Apply universal gemm to bwd_weight_cshuffle operator (#1873)

* Universal gemm - initial commit

* Review part 1

* Fix tests

* Remove instances

* Remove comp instances

example/20_grouped_conv_bwd_weight/CMakeLists.txt

@@ -13,3 +13,9 @@ add_example_dependencies(example_grouped_conv_bwd_weight example_grouped_conv_bw
 
 add_example_executable(example_grouped_conv_bwd_weight_dl_fp16 grouped_conv_bwd_weight_dl_fp16.cpp)
 add_example_dependencies(example_grouped_conv_bwd_weight example_grouped_conv_bwd_weight_dl_fp16)
+
+add_example_executable(example_grouped_conv_bwd_weight_v3_xdl_bf16 grouped_conv_bwd_weight_v3_xdl_bf16.cpp)
+add_example_dependencies(example_grouped_conv_bwd_weight example_grouped_conv_bwd_weight_v3_xdl_bf16)
+
+add_example_executable(example_grouped_conv_bwd_weight_v3_xdl_fp16 grouped_conv_bwd_weight_v3_xdl_fp16.cpp)
+add_example_dependencies(example_grouped_conv_bwd_weight example_grouped_conv_bwd_weight_v3_xdl_fp16)

example/20_grouped_conv_bwd_weight/grouped_conv_bwd_weight_v3_xdl_bf16.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
+
+#include "common.hpp"
+
+#include "ck/tensor_operation/gpu/device/impl/device_grouped_conv_bwd_weight_xdl_cshuffle_v3.hpp"
+
+using InDataType = BF16;
+// bf16 kernel use fp32 atomic add to accumulate Weight tensor into global memory
+using WeiDataType = F32;
+using OutDataType = BF16;
+using AccDataType = F32;
+
+using InElementOp  = PassThrough;
+using WeiElementOp = PassThrough;
+using OutElementOp = PassThrough;
+
+template <ck::index_t NDimSpatial>
+using DeviceConvBwdWeightInstance =
+    // clang-format on
+    ck::tensor_operation::device::DeviceGroupedConvBwdWeight_Xdl_CShuffleV3<
+        NDimSpatial,
+        ck::tuple_element_t<NDimSpatial - 1,
+                            ck::Tuple<ck::tensor_layout::convolution::GNWC,
+                                      ck::tensor_layout::convolution::GNHWC,
+                                      ck::tensor_layout::convolution::GNDHWC>>,
+        ck::tuple_element_t<NDimSpatial - 1,
+                            ck::Tuple<ck::tensor_layout::convolution::GKXC,
+                                      ck::tensor_layout::convolution::GKYXC,
+                                      ck::tensor_layout::convolution::GKZYXC>>,
+        ck::tuple_element_t<NDimSpatial - 1,
+                            ck::Tuple<ck::tensor_layout::convolution::GNWK,
+                                      ck::tensor_layout::convolution::GNHWK,
+                                      ck::tensor_layout::convolution::GNDHWK>>,
+        InDataType,           // InDataType
+        WeiDataType,          // WeiDataType
+        OutDataType,          // OutDataType
+        AccDataType,          // AccDataType
+        InElementOp,          // InElementwiseOperation
+        WeiElementOp,         // WeiElementwiseOperation
+        OutElementOp,         // OutElementwiseOperation
+        ConvBwdWeightDefault, // ConvolutionBackwardWeightSpecialization
+        256,                  // BlockSize
+        128,                  // MPerBlock
+        128,                  // NPerBlock
+        32,                   // K0PerBlock
+        8,                    // K1
+        32,                   // MPerXdl
+        32,                   // NPerXdl
+        2,                    // MXdlPerWave
+        2,                    // NXdlPerWave
+        S<4, 16, 1>,          // ABlockTransferThreadClusterLengths_K0_M_K1
+        S<2, 0, 1>,           // ABlockTransferThreadClusterArrangeOrder
+        S<1, 0, 2>,           // ABlockTransferSrcAccessOrder
+        2,                    // ABlockTransferSrcVectorDim
+        1,                    // ABlockTransferSrcScalarPerVector
+        2,                    // ABlockTransferDstScalarPerVector_K1
+        true,                 // ABlockLdsAddExtraM
+        S<4, 16, 1>,          // BBlockTransferThreadClusterLengths_K0_N_K1
+        S<2, 0, 1>,           // BBlockTransferThreadClusterArrangeOrder
+        S<1, 0, 2>,           // BBlockTransferSrcAccessOrder
+        2,                    // BBlockTransferSrcVectorDim
+        1,                    // BBlockTransferSrcScalarPerVector
+        2,                    // BBlockTransferDstScalarPerVector_K1
+        true,                 // BBlockLdsAddExtraN
+        1,                    // CShuffleMXdlPerWavePerShuffle
+        1,                    // CShuffleNXdlPerWavePerShuffle
+        S<1, 32, 1, 4>,       // CBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock
+        128 / (sizeof(WeiDataType) * CHAR_BIT)>; // CBlockTransferScalarPerVector_NWaveNPerXdl
+                                                 // clang-format off
+
+template <ck::index_t NDimSpatial>
+using HostConvBwdWeightInstance = ck::tensor_operation::host::ReferenceConvBwdWeight<NDimSpatial,
+                                                                                     InDataType,
+                                                                                     WeiDataType,
+                                                                                     OutDataType,
+                                                                                     InElementOp,
+                                                                                     WeiElementOp,
+                                                                                     OutElementOp>;
+
+#include "run_grouped_conv_bwd_weight_example.inc"
+
+int main(int argc, char* argv[])
+{
+    ExecutionConfig config;
+    ck::utils::conv::ConvParam conv_param = DefaultConvParam;
+
+    if(!parse_cmd_args(argc, argv, config, conv_param))
+    {
+        return 1;
+    }
+
+    switch(conv_param.num_dim_spatial_)
+    {
+    case 1: return !run_grouped_conv_bwd_weight<1>(config, conv_param);
+    case 2: return !run_grouped_conv_bwd_weight<2>(config, conv_param);
+    case 3: return !run_grouped_conv_bwd_weight<3>(config, conv_param);
+    default: break;
+    }
+
+    return 1;
+}

example/20_grouped_conv_bwd_weight/grouped_conv_bwd_weight_v3_xdl_fp16.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
+
+#include "common.hpp"
+
+#include "ck/tensor_operation/gpu/device/impl/device_grouped_conv_bwd_weight_xdl_cshuffle_v3.hpp"
+
+using InDataType  = F16;
+using WeiDataType = F16;
+using OutDataType = F16;
+using AccDataType = F32;
+
+using InElementOp  = PassThrough;
+using WeiElementOp = PassThrough;
+using OutElementOp = PassThrough;
+
+template <ck::index_t NDimSpatial>
+using DeviceConvBwdWeightInstance =
+    ck::tensor_operation::device::DeviceGroupedConvBwdWeight_Xdl_CShuffleV3<
+        NDimSpatial,
+        ck::tuple_element_t<NDimSpatial - 1,
+                            ck::Tuple<ck::tensor_layout::convolution::GNWC,
+                                      ck::tensor_layout::convolution::GNHWC,
+                                      ck::tensor_layout::convolution::GNDHWC>>,
+        ck::tuple_element_t<NDimSpatial - 1,
+                            ck::Tuple<ck::tensor_layout::convolution::GKXC,
+                                      ck::tensor_layout::convolution::GKYXC,
+                                      ck::tensor_layout::convolution::GKZYXC>>,
+        ck::tuple_element_t<NDimSpatial - 1,
+                            ck::Tuple<ck::tensor_layout::convolution::GNWK,
+                                      ck::tensor_layout::convolution::GNHWK,
+                                      ck::tensor_layout::convolution::GNDHWK>>,
+        InDataType,           // InDataType
+        WeiDataType,          // WeiDataType
+        OutDataType,          // OutDataType
+        AccDataType,          // AccDataType
+        InElementOp,          // InElementwiseOperation
+        WeiElementOp,         // WeiElementwiseOperation
+        OutElementOp,         // OutElementwiseOperation
+        ConvBwdWeightDefault, // ConvolutionBackwardWeightSpecialization
+        256,                  // BlockSize
+        128,                  // MPerBlock
+        128,                  // NPerBlock
+        32,                   // K0PerBlock
+        8,                    // K1
+        32,                   // MPerXdl
+        32,                   // NPerXdl
+        2,                    // MXdlPerWave
+        2,                    // NXdlPerWave
+        S<4, 16, 1>,          // ABlockTransferThreadClusterLengths_K0_M_K1
+        S<2, 0, 1>,           // ABlockTransferThreadClusterArrangeOrder
+        S<1, 0, 2>,           // ABlockTransferSrcAccessOrder
+        2,                    // ABlockTransferSrcVectorDim
+        1,                    // ABlockTransferSrcScalarPerVector
+        2,                    // ABlockTransferDstScalarPerVector_K1
+        false,                // ABlockLdsAddExtraM
+        S<4, 16, 1>,          // BBlockTransferThreadClusterLengths_K0_N_K1
+        S<2, 0, 1>,           // BBlockTransferThreadClusterArrangeOrder
+        S<1, 0, 2>,           // BBlockTransferSrcAccessOrder
+        2,                    // BBlockTransferSrcVectorDim
+        1,                    // BBlockTransferSrcScalarPerVector
+        2,                    // BBlockTransferDstScalarPerVector_K1
+        false,                // BBlockLdsAddExtraN
+        1,                    // CShuffleMXdlPerWavePerShuffle
+        1,                    // CShuffleNXdlPerWavePerShuffle
+        S<1, 32, 1, 4>,       // CBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock
+        128 / (sizeof(WeiDataType) * CHAR_BIT)>; // CBlockTransferScalarPerVector_NWaveNPerXdl
+
+template <ck::index_t NDimSpatial>
+using HostConvBwdWeightInstance = ck::tensor_operation::host::ReferenceConvBwdWeight<NDimSpatial,
+                                                                                     InDataType,
+                                                                                     WeiDataType,
+                                                                                     OutDataType,
+                                                                                     InElementOp,
+                                                                                     WeiElementOp,
+                                                                                     OutElementOp>;
+
+#include "run_grouped_conv_bwd_weight_example.inc"
+
+int main(int argc, char* argv[])
+{
+    ExecutionConfig config;
+    ck::utils::conv::ConvParam conv_param = DefaultConvParam;
+
+    if(!parse_cmd_args(argc, argv, config, conv_param))
+    {
+        return 1;
+    }
+
+    switch(conv_param.num_dim_spatial_)
+    {
+    case 1: return !run_grouped_conv_bwd_weight<1>(config, conv_param);
+    case 2: return !run_grouped_conv_bwd_weight<2>(config, conv_param);
+    case 3: return !run_grouped_conv_bwd_weight<3>(config, conv_param);
+    default: break;
+    }
+
+    return 1;
+}

