                               std::vector<ck::index_t> input_spatial_lengths,
+                                               std::vector<ck::index_t> output_spatial_lengths)
+    {
+        const index_t Di = input_spatial_lengths[0];
+        const index_t Hi = input_spatial_lengths[1];
+        const index_t Wi = input_spatial_lengths[2];
+
+        const index_t Do = output_spatial_lengths[0];
+        const index_t Ho = output_spatial_lengths[1];
+        const index_t Wo = output_spatial_lengths[2];
+
+        // N1 should satisfy that
+        //   1) N % N1 = 0;
+        //   2) N1 * (Do * Ho * Wo * K) < (2^31 - 1)
+        //   3) N1 * (Di * Hi * Wi * C) < (2^31 - 1)
+        //
+        // Do NOT confuse (B, N1) in this function with (B, N1) in gridewise GEMM.
+        auto N1 = N + 1;
+
+        const auto stride =
+            math::max(long_index_t(Do) * Ho * Wo * K, long_index_t(Di) * Hi * Wi * C);
+        const index_t max_stride = NumericLimits<index_t>::Max();
+
+        for(index_t n0 = 1; n0 <= N; ++n0)
+        {
+            index_t n1 = N / n0;
+            if(n0 * n1 == N && long_index_t(n1) * long_index_t(stride) < max_stride)
+            {
+                N1 = n1;
+                break;
+            }
+        }
+
+        const auto B = N / N1;
+        if(B * N1 != N)
+        {
+            throw std::runtime_error(__func__ +
+                                     std::string(": failed to find num_subbatches for conv3d.\n"));
+        }
+
+        return N1;
+    }
+
+    static auto
+    MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N(const index_t N,
+                                                    const index_t K,
+                                                    const index_t C,
+                                                    std::vector<ck::index_t> input_spatial_lengths,
+                                                    std::vector<ck::index_t> filter_spatial_lengths,
+                                                    std::vector<ck::index_t> output_spatial_lengths,
+                                                    std::vector<ck::index_t> conv_filter_strides,
+                                                    std::vector<ck::index_t> conv_filter_dilations,
+                                                    std::vector<ck::index_t> input_left_pads,
+                                                    std::vector<ck::index_t> input_right_pads)
+    {
+        assert(input_spatial_lengths.size() > 2);
+        assert(filter_spatial_lengths.size() > 2);
+        assert(conv_filter_strides.size() > 2);
+        assert(conv_filter_dilations.size() > 2);
+        assert(input_left_pads.size() > 2);
+        assert(input_right_pads.size() > 2);
+
+        const index_t Di = input_spatial_lengths[0];
+        const index_t Hi = input_spatial_lengths[1];
+        const index_t Wi = input_spatial_lengths[2];
+        const index_t Z  = filter_spatial_lengths[0];
+        const index_t Y  = filter_spatial_lengths[1];
+        const index_t X  = filter_spatial_lengths[2];
+
+        const index_t Do = output_spatial_lengths[0];
+        const index_t Ho = output_spatial_lengths[1];
+        const index_t Wo = output_spatial_lengths[2];
+
+        static_assert(ConvForwardSpecialization == ConvolutionForwardSpecialization::Default,
+                      "Wrong! This specialization not implemented!");
+
+        const auto in_desc_n_di_hi_wi_c =
+            make_naive_tensor_descriptor_packed(make_tuple(N, Di, Hi, Wi, C));
+        const auto wei_desc_k_z_y_x_c =
+            make_naive_tensor_descriptor_packed(make_tuple(K, Z, Y, X, C));
+        const auto out_desc_n_do_ho_wo_k =
+            make_naive_tensor_descriptor_packed(make_tuple(N, Do, Ho, Wo, K));
+
+        const auto descs = transform_forward_convolution3d_into_gemm_v4r4r4_ndhwc_kzyxc_ndhwk_pad(
+            in_desc_n_di_hi_wi_c,
+            wei_desc_k_z_y_x_c,
+            out_desc_n_do_ho_wo_k,
+            make_tuple(conv_filter_strides[0], conv_filter_strides[1], conv_filter_strides[2]),
+            make_tuple(
+                conv_filter_dilations[0], conv_filter_dilations[1], conv_filter_dilations[2]),
+            make_tuple(input_left_pads[0], input_left_pads[1], input_left_pads[2]),
+            make_tuple(input_right_pads[0], input_right_pads[1], input_right_pads[2]),
+            Number<K1>{});
+
+        return descs;
+    }
+
+    using ABCGridDescs = remove_cvref_t<decltype(MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N(
+        1, 1, 1, {1, 1, 1}, {1, 1, 1}, {1, 1, 1}, {1, 1, 1}, {1, 1, 1}, {1, 1, 1}, {1, 1, 1}))>;
+
+    using AGridDesc_K0_M_K1 = remove_cvref_t<decltype(ABCGridDescs{}[I0])>;
+    using BGridDesc_K0_N_K1 = remove_cvref_t<decltype(ABCGridDescs{}[I1])>;
+    using CGridDesc_M_N     = remove_cvref_t<decltype(ABCGridDescs{}[I2])>;
+
+    using GridwiseGemm = GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v2r3<
+        BlockSize,
+        InDataType,
+        AccDataType,
+        OutDataType,
+        InMemoryDataOperationEnum::Set,
+        AGridDesc_K0_M_K1,
+        BGridDesc_K0_N_K1,
+        CGridDesc_M_N,
+        InElementwiseOperation,
+        WeiElementwiseOperation,
+        OutElementwiseOperation,
+        MPerBlock,
+        NPerBlock,
+        K0PerBlock,
+        MPerXDL,
+        NPerXDL,
+        K1,
+        MXdlPerWave,
+        NXdlPerWave,
+        ABlockTransferThreadClusterLengths_K0_M_K1,
+        Sequence<1, 0, 2>, // ABlockTransferThreadClusterArrangeOrder,
+        Sequence<1, 0, 2>, // ABlockTransferSrcAccessOrder,
+        2,
+        ABlockTransferSrcScalarPerVector,
+        ABlockTransferDstScalarPerVector_K1,
+        false, // AThreadTransferSrcResetCoordinateAfterRun,
+        ABlockLdsAddExtraM,
+        BBlockTransferThreadClusterLengths_K0_N_K1,
+        Sequence<1, 0, 2>, // ABlockTransferThreadClusterArrangeOrder,
+        Sequence<1, 0, 2>, // ABlockTransferSrcAccessOrder,
+        2,
+        BBlockTransferSrcScalarPerVector,
+        BBlockTransferDstScalarPerVector_K1,
+        false, // BThreadTransferSrcResetCoordinateAfterRun,
+        BBlockLdsAddExtraN,
+        Sequence<2, 3, 0, 1, 7, 5, 4, 6>,
+        7,
+        CThreadTransferDstScalarPerVector>;
+
+    using CGridDesc_M0_N0_M1_N1_M2_M3_M4_N2 =
+        decltype(GridwiseGemm::MakeCGridDescriptor_M0_N0_M1_N1_M2_M3_M4_N2(CGridDesc_M_N{}));
+    using Block2CTileMap = typename GridwiseGemm::DefaultBlock2CTileMap;
+
+    // Argument
+    struct Argument : public BaseArgument
+    {
+        Argument(const InDataType* p_in,
+                 const WeiDataType* p_wei,
+                 OutDataType* p_out,
+                 const index_t N,
+                 const index_t K,
+                 const index_t C,
+                 std::vector<ck::index_t> input_spatial_lengths,
+                 std::vector<ck::index_t> filter_spatial_lengths,
+                 std::vector<ck::index_t> output_spatial_lengths,
+                 std::vector<ck::index_t> conv_filter_strides,
+                 std::vector<ck::index_t> conv_filter_dilations,
+                 std::vector<ck::index_t> input_left_pads,
+                 std::vector<ck::index_t> input_right_pads,
+                 index_t M01,
+                 index_t N01,
+                 InElementwiseOperation in_element_op,
+                 WeiElementwiseOperation wei_element_op,
+                 OutElementwiseOperation out_element_op)
+            : p_a_grid_{p_in},
+              p_b_grid_{p_wei},
+              p_c_grid_{p_out},
+              M01_{M01},
+              N01_{N01},
+              in_element_op_{in_element_op},
+              wei_element_op_{wei_element_op},
+              out_element_op_{out_element_op}
+        {
+            const index_t subbatch_size =
+                GetMaxAllowableSubBatchSize(N, K, C, input_spatial_lengths, output_spatial_lengths);
+            num_subbatches_ = N / subbatch_size;
+
+            const auto descs =
+                MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N(subbatch_size,
+                                                                K,
+                                                                C,
+                                                                input_spatial_lengths,
+                                                                filter_spatial_lengths,
+                                                                output_spatial_lengths,
+                                                                conv_filter_strides,
+                                                                conv_filter_dilations,
+                                                                input_left_pads,
+                                                                input_right_pads);
+
+            a_grid_desc_k0_m_k1_ = descs[I0];
+            b_grid_desc_k0_n_k1_ = descs[I1];
+            c_grid_desc_m_n_     = descs[I2];
+
+            block_2_ctile_map_ =
+                GridwiseGemm::MakeDefaultBlock2CTileMap(c_grid_desc_m_n_, M01, N01);
+
+            a_batch_stride_ = a_grid_desc_k0_m_k1_.GetElementSpaceSize();
+            b_batch_stride_ = 0;
+            c_batch_stride_ = c_grid_desc_m_n_.GetElementSpaceSize();
+
+            if(GridwiseGemm::CheckValidity(a_grid_desc_k0_m_k1_,
+                                           b_grid_desc_k0_n_k1_,
+                                           c_grid_desc_m_n_,
+                                           block_2_ctile_map_))
+            {
+                c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_ =
+                    GridwiseGemm::MakeCGridDescriptor_M0_N0_M1_N1_M2_M3_M4_N2(c_grid_desc_m_n_);
+            }
+        }
+
+        //  private:
+        const InDataType* p_a_grid_;
+        const WeiDataType* p_b_grid_;
+        OutDataType* p_c_grid_;
+        index_t num_subbatches_;
+        index_t a_batch_stride_;
+        index_t b_batch_stride_;
+        index_t c_batch_stride_;
+        AGridDesc_K0_M_K1 a_grid_desc_k0_m_k1_;
+        BGridDesc_K0_N_K1 b_grid_desc_k0_n_k1_;
+        CGridDesc_M_N c_grid_desc_m_n_;
+        CGridDesc_M0_N0_M1_N1_M2_M3_M4_N2 c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_;
+        Block2CTileMap block_2_ctile_map_;
+        index_t M01_;
+        index_t N01_;
+        InElementwiseOperation in_element_op_;
+        WeiElementwiseOperation wei_element_op_;
+        OutElementwiseOperation out_element_op_;
+    };
+
+    // Invoker
+    struct Invoker : public BaseInvoker
+    {
+        using Argument = DeviceOp::Argument;
+
+        float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
+        {
+            {
+                std::cout << "num_batches_of_GEMM = " << arg.num_subbatches_ << std::endl;
+                std::cout << "a_grid_desc_k0_m_k1{" << arg.a_grid_desc_k0_m_k1_.GetLength(I0)
+                          << ", " << arg.a_grid_desc_k0_m_k1_.GetLength(I1) << ", "
+                          << arg.a_grid_desc_k0_m_k1_.GetLength(I2) << "}" << std::endl;
+
+                std::cout << "b_grid_desc_k0_n_k1{" << arg.b_grid_desc_k0_n_k1_.GetLength(I0)
+                          << ", " << arg.b_grid_desc_k0_n_k1_.GetLength(I1) << ", "
+                          << arg.b_grid_desc_k0_n_k1_.GetLength(I2) << "}" << std::endl;
+
+                std::cout << "c_grid_desc_m_n{ " << arg.c_grid_desc_m_n_.GetLength(I0) << ", "
+                          << arg.c_grid_desc_m_n_.GetLength(I1) << "}" << std::endl;
+            }
+
+            if(!GridwiseGemm::CheckValidity(arg.a_grid_desc_k0_m_k1_,
+                                            arg.b_grid_desc_k0_n_k1_,
+                                            arg.c_grid_desc_m_n_,
+                                            arg.block_2_ctile_map_))
+            {
+                throw std::runtime_error(
+                    "wrong! GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v2r3 has invalid setting");
+            }
+
+            const index_t grid_size =
+                arg.block_2_ctile_map_.CalculateGridSize(arg.c_grid_desc_m_n_) *
+                arg.num_subbatches_;
+
+            const auto K0 = arg.a_grid_desc_k0_m_k1_.GetLength(I0);
+
+            const bool has_main_k0_block_loop = GridwiseGemm::CalculateHasMainK0BlockLoop(K0);
+
+            float ave_time = 0;
+            if(has_main_k0_block_loop)
+            {
+                const auto kernel = kernel_gemm_xdlops_v2r3_for_conv3d<
+                    GridwiseGemm,
+                    InDataType,
+                    OutDataType,
+                    remove_reference_t<AGridDesc_K0_M_K1>,
+                    remove_reference_t<BGridDesc_K0_N_K1>,
+                    remove_reference_t<CGridDesc_M0_N0_M1_N1_M2_M3_M4_N2>,
+                    InElementwiseOperation,
+                    WeiElementwiseOperation,
+                    OutElementwiseOperation,
+                    remove_reference_t<Block2CTileMap>,
+                    true>;
+                ave_time = launch_and_time_kernel(stream_config,
+                                                  kernel,
+                                                  dim3(grid_size),
+                                                  dim3(BlockSize),
+                                                  0,
+                                                  arg.p_a_grid_,
+                                                  arg.p_b_grid_,
+                                                  arg.p_c_grid_,
+                                                  arg.num_subbatches_,
+                                                  arg.a_batch_stride_,
+                                                  arg.b_batch_stride_,
+                                                  arg.c_batch_stride_,
+                                                  arg.a_grid_desc_k0_m_k1_,
+                                                  arg.b_grid_desc_k0_n_k1_,
+                                                  arg.c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_,
+                                                  arg.in_element_op_,
