Add grouped conv bwd weight multi d kernel (#1237)

* Add grouped conv bwd weight multi d kernel

* Reference fix

* Fix cmake files

* bwd weight scale only xdl

* Fixes

* Fix client conv fwd example

client_example/24_grouped_conv_activation/CMakeLists.txt

@@ -39,6 +39,10 @@ target_link_libraries(client_grouped_convnd_fwd_bilinear_residual_fp16 PRIVATE c
 add_executable(client_grouped_convnd_bwd_data_bilinear_residual_fp16
                grouped_convnd_bwd_data_bilinear/grouped_conv_bwd_data_bilinear_residual_fp16.cpp)
 target_link_libraries(client_grouped_convnd_bwd_data_bilinear_residual_fp16 PRIVATE composable_kernel::device_conv_operations)
+# Bwd weight bilinear
+add_executable(client_grouped_convnd_bwd_weight_bilinear_residual_fp16
+               grouped_convnd_bwd_weight_bilinear/grouped_conv_bwd_weight_bilinear_residual_fp16.cpp)
+target_link_libraries(client_grouped_convnd_bwd_weight_bilinear_residual_fp16 PRIVATE composable_kernel::device_conv_operations)
 # Fwd scale
 add_executable(client_grouped_convnd_fwd_scale_fp16
                grouped_convnd_fwd_scale/grouped_conv_fwd_scale_fp16.cpp)
@@ -47,4 +51,8 @@ target_link_libraries(client_grouped_convnd_fwd_scale_fp16 PRIVATE composable_ke
 add_executable(client_grouped_convnd_bwd_data_scale_fp16
                grouped_convnd_bwd_data_scale/grouped_conv_bwd_data_scale_fp16.cpp)
 target_link_libraries(client_grouped_convnd_bwd_data_scale_fp16 PRIVATE composable_kernel::device_conv_operations)
+# Bwd weight scale
+add_executable(client_grouped_convnd_bwd_weight_scale_fp16
+               grouped_convnd_bwd_weight_scale/grouped_conv_bwd_weight_scale_fp16.cpp)
+target_link_libraries(client_grouped_convnd_bwd_weight_scale_fp16 PRIVATE composable_kernel::device_conv_operations)
 endif()

client_example/24_grouped_conv_activation/grouped_convnd_bwd_weight_bilinear/grouped_conv_bwd_weight_bilinear_residual_fp16.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
+
+#include <tuple>
+#include <cstdlib>
+#include <iomanip>
+#include <iostream>
+#include <iterator>
+#include <numeric>
+#include <vector>
+
+#include "ck/utility/data_type.hpp"
+#include "ck/utility/tuple.hpp"
+#include "ck/ck.hpp"
+#include "ck/library/tensor_operation_instance/gpu/grouped_convolution_backward_weight_bilinear.hpp"
+#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
+#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
+
+using InDataType  = ck::half_t;
+using WeiDataType = ck::half_t;
+using OutDataType = ck::half_t;
+
+using InLayout    = ck::tensor_layout::convolution::NDHWGC;
+using WeiLayout   = ck::tensor_layout::convolution::GKZYXC;
+using OutLayout   = ck::tensor_layout::convolution::NDHWGK;
+using PassThrough = ck::tensor_operation::element_wise::PassThrough;
+using Bilinear    = ck::tensor_operation::element_wise::Bilinear;
+
+static constexpr ck::index_t NumDimSpatial = 3;
+static constexpr ck::index_t G             = 32;
+static constexpr ck::index_t N             = 32; // batch size
+static constexpr ck::index_t K             = 32; // output channel
+static constexpr ck::index_t C             = 32; // input channel (per group)
+static constexpr ck::index_t Z             = 3;  // filter D
+static constexpr ck::index_t Y             = 3;  // filter H
+static constexpr ck::index_t X             = 3;  // filter W
+static constexpr ck::index_t Di            = 14; // input D
+static constexpr ck::index_t Hi            = 14; // input H
+static constexpr ck::index_t Wi            = 14; // input W
+static constexpr ck::index_t Do            = 14; // output D
+static constexpr ck::index_t Ho            = 14; // output H
+static constexpr ck::index_t Wo            = 14; // output W
+
+struct SimpleDeviceMem
+{
+    SimpleDeviceMem() = delete;
+
+    SimpleDeviceMem(std::size_t mem_size) : p_mem_{}
+    {
+        (void)hipMalloc(static_cast<void**>(&p_mem_), mem_size);
+    }
+
+    void* GetDeviceBuffer() { return p_mem_; }
+
+    ~SimpleDeviceMem() { (void)hipFree(p_mem_); }
+
+    void* p_mem_;
+};
+
+int execute_conv_bwd_weight_bilinear()
+{
+    constexpr ck::index_t split_k = 2;
+
+    std::array<ck::index_t, NumDimSpatial + 3> in_lengths{G, N, C, Di, Hi, Wi};
+    std::array<ck::index_t, NumDimSpatial + 3> in_strides{
+        C, Di * Hi * Wi * G * C, 1, Hi * Wi * G * C, Wi * G * C, G * C};
+
+    std::array<ck::index_t, NumDimSpatial + 3> wei_lengths{G, K, C, Z, Y, X};
+    std::array<ck::index_t, NumDimSpatial + 3> wei_strides{
+        K * Z * Y * X * C, Z * Y * X * C, 1, Y * X * C, X * C, C};
+
+    std::array<ck::index_t, NumDimSpatial + 3> out_lengths{G, N, K, Do, Ho, Wo};
+    std::array<ck::index_t, NumDimSpatial + 3> out_strides{
+        K, Do * Ho * Wo * G * K, 1, Ho * Wo * G * K, Wo * G * K, G * K};
+
+    std::array<ck::index_t, NumDimSpatial> filter_strides{1, 1, 1};
+    std::array<ck::index_t, NumDimSpatial> filter_dilations{1, 1, 1};
+    std::array<ck::index_t, NumDimSpatial> input_left_pads{1, 1, 1};
+    std::array<ck::index_t, NumDimSpatial> input_right_pads{1, 1, 1};
+
+    SimpleDeviceMem in(sizeof(InDataType) * G * N * Di * Hi * Wi * C);
+    SimpleDeviceMem wei(sizeof(WeiDataType) * G * K * Z * Y * X * C);
+    SimpleDeviceMem out(sizeof(OutDataType) * G * N * Do * Ho * Wo * K);
+
+    using DeviceOp =
+        ck::tensor_operation::device::DeviceGroupedConvBwdWeightMultipleD<NumDimSpatial,
+                                                                          InLayout,
+                                                                          WeiLayout,
+                                                                          OutLayout,
+                                                                          ck::Tuple<WeiLayout>,
+                                                                          InDataType,
+                                                                          WeiDataType,
+                                                                          OutDataType,
+                                                                          ck::Tuple<WeiDataType>,
+                                                                          PassThrough,
+                                                                          Bilinear,
+                                                                          PassThrough>;
+
+    // get device op instances
+    const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
+        DeviceOp>::GetInstances();
+
+    std::cout << "found " << op_ptrs.size() << " instances" << std::endl;
+
+    std::string best_op_name;
+    int best_op_id        = -1;
+    float best_avg_time   = std::numeric_limits<float>::max();
+    float best_gb_per_sec = 0;
+    float best_tflops     = 0;
+
+    // profile device operation instances
+    std::cout << "Run all instances and do timing" << std::endl;
+
+    for(int i = 0; i < op_ptrs.size(); ++i)
+    {
+        auto& op_ptr = op_ptrs[i];
+        auto argument_ptr =
+            op_ptr->MakeArgumentPointer(static_cast<InDataType*>(in.GetDeviceBuffer()),
+                                        static_cast<WeiDataType*>(wei.GetDeviceBuffer()),
+                                        static_cast<OutDataType*>(out.GetDeviceBuffer()),
+                                        {wei.GetDeviceBuffer()},
+                                        in_lengths,
+                                        in_strides,
+                                        wei_lengths,
+                                        wei_strides,
+                                        out_lengths,
+                                        out_strides,
+                                        {wei_lengths},
+                                        {wei_strides},
+                                        filter_strides,
+                                        filter_dilations,
+                                        input_left_pads,
+                                        input_right_pads,
+                                        PassThrough{},
+                                        Bilinear{2.f, 2.f},
+                                        PassThrough{},
+                                        split_k);
+
+        SimpleDeviceMem workspace_buf(op_ptr->GetWorkSpaceSize(argument_ptr.get()));
+        op_ptr->SetWorkSpacePointer(argument_ptr.get(), workspace_buf.GetDeviceBuffer());
+
+        auto invoker_ptr    = op_ptr->MakeInvokerPointer();
+        std::string op_name = op_ptr->GetTypeString();
+
+        if(op_ptr->IsSupportedArgument(argument_ptr.get()))
+        {
+            float avg_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, true});
+
+            std::size_t flop =
+                std::size_t(2) * G * N * K * C * Do * Ho * Wo * Y * X + 3 * G * K * Z * Y * X * C;
+            std::size_t num_bytes = sizeof(InDataType) * G * N * Di * Hi * Wi * C +
+                                    2 * sizeof(WeiDataType) * G * K * Z * Y * X * C +
+                                    sizeof(OutDataType) * G * N * Do * Ho * Wo * K;
+
+            float tflops     = static_cast<float>(flop) / 1.E9 / avg_time;
+            float gb_per_sec = num_bytes / 1.E6 / avg_time;
+
+            std::cout << "Perf: " << std::setw(10) << avg_time << " ms, " << tflops << " TFlops, "
+                      << gb_per_sec << " GB/s, " << op_name << std::endl;
+
+            if(tflops > best_tflops)
+            {
+                best_op_id      = i;
+                best_op_name    = op_name;
+                best_avg_time   = avg_time;
+                best_gb_per_sec = gb_per_sec;
+                best_tflops     = tflops;
+            }
+        }
+        else
+        {
+            std::cerr << op_name << " does not support this problem" << std::endl;
+        }
+    }
+
+    if(best_op_id < 0)
+    {
+        std::cerr << "no suitable instance" << std::endl;
+        return EXIT_FAILURE;
+    }
+
+    std::cout << "Best Perf: " << std::setw(10) << best_avg_time << " ms, " << best_tflops
+              << " TFlops, " << best_gb_per_sec << " GB/s, " << best_op_name << std::endl;
+
+    // run the best intance
+    {
+        auto& op_ptr = op_ptrs[best_op_id];
+        std::cout << "Run the best instance without timing: " << op_ptr->GetTypeString()
+                  << std::endl;
+        auto argument_ptr =
+            op_ptr->MakeArgumentPointer(static_cast<InDataType*>(in.GetDeviceBuffer()),
+                                        static_cast<WeiDataType*>(wei.GetDeviceBuffer()),
+                                        static_cast<OutDataType*>(out.GetDeviceBuffer()),
+                                        {wei.GetDeviceBuffer()},
+                                        in_lengths,
+                                        in_strides,
+                                        wei_lengths,
+                                        wei_strides,
+                                        out_lengths,
+                                        out_strides,
+                                        {wei_lengths},
+                                        {wei_strides},
+                                        filter_strides,
+                                        filter_dilations,
+                                        input_left_pads,
+                                        input_right_pads,
+                                        PassThrough{},
+                                        Bilinear{2.f, 2.f},
+                                        PassThrough{},
+                                        split_k);
+
+        SimpleDeviceMem workspace_buf(op_ptr->GetWorkSpaceSize(argument_ptr.get()));
+        op_ptr->SetWorkSpacePointer(argument_ptr.get(), workspace_buf.GetDeviceBuffer());
+        auto invoker_ptr = op_ptr->MakeInvokerPointer();
+
+        if(op_ptr->IsSupportedArgument(argument_ptr.get()))
+        {
+            invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, false});
+        }
+
+        std::cout << "Done" << std::endl;
+    }
+    return 0;
+}
+
+int main() { return execute_conv_bwd_weight_bilinear(); }

client_example/24_grouped_conv_activation/grouped_convnd_bwd_weight_scale/grouped_conv_bwd_weight_scale_fp16.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
+
+#include <tuple>
+#include <cstdlib>
+#include <iomanip>
+#include <iostream>
+#include <iterator>
+#include <numeric>
+#include <vector>
+
+#include "ck/utility/data_type.hpp"
+#include "ck/utility/tuple.hpp"
+#include "ck/ck.hpp"
+#include "ck/library/tensor_operation_instance/gpu/grouped_convolution_backward_weight_scale.hpp"
+#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
+#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
+
+using InDataType  = ck::half_t;
+using WeiDataType = ck::half_t;
+using OutDataType = ck::half_t;
+
+using InLayout    = ck::tensor_layout::convolution::NDHWGC;
+using WeiLayout   = ck::tensor_layout::convolution::GKZYXC;
+using OutLayout   = ck::tensor_layout::convolution::NDHWGK;
+using PassThrough = ck::tensor_operation::element_wise::PassThrough;
+using Scale       = ck::tensor_operation::element_wise::Scale;
+
+static constexpr ck::index_t NumDimSpatial = 3;
+static constexpr ck::index_t G             = 32;
+static constexpr ck::index_t N             = 32; // batch size
+static constexpr ck::index_t K             = 32; // output channel
+static constexpr ck::index_t C             = 32; // input channel (per group)
+static constexpr ck::index_t Z             = 3;  // filter D
+static constexpr ck::index_t Y             = 3;  // filter H
+static constexpr ck::index_t X             = 3;  // filter W
+static constexpr ck::index_t Di            = 14; // input D
+static constexpr ck::index_t Hi            = 14; // input H
+static constexpr ck::index_t Wi            = 14; // input W
+static constexpr ck::index_t Do            = 14; // output D
+static constexpr ck::index_t Ho            = 14; // output H
+static constexpr ck::index_t Wo            = 14; // output W
+
+struct SimpleDeviceMem
+{
+    SimpleDeviceMem() = delete;
+
+    SimpleDeviceMem(std::size_t mem_size) : p_mem_{}
+    {
+        (void)hipMalloc(static_cast<void**>(&p_mem_), mem_size);
+    }
+
+    void* GetDeviceBuffer() { return p_mem_; }
+
+    ~SimpleDeviceMem() { (void)hipFree(p_mem_); }
+
+    void* p_mem_;
+};
+
+int execute_conv_bwd_weight_scale()
+{
+    constexpr ck::index_t split_k = 2;
+
+    std::array<ck::index_t, NumDimSpatial + 3> in_lengths{G, N, C, Di, Hi, Wi};
+    std::array<ck::index_t, NumDimSpatial + 3> in_strides{
+        C, Di * Hi * Wi * G * C, 1, Hi * Wi * G * C, Wi * G * C, G * C};
+
+    std::array<ck::index_t, NumDimSpatial + 3> wei_lengths{G, K, C, Z, Y, X};
+    std::array<ck::index_t, NumDimSpatial + 3> wei_strides{
+        K * Z * Y * X * C, Z * Y * X * C, 1, Y * X * C, X * C, C};
+
+    std::array<ck::index_t, NumDimSpatial + 3> out_lengths{G, N, K, Do, Ho, Wo};
+    std::array<ck::index_t, NumDimSpatial + 3> out_strides{
+        K, Do * Ho * Wo * G * K, 1, Ho * Wo * G * K, Wo * G * K, G * K};
+
+    std::array<ck::index_t, NumDimSpatial> filter_strides{1, 1, 1};
+    std::array<ck::index_t, NumDimSpatial> filter_dilations{1, 1, 1};
+    std::array<ck::index_t, NumDimSpatial> input_left_pads{1, 1, 1};
+    std::array<ck::index_t, NumDimSpatial> input_right_pads{1, 1, 1};
+
+    SimpleDeviceMem in(sizeof(InDataType) * G * N * Di * Hi * Wi * C);
+    SimpleDeviceMem wei(sizeof(WeiDataType) * G * K * Z * Y * X * C);
+    SimpleDeviceMem out(sizeof(OutDataType) * G * N * Do * Ho * Wo * K);
+
+    using DeviceOp =
+        ck::tensor_operation::device::DeviceGroupedConvBwdWeightMultipleD<NumDimSpatial,
+                                                                          InLayout,
+                                                                          WeiLayout,
+                                                                          OutLayout,
+                                                                          ck::Tuple<>,
+                                                                          InDataType,
+                                                                          WeiDataType,
+                                                                          OutDataType,
+                                                                          ck::Tuple<>,
+                                                                          PassThrough,
+                                                                          Scale,
+                                                                          PassThrough>;
+
+    // get device op instances
+    const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
+        DeviceOp>::GetInstances();
+
+    std::cout << "found " << op_ptrs.size() << " instances" << std::endl;
+
+    std::string best_op_name;
+    int best_op_id        = -1;
+    float best_avg_time   = std::numeric_limits<float>::max();
+    float best_gb_per_sec = 0;
+    float best_tflops     = 0;
+
+    // profile device operation instances
+    std::cout << "Run all instances and do timing" << std::endl;
+
+    for(int i = 0; i < op_ptrs.size(); ++i)
+    {
+        auto& op_ptr = op_ptrs[i];
+        auto argument_ptr =
+            op_ptr->MakeArgumentPointer(static_cast<InDataType*>(in.GetDeviceBuffer()),
+                                        static_cast<WeiDataType*>(wei.GetDeviceBuffer()),
+                                        static_cast<OutDataType*>(out.GetDeviceBuffer()),
+                                        {},
+                                        in_lengths,
+                                        in_strides,
+                                        wei_lengths,
+                                        wei_strides,
+                                        out_lengths,
+                                        out_strides,
+                                        {},
+                                        {},
+                                        filter_strides,
+                                        filter_dilations,
+                                        input_left_pads,
+                                        input_right_pads,
+                                        PassThrough{},
+                                        Scale{2.f},
+                                        PassThrough{},
+                                        split_k);
+
+        SimpleDeviceMem workspace_buf(op_ptr->GetWorkSpaceSize(argument_ptr.get()));
+        op_ptr->SetWorkSpacePointer(argument_ptr.get(), workspace_buf.GetDeviceBuffer());
+
+        auto invoker_ptr    = op_ptr->MakeInvokerPointer();
+        std::string op_name = op_ptr->GetTypeString();
+
+        if(op_ptr->IsSupportedArgument(argument_ptr.get()))
+        {
+            float avg_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, true});
+
+            std::size_t flop =
+                std::size_t(2) * G * N * K * C * Do * Ho * Wo * Y * X + G * K * Z * Y * X * C;
+            std::size_t num_bytes = sizeof(InDataType) * G * N * Di * Hi * Wi * C +
+                                    sizeof(WeiDataType) * G * K * Z * Y * X * C +
+                                    sizeof(OutDataType) * G * N * Do * Ho * Wo * K;
+
+            float tflops     = static_cast<float>(flop) / 1.E9 / avg_time;
+            float gb_per_sec = num_bytes / 1.E6 / avg_time;
+
+            std::cout << "Perf: " << std::setw(10) << avg_time << " ms, " << tflops << " TFlops, "
+                      << gb_per_sec << " GB/s, " << op_name << std::endl;
+
+            if(tflops > best_tflops)
+            {
+                best_op_id      = i;
+                best_op_name    = op_name;
+                best_avg_time   = avg_time;
+                best_gb_per_sec = gb_per_sec;
+                best_tflops     = tflops;
+            }
+        }
+        else
+        {
+            std::cerr << op_name << " does not support this problem" << std::endl;
+        }
+    }
+
+    if(best_op_id < 0)
+    {
+        std::cerr << "no suitable instance" << std::endl;
+        return EXIT_FAILURE;
+    }
+
+    std::cout << "Best Perf: " << std::setw(10) << best_avg_time << " ms, " << best_tflops
+              << " TFlops, " << best_gb_per_sec << " GB/s, " << best_op_name << std::endl;
+
+    // run the best intance
+    {
+        auto& op_ptr = op_ptrs[best_op_id];
+        std::cout << "Run the best instance without timing: " << op_ptr->GetTypeString()
+                  << std::endl;
+        auto argument_ptr =
+            op_ptr->MakeArgumentPointer(static_cast<InDataType*>(in.GetDeviceBuffer()),
+                                        static_cast<WeiDataType*>(wei.GetDeviceBuffer()),
+                                        static_cast<OutDataType*>(out.GetDeviceBuffer()),
+                                        {},
+                                        in_lengths,
+                                        in_strides,
+                                        wei_lengths,
+                                        wei_strides,
+                                        out_lengths,
+                                        out_strides,
+                                        {},
+                                        {},
+                                        filter_strides,
+                                        filter_dilations,
+                                        input_left_pads,
+                                        input_right_pads,
+                                        PassThrough{},
+                                        Scale{2.f},
+                                        PassThrough{},
+                                        split_k);
+
+        auto invoker_ptr = op_ptr->MakeInvokerPointer();
+        SimpleDeviceMem workspace_buf(op_ptr->GetWorkSpaceSize(argument_ptr.get()));
+        op_ptr->SetWorkSpacePointer(argument_ptr.get(), workspace_buf.GetDeviceBuffer());
+
+        if(op_ptr->IsSupportedArgument(argument_ptr.get()))
+        {
+            invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, false});
+        }
+
+        std::cout << "Done" << std::endl;
+    }
+    return 0;
+}
+
+int main() { return execute_conv_bwd_weight_scale(); }

example/20_grouped_conv_bwd_weight/grouped_conv_bwd_weight_xdl_fp16_comp_bf8_fp8.cpp