include/ck/tensor_operation/gpu/device/impl/device_grouped_conv_bwd_weight_xdl_cshuffle_v3.hpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include <iostream>
+#include <numeric>
+#include <sstream>
+
+#include "ck/utility/common_header.hpp"
+
+#include "ck/tensor_description/tensor_descriptor.hpp"
+#include "ck/tensor_description/tensor_descriptor_helper.hpp"
+#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
+#include "ck/tensor_operation/gpu/device/device_grouped_conv_bwd_weight.hpp"
+#include "ck/tensor_operation/operator_transform/transform_conv_bwd_weight_to_gemm.hpp"
+#include "ck/tensor_operation/operator_transform/transform_conv_bwd_weight_to_gemm_v2.hpp"
+#include "ck/tensor_operation/gpu/device/convolution_backward_weight_specialization.hpp"
+#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
+#include "ck/tensor_operation/gpu/grid/gridwise_gemm_xdl_cshuffle_bwd_weight_v3.hpp"
+#include <ck/tensor_operation/gpu/grid/block_to_ctile_map.hpp>
+#include "ck/tensor_operation/gpu/device/impl/device_grouped_conv_utils.hpp"
+#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
+#include "ck/tensor_operation/gpu/device/matrix_padder.hpp"
+
+#include "ck/host_utility/device_prop.hpp"
+#include "ck/host_utility/kernel_launch.hpp"
+#include "ck/host_utility/flush_cache.hpp"
+
+namespace ck {
+namespace tensor_operation {
+namespace device {
+
+template <typename GridwiseGemm,
+          typename AGridDesc_AK0_M_K1,
+          typename BGridDesc_BK0_N_K1,
+          typename CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock,
+          typename ComputePtrOffsetOfBatch,
+          bool HasMainKBlockLoop,
+          InMemoryDataOperationEnum CGlobalMemoryDataOperation,
+          index_t MinimumOccupancy = 1,
+          TailNumber TailNum       = TailNumber::Full>
+__global__ void
+#if CK_USE_LAUNCH_BOUNDS
+    __launch_bounds__(CK_MAX_THREAD_PER_BLOCK, MinimumOccupancy)
+#endif
+        kernel_grouped_conv_bwd_weight_xdl_cshuffle_v3(
+            typename GridwiseGemm::Argument karg,
+            const AGridDesc_AK0_M_K1 a_grid_desc_ak0_m_ak1,
+            const BGridDesc_BK0_N_K1 b_grid_desc_bk0_n_bk1,
+            const CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock
+                c_grid_desc_mblock_mperblock_nblock_nperblock,
+            const ComputePtrOffsetOfBatch compute_ptr_offset_of_batch,
+            const index_t num_k_per_block)
+{
+#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx908__) || defined(__gfx90a__) || \
+    defined(__gfx94__))
+    const index_t g_idx = __builtin_amdgcn_readfirstlane(blockIdx.z);
+    const index_t k_idx = __builtin_amdgcn_readfirstlane(blockIdx.y * num_k_per_block);
+
+    const long_index_t a_batch_offset =
+        amd_wave_read_first_lane(compute_ptr_offset_of_batch.GetAPtrOffset(g_idx));
+    const long_index_t b_batch_offset =
+        amd_wave_read_first_lane(compute_ptr_offset_of_batch.GetBPtrOffset(g_idx));
+    const long_index_t e_batch_offset =
+        amd_wave_read_first_lane(compute_ptr_offset_of_batch.GetEPtrOffset(g_idx));
+
+    __shared__ char p_shared[GridwiseGemm::GetSharedMemoryNumberOfByte()];
+    GridwiseGemm::template Run<AGridDesc_AK0_M_K1,
+                               BGridDesc_BK0_N_K1,
+                               CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock,
+                               HasMainKBlockLoop,
+                               CGlobalMemoryDataOperation,
+                               TailNum>(karg.p_a_grid + a_batch_offset,
+                                        karg.p_b_grid + b_batch_offset,
+                                        karg.p_c_grid + e_batch_offset,
+                                        p_shared,
+                                        karg,
+                                        a_grid_desc_ak0_m_ak1,
+                                        b_grid_desc_bk0_n_bk1,
+                                        c_grid_desc_mblock_mperblock_nblock_nperblock,
+                                        k_idx);
+#else
+    ignore = karg;
+#endif // end of if (defined(__gfx908__) || defined(__gfx90a__))
+}
+
+template <typename GridwiseGemm,
+          typename AGridDesc_AK0_M_K1,
+          typename BGridDesc_BK0_N_K1,
+          typename CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock,
+          typename ComputePtrOffsetOfBatch,
+          bool HasMainKBlockLoop,
+          InMemoryDataOperationEnum CGlobalMemoryDataOperation,
+          index_t MinimumOccupancy = 1,
+          TailNumber TailNum       = TailNumber::Full>
+__global__ void
+#if CK_USE_LAUNCH_BOUNDS
+    __launch_bounds__(CK_MAX_THREAD_PER_BLOCK, MinimumOccupancy)
+#endif
+        kernel_grouped_conv_bwd_weight_xdl_cshuffle_v3_2lds(
+            typename GridwiseGemm::Argument karg,
+            const AGridDesc_AK0_M_K1 a_grid_desc_ak0_m_ak1,
+            const BGridDesc_BK0_N_K1 b_grid_desc_bk0_n_bk1,
+            const CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock
+                c_grid_desc_mblock_mperblock_nblock_nperblock,
+            const ComputePtrOffsetOfBatch compute_ptr_offset_of_batch,
+            const index_t num_k_per_block)
+{
+#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx908__) || defined(__gfx90a__) || \
+    defined(__gfx940__) || defined(__gfx941__) || defined(__gfx942__))
+    // offset base pointer for each work-group
+    const index_t g_idx = __builtin_amdgcn_readfirstlane(blockIdx.z);
+    const index_t k_idx = __builtin_amdgcn_readfirstlane(blockIdx.y * num_k_per_block);
+
+    const long_index_t a_batch_offset =
+        amd_wave_read_first_lane(compute_ptr_offset_of_batch.GetAPtrOffset(g_idx));
+    const long_index_t b_batch_offset =
+        amd_wave_read_first_lane(compute_ptr_offset_of_batch.GetBPtrOffset(g_idx));
+    const long_index_t e_batch_offset =
+        amd_wave_read_first_lane(compute_ptr_offset_of_batch.GetEPtrOffset(g_idx));
+
+    // Pass two lds pointer is the key to tell compiler that ds_read/write
+    // operate on different lds chunk at same time without order dependecy
+    __shared__ char p_shared_0[GridwiseGemm::GetSharedMemoryNumberOfByte()];
+    __shared__ char p_shared_1[GridwiseGemm::GetSharedMemoryNumberOfByte()];
+
+    GridwiseGemm::template Run_2Lds<AGridDesc_AK0_M_K1,
+                                    BGridDesc_BK0_N_K1,
+                                    CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock,
+                                    HasMainKBlockLoop,
+                                    CGlobalMemoryDataOperation,
+                                    TailNum>(karg.p_a_grid + a_batch_offset,
+                                             karg.p_b_grid + b_batch_offset,
+                                             karg.p_c_grid + e_batch_offset,
+                                             p_shared_0,
+                                             p_shared_1,
+                                             karg,
+                                             a_grid_desc_ak0_m_ak1,
+                                             b_grid_desc_bk0_n_bk1,
+                                             c_grid_desc_mblock_mperblock_nblock_nperblock,
+                                             k_idx);
+#else
+    ignore = karg;
+#endif // end of if (defined(__gfx908__) || defined(__gfx90a__))
+}
+
+// out[N, Ho, Wo, K] = in[N, Hi, Wi, C] * wei[K, Y, X, C]
+template <ck::index_t NDimSpatial,
+          typename InLayout,
+          typename WeiLayout,
+          typename OutLayout,
+          typename InDataType,
+          typename WeiDataType,
+          typename OutDataType,
+          typename AccDataType,
+          typename InElementwiseOperation,
+          typename WeiElementwiseOperation,
+          typename OutElementwiseOperation,
+          ConvolutionBackwardWeightSpecialization ConvBackwardWeightSpecialization,
+          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,
+          BlockGemmPipelineScheduler BlkGemmPipeSched = BlockGemmPipelineScheduler::Intrawave,
+          BlockGemmPipelineVersion BlkGemmPipelineVer = BlockGemmPipelineVersion::v1,
+          typename ComputeTypeA                       = InDataType,
+          typename ComputeTypeB                       = ComputeTypeA>
+struct DeviceGroupedConvBwdWeight_Xdl_CShuffleV3
+    : public DeviceGroupedConvBwdWeight<NDimSpatial,
+                                        InLayout,
+                                        WeiLayout,
+                                        OutLayout,
+                                        InDataType,
+                                        WeiDataType,
+                                        OutDataType,
+                                        InElementwiseOperation,
+                                        WeiElementwiseOperation,
+                                        OutElementwiseOperation,
+                                        ComputeTypeA,
+                                        ComputeTypeB>
+{
+    static_assert(is_same_v<InElementwiseOperation, element_wise::PassThrough>);
+    static_assert(is_same_v<WeiElementwiseOperation, element_wise::PassThrough>);
+    static_assert(is_same_v<OutElementwiseOperation, element_wise::PassThrough>);
+
+    using DeviceOp = DeviceGroupedConvBwdWeight_Xdl_CShuffleV3;
+
+    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 inline auto I0 = Number<0>{};
+    static inline auto I1 = Number<1>{};
+    static inline auto I2 = Number<2>{};
+    static inline auto I3 = Number<3>{};
+    static inline auto I4 = Number<4>{};
+    static inline auto I5 = Number<5>{};
+
+    static constexpr GemmSpecialization GemmSpec = GemmSpecialization::Default;
+    static constexpr auto K1Number               = Number<K1>{};
+
+    static constexpr auto conv_to_gemm_transformer =
+        TransformConvBwdWeightToGemmV2<NDimSpatial,
+                                       MPerBlock,
+                                       NPerBlock,
+                                       K1Number,
+                                       K0PerBlock / K1Number,
+                                       1 /*NumGroupsToMerge*/,
+                                       ConvBackwardWeightSpecialization>{};
+
+    template <ck::index_t NDim, typename ck::enable_if<NDim == 1, bool>::type = false>
+    static auto GetABCGridDesc()
+    {
+        const ck::index_t dim   = 1;
+        const ck::index_t batch = 1;
+        const std::array<ck::index_t, NDimSpatial> lengths{1};
+        const std::array<ck::index_t, NDimSpatial + 3> strides{1, 1, 1, 1};
+        const std::array<ck::index_t, NDimSpatial> params{1};
+        return conv_to_gemm_transformer.template MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N<1>(
+            dim,
+            dim,
+            dim,
+            lengths,
+            lengths,
+            lengths,
+            strides,
+            strides,
+            strides,
+            params,
+            params,
+            params,
+            params,
+            batch);
+    }
+
+    template <ck::index_t NDim, typename ck::enable_if<NDim == 2, bool>::type = false>
+    static auto GetABCGridDesc()
+    {
+        const ck::index_t dim   = 1;
+        const ck::index_t batch = 1;
+        const std::array<ck::index_t, NDimSpatial> lengths{1, 1};
+        const std::array<ck::index_t, NDimSpatial + 3> strides{1, 1, 1, 1, 1};
+        const std::array<ck::index_t, NDimSpatial> params{1, 1};
+        return conv_to_gemm_transformer.template MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N<2>(
+            dim,
+            dim,
+            dim,
+            lengths,
+            lengths,
+            lengths,
+            strides,
+            strides,
+            strides,
+            params,
+            params,
+            params,
+            params,
+            batch);
+    }
+
+    template <ck::index_t NDim, typename ck::enable_if<NDim == 3, bool>::type = false>
+    static auto GetABCGridDesc()
+    {
+        const ck::index_t dim   = 1;
+        const ck::index_t batch = 1;
+        const std::array<ck::index_t, NDimSpatial> lengths{1, 1, 1};
+        const std::array<ck::index_t, NDimSpatial + 3> strides{1, 1, 1, 1, 1, 1};
+        const std::array<ck::index_t, NDimSpatial> params{1, 1, 1};
+        return conv_to_gemm_transformer.template MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N<3>(
+            dim,
+            dim,
+            dim,
+            lengths,
+            lengths,
+            lengths,
+            strides,
+            strides,
+            strides,
+            params,
+            params,
+            params,
+            params,
+            batch);
+    }
+
+    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])>;
+
+    using GridwiseGemm =
+        GridwiseGemm_xdl_cshuffle_v3<tensor_layout::gemm::RowMajor,
+                                     tensor_layout::gemm::ColumnMajor,
+                                     tensor_layout::gemm::RowMajor,
+                                     ADataType,
+                                     BDataType,
+                                     AccDataType,
+                                     CDataType,
+                                     CDataType,
+                                     AElementwiseOperation,
+                                     BElementwiseOperation,
+                                     CElementwiseOperation,
+                                     GemmSpec,
+                                     BlockSize,
+                                     MPerBlock,
+                                     NPerBlock,
+                                     K0PerBlock,
+                                     K1,
+                                     K1,
+                                     MPerXdl,
+                                     NPerXdl,
+                                     MXdlPerWave,
+                                     NXdlPerWave,
+                                     ABlockTransferThreadClusterLengths_K0_M_K1,
+                                     ABlockTransferThreadClusterArrangeOrder,
+                                     ABlockTransferSrcAccessOrder,
+                                     ABlockTransferSrcVectorDim,
+                                     ABlockTransferSrcScalarPerVector,
+                                     ABlockTransferDstScalarPerVector_K1,
+                                     false,
+                                     ABlockLdsAddExtraM,
+                                     BBlockTransferThreadClusterLengths_K0_N_K1,
+                                     BBlockTransferThreadClusterArrangeOrder,
+                                     BBlockTransferSrcAccessOrder,
+                                     BBlockTransferSrcVectorDim,
+                                     BBlockTransferSrcScalarPerVector,
+                                     BBlockTransferDstScalarPerVector_K1,
+                                     false,
+                                     BBlockLdsAddExtraN,
+                                     CShuffleMXdlPerWavePerShuffle,
+                                     CShuffleNXdlPerWavePerShuffle,
+                                     CBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
+                                     CBlockTransferScalarPerVector_NWaveNPerXdl,
+                                     BlkGemmPipeSched,
+                                     BlkGemmPipelineVer,
+                                     ComputeTypeA,
+                                     ComputeTypeB>;
+
+    // Argument
+    using CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock =
+        decltype(GridwiseGemm::MakeCGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(
+            CGridDesc_M_N{}, 1, 1));
+
+    struct Argument : public BaseArgument
+    {
+        Argument(const InDataType* p_in_grid,
+                 WeiDataType* p_wei_grid,
+                 const OutDataType* p_out_grid,
+                 const std::array<index_t, NDimSpatial + 3>& b_g_n_c_wis_lengths, // input
+                 const std::array<index_t, NDimSpatial + 3>& b_g_n_c_wis_strides,
+                 const std::array<index_t, NDimSpatial + 3>& e_g_k_c_xs_lengths, // weight
+                 const std::array<index_t, NDimSpatial + 3>& e_g_k_c_xs_strides,
+                 const std::array<index_t, NDimSpatial + 3>& a_g_n_k_wos_lengths, // output
+                 const std::array<index_t, NDimSpatial + 3>& a_g_n_k_wos_strides,
+                 const std::array<ck::index_t, NDimSpatial>& conv_filter_strides,
+                 const std::array<ck::index_t, NDimSpatial>& conv_filter_dilations,
+                 const std::array<ck::index_t, NDimSpatial>& input_left_pads,
+                 const std::array<ck::index_t, NDimSpatial>& input_right_pads,
+                 const ck::index_t M01,
+                 const 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_{},
+              compute_ptr_offset_of_batch_{},
+              M01_{M01},
+              N01_{N01},
+              a_element_op_{out_element_op},
+              b_element_op_{in_element_op},
+              c_element_op_{wei_element_op},
+              Conv_G_{b_g_n_c_wis_lengths[0]},
+              Conv_N_{b_g_n_c_wis_lengths[1]},
+              Conv_K_{e_g_k_c_xs_lengths[1]},
+              Conv_C_{b_g_n_c_wis_lengths[2]},
+              input_spatial_lengths_{},
+              filter_spatial_lengths_{},
+              output_spatial_lengths_{},
+              conv_filter_strides_{conv_filter_strides},
+              input_left_pads_{input_left_pads},
+              input_right_pads_{input_right_pads},
+              k_batch_{split_k}
+        {
+            constexpr index_t spatial_offset = 3;
+            std::copy(begin(b_g_n_c_wis_lengths) + spatial_offset,
+                      end(b_g_n_c_wis_lengths),
+                      begin(input_spatial_lengths_));
+            std::copy(begin(e_g_k_c_xs_lengths) + spatial_offset,
+                      end(e_g_k_c_xs_lengths),
+                      begin(filter_spatial_lengths_));
+            std::copy(begin(a_g_n_k_wos_lengths) + spatial_offset,
+                      end(a_g_n_k_wos_lengths),
+                      begin(output_spatial_lengths_));
+
+            const auto descs =
+                conv_to_gemm_transformer
+                    .template 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_,
+                        b_g_n_c_wis_strides,
+                        e_g_k_c_xs_strides,
+                        a_g_n_k_wos_strides,
+                        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];
+
+            // A/B/C Batch Stride
+            compute_ptr_offset_of_batch_.BatchStrideA_ = a_g_n_k_wos_strides[0];
+            compute_ptr_offset_of_batch_.BatchStrideB_ = b_g_n_c_wis_strides[0];
+            compute_ptr_offset_of_batch_.BatchStrideC_ =
+                Conv_K_ * Conv_C_ *
+                std::accumulate(begin(filter_spatial_lengths_),
+                                end(filter_spatial_lengths_),
+                                index_t{1},
+                                std::multiplies<>{});
+            const index_t GemmM = a_grid_desc_kbatch_k0_m_k1_.GetLength(I1);
+            const index_t GemmN = b_grid_desc_kbatch_k0_n_k1_.GetLength(I1);
+
+            c_grid_desc_mblock_mperblock_nblock_nperblock_ =
+                GridwiseGemm::MakeCGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(
+                    c_grid_desc_m_n_,
+                    GridwiseGemm::CalculateMBlock(GemmM),
+                    GridwiseGemm::CalculateNBlock(GemmN));
+        }
+
+        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_;
+
+        // for computing batch offset
+        ComputePtrOffsetOfStridedBatch<I1, I1, I0> compute_ptr_offset_of_batch_;
+
+        index_t M01_;
+        index_t N01_;
+
+        OutElementwiseOperation a_element_op_;
+        InElementwiseOperation b_element_op_;
+        WeiElementwiseOperation c_element_op_;
+
+        // for checking IsSupportedArgument()
+        const index_t Conv_G_;
+        const index_t Conv_N_;
+        const index_t Conv_K_;
+        const index_t Conv_C_;
+        std::array<ck::index_t, NDimSpatial> input_spatial_lengths_;
+        std::array<ck::index_t, NDimSpatial> filter_spatial_lengths_;
+        std::array<ck::index_t, NDimSpatial> output_spatial_lengths_;
+        const std::array<ck::index_t, NDimSpatial>& conv_filter_strides_;
+        const std::array<ck::index_t, NDimSpatial>& input_left_pads_;
+        const std::array<ck::index_t, NDimSpatial>& input_right_pads_;
+        const 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{})
+        {
+            const index_t GemmM = arg.a_grid_desc_kbatch_k0_m_k1_.GetLength(I1);
+            const index_t GemmN = arg.b_grid_desc_kbatch_k0_n_k1_.GetLength(I1);
+            const index_t GemmK = arg.a_grid_desc_kbatch_k0_m_k1_.GetLength(I0) *
+                                  arg.a_grid_desc_kbatch_k0_m_k1_.GetLength(I2);
+
+            const ADataType* p_a_grid = arg.p_a_grid_;
+            const BDataType* p_b_grid = arg.p_b_grid_;
+            typename GridwiseGemm::Argument gemm_arg{
+                p_a_grid, p_b_grid, arg.p_c_grid_, GemmM, GemmN, GemmK, I0, I0, I0, arg.k_batch_};
+
+            index_t gdx, gdy, gdz;
+            std::tie(gdx, gdy, gdz) = GridwiseGemm::CalculateGridSize(
+                gemm_arg.M, gemm_arg.N, gemm_arg.KBatch, arg.Conv_G_);
+
+            float ave_time = 0;
+
+            index_t k_grain                  = gemm_arg.KBatch * K0PerBlock;
+            index_t K_split                  = (gemm_arg.K + k_grain - 1) / k_grain * K0PerBlock;
+            const bool has_main_k_block_loop = GridwiseGemm::CalculateHasMainKBlockLoop(K_split);
+
+            const auto num_k_per_block =
+                arg.a_grid_desc_kbatch_k0_m_k1_.GetLength(Number<0>{}) / gemm_arg.KBatch;
+
+            const auto Run = [&](const auto& kernel) {
+                if(stream_config.flush_cache)
+                {
+                    typename GridwiseGemm::Argument gemm_arg_ = gemm_arg;
+                    ck::utility::RotatingMemWrapper<typename GridwiseGemm::Argument> rotating_mem(
+                        gemm_arg_,
+                        stream_config.rotating_count,
+                        gemm_arg_.M * gemm_arg_.K * sizeof(ADataType),
+                        gemm_arg_.K * gemm_arg_.N * sizeof(BDataType));
+                    rotating_mem.Print();
+
+                    auto run_flush_cache = [&]() {
+                        // flush icache
+                        ck::utility::flush_icache();
+                        // rotating mem
+                        rotating_mem.Next();
+                    };
+                    ave_time += ck::utility::launch_and_time_kernel_with_preprocess<false>(
+                        stream_config,
+                        run_flush_cache,
+                        kernel,
+                        dim3(gdx, gdy, gdz),
+                        dim3(BlockSize),
+                        0,
+                        gemm_arg_,
+                        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.compute_ptr_offset_of_batch_,
+                        num_k_per_block);
+                }
+                else
+                {
+                    ave_time +=
+                        launch_and_time_kernel(stream_config,
+                                               kernel,
+                                               dim3(gdx, gdy, gdz),
+                                               dim3(BlockSize),
+                                               0,
+                                               gemm_arg,
+                                               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.compute_ptr_offset_of_batch_,
+                                               num_k_per_block);
+                }
+            };
+
+            constexpr index_t minimum_occupancy =
+                BlkGemmPipeSched == BlockGemmPipelineScheduler::Intrawave ? 1 : 2;
+
+            if(has_main_k_block_loop)
+            {
+                // Tail number always full
+                if constexpr(BlkGemmPipelineVer == BlockGemmPipelineVersion::v1 ||
+                             BlkGemmPipelineVer == BlockGemmPipelineVersion::v3)
+                {
+                    if(gemm_arg.KBatch > 1)
+                    {
+                        const auto kernel = kernel_grouped_conv_bwd_weight_xdl_cshuffle_v3<
+                            GridwiseGemm,
+                            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>,
+                            ComputePtrOffsetOfStridedBatch<I1, I1, I0>,
+                            true,
+                            InMemoryDataOperationEnum::AtomicAdd,
+                            minimum_occupancy>;
+                        Run(kernel);
+                    }
+                    else
+                    {
+                        const auto kernel = kernel_grouped_conv_bwd_weight_xdl_cshuffle_v3<
+                            GridwiseGemm,
+                            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>,
+                            ComputePtrOffsetOfStridedBatch<I1, I1, I0>,
+                            true,
+                            InMemoryDataOperationEnum::Set,
+                            minimum_occupancy>;
+                        Run(kernel);
+                    }
+                }
+                // Tail number could be One to Seven
+                else if constexpr(BlkGemmPipelineVer == BlockGemmPipelineVersion::v2)
+                {
+                    if(gemm_arg.KBatch > 1)
+                    {
+                        if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) == TailNumber::One)
+                        {
+                            const auto kernel = kernel_grouped_conv_bwd_weight_xdl_cshuffle_v3<
+                                GridwiseGemm,
+                                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>,
+                                ComputePtrOffsetOfStridedBatch<I1, I1, I0>,
+                                true,
+                                InMemoryDataOperationEnum::AtomicAdd,
+                                minimum_occupancy,
+                                TailNumber::One>;
+                            Run(kernel);
+                        }
+                        else if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) ==
+                                TailNumber::Full)
+                        {
+                            const auto kernel = kernel_grouped_conv_bwd_weight_xdl_cshuffle_v3<
+                                GridwiseGemm,
+                                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>,
+                                ComputePtrOffsetOfStridedBatch<I1, I1, I0>,
+                                true,