+                                                  arg.wei_element_op_,
+                                                  arg.out_element_op_,
+                                                  arg.block_2_ctile_map_);
+            }
+            else
+            {
+                const auto kernel = kernel_gemm_xdlops_v2r3_for_conv3d<
+                    GridwiseGemm,
+                    InDataType,
+                    OutDataType,
+                    remove_reference_t<AGridDesc_K0_M_K1>,
+                    remove_reference_t<BGridDesc_K0_N_K1>,
+                    remove_reference_t<CGridDesc_M0_N0_M1_N1_M2_M3_M4_N2>,
+                    InElementwiseOperation,
+                    WeiElementwiseOperation,
+                    OutElementwiseOperation,
+                    remove_reference_t<Block2CTileMap>,
+                    false>;
+
+                ave_time = launch_and_time_kernel(stream_config,
+                                                  kernel,
+                                                  dim3(grid_size),
+                                                  dim3(BlockSize),
+                                                  0,
+                                                  arg.p_a_grid_,
+                                                  arg.p_b_grid_,
+                                                  arg.p_c_grid_,
+                                                  arg.num_subbatches_,
+                                                  arg.a_batch_stride_,
+                                                  arg.b_batch_stride_,
+                                                  arg.c_batch_stride_,
+                                                  arg.a_grid_desc_k0_m_k1_,
+                                                  arg.b_grid_desc_k0_n_k1_,
+                                                  arg.c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_,
+                                                  arg.in_element_op_,
+                                                  arg.wei_element_op_,
+                                                  arg.out_element_op_,
+                                                  arg.block_2_ctile_map_);
+            }
+
+            return ave_time;
+        }
+
+        // polymorphic
+        float Run(const BaseArgument* p_arg,
+                  const StreamConfig& stream_config = StreamConfig{}) override
+        {
+            return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
+        }
+    };
+
+    static constexpr bool IsValidCompilationParameter()
+    {
+        // TODO: properly implement this check
+        return true;
+    }
+
+    static bool IsSupportedArgument(const Argument& arg)
+    {
+        return GridwiseGemm::CheckValidity(arg.a_grid_desc_k0_m_k1_,
+                                           arg.b_grid_desc_k0_n_k1_,
+                                           arg.c_grid_desc_m_n_,
+                                           arg.block_2_ctile_map_);
+    }
+
+    // polymorphic
+    bool IsSupportedArgument(const BaseArgument* p_arg) override
+    {
+        return IsSupportedArgument(*dynamic_cast<const Argument*>(p_arg));
+    }
+
+    static auto MakeArgument(const InDataType* p_in,
+                             const WeiDataType* p_wei,
+                             OutDataType* p_out,
+                             const index_t N,
+                             const index_t K,
+                             const index_t C,
+                             std::vector<ck::index_t> input_spatial_lengths,
+                             std::vector<ck::index_t> filter_spatial_lengths,
+                             std::vector<ck::index_t> output_spatial_lengths,
+                             std::vector<ck::index_t> conv_filter_strides,
+                             std::vector<ck::index_t> conv_filter_dilations,
+                             std::vector<ck::index_t> input_left_pads,
+                             std::vector<ck::index_t> input_right_pads,
+                             InElementwiseOperation in_element_op,
+                             WeiElementwiseOperation wei_element_op,
+                             OutElementwiseOperation out_element_op)
+    {
+        return Argument{p_in,
+                        p_wei,
+                        p_out,
+                        N,
+                        K,
+                        C,
+                        input_spatial_lengths,
+                        filter_spatial_lengths,
+                        output_spatial_lengths,
+                        conv_filter_strides,
+                        conv_filter_dilations,
+                        input_left_pads,
+                        input_right_pads,
+                        1,
+                        1,
+                        in_element_op,
+                        wei_element_op,
+                        out_element_op};
+    }
+
+    static auto MakeInvoker() { return Invoker{}; }
+
+    // polymorphic
+    std::unique_ptr<BaseArgument>
+    MakeArgumentPointer(const void* p_in,
+                        const void* p_wei,
+                        void* p_out,
+                        const index_t N,
+                        const index_t K,
+                        const index_t C,
+                        std::vector<ck::index_t> input_spatial_lengths,
+                        std::vector<ck::index_t> filter_spatial_lengths,
+                        std::vector<ck::index_t> output_spatial_lengths,
+                        std::vector<ck::index_t> conv_filter_strides,
+                        std::vector<ck::index_t> conv_filter_dilations,
+                        std::vector<ck::index_t> input_left_pads,
+                        std::vector<ck::index_t> input_right_pads,
+                        InElementwiseOperation in_element_op,
+                        WeiElementwiseOperation wei_element_op,
+                        OutElementwiseOperation out_element_op) override
+
+    {
+        return std::make_unique<Argument>(static_cast<const InDataType*>(p_in),
+                                          static_cast<const WeiDataType*>(p_wei),
+                                          static_cast<OutDataType*>(p_out),
+                                          N,
+                                          K,
+                                          C,
+                                          input_spatial_lengths,
+                                          filter_spatial_lengths,
+                                          output_spatial_lengths,
+                                          conv_filter_strides,
+                                          conv_filter_dilations,
+                                          input_left_pads,
+                                          input_right_pads,
+                                          1,
+                                          1,
+                                          in_element_op,
+                                          wei_element_op,
+                                          out_element_op);
+    }
+
+    // polymorphic
+    std::unique_ptr<BaseInvoker> MakeInvokerPointer() override
+    {
+        return std::make_unique<Invoker>(Invoker{});
+    }
+
+    std::string GetTypeString() const override
+    {
+        auto str = std::stringstream();
+
+        // clang-format off
+        str << "DeviceConv3dFwdXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho_Wo_K"
+            << "<"
+            << BlockSize << ", "
+            << MPerBlock << ", "
+            << NPerBlock << ", "
+            << K0PerBlock
+            << ">";
+        // clang-format on
+
+        return str.str();
+    }
+};
+
+} // namespace device
+} // namespace tensor_operation
+} // namespace ck
+#endif

deps/cget/pkg/ROCmSoftwarePlatform__composable_kernel/install/include/ck/tensor_operation/gpu/device/device_conv_bwd_data.hpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include <vector>
+
+#include "ck/tensor_operation/gpu/device/device_base.hpp"
+
+namespace ck {
+namespace tensor_operation {
+namespace device {
+
+template <ck::index_t NumDimSpatial,
+          typename InLayout,
+          typename WeiLayout,
+          typename OutLayout,
+          typename InDataType,
+          typename WeiDataType,
+          typename OutDataType,
+          typename InElementwiseOperation,
+          typename WeiElementwiseOperation,
+          typename OutElementwiseOperation>
+struct DeviceConvBwdData : public BaseOperator
+{
+    virtual std::unique_ptr<BaseArgument>
+    MakeArgumentPointer(void* p_in,
+                        const void* p_wei,
+                        const void* p_out,
+                        ck::index_t N,
+                        ck::index_t K,
+                        ck::index_t C,
+                        std::vector<ck::index_t> input_spatial_lengths,
+                        std::vector<ck::index_t> filter_spatial_lengths,
+                        std::vector<ck::index_t> output_spatial_lengths,
+                        std::vector<ck::index_t> conv_filter_strides,
+                        std::vector<ck::index_t> conv_filter_dilations,
+                        std::vector<ck::index_t> input_left_pads,
+                        std::vector<ck::index_t> input_right_pads,
+                        InElementwiseOperation in_element_op,
+                        WeiElementwiseOperation wei_element_op,
+                        OutElementwiseOperation out_element_op) = 0;
+
+    virtual std::unique_ptr<BaseInvoker> MakeInvokerPointer() = 0;
+};
+
+} // namespace device
+} // namespace tensor_operation
+} // namespace ck

deps/cget/pkg/ROCmSoftwarePlatform__composable_kernel/install/include/ck/tensor_operation/gpu/device/device_conv_bwd_weight.hpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include <vector>
+
+#include "ck/tensor_operation/gpu/device/device_base.hpp"
+
+namespace ck {
+namespace tensor_operation {
+namespace device {
+
+template <ck::index_t NumDimSpatial,
+          typename InLayout,
+          typename WeiLayout,
+          typename OutLayout,
+          typename InDataType,
+          typename WeiDataType,
+          typename OutDataType,
+          typename InElementwiseOperation,
+          typename WeiElementwiseOperation,
+          typename OutElementwiseOperation>
+struct DeviceConvBwdWeight : public BaseOperator
+{
+    virtual std::unique_ptr<BaseArgument>
+    MakeArgumentPointer(const void* p_in,
+                        void* p_wei,
+                        const void* p_out,
+                        ck::index_t N,
+                        ck::index_t K,
+                        ck::index_t C,
+                        std::vector<ck::index_t> input_spatial_lengths,
+                        std::vector<ck::index_t> filter_spatial_lengths,
+                        std::vector<ck::index_t> output_spatial_lengths,
+                        std::vector<ck::index_t> conv_filter_strides,
+                        std::vector<ck::index_t> conv_filter_dilations,
+                        std::vector<ck::index_t> input_left_pads,
+                        std::vector<ck::index_t> input_right_pads,
+                        InElementwiseOperation in_element_op,
+                        WeiElementwiseOperation wei_element_op,
+                        OutElementwiseOperation out_element_op,
+                        ck::index_t split_k) = 0;
+
+    virtual std::unique_ptr<BaseInvoker> MakeInvokerPointer() = 0;
+};
+
+} // namespace device
+} // namespace tensor_operation
+} // namespace ck

deps/cget/pkg/ROCmSoftwarePlatform__composable_kernel/install/include/ck/tensor_operation/gpu/device/device_conv_fwd.hpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include <vector>
+
+#include "ck/tensor_operation/gpu/device/device_base.hpp"
+
+namespace ck {
+namespace tensor_operation {
+namespace device {
+
+template <ck::index_t NumDimSpatial,
+          typename InLayout,
+          typename WeiLayout,
+          typename OutLayout,
+          typename InDataType,
+          typename WeiDataType,
+          typename OutDataType,
+          typename InElementwiseOperation,
+          typename WeiElementwiseOperation,
+          typename OutElementwiseOperation>
+struct DeviceConvFwd : public BaseOperator
+{
+    virtual std::unique_ptr<BaseArgument>
+    MakeArgumentPointer(const void* p_in,
+                        const void* p_wei,
+                        void* p_out,
+                        ck::index_t N,
+                        ck::index_t K,
+                        ck::index_t C,
+                        std::vector<ck::index_t> input_spatial_lengths,
+                        std::vector<ck::index_t> filter_spatial_lengths,
+                        std::vector<ck::index_t> output_spatial_lengths,
+                        std::vector<ck::index_t> conv_filter_strides,
+                        std::vector<ck::index_t> conv_filter_dilations,
+                        std::vector<ck::index_t> input_left_pads,
+                        std::vector<ck::index_t> input_right_pads,
+                        InElementwiseOperation in_element_op,
+                        WeiElementwiseOperation wei_element_op,
+                        OutElementwiseOperation out_element_op) = 0;
+
+    virtual std::unique_ptr<BaseInvoker> MakeInvokerPointer() = 0;
+};
+
+} // namespace device
+} // namespace tensor_operation
+} // namespace ck

deps/cget/pkg/ROCmSoftwarePlatform__composable_kernel/install/include/ck/tensor_operation/gpu/device/device_conv_fwd_bias_activation.hpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include <vector>
+#include <iostream>
+
+#include "ck/tensor_operation/gpu/device/device_base.hpp"
+#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
+
+namespace ck {
+namespace tensor_operation {
+namespace device {
+
+template <typename InElementwiseOperation,
+          typename WeiElementwiseOperation,
+          typename OutElementwiseOperation>
+struct DeviceConvFwdBiasActivation : public BaseOperator
+{
+    virtual std::unique_ptr<BaseArgument>