 // SPDX-License-Identifier: MIT
-// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
 
 #include "common.hpp"
 
@@ -78,6 +78,9 @@ using HostConvBwdWeightInstance = ck::tensor_operation::host::ReferenceConvBwdWe
                                                                                      InElementOp,
                                                                                      WeiElementOp,
                                                                                      OutElementOp,
+                                                                                     0,
+                                                                                     0,
+                                                                                     0,
                                                                                      ComputeTypeA,
                                                                                      ComputeTypeB>;
 

example/20_grouped_conv_bwd_weight/run_grouped_conv_bwd_weight_example.inc

@@ -119,7 +119,10 @@ bool run_grouped_conv_bwd_weight(const ExecutionConfig& config,
                                                   conv_param.input_right_pads_,
                                                   InElementOp{},
                                                   WeiElementOp{},
-                                                  OutElementOp{});
+                                                  OutElementOp{},
+                                                  {},
+                                                  {},
+                                                  {});
 
         ref_invoker.Run(ref_argument);
 

example/62_convnd_activ/binary/CMakeLists.txt

@@ -8,6 +8,8 @@ foreach(gpu IN LISTS GPU_TARGETS)
       add_example_dependencies(example_convnd_activ_binary_xdl example_convnd_fwd_xdl_bilinear_residual_fp16)
       add_example_executable(example_convnd_bwd_data_xdl_bilinear_residual_fp16 convnd_bwd_data_xdl_bilinear_residual_fp16.cpp)
       add_example_dependencies(example_convnd_activ_binary_xdl example_convnd_bwd_data_xdl_bilinear_residual_fp16)
+      add_example_executable(example_convnd_bwd_weight_xdl_bilinear_residual_fp16 convnd_bwd_weight_xdl_bilinear_residual_fp16.cpp)
+      add_example_dependencies(example_convnd_activ_binary_xdl example_convnd_bwd_weight_xdl_bilinear_residual_fp16)
       set(target 1)
  endif()
 endforeach()

example/62_convnd_activ/binary/convnd_bwd_weight_xdl_bilinear_residual_fp16.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
+
+#include <cstdlib>
+#include <iostream>
+#include <numeric>
+#include <type_traits>
+
+#include "ck/ck.hpp"
+#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
+#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
+#include "ck/tensor_operation/gpu/device/impl/device_grouped_conv_bwd_weight_multiple_d_xdl_cshuffle.hpp"
+
+#include "ck/library/utility/algorithm.hpp"
+#include "ck/library/utility/check_err.hpp"
+#include "ck/library/utility/device_memory.hpp"
+#include "ck/library/utility/host_tensor.hpp"
+#include "ck/library/utility/host_tensor_generator.hpp"
+#include "ck/library/utility/convolution_parameter.hpp"
+#include "ck/library/utility/convolution_host_tensor_descriptor_helper.hpp"
+#include "ck/library/reference_tensor_operation/cpu/reference_conv_bwd_weight.hpp"
+#include "ck/library/utility/convolution_host_tensor_descriptor_helper.hpp"
+
+constexpr ck::index_t NDimSpatial = 3;
+using InDataType                  = ck::half_t;
+using WeiDataType                 = ck::half_t;
+using AccDataType                 = float;
+using OutDataType                 = ck::half_t;
+
+template <ck::index_t... Is>
+using S = ck::Sequence<Is...>;
+
+using InLayout  = ck::tensor_layout::convolution::GNDHWC;
+using WeiLayout = ck::tensor_layout::convolution::GKZYXC;
+using OutLayout = ck::tensor_layout::convolution::GNDHWK;
+
+using InElementOp  = ck::tensor_operation::element_wise::PassThrough;
+using WeiElementOp = ck::tensor_operation::element_wise::Bilinear;
+using OutElementOp = ck::tensor_operation::element_wise::PassThrough;
+
+static constexpr auto ConvBwdWeightDefault =
+    ck::tensor_operation::device::ConvolutionBackwardWeightSpecialization::Default;
+
+template <typename WeiElementOp>
+using DeviceGroupedConvNDBwdWeightInstance =
+    ck::tensor_operation::device::DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle<
+        NDimSpatial,
+        InLayout,               // InLayout
+        WeiLayout,              // WeiLayout
+        OutLayout,              // OutLayout
+        ck::Tuple<WeiLayout>,   // DsLayout
+        InDataType,             // InDataType
+        WeiDataType,            // WeiDataType
+        OutDataType,            // OutDataType
+        AccDataType,            // AccDataType
+        ck::Tuple<WeiDataType>, // DsLayout
+        InElementOp,            // InElementwiseOperation
+        WeiElementOp,           // WeiElementwiseOperation
+        OutElementOp,           // OutElementwiseOperation
+        ConvBwdWeightDefault,   // ConvolutionBackwardWeightSpecialization
+        256,                    // BlockSize
+        128,                    // MPerBlock
+        128,                    // NPerBlock
+        4,                      // K0PerBlock
+        8,                      // K1
+        32,                     // MPerXdl
+        32,                     // NPerXdl
+        2,                      // MXdlPerWave
+        2,                      // NXdlPerWave
+        S<1, 4, 16, 4>,         // ABlockTransferThreadClusterLengths_K0_M_K1
+        S<0, 3, 1, 2>,          // ABlockTransferThreadClusterArrangeOrder
+        S<0, 2, 1, 3>,          // ABlockTransferSrcAccessOrder
+        2,                      // ABlockTransferSrcVectorDim
+        8,                      // ABlockTransferSrcScalarPerVector
+        2,                      // ABlockTransferDstScalarPerVector_K1
+        true,                   // ABlockLdsAddExtraM
+        S<1, 4, 16, 4>,         // BBlockTransferThreadClusterLengths_K0_N_K1
+        S<0, 3, 1, 2>,          // BBlockTransferThreadClusterArrangeOrder
+        S<0, 2, 1, 3>,          // BBlockTransferSrcAccessOrder
+        2,                      // BBlockTransferSrcVectorDim
+        8,                      // 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
+using DeviceGroupedConvNDActivInstance = DeviceGroupedConvNDBwdWeightInstance<WeiElementOp>;
+
+namespace {
+// Use custom implementation to pass two more tensors for post op
+template <ck::index_t NDimSpatial,
+          typename InDataType,
+          typename WeiDataType,
+          typename OutDataType,
+          typename InElementOp,
+          typename WeiElementOp,
+          typename OutElementOp,
+          typename DeviceConvNDFwdInstance>
+bool run_grouped_conv(bool do_verification,
+                      int init_method,
+                      bool time_kernel,
+                      const ck::utils::conv::ConvParam& conv_param,
+                      const HostTensorDescriptor& in_g_n_c_wis_desc,
+                      const HostTensorDescriptor& wei_g_k_c_xs_desc,
+                      const HostTensorDescriptor& out_g_n_k_wos_desc,
+                      const InElementOp& in_element_op,
+                      const WeiElementOp& wei_element_op,
+                      const OutElementOp& out_element_op)
+{
+    constexpr ck::index_t split_k = 1;
+    constexpr ck::index_t NumDs   = 1;
+    Tensor<InDataType> in(in_g_n_c_wis_desc);
+    Tensor<WeiDataType> wei_host(wei_g_k_c_xs_desc);
+    Tensor<OutDataType> out(out_g_n_k_wos_desc);
+
+    std::cout << "in: " << in.mDesc << std::endl;
+    std::cout << "wei: " << wei_host.mDesc << std::endl;
+    std::cout << "out: " << out.mDesc << std::endl;
+
+    switch(init_method)
+    {
+    case 0: break;
+    case 1:
+        in.GenerateTensorValue(GeneratorTensor_2<InDataType>{-5, 5});
+        out.GenerateTensorValue(GeneratorTensor_2<OutDataType>{-5, 5});
+        wei_host.GenerateTensorValue(GeneratorTensor_2<WeiDataType>{-5, 5});
+        break;
+    default:
+        in.GenerateTensorValue(GeneratorTensor_3<InDataType>{0.0, 1.0});
+        out.GenerateTensorValue(GeneratorTensor_3<OutDataType>{0.0, 1.0});
+        wei_host.GenerateTensorValue(GeneratorTensor_3<WeiDataType>{-0.5, 0.5});
+    }
+
+    // Initialize based on wei_host
+    Tensor<WeiDataType> wei_device(wei_host);
+
+    DeviceMem in_device_buf(sizeof(InDataType) * in.mDesc.GetElementSpaceSize());
+    DeviceMem wei_device_buf(sizeof(WeiDataType) * wei_device.mDesc.GetElementSpaceSize());
+    DeviceMem out_device_buf(sizeof(OutDataType) * out.mDesc.GetElementSpaceSize());
+
+    in_device_buf.ToDevice(in.mData.data());
+    wei_device_buf.ToDevice(wei_device.mData.data());
+    out_device_buf.ToDevice(out.mData.data());
+
+    std::array<ck::index_t, NDimSpatial + 3> b_g_n_c_wis_lengths{};
+    std::array<ck::index_t, NDimSpatial + 3> b_g_n_c_wis_strides{};
+    std::array<ck::index_t, NDimSpatial + 3> e_g_k_c_xs_lengths{};
+    std::array<ck::index_t, NDimSpatial + 3> e_g_k_c_xs_strides{};
+    std::array<ck::index_t, NDimSpatial + 3> a_g_n_k_wos_lengths{};
+    std::array<ck::index_t, NDimSpatial + 3> a_g_n_k_wos_strides{};
+    std::array<ck::index_t, NDimSpatial> conv_filter_strides{};
+    std::array<ck::index_t, NDimSpatial> conv_filter_dilations{};
+    std::array<ck::index_t, NDimSpatial> input_left_pads{};
+    std::array<ck::index_t, NDimSpatial> input_right_pads{};
+
+    auto copy = [](const auto& x, auto& y) { ck::ranges::copy(x, y.begin()); };
+
+    copy(in_g_n_c_wis_desc.GetLengths(), b_g_n_c_wis_lengths);
+    copy(in_g_n_c_wis_desc.GetStrides(), b_g_n_c_wis_strides);
+    copy(wei_g_k_c_xs_desc.GetLengths(), e_g_k_c_xs_lengths);
+    copy(wei_g_k_c_xs_desc.GetStrides(), e_g_k_c_xs_strides);
+    copy(out_g_n_k_wos_desc.GetLengths(), a_g_n_k_wos_lengths);
+    copy(out_g_n_k_wos_desc.GetStrides(), a_g_n_k_wos_strides);
+    copy(conv_param.conv_filter_strides_, conv_filter_strides);
+    copy(conv_param.conv_filter_dilations_, conv_filter_dilations);
+    copy(conv_param.input_left_pads_, input_left_pads);
+    copy(conv_param.input_right_pads_, input_right_pads);
+
+    // Use weight as D
+    const std::array<const void*, NumDs> ds = {wei_device_buf.GetDeviceBuffer()};
+
+    auto conv     = DeviceConvNDFwdInstance{};
+    auto invoker  = conv.MakeInvoker();
+    auto argument = conv.MakeArgument(
+        static_cast<InDataType*>(in_device_buf.GetDeviceBuffer()),
+        static_cast<WeiDataType*>(wei_device_buf.GetDeviceBuffer()),
+        static_cast<OutDataType*>(out_device_buf.GetDeviceBuffer()),
+        ds,
+        b_g_n_c_wis_lengths,
+        b_g_n_c_wis_strides,
+        e_g_k_c_xs_lengths,
+        e_g_k_c_xs_strides,
+        a_g_n_k_wos_lengths,
+        a_g_n_k_wos_strides,
+        std::array<std::array<ck::index_t, NDimSpatial + 3>, NumDs>{e_g_k_c_xs_lengths},
+        std::array<std::array<ck::index_t, NDimSpatial + 3>, NumDs>{e_g_k_c_xs_strides},
+        conv_filter_strides,
+        conv_filter_dilations,
+        input_left_pads,
+        input_right_pads,
+        in_element_op,
+        wei_element_op,
+        out_element_op,
+        split_k);
+
+    DeviceMem workspace_buf(argument.GetWorkspaceSizeBytes());
+    conv.SetWorkSpacePointer(&argument, workspace_buf.GetDeviceBuffer());
+
+    if(!conv.IsSupportedArgument(argument))
+    {
+        throw std::runtime_error("The device op with the specified compilation parameters does "
+                                 "not support this convolution problem.");
+    }
+    float avg_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
+
+    std::size_t flop =
+        conv_param.GetFlops() + 3 * conv_param.GetOutputByte<WeiDataType>() / sizeof(WeiDataType);
+    std::size_t num_btype = conv_param.GetByte<InDataType, WeiDataType, OutDataType>() +
+                            conv_param.GetOutputByte<WeiDataType>();
+
+    float tflops     = static_cast<float>(flop) / 1.E9 / avg_time;
+    float gb_per_sec = num_btype / 1.E6 / avg_time;
+    std::cout << "Perf: " << avg_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s, "
+              << conv.GetTypeString() << std::endl;
+
+    if(do_verification)
+    {
+        std::array<Tensor<OutDataType>, NumDs> d_tensors = {wei_host};
+        auto ref_conv =
+            ck::tensor_operation::host::ReferenceConvBwdWeight<NDimSpatial,
+                                                               InDataType,
+                                                               WeiDataType,
+                                                               OutDataType,
+                                                               InElementOp,
+                                                               WeiElementOp,
+                                                               OutElementOp,
+                                                               0, /*Num A Elementwise Tensors*/
+                                                               0, /*Num B Elementwise Tensors*/
+                                                               NumDs>{};
+
+        auto ref_invoker  = ref_conv.MakeInvoker();
+        auto ref_argument = ref_conv.MakeArgument(in,
+                                                  wei_host,
+                                                  out,
+                                                  conv_param.conv_filter_strides_,
+                                                  conv_param.conv_filter_dilations_,
+                                                  conv_param.input_left_pads_,
+                                                  conv_param.input_right_pads_,
+                                                  in_element_op,
+                                                  wei_element_op,
+                                                  out_element_op,
+                                                  {},
+                                                  {},
+                                                  d_tensors);
+
+        ref_invoker.Run(ref_argument);
+        wei_device_buf.FromDevice(wei_device.mData.data());
+
+        return ck::utils::check_err(wei_device, wei_host, "Error: incorrect results!");
+    }
+
+    return true;
+}
+
+} // namespace
+
+#include "../run_convnd_activ_example.inc"
+
+int main(int argc, char* argv[]) { return !run_convnd_example(argc, argv); }

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

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

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

 // SPDX-License-Identifier: MIT
-// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
 
 #pragma once
 
@@ -137,34 +137,6 @@ struct DeviceGroupedConvBwdWeight_Dl : public DeviceGroupedConvBwdWeight<NDimSpa
                                                                          WeiElementwiseOperation,
                                                                          OutElementwiseOperation>
 {
-    // 1d
-    static constexpr bool is_NWGK_GKXC_NWGC =
-        is_same_v<InLayout, tensor_layout::convolution::NWGC> &&
-        is_same_v<WeiLayout, tensor_layout::convolution::GKXC> &&
-        is_same_v<OutLayout, tensor_layout::convolution::NWGK>;
-    static constexpr bool is_GNWK_GKXC_GNWC =
-        is_same_v<InLayout, tensor_layout::convolution::GNWC> &&
-        is_same_v<WeiLayout, tensor_layout::convolution::GKXC> &&
-        is_same_v<OutLayout, tensor_layout::convolution::GNWK>;
-    // 2d
-    static constexpr bool is_NHWGK_GKYXC_NHWGC =
-        is_same_v<InLayout, tensor_layout::convolution::NHWGC> &&
-        is_same_v<WeiLayout, tensor_layout::convolution::GKYXC> &&
-        is_same_v<OutLayout, tensor_layout::convolution::NHWGK>;
-    static constexpr bool is_GNHWK_GKYXC_GNHWC =
-        is_same_v<InLayout, tensor_layout::convolution::GNHWC> &&
-        is_same_v<WeiLayout, tensor_layout::convolution::GKYXC> &&
-        is_same_v<OutLayout, tensor_layout::convolution::GNHWK>;
-    // 3d
-    static constexpr bool is_NDHWGK_GKZYXC_NDHWGC =
-        is_same_v<InLayout, tensor_layout::convolution::NDHWGC> &&
-        is_same_v<WeiLayout, tensor_layout::convolution::GKZYXC> &&
-        is_same_v<OutLayout, tensor_layout::convolution::NDHWGK>;
-    static constexpr bool is_GNDHWK_GKZYXC_GNDHWC =
-        is_same_v<InLayout, tensor_layout::convolution::GNDHWC> &&
-        is_same_v<WeiLayout, tensor_layout::convolution::GKZYXC> &&
-        is_same_v<OutLayout, tensor_layout::convolution::GNDHWK>;
-
     using DeviceOp = DeviceGroupedConvBwdWeight_Dl;
 
     using ADataType = OutDataType;
@@ -1065,9 +1037,15 @@ struct DeviceGroupedConvBwdWeight_Dl : public DeviceGroupedConvBwdWeight<NDimSpa
         if(arg.k_batch_ != 1)
             return false;
 