+                                InMemoryDataOperationEnum::AtomicAdd,
+                                minimum_occupancy,
+                                TailNumber::Full>;
+                            Run(kernel);
+                        }
+
+                        if constexpr(GridwiseGemm::BlockwiseGemmPipe::PrefetchStages > 2)
+                        {
+                            if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) == TailNumber::Two)
+                            {
+                                const auto kernel = kernel_grouped_conv_bwd_weight_xdl_cshuffle_v3<
+                                    GridwiseGemm,
+                                    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>,
+                                    ComputePtrOffsetOfStridedBatch<I1, I1, I0>,
+                                    true,
+                                    InMemoryDataOperationEnum::AtomicAdd,
+                                    minimum_occupancy,
+                                    TailNumber::Two>;
+                                Run(kernel);
+                            }
+                        }
+
+                        if constexpr(GridwiseGemm::BlockwiseGemmPipe::PrefetchStages > 3)
+                        {
+                            if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) ==
+                               TailNumber::Three)
+                            {
+                                const auto kernel = kernel_grouped_conv_bwd_weight_xdl_cshuffle_v3<
+                                    GridwiseGemm,
+                                    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>,
+                                    ComputePtrOffsetOfStridedBatch<I1, I1, I0>,
+                                    true,
+                                    InMemoryDataOperationEnum::AtomicAdd,
+                                    minimum_occupancy,
+                                    TailNumber::Three>;
+                                Run(kernel);
+                            }
+                        }
+
+                        if constexpr(GridwiseGemm::BlockwiseGemmPipe::PrefetchStages > 4)
+                        {
+                            if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) ==
+                               TailNumber::Four)
+                            {
+                                const auto kernel = kernel_grouped_conv_bwd_weight_xdl_cshuffle_v3<
+                                    GridwiseGemm,
+                                    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>,
+                                    ComputePtrOffsetOfStridedBatch<I1, I1, I0>,
+                                    true,
+                                    InMemoryDataOperationEnum::AtomicAdd,
+                                    minimum_occupancy,
+                                    TailNumber::Four>;
+                                Run(kernel);
+                            }
+                        }
+
+                        if constexpr(GridwiseGemm::BlockwiseGemmPipe::PrefetchStages > 5)
+                        {
+                            if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) ==
+                               TailNumber::Five)
+                            {
+                                const auto kernel = kernel_grouped_conv_bwd_weight_xdl_cshuffle_v3<
+                                    GridwiseGemm,
+                                    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>,
+                                    ComputePtrOffsetOfStridedBatch<I1, I1, I0>,
+                                    true,
+                                    InMemoryDataOperationEnum::AtomicAdd,
+                                    minimum_occupancy,
+                                    TailNumber::Five>;
+                                Run(kernel);
+                            }
+                        }
+
+                        if constexpr(GridwiseGemm::BlockwiseGemmPipe::PrefetchStages > 6)
+                        {
+                            if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) == TailNumber::Six)
+                            {
+                                const auto kernel = kernel_grouped_conv_bwd_weight_xdl_cshuffle_v3<
+                                    GridwiseGemm,
+                                    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>,
+                                    ComputePtrOffsetOfStridedBatch<I1, I1, I0>,
+                                    true,
+                                    InMemoryDataOperationEnum::AtomicAdd,
+                                    minimum_occupancy,
+                                    TailNumber::Six>;
+                                Run(kernel);
+                            }
+                        }
+
+                        if constexpr(GridwiseGemm::BlockwiseGemmPipe::PrefetchStages > 7)
+                        {
+                            if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) ==
+                               TailNumber::Seven)
+                            {
+                                const auto kernel = kernel_grouped_conv_bwd_weight_xdl_cshuffle_v3<
+                                    GridwiseGemm,
+                                    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>,
+                                    ComputePtrOffsetOfStridedBatch<I1, I1, I0>,
+                                    true,
+                                    InMemoryDataOperationEnum::AtomicAdd,
+                                    minimum_occupancy,
+                                    TailNumber::Seven>;
+                                Run(kernel);
+                            }
+                        }
+                    }
+                    else
+                    {
+                        if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) == TailNumber::One)
+                        {
+                            const auto kernel = kernel_grouped_conv_bwd_weight_xdl_cshuffle_v3<
+                                GridwiseGemm,
+                                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>,
+                                ComputePtrOffsetOfStridedBatch<I1, I1, I0>,
+                                true,
+                                InMemoryDataOperationEnum::Set,
+                                minimum_occupancy,
+                                TailNumber::One>;
+                            Run(kernel);
+                        }
+                        else if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) ==
+                                TailNumber::Full)
+                        {
+                            const auto kernel = kernel_grouped_conv_bwd_weight_xdl_cshuffle_v3<
+                                GridwiseGemm,
+                                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>,
+                                ComputePtrOffsetOfStridedBatch<I1, I1, I0>,
+                                true,
+                                InMemoryDataOperationEnum::Set,
+                                minimum_occupancy,
+                                TailNumber::Full>;
+                            Run(kernel);
+                        }
+
+                        if constexpr(GridwiseGemm::BlockwiseGemmPipe::PrefetchStages > 2)
+                        {
+                            if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) == TailNumber::Two)
+                            {
+                                const auto kernel = kernel_grouped_conv_bwd_weight_xdl_cshuffle_v3<
+                                    GridwiseGemm,
+                                    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>,
+                                    ComputePtrOffsetOfStridedBatch<I1, I1, I0>,
+                                    true,
+                                    InMemoryDataOperationEnum::Set,
+                                    minimum_occupancy,
+                                    TailNumber::Two>;
+                                Run(kernel);
+                            }
+                        }
+
+                        if constexpr(GridwiseGemm::BlockwiseGemmPipe::PrefetchStages > 3)
+                        {
+                            if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) ==
+                               TailNumber::Three)
+                            {
+                                const auto kernel = kernel_grouped_conv_bwd_weight_xdl_cshuffle_v3<
+                                    GridwiseGemm,
+                                    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>,
+                                    ComputePtrOffsetOfStridedBatch<I1, I1, I0>,
+                                    true,
+                                    InMemoryDataOperationEnum::Set,
+                                    minimum_occupancy,
+                                    TailNumber::Three>;
+                                Run(kernel);
+                            }
+                        }
+
+                        if constexpr(GridwiseGemm::BlockwiseGemmPipe::PrefetchStages > 4)
+                        {
+                            if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) ==
+                               TailNumber::Four)
+                            {
+                                const auto kernel = kernel_grouped_conv_bwd_weight_xdl_cshuffle_v3<
+                                    GridwiseGemm,
+                                    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>,
+                                    ComputePtrOffsetOfStridedBatch<I1, I1, I0>,
+                                    true,
+                                    InMemoryDataOperationEnum::Set,
+                                    minimum_occupancy,
+                                    TailNumber::Four>;
+                                Run(kernel);
+                            }
+                        }
+
+                        if constexpr(GridwiseGemm::BlockwiseGemmPipe::PrefetchStages > 5)
+                        {
+                            if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) ==
+                               TailNumber::Five)
+                            {
+                                const auto kernel = kernel_grouped_conv_bwd_weight_xdl_cshuffle_v3<
+                                    GridwiseGemm,
+                                    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>,
+                                    ComputePtrOffsetOfStridedBatch<I1, I1, I0>,
+                                    true,
+                                    InMemoryDataOperationEnum::Set,
+                                    minimum_occupancy,
+                                    TailNumber::Five>;
+                                Run(kernel);
+                            }
+                        }
+
+                        if constexpr(GridwiseGemm::BlockwiseGemmPipe::PrefetchStages > 6)
+                        {
+                            if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) == TailNumber::Six)
+                            {
+                                const auto kernel = kernel_grouped_conv_bwd_weight_xdl_cshuffle_v3<
+                                    GridwiseGemm,
+                                    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>,
+                                    ComputePtrOffsetOfStridedBatch<I1, I1, I0>,
+                                    true,
+                                    InMemoryDataOperationEnum::Set,
+                                    minimum_occupancy,
+                                    TailNumber::Six>;
+                                Run(kernel);
+                            }
+                        }
+
+                        if constexpr(GridwiseGemm::BlockwiseGemmPipe::PrefetchStages > 7)
+                        {
+                            if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) ==
+                               TailNumber::Seven)
+                            {
+                                const auto kernel = kernel_grouped_conv_bwd_weight_xdl_cshuffle_v3<
+                                    GridwiseGemm,
+                                    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>,
+                                    ComputePtrOffsetOfStridedBatch<I1, I1, I0>,
+                                    true,
+                                    InMemoryDataOperationEnum::Set,
+                                    minimum_occupancy,
+                                    TailNumber::Seven>;
+                                Run(kernel);
+                            }
+                        }
+                    }
+                }
+
+                // Tail number could be Odd or Even
+                else if constexpr(BlkGemmPipelineVer == BlockGemmPipelineVersion::v4)
+                {
+                    if(gemm_arg.KBatch > 1)
+                    {
+                        if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) == TailNumber::Odd)
+                        {
+                            const auto kernel = kernel_grouped_conv_bwd_weight_xdl_cshuffle_v3_2lds<
+                                GridwiseGemm,
+                                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>,
+                                ComputePtrOffsetOfStridedBatch<I1, I1, I0>,
+                                true,
+                                InMemoryDataOperationEnum::AtomicAdd,
+                                minimum_occupancy,
+                                TailNumber::Odd>;
+                            Run(kernel);
+                        }
+                        else
+                        {
+                            const auto kernel = kernel_grouped_conv_bwd_weight_xdl_cshuffle_v3_2lds<
+                                GridwiseGemm,
+                                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>,
+                                ComputePtrOffsetOfStridedBatch<I1, I1, I0>,
+                                true,
+                                InMemoryDataOperationEnum::AtomicAdd,
+                                minimum_occupancy,
+                                TailNumber::Even>;
+                            Run(kernel);
+                        }
+                    }
+                    else
+                    {
+                        if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) == TailNumber::Odd)
+                        {
+                            const auto kernel = kernel_grouped_conv_bwd_weight_xdl_cshuffle_v3_2lds<
+                                GridwiseGemm,
+                                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>,
+                                ComputePtrOffsetOfStridedBatch<I1, I1, I0>,
+                                true,
+                                InMemoryDataOperationEnum::Set,
+                                minimum_occupancy,
+                                TailNumber::Odd>;
+                            Run(kernel);
+                        }
+                        else
+                        {
+                            const auto kernel = kernel_grouped_conv_bwd_weight_xdl_cshuffle_v3_2lds<
+                                GridwiseGemm,
+                                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>,
+                                ComputePtrOffsetOfStridedBatch<I1, I1, I0>,
+                                true,
+                                InMemoryDataOperationEnum::Set,
+                                minimum_occupancy,
+                                TailNumber::Even>;
+                            Run(kernel);
+                        }
+                    }
+                }
+                else
+                {
+                    if(gemm_arg.KBatch > 1)
+                    {
+                        if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) == TailNumber::Odd)
+                        {
+                            const auto kernel = kernel_grouped_conv_bwd_weight_xdl_cshuffle_v3<
+                                GridwiseGemm,
+                                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>,
+                                ComputePtrOffsetOfStridedBatch<I1, I1, I0>,
+                                true,
+                                InMemoryDataOperationEnum::AtomicAdd,
+                                minimum_occupancy,
+                                TailNumber::Odd>;
+                            Run(kernel);
+                        }
+                        else
+                        {
+                            const auto kernel = kernel_grouped_conv_bwd_weight_xdl_cshuffle_v3<
+                                GridwiseGemm,
+                                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>,
+                                ComputePtrOffsetOfStridedBatch<I1, I1, I0>,
+                                true,
+                                InMemoryDataOperationEnum::AtomicAdd,
+                                minimum_occupancy,
+                                TailNumber::Even>;
+                            Run(kernel);
+                        }
+                    }
+                    else
+                    {
+                        if(GridwiseGemm::CalculateKBlockLoopTailNum(K_split) == TailNumber::Odd)
+                        {
+                            const auto kernel = kernel_grouped_conv_bwd_weight_xdl_cshuffle_v3<
+                                GridwiseGemm,
+                                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>,
+                                ComputePtrOffsetOfStridedBatch<I1, I1, I0>,
+                                true,
+                                InMemoryDataOperationEnum::Set,
+                                minimum_occupancy,
+                                TailNumber::Odd>;
+                            Run(kernel);
+                        }
+                        else
+                        {
+                            const auto kernel = kernel_grouped_conv_bwd_weight_xdl_cshuffle_v3<
+                                GridwiseGemm,
+                                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>,
+                                ComputePtrOffsetOfStridedBatch<I1, I1, I0>,
+                                true,
+                                InMemoryDataOperationEnum::Set,
+                                minimum_occupancy,
+                                TailNumber::Even>;
+                            Run(kernel);
+                        }
+                    }
+                }
+            }
+            else
+            {
+                // Tail number always 1
+                if constexpr(BlkGemmPipelineVer == BlockGemmPipelineVersion::v1)
+                {
+                    if(gemm_arg.KBatch > 1)
+                    {
+                        const auto kernel = kernel_grouped_conv_bwd_weight_xdl_cshuffle_v3<
+                            GridwiseGemm,
+                            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>,
+                            ComputePtrOffsetOfStridedBatch<I1, I1, I0>,
+                            false,
+                            InMemoryDataOperationEnum::AtomicAdd,
+                            minimum_occupancy>;
+                        Run(kernel);
+                    }
+                    else
+                    {
+                        const auto kernel = kernel_grouped_conv_bwd_weight_xdl_cshuffle_v3<
+                            GridwiseGemm,
+                            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>,
+                            ComputePtrOffsetOfStridedBatch<I1, I1, I0>,
+                            false,
+                            InMemoryDataOperationEnum::Set,
+                            minimum_occupancy>;
+                        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)
+    {
+        const index_t GemmM = arg.a_grid_desc_kbatch_k0_m_k1_.GetLength(I1);
+        const index_t GemmN = arg.b_grid_desc_kbatch_k0_n_k1_.GetLength(I1);
+        const index_t GemmK = arg.a_grid_desc_kbatch_k0_m_k1_.GetLength(I0) *
+                              arg.a_grid_desc_kbatch_k0_m_k1_.GetLength(I2);
+
+        typename GridwiseGemm::Argument gemm_arg{
+            nullptr, nullptr, nullptr, GemmM, GemmN, GemmK, I0, I0, I0, arg.k_batch_};
+
+        const auto num_k_loop = gemm_arg.AK0 / (K0PerBlock / K1);
+        if constexpr(BlkGemmPipelineVer != BlockGemmPipelineVersion::v1)
+        {
+            if(num_k_loop <= GridwiseGemm::BlockwiseGemmPipe::PrefetchStages)
+            {
+                return false;
+            }
+        }
+
+        if(!ck::is_xdl_supported())
+        {
+            return false;
+        }
+
+        if(!is_bf16_atomic_supported() && std::is_same_v<CDataType, ck::bhalf_t> &&
+           arg.k_batch_ > 1)
+        {
+            return false;
+        }
+
+        if constexpr(NDimSpatial == 1)
+        {
+            if constexpr(!is_GNWC_GKXC_GNWK<InLayout, WeiLayout, OutLayout>())
+            {
+                return false;
+            }
+        }
+        else if constexpr(NDimSpatial == 2)
+        {
+            if constexpr(!(is_NHWGC_GKYXC_NHWGK<InLayout, WeiLayout, OutLayout>() ||
+                           is_GNHWC_GKYXC_GNHWK<InLayout, WeiLayout, OutLayout>()))
+            {
+                return false;
+            }
+        }
+        else if constexpr(NDimSpatial == 3)
+        {
+            if constexpr(!(is_NDHWGC_GKZYXC_NDHWGK<InLayout, WeiLayout, OutLayout>() ||
+                           is_GNDHWC_GKZYXC_GNDHWK<InLayout, WeiLayout, OutLayout>()))
+            {
+                return false;
+            }
+        }
+        else
+        {
+            return false;
+        }
+
+        if constexpr(ConvBackwardWeightSpecialization ==
+                     ConvolutionBackwardWeightSpecialization::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;
+                }
+            }
+        }
+        if(!(ABlockTransferSrcVectorDim == 1 && 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_ % CBlockTransferScalarPerVector_NWaveNPerXdl == 0))
+        {
+            return false;
+        }
+
+        // Gridwise GEMM size
+        return true;
+    }
+
+    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,
+                 const std::array<index_t, NDimSpatial + 3>& b_g_n_c_wis_lengths, // input
+                 const std::array<index_t, NDimSpatial + 3>& b_g_n_c_wis_strides,
+                 const std::array<index_t, NDimSpatial + 3>& e_g_k_c_xs_lengths, // weight
+                 const std::array<index_t, NDimSpatial + 3>& e_g_k_c_xs_strides,
+                 const std::array<index_t, NDimSpatial + 3>& a_g_n_k_wos_lengths, // output
+                 const std::array<index_t, NDimSpatial + 3>& a_g_n_k_wos_strides,
+                 const std::array<ck::index_t, NDimSpatial>& conv_filter_strides,
+                 const std::array<ck::index_t, NDimSpatial>& conv_filter_dilations,
+                 const std::array<ck::index_t, NDimSpatial>& input_left_pads,
+                 const std::array<ck::index_t, NDimSpatial>& input_right_pads,
+                 InElementwiseOperation in_element_op,
+                 WeiElementwiseOperation wei_element_op,
+                 OutElementwiseOperation out_element_op,
+                 const ck::index_t split_k)
+    {
+        return Argument{p_in_grid,
+                        p_wei_grid,
+                        p_out_grid,
+                        b_g_n_c_wis_lengths, // input
+                        b_g_n_c_wis_strides,
+                        e_g_k_c_xs_lengths, // weight
+                        e_g_k_c_xs_strides,
+                        a_g_n_k_wos_lengths, // output
+                        a_g_n_k_wos_strides,
+                        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,
+                        const std::array<index_t, NDimSpatial + 3>& b_g_n_c_wis_lengths, // input
+                        const std::array<index_t, NDimSpatial + 3>& b_g_n_c_wis_strides,
+                        const std::array<index_t, NDimSpatial + 3>& e_g_k_c_xs_lengths, // weight
+                        const std::array<index_t, NDimSpatial + 3>& e_g_k_c_xs_strides,
+                        const std::array<index_t, NDimSpatial + 3>& a_g_n_k_wos_lengths, // output
+                        const std::array<index_t, NDimSpatial + 3>& a_g_n_k_wos_strides,
+                        const std::array<ck::index_t, NDimSpatial>& conv_filter_strides,
+                        const std::array<ck::index_t, NDimSpatial>& conv_filter_dilations,
+                        const std::array<ck::index_t, NDimSpatial>& input_left_pads,
+                        const std::array<ck::index_t, NDimSpatial>& input_right_pads,
+                        InElementwiseOperation in_element_op,
+                        WeiElementwiseOperation wei_element_op,
+                        OutElementwiseOperation out_element_op,
+                        const ck::index_t split_k) 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),
+                                          b_g_n_c_wis_lengths, // input
+                                          b_g_n_c_wis_strides,
+                                          e_g_k_c_xs_lengths, // weight
+                                          e_g_k_c_xs_strides,
+                                          a_g_n_k_wos_lengths, // output
+                                          a_g_n_k_wos_strides,
+                                          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 << "DeviceGroupedConvBwdWeight_Xdl_CShuffleV3"
+            << "<"
+            << BlockSize << ", "
+            << MPerBlock << ", "
+            << NPerBlock << ", "
+            << K0PerBlock << ", "
+            << getConvBackwardWeightSpecializationString(ConvBackwardWeightSpecialization) << ", "
+            << K1 << ", "
+            << MXdlPerWave << ", "
+            << NXdlPerWave << ", "
+            << ABlockTransferSrcScalarPerVector << ", "
+            << ABlockTransferDstScalarPerVector_K1 << ", "
+            << BBlockTransferSrcScalarPerVector << ", "
+            << BBlockTransferDstScalarPerVector_K1 << ", "
+            << CShuffleMXdlPerWavePerShuffle << ", "
+            << CShuffleNXdlPerWavePerShuffle << ", "
+            << CBlockTransferScalarPerVector_NWaveNPerXdl
+            << ">";
+        // clang-format on
+
+        return str.str();
+    }
+};
+
+} // namespace device
+} // namespace tensor_operation
+} // namespace ck