+    MakeArgumentPointer(const void* p_in,
+                        const void* p_wei,
+                        void* p_out,
+                        const void* p_bias,
+                        ck::index_t N,
+                        ck::index_t K,
+                        ck::index_t C,
+                        std::vector<ck::index_t> input_spatial_lengths,
+                        std::vector<ck::index_t> filter_spatial_lengths,
+                        std::vector<ck::index_t> output_spatial_lengths,
+                        std::vector<ck::index_t> conv_filter_strides,
+                        std::vector<ck::index_t> conv_filter_dilations,
+                        std::vector<ck::index_t> input_left_pads,
+                        std::vector<ck::index_t> input_right_pads,
+                        InElementwiseOperation in_element_op,
+                        WeiElementwiseOperation wei_element_op,
+                        OutElementwiseOperation out_element_op) = 0;
+
+    virtual std::unique_ptr<BaseInvoker> MakeInvokerPointer() = 0;
+};
+
+template <typename InElementwiseOperation,
+          typename WeiElementwiseOperation,
+          typename OutElementwiseOperation>
+using DeviceConvFwdBiasActivationPtr =
+    std::unique_ptr<DeviceConvFwdBiasActivation<InElementwiseOperation,
+                                                WeiElementwiseOperation,
+                                                OutElementwiseOperation>>;
+
+} // namespace device
+} // namespace tensor_operation
+} // namespace ck

deps/cget/pkg/ROCmSoftwarePlatform__composable_kernel/install/include/ck/tensor_operation/gpu/device/device_conv_fwd_bias_activation_add.hpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include <vector>
+#include <iostream>
+
+#include "ck/tensor_operation/gpu/device/device_base.hpp"
+
+namespace ck {
+namespace tensor_operation {
+namespace device {
+
+template <typename InElementwiseOperation,
+          typename WeiElementwiseOperation,
+          typename OutElementwiseOperation>
+struct DeviceConvFwdBiasActivationAdd : public BaseOperator
+{
+    virtual std::unique_ptr<BaseArgument>
+    MakeArgumentPointer(const void* p_in,
+                        const void* p_wei,
+                        void* p_out,
+                        const void* p_bias,
+                        const void* p_resi,
+                        ck::index_t N,
+                        ck::index_t K,
+                        ck::index_t C,
+                        std::vector<ck::index_t> input_spatial_lengths,
+                        std::vector<ck::index_t> filter_spatial_lengths,
+                        std::vector<ck::index_t> output_spatial_lengths,
+                        std::vector<ck::index_t> conv_filter_strides,
+                        std::vector<ck::index_t> conv_filter_dilations,
+                        std::vector<ck::index_t> input_left_pads,
+                        std::vector<ck::index_t> input_right_pads,
+                        InElementwiseOperation in_element_op,
+                        WeiElementwiseOperation wei_element_op,
+                        OutElementwiseOperation out_element_op) = 0;
+
+    virtual std::unique_ptr<BaseInvoker> MakeInvokerPointer() = 0;
+};
+
+template <typename InElementwiseOperation,
+          typename WeiElementwiseOperation,
+          typename OutElementwiseOperation>
+using DeviceConvFwdBiasActivationAddPtr =
+    std::unique_ptr<DeviceConvFwdBiasActivationAdd<InElementwiseOperation,
+                                                   WeiElementwiseOperation,
+                                                   OutElementwiseOperation>>;
+
+} // namespace device
+} // namespace tensor_operation
+} // namespace ck

deps/cget/pkg/ROCmSoftwarePlatform__composable_kernel/install/include/ck/tensor_operation/gpu/device/device_convnd_bwd_data_nwc_kxc_nwk_xdl.hpp

+// 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_conv_bwd_data.hpp"
+#include "ck/tensor_operation/gpu/device/convolution_backward_data_specialization.hpp"
+#include "ck/tensor_operation/gpu/grid/gridwise_gemm_xdlops_v2r3.hpp"
+#include "ck/host_utility/device_prop.hpp"
+#include "ck/host_utility/kernel_launch.hpp"
+
+namespace ck {
+namespace tensor_operation {
+namespace device {
+
+// out[N, Ho, Wo, K] = in[N, Hi, Wi, C] * wei[K, Y, X, C]
+template <ck::index_t NDimSpatial,
+          typename InDataType,
+          typename WeiDataType,
+          typename OutDataType,
+          typename AccDataType,
+          typename InElementwiseOperation,
+          typename WeiElementwiseOperation,
+          typename OutElementwiseOperation,
+          ConvolutionBackwardDataSpecialization ConvBackwardDataSpecialization,
+          ck::index_t BlockSize,
+          ck::index_t MPerBlock,
+          ck::index_t NPerBlock,
+          ck::index_t K0PerBlock,
+          ck::index_t K1,
+          ck::index_t MPerXdl,
+          ck::index_t NPerXdl,
+          ck::index_t MXdlPerWave,
+          ck::index_t NXdlPerWave,
+          typename ABlockTransferThreadClusterLengths_K0_M_K1,
+          typename ABlockTransferThreadClusterArrangeOrder,
+          typename ABlockTransferSrcAccessOrder,
+          ck::index_t ABlockTransferSrcVectorDim,
+          ck::index_t ABlockTransferSrcScalarPerVector,
+          ck::index_t ABlockTransferDstScalarPerVector_K1,
+          bool ABlockLdsAddExtraM,
+          typename BBlockTransferThreadClusterLengths_K0_N_K1,
+          typename BBlockTransferThreadClusterArrangeOrder,
+          typename BBlockTransferSrcAccessOrder,
+          ck::index_t BBlockTransferSrcVectorDim,
+          ck::index_t BBlockTransferSrcScalarPerVector,
+          ck::index_t BBlockTransferDstScalarPerVector_K1,
+          bool BBlockLdsAddExtraN,
+          ck::index_t CThreadTransferSrcDstVectorDim,
+          ck::index_t CThreadTransferDstScalarPerVector>
+struct DeviceConvNdBwdDataNwcKxcNwk_Xdl
+    : public DeviceConvBwdData<
+          NDimSpatial,
+          ck::tuple_element_t<NDimSpatial - 1,
+                              ck::Tuple<ck::tensor_layout::convolution::NWC,
+                                        ck::tensor_layout::convolution::NHWC,
+                                        ck::tensor_layout::convolution::NDHWC>>,
+          ck::tuple_element_t<NDimSpatial - 1,
+                              ck::Tuple<ck::tensor_layout::convolution::KXC,
+                                        ck::tensor_layout::convolution::KYXC,
+                                        ck::tensor_layout::convolution::KZYXC>>,
+          ck::tuple_element_t<NDimSpatial - 1,
+                              ck::Tuple<ck::tensor_layout::convolution::NWK,
+                                        ck::tensor_layout::convolution::NHWK,
+                                        ck::tensor_layout::convolution::NDHWK>>,
+          InDataType,
+          WeiDataType,
+          OutDataType,
+          InElementwiseOperation,
+          WeiElementwiseOperation,
+          OutElementwiseOperation>
+{
+    using DeviceOp = DeviceConvNdBwdDataNwcKxcNwk_Xdl;
+
+    using ADataType = OutDataType;
+    using BDataType = WeiDataType;
+    using CDataType = InDataType;
+
+    // TODO make A/B datatype different
+    using ABDataType = InDataType;
+
+    static constexpr auto I0 = Number<0>{};
+    static constexpr auto I1 = Number<1>{};
+    static constexpr auto I2 = Number<2>{};
+    static constexpr auto I3 = Number<3>{};
+    static constexpr auto I4 = Number<4>{};
+    static constexpr auto I5 = Number<5>{};
+    static constexpr auto I6 = Number<6>{};
+    static constexpr auto I7 = Number<7>{};
+
+    static_assert((K1 % ABlockTransferThreadClusterLengths_K0_M_K1{}[I2]) %
+                      ABlockTransferSrcScalarPerVector ==
+                  0);
+    static_assert((NPerBlock / BBlockTransferThreadClusterLengths_K0_N_K1{}[I1]) %
+                      BBlockTransferSrcScalarPerVector ==
+                  0);
+
+    static constexpr auto K1Number     = Number<K1>{};
+    static constexpr auto GemmK1Number = K1Number;
+
+    template <ck::index_t NDim, typename ck::enable_if<NDim == 1, bool>::type = false>
+    static auto
+    MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N(ck::index_t N,
+                                                    ck::index_t K,
+                                                    ck::index_t C,
+                                                    std::vector<ck::index_t> input_spatial_lengths,
+                                                    std::vector<ck::index_t> filter_spatial_lengths,
+                                                    std::vector<ck::index_t> output_spatial_lengths,
+                                                    std::vector<ck::index_t> conv_filter_strides,
+                                                    std::vector<ck::index_t> conv_filter_dilations,
+                                                    std::vector<ck::index_t> input_left_pads,
+                                                    std::vector<ck::index_t> input_right_pads,
+                                                    std::vector<ck::index_t> tildes)
+    {
+        using namespace ck;
+
+        index_t i_xtilde = tildes[0];
+
+        const index_t Wi            = input_spatial_lengths[0];
+        const index_t Wo            = output_spatial_lengths[0];
+        const index_t X             = filter_spatial_lengths[0];
+        const index_t InLeftPadW    = input_left_pads[0];
+        const index_t InRightPadW   = input_right_pads[0];
+        const index_t ConvStrideW   = conv_filter_strides[0];
+        const index_t ConvDilationW = conv_filter_dilations[0];
+
+        const auto K0 = K / K1;
+
+        const auto in_n_wi_c_grid_desc = make_naive_tensor_descriptor_packed(make_tuple(N, Wi, C));
+
+        if constexpr(ConvBackwardDataSpecialization ==
+                     ConvolutionBackwardDataSpecialization::Filter1x1Stride1Pad0)
+        {
+            // A: output tensor
+            const auto out_gemmk0_gemmm_gemmk1_grid_desc = transform_tensor_descriptor(
+                make_naive_tensor_descriptor_packed(make_tuple(N * Wo, K)),
+                make_tuple(make_pass_through_transform(N * Wo),
+                           make_unmerge_transform(make_tuple(K0, K1))),
+                make_tuple(Sequence<0>{}, Sequence<1>{}),
+                make_tuple(Sequence<1>{}, Sequence<0, 2>{}));
+
+            // B: weight tensor
+            const auto wei_gemmk0_gemmn_gemmk1_grid_desc =
+                transform_tensor_descriptor(make_naive_tensor_descriptor_packed(make_tuple(K, C)),
+                                            make_tuple(make_unmerge_transform(make_tuple(K0, K1)),
+                                                       make_pass_through_transform(C)),
+                                            make_tuple(Sequence<0>{}, Sequence<1>{}),
+                                            make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
+
+            // C: input tensor
+            const auto in_n_x_wo_c_grid_desc = transform_tensor_descriptor(
+                in_n_wi_c_grid_desc,
+                make_tuple(make_pass_through_transform(N),
+                           make_embed_transform(make_tuple(I1, Wo), make_tuple(I1, ConvStrideW)),
+                           make_pass_through_transform(C)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
+                make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3>{}));
+
+            const auto in_gemmm_gemmn_grid_desc = transform_tensor_descriptor(
+                in_n_x_wo_c_grid_desc,
+                make_tuple(make_freeze_transform(I0),
+                           make_merge_transform(make_tuple(N, Wo)),
+                           make_pass_through_transform(C)),
+                make_tuple(Sequence<1>{}, Sequence<0, 2>{}, Sequence<3>{}),
+                make_tuple(Sequence<>{}, Sequence<0>{}, Sequence<1>{}));
+
+            return make_tuple(out_gemmk0_gemmm_gemmk1_grid_desc,
+                              wei_gemmk0_gemmn_gemmk1_grid_desc,
+                              in_gemmm_gemmn_grid_desc);
+        }
+        else
+        {
+            const auto out_n_wo_k_grid_desc =
+                make_naive_tensor_descriptor_packed(make_tuple(N, Wo, K));
+            const auto wei_k_x_c_grid_desc =
+                make_naive_tensor_descriptor_packed(make_tuple(K, X, C));
+
+            const auto GcdStrideDilationW = math::gcd(ConvStrideW, ConvDilationW);
+
+            const auto XTilde = ConvStrideW / GcdStrideDilationW;
+
+            const auto XDot = math::integer_divide_ceil(X, XTilde);
+
+            const auto WTilde =
+                Wo + math::integer_divide_ceil(ConvDilationW * (X - I1), ConvStrideW);
+
+            // only work on HTilde and WTilde that contribute to non-padding area of input tensor
+            const auto IWTildeSliceBegin = math::integer_divide_floor(
+                math::max(I0, InLeftPadW - ConvDilationW * (XTilde - I1)), ConvStrideW);
+
+            const auto IWTildeSliceEnd = math::min(
+                WTilde, math::integer_divide_ceil(InLeftPadW + Wi - I1, ConvStrideW) + I1);
+
+            const auto WTildeSlice = IWTildeSliceEnd - IWTildeSliceBegin;
+
+            // GemmK is different for each GEMM
+            const auto XDotSlice = math::integer_divide_ceil(X - i_xtilde, XTilde);
+
+            // A: output tensor
+            const auto out_n_wop_k_grid_desc = transform_tensor_descriptor(
+                out_n_wo_k_grid_desc,
+                make_tuple(make_pass_through_transform(N),
+                           make_pad_transform(Wo, I0, I0),
+                           make_pass_through_transform(K)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
+
+            const auto out_n_xdot_wtilde_k_grid_desc = transform_tensor_descriptor(
+                out_n_wop_k_grid_desc,
+                make_tuple(
+                    make_pass_through_transform(N),
+                    make_embed_transform(make_tuple(XDot, WTilde),
+                                         make_tuple(-ConvDilationW / GcdStrideDilationW, I1)),