-        if constexpr(!((NDimSpatial == 1 && (is_NWGK_GKXC_NWGC || is_GNWK_GKXC_GNWC)) ||
-                       (NDimSpatial == 2 && (is_NHWGK_GKYXC_NHWGC || is_GNHWK_GKYXC_GNHWC)) ||
-                       (NDimSpatial == 3 && (is_NDHWGK_GKZYXC_NDHWGC || is_GNDHWK_GKZYXC_GNDHWC))))
+        if constexpr(!((NDimSpatial == 1 &&
+                        (is_NWGK_GKXC_NWGC<InLayout, WeiLayout, OutLayout>() ||
+                         is_GNWK_GKXC_GNWC<InLayout, WeiLayout, OutLayout>())) ||
+                       (NDimSpatial == 2 &&
+                        (is_NHWGK_GKYXC_NHWGC<InLayout, WeiLayout, OutLayout>() ||
+                         is_GNHWK_GKYXC_GNHWC<InLayout, WeiLayout, OutLayout>())) ||
+                       (NDimSpatial == 3 &&
+                        (is_NDHWGK_GKZYXC_NDHWGC<InLayout, WeiLayout, OutLayout>() ||
+                         is_GNDHWK_GKZYXC_GNDHWC<InLayout, WeiLayout, OutLayout>()))))
         {
             return false;
         }