include/ck/tensor_operation/operator_transform/transform_conv_bwd_weight_to_gemm_v2.hpp

 // SPDX-License-Identifier: MIT
-// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
+// Copyright (c) 2024-2025, Advanced Micro Devices, Inc. All rights reserved.
 
 #pragma once
 
@@ -34,6 +34,94 @@ struct TransformConvBwdWeightToGemmV2
     static constexpr auto I0 = Number<0>{};
     static constexpr auto I1 = Number<1>{};
 
+    template <index_t NDim, typename enable_if<NDim == 1, bool>::type = false>
+    constexpr static auto
+    make_out_grid_desc(const index_t N,
+                       const index_t Wo,
+                       const index_t K,
+                       const std::array<index_t, NDimSpatial + 3>& output_strides)
+    {
+        const index_t BatchStride = output_strides[0];
+        const index_t WoStride    = output_strides[3];
+        const auto KStride        = Number<1>{};
+        return make_naive_tensor_descriptor(make_tuple(N * Wo, NumGroupsToMerge, K),
+                                            make_tuple(WoStride, BatchStride, KStride));
+    }
+
+    template <index_t NDim, typename enable_if<NDim == 1, bool>::type = false>
+    constexpr static auto
+    make_in_grid_desc(const index_t N,
+                      const index_t Wi,
+                      const index_t C,
+                      const std::array<index_t, NDimSpatial + 3>& input_strides)
+    {
+        const index_t BatchStride = input_strides[0];
+        const index_t NStride     = input_strides[1];
+        const index_t WiStride    = input_strides[3];
+        const auto CStride        = input_strides[2];
+        if constexpr(ConvBackwardWeightSpecialization ==
+                     device::ConvolutionBackwardWeightSpecialization::Filter1x1Stride1Pad0)
+        {
+            return make_naive_tensor_descriptor(make_tuple(N * Wi, NumGroupsToMerge, C),
+                                                make_tuple(WiStride, BatchStride, CStride));
+        }
+        else
+        {
+            return make_naive_tensor_descriptor(
+                make_tuple(N, Wi, NumGroupsToMerge, C),
+                make_tuple(NStride, WiStride, BatchStride, CStride));
+        }
+    }
+
+    template <index_t NDim, typename enable_if<NDim == 1, bool>::type = false>
+    constexpr static auto
+    make_wei_grid_desc(const index_t K,
+                       const index_t X,
+                       const index_t C,
+                       const std::array<index_t, NDimSpatial + 3>& weights_strides)
+    {
+        const auto CStride     = Number<1>{};
+        const auto KStride     = weights_strides[1];
+        const auto XStride     = weights_strides[3];
+        const auto BatchStride = weights_strides[0];
+        // Add NumGroupsToMerge for Batch+M dimension and, 1 as a placehorder
+        // for Batch+N dimension
+        const auto desc = make_naive_tensor_descriptor(
+            make_tuple(NumGroupsToMerge, K, X, 1, C),
+            make_tuple(BatchStride, KStride, XStride, BatchStride, CStride));
+        // Padd 1 to NumGroupsToMerge
+        const auto padded_desc = transform_tensor_descriptor(
+            desc,
+            make_tuple(make_pass_through_transform(NumGroupsToMerge),
+                       make_pass_through_transform(K),
+                       make_pass_through_transform(X),
+                       make_pad_transform(1, 0, NumGroupsToMerge - 1),
+                       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>{}));
+        // We need only matrices from diagonal. Xor returns 0 for the same
+        // values. So if matrices is not on diagonal then it will be stored in padding.
+        // To avoid use of modulo after xor we assume that NumBatch to merge is power of 2.
+        static_assert(NumGroupsToMerge == 1 || NumGroupsToMerge == 2 || NumGroupsToMerge == 4 ||
+                      NumGroupsToMerge == 8 || NumGroupsToMerge == 16 || NumGroupsToMerge == 32 ||
+                      NumGroupsToMerge == 64);
+        const auto unmerged_padded_desc = transform_tensor_descriptor(
+            padded_desc,
+            make_tuple(make_xor_transform(make_tuple(NumGroupsToMerge, NumGroupsToMerge)),
+                       make_pass_through_transform(K),
+                       make_pass_through_transform(X),
+                       make_pass_through_transform(C)),
+            make_tuple(Sequence<0, 3>{}, Sequence<1>{}, Sequence<2>{}, Sequence<4>{}),
+            make_tuple(Sequence<0, 3>{}, Sequence<1>{}, Sequence<2>{}, Sequence<4>{}));
+        // Merge To M, N
+        return transform_tensor_descriptor(
+            unmerged_padded_desc,
+            make_tuple(make_merge_transform(make_tuple(NumGroupsToMerge, K)),
+                       make_merge_transform(make_tuple(X, NumGroupsToMerge, C))),
+            make_tuple(Sequence<0, 1>{}, Sequence<2, 3, 4>{}),
+            make_tuple(Sequence<0>{}, Sequence<1>{}));
+    }
+
     template <index_t NDim, typename enable_if<NDim == 2, bool>::type = false>
     constexpr static auto
     make_out_grid_desc(const index_t N,
@@ -221,6 +309,187 @@ struct TransformConvBwdWeightToGemmV2
             make_tuple(Sequence<0>{}, Sequence<1>{}));
     }
 