+                    make_pass_through_transform(K)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
+                make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3>{}));
+
+            const auto out_n_xdotslice_wtildeslice_k0_k1_grid_desc = transform_tensor_descriptor(
+                out_n_xdot_wtilde_k_grid_desc,
+                make_tuple(make_pass_through_transform(N),
+                           make_slice_transform(XDot, I0, XDotSlice),
+                           make_slice_transform(WTilde, IWTildeSliceBegin, WTildeSlice),
+                           make_unmerge_transform(make_tuple(K0, K1))),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3, 4>{}));
+
+            const auto out_gemmk0_gemmm_gemmk1_grid_desc = transform_tensor_descriptor(
+                out_n_xdotslice_wtildeslice_k0_k1_grid_desc,
+                make_tuple(make_merge_transform(make_tuple(XDotSlice, K0)),
+                           make_merge_transform(make_tuple(N, WTildeSlice)),
+                           make_pass_through_transform(K1)),
+                make_tuple(Sequence<1, 3>{}, Sequence<0, 2>{}, Sequence<4>{}),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
+
+            // B weight tensor
+            const auto wei_k_xdot_xtilde_c_grid_desc = transform_tensor_descriptor(
+                wei_k_x_c_grid_desc,
+                make_tuple(make_pass_through_transform(K),
+                           make_embed_transform(make_tuple(XDot, XTilde),
+                                                make_tuple(ConvStrideW / GcdStrideDilationW, I1)),
+                           make_pass_through_transform(C)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
+                make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3>{}));
+
+            const auto wei_k0_k1_xdotslice_c_grid_desc = transform_tensor_descriptor(
+                wei_k_xdot_xtilde_c_grid_desc,
+                make_tuple(make_unmerge_transform(make_tuple(K0, K1)),
+                           make_slice_transform(XDot, I0, XDotSlice),
+                           make_freeze_transform(i_xtilde),
+                           make_pass_through_transform(C)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
+                make_tuple(Sequence<0, 1>{}, Sequence<2>{}, Sequence<>{}, Sequence<3>{}));
+
+            const auto wei_gemmk0_gemmn_gemmk1_grid_desc = transform_tensor_descriptor(
+                wei_k0_k1_xdotslice_c_grid_desc,
+                make_tuple(make_merge_transform(make_tuple(XDotSlice, K0)),
+                           make_pass_through_transform(C),
+                           make_pass_through_transform(K1)),
+                make_tuple(Sequence<2, 0>{}, Sequence<3>{}, Sequence<1>{}),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
+
+            // C: input tensor
+            const auto in_n_wip_c_grid_desc = transform_tensor_descriptor(
+                in_n_wi_c_grid_desc,
+                make_tuple(make_pass_through_transform(N),
+                           make_pad_transform(Wi, InLeftPadW, InRightPadW),
+                           make_pass_through_transform(C)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
+
+            const auto in_n_xtilde_wtilde_c_grid_desc = transform_tensor_descriptor(
+                in_n_wip_c_grid_desc,
+                make_tuple(make_pass_through_transform(N),
+                           make_embed_transform(make_tuple(XTilde, WTilde),
+                                                make_tuple(ConvDilationW, ConvStrideW)),
+                           make_pass_through_transform(C)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
+                make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3>{}));
+
+            const auto in_n_wtildeslice_c_grid_desc = transform_tensor_descriptor(
+                in_n_xtilde_wtilde_c_grid_desc,
+                make_tuple(make_pass_through_transform(N),
+                           make_freeze_transform(i_xtilde),
+                           make_slice_transform(WTilde, IWTildeSliceBegin, WTildeSlice),
+                           make_pass_through_transform(C)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
+                make_tuple(Sequence<0>{}, Sequence<>{}, Sequence<1>{}, Sequence<2>{}));
+
+            const auto in_gemmm_gemmn_grid_desc = transform_tensor_descriptor(
+                in_n_wtildeslice_c_grid_desc,
+                make_tuple(make_merge_transform(make_tuple(N, WTildeSlice)),
+                           make_pass_through_transform(C)),
+                make_tuple(Sequence<0, 1>{}, Sequence<2>{}),
+                make_tuple(Sequence<0>{}, Sequence<1>{}));
+
+            return make_tuple(out_gemmk0_gemmm_gemmk1_grid_desc,
+                              wei_gemmk0_gemmn_gemmk1_grid_desc,
+                              in_gemmm_gemmn_grid_desc);
+        }
+
+    } // function end
+    template <ck::index_t NDim, typename ck::enable_if<NDim == 2, bool>::type = false>
+    static auto
+    MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N(ck::index_t N,
+                                                    ck::index_t K,
+                                                    ck::index_t C,
+                                                    std::vector<ck::index_t> input_spatial_lengths,
+                                                    std::vector<ck::index_t> filter_spatial_lengths,
+                                                    std::vector<ck::index_t> output_spatial_lengths,
+                                                    std::vector<ck::index_t> conv_filter_strides,
+                                                    std::vector<ck::index_t> conv_filter_dilations,
+                                                    std::vector<ck::index_t> input_left_pads,
+                                                    std::vector<ck::index_t> input_right_pads,
+                                                    std::vector<ck::index_t> tildes)
+    {
+        using namespace ck;
+
+        index_t i_ytilde = tildes[0];
+        index_t i_xtilde = tildes[1];
+
+        const index_t Hi = input_spatial_lengths[0];
+        const index_t Wi = input_spatial_lengths[1];
+
+        const index_t Ho = output_spatial_lengths[0];
+        const index_t Wo = output_spatial_lengths[1];
+
+        const index_t Y = filter_spatial_lengths[0];
+        const index_t X = filter_spatial_lengths[1];
+
+        const index_t InLeftPadH = input_left_pads[0];
+        const index_t InLeftPadW = input_left_pads[1];
+
+        const index_t InRightPadH = input_right_pads[0];
+        const index_t InRightPadW = input_right_pads[1];
+
+        const index_t ConvStrideH = conv_filter_strides[0];
+        const index_t ConvStrideW = conv_filter_strides[1];
+
+        const index_t ConvDilationH = conv_filter_dilations[0];
+        const index_t ConvDilationW = conv_filter_dilations[1];
+
+        const auto K0 = K / K1;
+
+        const auto out_n_ho_wo_k_grid_desc =
+            make_naive_tensor_descriptor_packed(make_tuple(N, Ho, Wo, K));
+        const auto wei_k_y_x_c_grid_desc =
+            make_naive_tensor_descriptor_packed(make_tuple(K, Y, X, C));
+        const auto in_n_hi_wi_c_grid_desc =
+            make_naive_tensor_descriptor_packed(make_tuple(N, Hi, Wi, C));
+
+        if constexpr(ConvBackwardDataSpecialization ==
+                     ConvolutionBackwardDataSpecialization::Filter1x1Stride1Pad0)
+        {
+            // A: output tensor
+            const auto out_gemmk0_gemmm_gemmk1_grid_desc = transform_tensor_descriptor(
+                make_naive_tensor_descriptor_packed(make_tuple(N * Ho * Wo, K)),
+                make_tuple(make_pass_through_transform(N * Ho * Wo),
+                           make_unmerge_transform(make_tuple(K0, K1))),
+                make_tuple(Sequence<0>{}, Sequence<1>{}),
+                make_tuple(Sequence<1>{}, Sequence<0, 2>{}));
+
+            // B: weight tensor
+            const auto wei_gemmk0_gemmn_gemmk1_grid_desc =
+                transform_tensor_descriptor(make_naive_tensor_descriptor_packed(make_tuple(K, C)),
+                                            make_tuple(make_unmerge_transform(make_tuple(K0, K1)),
+                                                       make_pass_through_transform(C)),
+                                            make_tuple(Sequence<0>{}, Sequence<1>{}),
+                                            make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
+
+            // C: input tensor
+            const auto in_n_y_ho_x_wo_c_grid_desc = transform_tensor_descriptor(
+                in_n_hi_wi_c_grid_desc,
+                make_tuple(make_pass_through_transform(N),
+                           make_embed_transform(make_tuple(I1, Ho), make_tuple(I1, ConvStrideH)),
+                           make_embed_transform(make_tuple(I1, Wo), make_tuple(I1, ConvStrideW)),
+                           make_pass_through_transform(C)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
+                make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}));
+
+            const auto in_gemmm_gemmn_grid_desc = transform_tensor_descriptor(
+                in_n_y_ho_x_wo_c_grid_desc,
+                make_tuple(make_freeze_transform(I0),
+                           make_freeze_transform(I0),
+                           make_merge_transform(make_tuple(N, Ho, Wo)),
+                           make_pass_through_transform(C)),
+                make_tuple(Sequence<1>{}, Sequence<3>{}, Sequence<0, 2, 4>{}, Sequence<5>{}),
+                make_tuple(Sequence<>{}, Sequence<>{}, Sequence<0>{}, Sequence<1>{}));
+
+            return make_tuple(out_gemmk0_gemmm_gemmk1_grid_desc,
+                              wei_gemmk0_gemmn_gemmk1_grid_desc,
+                              in_gemmm_gemmn_grid_desc);
+        }
+        else
+        {
+            const auto GcdStrideDilationH = math::gcd(ConvStrideH, ConvDilationH);
+            const auto GcdStrideDilationW = math::gcd(ConvStrideW, ConvDilationW);
+
+            const auto YTilde = ConvStrideH / GcdStrideDilationH;
+            const auto XTilde = ConvStrideW / GcdStrideDilationW;
+
+            const auto YDot = math::integer_divide_ceil(Y, YTilde);
+            const auto XDot = math::integer_divide_ceil(X, XTilde);
+
+            const auto HTilde =
+                Ho + math::integer_divide_ceil(ConvDilationH * (Y - I1), ConvStrideH);
+            const auto WTilde =
+                Wo + math::integer_divide_ceil(ConvDilationW * (X - I1), ConvStrideW);
+
+            // only work on HTilde and WTilde that contribute to non-padding area of input tensor
+            const auto IHTildeSliceBegin = math::integer_divide_floor(
+                math::max(I0, InLeftPadH - ConvDilationH * (YTilde - I1)), ConvStrideH);
+            const auto IWTildeSliceBegin = math::integer_divide_floor(
+                math::max(I0, InLeftPadW - ConvDilationW * (XTilde - I1)), ConvStrideW);
+
+            const auto IHTildeSliceEnd = math::min(
+                HTilde, math::integer_divide_ceil(InLeftPadH + Hi - I1, ConvStrideH) + I1);
+            const auto IWTildeSliceEnd = math::min(
+                WTilde, math::integer_divide_ceil(InLeftPadW + Wi - I1, ConvStrideW) + I1);
+
+            const auto HTildeSlice = IHTildeSliceEnd - IHTildeSliceBegin;
+            const auto WTildeSlice = IWTildeSliceEnd - IWTildeSliceBegin;
+
+            // GemmK is different for each GEMM
+            const auto YDotSlice = math::integer_divide_ceil(Y - i_ytilde, YTilde);
+            const auto XDotSlice = math::integer_divide_ceil(X - i_xtilde, XTilde);
+
+            // A: output tensor
+            const auto out_n_hop_wop_k_grid_desc = transform_tensor_descriptor(
+                out_n_ho_wo_k_grid_desc,
+                make_tuple(make_pass_through_transform(N),
+                           make_pad_transform(Ho, I0, I0),
+                           make_pad_transform(Wo, I0, I0),
+                           make_pass_through_transform(K)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
+
+            const auto out_n_ydot_htilde_xdot_wtilde_k_grid_desc = transform_tensor_descriptor(
+                out_n_hop_wop_k_grid_desc,
+                make_tuple(
+                    make_pass_through_transform(N),
+                    make_embed_transform(make_tuple(YDot, HTilde),
+                                         make_tuple(-ConvDilationH / GcdStrideDilationH, I1)),
+                    make_embed_transform(make_tuple(XDot, WTilde),
+                                         make_tuple(-ConvDilationW / GcdStrideDilationW, I1)),
+                    make_pass_through_transform(K)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
+                make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}));
+
+            const auto out_n_ydotslice_htildeslice_xdotslice_wtildeslice_k0_k1_grid_desc =
+                transform_tensor_descriptor(
+                    out_n_ydot_htilde_xdot_wtilde_k_grid_desc,
+                    make_tuple(make_pass_through_transform(N),
+                               make_slice_transform(YDot, I0, YDotSlice),
+                               make_slice_transform(HTilde, IHTildeSliceBegin, HTildeSlice),
+                               make_slice_transform(XDot, I0, XDotSlice),
+                               make_slice_transform(WTilde, IWTildeSliceBegin, WTildeSlice),
+                               make_unmerge_transform(make_tuple(K0, K1))),
+                    make_tuple(Sequence<0>{},
+                               Sequence<1>{},
+                               Sequence<2>{},
+                               Sequence<3>{},
+                               Sequence<4>{},
+                               Sequence<5>{}),
+                    make_tuple(Sequence<0>{},
+                               Sequence<1>{},
+                               Sequence<2>{},
+                               Sequence<3>{},
+                               Sequence<4>{},
+                               Sequence<5, 6>{}));
+