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

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2024, 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_multiple_d.hpp"
+#include "ck/tensor_operation/operator_transform/transform_conv_bwd_weight_to_gemm.hpp"
+#include "ck/tensor_operation/gpu/device/convolution_backward_weight_specialization.hpp"
+#include "ck/tensor_operation/gpu/grid/gridwise_elementwise_dynamic_vector_dims.hpp"
+#include "ck/tensor_operation/gpu/grid/gridwise_gemm_xdlops_bwd_weight.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/host_utility/device_prop.hpp"
+#include "ck/host_utility/kernel_launch.hpp"
+
+namespace ck {
+namespace tensor_operation {
+namespace device {
+
+template <typename GridwiseGemm,
+          typename FloatA,
+          typename FloatB,
+          typename FloatC,
+          typename AElementwiseOperation,
+          typename BElementwiseOperation,
+          typename CElementwiseOperation,
+          typename AGridDesc_B_K0_M_K1,
+          typename BGridDesc_B_K0_N_K1,
+          typename CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock,
+          typename Block2CTileMap,
+          typename ComputePtrOffsetOfBatch,
+          bool HasMainKBlockLoop>
+__global__ void
+#if CK_USE_LAUNCH_BOUNDS
+    __launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
+#endif
+        kernel_batched_gemm_xdlops_bwd_weight(
+            const FloatA* __restrict__ p_a_grid,
+            const FloatB* __restrict__ p_b_grid,
+            FloatC* __restrict__ p_c_grid,
+            const AElementwiseOperation a_element_op,
+            const BElementwiseOperation b_element_op,
+            const CElementwiseOperation c_element_op,
+            const index_t batch_count,
+            const AGridDesc_B_K0_M_K1 a_b_k0_m_k1_grid_desc,
+            const BGridDesc_B_K0_N_K1 b_b_k0_n_k1_grid_desc,
+            const CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock
+                c_grid_desc_mblock_mperblock_nblock_nperblock,
+            const Block2CTileMap block_2_ctile_map,
+            const ComputePtrOffsetOfBatch compute_ptr_offset_of_batch)
+{
+#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx908__) || defined(__gfx90a__) || \
+    defined(__gfx94__))
+    const index_t num_blocks_per_batch =
+        __builtin_amdgcn_readfirstlane(get_grid_size() / batch_count);
+    const index_t g_idx = __builtin_amdgcn_readfirstlane(get_block_1d_id() / num_blocks_per_batch);
+
+    const long_index_t a_batch_offset = __builtin_amdgcn_readfirstlane(
+        static_cast<long_index_t>(compute_ptr_offset_of_batch.GetAPtrOffset(g_idx)));
+    const long_index_t b_batch_offset = __builtin_amdgcn_readfirstlane(
+        static_cast<long_index_t>(compute_ptr_offset_of_batch.GetBPtrOffset(g_idx)));
+    const long_index_t c_batch_offset = __builtin_amdgcn_readfirstlane(
+        static_cast<long_index_t>(compute_ptr_offset_of_batch.GetCPtrOffset(g_idx)));
+
+    __shared__ FloatA p_shared[GridwiseGemm::GetSharedMemoryNumberOfByte() / sizeof(FloatA)];
+
+    GridwiseGemm::template Run<HasMainKBlockLoop>(p_a_grid + a_batch_offset,
+                                                  p_b_grid + b_batch_offset,
+                                                  p_c_grid + c_batch_offset,
+                                                  p_shared,
+                                                  a_b_k0_m_k1_grid_desc,
+                                                  b_b_k0_n_k1_grid_desc,
+                                                  c_grid_desc_mblock_mperblock_nblock_nperblock,
+                                                  a_element_op,
+                                                  b_element_op,
+                                                  c_element_op,
+                                                  block_2_ctile_map);
+#else
+    ignore = p_a_grid;
+    ignore = p_b_grid;
+    ignore = p_c_grid;
+    ignore = a_b_k0_m_k1_grid_desc;
+    ignore = b_b_k0_n_k1_grid_desc;
+    ignore = c_grid_desc_mblock_mperblock_nblock_nperblock;
+    ignore = a_element_op;
+    ignore = b_element_op;
+    ignore = c_element_op;
+    ignore = batch_count;
+    ignore = block_2_ctile_map;
+    ignore = compute_ptr_offset_of_batch;
+
+    compute_ptr_offset_of_batch.GetAPtrOffset(0);
+    compute_ptr_offset_of_batch.GetBPtrOffset(0);
+    compute_ptr_offset_of_batch.GetCPtrOffset(0);
+#endif // end of if (defined(__gfx908__) || defined(__gfx90a__))
+}
+
+template <index_t NDimSpatial,
+          typename InLayout,
+          typename WeiLayout,
+          typename OutLayout,
+          typename DsLayout,
+          typename InDataType,
+          typename WeiDataType,
+          typename OutDataType,
+          typename AccDataType,
+          typename DsDataType,
+          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,
+          typename ComputeTypeA = InDataType,
+          typename ComputeTypeB = ComputeTypeA>
+struct DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle
+    : public DeviceGroupedConvBwdWeightMultipleD<NDimSpatial,
+                                                 InLayout,
+                                                 WeiLayout,
+                                                 OutLayout,
+                                                 DsLayout,
+                                                 InDataType,
+                                                 WeiDataType,
+                                                 OutDataType,
+                                                 DsDataType,
+                                                 InElementwiseOperation,
+                                                 WeiElementwiseOperation,
+                                                 OutElementwiseOperation,
+                                                 ComputeTypeA,
+                                                 ComputeTypeB>
+{
+    using DeviceOp = DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle;
+
+    using ADataType = OutDataType;
+    using BDataType = InDataType;
+    using EDataType = WeiDataType;
+
+    static constexpr index_t NumDTensor = DsLayout::Size();
+
+    using AElementwiseOperation   = OutElementwiseOperation;
+    using BElementwiseOperation   = InElementwiseOperation;
+    using CDEElementwiseOperation = WeiElementwiseOperation;
+
+    // TODO make A/B datatype different
+    using ABDataType = InDataType;
+
+    static constexpr auto I0 = Number<0>{};
+    static constexpr auto I1 = Number<1>{};
+    static constexpr auto I2 = Number<2>{};
+    static constexpr auto I3 = Number<3>{};
+    static constexpr auto I4 = Number<4>{};
+    static constexpr auto I5 = Number<5>{};
+
+    static constexpr auto K1Number = Number<K1>{};
+
+    static constexpr auto conv_to_gemm_transformer =
+        TransformConvBwdWeightToGemm<NDimSpatial,
+                                     MPerBlock,
+                                     NPerBlock,
+                                     K1Number,
+                                     K0PerBlock,
+                                     ConvBackwardWeightSpecialization>{};
+
+    // Bytes per 32 lds bank: 32 * 4 bytes
+    static constexpr auto BankLength = 128;
+    static constexpr auto ElePerBank = BankLength / sizeof(ADataType);
+
+    // M1 & M0
+    static constexpr auto ABlockLdsM1PerBlock = ElePerBank / K1;
+    static constexpr auto ABlockLdsM0PerBlock = MPerBlock / ABlockLdsM1PerBlock;
+    static constexpr auto ABlockLdsM1Padding  = 4;
+
+    // N1 & N0
+    static constexpr auto BBlockLdsN1PerBlock = ElePerBank / K1;
+    static constexpr auto BBlockLdsN0PerBlock = NPerBlock / BBlockLdsN1PerBlock;
+    static constexpr auto BBlockLdsN1Padding  = 4;
+
+    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_bk0mk1_bk0nk1_mn_xdlops_bwd_weight<
+        BlockSize,
+        ADataType,
+        BDataType,
+        AccDataType,
+        EDataType,
+        InMemoryDataOperationEnum::AtomicAdd,
+        AGridDesc_K0_M_K1,
+        BGridDesc_K0_N_K1,
+        CGridDesc_M_N,
+        AElementwiseOperation,
+        BElementwiseOperation,
+        element_wise::PassThrough,
+        MPerBlock,
+        NPerBlock,
+        K0PerBlock,
+        MPerXdl,
+        NPerXdl,
+        K1,
+        MXdlPerWave,
+        NXdlPerWave,
+        ABlockTransferThreadClusterLengths_K0_M_K1,
+        ABlockTransferThreadClusterArrangeOrder,
+        ABlockTransferSrcAccessOrder,
+        ABlockTransferSrcVectorDim,
+        ABlockTransferSrcScalarPerVector,
+        ABlockTransferDstScalarPerVector_K1,
+        false, // AThreadTransferSrcResetCoordinateAfterRun,
+        ABlockLdsAddExtraM,
+        ABlockLdsM1PerBlock,
+        ABlockLdsM0PerBlock,
+        ABlockLdsM1Padding,
+        BBlockTransferThreadClusterLengths_K0_N_K1,
+        BBlockTransferThreadClusterArrangeOrder,
+        BBlockTransferSrcAccessOrder,
+        BBlockTransferSrcVectorDim,
+        BBlockTransferSrcScalarPerVector,
+        BBlockTransferDstScalarPerVector_K1,
+        false, // BThreadTransferSrcResetCoordinateAfterRun,
+        BBlockLdsAddExtraN,
+        BBlockLdsN1PerBlock,
+        BBlockLdsN0PerBlock,
+        BBlockLdsN1Padding,
+        CShuffleMXdlPerWavePerShuffle,
+        CShuffleNXdlPerWavePerShuffle,
+        CBlockTransferScalarPerVector_NWaveNPerXdl,
+        CBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
+        true,
+        true,
+        1,
+        PipelineVersion::v1,
+        ComputeTypeA,
+        ComputeTypeB>;
+
+    static constexpr auto MakeElementwiseInputSequence()
+    {
+        return generate_sequence_v2(
+            [&](auto) constexpr { return Number<CBlockTransferScalarPerVector_NWaveNPerXdl>{}; },
+            Number<NumDTensor + 1>{});
+    }
+
+    static constexpr auto GetDsGridPointerTuple()
+    {
+        return generate_tuple(
+            [&](auto i) {
+                using DDataType = remove_cvref_t<tuple_element_t<i.value, DsDataType>>;
+                return static_cast<const DDataType*>(nullptr);
+            },
+            Number<NumDTensor>{});
+    }
+
+    template <index_t NDim, typename ck::enable_if<NDim == 1, bool>::type = false>
+    static auto MakeDsGridDescriptor_M_N(
+        const std::array<std::array<index_t, NDim + 3>, NumDTensor>& ds_g_k_c_xs_lengths,
+        const std::array<std::array<index_t, NDim + 3>, NumDTensor>& ds_g_k_c_xs_strides)
+    {
+        return generate_tuple(
+            [&](auto i) {
+                const index_t K       = ds_g_k_c_xs_lengths[i][I1];
+                const index_t C       = ds_g_k_c_xs_lengths[i][I2];
+                const index_t X       = ds_g_k_c_xs_lengths[i][I3];
+                const index_t CStride = ds_g_k_c_xs_strides[I2];
+                const index_t KStride = ds_g_k_c_xs_strides[I1];
+
+                const auto wei_grid_desc = make_naive_tensor_descriptor(
+                    make_tuple(K, X * C), make_tuple(KStride, CStride));
+
+                if constexpr(ConvBackwardWeightSpecialization ==
+                             device::ConvolutionBackwardWeightSpecialization::Filter1x1Stride1Pad0)
+                {
+                    return wei_grid_desc;
+                }
+                else
+                {
+                    const index_t GemmM = K;
+                    const index_t GemmN = C * X;
+                    const auto PadGemmM = MPerBlock - GemmM % MPerBlock;
+                    const auto PadGemmN = NPerBlock - GemmN % NPerBlock;
+
+                    return 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>{}));
+                }
+            },
+            Number<NumDTensor>{});
+    }
+
+    template <index_t NDim, typename ck::enable_if<NDim == 2, bool>::type = false>
+    static auto MakeDsGridDescriptor_M_N(
+        const std::array<std::array<index_t, NDim + 3>, NumDTensor>& ds_g_k_c_xs_lengths,
+        const std::array<std::array<index_t, NDim + 3>, NumDTensor>& ds_g_k_c_xs_strides)
+    {
+        return generate_tuple(
+            [&](auto i) {
+                const index_t K = ds_g_k_c_xs_lengths[i][I1];
+                const index_t C = ds_g_k_c_xs_lengths[i][I2];
+                const index_t Y = ds_g_k_c_xs_lengths[i][I3];
+                const index_t X = ds_g_k_c_xs_lengths[i][I4];
+
+                const auto wei_grid_desc =
+                    conv_to_gemm_transformer.template make_wei_grid_desc<NDim>(
+                        K, Y, X, C, ds_g_k_c_xs_strides[i]);
+
+                if constexpr(ConvBackwardWeightSpecialization ==
+                             device::ConvolutionBackwardWeightSpecialization::Filter1x1Stride1Pad0)
+                {
+                    return wei_grid_desc;
+                }
+                else
+                {
+                    const index_t GemmM = K;
+                    const index_t GemmN = C * X * Y;
+                    const auto PadGemmM = MPerBlock - GemmM % MPerBlock;
+                    const auto PadGemmN = NPerBlock - GemmN % NPerBlock;
+
+                    return 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>{}));
+                }
+            },
+            Number<NumDTensor>{});
+    }
+
+    template <index_t NDim, typename ck::enable_if<NDim == 3, bool>::type = false>
+    static auto MakeDsGridDescriptor_M_N(
+        const std::array<std::array<index_t, NDim + 3>, NumDTensor>& ds_g_k_c_xs_lengths,
+        const std::array<std::array<index_t, NDim + 3>, NumDTensor>& ds_g_k_c_xs_strides)
+    {
+        return generate_tuple(
+            [&](auto i) {
+                const index_t K = ds_g_k_c_xs_lengths[i][I1];
+                const index_t C = ds_g_k_c_xs_lengths[i][I2];
+                const index_t Z = ds_g_k_c_xs_lengths[i][I3];
+                const index_t Y = ds_g_k_c_xs_lengths[i][I4];
+                const index_t X = ds_g_k_c_xs_lengths[i][I5];
+
+                const auto wei_grid_desc =
+                    conv_to_gemm_transformer.template make_wei_grid_desc<NDim>(
+                        K, Z, Y, X, C, ds_g_k_c_xs_strides[i]);
+
+                if constexpr(ConvBackwardWeightSpecialization ==
+                             device::ConvolutionBackwardWeightSpecialization::Filter1x1Stride1Pad0)
+                {
+                    return wei_grid_desc;
+                }
+                else
+                {
+                    const index_t GemmM = K;
+                    const index_t GemmN = C * X * Y * Z;
+                    const auto PadGemmM = MPerBlock - GemmM % MPerBlock;
+                    const auto PadGemmN = NPerBlock - GemmN % NPerBlock;
+
+                    return 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>{}));
+                }
+            },
+            Number<NumDTensor>{});
+    }
+
+    template <typename ComputePtrOffsetOfBatch>
+    static void
+    InitElementwiseBatchStrides(const ComputePtrOffsetOfBatch& compute_ptr_offset_of_batch_,
+                                std::array<index_t, NumDTensor + I1>& input_batch_strides,
+                                std::array<index_t, I1>& output_batch_strides)
+    {
+        input_batch_strides[I0]  = compute_ptr_offset_of_batch_.BatchStrideC_;
+        output_batch_strides[I0] = compute_ptr_offset_of_batch_.BatchStrideC_;
+
+        // input_batch_strides = {C, Ds...}
+        static_for<0, NumDTensor, 1>{}([&](auto i) {
+            input_batch_strides[i + 1] = compute_ptr_offset_of_batch_.BatchStrideDs_[i];
+        });
+    }
+
+    using DsGridDesc_M_N     = decltype(MakeDsGridDescriptor_M_N<NDimSpatial>({}, {}));
+    using CDGridDesc_M_N     = decltype(concat_tuple(Tuple<CGridDesc_M_N>{}, DsGridDesc_M_N{}));
+    using DsGridPointerTuple = decltype(GetDsGridPointerTuple());
+    using CDDataTypes   = decltype(concat_tuple(Tuple<const EDataType*>{}, DsGridPointerTuple{}));
+    using EGridDesc_M_N = CGridDesc_M_N;
+    static constexpr index_t ClusterLengthMPerBlock =
+        CBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock::At(1);
+    static constexpr index_t ClusterLengthNPerBlock =
+        CBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock::At(3);
+    using Block2TileMapElementwise = BlockToCTileMap_M00_N0_M01Adapt<MPerBlock, NPerBlock>;
+
+    using GridwiseElementwise =
+        GridwiseElementwise<CDGridDesc_M_N,
+                            Tuple<EGridDesc_M_N>,
+                            CDDataTypes,
+                            Tuple<EDataType*>,
+                            Block2TileMapElementwise,
+                            CDEElementwiseOperation,
+                            BlockSize,
+                            MPerBlock,
+                            NPerBlock,
+                            MPerBlock / ClusterLengthMPerBlock,
+                            NPerBlock / ClusterLengthNPerBlock,
+                            Sequence<0, 1>,
+                            decltype(MakeElementwiseInputSequence()),
+                            Sequence<CBlockTransferScalarPerVector_NWaveNPerXdl>,
+                            true>;
+
+    // Argument
+    using CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock =
+        decltype(GridwiseGemm::MakeCGridDesc_MBlock_MPerBlock_NBlock_NPerBlock(CGridDesc_M_N{}));
+
+    using Block2CTileMap =
+        decltype(GridwiseGemm::MakeCBlockClusterAdaptor(CGridDesc_M_N{}, 1, 1, 1));
+
+    struct Argument : public BaseArgument
+    {
+        Argument(
+            const InDataType* p_in_grid,
+            WeiDataType* p_wei_grid,
+            const OutDataType* p_out_grid,
+            const std::array<const void*, NumDTensor>& p_ds,
+            const std::array<index_t, NDimSpatial + 3>& b_g_n_c_wis_lengths, // input
+            const std::array<index_t, NDimSpatial + 3>& b_g_n_c_wis_strides,
+            const std::array<index_t, NDimSpatial + 3>& e_g_k_c_xs_lengths, // weight
+            const std::array<index_t, NDimSpatial + 3>& e_g_k_c_xs_strides,
+            const std::array<index_t, NDimSpatial + 3>& a_g_n_k_wos_lengths, // output
+            const std::array<index_t, NDimSpatial + 3>& a_g_n_k_wos_strides,
+            const std::array<std::array<index_t, NDimSpatial + 3>, NumDTensor>& ds_g_k_c_xs_lengths,
+            const std::array<std::array<index_t, NDimSpatial + 3>, NumDTensor>& ds_g_k_c_xs_strides,
+            const std::array<ck::index_t, NDimSpatial>& conv_filter_strides,
+            const std::array<ck::index_t, NDimSpatial>& conv_filter_dilations,
+            const std::array<ck::index_t, NDimSpatial>& input_left_pads,
+            const std::array<ck::index_t, NDimSpatial>& input_right_pads,
+            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_ds_grid_{},
+              p_e_grid_{p_wei_grid},
+              a_grid_desc_kbatch_k0_m_k1_{},
+              b_grid_desc_kbatch_k0_n_k1_{},
+              ce_grid_desc_m_n_{},
+              c_grid_desc_mblock_mperblock_nblock_nperblock_{},
+              block_2_ctile_map_{},
+              compute_ptr_offset_of_batch_{},
+              M01_{M01},
+              N01_{N01},
+              a_element_op_{out_element_op},
+              b_element_op_{in_element_op},
+              cde_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_);
+
+            static_for<0, NumDTensor, 1>{}([&](auto i) {
+                using DLayout   = remove_cvref_t<tuple_element_t<i.value, DsLayout>>;
+                using DDataType = remove_cvref_t<tuple_element_t<i.value, DsDataType>>;
+
+                static_assert(is_same_v<DLayout, WeiLayout>, "Not supported D data layout");
+
+                // D pointer
+                p_ds_grid_(i) = static_cast<const DDataType*>(p_ds[i]);
+                compute_ptr_offset_of_batch_.BatchStrideDs_(i) = ds_g_k_c_xs_strides[i][0];
+            });
+
+            a_grid_desc_kbatch_k0_m_k1_ = descs[I0];
+            b_grid_desc_kbatch_k0_n_k1_ = descs[I1];
+            ce_grid_desc_m_n_           = descs[I2];
+
+            ds_grid_descs_tuple_ =
+                MakeDsGridDescriptor_M_N<NDimSpatial>(ds_g_k_c_xs_lengths, ds_g_k_c_xs_strides);
+
+            block_2_ctile_map_ =
+                GridwiseGemm::MakeCBlockClusterAdaptor(ce_grid_desc_m_n_, M01, N01, k_batch_);
+            elementwise_block_2_ctile_map_ = Block2TileMapElementwise{
+                ce_grid_desc_m_n_.GetLength(I0), ce_grid_desc_m_n_.GetLength(I1)};
+
+            // 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<>{});
+
+            if(GridwiseGemm::CheckValidity(a_grid_desc_kbatch_k0_m_k1_,
+                                           b_grid_desc_kbatch_k0_n_k1_,
+                                           ce_grid_desc_m_n_,
+                                           block_2_ctile_map_))
+            {
+                c_grid_desc_mblock_mperblock_nblock_nperblock_ =
+                    GridwiseGemm::MakeCGridDesc_MBlock_MPerBlock_NBlock_NPerBlock(
+                        ce_grid_desc_m_n_);
+            }
+        }
+
+        std::size_t GetWorkspaceSizeBytes() const
+        {
+            return sizeof(EDataType) * ce_grid_desc_m_n_.GetElementSpaceSize() * Conv_G_;
+        }
+
+        const ADataType* p_a_grid_;
+        const BDataType* p_b_grid_;
+        DsGridPointerTuple p_ds_grid_;
+        EDataType* p_e_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 ce_grid_desc_m_n_;
+        CGridDesc_MBlock_MPerBlock_NBlock_NPerBlock c_grid_desc_mblock_mperblock_nblock_nperblock_;
+        DsGridDesc_M_N ds_grid_descs_tuple_;
+
+        Block2CTileMap block_2_ctile_map_;
+        Block2TileMapElementwise elementwise_block_2_ctile_map_;
+
+        // for computing batch offset
+        ComputePtrOffsetOfStridedBatch<I1, I1, NumDTensor> compute_ptr_offset_of_batch_;
+
+        index_t M01_;
+        index_t N01_;
+
+        OutElementwiseOperation a_element_op_;
+        InElementwiseOperation b_element_op_;
+        WeiElementwiseOperation cde_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.ce_grid_desc_m_n_{" << arg.ce_grid_desc_m_n_.GetLength(I0) << ", "
+                      << arg.ce_grid_desc_m_n_.GetLength(I1) << "}" << std::endl;
+        }
+
+        float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
+        {
+            if(!GridwiseGemm::CheckValidity(arg.a_grid_desc_kbatch_k0_m_k1_,
+                                            arg.b_grid_desc_kbatch_k0_n_k1_,
+                                            arg.ce_grid_desc_m_n_,
+                                            arg.block_2_ctile_map_))
+            {
+                throw std::runtime_error(
+                    "wrong! GridwiseGemm_km_kn_m0m1n0n1_xdlops_v3r1 has invalid setting");
+            }
+
+            const auto K0                     = arg.a_grid_desc_kbatch_k0_m_k1_.GetLength(I1);
+            const bool has_main_k0_block_loop = GridwiseGemm::CalculateHasMainK0BlockLoop(K0);
+
+            auto launch_gemm_kernel = [&](auto has_main_k_block_loop) {
+                EDataType* p_c_grid = type_convert<EDataType*>(arg.p_workspace_);
+                const index_t grid_size =
+                    arg.block_2_ctile_map_.CalculateGridSize(arg.ce_grid_desc_m_n_) * arg.Conv_G_;
+
+                constexpr bool has_main_loop = has_main_k_block_loop.value;
+
+                auto preprocess = [&]() {
+                    hip_check_error(hipMemsetAsync(
+                        p_c_grid, 0, arg.GetWorkspaceSizeBytes(), stream_config.stream_id_));
+                };
+
+                const auto kernel = kernel_batched_gemm_xdlops_bwd_weight<
+                    GridwiseGemm,
+                    ADataType,
+                    BDataType,
+                    EDataType,
+                    OutElementwiseOperation,
+                    InElementwiseOperation,
+                    element_wise::PassThrough,
+                    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>,
+                    remove_reference_t<DeviceOp::Block2CTileMap>,
+                    ComputePtrOffsetOfStridedBatch<I1, I1, NumDTensor>,
+                    has_main_loop>;
+
+                return launch_and_time_kernel_with_preprocess(
+                    stream_config,
+                    preprocess,
+                    kernel,
+                    dim3(grid_size),
+                    dim3(BlockSize),
+                    0,
+                    arg.p_a_grid_,
+                    arg.p_b_grid_,
+                    p_c_grid,
+                    arg.a_element_op_,
+                    arg.b_element_op_,
+                    element_wise::PassThrough{},
+                    arg.Conv_G_,
+                    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.block_2_ctile_map_,
+                    arg.compute_ptr_offset_of_batch_);
+            };
+
+            auto launch_elementwise_kernel = [&]() {
+                const EDataType* p_c_grid = type_convert<const EDataType*>(arg.p_workspace_);
+                const index_t grid_size =
+                    arg.elementwise_block_2_ctile_map_.CalculateGridSize(arg.ce_grid_desc_m_n_) *
+                    arg.Conv_G_;
+
+                std::array<index_t, NumDTensor + I1> input_batch_strides;
+                std::array<index_t, I1> output_batch_strides;
+                InitElementwiseBatchStrides(
+                    arg.compute_ptr_offset_of_batch_, input_batch_strides, output_batch_strides);
+
+                const auto kernel = kernel_batched_elementwise<GridwiseElementwise,
+                                                               CDGridDesc_M_N,
+                                                               ck::Tuple<EGridDesc_M_N>,
+                                                               CDDataTypes,
+                                                               ck::Tuple<EDataType*>,
+                                                               Block2TileMapElementwise,
+                                                               CDEElementwiseOperation,
+                                                               NumDTensor + I1,
+                                                               I1>;
+
+                return launch_and_time_kernel(
+                    stream_config,
+                    kernel,
+                    dim3(grid_size),
+                    dim3(BlockSize),
+                    0,
+                    concat_tuple(make_tuple(arg.ce_grid_desc_m_n_), arg.ds_grid_descs_tuple_),
+                    make_tuple(arg.ce_grid_desc_m_n_),
+                    concat_tuple(make_tuple(p_c_grid), arg.p_ds_grid_),
+                    arg.p_e_grid_,
+                    arg.elementwise_block_2_ctile_map_,
+                    arg.cde_element_op_,
+                    arg.Conv_G_,
+                    input_batch_strides,
+                    output_batch_strides);
+            };
+
+            float avg_time = 0;
+            if(has_main_k0_block_loop)
+            {
+                avg_time = launch_gemm_kernel(integral_constant<bool, true>{});
+            }
+            else
+            {
+                avg_time = launch_gemm_kernel(integral_constant<bool, false>{});
+            }
+
+            avg_time += launch_elementwise_kernel();
+            return avg_time;
+        }
+
+        float Run(const BaseArgument* p_arg,
+                  const StreamConfig& stream_config = StreamConfig{}) override
+        {
+            return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
+        }
+    };
+
+    static constexpr bool IsValidCompilationParameter()
+    {
+        // TODO: properly implement this check
+        return true;
+    }
+
+    static bool IsSupportedArgument(const Argument& arg)
+    {
+        if(!ck::is_xdl_supported())
+        {
+            return false;
+        }
+        if constexpr(NDimSpatial == 1)
+        {
+            if constexpr(!is_GNWK_GKXC_GNWC<InLayout, WeiLayout, OutLayout>())
+            {
+                return false;
+            }
+        }
+        else if constexpr(NDimSpatial == 2)
+        {
+            if constexpr(!(is_NHWGK_GKYXC_NHWGC<InLayout, WeiLayout, OutLayout>() ||
+                           is_GNHWK_GKYXC_GNHWC<InLayout, WeiLayout, OutLayout>()))
+            {
+                return false;
+            }
+        }
+        else if constexpr(NDimSpatial == 3)
+        {
+            if constexpr(!(is_NDHWGK_GKZYXC_NDHWGC<InLayout, WeiLayout, OutLayout>() ||
+                           is_GNDHWK_GKZYXC_GNDHWC<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;
+                }
+            }
+        }
+
+        // vector load A/B matrix from global memory
+        if(!(ABlockTransferSrcVectorDim == 2 && BBlockTransferSrcVectorDim == 2 &&
+             arg.Conv_K_ % ABlockTransferSrcScalarPerVector == 0 &&
+             arg.Conv_C_ % BBlockTransferSrcScalarPerVector == 0))
+        {
+            return false;
+        }
+
+        // vector store C matrix into global memory
+        if(!(arg.Conv_C_ % CBlockTransferScalarPerVector_NWaveNPerXdl == 0))
+        {
+            return false;
+        }
+
+        // Gridwise GEMM size
+        return GridwiseGemm::CheckValidity(arg.a_grid_desc_kbatch_k0_m_k1_,
+                                           arg.b_grid_desc_kbatch_k0_n_k1_,
+                                           arg.ce_grid_desc_m_n_,
+                                           arg.block_2_ctile_map_);
+    }
+
+    bool IsSupportedArgument(const BaseArgument* p_arg) override
+    {
+        return IsSupportedArgument(*dynamic_cast<const Argument*>(p_arg));
+    }
+
+    static auto MakeArgument(
+        const InDataType* p_in_grid,
+        WeiDataType* p_wei_grid,
+        const OutDataType* p_out_grid,
+        const std::array<const void*, NumDTensor>& p_ds,
+        const std::array<index_t, NDimSpatial + 3>& b_g_n_c_wis_lengths, // input
+        const std::array<index_t, NDimSpatial + 3>& b_g_n_c_wis_strides,
+        const std::array<index_t, NDimSpatial + 3>& e_g_k_c_xs_lengths, // weight
+        const std::array<index_t, NDimSpatial + 3>& e_g_k_c_xs_strides,
+        const std::array<index_t, NDimSpatial + 3>& a_g_n_k_wos_lengths, // output
+        const std::array<index_t, NDimSpatial + 3>& a_g_n_k_wos_strides,
+        const std::array<std::array<index_t, NDimSpatial + 3>, NumDTensor>& ds_g_k_c_xs_lengths,
+        const std::array<std::array<index_t, NDimSpatial + 3>, NumDTensor>& ds_g_k_c_xs_strides,
+        const std::array<ck::index_t, NDimSpatial>& conv_filter_strides,
+        const std::array<ck::index_t, NDimSpatial>& conv_filter_dilations,
+        const std::array<ck::index_t, NDimSpatial>& input_left_pads,
+        const std::array<ck::index_t, NDimSpatial>& input_right_pads,
+        InElementwiseOperation in_element_op,
+        WeiElementwiseOperation wei_element_op,
+        OutElementwiseOperation out_element_op,
+        const ck::index_t split_k)
+    {
+        return Argument{p_in_grid,
+                        p_wei_grid,
+                        p_out_grid,
+                        p_ds,
+                        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,
+                        ds_g_k_c_xs_lengths,
+                        ds_g_k_c_xs_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<const void*, NumDTensor>& p_ds,
+        const std::array<index_t, NDimSpatial + 3>& b_g_n_c_wis_lengths, // input
+        const std::array<index_t, NDimSpatial + 3>& b_g_n_c_wis_strides,
+        const std::array<index_t, NDimSpatial + 3>& e_g_k_c_xs_lengths, // weight
+        const std::array<index_t, NDimSpatial + 3>& e_g_k_c_xs_strides,
+        const std::array<index_t, NDimSpatial + 3>& a_g_n_k_wos_lengths, // output
+        const std::array<index_t, NDimSpatial + 3>& a_g_n_k_wos_strides,
+        const std::array<std::array<index_t, NDimSpatial + 3>, NumDTensor>& ds_g_k_c_xs_lengths,
+        const std::array<std::array<index_t, NDimSpatial + 3>, NumDTensor>& ds_g_k_c_xs_strides,
+        const std::array<ck::index_t, NDimSpatial>& conv_filter_strides,
+        const std::array<ck::index_t, NDimSpatial>& conv_filter_dilations,
+        const std::array<ck::index_t, NDimSpatial>& input_left_pads,
+        const std::array<ck::index_t, NDimSpatial>& input_right_pads,
+        InElementwiseOperation in_element_op,
+        WeiElementwiseOperation wei_element_op,
+        OutElementwiseOperation out_element_op,
+        const ck::index_t split_k) 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),
+                                          p_ds,
+                                          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,
+                                          ds_g_k_c_xs_lengths,
+                                          ds_g_k_c_xs_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 << "DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle"
+            << "<"
+            << 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();
+    }
+
+    size_t GetWorkSpaceSize(const BaseArgument* p_arg) const override
+    {
+        auto arg = dynamic_cast<const Argument*>(p_arg);
+        if(arg)
+        {
+            return arg->GetWorkspaceSizeBytes();
+        }
+        else
+            throw std::runtime_error(
+                "The argument pointer is not an object of "
+                "DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle::Argument structure!");
+    }
+
+    void SetWorkSpacePointer(BaseArgument* p_arg,
+                             void* p_workspace,
+                             const StreamConfig& = StreamConfig{}) const override
+    {
+        auto p_arg_ = dynamic_cast<Argument*>(p_arg);
+        if(p_arg_)
+        {
+            p_arg_->p_workspace_ = p_workspace;
+        }
+        else
+            throw std::runtime_error(
+                "The argument pointer is not an object of "
+                "DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle::Argument structure!");
+    }
+};
+
+} // namespace device
+} // namespace tensor_operation
+} // namespace ck

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

 // SPDX-License-Identifier: MIT
-// Copyright (c) 2023, Advanced Micro Devices, Inc. All rights reserved.
+// Copyright (c) 2023-2024, Advanced Micro Devices, Inc. All rights reserved.
 