+    template <index_t NDim, typename enable_if<NDim == 1, bool>::type = false>
+    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,
+        const std::array<index_t, NDimSpatial>& input_spatial_lengths,
+        const std::array<index_t, NDimSpatial>& filter_spatial_lengths,
+        const std::array<index_t, NDimSpatial>& output_spatial_lengths,
+        const std::array<index_t, NDimSpatial + 3>& input_strides,
+        const std::array<index_t, NDimSpatial + 3>& weights_strides,
+        const std::array<index_t, NDimSpatial + 3>& output_strides,
+        const std::array<index_t, NDimSpatial>& conv_filter_strides,
+        const std::array<index_t, NDimSpatial>& conv_filter_dilations,
+        const std::array<index_t, NDimSpatial>& input_left_pads,
+        const std::array<index_t, NDimSpatial>& input_right_pads,
+        const index_t batch_k)
+    {
+        using namespace ck;
+
+        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 ConvStrideW = conv_filter_strides[0];
+
+        const index_t ConvDilationW = conv_filter_dilations[0];
+
+        const index_t InLeftPadW = input_left_pads[0];
+
+        const index_t InRightPadW = input_right_pads[0];
+
+        const index_t GemmKTotal = N * Wo;
+        const index_t GemmM      = K * NumGroupsToMerge;
+        const index_t GemmN      = C * X * NumGroupsToMerge;
+
+        const auto PadGemmM = MPerBlock - GemmM % MPerBlock;
+        const auto PadGemmN = NPerBlock - GemmN % NPerBlock;
+
+        const index_t GemmKBatch = batch_k;
+        const index_t GemmK0 =
+            math::integer_divide_ceil(GemmKTotal, GemmK1Number * K0PerBlock * GemmKBatch) *
+            K0PerBlock;
+        const index_t GemmKPad = GemmKBatch * GemmK0 * GemmK1Number;
+
+        const auto out_grid_desc = make_out_grid_desc<NDim>(N, Wo, K, output_strides);
+        const auto in_grid_desc  = make_in_grid_desc<NDim>(N, Wi, C, input_strides);
+        const auto wei_grid_desc = make_wei_grid_desc<NDim>(K, X, C, weights_strides);
+
+        if constexpr(ConvBackwardWeightSpecialization ==
+                     device::ConvolutionBackwardWeightSpecialization::Filter1x1Stride1Pad0)
+        {
+            // A: output tensor
+            const auto out_gemmkpad_gemmm_grid_desc = transform_tensor_descriptor(
+                out_grid_desc,
+                make_tuple(
+                    make_right_pad_transform(GemmKTotal, GemmKPad - GemmKTotal),
+                    make_merge_transform(make_tuple(NumGroupsToMerge, GemmM / NumGroupsToMerge))),
+                make_tuple(Sequence<0>{}, Sequence<1, 2>{}),
+                make_tuple(Sequence<0>{}, Sequence<1>{}));
+
+            const auto out_gemmkbatch_gemmk0_gemmm_gemmk1_grid_desc = transform_tensor_descriptor(
+                out_gemmkpad_gemmm_grid_desc,
+                make_tuple(make_unmerge_transform(make_tuple(GemmKBatch * GemmK0, GemmK1Number)),
+                           make_pass_through_transform(GemmM)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}),
+                make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
+
+            // B: input tensor
+            const auto in_gemmkpad_gemmn_grid_desc = transform_tensor_descriptor(
+                in_grid_desc,
+                make_tuple(
+                    make_right_pad_transform(GemmKTotal, GemmKPad - GemmKTotal),
+                    make_merge_transform(make_tuple(NumGroupsToMerge, GemmN / NumGroupsToMerge))),
+                make_tuple(Sequence<0>{}, Sequence<1, 2>{}),
+                make_tuple(Sequence<0>{}, Sequence<1>{}));
+
+            const auto in_gemmkbatch_gemmk0_gemmn_gemmk1_grid_desc = transform_tensor_descriptor(
+                in_gemmkpad_gemmn_grid_desc,
+                make_tuple(make_unmerge_transform(make_tuple(GemmKBatch * GemmK0, GemmK1Number)),
+                           make_pass_through_transform(GemmN)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}),
+                make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
+
+            return make_tuple(out_gemmkbatch_gemmk0_gemmm_gemmk1_grid_desc,
+                              in_gemmkbatch_gemmk0_gemmn_gemmk1_grid_desc,
+                              wei_grid_desc);
+        }
+        else
+        {
+            // A: output tensor
+            const auto out_gemmkpad_gemmm_grid_desc = transform_tensor_descriptor(
+                out_grid_desc,
+                make_tuple(
+                    make_right_pad_transform(GemmKTotal, GemmKPad - GemmKTotal),
+                    make_merge_transform(make_tuple(NumGroupsToMerge, GemmM / NumGroupsToMerge))),
+                make_tuple(Sequence<0>{}, Sequence<1, 2>{}),
+                make_tuple(Sequence<0>{}, Sequence<1>{}));
+
+            const auto out_gemmkbatch_gemmk0_gemmm_gemmk1_grid_desc = transform_tensor_descriptor(
+                out_gemmkpad_gemmm_grid_desc,
+                make_tuple(make_unmerge_transform(make_tuple(GemmKBatch * GemmK0, GemmK1Number)),
+                           make_pass_through_transform(GemmM)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}),
+                make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
+
+            // B: input tensor
+            const auto in_n_hip_wip_c_grid_desc = transform_tensor_descriptor(
+                in_grid_desc,
+                make_tuple(make_pass_through_transform(N),
+                           make_pad_transform(Wi, InLeftPadW, InRightPadW),
+                           make_pass_through_transform(NumGroupsToMerge),
+                           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(X, Wo), make_tuple(ConvDilationW, ConvStrideW)),
+                    make_pass_through_transform(NumGroupsToMerge),
+                    make_pass_through_transform(C)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
+                make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3>{}, Sequence<4>{}));
+
+            const auto in_gemmktotal_gemmn_grid_desc = transform_tensor_descriptor(
+                in_n_y_ho_x_wo_c_grid_desc,
+                make_tuple(make_merge_transform(make_tuple(X, NumGroupsToMerge, C)),
+                           make_merge_transform(make_tuple(N, Wo))),
+                make_tuple(Sequence<1, 3, 4>{}, Sequence<0, 2>{}),
+                make_tuple(Sequence<1>{}, Sequence<0>{}));
+
+            const auto in_gemmkpad_gemmn_grid_desc = transform_tensor_descriptor(
+                in_gemmktotal_gemmn_grid_desc,
+                make_tuple(make_right_pad_transform(GemmKTotal, GemmKPad - GemmKTotal),
+                           make_pass_through_transform(GemmN)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}),
+                make_tuple(Sequence<0>{}, Sequence<1>{}));
+
+            const auto in_gemmkbatch_gemmk0_gemmn_gemmk1_grid_desc = transform_tensor_descriptor(
+                in_gemmkpad_gemmn_grid_desc,
+                make_tuple(make_unmerge_transform(make_tuple(GemmKBatch * GemmK0, GemmK1Number)),
+                           make_pass_through_transform(GemmN)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}),
+                make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
+
+            // Padd
+            const auto out_gemmkbatch_gemmk0_gemmm_gemmk1_pad_grid_desc =
+                transform_tensor_descriptor(
+                    out_gemmkbatch_gemmk0_gemmm_gemmk1_grid_desc,
+                    make_tuple(make_pass_through_transform(GemmKBatch * GemmK0),
+                               make_right_pad_transform(GemmM, PadGemmM),
+                               make_pass_through_transform(GemmK1Number)),
+                    make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
+                    make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
+
+            const auto in_gemmkbatch_gemmk0_gemmn_gemmk1_pad_grid_desc =
+                transform_tensor_descriptor(
+                    in_gemmkbatch_gemmk0_gemmn_gemmk1_grid_desc,
+                    make_tuple(make_pass_through_transform(GemmKBatch * GemmK0),
+                               make_right_pad_transform(GemmN, PadGemmN),
+                               make_pass_through_transform(GemmK1Number)),
+                    make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
+                    make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
+
+            const auto wei_gemmm_gemmn_pad_grid_desc =
+                transform_tensor_descriptor(wei_grid_desc,
+                                            make_tuple(make_right_pad_transform(GemmM, PadGemmM),
+                                                       make_right_pad_transform(GemmN, PadGemmN)),
+                                            make_tuple(Sequence<0>{}, Sequence<1>{}),
+                                            make_tuple(Sequence<0>{}, Sequence<1>{}));
+
+            return make_tuple(out_gemmkbatch_gemmk0_gemmm_gemmk1_pad_grid_desc,
+                              in_gemmkbatch_gemmk0_gemmn_gemmk1_pad_grid_desc,
+                              wei_gemmm_gemmn_pad_grid_desc);
+        }
+
+    } // function end
+
     template <index_t NDim, typename enable_if<NDim == 2, bool>::type = false>
     static auto MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N(
         const index_t N,

library/include/ck/library/tensor_operation_instance/gpu/grouped_conv_bwd_weight/device_grouped_conv_bwd_weight_v3_xdl_instance.hpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
+
+#include "ck/ck.hpp"
+#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
+#include "ck/tensor_operation/gpu/device/impl/device_grouped_conv_bwd_weight_xdl_cshuffle_v3.hpp"
+#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
+
+#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
+
+namespace ck {
+namespace tensor_operation {
+namespace device {
+namespace instance {
+
+using namespace ck::tensor_layout::convolution;
+
+using BF16 = ck::bhalf_t;
+using F16  = ck::half_t;
+using F32  = float;
+
+#ifdef CK_ENABLE_FP8
+using F8 = ck::f8_t;
+#endif
+
+#ifdef CK_ENABLE_BF8
+using BF8 = ck::bf8_t;
+#endif
+
+using Empty_Tuple = ck::Tuple<>;
+
+template <ck::index_t... Is>
+using S = ck::Sequence<Is...>;
+
+using PassThrough = ck::tensor_operation::element_wise::PassThrough;
+
+static constexpr auto ConvBwdWeightDefault =
+    ck::tensor_operation::device::ConvolutionBackwardWeightSpecialization::Default;
+
+static constexpr auto ConvBwdWeightFilter1x1Stride1Pad0 =
+    ck::tensor_operation::device::ConvolutionBackwardWeightSpecialization::Filter1x1Stride1Pad0;
+
+template <ck::index_t NDimSpatial,
+          typename ALayout,
+          typename BLayout,
+          typename ELayout,
+          ConvolutionBackwardWeightSpecialization ConvSpec,
+          BlockGemmPipelineScheduler Scheduler,
+          BlockGemmPipelineVersion PipelineVersion>
+using device_grouped_conv_bwd_weight_v3_xdl_c_shuffle_f32_instances = std::tuple<
+    // clang-format off
+        //#########################################|     Num| InLayout| WeiLayout| OutLayout| InData| WeiData| OutData| AccData|          In|         Wei|         Out|              ConvBackward| Block|  MPer|  NPer| K0Per| K1| MPer| NPer| MXdl| NXdl|  ABlockTransfer|   ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds|  BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds|    CShuffle|    CShuffle|   CBlockTransfer|  CBlockTransfer| BlockGemm| BlockGemm|
+        //#########################################|     Dim|         |          |          |   Type|    Type|    Type|    Type| Elementwise| Elementwise| Elementwise|                    Weight|  Size| Block| Block| Block|   |  XDL|  XDL|  Per|  Per|   ThreadCluster|    ThreadCluster| SrcAccessOrder|   SrcVectorDim|      SrcScalar|      DstScalar| AddExtraM|   ThreadCluster|  ThreadCluster| SrcAccessOrder|  SrcVectorDim|      SrcScalar|      DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave|   ClusterLengths| ScalarPerVector|  Pipeline|  Pipeline|
+        //#########################################| Spatial|         |          |          |       |        |        |        |   Operation|   Operation|   Operation|            Specialization|      |      |      |      |   |     |     | Wave| Wave| Lengths_K0_M_K1|     ArrangeOrder|               |               |      PerVector|   PerVector_K1|          | Lengths_K0_N_K1|   ArrangeOrder|               |              |      PerVector|   PerVector_K1|          |  PerShuffle|  PerShuffle| MBlock_MPerBlock|    NWaveNPerXdl| Scheduler|   Version|
+        //#########################################|        |         |          |          |       |        |        |        |            |            |            |                          |      |      |      |      |   |     |     |     |     |                |                 |               |               |               |               |          |                |               |               |              |               |               |          |            |            | NBlock_NPerBlock|                |          |          |
+        // generic instance
+        DeviceGroupedConvBwdWeight_Xdl_CShuffleV3< NDimSpatial,  ALayout,   BLayout,   ELayout,    F32,     F32,     F32,     F32, PassThrough, PassThrough, PassThrough,                  ConvSpec,    64,    16,    16,     32,   8,   16,   16,    1,    1,  S<4, 8,  1>, S<2, 0, 1>,  S<1, 0, 2>,                   1,              1,              4,      false,  S<4, 8,  1>,  S<2, 0, 1>,  S<1, 0, 2>,                1,              1,              4,      false,           1,           1,   S<1, 8, 1, 8>,                  2, Scheduler, PipelineVersion>
+    // clang-format on
+    >;
+
+template <ck::index_t NDimSpatial,
+          typename ALayout,
+          typename BLayout,
+          typename ELayout,
+          ConvolutionBackwardWeightSpecialization ConvSpec,
+          BlockGemmPipelineScheduler Scheduler,
+          BlockGemmPipelineVersion PipelineVersion>
+using device_grouped_conv_bwd_weight_v3_xdl_c_shuffle_f16_instances = std::tuple<
+    // clang-format off
+        //#########################################|     Num| InLayout| WeiLayout| OutLayout| InData| WeiData| OutData| AccData|          In|         Wei|         Out|              ConvBackward| Block|  MPer|  NPer| K0Per| K1| MPer| NPer| MXdl| NXdl|  ABlockTransfer|   ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds|  BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds|    CShuffle|    CShuffle|   CBlockTransfer|  CBlockTransfer| BlockGemm| BlockGemm|
+        //#########################################|     Dim|         |          |          |   Type|    Type|    Type|    Type| Elementwise| Elementwise| Elementwise|                    Weight|  Size| Block| Block| Block|   |  XDL|  XDL|  Per|  Per|   ThreadCluster|    ThreadCluster| SrcAccessOrder|   SrcVectorDim|      SrcScalar|      DstScalar| AddExtraM|   ThreadCluster|  ThreadCluster| SrcAccessOrder|  SrcVectorDim|      SrcScalar|      DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave|   ClusterLengths| ScalarPerVector|  Pipeline|  Pipeline|
+        //#########################################| Spatial|         |          |          |       |        |        |        |   Operation|   Operation|   Operation|            Specialization|      |      |      |      |   |     |     | Wave| Wave| Lengths_K0_M_K1|     ArrangeOrder|               |               |      PerVector|   PerVector_K1|          | Lengths_K0_N_K1|   ArrangeOrder|               |              |      PerVector|   PerVector_K1|          |  PerShuffle|  PerShuffle| MBlock_MPerBlock|    NWaveNPerXdl| Scheduler|   Version|
+        //#########################################|        |         |          |          |       |        |        |        |            |            |            |                          |      |      |      |      |   |     |     |     |     |                |                 |               |               |               |               |          |                |               |               |              |               |               |          |            |            | NBlock_NPerBlock|                |          |          |
+        // generic instance
+        DeviceGroupedConvBwdWeight_Xdl_CShuffleV3< NDimSpatial,  ALayout,   BLayout,   ELayout,    F16,     F16,     F16,     F32, PassThrough, PassThrough, PassThrough,                  ConvSpec,    64,    32,    32,     32,   8,   32,   32,    1,    1,  S<4, 8,  1>, S<2, 0, 1>,  S<1, 0, 2>,                   1,              2,              2,      false,  S<4, 16, 1>,  S<2, 0, 1>,  S<1, 0, 2>,                1,              2,              2,      false,           1,           1,   S<1, 8, 1, 8>,                  2, Scheduler, PipelineVersion>,
+        DeviceGroupedConvBwdWeight_Xdl_CShuffleV3< NDimSpatial,  ALayout,   BLayout,   ELayout,    F16,     F16,     F16,     F32, PassThrough, PassThrough, PassThrough,                  ConvSpec,    64,    32,    64,     32,   8,   32,   32,    1,    2,  S<4, 8,  1>, S<2, 0, 1>,  S<1, 0, 2>,                   1,              4,              4,      false,  S<4, 16, 1>,  S<2, 0, 1>,  S<1, 0, 2>,                1,              4,              4,      false,           1,           1,   S<1, 8, 1, 8>,                  2, Scheduler, PipelineVersion>,
+        DeviceGroupedConvBwdWeight_Xdl_CShuffleV3< NDimSpatial,  ALayout,   BLayout,   ELayout,    F16,     F16,     F16,     F32, PassThrough, PassThrough, PassThrough,                  ConvSpec,    64,    32,   128,     32,   8,   32,   32,    1,    4,  S<4, 4,  1>, S<2, 0, 1>,  S<1, 0, 2>,                   1,              8,              8,      false,  S<4, 16, 1>,  S<2, 0, 1>,  S<1, 0, 2>,                1,              8,              8,      false,           1,           1,   S<1, 8, 1, 8>,                  2, Scheduler, PipelineVersion>,
+        DeviceGroupedConvBwdWeight_Xdl_CShuffleV3< NDimSpatial,  ALayout,   BLayout,   ELayout,    F16,     F16,     F16,     F32, PassThrough, PassThrough, PassThrough,                  ConvSpec,    64,    64,    32,     32,   8,   32,   32,    2,    1,  S<4, 16, 1>, S<2, 0, 1>,  S<1, 0, 2>,                   1,              4,              4,      false,  S<4, 8,  1>,  S<2, 0, 1>,  S<1, 0, 2>,                1,              4,              4,      false,           1,           1,   S<1, 8, 1, 8>,                  2, Scheduler, PipelineVersion>,
+        DeviceGroupedConvBwdWeight_Xdl_CShuffleV3< NDimSpatial,  ALayout,   BLayout,   ELayout,    F16,     F16,     F16,     F32, PassThrough, PassThrough, PassThrough,                  ConvSpec,    64,   128,    32,     32,   8,   32,   32,    4,    1,  S<4, 16, 1>, S<2, 0, 1>,  S<1, 0, 2>,                   1,              8,              8,      false,  S<4, 4,  1>,  S<2, 0, 1>,  S<1, 0, 2>,                1,              8,              8,      false,           1,           1,   S<1, 8, 1, 8>,                  2, Scheduler, PipelineVersion>,       
+        DeviceGroupedConvBwdWeight_Xdl_CShuffleV3< NDimSpatial,  ALayout,   BLayout,   ELayout,    F16,     F16,     F16,     F32, PassThrough, PassThrough, PassThrough,                  ConvSpec,    64,    64,    80,     32,   8,   16,   16,    4,    5,  S<4, 16, 1>, S<2, 0, 1>,  S<2, 0, 1>,                   1,              4,              4,      false,  S<4, 16, 1>,  S<2, 0, 1>,  S<2, 0, 1>,                1,              5,              4,      false,           1,           1,   S<1, 8, 1, 8>,                  2, Scheduler, PipelineVersion>,
+        DeviceGroupedConvBwdWeight_Xdl_CShuffleV3< NDimSpatial,  ALayout,   BLayout,   ELayout,    F16,     F16,     F16,     F32, PassThrough, PassThrough, PassThrough,                  ConvSpec,    64,    64,   112,     32,   8,   16,   16,    4,    7,  S<4, 16, 1>, S<2, 0, 1>,  S<2, 0, 1>,                   1,              4,              4,      false,  S<4, 16, 1>,  S<2, 0, 1>,  S<2, 0, 1>,                1,              7,              4,      false,           1,           1,   S<1, 8, 1, 8>,                  2, Scheduler, PipelineVersion>
+    // clang-format on
+    >;
+
+template <ck::index_t NDimSpatial,
+          typename ALayout,
+          typename BLayout,
+          typename ELayout,
+          ConvolutionBackwardWeightSpecialization ConvSpec,
+          BlockGemmPipelineScheduler Scheduler,
+          BlockGemmPipelineVersion PipelineVersion>
+using device_grouped_conv_bwd_weight_v3_xdl_c_shuffle_bf16_instances = std::tuple<
+    // clang-format off
+        //#########################################|     Num| InLayout| WeiLayout| OutLayout| InData| WeiData| OutData| AccData|          In|         Wei|         Out|              ConvBackward| Block|  MPer|  NPer| K0Per| K1| MPer| NPer| MXdl| NXdl|  ABlockTransfer|   ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds|  BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds|    CShuffle|    CShuffle|   CBlockTransfer|  CBlockTransfer| BlockGemm| BlockGemm|
+        //#########################################|     Dim|         |          |          |   Type|    Type|    Type|    Type| Elementwise| Elementwise| Elementwise|                    Weight|  Size| Block| Block| Block|   |  XDL|  XDL|  Per|  Per|   ThreadCluster|    ThreadCluster| SrcAccessOrder|   SrcVectorDim|      SrcScalar|      DstScalar| AddExtraM|   ThreadCluster|  ThreadCluster| SrcAccessOrder|  SrcVectorDim|      SrcScalar|      DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave|   ClusterLengths| ScalarPerVector|  Pipeline|  Pipeline|
+        //#########################################| Spatial|         |          |          |       |        |        |        |   Operation|   Operation|   Operation|            Specialization|      |      |      |      |   |     |     | Wave| Wave| Lengths_K0_M_K1|     ArrangeOrder|               |               |      PerVector|   PerVector_K1|          | Lengths_K0_N_K1|   ArrangeOrder|               |              |      PerVector|   PerVector_K1|          |  PerShuffle|  PerShuffle| MBlock_MPerBlock|    NWaveNPerXdl| Scheduler|   Version|
+        //#########################################|        |         |          |          |       |        |        |        |            |            |            |                          |      |      |      |      |   |     |     |     |     |                |                 |               |               |               |               |          |                |               |               |              |               |               |          |            |            | NBlock_NPerBlock|                |          |          |
+        // generic instance
+        DeviceGroupedConvBwdWeight_Xdl_CShuffleV3< NDimSpatial,  ALayout,   BLayout,   ELayout,   BF16,    BF16,    BF16,     F32, PassThrough, PassThrough, PassThrough,                  ConvSpec,    64,    32,    32,     32,   8,   32,   32,    1,    1,  S<4, 8,  1>,  S<2, 0, 1>,  S<1, 0, 2>,                  1,              2,              2,      false,  S<4, 16,  1>, S<2, 0, 1>,  S<1, 0, 2>,                1,              2,              2,      false,           1,           1,   S<1, 8, 1, 8>,                  2, Scheduler, PipelineVersion>,
+        DeviceGroupedConvBwdWeight_Xdl_CShuffleV3< NDimSpatial,  ALayout,   BLayout,   ELayout,   BF16,    BF16,    BF16,     F32, PassThrough, PassThrough, PassThrough,                  ConvSpec,    64,    32,    64,     32,   8,   32,   32,    1,    2,  S<4, 8,  1>,  S<2, 0, 1>,  S<1, 0, 2>,                  1,              4,              4,      false,  S<4, 16,  1>, S<2, 0, 1>,  S<1, 0, 2>,                1,              4,              4,      false,           1,           1,   S<1, 8, 1, 8>,                  2, Scheduler, PipelineVersion>,
+        DeviceGroupedConvBwdWeight_Xdl_CShuffleV3< NDimSpatial,  ALayout,   BLayout,   ELayout,   BF16,    BF16,    BF16,     F32, PassThrough, PassThrough, PassThrough,                  ConvSpec,    64,    32,   128,     32,   8,   32,   32,    1,    4,  S<4, 4,  1>,  S<2, 0, 1>,  S<1, 0, 2>,                  1,              8,              8,      false,  S<4, 16,  1>, S<2, 0, 1>,  S<1, 0, 2>,                1,              8,              8,      false,           1,           1,   S<1, 8, 1, 8>,                  2, Scheduler, PipelineVersion>,
+        DeviceGroupedConvBwdWeight_Xdl_CShuffleV3< NDimSpatial,  ALayout,   BLayout,   ELayout,   BF16,    BF16,    BF16,     F32, PassThrough, PassThrough, PassThrough,                  ConvSpec,    64,    64,    32,     32,   8,   32,   32,    2,    1,  S<4, 16, 1>,  S<2, 0, 1>,  S<1, 0, 2>,                  1,              4,              4,      false,  S<4, 8,  1>,  S<2, 0, 1>,  S<1, 0, 2>,                1,              4,              4,      false,           1,           1,   S<1, 8, 1, 8>,                  2, Scheduler, PipelineVersion>,
+        DeviceGroupedConvBwdWeight_Xdl_CShuffleV3< NDimSpatial,  ALayout,   BLayout,   ELayout,   BF16,    BF16,    BF16,     F32, PassThrough, PassThrough, PassThrough,                  ConvSpec,    64,   128,    32,     32,   8,   32,   32,    4,    1,  S<4, 16, 1>,  S<2, 0, 1>,  S<1, 0, 2>,                  1,              8,              8,      false,  S<4, 4,  1>,  S<2, 0, 1>,  S<1, 0, 2>,                1,              8,              8,      false,           1,           1,   S<1, 8, 1, 8>,                  2, Scheduler, PipelineVersion>,
+        DeviceGroupedConvBwdWeight_Xdl_CShuffleV3< NDimSpatial,  ALayout,   BLayout,   ELayout,   BF16,    BF16,    BF16,     F32, PassThrough, PassThrough, PassThrough,                  ConvSpec,    64,    64,    80,     32,   8,   16,   16,    4,    5,  S<4, 16, 1>,  S<2, 0, 1>,  S<2, 0, 1>,                  1,              4,              4,      false,  S<4, 16,  1>,  S<2, 0, 1>,  S<2, 0, 1>,               1,              5,              4,      false,           1,           1,   S<1, 8, 1, 8>,                  2, Scheduler, PipelineVersion>,
+        DeviceGroupedConvBwdWeight_Xdl_CShuffleV3< NDimSpatial,  ALayout,   BLayout,   ELayout,   BF16,    BF16,    BF16,     F32, PassThrough, PassThrough, PassThrough,                  ConvSpec,    64,    64,   112,     32,   8,   16,   16,    4,    7,  S<4, 16, 1>,  S<2, 0, 1>,  S<2, 0, 1>,                  1,              4,              4,      false,  S<4, 16,  1>,  S<2, 0, 1>,  S<2, 0, 1>,               1,              7,              4,      false,           1,           1,   S<1, 8, 1, 8>,                  2, Scheduler, PipelineVersion>
+    //clang-format on
+    >;
+
+} // namespace instance
+} // namespace device
+} // namespace tensor_operation
+} // namespace ck