+            const auto out_gemmk0_gemmm_gemmk1_grid_desc = transform_tensor_descriptor(
+                out_n_ydotslice_htildeslice_xdotslice_wtildeslice_k0_k1_grid_desc,
+                make_tuple(make_merge_transform(make_tuple(YDotSlice, XDotSlice, K0)),
+                           make_merge_transform(make_tuple(N, HTildeSlice, WTildeSlice)),
+                           make_pass_through_transform(K1)),
+                make_tuple(Sequence<1, 3, 5>{}, Sequence<0, 2, 4>{}, Sequence<6>{}),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
+
+            // B weight tensor
+            const auto wei_k_ydot_ytilde_xdot_xtilde_c_grid_desc = transform_tensor_descriptor(
+                wei_k_y_x_c_grid_desc,
+                make_tuple(make_pass_through_transform(K),
+                           make_embed_transform(make_tuple(YDot, YTilde),
+                                                make_tuple(ConvStrideH / GcdStrideDilationH, I1)),
+                           make_embed_transform(make_tuple(XDot, XTilde),
+                                                make_tuple(ConvStrideW / GcdStrideDilationW, I1)),
+                           make_pass_through_transform(C)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
+                make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}));
+
+            const auto wei_k0_k1_ydotslice_xdotslice_c_grid_desc =
+                transform_tensor_descriptor(wei_k_ydot_ytilde_xdot_xtilde_c_grid_desc,
+                                            make_tuple(make_unmerge_transform(make_tuple(K0, K1)),
+                                                       make_slice_transform(YDot, I0, YDotSlice),
+                                                       make_slice_transform(XDot, I0, XDotSlice),
+                                                       make_freeze_transform(i_ytilde),
+                                                       make_freeze_transform(i_xtilde),
+                                                       make_pass_through_transform(C)),
+                                            make_tuple(Sequence<0>{},
+                                                       Sequence<1>{},
+                                                       Sequence<3>{},
+                                                       Sequence<2>{},
+                                                       Sequence<4>{},
+                                                       Sequence<5>{}),
+                                            make_tuple(Sequence<0, 1>{},
+                                                       Sequence<2>{},
+                                                       Sequence<3>{},
+                                                       Sequence<>{},
+                                                       Sequence<>{},
+                                                       Sequence<4>{}));
+
+            const auto wei_gemmk0_gemmn_gemmk1_grid_desc = transform_tensor_descriptor(
+                wei_k0_k1_ydotslice_xdotslice_c_grid_desc,
+                make_tuple(make_merge_transform(make_tuple(YDotSlice, XDotSlice, K0)),
+                           make_pass_through_transform(C),
+                           make_pass_through_transform(K1)),
+                make_tuple(Sequence<2, 3, 0>{}, Sequence<4>{}, Sequence<1>{}),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
+
+            // C: input tensor
+            const auto in_n_hip_wip_c_grid_desc = transform_tensor_descriptor(
+                in_n_hi_wi_c_grid_desc,
+                make_tuple(make_pass_through_transform(N),
+                           make_pad_transform(Hi, InLeftPadH, InRightPadH),
+                           make_pad_transform(Wi, InLeftPadW, InRightPadW),
+                           make_pass_through_transform(C)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
+
+            const auto in_n_ytilde_htilde_xtilde_wtilde_c_grid_desc = transform_tensor_descriptor(
+                in_n_hip_wip_c_grid_desc,
+                make_tuple(make_pass_through_transform(N),
+                           make_embed_transform(make_tuple(YTilde, HTilde),
+                                                make_tuple(ConvDilationH, ConvStrideH)),
+                           make_embed_transform(make_tuple(XTilde, WTilde),
+                                                make_tuple(ConvDilationW, ConvStrideW)),
+                           make_pass_through_transform(C)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
+                make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}));
+
+            const auto in_n_htildeslice_wtildeslice_c_grid_desc = transform_tensor_descriptor(
+                in_n_ytilde_htilde_xtilde_wtilde_c_grid_desc,
+                make_tuple(make_pass_through_transform(N),
+                           make_freeze_transform(i_ytilde),
+                           make_slice_transform(HTilde, IHTildeSliceBegin, HTildeSlice),
+                           make_freeze_transform(i_xtilde),
+                           make_slice_transform(WTilde, IWTildeSliceBegin, WTildeSlice),
+                           make_pass_through_transform(C)),
+                make_tuple(Sequence<0>{},
+                           Sequence<1>{},
+                           Sequence<2>{},
+                           Sequence<3>{},
+                           Sequence<4>{},
+                           Sequence<5>{}),
+                make_tuple(Sequence<0>{},
+                           Sequence<>{},
+                           Sequence<1>{},
+                           Sequence<>{},
+                           Sequence<2>{},
+                           Sequence<3>{}));
+
+            const auto in_gemmm_gemmn_grid_desc = transform_tensor_descriptor(
+                in_n_htildeslice_wtildeslice_c_grid_desc,
+                make_tuple(make_merge_transform(make_tuple(N, HTildeSlice, WTildeSlice)),
+                           make_pass_through_transform(C)),
+                make_tuple(Sequence<0, 1, 2>{}, Sequence<3>{}),
+                make_tuple(Sequence<0>{}, Sequence<1>{}));
+
+            return make_tuple(out_gemmk0_gemmm_gemmk1_grid_desc,
+                              wei_gemmk0_gemmn_gemmk1_grid_desc,
+                              in_gemmm_gemmn_grid_desc);
+        }
+
+    } // function end
+
+    template <ck::index_t NDim, typename ck::enable_if<NDim == 3, bool>::type = false>
+    static auto
+    MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N(ck::index_t N,
+                                                    ck::index_t K,
+                                                    ck::index_t C,
+                                                    std::vector<ck::index_t> input_spatial_lengths,
+                                                    std::vector<ck::index_t> filter_spatial_lengths,
+                                                    std::vector<ck::index_t> output_spatial_lengths,
+                                                    std::vector<ck::index_t> conv_filter_strides,
+                                                    std::vector<ck::index_t> conv_filter_dilations,
+                                                    std::vector<ck::index_t> input_left_pads,
+                                                    std::vector<ck::index_t> input_right_pads,
+                                                    std::vector<ck::index_t> tildes)
+    {
+        using namespace ck;
+
+        const index_t i_ztilde = tildes[0];
+        const index_t i_ytilde = tildes[1];
+        const index_t i_xtilde = tildes[2];
+
+        const index_t Di = input_spatial_lengths[0];
+        const index_t Hi = input_spatial_lengths[1];
+        const index_t Wi = input_spatial_lengths[2];
+
+        const index_t Do = output_spatial_lengths[0];
+        const index_t Ho = output_spatial_lengths[1];
+        const index_t Wo = output_spatial_lengths[2];
+
+        const index_t Z = filter_spatial_lengths[0];
+        const index_t Y = filter_spatial_lengths[1];
+        const index_t X = filter_spatial_lengths[2];
+
+        const index_t InLeftPadD = input_left_pads[0];
+        const index_t InLeftPadH = input_left_pads[1];
+        const index_t InLeftPadW = input_left_pads[2];
+
+        const index_t InRightPadD = input_right_pads[0];
+        const index_t InRightPadH = input_right_pads[1];
+        const index_t InRightPadW = input_right_pads[2];
+
+        const index_t ConvStrideD = conv_filter_strides[0];
+        const index_t ConvStrideH = conv_filter_strides[1];
+        const index_t ConvStrideW = conv_filter_strides[2];
+
+        const index_t ConvDilationD = conv_filter_dilations[0];
+        const index_t ConvDilationH = conv_filter_dilations[1];
+        const index_t ConvDilationW = conv_filter_dilations[2];
+
+        const auto K0 = K / K1;
+
+        const auto out_n_do_ho_wo_k_grid_desc =
+            make_naive_tensor_descriptor_packed(make_tuple(N, Do, Ho, Wo, K));
+        const auto wei_k_z_y_x_c_grid_desc =
+            make_naive_tensor_descriptor_packed(make_tuple(K, Z, Y, X, C));
+        const auto in_n_di_hi_wi_c_grid_desc =
+            make_naive_tensor_descriptor_packed(make_tuple(N, Di, Hi, Wi, C));
+
+        if constexpr(ConvBackwardDataSpecialization ==
+                     ConvolutionBackwardDataSpecialization::Filter1x1Stride1Pad0)
+        {
+            // A: output tensor
+            const auto out_gemmk0_gemmm_gemmk1_grid_desc = transform_tensor_descriptor(
+                make_naive_tensor_descriptor_packed(make_tuple(N * Do * Ho * Wo, K)),
+                make_tuple(make_pass_through_transform(N * Do * Ho * Wo),
+                           make_unmerge_transform(make_tuple(K0, K1))),
+                make_tuple(Sequence<0>{}, Sequence<1>{}),
+                make_tuple(Sequence<1>{}, Sequence<0, 2>{}));
+
+            // B: weight tensor
+            const auto wei_gemmk0_gemmn_gemmk1_grid_desc =
+                transform_tensor_descriptor(make_naive_tensor_descriptor_packed(make_tuple(K, C)),
+                                            make_tuple(make_unmerge_transform(make_tuple(K0, K1)),
+                                                       make_pass_through_transform(C)),
+                                            make_tuple(Sequence<0>{}, Sequence<1>{}),
+                                            make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
+
+            // C: input tensor
+            const auto in_n_z_do_y_ho_x_wo_c_grid_desc = transform_tensor_descriptor(
+                in_n_di_hi_wi_c_grid_desc,
+                make_tuple(make_pass_through_transform(N),
+                           make_embed_transform(make_tuple(I1, Do), make_tuple(I1, ConvStrideD)),
+                           make_embed_transform(make_tuple(I1, Ho), make_tuple(I1, ConvStrideH)),
+                           make_embed_transform(make_tuple(I1, Wo), make_tuple(I1, ConvStrideW)),
+                           make_pass_through_transform(C)),
+                make_tuple(
+                    Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}, Sequence<4>{}),
+                make_tuple(Sequence<0>{},
+                           Sequence<1, 2>{},
+                           Sequence<3, 4>{},
+                           Sequence<5, 6>{},
+                           Sequence<7>{}));
+
+            const auto in_gemmm_gemmn_grid_desc = transform_tensor_descriptor(
+                in_n_z_do_y_ho_x_wo_c_grid_desc,
+                make_tuple(make_freeze_transform(I0),
+                           make_freeze_transform(I0),
+                           make_freeze_transform(I0),
+                           make_merge_transform(make_tuple(N, Do, Ho, Wo)),
+                           make_pass_through_transform(C)),
+                make_tuple(Sequence<1>{},
+                           Sequence<3>{},
+                           Sequence<5>{},
+                           Sequence<0, 2, 4, 6>{},
+                           Sequence<7>{}),
+                make_tuple(Sequence<>{}, Sequence<>{}, Sequence<>{}, Sequence<0>{}, Sequence<1>{}));
+
+            return make_tuple(out_gemmk0_gemmm_gemmk1_grid_desc,
+                              wei_gemmk0_gemmn_gemmk1_grid_desc,
+                              in_gemmm_gemmn_grid_desc);
+        }
+        else
+        {
+            const auto GcdStrideDilationD = math::gcd(ConvStrideD, ConvDilationD);
+            const auto GcdStrideDilationH = math::gcd(ConvStrideH, ConvDilationH);
+            const auto GcdStrideDilationW = math::gcd(ConvStrideW, ConvDilationW);
+
+            const auto ZTilde = ConvStrideD / GcdStrideDilationD;
+            const auto YTilde = ConvStrideH / GcdStrideDilationH;
+            const auto XTilde = ConvStrideW / GcdStrideDilationW;
+
+            const auto ZDot = math::integer_divide_ceil(Z, ZTilde);
+            const auto YDot = math::integer_divide_ceil(Y, YTilde);
+            const auto XDot = math::integer_divide_ceil(X, XTilde);
+
+            const auto DTilde =
+                Do + math::integer_divide_ceil(ConvDilationD * (Z - I1), ConvStrideD);
+            const auto HTilde =
+                Ho + math::integer_divide_ceil(ConvDilationH * (Y - I1), ConvStrideH);
+            const auto WTilde =
+                Wo + math::integer_divide_ceil(ConvDilationW * (X - I1), ConvStrideW);
+
+            // only work on HTilde and WTilde that contribute to non-padding area of input tensor
+            const auto IDTildeSliceBegin = math::integer_divide_floor(
+                math::max(I0, InLeftPadD - ConvDilationD * (ZTilde - I1)), ConvStrideD);
+            const auto IHTildeSliceBegin = math::integer_divide_floor(
+                math::max(I0, InLeftPadH - ConvDilationH * (YTilde - I1)), ConvStrideH);
+            const auto IWTildeSliceBegin = math::integer_divide_floor(
+                math::max(I0, InLeftPadW - ConvDilationW * (XTilde - I1)), ConvStrideW);
+
+            const auto IDTildeSliceEnd = math::min(
+                DTilde, math::integer_divide_ceil(InLeftPadD + Di - I1, ConvStrideD) + I1);