 #pragma once
 
@@ -90,16 +90,6 @@ struct DeviceGroupedConvBwdWeight_Wmma_CShuffle
     // TODO make A/B datatype different
     using ABDataType = InDataType;
 
-    // 3d
-    static constexpr bool is_NDHWGK_GKZYXC_NDHWGC =
-        is_same_v<InLayout, tensor_layout::convolution::NDHWGC> &&
-        is_same_v<WeiLayout, tensor_layout::convolution::GKZYXC> &&
-        is_same_v<OutLayout, tensor_layout::convolution::NDHWGK>;
-    static constexpr bool is_GNDHWK_GKZYXC_GNDHWC =
-        is_same_v<InLayout, tensor_layout::convolution::GNDHWC> &&
-        is_same_v<WeiLayout, tensor_layout::convolution::GKZYXC> &&
-        is_same_v<OutLayout, tensor_layout::convolution::GNDHWK>;
-
     static constexpr auto I0 = Number<0>{};
     static constexpr auto I1 = Number<1>{};
     static constexpr auto I2 = Number<2>{};
@@ -218,8 +208,8 @@ struct DeviceGroupedConvBwdWeight_Wmma_CShuffle
         const index_t GemmM      = K;
         const index_t GemmN      = C * Z * X * Y;
 
-        const auto PadGemmM = (MPerBlock - GemmM % MPerBlock) % MPerBlock;
-        const auto PadGemmN = (NPerBlock - GemmN % NPerBlock) % NPerBlock;
+        const auto PadGemmM = MPerBlock - GemmM % MPerBlock;
+        const auto PadGemmN = NPerBlock - GemmN % NPerBlock;
 
         const index_t GemmK0 =
             math::integer_divide_ceil(GemmKTotal, GemmK1Number * K0PerBlock) * K0PerBlock;
@@ -720,7 +710,8 @@ struct DeviceGroupedConvBwdWeight_Wmma_CShuffle
             return false;
         }
 
-        if constexpr(!(is_NDHWGK_GKZYXC_NDHWGC || is_GNDHWK_GKZYXC_GNDHWC))
+        if constexpr(!(is_NDHWGK_GKZYXC_NDHWGC<InLayout, WeiLayout, OutLayout>() ||
+                       is_GNDHWK_GKZYXC_GNDHWC<InLayout, WeiLayout, OutLayout>()))
         {
             return false;
         }

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

 // SPDX-License-Identifier: MIT
-// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
 
 #pragma once
 
@@ -12,6 +12,7 @@
 #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/gpu/device/convolution_backward_weight_specialization.hpp"
 #include "ck/tensor_operation/gpu/grid/gridwise_gemm_xdlops_bwd_weight.hpp"
 #include "ck/tensor_operation/gpu/device/impl/device_grouped_conv_utils.hpp"
@@ -169,30 +170,6 @@ struct DeviceGroupedConvBwdWeight_Xdl_CShuffle
     // TODO make A/B datatype different
     using ABDataType = InDataType;
 
-    // 1d
-    static constexpr bool is_GNWK_GKXC_GNWC =
-        is_same_v<InLayout, tensor_layout::convolution::GNWC> &&
-        is_same_v<WeiLayout, tensor_layout::convolution::GKXC> &&
-        is_same_v<OutLayout, tensor_layout::convolution::GNWK>;
-    // 2d
-    static constexpr bool is_NHWGK_GKYXC_NHWGC =
-        is_same_v<InLayout, tensor_layout::convolution::NHWGC> &&
-        is_same_v<WeiLayout, tensor_layout::convolution::GKYXC> &&
-        is_same_v<OutLayout, tensor_layout::convolution::NHWGK>;
-    static constexpr bool is_GNHWK_GKYXC_GNHWC =
-        is_same_v<InLayout, tensor_layout::convolution::GNHWC> &&
-        is_same_v<WeiLayout, tensor_layout::convolution::GKYXC> &&
-        is_same_v<OutLayout, tensor_layout::convolution::GNHWK>;
-    // 3d
-    static constexpr bool is_NDHWGK_GKZYXC_NDHWGC =
-        is_same_v<InLayout, tensor_layout::convolution::NDHWGC> &&
-        is_same_v<WeiLayout, tensor_layout::convolution::GKZYXC> &&
-        is_same_v<OutLayout, tensor_layout::convolution::NDHWGK>;
-    static constexpr bool is_GNDHWK_GKZYXC_GNDHWC =
-        is_same_v<InLayout, tensor_layout::convolution::GNDHWC> &&
-        is_same_v<WeiLayout, tensor_layout::convolution::GKZYXC> &&
-        is_same_v<OutLayout, tensor_layout::convolution::GNDHWK>;
-
     static constexpr auto I0 = Number<0>{};
     static constexpr auto I1 = Number<1>{};
     static constexpr auto I2 = Number<2>{};
@@ -200,8 +177,15 @@ struct DeviceGroupedConvBwdWeight_Xdl_CShuffle
     static constexpr auto I4 = Number<4>{};
     static constexpr auto I5 = Number<5>{};
 
-    static constexpr auto K1Number     = Number<K1>{};
-    static constexpr auto GemmK1Number = K1Number;
+    static constexpr auto K1Number = Number<K1>{};
+
+    static constexpr auto conv_to_gemm_transformer =
+        TransformConvBwdWeightToGemm<NDimSpatial,
+                                     MPerBlock,
+                                     NPerBlock,
+                                     K1Number,
+                                     K0PerBlock,
+                                     ConvBackwardWeightSpecialization>{};
 
     // Bytes per 32 lds bank: 32 * 4 bytes
     static constexpr auto BankLength = 128;
@@ -217,690 +201,6 @@ struct DeviceGroupedConvBwdWeight_Xdl_CShuffle
     static constexpr auto BBlockLdsN0PerBlock = NPerBlock / BBlockLdsN1PerBlock;
     static constexpr auto BBlockLdsN1Padding  = 4;
 
-    template <ck::index_t NDim, typename ck::enable_if<NDim == 2, bool>::type = false>
-    constexpr static auto
-    make_out_grid_desc(const ck::index_t N,
-                       const ck::index_t Ho,
-                       const ck::index_t Wo,
-                       const ck::index_t K,
-                       const std::array<ck::index_t, NDimSpatial + 3>& output_strides)
-    {
-        const index_t WoStride = output_strides[4];
-        const auto KStride     = Number<1>{};
-        return make_naive_tensor_descriptor(make_tuple(N * Ho * Wo, K),
-                                            make_tuple(WoStride, KStride));
-    }
-
-    template <ck::index_t NDim, typename ck::enable_if<NDim == 2, bool>::type = false>
-    constexpr static auto
-    make_in_grid_desc(const ck::index_t N,
-                      const ck::index_t Hi,
-                      const ck::index_t Wi,
-                      const ck::index_t C,
-                      const std::array<ck::index_t, NDimSpatial + 3>& input_strides)
-    {
-        const index_t NStride  = input_strides[1];
-        const index_t HiStride = input_strides[3];
-        const index_t WiStride = input_strides[4];
-        const auto CStride     = input_strides[2];
-        if constexpr(ConvBackwardWeightSpecialization ==
-                     ConvolutionBackwardWeightSpecialization::Filter1x1Stride1Pad0)
-        {
-            return make_naive_tensor_descriptor(make_tuple(N * Hi * Wi, C),
-                                                make_tuple(WiStride, CStride));
-        }
-        else
-        {
-            return make_naive_tensor_descriptor(make_tuple(N, Hi, Wi, C),
-                                                make_tuple(NStride, HiStride, WiStride, CStride));
-        }
-    }
-
-    template <ck::index_t NDim, typename ck::enable_if<NDim == 2, bool>::type = false>
-    constexpr static auto
-    make_wei_grid_desc(const ck::index_t K,
-                       const ck::index_t Y,
-                       const ck::index_t X,
-                       const ck::index_t C,
-                       const std::array<ck::index_t, NDimSpatial + 3>& weights_strides)
-    {
-        const auto CStride = Number<1>{};
-        const auto KStride = weights_strides[1];
-        return make_naive_tensor_descriptor(make_tuple(K, Y * X * C), make_tuple(KStride, CStride));
-    }
-
-    template <ck::index_t NDim, typename ck::enable_if<NDim == 3, bool>::type = false>
-    constexpr static auto
-    make_out_grid_desc(const ck::index_t N,
-                       const ck::index_t Do,
-                       const ck::index_t Ho,
-                       const ck::index_t Wo,
-                       const ck::index_t K,
-                       const std::array<ck::index_t, NDimSpatial + 3>& output_strides)
-    {
-        const index_t WoStride = output_strides[5];
-        const auto KStride     = Number<1>{};
-        return make_naive_tensor_descriptor(make_tuple(N * Do * Ho * Wo, K),
-                                            make_tuple(WoStride, KStride));
-    }
-
-    template <ck::index_t NDim, typename ck::enable_if<NDim == 3, bool>::type = false>
-    constexpr static auto
-    make_in_grid_desc(const ck::index_t N,
-                      const ck::index_t Di,
-                      const ck::index_t Hi,
-                      const ck::index_t Wi,
-                      const ck::index_t C,
-                      const std::array<ck::index_t, NDimSpatial + 3>& input_strides)
-    {
-        const index_t NStride  = input_strides[1];
-        const index_t DiStride = input_strides[3];
-        const index_t HiStride = input_strides[4];
-        const index_t WiStride = input_strides[5];
-        const auto CStride     = input_strides[2];
-        if constexpr(ConvBackwardWeightSpecialization ==
-                     ConvolutionBackwardWeightSpecialization::Filter1x1Stride1Pad0)
-        {
-            return make_naive_tensor_descriptor(make_tuple(N * Di * Hi * Wi, C),
-                                                make_tuple(WiStride, CStride));
-        }
-        else
-        {
-            return make_naive_tensor_descriptor(
-                make_tuple(N, Di, Hi, Wi, C),
-                make_tuple(NStride, DiStride, HiStride, WiStride, CStride));
-        }
-    }
-
-    template <ck::index_t NDim, typename ck::enable_if<NDim == 3, bool>::type = false>
-    constexpr static auto
-    make_wei_grid_desc(const ck::index_t K,
-                       const ck::index_t Z,
-                       const ck::index_t Y,
-                       const ck::index_t X,
-                       const ck::index_t C,
-                       const std::array<ck::index_t, NDimSpatial + 3>& weights_strides)
-    {
-        const auto CStride = Number<1>{};
-        const auto KStride = weights_strides[1];
-        return make_naive_tensor_descriptor(make_tuple(K, Z * Y * X * C),
-                                            make_tuple(KStride, CStride));
-    }
-
-    template <ck::index_t NDim, typename ck::enable_if<NDim == 1, bool>::type = false>
-    static auto MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N(
-        const ck::index_t N,
-        const ck::index_t K,
-        const ck::index_t C,
-        const std::array<ck::index_t, NDimSpatial>& input_spatial_lengths,
-        const std::array<ck::index_t, NDimSpatial>& filter_spatial_lengths,
-        const std::array<ck::index_t, NDimSpatial>& output_spatial_lengths,
-        const std::array<ck::index_t, NDimSpatial + 3>& /* input_strides */,
-        const std::array<ck::index_t, NDimSpatial + 3>& /* weights_strides */,
-        const std::array<ck::index_t, NDimSpatial + 3>& /* output_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 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;
-        const index_t GemmN      = C * X;
-
-        const auto PadGemmM = (MPerBlock - GemmM % MPerBlock) % MPerBlock;
-        const auto PadGemmN = (NPerBlock - GemmN % NPerBlock) % 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;
-
-        if constexpr(ConvBackwardWeightSpecialization ==
-                     ConvolutionBackwardWeightSpecialization::Filter1x1Stride1Pad0)
-        {
-            // A: output tensor
-            const auto out_gemmktotal_gemmm_grid_desc =
-                make_naive_tensor_descriptor_packed(make_tuple(N * Wo, K));
-
-            const auto out_gemmkpad_gemmm_grid_desc = transform_tensor_descriptor(
-                out_gemmktotal_gemmm_grid_desc,
-                make_tuple(make_right_pad_transform(GemmKTotal, GemmKPad - GemmKTotal),
-                           make_pass_through_transform(GemmM)),
-                make_tuple(Sequence<0>{}, Sequence<1>{}),
-                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, 1, 3>{}, Sequence<2>{}));
-
-            // B: input tensor
-            const auto in_gemmktotal_gemmn_grid_desc =
-                make_naive_tensor_descriptor_packed(make_tuple(N * Wi, C));
-
-            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, 1, 3>{}, Sequence<2>{}));
-
-            // C: weight tensor
-            const auto wei_gemmm_gemmn_grid_desc =
-                make_naive_tensor_descriptor_packed(make_tuple(K, X * C));
-
-            return make_tuple(out_gemmkbatch_gemmk0_gemmm_gemmk1_grid_desc,
-                              in_gemmkbatch_gemmk0_gemmn_gemmk1_grid_desc,
-                              wei_gemmm_gemmn_grid_desc);
-        }
-        else
-        {
-            const auto out_gemmktotal_gemmm_grid_desc =
-                make_naive_tensor_descriptor_packed(make_tuple(N * Wo, K));
-            const auto in_n_wi_c_grid_desc =
-                make_naive_tensor_descriptor_packed(make_tuple(N, Wi, C));
-
-            // A: output tensor
-            const auto out_gemmkpad_gemmm_grid_desc = transform_tensor_descriptor(
-                out_gemmktotal_gemmm_grid_desc,
-                make_tuple(make_right_pad_transform(GemmKTotal, GemmKPad - GemmKTotal),
-                           make_pass_through_transform(GemmM)),
-                make_tuple(Sequence<0>{}, Sequence<1>{}),
-                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, 1, 3>{}, Sequence<2>{}));
-
-            // B: input tensor
-            const auto in_n_wip_c_grid_desc = transform_tensor_descriptor(
-                in_n_wi_c_grid_desc,
-                make_tuple(make_pass_through_transform(N),
-                           make_pad_transform(Wi, InLeftPadW, InRightPadW),
-                           make_pass_through_transform(C)),
-                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
-                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
-
-            const auto in_n_x_wo_c_grid_desc = transform_tensor_descriptor(
-                in_n_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(C)),
-                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
-                make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3>{}));
-
-            const auto in_gemmktotal_gemmn_grid_desc =
-                transform_tensor_descriptor(in_n_x_wo_c_grid_desc,
-                                            make_tuple(make_merge_transform(make_tuple(X, C)),
-                                                       make_merge_transform(make_tuple(N, Wo))),
-                                            make_tuple(Sequence<1, 3>{}, 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, 1, 3>{}, Sequence<2>{}));
-
-            // C: weight tensor
-            const auto wei_gemmm_gemmn_grid_desc =
-                make_naive_tensor_descriptor_packed(make_tuple(K, X * C));
-
-            // 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),
-                               make_pass_through_transform(GemmK0),
-                               make_right_pad_transform(GemmM, PadGemmM),
-                               make_pass_through_transform(GemmK1Number)),
-                    make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
-                    make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
-
-            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),
-                               make_pass_through_transform(GemmK0),
-                               make_right_pad_transform(GemmN, PadGemmN),
-                               make_pass_through_transform(GemmK1Number)),
-                    make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
-                    make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
-
-            const auto wei_gemmm_gemmn_pad_grid_desc =
-                transform_tensor_descriptor(wei_gemmm_gemmn_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);
-        }
-    }
-
-    template <ck::index_t NDim, typename ck::enable_if<NDim == 2, bool>::type = false>
-    static auto MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N(
-        const ck::index_t N,
-        const ck::index_t K,
-        const ck::index_t C,
-        const std::array<ck::index_t, NDimSpatial>& input_spatial_lengths,
-        const std::array<ck::index_t, NDimSpatial>& filter_spatial_lengths,
-        const std::array<ck::index_t, NDimSpatial>& output_spatial_lengths,
-        const std::array<ck::index_t, NDimSpatial + 3>& input_strides,
-        const std::array<ck::index_t, NDimSpatial + 3>& weights_strides,
-        const std::array<ck::index_t, NDimSpatial + 3>& output_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 batch_k)
-    {
-        using namespace ck;
-
-        const index_t Hi = input_spatial_lengths[0];
-        const index_t Wi = input_spatial_lengths[1];
-
-        const index_t Ho = output_spatial_lengths[0];
-        const index_t Wo = output_spatial_lengths[1];
-
-        const index_t Y = filter_spatial_lengths[0];
-        const index_t X = filter_spatial_lengths[1];
-
-        const index_t ConvStrideH = conv_filter_strides[0];
-        const index_t ConvStrideW = conv_filter_strides[1];
-
-        const index_t ConvDilationH = conv_filter_dilations[0];
-        const index_t ConvDilationW = conv_filter_dilations[1];
-
-        const index_t InLeftPadH = input_left_pads[0];
-        const index_t InLeftPadW = input_left_pads[1];
-
-        const index_t InRightPadH = input_right_pads[0];
-        const index_t InRightPadW = input_right_pads[1];
-
-        const index_t GemmKTotal = N * Ho * Wo;
-        const index_t GemmM      = K;
-        const index_t GemmN      = C * X * Y;
-
-        const auto PadGemmM = (MPerBlock - GemmM % MPerBlock) % MPerBlock;
-        const auto PadGemmN = (NPerBlock - GemmN % NPerBlock) % 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, Ho, Wo, K, output_strides);
-        const auto in_grid_desc  = make_in_grid_desc<NDim>(N, Hi, Wi, C, input_strides);
-        const auto wei_grid_desc = make_wei_grid_desc<NDim>(K, Y, X, C, weights_strides);
-
-        if constexpr(ConvBackwardWeightSpecialization ==
-                     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_pass_through_transform(GemmM)),
-                make_tuple(Sequence<0>{}, Sequence<1>{}),
-                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, 1, 3>{}, Sequence<2>{}));
-
-            // 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_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, 1, 3>{}, Sequence<2>{}));
-
-            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_pass_through_transform(GemmM)),
-                make_tuple(Sequence<0>{}, Sequence<1>{}),
-                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, 1, 3>{}, Sequence<2>{}));
-
-            // 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(Hi, InLeftPadH, InRightPadH),
-                           make_pad_transform(Wi, InLeftPadW, InRightPadW),
-                           make_pass_through_transform(C)),
-                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
-                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
-
-            const auto in_n_y_ho_x_wo_c_grid_desc = transform_tensor_descriptor(
-                in_n_hip_wip_c_grid_desc,
-                make_tuple(
-                    make_pass_through_transform(N),
-                    make_embed_transform(make_tuple(Y, Ho), make_tuple(ConvDilationH, ConvStrideH)),
-                    make_embed_transform(make_tuple(X, Wo), make_tuple(ConvDilationW, ConvStrideW)),
-                    make_pass_through_transform(C)),
-                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
-                make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}));
-
-            const auto in_gemmktotal_gemmn_grid_desc =
-                transform_tensor_descriptor(in_n_y_ho_x_wo_c_grid_desc,
-                                            make_tuple(make_merge_transform(make_tuple(Y, X, C)),
-                                                       make_merge_transform(make_tuple(N, Ho, Wo))),
-                                            make_tuple(Sequence<1, 3, 5>{}, Sequence<0, 2, 4>{}),
-                                            make_tuple(Sequence<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, 1, 3>{}, Sequence<2>{}));
-
-            // 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),
-                               make_pass_through_transform(GemmK0),
-                               make_right_pad_transform(GemmM, PadGemmM),
-                               make_pass_through_transform(GemmK1Number)),
-                    make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
-                    make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
-
-            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),
-                               make_pass_through_transform(GemmK0),
-                               make_right_pad_transform(GemmN, PadGemmN),
-                               make_pass_through_transform(GemmK1Number)),
-                    make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
-                    make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
-
-            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);
-        }
-    }
-
-    template <ck::index_t NDim, typename ck::enable_if<NDim == 3, bool>::type = false>
-    static auto MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N(
-        const ck::index_t N,
-        const ck::index_t K,
-        const ck::index_t C,
-        const std::array<ck::index_t, NDimSpatial>& input_spatial_lengths,
-        const std::array<ck::index_t, NDimSpatial>& filter_spatial_lengths,
-        const std::array<ck::index_t, NDimSpatial>& output_spatial_lengths,
-        const std::array<ck::index_t, NDimSpatial + 3>& input_strides,
-        const std::array<ck::index_t, NDimSpatial + 3>& weights_strides,
-        const std::array<ck::index_t, NDimSpatial + 3>& output_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 batch_k)
-    {
-        using namespace ck;
-
-        const index_t Di = input_spatial_lengths[0];
-        const index_t Hi = input_spatial_lengths[1];
-        const index_t Wi = input_spatial_lengths[2];
-
-        const index_t Do = output_spatial_lengths[0];
-        const index_t Ho = output_spatial_lengths[1];
-        const index_t Wo = output_spatial_lengths[2];
-
-        const index_t Z = filter_spatial_lengths[0];
-        const index_t Y = filter_spatial_lengths[1];
-        const index_t X = filter_spatial_lengths[2];
-
-        const index_t ConvStrideD = conv_filter_strides[0];
-        const index_t ConvStrideH = conv_filter_strides[1];
-        const index_t ConvStrideW = conv_filter_strides[2];
-
-        const index_t ConvDilationD = conv_filter_dilations[0];
-        const index_t ConvDilationH = conv_filter_dilations[1];
-        const index_t ConvDilationW = conv_filter_dilations[2];
-
-        const index_t InLeftPadD = input_left_pads[0];
-        const index_t InLeftPadH = input_left_pads[1];
-        const index_t InLeftPadW = input_left_pads[2];
-
-        const index_t InRightPadD = input_right_pads[0];
-        const index_t InRightPadH = input_right_pads[1];
-        const index_t InRightPadW = input_right_pads[2];
-
-        const index_t GemmKTotal = N * Do * Ho * Wo;
-        const index_t GemmM      = K;
-        const index_t GemmN      = C * Z * X * Y;
-
-        const auto PadGemmM = (MPerBlock - GemmM % MPerBlock) % MPerBlock;
-        const auto PadGemmN = (NPerBlock - GemmN % NPerBlock) % 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, Do, Ho, Wo, K, output_strides);
-        const auto in_grid_desc  = make_in_grid_desc<NDim>(N, Di, Hi, Wi, C, input_strides);
-        const auto wei_grid_desc = make_wei_grid_desc<NDim>(K, Z, Y, X, C, weights_strides);
-
-        if constexpr(ConvBackwardWeightSpecialization ==
-                     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_pass_through_transform(GemmM)),
-                make_tuple(Sequence<0>{}, Sequence<1>{}),
-                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, 1, 3>{}, Sequence<2>{}));
-
-            // 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_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, 1, 3>{}, Sequence<2>{}));
-
-            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_pass_through_transform(GemmM)),
-                make_tuple(Sequence<0>{}, Sequence<1>{}),
-                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, 1, 3>{}, Sequence<2>{}));
-
-            // B: input tensor
-            const auto in_n_dip_hip_wip_c_grid_desc = transform_tensor_descriptor(
-                in_grid_desc,
-                make_tuple(make_pass_through_transform(N),
-                           make_pad_transform(Di, InLeftPadD, InRightPadD),
-                           make_pad_transform(Hi, InLeftPadH, InRightPadH),
-                           make_pad_transform(Wi, InLeftPadW, InRightPadW),
-                           make_pass_through_transform(C)),
-                make_tuple(
-                    Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}, Sequence<4>{}),
-                make_tuple(
-                    Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}, Sequence<4>{}));
-
-            const auto in_n_z_do_y_ho_x_wo_c_grid_desc = transform_tensor_descriptor(
-                in_n_dip_hip_wip_c_grid_desc,
-                make_tuple(
-                    make_pass_through_transform(N),
-                    make_embed_transform(make_tuple(Z, Do), make_tuple(ConvDilationD, ConvStrideD)),
-                    make_embed_transform(make_tuple(Y, Ho), make_tuple(ConvDilationH, ConvStrideH)),
-                    make_embed_transform(make_tuple(X, Wo), make_tuple(ConvDilationW, ConvStrideW)),
-                    make_pass_through_transform(C)),
-                make_tuple(
-                    Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}, Sequence<4>{}),
-                make_tuple(Sequence<0>{},
-                           Sequence<1, 2>{},
-                           Sequence<3, 4>{},
-                           Sequence<5, 6>{},
-                           Sequence<7>{}));
-
-            const auto in_gemmktotal_gemmn_grid_desc = transform_tensor_descriptor(
-                in_n_z_do_y_ho_x_wo_c_grid_desc,
-                make_tuple(make_merge_transform(make_tuple(Z, Y, X, C)),
-                           make_merge_transform(make_tuple(N, Do, Ho, Wo))),
-                make_tuple(Sequence<1, 3, 5, 7>{}, Sequence<0, 2, 4, 6>{}),
-                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, 1, 3>{}, Sequence<2>{}));
-
-            // 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),
-                               make_pass_through_transform(GemmK0),
-                               make_right_pad_transform(GemmM, PadGemmM),
-                               make_pass_through_transform(GemmK1Number)),
-                    make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
-                    make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
-
-            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),
-                               make_pass_through_transform(GemmK0),
-                               make_right_pad_transform(GemmN, PadGemmN),
-                               make_pass_through_transform(GemmK1Number)),
-                    make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
-                    make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
-
-            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 <ck::index_t NDim, typename ck::enable_if<NDim == 1, bool>::type = false>
     static auto GetABCGridDesc()
     {
@@ -909,20 +209,21 @@ struct DeviceGroupedConvBwdWeight_Xdl_CShuffle
         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 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);
+        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>
@@ -933,20 +234,21 @@ struct DeviceGroupedConvBwdWeight_Xdl_CShuffle
         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 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);
+        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>
@@ -957,66 +259,23 @@ struct DeviceGroupedConvBwdWeight_Xdl_CShuffle
         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 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);
-    }
-
-    // type convert descs
-    template <typename Desc_M0>
-    static auto PadDescriptor_M0_1d(Desc_M0 desc_m0, index_t gridSize, index_t blockSize)
-    {
-        const auto m0           = desc_m0.GetLength(I0);
-        const index_t loop_step = gridSize * blockSize * 4;
-        const auto pad          = math::integer_least_multiple(m0, loop_step) - m0;
-        const auto desc_m0_pad =
-            transform_tensor_descriptor(desc_m0,
-                                        make_tuple(make_right_pad_transform(m0, pad)),
-                                        make_tuple(Sequence<0>{}),
-                                        make_tuple(Sequence<0>{}));
-        return desc_m0_pad;
+        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);
     }
 