library/include/ck/library/tensor_operation_instance/gpu/grouped_convolution_backward_weight.hpp

@@ -311,6 +311,11 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe
                 {
                     add_device_grouped_conv2d_bwd_weight_xdl_gnhwc_gkyxc_gnhwk_f32_instances(
                         op_ptrs);
+
+                    add_device_grouped_conv2d_bwd_weight_xdl_gnhwc_gkyxc_gnhwk_f32_default_pipev1_instances(
+                        op_ptrs);
+                    add_device_grouped_conv2d_bwd_weight_xdl_gnhwc_gkyxc_gnhwk_f32_pad0_pipev1_instances(
+                        op_ptrs);
                 }
 #endif
 #ifdef CK_ENABLE_FP16
@@ -320,6 +325,11 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe
                 {
                     add_device_grouped_conv2d_bwd_weight_xdl_gnhwc_gkyxc_gnhwk_f16_instances(
                         op_ptrs);
+
+                    add_device_grouped_conv2d_bwd_weight_xdl_gnhwc_gkyxc_gnhwk_f16_default_pipev1_instances(
+                        op_ptrs);
+                    add_device_grouped_conv2d_bwd_weight_xdl_gnhwc_gkyxc_gnhwk_f16_pad0_pipev1_instances(
+                        op_ptrs);
                 }
 #endif
 #ifdef CK_ENABLE_BF16
@@ -343,6 +353,15 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe
                 {
                     add_device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_f32_instances(
                         op_ptrs);
+
+                    add_device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_f32_default_pipev2_instances(
+                        op_ptrs);
+                    add_device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_f32_default_pipev5_instances(
+                        op_ptrs);
+                    add_device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_f32_pad0_pipev2_instances(
+                        op_ptrs);
+                    add_device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_f32_pad0_pipev5_instances(
+                        op_ptrs);
                 }
 #endif
 #ifdef CK_ENABLE_FP16
@@ -352,6 +371,16 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe
                 {
                     add_device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_f16_instances(
                         op_ptrs);
+
+                    add_device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_f16_default_pipev2_instances(
+                        op_ptrs);
+                    add_device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_f16_default_pipev5_instances(
+                        op_ptrs);
+                    add_device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_f16_pad0_pipev2_instances(
+                        op_ptrs);
+                    add_device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_f16_pad0_pipev5_instances(
+                        op_ptrs);
+
                     add_device_grouped_conv2d_bwd_weight_two_stage_xdl_nhwgc_gkyxc_nhwgk_f16_pipev1_instances(
                         op_ptrs);
                     add_device_grouped_conv2d_bwd_weight_two_stage_xdl_nhwgc_gkyxc_nhwgk_f16_pipev2_instances(
@@ -381,6 +410,16 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe
                 {
                     add_device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_bf16_instances(
                         op_ptrs);
+
+                    add_device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_bf16_default_pipev2_instances(
+                        op_ptrs);
+                    add_device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_bf16_default_pipev5_instances(
+                        op_ptrs);
+                    add_device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_bf16_pad0_pipev2_instances(
+                        op_ptrs);
+                    add_device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_bf16_pad0_pipev5_instances(
+                        op_ptrs);
+
                     add_device_grouped_conv2d_bwd_weight_two_stage_xdl_nhwgc_gkyxc_nhwgk_bf16_pipev1_instances(
                         op_ptrs);
                     add_device_grouped_conv2d_bwd_weight_two_stage_xdl_nhwgc_gkyxc_nhwgk_bf16_pipev2_instances(
@@ -471,6 +510,15 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe
                 {
                     add_device_grouped_conv3d_bwd_weight_xdl_ndhwgc_gkzyxc_ndhwgk_f32_instances(
                         op_ptrs);
+
+                    add_device_grouped_conv3d_bwd_weight_xdl_ndhwgc_gkzyxc_ndhwgk_f32_default_pipev2_instances(
+                        op_ptrs);
+                    add_device_grouped_conv3d_bwd_weight_xdl_ndhwgc_gkzyxc_ndhwgk_f32_default_pipev5_instances(
+                        op_ptrs);
+                    add_device_grouped_conv3d_bwd_weight_xdl_ndhwgc_gkzyxc_ndhwgk_f32_pad0_pipev2_instances(
+                        op_ptrs);
+                    add_device_grouped_conv3d_bwd_weight_xdl_ndhwgc_gkzyxc_ndhwgk_f32_pad0_pipev2_instances(
+                        op_ptrs);
                 }
 #endif
 #ifdef CK_ENABLE_FP16
@@ -480,6 +528,16 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe
                 {
                     add_device_grouped_conv3d_bwd_weight_xdl_ndhwgc_gkzyxc_ndhwgk_f16_instances(
                         op_ptrs);
+
+                    add_device_grouped_conv3d_bwd_weight_xdl_ndhwgc_gkzyxc_ndhwgk_f16_default_pipev2_instances(
+                        op_ptrs);
+                    add_device_grouped_conv3d_bwd_weight_xdl_ndhwgc_gkzyxc_ndhwgk_f16_default_pipev5_instances(
+                        op_ptrs);
+                    add_device_grouped_conv3d_bwd_weight_xdl_ndhwgc_gkzyxc_ndhwgk_f16_pad0_pipev2_instances(
+                        op_ptrs);
+                    add_device_grouped_conv3d_bwd_weight_xdl_ndhwgc_gkzyxc_ndhwgk_f16_pad0_pipev5_instances(
+                        op_ptrs);
+
                     add_device_grouped_conv3d_bwd_weight_two_stage_xdl_ndhwgc_gkzyxc_ndhwgk_f16_pipev1_instances(
                         op_ptrs);
                     add_device_grouped_conv3d_bwd_weight_two_stage_xdl_ndhwgc_gkzyxc_ndhwgk_f16_pipev2_instances(
@@ -509,6 +567,16 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe
                 {
                     add_device_grouped_conv3d_bwd_weight_xdl_ndhwgc_gkzyxc_ndhwgk_bf16_instances(
                         op_ptrs);
+
+                    add_device_grouped_conv3d_bwd_weight_xdl_ndhwgc_gkzyxc_ndhwgk_bf16_default_pipev2_instances(
+                        op_ptrs);
+                    add_device_grouped_conv3d_bwd_weight_xdl_ndhwgc_gkzyxc_ndhwgk_bf16_default_pipev5_instances(
+                        op_ptrs);
+                    add_device_grouped_conv3d_bwd_weight_xdl_ndhwgc_gkzyxc_ndhwgk_bf16_pad0_pipev2_instances(
+                        op_ptrs);
+                    add_device_grouped_conv3d_bwd_weight_xdl_ndhwgc_gkzyxc_ndhwgk_bf16_pad0_pipev5_instances(
+                        op_ptrs);
+
                     add_device_grouped_conv3d_bwd_weight_two_stage_xdl_ndhwgc_gkzyxc_ndhwgk_bf16_pipev1_instances(
                         op_ptrs);
                     add_device_grouped_conv3d_bwd_weight_two_stage_xdl_ndhwgc_gkzyxc_ndhwgk_bf16_pipev2_instances(

library/include/ck/library/tensor_operation_instance/gpu/grouped_convolution_backward_weight_xdl.inc