+            const auto IHTildeSliceEnd = math::min(
+                HTilde, math::integer_divide_ceil(InLeftPadH + Hi - I1, ConvStrideH) + I1);
+            const auto IWTildeSliceEnd = math::min(
+                WTilde, math::integer_divide_ceil(InLeftPadW + Wi - I1, ConvStrideW) + I1);
+
+            const auto DTildeSlice = IDTildeSliceEnd - IDTildeSliceBegin;
+            const auto HTildeSlice = IHTildeSliceEnd - IHTildeSliceBegin;
+            const auto WTildeSlice = IWTildeSliceEnd - IWTildeSliceBegin;
+
+            // GemmK is different for each GEMM
+            const auto ZDotSlice = math::integer_divide_ceil(Z - i_ztilde, ZTilde);
+            const auto YDotSlice = math::integer_divide_ceil(Y - i_ytilde, YTilde);
+            const auto XDotSlice = math::integer_divide_ceil(X - i_xtilde, XTilde);
+
+            // A: output tensor
+            const auto out_n_dop_hop_wop_k_grid_desc = transform_tensor_descriptor(
+                out_n_do_ho_wo_k_grid_desc,
+                make_tuple(make_pass_through_transform(N),
+                           make_pad_transform(Do, I0, I0),
+                           make_pad_transform(Ho, I0, I0),
+                           make_pad_transform(Wo, I0, I0),
+                           make_pass_through_transform(K)),
+                make_tuple(
+                    Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}, Sequence<4>{}),
+                make_tuple(
+                    Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}, Sequence<4>{}));
+
+            const auto out_n_zdot_dtilde_ydot_htilde_xdot_wtilde_k_grid_desc =
+                transform_tensor_descriptor(
+                    out_n_dop_hop_wop_k_grid_desc,
+                    make_tuple(
+                        make_pass_through_transform(N),
+                        make_embed_transform(make_tuple(ZDot, DTilde),
+                                             make_tuple(-ConvDilationD / GcdStrideDilationD, I1)),
+                        make_embed_transform(make_tuple(YDot, HTilde),
+                                             make_tuple(-ConvDilationH / GcdStrideDilationH, I1)),
+                        make_embed_transform(make_tuple(XDot, WTilde),
+                                             make_tuple(-ConvDilationW / GcdStrideDilationW, I1)),
+                        make_pass_through_transform(K)),
+                    make_tuple(
+                        Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}, Sequence<4>{}),
+                    make_tuple(Sequence<0>{},
+                               Sequence<1, 2>{},
+                               Sequence<3, 4>{},
+                               Sequence<5, 6>{},
+                               Sequence<7>{}));
+
+            const auto
+                out_n_zdotslice_dtildeslice_ydotslice_htildeslice_xdotslice_wtildeslice_k0_k1_grid_desc =
+                    transform_tensor_descriptor(
+                        out_n_zdot_dtilde_ydot_htilde_xdot_wtilde_k_grid_desc,
+                        make_tuple(make_pass_through_transform(N),
+                                   make_slice_transform(ZDot, I0, ZDotSlice),
+                                   make_slice_transform(DTilde, IDTildeSliceBegin, DTildeSlice),
+                                   make_slice_transform(YDot, I0, YDotSlice),
+                                   make_slice_transform(HTilde, IHTildeSliceBegin, HTildeSlice),
+                                   make_slice_transform(XDot, I0, XDotSlice),
+                                   make_slice_transform(WTilde, IWTildeSliceBegin, WTildeSlice),
+                                   make_unmerge_transform(make_tuple(K0, K1))),
+                        make_tuple(Sequence<0>{},
+                                   Sequence<1>{},
+                                   Sequence<2>{},
+                                   Sequence<3>{},
+                                   Sequence<4>{},
+                                   Sequence<5>{},
+                                   Sequence<6>{},
+                                   Sequence<7>{}),
+                        make_tuple(Sequence<0>{},
+                                   Sequence<1>{},
+                                   Sequence<2>{},
+                                   Sequence<3>{},
+                                   Sequence<4>{},
+                                   Sequence<5>{},
+                                   Sequence<6>{},
+                                   Sequence<7, 8>{}));
+
+            const auto out_gemmk0_gemmm_gemmk1_grid_desc = transform_tensor_descriptor(
+                out_n_zdotslice_dtildeslice_ydotslice_htildeslice_xdotslice_wtildeslice_k0_k1_grid_desc,
+                make_tuple(
+                    make_merge_transform(make_tuple(ZDotSlice, YDotSlice, XDotSlice, K0)),
+                    make_merge_transform(make_tuple(N, DTildeSlice, HTildeSlice, WTildeSlice)),
+                    make_pass_through_transform(K1)),
+                make_tuple(Sequence<1, 3, 5, 7>{}, Sequence<0, 2, 4, 6>{}, Sequence<8>{}),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
+
+            // B weight tensor
+            const auto wei_k_zdot_ztilde_ydot_ytilde_xdot_xtilde_c_grid_desc =
+                transform_tensor_descriptor(
+                    wei_k_z_y_x_c_grid_desc,
+                    make_tuple(
+                        make_pass_through_transform(K),
+                        make_embed_transform(make_tuple(ZDot, ZTilde),
+                                             make_tuple(ConvStrideD / GcdStrideDilationD, I1)),
+                        make_embed_transform(make_tuple(YDot, YTilde),
+                                             make_tuple(ConvStrideH / GcdStrideDilationH, I1)),
+                        make_embed_transform(make_tuple(XDot, XTilde),
+                                             make_tuple(ConvStrideW / GcdStrideDilationW, I1)),
+                        make_pass_through_transform(C)),
+                    make_tuple(
+                        Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}, Sequence<4>{}),
+                    make_tuple(Sequence<0>{},
+                               Sequence<1, 2>{},
+                               Sequence<3, 4>{},
+                               Sequence<5, 6>{},
+                               Sequence<7>{}));
+
+            const auto wei_k0_k1_zdotslice_ydotslice_xdotslice_c_grid_desc =
+                transform_tensor_descriptor(wei_k_zdot_ztilde_ydot_ytilde_xdot_xtilde_c_grid_desc,
+                                            make_tuple(make_unmerge_transform(make_tuple(K0, K1)),
+                                                       make_slice_transform(ZDot, I0, ZDotSlice),
+                                                       make_slice_transform(YDot, I0, YDotSlice),
+                                                       make_slice_transform(XDot, I0, XDotSlice),
+                                                       make_freeze_transform(i_ztilde),
+                                                       make_freeze_transform(i_ytilde),
+                                                       make_freeze_transform(i_xtilde),
+                                                       make_pass_through_transform(C)),
+                                            make_tuple(Sequence<0>{},
+                                                       Sequence<1>{},
+                                                       Sequence<3>{},
+                                                       Sequence<5>{},
+                                                       Sequence<2>{},
+                                                       Sequence<4>{},
+                                                       Sequence<6>{},
+                                                       Sequence<7>{}),
+                                            make_tuple(Sequence<0, 1>{},
+                                                       Sequence<2>{},
+                                                       Sequence<3>{},
+                                                       Sequence<4>{},
+                                                       Sequence<>{},
+                                                       Sequence<>{},
+                                                       Sequence<>{},
+                                                       Sequence<5>{}));
+
+            const auto wei_gemmk0_gemmn_gemmk1_grid_desc = transform_tensor_descriptor(
+                wei_k0_k1_zdotslice_ydotslice_xdotslice_c_grid_desc,
+                make_tuple(make_merge_transform(make_tuple(ZDotSlice, YDotSlice, XDotSlice, K0)),
+                           make_pass_through_transform(C),
+                           make_pass_through_transform(K1)),
+                make_tuple(Sequence<2, 3, 4, 0>{}, Sequence<5>{}, Sequence<1>{}),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
+
+            // C: input tensor
+            const auto in_n_dip_hip_wip_c_grid_desc = transform_tensor_descriptor(
+                in_n_di_hi_wi_c_grid_desc,
+                make_tuple(make_pass_through_transform(N),
+                           make_pad_transform(Di, InLeftPadD, InRightPadD),
+                           make_pad_transform(Hi, InLeftPadH, InRightPadH),
+                           make_pad_transform(Wi, InLeftPadW, InRightPadW),
+                           make_pass_through_transform(C)),
+                make_tuple(
+                    Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}, Sequence<4>{}),
+                make_tuple(
+                    Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}, Sequence<4>{}));
+
+            const auto in_n_ztilde_dtilde_ytilde_htilde_xtilde_wtilde_c_grid_desc =
+                transform_tensor_descriptor(
+                    in_n_dip_hip_wip_c_grid_desc,
+                    make_tuple(make_pass_through_transform(N),
+                               make_embed_transform(make_tuple(ZTilde, DTilde),
+                                                    make_tuple(ConvDilationD, ConvStrideD)),
+                               make_embed_transform(make_tuple(YTilde, HTilde),
+                                                    make_tuple(ConvDilationH, ConvStrideH)),
+                               make_embed_transform(make_tuple(XTilde, WTilde),
+                                                    make_tuple(ConvDilationW, ConvStrideW)),
+                               make_pass_through_transform(C)),
+                    make_tuple(
+                        Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}, Sequence<4>{}),
+                    make_tuple(Sequence<0>{},
+                               Sequence<1, 2>{},
+                               Sequence<3, 4>{},
+                               Sequence<5, 6>{},
+                               Sequence<7>{}));
+
+            const auto in_n_dtildeslice_htildeslice_wtildeslice_c_grid_desc =
+                transform_tensor_descriptor(
+                    in_n_ztilde_dtilde_ytilde_htilde_xtilde_wtilde_c_grid_desc,
+                    make_tuple(make_pass_through_transform(N),
+                               make_freeze_transform(i_ztilde),
+                               make_slice_transform(DTilde, IDTildeSliceBegin, DTildeSlice),
+                               make_freeze_transform(i_ytilde),
+                               make_slice_transform(HTilde, IHTildeSliceBegin, HTildeSlice),
+                               make_freeze_transform(i_xtilde),
+                               make_slice_transform(WTilde, IWTildeSliceBegin, WTildeSlice),
+                               make_pass_through_transform(C)),
+                    make_tuple(Sequence<0>{},
+                               Sequence<1>{},
+                               Sequence<2>{},
+                               Sequence<3>{},
+                               Sequence<4>{},
+                               Sequence<5>{},
+                               Sequence<6>{},
+                               Sequence<7>{}),
+                    make_tuple(Sequence<0>{},
+                               Sequence<>{},
+                               Sequence<1>{},
+                               Sequence<>{},
+                               Sequence<2>{},
+                               Sequence<>{},
+                               Sequence<3>{},
+                               Sequence<4>{}));
+
+            const auto in_gemmm_gemmn_grid_desc = transform_tensor_descriptor(
+                in_n_dtildeslice_htildeslice_wtildeslice_c_grid_desc,
+                make_tuple(
+                    make_merge_transform(make_tuple(N, DTildeSlice, HTildeSlice, WTildeSlice)),
+                    make_pass_through_transform(C)),
+                make_tuple(Sequence<0, 1, 2, 3>{}, Sequence<4>{}),
+                make_tuple(Sequence<0>{}, Sequence<1>{}));
+
+            return make_tuple(out_gemmk0_gemmm_gemmk1_grid_desc,
+                              wei_gemmk0_gemmn_gemmk1_grid_desc,
+                              in_gemmm_gemmn_grid_desc);
+        }
+
+    } // function end
+
+    template <ck::index_t NDim, typename ck::enable_if<NDim == 1, bool>::type = false>
+    static auto GetABCGridDesc()
+    {
+        return MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N<1>(
+            1, 1, 1, {1}, {1}, {1}, {1}, {1}, {1}, {1}, {0});
+    }
+
+    template <ck::index_t NDim, typename ck::enable_if<NDim == 2, bool>::type = false>
+    static auto GetABCGridDesc()
+    {
+        return MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N<2>(
+            1, 1, 1, {1, 1}, {1, 1}, {1, 1}, {1, 1}, {1, 1}, {1, 1}, {1, 1}, {0, 0});
+    }
+
+    template <ck::index_t NDim, typename ck::enable_if<NDim == 3, bool>::type = false>
+    static auto GetABCGridDesc()
+    {
+        return MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N<3>(1,
+                                                                  1,
+                                                                  1,
+                                                                  {1, 1, 1},