-    template <index_t Dim>
-    static auto MakeDescriptor_M0(const std::array<index_t, Dim>& shape,
-                                  const std::array<index_t, Dim>& stride,
-                                  index_t gridSize,
-                                  index_t blockSize)
-    {
-        auto tupleOfShape  = generate_tuple([&](auto I) { return shape[I]; }, Number<Dim>{});
-        auto tupleOfStride = generate_tuple([&](auto I) { return stride[I]; }, Number<Dim>{});
-
-        // nd desc - [s0, s1, s2, ...]
-        const auto desc = make_naive_tensor_descriptor(tupleOfShape, tupleOfStride);
-
-        // merge nd to 1d desc - [s0 * s1 * ...]
-        if constexpr(Dim > 1)
-        {
-            const auto desc_m0 = transform_tensor_descriptor(
-                desc,
-                make_tuple(make_merge_transform(tupleOfShape)),
-                make_tuple(generate_sequence_v2([&](auto I) { return I; }, Number<Dim>{})),
-                make_tuple(Sequence<0>{}));
-
-            return PadDescriptor_M0_1d(desc_m0, gridSize, blockSize);
-        }
-        else
-            return PadDescriptor_M0_1d(desc, gridSize, blockSize);
-    }
-
-    using GridDesc_M0 = decltype(MakeDescriptor_M0<1>({1}, {1}, 1, 1));
-
     using ABCGridDescs = decltype(GetABCGridDesc<NDimSpatial>());
 
     using AGridDesc_K0_M_K1 = remove_cvref_t<decltype(ABCGridDescs{}[I0])>;
@@ -1089,12 +348,12 @@ struct DeviceGroupedConvBwdWeight_Xdl_CShuffle
         Argument(const InDataType* p_in_grid,
                  WeiDataType* p_wei_grid,
                  const OutDataType* p_out_grid,
-                 const std::array<index_t, NDimSpatial + 3>& a_g_n_c_wis_lengths, // input
-                 const std::array<index_t, NDimSpatial + 3>& a_g_n_c_wis_strides,
-                 const std::array<index_t, NDimSpatial + 3>& b_g_k_c_xs_lengths, // weight
-                 const std::array<index_t, NDimSpatial + 3>& b_g_k_c_xs_strides,
-                 const std::array<index_t, NDimSpatial + 3>& e_g_n_k_wos_lengths, // output
-                 const std::array<index_t, NDimSpatial + 3>& e_g_n_k_wos_strides,
+                 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,
@@ -1119,10 +378,10 @@ struct DeviceGroupedConvBwdWeight_Xdl_CShuffle
               a_element_op_{out_element_op},
               b_element_op_{in_element_op},
               c_element_op_{wei_element_op},
-              Conv_G_{a_g_n_c_wis_lengths[0]},
-              Conv_N_{a_g_n_c_wis_lengths[1]},
-              Conv_K_{b_g_k_c_xs_lengths[1]},
-              Conv_C_{a_g_n_c_wis_lengths[2]},
+              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_{},
@@ -1132,32 +391,33 @@ struct DeviceGroupedConvBwdWeight_Xdl_CShuffle
               k_batch_{split_k}
         {
             constexpr index_t spatial_offset = 3;
-            std::copy(begin(a_g_n_c_wis_lengths) + spatial_offset,
-                      end(a_g_n_c_wis_lengths),
+            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(b_g_k_c_xs_lengths) + spatial_offset,
-                      end(b_g_k_c_xs_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(e_g_n_k_wos_lengths) + spatial_offset,
-                      end(e_g_n_k_wos_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 =
-                DeviceOp::MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N<NDimSpatial>(
-                    Conv_N_,
-                    Conv_K_,
-                    Conv_C_,
-                    input_spatial_lengths_,
-                    filter_spatial_lengths_,
-                    output_spatial_lengths_,
-                    a_g_n_c_wis_strides,
-                    b_g_k_c_xs_strides,
-                    e_g_n_k_wos_strides,
-                    conv_filter_strides,
-                    conv_filter_dilations,
-                    input_left_pads,
-                    input_right_pads,
-                    k_batch_);
+                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];
@@ -1167,8 +427,8 @@ struct DeviceGroupedConvBwdWeight_Xdl_CShuffle
                 GridwiseGemm::MakeCBlockClusterAdaptor(c_grid_desc_m_n_, M01, N01, k_batch_);
 
             // A/B/C Batch Stride
-            compute_ptr_offset_of_batch_.BatchStrideA_ = e_g_n_k_wos_strides[0];
-            compute_ptr_offset_of_batch_.BatchStrideB_ = a_g_n_c_wis_strides[0];
+            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_),
@@ -1329,21 +589,23 @@ struct DeviceGroupedConvBwdWeight_Xdl_CShuffle
         }
         if constexpr(NDimSpatial == 1)
         {
-            if constexpr(!is_GNWK_GKXC_GNWC)
+            if constexpr(!is_GNWK_GKXC_GNWC<InLayout, WeiLayout, OutLayout>())
             {
                 return false;
             }
         }
         else if constexpr(NDimSpatial == 2)
         {
-            if constexpr(!(is_NHWGK_GKYXC_NHWGC || is_GNHWK_GKYXC_GNHWC))
+            if constexpr(!(is_NHWGK_GKYXC_NHWGC<InLayout, WeiLayout, OutLayout>() ||
+                           is_GNHWK_GKYXC_GNHWC<InLayout, WeiLayout, OutLayout>()))
             {
                 return false;
             }
         }
         else if constexpr(NDimSpatial == 3)
         {
-            if constexpr(!(is_NDHWGK_GKZYXC_NDHWGC || is_GNDHWK_GKZYXC_GNDHWC))
+            if constexpr(!(is_NDHWGK_GKZYXC_NDHWGC<InLayout, WeiLayout, OutLayout>() ||
+                           is_GNDHWK_GKZYXC_GNDHWC<InLayout, WeiLayout, OutLayout>()))
             {
                 return false;
             }
@@ -1397,12 +659,12 @@ struct DeviceGroupedConvBwdWeight_Xdl_CShuffle
     MakeArgument(const InDataType* p_in_grid,
                  WeiDataType* p_wei_grid,
                  const OutDataType* p_out_grid,
-                 const std::array<index_t, NDimSpatial + 3>& a_g_n_c_wis_lengths, // input
-                 const std::array<index_t, NDimSpatial + 3>& a_g_n_c_wis_strides,
-                 const std::array<index_t, NDimSpatial + 3>& b_g_k_c_xs_lengths, // weight
-                 const std::array<index_t, NDimSpatial + 3>& b_g_k_c_xs_strides,
-                 const std::array<index_t, NDimSpatial + 3>& e_g_n_k_wos_lengths, // output
-                 const std::array<index_t, NDimSpatial + 3>& e_g_n_k_wos_strides,
+                 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,
@@ -1415,12 +677,12 @@ struct DeviceGroupedConvBwdWeight_Xdl_CShuffle
         return Argument{p_in_grid,
                         p_wei_grid,
                         p_out_grid,
-                        a_g_n_c_wis_lengths, // input
-                        a_g_n_c_wis_strides,
-                        b_g_k_c_xs_lengths, // weight
-                        b_g_k_c_xs_strides,
-                        e_g_n_k_wos_lengths, // output
-                        e_g_n_k_wos_strides,
+                        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,
@@ -1439,12 +701,12 @@ struct DeviceGroupedConvBwdWeight_Xdl_CShuffle
     MakeArgumentPointer(const void* p_in_grid,
                         void* p_wei_grid,
                         const void* p_out_grid,
-                        const std::array<index_t, NDimSpatial + 3>& a_g_n_c_wis_lengths, // input
-                        const std::array<index_t, NDimSpatial + 3>& a_g_n_c_wis_strides,
-                        const std::array<index_t, NDimSpatial + 3>& b_g_k_c_xs_lengths, // weight
-                        const std::array<index_t, NDimSpatial + 3>& b_g_k_c_xs_strides,
-                        const std::array<index_t, NDimSpatial + 3>& e_g_n_k_wos_lengths, // output
-                        const std::array<index_t, NDimSpatial + 3>& e_g_n_k_wos_strides,
+                        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,
@@ -1457,12 +719,12 @@ struct DeviceGroupedConvBwdWeight_Xdl_CShuffle
         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),
-                                          a_g_n_c_wis_lengths, // input
-                                          a_g_n_c_wis_strides,
-                                          b_g_k_c_xs_lengths, // weight
-                                          b_g_k_c_xs_strides,
-                                          e_g_n_k_wos_lengths, // output
-                                          e_g_n_k_wos_strides,
+                                          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,

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

 // SPDX-License-Identifier: MIT
-// Copyright (c) 2023, Advanced Micro Devices, Inc. All rights reserved.
+// Copyright (c) 2023-2024, Advanced Micro Devices, Inc. All rights reserved.
 