@@ -74,6 +74,30 @@ void add_device_grouped_conv2d_bwd_weight_xdl_gnhwc_gkyxc_gnhwk_f16_instances(
                                                            PassThrough,
                                                            PassThrough,
                                                            PassThrough>>>& instances);
+
+void add_device_grouped_conv2d_bwd_weight_xdl_gnhwc_gkyxc_gnhwk_f16_default_pipev1_instances(
+    std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<2,
+                                                           GNHWC,
+                                                           GKYXC,
+                                                           GNHWK,
+                                                           F16,
+                                                           F16,
+                                                           F16,
+                                                           PassThrough,
+                                                           PassThrough,
+                                                           PassThrough>>>& instances);
+
+void add_device_grouped_conv2d_bwd_weight_xdl_gnhwc_gkyxc_gnhwk_f16_pad0_pipev1_instances(
+    std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<2,
+                                                           GNHWC,
+                                                           GKYXC,
+                                                           GNHWK,
+                                                           F16,
+                                                           F16,
+                                                           F16,
+                                                           PassThrough,
+                                                           PassThrough,
+                                                           PassThrough>>>& instances);
 #endif
 #ifdef CK_ENABLE_FP32
 void add_device_grouped_conv2d_bwd_weight_xdl_gnhwc_gkyxc_gnhwk_f32_instances(
@@ -87,6 +111,30 @@ void add_device_grouped_conv2d_bwd_weight_xdl_gnhwc_gkyxc_gnhwk_f32_instances(
                                                            PassThrough,
                                                            PassThrough,
                                                            PassThrough>>>& instances);
+
+void add_device_grouped_conv2d_bwd_weight_xdl_gnhwc_gkyxc_gnhwk_f32_default_pipev1_instances(
+    std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<2,
+                                                           GNHWC,
+                                                           GKYXC,
+                                                           GNHWK,
+                                                           F32,
+                                                           F32,
+                                                           F32,
+                                                           PassThrough,
+                                                           PassThrough,
+                                                           PassThrough>>>& instances);
+
+void add_device_grouped_conv2d_bwd_weight_xdl_gnhwc_gkyxc_gnhwk_f32_pad0_pipev1_instances(
+    std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<2,
+                                                           GNHWC,
+                                                           GKYXC,
+                                                           GNHWK,
+                                                           F32,
+                                                           F32,
+                                                           F32,
+                                                           PassThrough,
+                                                           PassThrough,
+                                                           PassThrough>>>& instances);
 #endif
 #ifdef CK_ENABLE_BF16
 void add_device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_bf16_instances(
@@ -112,6 +160,53 @@ void add_device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_bf16_f32_bf16_in
                                                            PassThrough,
                                                            PassThrough,
                                                            PassThrough>>>& instances);
+void add_device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_bf16_default_pipev2_instances(
+    std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<2,
+                                                           NHWGC,
+                                                           GKYXC,
+                                                           NHWGK,
+                                                           BF16,
+                                                           BF16,
+                                                           BF16,
+                                                           PassThrough,
+                                                           PassThrough,
+                                                           PassThrough>>>& instances);
+
+void add_device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_bf16_default_pipev5_instances(
+    std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<2,
+                                                           NHWGC,
+                                                           GKYXC,
+                                                           NHWGK,
+                                                           BF16,
+                                                           BF16,
+                                                           BF16,
+                                                           PassThrough,
+                                                           PassThrough,
+                                                           PassThrough>>>& instances);
+
+void add_device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_bf16_pad0_pipev2_instances(
+    std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<2,
+                                                           NHWGC,
+                                                           GKYXC,
+                                                           NHWGK,
+                                                           BF16,
+                                                           BF16,
+                                                           BF16,
+                                                           PassThrough,
+                                                           PassThrough,
+                                                           PassThrough>>>& instances);
+
+void add_device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_bf16_pad0_pipev5_instances(
+    std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<2,
+                                                           NHWGC,
+                                                           GKYXC,
+                                                           NHWGK,
+                                                           BF16,
+                                                           BF16,
+                                                           BF16,
+                                                           PassThrough,
+                                                           PassThrough,
+                                                           PassThrough>>>& instances);
 
 void add_device_grouped_conv2d_bwd_weight_two_stage_xdl_nhwgc_gkyxc_nhwgk_bf16_pipev1_instances(
     std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<2,
@@ -222,6 +317,54 @@ void add_device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_f16_instances(
                                                            PassThrough,
                                                            PassThrough>>>& instances);
 
+void add_device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_f16_default_pipev2_instances(
+    std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<2,
+                                                           NHWGC,
+                                                           GKYXC,
+                                                           NHWGK,
+                                                           F16,
+                                                           F16,
+                                                           F16,
+                                                           PassThrough,
+                                                           PassThrough,
+                                                           PassThrough>>>& instances);
+
+void add_device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_f16_default_pipev5_instances(
+    std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<2,
+                                                           NHWGC,
+                                                           GKYXC,
+                                                           NHWGK,
+                                                           F16,
+                                                           F16,
+                                                           F16,
+                                                           PassThrough,
+                                                           PassThrough,
+                                                           PassThrough>>>& instances);
+
+void add_device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_f16_pad0_pipev2_instances(
+    std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<2,
+                                                           NHWGC,
+                                                           GKYXC,
+                                                           NHWGK,
+                                                           F16,
+                                                           F16,
+                                                           F16,
+                                                           PassThrough,
+                                                           PassThrough,
+                                                           PassThrough>>>& instances);
+
+void add_device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_f16_pad0_pipev5_instances(
+    std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<2,
+                                                           NHWGC,
+                                                           GKYXC,
+                                                           NHWGK,
+                                                           F16,
+                                                           F16,
+                                                           F16,
+                                                           PassThrough,
+                                                           PassThrough,
+                                                           PassThrough>>>& instances);
+
 void add_device_grouped_conv2d_bwd_weight_two_stage_xdl_nhwgc_gkyxc_nhwgk_f16_pipev1_instances(
     std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<2,
                                                            NHWGC,
@@ -330,6 +473,54 @@ void add_device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_f32_instances(
                                                            PassThrough,
                                                            PassThrough,
                                                            PassThrough>>>& instances);
+
+void add_device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_f32_default_pipev2_instances(
+    std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<2,
+                                                           NHWGC,
+                                                           GKYXC,
+                                                           NHWGK,
+                                                           F32,
+                                                           F32,
+                                                           F32,
+                                                           PassThrough,
+                                                           PassThrough,
+                                                           PassThrough>>>& instances);
+
+void add_device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_f32_default_pipev5_instances(
+    std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<2,
+                                                           NHWGC,
+                                                           GKYXC,
+                                                           NHWGK,
+                                                           F32,
+                                                           F32,
+                                                           F32,
+                                                           PassThrough,
+                                                           PassThrough,
+                                                           PassThrough>>>& instances);
+
+void add_device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_f32_pad0_pipev2_instances(
+    std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<2,
+                                                           NHWGC,
+                                                           GKYXC,
+                                                           NHWGK,
+                                                           F32,
+                                                           F32,
+                                                           F32,
+                                                           PassThrough,
+                                                           PassThrough,
+                                                           PassThrough>>>& instances);
+
+void add_device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_f32_pad0_pipev5_instances(
+    std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<2,
+                                                           NHWGC,
+                                                           GKYXC,
+                                                           NHWGK,
+                                                           F32,
+                                                           F32,
+                                                           F32,
+                                                           PassThrough,
+                                                           PassThrough,
+                                                           PassThrough>>>& instances);
 #endif
 // conv3d backward weight
 #ifdef CK_ENABLE_BF16
@@ -384,6 +575,54 @@ void add_device_grouped_conv3d_bwd_weight_xdl_ndhwgc_gkzyxc_ndhwgk_bf16_instance
                                                            PassThrough,
                                                            PassThrough>>>& instances);
 
+void add_device_grouped_conv3d_bwd_weight_xdl_ndhwgc_gkzyxc_ndhwgk_bf16_default_pipev2_instances(
+    std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<3,
+                                                           NDHWGC,
+                                                           GKZYXC,
+                                                           NDHWGK,
+                                                           BF16,
+                                                           BF16,
+                                                           BF16,
+                                                           PassThrough,
+                                                           PassThrough,
+                                                           PassThrough>>>& instances);
+
+void add_device_grouped_conv3d_bwd_weight_xdl_ndhwgc_gkzyxc_ndhwgk_bf16_default_pipev5_instances(
+    std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<3,
+                                                           NDHWGC,
+                                                           GKZYXC,
+                                                           NDHWGK,
+                                                           BF16,
+                                                           BF16,
+                                                           BF16,
+                                                           PassThrough,
+                                                           PassThrough,
+                                                           PassThrough>>>& instances);
+
+void add_device_grouped_conv3d_bwd_weight_xdl_ndhwgc_gkzyxc_ndhwgk_bf16_pad0_pipev2_instances(
+    std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<3,
+                                                           NDHWGC,
+                                                           GKZYXC,
+                                                           NDHWGK,
+                                                           BF16,
+                                                           BF16,
+                                                           BF16,
+                                                           PassThrough,
+                                                           PassThrough,
+                                                           PassThrough>>>& instances);
+
+void add_device_grouped_conv3d_bwd_weight_xdl_ndhwgc_gkzyxc_ndhwgk_bf16_pad0_pipev5_instances(
+    std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<3,
+                                                           NDHWGC,
+                                                           GKZYXC,
+                                                           NDHWGK,
+                                                           BF16,
+                                                           BF16,
+                                                           BF16,
+                                                           PassThrough,
+                                                           PassThrough,
+                                                           PassThrough>>>& instances);
+
 void add_device_grouped_conv3d_bwd_weight_xdl_ndhwgc_gkzyxc_ndhwgk_bf16_f32_bf16_instances(
     std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<3,
                                                            NDHWGC,
@@ -505,6 +744,54 @@ void add_device_grouped_conv3d_bwd_weight_xdl_ndhwgc_gkzyxc_ndhwgk_f16_instances
                                                            PassThrough,
                                                            PassThrough>>>& instances);
 
+void add_device_grouped_conv3d_bwd_weight_xdl_ndhwgc_gkzyxc_ndhwgk_f16_default_pipev2_instances(
+    std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<3,
+                                                           NDHWGC,
+                                                           GKZYXC,
+                                                           NDHWGK,
+                                                           F16,
+                                                           F16,
+                                                           F16,
+                                                           PassThrough,
+                                                           PassThrough,
+                                                           PassThrough>>>& instances);
+
+void add_device_grouped_conv3d_bwd_weight_xdl_ndhwgc_gkzyxc_ndhwgk_f16_default_pipev5_instances(
+    std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<3,
+                                                           NDHWGC,
+                                                           GKZYXC,
+                                                           NDHWGK,
+                                                           F16,
+                                                           F16,
+                                                           F16,
+                                                           PassThrough,
+                                                           PassThrough,
+                                                           PassThrough>>>& instances);
+
+void add_device_grouped_conv3d_bwd_weight_xdl_ndhwgc_gkzyxc_ndhwgk_f16_pad0_pipev2_instances(
+    std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<3,
+                                                           NDHWGC,
+                                                           GKZYXC,
+                                                           NDHWGK,
+                                                           F16,
+                                                           F16,
+                                                           F16,
+                                                           PassThrough,
+                                                           PassThrough,
+                                                           PassThrough>>>& instances);
+
+void add_device_grouped_conv3d_bwd_weight_xdl_ndhwgc_gkzyxc_ndhwgk_f16_pad0_pipev5_instances(
+    std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<3,
+                                                           NDHWGC,
+                                                           GKZYXC,
+                                                           NDHWGK,
+                                                           F16,
+                                                           F16,
+                                                           F16,
+                                                           PassThrough,
+                                                           PassThrough,
+                                                           PassThrough>>>& instances);
+
 void add_device_grouped_conv3d_bwd_weight_two_stage_xdl_ndhwgc_gkzyxc_ndhwgk_f16_pipev1_instances(
     std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<3,
                                                            NDHWGC,
@@ -613,6 +900,54 @@ void add_device_grouped_conv3d_bwd_weight_xdl_ndhwgc_gkzyxc_ndhwgk_f32_instances
                                                            PassThrough,
                                                            PassThrough,
                                                            PassThrough>>>& instances);
+
+void add_device_grouped_conv3d_bwd_weight_xdl_ndhwgc_gkzyxc_ndhwgk_f32_default_pipev2_instances(
+    std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<3,
+                                                           NDHWGC,
+                                                           GKZYXC,
+                                                           NDHWGK,
+                                                           F32,
+                                                           F32,
+                                                           F32,
+                                                           PassThrough,
+                                                           PassThrough,
+                                                           PassThrough>>>& instances);
+
+void add_device_grouped_conv3d_bwd_weight_xdl_ndhwgc_gkzyxc_ndhwgk_f32_default_pipev5_instances(
+    std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<3,
+                                                           NDHWGC,
+                                                           GKZYXC,
+                                                           NDHWGK,
+                                                           F32,
+                                                           F32,
+                                                           F32,
+                                                           PassThrough,
+                                                           PassThrough,
+                                                           PassThrough>>>& instances);
+
+void add_device_grouped_conv3d_bwd_weight_xdl_ndhwgc_gkzyxc_ndhwgk_f32_pad0_pipev2_instances(
+    std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<3,
+                                                           NDHWGC,
+                                                           GKZYXC,
+                                                           NDHWGK,
+                                                           F32,
+                                                           F32,
+                                                           F32,
+                                                           PassThrough,
+                                                           PassThrough,
+                                                           PassThrough>>>& instances);
+
+void add_device_grouped_conv3d_bwd_weight_xdl_ndhwgc_gkzyxc_ndhwgk_f32_pad0_pipev2_instances(
+    std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<3,
+                                                           NDHWGC,
+                                                           GKZYXC,
+                                                           NDHWGK,
+                                                           F32,
+                                                           F32,
+                                                           F32,
+                                                           PassThrough,
+                                                           PassThrough,
+                                                           PassThrough>>>& instances);
 #endif
 #if defined CK_ENABLE_FP16 && defined CK_ENABLE_FP8 && defined CK_ENABLE_BF8
 void add_device_grouped_conv3d_bwd_weight_xdl_ndhwgc_gkzyxc_ndhwgk_f16_comp_bf8_f8_instances(

library/src/tensor_operation_instance/gpu/grouped_conv2d_bwd_weight/CMakeLists.txt

@@ -7,6 +7,22 @@ set(GROUPED_CONV2D_BWD_WEIGHT
     xdl/device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_f32_instance.cpp
     xdl/device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_bf16_f32_bf16_instance.cpp
     xdl/device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_bf16_instance.cpp
+    xdl/device_grouped_conv2d_bwd_weight_xdl_gnhwc_gkyxc_gnhwk_f16_default_pipev1_instance.cpp
+    xdl/device_grouped_conv2d_bwd_weight_xdl_gnhwc_gkyxc_gnhwk_f16_pad0_pipev1_instance.cpp
+    xdl/device_grouped_conv2d_bwd_weight_xdl_gnhwc_gkyxc_gnhwk_f32_default_pipev1_instance.cpp
+    xdl/device_grouped_conv2d_bwd_weight_xdl_gnhwc_gkyxc_gnhwk_f32_pad0_pipev1_instance.cpp
+    xdl/device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_bf16_default_pipev2_instance.cpp
+    xdl/device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_bf16_default_pipev5_instance.cpp
+    xdl/device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_bf16_pad0_pipev2_instance.cpp
+    xdl/device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_bf16_pad0_pipev5_instance.cpp
+    xdl/device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_f16_default_pipev2_instance.cpp
+    xdl/device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_f16_default_pipev5_instance.cpp
+    xdl/device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_f16_pad0_pipev2_instance.cpp
+    xdl/device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_f16_pad0_pipev5_instance.cpp
+    xdl/device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_f32_default_pipev2_instance.cpp
+    xdl/device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_f32_default_pipev5_instance.cpp
+    xdl/device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_f32_pad0_pipev2_instance.cpp
+    xdl/device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_f32_pad0_pipev5_instance.cpp
     xdl/device_grouped_conv2d_bwd_weight_two_stage_xdl_nhwgc_gkyxc_nhwgk_f16_pipev2_instance.cpp
     xdl/device_grouped_conv2d_bwd_weight_two_stage_xdl_nhwgc_gkyxc_nhwgk_f16_pipev5_instance.cpp
     xdl/device_grouped_conv2d_bwd_weight_two_stage_xdl_ngchw_gkyxc_ngkhw_f16_pipev2_instance.cpp