+                                                                  {1, 1, 1},
+                                                                  {1, 1, 1},
+                                                                  {1, 1, 1},
+                                                                  {1, 1, 1},
+                                                                  {1, 1, 1},
+                                                                  {1, 1, 1},
+                                                                  {0, 0, 0});
+    }
+
+    using ABCGridDescs = decltype(GetABCGridDesc<NDimSpatial>());
+
+    using AGridDesc_K0_M_K1 = remove_cvref_t<decltype(ABCGridDescs{}[I0])>;
+    using BGridDesc_K0_N_K1 = remove_cvref_t<decltype(ABCGridDescs{}[I1])>;
+    using CGridDesc_M_N     = remove_cvref_t<decltype(ABCGridDescs{}[I2])>;
+
+    // GridwiseGemm
+    using GridwiseGemm = GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v2r3<
+        BlockSize,
+        ABDataType, // TODO: distinguish A/B datatype
+        AccDataType,
+        CDataType,
+        InMemoryDataOperationEnum::Set,
+        AGridDesc_K0_M_K1,
+        BGridDesc_K0_N_K1,
+        CGridDesc_M_N,
+        InElementwiseOperation,
+        WeiElementwiseOperation,
+        OutElementwiseOperation,
+        MPerBlock,
+        NPerBlock,
+        K0PerBlock,
+        MPerXdl,
+        NPerXdl,
+        K1,
+        MXdlPerWave,
+        NXdlPerWave,
+        ABlockTransferThreadClusterLengths_K0_M_K1,
+        ABlockTransferThreadClusterArrangeOrder,
+        ABlockTransferSrcAccessOrder,
+        ABlockTransferSrcVectorDim,
+        ABlockTransferSrcScalarPerVector,
+        ABlockTransferDstScalarPerVector_K1,
+        false, // AThreadTransferSrcResetCoordinateAfterRun,
+        ABlockLdsAddExtraM,
+        BBlockTransferThreadClusterLengths_K0_N_K1,
+        BBlockTransferThreadClusterArrangeOrder,
+        BBlockTransferSrcAccessOrder,
+        BBlockTransferSrcVectorDim,
+        BBlockTransferSrcScalarPerVector,
+        BBlockTransferDstScalarPerVector_K1,
+        false, // BThreadTransferSrcResetCoordinateAfterRun,
+        BBlockLdsAddExtraN,
+        Sequence<2, 3, 0, 1, 7, 5, 4, 6>, // CThreadTransferSrcDstAccessOrder,
+        7,                                // CThreadTransferSrcDstVectorDim,
+        CThreadTransferDstScalarPerVector>;
+
+    // Argument
+    struct Argument : public BaseArgument
+    {
+        Argument(InDataType* p_in_grid,
+                 const WeiDataType* p_wei_grid,
+                 const OutDataType* p_out_grid,
+                 ck::index_t N,
+                 ck::index_t K,
+                 ck::index_t C,
+                 std::vector<ck::index_t> input_spatial_lengths,
+                 std::vector<ck::index_t> filter_spatial_lengths,
+                 std::vector<ck::index_t> output_spatial_lengths,
+                 std::vector<ck::index_t> conv_filter_strides,
+                 std::vector<ck::index_t> conv_filter_dilations,
+                 std::vector<ck::index_t> input_left_pads,
+                 std::vector<ck::index_t> input_right_pads,
+                 ck::index_t M01,
+                 ck::index_t N01,
+                 InElementwiseOperation in_element_op,
+                 WeiElementwiseOperation wei_element_op,
+                 OutElementwiseOperation out_element_op)
+            : p_a_grid_{p_out_grid},
+              p_b_grid_{p_wei_grid},
+              p_c_grid_{p_in_grid},
+              M01_{M01},
+              N01_{N01},
+              a_element_op_{out_element_op},
+              b_element_op_{wei_element_op},
+              c_element_op_{in_element_op},
+              Conv_N_{N},
+              Conv_K_{K},
+              Conv_C_{C},
+              input_spatial_lengths_{input_spatial_lengths},
+              filter_spatial_lengths_{filter_spatial_lengths},
+              output_spatial_lengths_{output_spatial_lengths},
+              conv_filter_strides_{conv_filter_strides},
+              conv_filter_dilations_{conv_filter_dilations},
+              input_left_pads_{input_left_pads},
+              input_right_pads_{input_right_pads}
+        {
+            CreateABCDesc<NDimSpatial>();
+        }
+
+        template <ck::index_t NDim, typename ck::enable_if<NDim == 1, bool>::type = false>
+        void CreateABCDesc()
+        {
+            const index_t ConvStrideW     = conv_filter_strides_[0];
+            const index_t ConvDilationW   = conv_filter_dilations_[0];
+            const auto GcdStrideDilationW = math::gcd(ConvStrideW, ConvDilationW);
+            const auto XTilde             = ConvStrideW / GcdStrideDilationW;
+
+            const index_t X = filter_spatial_lengths_[0];
+
+            for(index_t i_xtilde = 0; i_xtilde < XTilde; ++i_xtilde)
+            {
+                // check slice is valid
+                const auto XDotSlice = math::integer_divide_ceil(X - i_xtilde, XTilde);
+                if(XDotSlice <= 0)
+                {
+                    continue;
+                }
+
+                const auto descs =
+                    DeviceOp::MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N<NDimSpatial>(
+                        Conv_N_,
+                        Conv_K_,
+                        Conv_C_,
+                        input_spatial_lengths_,
+                        filter_spatial_lengths_,
+                        output_spatial_lengths_,
+                        conv_filter_strides_,
+                        conv_filter_dilations_,
+                        input_left_pads_,
+                        input_right_pads_,
+                        {i_xtilde});
+                a_grid_desc_k0_m_k1_container_.push_back(descs[I0]);
+                b_grid_desc_k0_n_k1_container_.push_back(descs[I1]);
+                c_grid_desc_m_n_container_.push_back(descs[I2]);
+
+                auto block_2_ctile_map =
+                    GridwiseGemm::MakeDefaultBlock2CTileMap(descs[I2], M01_, N01_);
+
+                if(GridwiseGemm::CheckValidity(descs[I0], descs[I1], descs[I2], block_2_ctile_map))
+                {
+                    c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_container_.push_back(
+                        GridwiseGemm::MakeCGridDescriptor_M0_N0_M1_N1_M2_M3_M4_N2(descs[I2]));
+
+                    block_2_ctile_map_container_.push_back(block_2_ctile_map);
+                }
+            }
+        }
+        template <ck::index_t NDim, typename ck::enable_if<NDim == 2, bool>::type = false>
+        void CreateABCDesc()
+        {
+            const index_t ConvStrideH = conv_filter_strides_[0];
+            const index_t ConvStrideW = conv_filter_strides_[1];
+
+            const index_t ConvDilationH = conv_filter_dilations_[0];
+            const index_t ConvDilationW = conv_filter_dilations_[1];
+
+            const auto GcdStrideDilationH = math::gcd(ConvStrideH, ConvDilationH);
+            const auto GcdStrideDilationW = math::gcd(ConvStrideW, ConvDilationW);
+
+            const auto YTilde = ConvStrideH / GcdStrideDilationH;
+            const auto XTilde = ConvStrideW / GcdStrideDilationW;
+
+            const index_t Y = filter_spatial_lengths_[0];
+            const index_t X = filter_spatial_lengths_[1];
+            for(index_t i_ytilde = 0; i_ytilde < YTilde; ++i_ytilde)
+            {
+                for(index_t i_xtilde = 0; i_xtilde < XTilde; ++i_xtilde)
+                {
+                    // check slice is valid
+                    const auto YDotSlice = math::integer_divide_ceil(Y - i_ytilde, YTilde);
+                    const auto XDotSlice = math::integer_divide_ceil(X - i_xtilde, XTilde);
+                    if(YDotSlice * XDotSlice <= 0)
+                    {
+                        continue;
+                    }
+
+                    const auto descs =
+                        DeviceOp::MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N<NDimSpatial>(
+                            Conv_N_,
+                            Conv_K_,
+                            Conv_C_,
+                            input_spatial_lengths_,
+                            filter_spatial_lengths_,
+                            output_spatial_lengths_,
+                            conv_filter_strides_,
+                            conv_filter_dilations_,
+                            input_left_pads_,
+                            input_right_pads_,
+                            {i_ytilde, i_xtilde});
+                    a_grid_desc_k0_m_k1_container_.push_back(descs[I0]);
+                    b_grid_desc_k0_n_k1_container_.push_back(descs[I1]);
+                    c_grid_desc_m_n_container_.push_back(descs[I2]);
+
+                    auto block_2_ctile_map =
+                        GridwiseGemm::MakeDefaultBlock2CTileMap(descs[I2], M01_, N01_);
+
+                    if(GridwiseGemm::CheckValidity(
+                           descs[I0], descs[I1], descs[I2], block_2_ctile_map))
+                    {
+                        c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_container_.push_back(
+                            GridwiseGemm::MakeCGridDescriptor_M0_N0_M1_N1_M2_M3_M4_N2(descs[I2]));
+
+                        block_2_ctile_map_container_.push_back(block_2_ctile_map);
+                    }
+                }
+            }
+        }
+        template <ck::index_t NDim, typename ck::enable_if<NDim == 3, bool>::type = false>
+        void CreateABCDesc()
+        {
+            const index_t ConvStrideD = conv_filter_strides_[0];
+            const index_t ConvStrideH = conv_filter_strides_[1];
+            const index_t ConvStrideW = conv_filter_strides_[2];
+
+            const index_t ConvDilationD = conv_filter_dilations_[0];
+            const index_t ConvDilationH = conv_filter_dilations_[1];
+            const index_t ConvDilationW = conv_filter_dilations_[2];
+
+            const auto GcdStrideDilationD = math::gcd(ConvStrideD, ConvDilationD);
+            const auto GcdStrideDilationH = math::gcd(ConvStrideH, ConvDilationH);
+            const auto GcdStrideDilationW = math::gcd(ConvStrideW, ConvDilationW);
+
+            const auto ZTilde = ConvStrideD / GcdStrideDilationD;
+            const auto YTilde = ConvStrideH / GcdStrideDilationH;
+            const auto XTilde = ConvStrideW / GcdStrideDilationW;
+
+            const index_t Z = filter_spatial_lengths_[0];
+            const index_t Y = filter_spatial_lengths_[1];
+            const index_t X = filter_spatial_lengths_[2];
+            for(index_t i_ztilde = 0; i_ztilde < ZTilde; ++i_ztilde)
+            {
+                for(index_t i_ytilde = 0; i_ytilde < YTilde; ++i_ytilde)
+                {
+                    for(index_t i_xtilde = 0; i_xtilde < XTilde; ++i_xtilde)
+                    {
+                        // check slice is valid
+                        const auto ZDotSlice = math::integer_divide_ceil(Z - i_ztilde, ZTilde);
+                        const auto YDotSlice = math::integer_divide_ceil(Y - i_ytilde, YTilde);
+                        const auto XDotSlice = math::integer_divide_ceil(X - i_xtilde, XTilde);
+                        if(ZDotSlice * YDotSlice * XDotSlice <= 0)
+                        {
+                            continue;
+                        }
+
+                        const auto descs =
+                            DeviceOp::MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N<NDimSpatial>(
+                                Conv_N_,
+                                Conv_K_,
+                                Conv_C_,
+                                input_spatial_lengths_,
+                                filter_spatial_lengths_,
+                                output_spatial_lengths_,
+                                conv_filter_strides_,
+                                conv_filter_dilations_,
+                                input_left_pads_,
+                                input_right_pads_,
+                                {i_ztilde, i_ytilde, i_xtilde});
+                        a_grid_desc_k0_m_k1_container_.push_back(descs[I0]);
+                        b_grid_desc_k0_n_k1_container_.push_back(descs[I1]);
+                        c_grid_desc_m_n_container_.push_back(descs[I2]);
+
+                        auto block_2_ctile_map =
+                            GridwiseGemm::MakeDefaultBlock2CTileMap(descs[I2], M01_, N01_);
+
+                        if(GridwiseGemm::CheckValidity(
+                               descs[I0], descs[I1], descs[I2], block_2_ctile_map))
+                        {
+                            c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_container_.push_back(
+                                GridwiseGemm::MakeCGridDescriptor_M0_N0_M1_N1_M2_M3_M4_N2(
+                                    descs[I2]));
+
+                            block_2_ctile_map_container_.push_back(block_2_ctile_map);
+                        }
+                    }
+                }
+            }
+        }
+
+        const ADataType* p_a_grid_;
+        const BDataType* p_b_grid_;
+        CDataType* p_c_grid_;
+        std::vector<AGridDesc_K0_M_K1> a_grid_desc_k0_m_k1_container_;
+        std::vector<BGridDesc_K0_N_K1> b_grid_desc_k0_n_k1_container_;
+        std::vector<CGridDesc_M_N> c_grid_desc_m_n_container_;
+        std::vector<typename GridwiseGemm::CGridDesc_M0_N0_M1_N1_M2_M3_M4_N2>
+            c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_container_;
+        std::vector<typename GridwiseGemm::DefaultBlock2CTileMap> block_2_ctile_map_container_;
+        index_t M01_;
+        index_t N01_;
+        OutElementwiseOperation a_element_op_;
+        WeiElementwiseOperation b_element_op_;
+        InElementwiseOperation c_element_op_;
+        // for checking IsSupportedArgument()
+        index_t Conv_N_;
+        index_t Conv_K_;
+        index_t Conv_C_;
+
+        std::vector<ck::index_t> input_spatial_lengths_;
+        std::vector<ck::index_t> filter_spatial_lengths_;
+        std::vector<ck::index_t> output_spatial_lengths_;
+        std::vector<ck::index_t> conv_filter_strides_;