 #pragma once
 
 #include "ck/utility/common_header.hpp"
+#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
 
 namespace ck {
 namespace tensor_operation {
 namespace device {
 
+// 1d
+template <typename InLayout, typename WeiLayout, typename OutLayout>
+constexpr bool is_NWGK_GKXC_NWGC()
+{
+    return is_same_v<InLayout, tensor_layout::convolution::NWGC> &&
+           is_same_v<WeiLayout, tensor_layout::convolution::GKXC> &&
+           is_same_v<OutLayout, tensor_layout::convolution::NWGK>;
+}
+
+template <typename InLayout, typename WeiLayout, typename OutLayout>
+constexpr bool is_GNWK_GKXC_GNWC()
+{
+    return is_same_v<InLayout, tensor_layout::convolution::GNWC> &&
+           is_same_v<WeiLayout, tensor_layout::convolution::GKXC> &&
+           is_same_v<OutLayout, tensor_layout::convolution::GNWK>;
+}
+// 2d
+template <typename InLayout, typename WeiLayout, typename OutLayout>
+constexpr bool is_NHWGK_GKYXC_NHWGC()
+{
+    return is_same_v<InLayout, tensor_layout::convolution::NHWGC> &&
+           is_same_v<WeiLayout, tensor_layout::convolution::GKYXC> &&
+           is_same_v<OutLayout, tensor_layout::convolution::NHWGK>;
+}
+
+template <typename InLayout, typename WeiLayout, typename OutLayout>
+constexpr bool is_GNHWK_GKYXC_GNHWC()
+{
+    return is_same_v<InLayout, tensor_layout::convolution::GNHWC> &&
+           is_same_v<WeiLayout, tensor_layout::convolution::GKYXC> &&
+           is_same_v<OutLayout, tensor_layout::convolution::GNHWK>;
+}
+// 3d
+template <typename InLayout, typename WeiLayout, typename OutLayout>
+constexpr bool is_NDHWGK_GKZYXC_NDHWGC()
+{
+    return is_same_v<InLayout, tensor_layout::convolution::NDHWGC> &&
+           is_same_v<WeiLayout, tensor_layout::convolution::GKZYXC> &&
+           is_same_v<OutLayout, tensor_layout::convolution::NDHWGK>;
+}
+
+template <typename InLayout, typename WeiLayout, typename OutLayout>
+constexpr bool is_GNDHWK_GKZYXC_GNDHWC()
+{
+    return is_same_v<InLayout, tensor_layout::convolution::GNDHWC> &&
+           is_same_v<WeiLayout, tensor_layout::convolution::GKZYXC> &&
+           is_same_v<OutLayout, tensor_layout::convolution::GNDHWK>;
+}
+
 template <index_t NumATensor = 1, index_t NumBTensor = 1, index_t NumDTensor = 0, typename = void>
 struct ComputePtrOffsetOfStridedBatch
 {

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

@@ -41,6 +41,58 @@ __global__ void
                                     elementwise_op);
 }
 
+template <typename GridwiseElementwiseFunctor,
+          typename InGridDescTuple,
+          typename OutGridDescTuple,
+          typename InDataTypePointerTuple,
+          typename OutDataTypePointerTuple,
+          typename Block2TileMap,
+          typename ElementwiseOperation,
+          index_t NumInputs,
+          index_t NumOutputs>
+__global__ void
+#if CK_USE_LAUNCH_BOUNDS
+    __launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
+#endif
+        kernel_batched_elementwise(const InGridDescTuple in_grid_desc_tuple,
+                                   const OutGridDescTuple out_grid_desc_tuple,
+                                   const InDataTypePointerTuple p_in_global_tuple,
+                                   const OutDataTypePointerTuple p_out_global_tuple,
+                                   const Block2TileMap block_2_tile_map,
+                                   const ElementwiseOperation elementwise_op,
+                                   const index_t batch_count,
+                                   const std::array<index_t, NumInputs> input_batch_strides,
+                                   const std::array<index_t, NumOutputs> output_batch_strides)
+{
+    static_assert(InGridDescTuple::Size() == NumInputs &&
+                  InDataTypePointerTuple::Size() == NumInputs);
+    static_assert(OutGridDescTuple::Size() == NumOutputs &&
+                  OutDataTypePointerTuple::Size() == NumOutputs);
+
+    const index_t num_blocks_per_batch =
+        __builtin_amdgcn_readfirstlane(get_grid_size() / batch_count);
+    const index_t g_idx = __builtin_amdgcn_readfirstlane(get_block_1d_id() / num_blocks_per_batch);
+
+    InDataTypePointerTuple p_in_global_with_offset_tuple;
+    OutDataTypePointerTuple p_out_global_with_offset_tuple;
+
+    static_for<0, InDataTypePointerTuple::Size(), 1>{}([&](auto i) {
+        p_in_global_with_offset_tuple(i) = p_in_global_tuple.At(i) + input_batch_strides[i] * g_idx;
+    });
+
+    static_for<0, OutDataTypePointerTuple::Size(), 1>{}([&](auto i) {
+        p_out_global_with_offset_tuple(i) =
+            p_out_global_tuple.At(i) + output_batch_strides[i] * g_idx;
+    });
+
+    GridwiseElementwiseFunctor::Run(in_grid_desc_tuple,
+                                    out_grid_desc_tuple,
+                                    p_in_global_with_offset_tuple,
+                                    p_out_global_with_offset_tuple,
+                                    block_2_tile_map,
+                                    elementwise_op);
+}
+
 template <typename InGridDescTuple,
           typename OutGridDescTuple,
           typename InDataTypePointerTuple,

include/ck/tensor_operation/operator_transform/transform_conv_bwd_weight_to_gemm.hpp

+
+// SPDX-License-Identifier: MIT
+// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include "ck/library/utility/numeric.hpp"
+#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/convolution_backward_weight_specialization.hpp"
+
+namespace ck {
+namespace tensor_operation {
+
+template <index_t NDimSpatial,
+          index_t MPerBlock,
+          index_t NPerBlock,
+          index_t GemmK1Number,
+          index_t K0PerBlock,
+          device::ConvolutionBackwardWeightSpecialization ConvBackwardWeightSpecialization>
+struct TransformConvBwdWeightToGemm
+{
+    static constexpr auto I0 = Number<0>{};
+    static constexpr auto I1 = Number<1>{};
+
+    template <index_t NDim, typename enable_if<NDim == 2, bool>::type = false>
+    constexpr static auto
+    make_out_grid_desc(const index_t N,
+                       const index_t Ho,
+                       const index_t Wo,
+                       const index_t K,
+                       const std::array<index_t, NDimSpatial + 3>& output_strides)
+    {
+        const index_t WoStride = output_strides[4];
+        const auto KStride     = Number<1>{};
+        return make_naive_tensor_descriptor(make_tuple(N * Ho * Wo, K),
+                                            make_tuple(WoStride, KStride));
+    }
+
+    template <index_t NDim, typename enable_if<NDim == 2, bool>::type = false>
+    constexpr static auto
+    make_in_grid_desc(const index_t N,
+                      const index_t Hi,
+                      const index_t Wi,
+                      const index_t C,
+                      const std::array<index_t, NDimSpatial + 3>& input_strides)
+    {
+        const index_t NStride  = input_strides[1];
+        const index_t HiStride = input_strides[3];
+        const index_t WiStride = input_strides[4];
+        const auto CStride     = input_strides[2];
+        if constexpr(ConvBackwardWeightSpecialization ==
+                     device::ConvolutionBackwardWeightSpecialization::Filter1x1Stride1Pad0)
+        {
+            return make_naive_tensor_descriptor(make_tuple(N * Hi * Wi, C),
+                                                make_tuple(WiStride, CStride));
+        }
+        else
+        {
+            return make_naive_tensor_descriptor(make_tuple(N, Hi, Wi, C),
+                                                make_tuple(NStride, HiStride, WiStride, CStride));
+        }
+    }
+
+    template <index_t NDim, typename enable_if<NDim == 2, bool>::type = false>
+    constexpr static auto
+    make_wei_grid_desc(const index_t K,
+                       const index_t Y,
+                       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];
+        return make_naive_tensor_descriptor(make_tuple(K, Y * X * C), make_tuple(KStride, CStride));
+    }
+
+    template <index_t NDim, typename enable_if<NDim == 3, bool>::type = false>
+    constexpr static auto
+    make_out_grid_desc(const index_t N,
+                       const index_t Do,
+                       const index_t Ho,
+                       const index_t Wo,
+                       const index_t K,
+                       const std::array<index_t, NDimSpatial + 3>& output_strides)
+    {
+        const index_t WoStride = output_strides[5];
+        const auto KStride     = Number<1>{};
+        return make_naive_tensor_descriptor(make_tuple(N * Do * Ho * Wo, K),
+                                            make_tuple(WoStride, KStride));
+    }
+
+    template <index_t NDim, typename enable_if<NDim == 3, bool>::type = false>
+    constexpr static auto
+    make_in_grid_desc(const index_t N,
+                      const index_t Di,
+                      const index_t Hi,
+                      const index_t Wi,
+                      const index_t C,
+                      const std::array<index_t, NDimSpatial + 3>& input_strides)
+    {
+        const index_t NStride  = input_strides[1];
+        const index_t DiStride = input_strides[3];
+        const index_t HiStride = input_strides[4];
+        const index_t WiStride = input_strides[5];
+        const auto CStride     = input_strides[2];
+        if constexpr(ConvBackwardWeightSpecialization ==
+                     device::ConvolutionBackwardWeightSpecialization::Filter1x1Stride1Pad0)
+        {
+            return make_naive_tensor_descriptor(make_tuple(N * Di * Hi * Wi, C),
+                                                make_tuple(WiStride, CStride));
+        }
+        else
+        {
+            return make_naive_tensor_descriptor(
+                make_tuple(N, Di, Hi, Wi, C),
+                make_tuple(NStride, DiStride, HiStride, WiStride, CStride));
+        }
+    }
+
+    template <index_t NDim, typename enable_if<NDim == 3, bool>::type = false>
+    constexpr static auto
+    make_wei_grid_desc(const index_t K,
+                       const index_t Z,
+                       const index_t Y,
+                       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];
+        return make_naive_tensor_descriptor(make_tuple(K, Z * Y * X * C),
+                                            make_tuple(KStride, CStride));
+    }
+
+    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;
+        const index_t GemmN      = C * X;
+
+        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;
+
+        if constexpr(ConvBackwardWeightSpecialization ==
+                     device::ConvolutionBackwardWeightSpecialization::Filter1x1Stride1Pad0)
+        {
+            // A: output tensor
+            const auto out_gemmktotal_gemmm_grid_desc =
+                make_naive_tensor_descriptor_packed(make_tuple(N * Wo, K));
+
+            const auto out_gemmkpad_gemmm_grid_desc = transform_tensor_descriptor(
+                out_gemmktotal_gemmm_grid_desc,
+                make_tuple(make_right_pad_transform(GemmKTotal, GemmKPad - GemmKTotal),
+                           make_pass_through_transform(GemmM)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}),
+                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, 1, 3>{}, Sequence<2>{}));
+
+            // B: input tensor
+            const auto in_gemmktotal_gemmn_grid_desc =
+                make_naive_tensor_descriptor_packed(make_tuple(N * Wi, C));
+
+            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, 1, 3>{}, Sequence<2>{}));
+
+            // C: weight tensor
+            const auto wei_gemmm_gemmn_grid_desc =
+                make_naive_tensor_descriptor_packed(make_tuple(K, X * C));
+
+            return make_tuple(out_gemmkbatch_gemmk0_gemmm_gemmk1_grid_desc,
+                              in_gemmkbatch_gemmk0_gemmn_gemmk1_grid_desc,
+                              wei_gemmm_gemmn_grid_desc);
+        }
+        else
+        {
+            const auto out_gemmktotal_gemmm_grid_desc =
+                make_naive_tensor_descriptor_packed(make_tuple(N * Wo, K));
+            const auto in_n_wi_c_grid_desc =
+                make_naive_tensor_descriptor_packed(make_tuple(N, Wi, C));
+
+            // A: output tensor
+            const auto out_gemmkpad_gemmm_grid_desc = transform_tensor_descriptor(
+                out_gemmktotal_gemmm_grid_desc,
+                make_tuple(make_right_pad_transform(GemmKTotal, GemmKPad - GemmKTotal),
+                           make_pass_through_transform(GemmM)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}),
+                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, 1, 3>{}, Sequence<2>{}));
+
+            // B: input tensor
+            const auto in_n_wip_c_grid_desc = transform_tensor_descriptor(
+                in_n_wi_c_grid_desc,
+                make_tuple(make_pass_through_transform(N),
+                           make_pad_transform(Wi, InLeftPadW, InRightPadW),
+                           make_pass_through_transform(C)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
+
+            const auto in_n_x_wo_c_grid_desc = transform_tensor_descriptor(
+                in_n_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(C)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
+                make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3>{}));
+
+            const auto in_gemmktotal_gemmn_grid_desc =
+                transform_tensor_descriptor(in_n_x_wo_c_grid_desc,
+                                            make_tuple(make_merge_transform(make_tuple(X, C)),
+                                                       make_merge_transform(make_tuple(N, Wo))),
+                                            make_tuple(Sequence<1, 3>{}, 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, 1, 3>{}, Sequence<2>{}));
+
+            // C: weight tensor
+            const auto wei_gemmm_gemmn_grid_desc =
+                make_naive_tensor_descriptor_packed(make_tuple(K, X * C));
+
+            // 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),
+                               make_pass_through_transform(GemmK0),
+                               make_right_pad_transform(GemmM, PadGemmM),
+                               make_pass_through_transform(GemmK1Number)),
+                    make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
+                    make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
+
+            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),
+                               make_pass_through_transform(GemmK0),
+                               make_right_pad_transform(GemmN, PadGemmN),
+                               make_pass_through_transform(GemmK1Number)),
+                    make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
+                    make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
+
+            const auto wei_gemmm_gemmn_pad_grid_desc =
+                transform_tensor_descriptor(wei_gemmm_gemmn_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);
+        }
+    }
+
+    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,
+        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 Hi = input_spatial_lengths[0];
+        const index_t Wi = input_spatial_lengths[1];
+
+        const index_t Ho = output_spatial_lengths[0];
+        const index_t Wo = output_spatial_lengths[1];
+
+        const index_t Y = filter_spatial_lengths[0];
+        const index_t X = filter_spatial_lengths[1];
+
+        const index_t ConvStrideH = conv_filter_strides[0];
+        const index_t ConvStrideW = conv_filter_strides[1];
+
+        const index_t ConvDilationH = conv_filter_dilations[0];
+        const index_t ConvDilationW = conv_filter_dilations[1];
+
+        const index_t InLeftPadH = input_left_pads[0];
+        const index_t InLeftPadW = input_left_pads[1];
+
+        const index_t InRightPadH = input_right_pads[0];
+        const index_t InRightPadW = input_right_pads[1];
+
+        const index_t GemmKTotal = N * Ho * Wo;
+        const index_t GemmM      = K;
+        const index_t GemmN      = C * X * Y;
+
+        const 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, Ho, Wo, K, output_strides);
+        const auto in_grid_desc  = make_in_grid_desc<NDim>(N, Hi, Wi, C, input_strides);
+        const auto wei_grid_desc = make_wei_grid_desc<NDim>(K, Y, 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_pass_through_transform(GemmM)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}),
+                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, 1, 3>{}, Sequence<2>{}));
+
+            // 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_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, 1, 3>{}, Sequence<2>{}));
+
+            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_pass_through_transform(GemmM)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}),
+                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, 1, 3>{}, Sequence<2>{}));
+
+            // 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(Hi, InLeftPadH, InRightPadH),
+                           make_pad_transform(Wi, InLeftPadW, InRightPadW),
+                           make_pass_through_transform(C)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
+
+            const auto in_n_y_ho_x_wo_c_grid_desc = transform_tensor_descriptor(
+                in_n_hip_wip_c_grid_desc,
+                make_tuple(
+                    make_pass_through_transform(N),
+                    make_embed_transform(make_tuple(Y, Ho), make_tuple(ConvDilationH, ConvStrideH)),
+                    make_embed_transform(make_tuple(X, Wo), make_tuple(ConvDilationW, ConvStrideW)),
+                    make_pass_through_transform(C)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
+                make_tuple(Sequence<0>{}, Sequence<1, 2>{}, Sequence<3, 4>{}, Sequence<5>{}));
+
+            const auto in_gemmktotal_gemmn_grid_desc =
+                transform_tensor_descriptor(in_n_y_ho_x_wo_c_grid_desc,
+                                            make_tuple(make_merge_transform(make_tuple(Y, X, C)),
+                                                       make_merge_transform(make_tuple(N, Ho, Wo))),
+                                            make_tuple(Sequence<1, 3, 5>{}, Sequence<0, 2, 4>{}),
+                                            make_tuple(Sequence<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, 1, 3>{}, Sequence<2>{}));
+
+            // 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),
+                               make_pass_through_transform(GemmK0),
+                               make_right_pad_transform(GemmM, PadGemmM),
+                               make_pass_through_transform(GemmK1Number)),
+                    make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
+                    make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
+
+            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),
+                               make_pass_through_transform(GemmK0),
+                               make_right_pad_transform(GemmN, PadGemmN),
+                               make_pass_through_transform(GemmK1Number)),
+                    make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
+                    make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
+
+            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);
+        }
+    }
+
+    template <index_t NDim, typename enable_if<NDim == 3, 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 Di = input_spatial_lengths[0];
+        const index_t Hi = input_spatial_lengths[1];
+        const index_t Wi = input_spatial_lengths[2];
+
+        const index_t Do = output_spatial_lengths[0];
+        const index_t Ho = output_spatial_lengths[1];
+        const index_t Wo = output_spatial_lengths[2];
+
+        const index_t Z = filter_spatial_lengths[0];
+        const index_t Y = filter_spatial_lengths[1];
+        const index_t X = filter_spatial_lengths[2];
+
+        const index_t ConvStrideD = conv_filter_strides[0];
+        const index_t ConvStrideH = conv_filter_strides[1];
+        const index_t ConvStrideW = conv_filter_strides[2];
+
+        const index_t ConvDilationD = conv_filter_dilations[0];
+        const index_t ConvDilationH = conv_filter_dilations[1];
+        const index_t ConvDilationW = conv_filter_dilations[2];
+
+        const index_t InLeftPadD = input_left_pads[0];
+        const index_t InLeftPadH = input_left_pads[1];
+        const index_t InLeftPadW = input_left_pads[2];
+
+        const index_t InRightPadD = input_right_pads[0];
+        const index_t InRightPadH = input_right_pads[1];
+        const index_t InRightPadW = input_right_pads[2];
+
+        const index_t GemmKTotal = N * Do * Ho * Wo;
+        const index_t GemmM      = K;
+        const index_t GemmN      = C * Z * X * Y;
+
+        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, Do, Ho, Wo, K, output_strides);
+        const auto in_grid_desc  = make_in_grid_desc<NDim>(N, Di, Hi, Wi, C, input_strides);
+        const auto wei_grid_desc = make_wei_grid_desc<NDim>(K, Z, Y, 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_pass_through_transform(GemmM)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}),
+                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, 1, 3>{}, Sequence<2>{}));
+
+            // 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_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, 1, 3>{}, Sequence<2>{}));
+
+            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_pass_through_transform(GemmM)),
+                make_tuple(Sequence<0>{}, Sequence<1>{}),
+                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, 1, 3>{}, Sequence<2>{}));
+
+            // B: input tensor
+            const auto in_n_dip_hip_wip_c_grid_desc = transform_tensor_descriptor(
+                in_grid_desc,
+                make_tuple(make_pass_through_transform(N),
+                           make_pad_transform(Di, InLeftPadD, InRightPadD),
+                           make_pad_transform(Hi, InLeftPadH, InRightPadH),
+                           make_pad_transform(Wi, InLeftPadW, InRightPadW),
+                           make_pass_through_transform(C)),
+                make_tuple(
+                    Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}, Sequence<4>{}),
+                make_tuple(
+                    Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}, Sequence<4>{}));
+
+            const auto in_n_z_do_y_ho_x_wo_c_grid_desc = transform_tensor_descriptor(
+                in_n_dip_hip_wip_c_grid_desc,
+                make_tuple(
+                    make_pass_through_transform(N),
+                    make_embed_transform(make_tuple(Z, Do), make_tuple(ConvDilationD, ConvStrideD)),
+                    make_embed_transform(make_tuple(Y, Ho), make_tuple(ConvDilationH, ConvStrideH)),
+                    make_embed_transform(make_tuple(X, Wo), make_tuple(ConvDilationW, ConvStrideW)),
+                    make_pass_through_transform(C)),
+                make_tuple(
+                    Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}, Sequence<4>{}),
+                make_tuple(Sequence<0>{},
+                           Sequence<1, 2>{},
+                           Sequence<3, 4>{},
+                           Sequence<5, 6>{},
+                           Sequence<7>{}));
+
+            const auto in_gemmktotal_gemmn_grid_desc = transform_tensor_descriptor(
+                in_n_z_do_y_ho_x_wo_c_grid_desc,
+                make_tuple(make_merge_transform(make_tuple(Z, Y, X, C)),
+                           make_merge_transform(make_tuple(N, Do, Ho, Wo))),
+                make_tuple(Sequence<1, 3, 5, 7>{}, Sequence<0, 2, 4, 6>{}),
+                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, 1, 3>{}, Sequence<2>{}));
+
+            // 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),
+                               make_pass_through_transform(GemmK0),
+                               make_right_pad_transform(GemmM, PadGemmM),
+                               make_pass_through_transform(GemmK1Number)),
+                    make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
+                    make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
+
+            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),
+                               make_pass_through_transform(GemmK0),
+                               make_right_pad_transform(GemmN, PadGemmN),
+                               make_pass_through_transform(GemmK1Number)),
+                    make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
+                    make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}));
+
+            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
+};
+
+} // namespace tensor_operation
+} // namespace ck

library/include/ck/library/reference_tensor_operation/cpu/reference_conv_bwd_weight.hpp