library/src/tensor_operation_instance/gpu/grouped_conv2d_bwd_weight/xdl/device_grouped_conv2d_bwd_weight_xdl_gnhwc_gkyxc_gnhwk_f16_default_pipev1_instance.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
+
+#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
+#include "ck/library/tensor_operation_instance/gpu/grouped_conv_bwd_weight/device_grouped_conv_bwd_weight_v3_xdl_instance.hpp"
+
+namespace ck {
+namespace tensor_operation {
+namespace device {
+namespace instance {
+
+// Compilation parameters for in[g, n, hi, wi, c] * wei[g, k, y, x, c] = out[g, n, ho, wo, k]
+void add_device_grouped_conv2d_bwd_weight_xdl_gnhwc_gkyxc_gnhwk_f16_default_pipev1_instances(
+    std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<2,
+                                                           GNHWC,
+                                                           GKYXC,
+                                                           GNHWK,
+                                                           F16,
+                                                           F16,
+                                                           F16,
+                                                           PassThrough,
+                                                           PassThrough,
+                                                           PassThrough>>>& instances)
+{
+    add_device_operation_instances(instances,
+                                   device_grouped_conv_bwd_weight_v3_xdl_c_shuffle_f16_instances<
+                                       2,
+                                       GNHWC,
+                                       GKYXC,
+                                       GNHWK,
+                                       ConvBwdWeightDefault,
+                                       BlockGemmPipelineScheduler::Intrawave,
+                                       BlockGemmPipelineVersion::v1>{});
+}
+} // namespace instance
+} // namespace device
+} // namespace tensor_operation
+} // namespace ck

library/src/tensor_operation_instance/gpu/grouped_conv2d_bwd_weight/xdl/device_grouped_conv2d_bwd_weight_xdl_gnhwc_gkyxc_gnhwk_f16_pad0_pipev1_instance.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
+
+#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
+#include "ck/library/tensor_operation_instance/gpu/grouped_conv_bwd_weight/device_grouped_conv_bwd_weight_v3_xdl_instance.hpp"
+
+namespace ck {
+namespace tensor_operation {
+namespace device {
+namespace instance {
+
+// Compilation parameters for in[g, n, hi, wi, c] * wei[g, k, y, x, c] = out[g, n, ho, wo, k]
+void add_device_grouped_conv2d_bwd_weight_xdl_gnhwc_gkyxc_gnhwk_f16_pad0_pipev1_instances(
+    std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<2,
+                                                           GNHWC,
+                                                           GKYXC,
+                                                           GNHWK,
+                                                           F16,
+                                                           F16,
+                                                           F16,
+                                                           PassThrough,
+                                                           PassThrough,
+                                                           PassThrough>>>& instances)
+{
+    add_device_operation_instances(instances,
+                                   device_grouped_conv_bwd_weight_v3_xdl_c_shuffle_f16_instances<
+                                       2,
+                                       GNHWC,
+                                       GKYXC,
+                                       GNHWK,
+                                       ConvBwdWeightFilter1x1Stride1Pad0,
+                                       BlockGemmPipelineScheduler::Intrawave,
+                                       BlockGemmPipelineVersion::v1>{});
+}
+
+} // namespace instance
+} // namespace device
+} // namespace tensor_operation
+} // namespace ck

library/src/tensor_operation_instance/gpu/grouped_conv2d_bwd_weight/xdl/device_grouped_conv2d_bwd_weight_xdl_gnhwc_gkyxc_gnhwk_f32_default_pipev1_instance.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
+
+#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
+#include "ck/library/tensor_operation_instance/gpu/grouped_conv_bwd_weight/device_grouped_conv_bwd_weight_v3_xdl_instance.hpp"
+
+namespace ck {
+namespace tensor_operation {
+namespace device {
+namespace instance {
+
+// Compilation parameters for in[g, n, hi, wi, c] * wei[g, k, y, x, c] = out[g, n, ho, wo, k]
+void add_device_grouped_conv2d_bwd_weight_xdl_gnhwc_gkyxc_gnhwk_f32_default_pipev1_instances(
+    std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<2,
+                                                           GNHWC,
+                                                           GKYXC,
+                                                           GNHWK,
+                                                           F32,
+                                                           F32,
+                                                           F32,
+                                                           PassThrough,
+                                                           PassThrough,
+                                                           PassThrough>>>& instances)
+{
+    add_device_operation_instances(instances,
+                                   device_grouped_conv_bwd_weight_v3_xdl_c_shuffle_f32_instances<
+                                       2,
+                                       GNHWC,
+                                       GKYXC,
+                                       GNHWK,
+                                       ConvBwdWeightDefault,
+                                       BlockGemmPipelineScheduler::Intrawave,
+                                       BlockGemmPipelineVersion::v1>{});
+}
+
+} // namespace instance
+} // namespace device
+} // namespace tensor_operation
+} // namespace ck

library/src/tensor_operation_instance/gpu/grouped_conv2d_bwd_weight/xdl/device_grouped_conv2d_bwd_weight_xdl_gnhwc_gkyxc_gnhwk_f32_pad0_pipev1_instance.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
+
+#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
+#include "ck/library/tensor_operation_instance/gpu/grouped_conv_bwd_weight/device_grouped_conv_bwd_weight_v3_xdl_instance.hpp"
+
+namespace ck {
+namespace tensor_operation {
+namespace device {
+namespace instance {
+
+// Compilation parameters for in[g, n, hi, wi, c] * wei[g, k, y, x, c] = out[g, n, ho, wo, k]
+void add_device_grouped_conv2d_bwd_weight_xdl_gnhwc_gkyxc_gnhwk_f32_pad0_pipev1_instances(
+    std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<2,
+                                                           GNHWC,
+                                                           GKYXC,
+                                                           GNHWK,
+                                                           F32,
+                                                           F32,
+                                                           F32,
+                                                           PassThrough,
+                                                           PassThrough,
+                                                           PassThrough>>>& instances)
+{
+    add_device_operation_instances(instances,
+                                   device_grouped_conv_bwd_weight_v3_xdl_c_shuffle_f32_instances<
+                                       2,
+                                       GNHWC,
+                                       GKYXC,
+                                       GNHWK,
+                                       ConvBwdWeightFilter1x1Stride1Pad0,
+                                       BlockGemmPipelineScheduler::Intrawave,
+                                       BlockGemmPipelineVersion::v1>{});
+}
+
+} // namespace instance
+} // namespace device
+} // namespace tensor_operation
+} // namespace ck

library/src/tensor_operation_instance/gpu/grouped_conv2d_bwd_weight/xdl/device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_bf16_default_pipev2_instance.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
+
+#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
+#include "ck/library/tensor_operation_instance/gpu/grouped_conv_bwd_weight/device_grouped_conv_bwd_weight_v3_xdl_instance.hpp"
+
+namespace ck {
+namespace tensor_operation {
+namespace device {
+namespace instance {
+
+// Compilation parameters for in[n, hi, wi, g, c] * wei[g, k, y, x, c] = out[n, ho, wo, g, k]
+void add_device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_bf16_default_pipev2_instances(
+    std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<2,
+                                                           NHWGC,
+                                                           GKYXC,
+                                                           NHWGK,
+                                                           BF16,
+                                                           BF16,
+                                                           BF16,
+                                                           PassThrough,
+                                                           PassThrough,
+                                                           PassThrough>>>& instances)
+{
+    add_device_operation_instances(instances,
+                                   device_grouped_conv_bwd_weight_v3_xdl_c_shuffle_bf16_instances<
+                                       2,
+                                       NHWGC,
+                                       GKYXC,
+                                       NHWGK,
+                                       ConvBwdWeightDefault,
+                                       BlockGemmPipelineScheduler::Intrawave,
+                                       BlockGemmPipelineVersion::v2>{});
+}
+
+} // namespace instance
+} // namespace device
+} // namespace tensor_operation
+} // namespace ck

library/src/tensor_operation_instance/gpu/grouped_conv2d_bwd_weight/xdl/device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_bf16_default_pipev5_instance.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
+
+#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
+#include "ck/library/tensor_operation_instance/gpu/grouped_conv_bwd_weight/device_grouped_conv_bwd_weight_v3_xdl_instance.hpp"
+
+namespace ck {
+namespace tensor_operation {
+namespace device {
+namespace instance {
+
+// Compilation parameters for in[n, hi, wi, g, c] * wei[g, k, y, x, c] = out[n, ho, wo, g, k]
+void add_device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_bf16_default_pipev5_instances(
+    std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<2,
+                                                           NHWGC,
+                                                           GKYXC,
+                                                           NHWGK,
+                                                           BF16,
+                                                           BF16,
+                                                           BF16,
+                                                           PassThrough,
+                                                           PassThrough,
+                                                           PassThrough>>>& instances)
+{
+    add_device_operation_instances(instances,
+                                   device_grouped_conv_bwd_weight_v3_xdl_c_shuffle_bf16_instances<
+                                       2,
+                                       NHWGC,
+                                       GKYXC,
+                                       NHWGK,
+                                       ConvBwdWeightDefault,
+                                       BlockGemmPipelineScheduler::Intrawave,
+                                       BlockGemmPipelineVersion::v5>{});
+}
+
+} // namespace instance
+} // namespace device
+} // namespace tensor_operation
+} // namespace ck

library/src/tensor_operation_instance/gpu/grouped_conv2d_bwd_weight/xdl/device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_bf16_pad0_pipev2_instance.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
+
+#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
+#include "ck/library/tensor_operation_instance/gpu/grouped_conv_bwd_weight/device_grouped_conv_bwd_weight_v3_xdl_instance.hpp"
+
+namespace ck {
+namespace tensor_operation {
+namespace device {
+namespace instance {
+
+// Compilation parameters for in[n, hi, wi, g, c] * wei[g, k, y, x, c] = out[n, ho, wo, g, k]
+void add_device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_bf16_pad0_pipev2_instances(
+    std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<2,
+                                                           NHWGC,
+                                                           GKYXC,
+                                                           NHWGK,
+                                                           BF16,
+                                                           BF16,
+                                                           BF16,
+                                                           PassThrough,
+                                                           PassThrough,
+                                                           PassThrough>>>& instances)
+{
+
+    add_device_operation_instances(instances,
+                                   device_grouped_conv_bwd_weight_v3_xdl_c_shuffle_bf16_instances<
+                                       2,
+                                       NHWGC,
+                                       GKYXC,
+                                       NHWGK,
+                                       ConvBwdWeightFilter1x1Stride1Pad0,
+                                       BlockGemmPipelineScheduler::Intrawave,
+                                       BlockGemmPipelineVersion::v2>{});
+}
+
+} // namespace instance
+} // namespace device
+} // namespace tensor_operation
+} // namespace ck

library/src/tensor_operation_instance/gpu/grouped_conv2d_bwd_weight/xdl/device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_bf16_pad0_pipev5_instance.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
+
+#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
+#include "ck/library/tensor_operation_instance/gpu/grouped_conv_bwd_weight/device_grouped_conv_bwd_weight_v3_xdl_instance.hpp"
+
+namespace ck {
+namespace tensor_operation {
+namespace device {
+namespace instance {
+
+// Compilation parameters for in[n, hi, wi, g, c] * wei[g, k, y, x, c] = out[n, ho, wo, g, k]
+void add_device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_bf16_pad0_pipev5_instances(
+    std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<2,
+                                                           NHWGC,
+                                                           GKYXC,
+                                                           NHWGK,
+                                                           BF16,
+                                                           BF16,
+                                                           BF16,
+                                                           PassThrough,
+                                                           PassThrough,
+                                                           PassThrough>>>& instances)
+{
+
+    add_device_operation_instances(instances,
+                                   device_grouped_conv_bwd_weight_v3_xdl_c_shuffle_bf16_instances<
+                                       2,
+                                       NHWGC,
+                                       GKYXC,
+                                       NHWGK,
+                                       ConvBwdWeightFilter1x1Stride1Pad0,
+                                       BlockGemmPipelineScheduler::Intrawave,
+                                       BlockGemmPipelineVersion::v5>{});
+}
+
+} // namespace instance
+} // namespace device
+} // namespace tensor_operation
+} // namespace ck

library/src/tensor_operation_instance/gpu/grouped_conv2d_bwd_weight/xdl/device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_f16_default_pipev2_instance.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
+
+#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
+#include "ck/library/tensor_operation_instance/gpu/grouped_conv_bwd_weight/device_grouped_conv_bwd_weight_v3_xdl_instance.hpp"
+
+namespace ck {
+namespace tensor_operation {
+namespace device {
+namespace instance {
+
+// Compilation parameters for in[n, hi, wi, g, c] * wei[g, k, y, x, c] = out[n, ho, wo, g, k]
+void add_device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_f16_default_pipev2_instances(
+    std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<2,
+                                                           NHWGC,
+                                                           GKYXC,
+                                                           NHWGK,
+                                                           F16,
+                                                           F16,
+                                                           F16,
+                                                           PassThrough,
+                                                           PassThrough,
+                                                           PassThrough>>>& instances)
+{
+    add_device_operation_instances(instances,
+                                   device_grouped_conv_bwd_weight_v3_xdl_c_shuffle_f16_instances<
+                                       2,
+                                       NHWGC,
+                                       GKYXC,
+                                       NHWGK,
+                                       ConvBwdWeightDefault,
+                                       BlockGemmPipelineScheduler::Intrawave,
+                                       BlockGemmPipelineVersion::v2>{});
+    ;
+}
+
+} // namespace instance
+} // namespace device
+} // namespace tensor_operation
+} // namespace ck

library/src/tensor_operation_instance/gpu/grouped_conv2d_bwd_weight/xdl/device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_f16_default_pipev5_instance.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
+
+#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
+#include "ck/library/tensor_operation_instance/gpu/grouped_conv_bwd_weight/device_grouped_conv_bwd_weight_v3_xdl_instance.hpp"
+
+namespace ck {
+namespace tensor_operation {
+namespace device {
+namespace instance {
+
+// Compilation parameters for in[n, hi, wi, g, c] * wei[g, k, y, x, c] = out[n, ho, wo, g, k]
+void add_device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_f16_default_pipev5_instances(
+    std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<2,
+                                                           NHWGC,
+                                                           GKYXC,
+                                                           NHWGK,
+                                                           F16,
+                                                           F16,
+                                                           F16,
+                                                           PassThrough,
+                                                           PassThrough,
+                                                           PassThrough>>>& instances)
+{
+    add_device_operation_instances(instances,
+                                   device_grouped_conv_bwd_weight_v3_xdl_c_shuffle_f16_instances<
+                                       2,
+                                       NHWGC,
+                                       GKYXC,
+                                       NHWGK,
+                                       ConvBwdWeightDefault,
+                                       BlockGemmPipelineScheduler::Intrawave,
+                                       BlockGemmPipelineVersion::v5>{});
+    ;
+}
+
+} // namespace instance
+} // namespace device
+} // namespace tensor_operation
+} // namespace ck

library/src/tensor_operation_instance/gpu/grouped_conv2d_bwd_weight/xdl/device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_f16_pad0_pipev2_instance.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
+
+#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
+#include "ck/library/tensor_operation_instance/gpu/grouped_conv_bwd_weight/device_grouped_conv_bwd_weight_v3_xdl_instance.hpp"
+
+namespace ck {
+namespace tensor_operation {
+namespace device {
+namespace instance {
+
+// Compilation parameters for in[n, hi, wi, g, c] * wei[g, k, y, x, c] = out[n, ho, wo, g, k]
+void add_device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_f16_pad0_pipev2_instances(
+    std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<2,
+                                                           NHWGC,
+                                                           GKYXC,
+                                                           NHWGK,
+                                                           F16,
+                                                           F16,
+                                                           F16,
+                                                           PassThrough,
+                                                           PassThrough,
+                                                           PassThrough>>>& instances)
+{
+    add_device_operation_instances(instances,
+                                   device_grouped_conv_bwd_weight_v3_xdl_c_shuffle_f16_instances<
+                                       2,
+                                       NHWGC,
+                                       GKYXC,
+                                       NHWGK,
+                                       ConvBwdWeightFilter1x1Stride1Pad0,
+                                       BlockGemmPipelineScheduler::Intrawave,
+                                       BlockGemmPipelineVersion::v2>{});
+}
+
+} // namespace instance
+} // namespace device
+} // namespace tensor_operation
+} // namespace ck