+        std::vector<ck::index_t> conv_filter_dilations_;
+        std::vector<ck::index_t> input_left_pads_;
+        std::vector<ck::index_t> input_right_pads_;
+    };
+
+    // Invoker
+    struct Invoker : public BaseInvoker
+    {
+        using Argument = DeviceOp::Argument;
+
+        float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
+        {
+            float ave_time = 0;
+            for(size_t i = 0; i < arg.a_grid_desc_k0_m_k1_container_.size(); i++)
+            {
+                {
+                    std::cout << "arg.a_grid_desc_k0_m_k1_container_{"
+                              << arg.a_grid_desc_k0_m_k1_container_[i].GetLength(I0) << ", "
+                              << arg.a_grid_desc_k0_m_k1_container_[i].GetLength(I1) << ", "
+                              << arg.a_grid_desc_k0_m_k1_container_[i].GetLength(I2) << "}"
+                              << std::endl;
+
+                    std::cout << "arg.b_grid_desc_k0_n_k1_container_{"
+                              << arg.b_grid_desc_k0_n_k1_container_[i].GetLength(I0) << ", "
+                              << arg.b_grid_desc_k0_n_k1_container_[i].GetLength(I1) << ", "
+                              << arg.b_grid_desc_k0_n_k1_container_[i].GetLength(I2) << "}"
+                              << std::endl;
+
+                    std::cout << "arg.c_grid_desc_m_n_container_{ "
+                              << arg.c_grid_desc_m_n_container_[i].GetLength(I0) << ", "
+                              << arg.c_grid_desc_m_n_container_[i].GetLength(I1) << "}"
+                              << std::endl;
+
+                    std::cout << "arg.c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_container_( "
+                              << arg.c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_container_[i].GetLength(I0)
+                              << ", "
+                              << arg.c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_container_[i].GetLength(I1)
+                              << ", "
+                              << arg.c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_container_[i].GetLength(I2)
+                              << ", "
+                              << arg.c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_container_[i].GetLength(I3)
+                              << ", "
+                              << arg.c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_container_[i].GetLength(I4)
+                              << ", "
+                              << arg.c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_container_[i].GetLength(I5)
+                              << ", "
+                              << arg.c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_container_[i].GetLength(I6)
+                              << ", "
+                              << arg.c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_container_[i].GetLength(I7)
+                              << " ) " << std::endl;
+                }
+
+                if(!GridwiseGemm::CheckValidity(arg.a_grid_desc_k0_m_k1_container_[i],
+                                                arg.b_grid_desc_k0_n_k1_container_[i],
+                                                arg.c_grid_desc_m_n_container_[i],
+                                                arg.block_2_ctile_map_container_[i]))
+                {
+                    throw std::runtime_error(
+                        "wrong! GridwiseGemm_km_kn_m0m1n0n1_xdlops_v3r1 has invalid setting");
+                }
+
+                const index_t grid_size = arg.block_2_ctile_map_container_[i].CalculateGridSize(
+                    arg.c_grid_desc_m_n_container_[i]);
+
+                const auto K = arg.a_grid_desc_k0_m_k1_container_[i].GetLength(I0) *
+                               arg.a_grid_desc_k0_m_k1_container_[i].GetLength(I2);
+
+                if(GridwiseGemm::CalculateHasMainKBlockLoop(K))
+                {
+                    const auto kernel = kernel_gemm_xdlops_v2r3<
+                        GridwiseGemm,
+                        ADataType, // TODO: distiguish A/B datatype
+                        CDataType,
+                        remove_reference_t<DeviceOp::AGridDesc_K0_M_K1>,
+                        remove_reference_t<DeviceOp::BGridDesc_K0_N_K1>,
+                        remove_reference_t<
+                            typename GridwiseGemm::CGridDesc_M0_N0_M1_N1_M2_M3_M4_N2>,
+                        OutElementwiseOperation,
+                        WeiElementwiseOperation,
+                        InElementwiseOperation,
+                        remove_reference_t<typename GridwiseGemm::DefaultBlock2CTileMap>,
+                        true>;
+
+                    ave_time += launch_and_time_kernel(
+                        stream_config,
+                        kernel,
+                        dim3(grid_size),
+                        dim3(BlockSize),
+                        0,
+                        arg.p_a_grid_,
+                        arg.p_b_grid_,
+                        arg.p_c_grid_,
+                        arg.a_grid_desc_k0_m_k1_container_[i],
+                        arg.b_grid_desc_k0_n_k1_container_[i],
+                        arg.c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_container_[i],
+                        arg.a_element_op_,
+                        arg.b_element_op_,
+                        arg.c_element_op_,
+                        arg.block_2_ctile_map_container_[i]);
+                }
+                else
+                {
+                    const auto kernel = kernel_gemm_xdlops_v2r3<
+                        GridwiseGemm,
+                        ADataType, // TODO: distiguish A/B datatype
+                        CDataType,
+                        remove_reference_t<DeviceOp::AGridDesc_K0_M_K1>,
+                        remove_reference_t<DeviceOp::BGridDesc_K0_N_K1>,
+                        remove_reference_t<
+                            typename GridwiseGemm::CGridDesc_M0_N0_M1_N1_M2_M3_M4_N2>,
+                        OutElementwiseOperation,
+                        WeiElementwiseOperation,
+                        InElementwiseOperation,
+                        remove_reference_t<typename GridwiseGemm::DefaultBlock2CTileMap>,
+                        false>;
+
+                    ave_time += launch_and_time_kernel(
+                        stream_config,
+                        kernel,
+                        dim3(grid_size),
+                        dim3(BlockSize),
+                        0,
+                        arg.p_a_grid_,
+                        arg.p_b_grid_,
+                        arg.p_c_grid_,
+                        arg.a_grid_desc_k0_m_k1_container_[i],
+                        arg.b_grid_desc_k0_n_k1_container_[i],
+                        arg.c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_container_[i],
+                        arg.a_element_op_,
+                        arg.b_element_op_,
+                        arg.c_element_op_,
+                        arg.block_2_ctile_map_container_[i]);
+                }
+            }
+            return ave_time;
+        }
+
+        float Run(const BaseArgument* p_arg,
+                  const StreamConfig& stream_config = StreamConfig{}) override
+        {
+            return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
+        }
+    };
+
+    static constexpr bool IsValidCompilationParameter()
+    {
+        // TODO: properly implement this check
+        return true;
+    }
+
+    static bool IsSupportedArgument(const Argument& arg)
+    {
+        if constexpr(ConvBackwardDataSpecialization ==
+                     ConvolutionBackwardDataSpecialization::Filter1x1Stride1Pad0)
+        {
+            // check if it's 1x1, stride=1 pad = 0 conv
+            for(int i = 0; i < NDimSpatial; i++)
+            {
+                if(!(arg.filter_spatial_lengths_[i] == 1 && arg.conv_filter_strides_[i] == 1 &&
+                     arg.input_left_pads_[i] == 0 && arg.input_right_pads_[i] == 0))
+                {
+                    return false;
+                }
+            }
+        }
+
+        // vector load A/B matrix from global memory
+        if(!(ABlockTransferSrcVectorDim == 2 && BBlockTransferSrcVectorDim == 1 &&
+             arg.Conv_K_ % ABlockTransferSrcScalarPerVector == 0 &&
+             arg.Conv_C_ % BBlockTransferSrcScalarPerVector == 0))
+        {
+            return false;
+        }
+
+        // vector store C matrix into global memory
+        if(!(arg.Conv_C_ % CThreadTransferDstScalarPerVector == 0))
+        {
+            return false;
+        }
+
+        // Gridwise GEMM size
+        for(std::size_t i = 0; i < arg.a_grid_desc_k0_m_k1_container_.size(); i++)
+        {
+            if(!GridwiseGemm::CheckValidity(arg.a_grid_desc_k0_m_k1_container_[i],
+                                            arg.b_grid_desc_k0_n_k1_container_[i],
+                                            arg.c_grid_desc_m_n_container_[i],
+                                            arg.block_2_ctile_map_container_[i]))
+            {
+                return false;
+            }
+        }
+        return true;
+    }
+
+    bool IsSupportedArgument(const BaseArgument* p_arg) override
+    {
+        return IsSupportedArgument(*dynamic_cast<const Argument*>(p_arg));
+    }
+
+    static auto MakeArgument(InDataType* p_in_grid,
+                             const WeiDataType* p_wei_grid,
+                             const OutDataType* p_out_grid,
+                             ck::index_t N,
+                             ck::index_t K,
+                             ck::index_t C,
+                             std::vector<ck::index_t> input_spatial_lengths,
+                             std::vector<ck::index_t> filter_spatial_lengths,
+                             std::vector<ck::index_t> output_spatial_lengths,
+                             std::vector<ck::index_t> conv_filter_strides,
+                             std::vector<ck::index_t> conv_filter_dilations,
+                             std::vector<ck::index_t> input_left_pads,
+                             std::vector<ck::index_t> input_right_pads,
+                             InElementwiseOperation in_element_op,
+                             WeiElementwiseOperation wei_element_op,
+                             OutElementwiseOperation out_element_op)
+    {
+        return Argument{p_in_grid,
+                        p_wei_grid,
+                        p_out_grid,
+                        N,
+                        K,
+                        C,
+                        input_spatial_lengths,
+                        filter_spatial_lengths,
+                        output_spatial_lengths,
+                        conv_filter_strides,
+                        conv_filter_dilations,
+                        input_left_pads,
+                        input_right_pads,
+                        1,
+                        1,
+                        in_element_op,
+                        wei_element_op,
+                        out_element_op};
+    }
+
+    static auto MakeInvoker() { return Invoker{}; }
+
+    std::unique_ptr<BaseArgument>
+    MakeArgumentPointer(void* p_in_grid,
+                        const void* p_wei_grid,
+                        const void* p_out_grid,
+                        ck::index_t N,
+                        ck::index_t K,
+                        ck::index_t C,
+                        std::vector<ck::index_t> input_spatial_lengths,
+                        std::vector<ck::index_t> filter_spatial_lengths,
+                        std::vector<ck::index_t> output_spatial_lengths,
+                        std::vector<ck::index_t> conv_filter_strides,
+                        std::vector<ck::index_t> conv_filter_dilations,
+                        std::vector<ck::index_t> input_left_pads,
+                        std::vector<ck::index_t> input_right_pads,
+                        InElementwiseOperation in_element_op,
+                        WeiElementwiseOperation wei_element_op,
+                        OutElementwiseOperation out_element_op) override
+    {
+        return std::make_unique<Argument>(static_cast<InDataType*>(p_in_grid),
+                                          static_cast<const WeiDataType*>(p_wei_grid),
+                                          static_cast<const OutDataType*>(p_out_grid),
+                                          N,
+                                          K,
+                                          C,
+                                          input_spatial_lengths,
+                                          filter_spatial_lengths,
+                                          output_spatial_lengths,
+                                          conv_filter_strides,
+                                          conv_filter_dilations,
+                                          input_left_pads,
+                                          input_right_pads,
+                                          1,
+                                          1,
+                                          in_element_op,
+                                          wei_element_op,
+                                          out_element_op);
+    }
+
+    std::unique_ptr<BaseInvoker> MakeInvokerPointer() override
+    {
+        return std::make_unique<Invoker>(Invoker{});
+    }
+
+    std::string GetTypeString() const override
+    {
+        auto str = std::stringstream();
+
+        // clang-format off
+        str << "DeviceConvNdBwdDataNwcKxcNwk_Xdl"
+            << "<"
+            << BlockSize << ", "
+            << MPerBlock << ", "
+            << NPerBlock << ", "
+            << K0PerBlock
+            << ">";
+        if constexpr(ConvBackwardDataSpecialization ==
+                     ConvolutionBackwardDataSpecialization::Filter1x1Stride1Pad0){
+
+            str<< " Filter1x1Stride1Pad0";
+        }
+
+
+        return str.str();
+    }
+};
+
+} // namespace device
+} // namespace tensor_operation
+} // namespace ck