 // SPDX-License-Identifier: MIT
-// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
 
 #pragma once
 
@@ -25,6 +25,9 @@ template <ck::index_t NDimSpatial,
           typename InElementwiseOperation,
           typename WeiElementwiseOperation,
           typename OutElementwiseOperation,
+          ck::index_t NumAElementwiseTensor                                         = 0,
+          ck::index_t NumBElementwiseTensor                                         = 0,
+          ck::index_t NumDElementwiseTensor                                         = 0,
           typename ComputeTypeA                                                     = OutDataType,
           typename ComputeTypeB                                                     = InDataType,
           typename std::enable_if<NDimSpatial >= 1 && NDimSpatial <= 3, bool>::type = false>
@@ -33,19 +36,26 @@ struct ReferenceConvBwdWeight : public device::BaseOperator
     // Argument
     struct Argument : public device::BaseArgument
     {
-        Argument(const Tensor<InDataType>& in_n_c_hi_wi,
-                 Tensor<WeiDataType>& wei_k_c_y_x,
-                 const Tensor<OutDataType>& out_n_k_ho_wo,
-                 std::vector<ck::index_t> conv_filter_strides,
-                 std::vector<ck::index_t> conv_filter_dilations,
-                 std::vector<ck::index_t> input_left_pads,
-                 std::vector<ck::index_t> input_right_pads,
-                 InElementwiseOperation in_element_op,
-                 WeiElementwiseOperation wei_element_op,
-                 OutElementwiseOperation out_element_op)
+        Argument(
+            const Tensor<InDataType>& in_n_c_hi_wi,
+            Tensor<WeiDataType>& wei_k_c_y_x,
+            const Tensor<OutDataType>& out_n_k_ho_wo,
+            std::vector<ck::index_t> conv_filter_strides,
+            std::vector<ck::index_t> conv_filter_dilations,
+            std::vector<ck::index_t> input_left_pads,
+            std::vector<ck::index_t> input_right_pads,
+            InElementwiseOperation in_element_op,
+            WeiElementwiseOperation wei_element_op,
+            OutElementwiseOperation out_element_op,
+            const std::array<Tensor<OutDataType>, NumAElementwiseTensor>& elementwise_a_tensors,
+            const std::array<Tensor<InDataType>, NumBElementwiseTensor>& elementwise_b_tensors,
+            const std::array<Tensor<WeiDataType>, NumDElementwiseTensor>& elementwise_d_tensors)
             : input_{in_n_c_hi_wi},
               weight_{wei_k_c_y_x},
               output_{out_n_k_ho_wo},
+              elementwise_a_tensors_{elementwise_a_tensors},
+              elementwise_b_tensors_{elementwise_b_tensors},
+              elementwise_d_tensors_{elementwise_d_tensors},
               conv_strides_{conv_filter_strides},
               conv_dilations_{conv_filter_dilations},
               in_left_pads_{input_left_pads},
@@ -60,6 +70,10 @@ struct ReferenceConvBwdWeight : public device::BaseOperator
         Tensor<WeiDataType>& weight_;
         const Tensor<OutDataType>& output_;
 
+        const std::array<Tensor<OutDataType>, NumAElementwiseTensor>& elementwise_a_tensors_;
+        const std::array<Tensor<InDataType>, NumBElementwiseTensor>& elementwise_b_tensors_;
+        const std::array<Tensor<WeiDataType>, NumDElementwiseTensor>& elementwise_d_tensors_;
+
         std::vector<index_t> conv_strides_;
         std::vector<index_t> conv_dilations_;
         std::vector<index_t> in_left_pads_;
@@ -103,22 +117,43 @@ struct ReferenceConvBwdWeight : public device::BaseOperator
                                 ComputeTypeA v_out;
                                 ComputeTypeB v_in;
 
-                                arg.out_element_op_(
-                                    v_out, ck::type_convert<float>(arg.output_(g, n, k, wo)));
-
-                                arg.in_element_op_(
-                                    v_in, ck::type_convert<float>(arg.input_(g, n, c, wi)));
+                                ExecuteElementwiseOp(
+                                    arg.out_element_op_,
+                                    arg.elementwise_a_tensors_,
+                                    Number<NumAElementwiseTensor>{},
+                                    v_out,
+                                    ck::type_convert<float>(arg.output_(g, n, k, wo)),
+                                    g,
+                                    n,
+                                    k,
+                                    wo);
+                                ExecuteElementwiseOp(
+                                    arg.in_element_op_,
+                                    arg.elementwise_b_tensors_,
+                                    Number<NumBElementwiseTensor>{},
+                                    v_in,
+                                    ck::type_convert<float>(arg.input_(g, n, c, wi)),
+                                    g,
+                                    n,
+                                    c,
+                                    wi);
 
                                 v_acc += type_convert<float>(v_out) * type_convert<float>(v_in);
                             }
                         }
                     }
 
-                    float v_wei;
-
-                    arg.wei_element_op_(v_wei, v_acc);
-
-                    arg.weight_(g, k, c, x) = ck::type_convert<WeiDataType>(v_wei);
+                    WeiDataType v_acc_converted = ck::type_convert<WeiDataType>(v_acc);
+                    WeiDataType& v_wei          = arg.weight_(g, k, c, x);
+                    ExecuteElementwiseOp(arg.wei_element_op_,
+                                         arg.elementwise_d_tensors_,
+                                         Number<NumDElementwiseTensor>{},
+                                         v_wei,
+                                         v_acc_converted,
+                                         g,
+                                         k,
+                                         c,
+                                         x);
                 };
 
                 make_ParallelTensorFunctor(f_kcx,
@@ -163,12 +198,28 @@ struct ReferenceConvBwdWeight : public device::BaseOperator
                                     ComputeTypeA v_out;
                                     ComputeTypeB v_in;
 
-                                    arg.out_element_op_(
+                                    ExecuteElementwiseOp(
+                                        arg.out_element_op_,
+                                        arg.elementwise_a_tensors_,
+                                        Number<NumAElementwiseTensor>{},
                                         v_out,
-                                        ck::type_convert<float>(arg.output_(g, n, k, ho, wo)));
-
-                                    arg.in_element_op_(
-                                        v_in, ck::type_convert<float>(arg.input_(g, n, c, hi, wi)));
+                                        ck::type_convert<float>(arg.output_(g, n, k, ho, wo)),
+                                        g,
+                                        n,
+                                        k,
+                                        ho,
+                                        wo);
+                                    ExecuteElementwiseOp(
+                                        arg.in_element_op_,
+                                        arg.elementwise_b_tensors_,
+                                        Number<NumBElementwiseTensor>{},
+                                        v_in,
+                                        ck::type_convert<float>(arg.input_(g, n, c, hi, wi)),
+                                        g,
+                                        n,
+                                        c,
+                                        hi,
+                                        wi);
 
                                     v_acc += type_convert<float>(v_out) * type_convert<float>(v_in);
                                 }
@@ -176,11 +227,18 @@ struct ReferenceConvBwdWeight : public device::BaseOperator
                         }
                     }
 
-                    float v_wei;
-
-                    arg.wei_element_op_(v_wei, v_acc);
-
-                    arg.weight_(g, k, c, y, x) = ck::type_convert<WeiDataType>(v_wei);
+                    WeiDataType v_acc_converted = ck::type_convert<WeiDataType>(v_acc);
+                    WeiDataType& v_wei          = arg.weight_(g, k, c, y, x);
+                    ExecuteElementwiseOp(arg.wei_element_op_,
+                                         arg.elementwise_d_tensors_,
+                                         Number<NumDElementwiseTensor>{},
+                                         v_wei,
+                                         v_acc_converted,
+                                         g,
+                                         k,
+                                         c,
+                                         y,
+                                         x);
                 };
 
                 make_ParallelTensorFunctor(f_kcyx,
@@ -231,13 +289,30 @@ struct ReferenceConvBwdWeight : public device::BaseOperator
                                         ComputeTypeA v_out;
                                         ComputeTypeB v_in;
 
-                                        arg.out_element_op_(v_out,
-                                                            ck::type_convert<float>(
-                                                                arg.output_(g, n, k, do_, ho, wo)));
-
-                                        arg.in_element_op_(v_in,
-                                                           ck::type_convert<float>(
-                                                               arg.input_(g, n, c, di, hi, wi)));
+                                        ExecuteElementwiseOp(arg.out_element_op_,
+                                                             arg.elementwise_a_tensors_,
+                                                             Number<NumAElementwiseTensor>{},
+                                                             v_out,
+                                                             ck::type_convert<float>(
+                                                                 arg.output_(g, n, k, do_, ho, wo)),
+                                                             g,
+                                                             n,
+                                                             k,
+                                                             do_,
+                                                             ho,
+                                                             wo);
+                                        ExecuteElementwiseOp(arg.in_element_op_,
+                                                             arg.elementwise_b_tensors_,
+                                                             Number<NumBElementwiseTensor>{},
+                                                             v_in,
+                                                             ck::type_convert<float>(
+                                                                 arg.input_(g, n, c, di, hi, wi)),
+                                                             g,
+                                                             n,
+                                                             c,
+                                                             di,
+                                                             hi,
+                                                             wi);
 
                                         v_acc +=
                                             type_convert<float>(v_out) * type_convert<float>(v_in);
@@ -247,11 +322,19 @@ struct ReferenceConvBwdWeight : public device::BaseOperator
                         }
                     }
 
-                    float v_wei;
-
-                    arg.wei_element_op_(v_wei, v_acc);
-
-                    arg.weight_(g, k, c, z, y, x) = ck::type_convert<WeiDataType>(v_wei);
+                    WeiDataType v_acc_converted = ck::type_convert<WeiDataType>(v_acc);
+                    WeiDataType& v_wei          = arg.weight_(g, k, c, z, y, x);
+                    ExecuteElementwiseOp(arg.wei_element_op_,
+                                         arg.elementwise_d_tensors_,
+                                         Number<NumDElementwiseTensor>{},
+                                         v_wei,
+                                         v_acc_converted,
+                                         g,
+                                         k,
+                                         c,
+                                         z,
+                                         y,
+                                         x);
                 };
 
                 make_ParallelTensorFunctor(f_kczyx,
@@ -276,6 +359,37 @@ struct ReferenceConvBwdWeight : public device::BaseOperator
         }
     };
 
+    template <typename... Args,
+              typename ElementwiseOp,
+              typename ElementwiseTensor,
+              typename NumTensor,
+              typename Y,
+              typename X>
+    static void ExecuteElementwiseOp(ElementwiseOp& elementwise_op,
+                                     ElementwiseTensor& elementwise_tensors,
+                                     NumTensor,
+                                     Y& y,
+                                     const X& x,
+                                     Args... dims)
+    {
+        if constexpr(NumTensor::value == 0)
+        {
+            elementwise_op(y, x);
+        }
+        else if constexpr(NumTensor::value == 1)
+        {
+            elementwise_op(y, x, elementwise_tensors[0](dims...));
+        }
+        else if constexpr(NumTensor::value == 2)
+        {
+            elementwise_op(y, x, elementwise_tensors[0](dims...), elementwise_tensors[1](dims...));
+        }
+        else
+        {
+            throw std::runtime_error("ElementOp not supported in reference.");
+        }
+    }
+
     static constexpr bool IsValidCompilationParameter()
     {
         // TODO: properly implement this check
@@ -284,16 +398,20 @@ struct ReferenceConvBwdWeight : public device::BaseOperator
 
     bool IsSupportedArgument(const device::BaseArgument*) override { return true; }
 
-    static auto MakeArgument(const Tensor<InDataType>& in_n_c_hi_wi,
-                             Tensor<WeiDataType>& wei_k_c_y_x,
-                             const Tensor<OutDataType>& out_n_k_ho_wo,
-                             std::vector<ck::index_t> conv_filter_strides,
-                             std::vector<ck::index_t> conv_filter_dilations,
-                             std::vector<ck::index_t> input_left_pads,
-                             std::vector<ck::index_t> input_right_pads,
-                             InElementwiseOperation in_element_op,
-                             WeiElementwiseOperation wei_element_op,
-                             OutElementwiseOperation out_element_op)
+    static auto MakeArgument(
+        const Tensor<InDataType>& in_n_c_hi_wi,
+        Tensor<WeiDataType>& wei_k_c_y_x,
+        const Tensor<OutDataType>& out_n_k_ho_wo,
+        std::vector<ck::index_t> conv_filter_strides,
+        std::vector<ck::index_t> conv_filter_dilations,
+        std::vector<ck::index_t> input_left_pads,
+        std::vector<ck::index_t> input_right_pads,
+        InElementwiseOperation in_element_op,
+        WeiElementwiseOperation wei_element_op,
+        OutElementwiseOperation out_element_op,
+        const std::array<Tensor<OutDataType>, NumAElementwiseTensor>& elementwise_a_tensors = {},
+        const std::array<Tensor<InDataType>, NumBElementwiseTensor>& elementwise_b_tensors  = {},
+        const std::array<Tensor<WeiDataType>, NumDElementwiseTensor>& elementwise_d_tensors = {})
     {
         return Argument{in_n_c_hi_wi,
                         wei_k_c_y_x,
@@ -304,7 +422,10 @@ struct ReferenceConvBwdWeight : public device::BaseOperator
                         input_right_pads,
                         in_element_op,
                         wei_element_op,
-                        out_element_op};
+                        out_element_op,
+                        elementwise_a_tensors,
+                        elementwise_b_tensors,
+                        elementwise_d_tensors};
     }
 
     static auto MakeInvoker() { return Invoker{}; }

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

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2024, 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_multiple_d_xdl_cshuffle.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;
+using Bilinear    = ck::tensor_operation::element_wise::Bilinear;
+
+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>
+using device_grouped_conv_bwd_weight_xdl_c_shuffle_f32_bilinear_instances = std::tuple<
+    // clang-format off
+        //#########################################|     Num| InLayout| WeiLayout| OutLayout| DsData| InData| WeiData| OutData| AccData| DsData|          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|
+        //#########################################|     Dim|         |          |          | Layout|   Type|    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|
+        //#########################################| 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|
+        //#########################################|        |         |          |          |       |       |        |        |        |       |            |            |            |                          |      |      |      |      |   |     |     |     |     |                |                 |               |               |               |               |          |                |               |               |              |               |               |          |            |            | NBlock_NPerBlock|                |
+        // generic instance
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Tuple<BLayout>, F32,     F32,     F32,     F32, Tuple<F32>, PassThrough, Bilinear, PassThrough,  ConvSpec,    64,    64,    64,     4,  4,   32,   32,    2,    2,  S<1, 4, 16, 1>,   S<0, 3, 1, 2>,   S<0, 2, 1, 3>,              2,              1,              4,      true,  S<1, 4, 16, 1>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              1,              4,      true,           1,           1,   S<1, 16, 1, 4>,               1>,
+        // instances for small conv.K and conv.C
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Tuple<BLayout>, F32,     F32,     F32,     F32, Tuple<F32>, PassThrough, Bilinear, PassThrough,  ConvSpec,   128,   128,    32,     4,  4,   32,   32,    2,    1,  S<1, 4, 32, 1>,   S<0, 3, 1, 2>,   S<0, 2, 1, 3>,              2,              4,              4,      true,  S<1, 4, 8,  4>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              1,              1,      true,           1,           1,   S<1, 32, 1, 4>,               1>,
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Tuple<BLayout>, F32,     F32,     F32,     F32, Tuple<F32>, PassThrough, Bilinear, PassThrough,  ConvSpec,    64,    32,    64,     4,  4,   32,   32,    1,    2,  S<1, 4, 8,  2>,   S<0, 3, 1, 2>,   S<0, 2, 1, 3>,              2,              1,              2,      true,  S<1, 4, 16, 1>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              4,              4,      true,           1,           1,   S<1, 16, 1, 4>,               4>,
+
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Tuple<BLayout>, F32,     F32,     F32,     F32, Tuple<F32>, PassThrough, Bilinear, PassThrough,  ConvSpec,   256,   256,   128,     4,  4,   32,   32,    4,    2,  S<1, 4, 64, 1>,   S<0, 3, 1, 2>,   S<0, 2, 1, 3>,              2,              4,              4,      true,  S<1, 4, 32, 2>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              4,              2,      true,           1,           1,   S<1, 32, 1, 8>,               4>,
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Tuple<BLayout>, F32,     F32,     F32,     F32, Tuple<F32>, PassThrough, Bilinear, PassThrough,  ConvSpec,   256,   128,   256,     4,  4,   32,   32,    2,    4,  S<1, 4, 32, 2>,   S<0, 3, 1, 2>,   S<0, 2, 1, 3>,              2,              4,              2,      true,  S<1, 4, 64, 1>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              4,              4,      true,           1,           1,   S<1, 32, 1, 8>,               4>,
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Tuple<BLayout>, F32,     F32,     F32,     F32, Tuple<F32>, PassThrough, Bilinear, PassThrough,  ConvSpec,   128,   128,   128,     4,  4,   32,   32,    4,    2,  S<1, 4, 32, 1>,   S<0, 3, 1, 2>,   S<0, 2, 1, 3>,              2,              4,              4,      true,  S<1, 4, 32, 1>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              4,              4,      true,           1,           1,   S<1, 32, 1, 4>,               4>,
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Tuple<BLayout>, F32,     F32,     F32,     F32, Tuple<F32>, PassThrough, Bilinear, PassThrough,  ConvSpec,   256,   128,   128,     4,  4,   32,   32,    2,    2,  S<1, 4, 32, 2>,   S<0, 3, 1, 2>,   S<0, 2, 1, 3>,              2,              4,              2,      true,  S<1, 4, 32, 2>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              4,              2,      true,           1,           1,   S<1, 32, 1, 4>,               4>,
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Tuple<BLayout>, F32,     F32,     F32,     F32, Tuple<F32>, PassThrough, Bilinear, PassThrough,  ConvSpec,   128,   128,    64,     4,  4,   32,   32,    2,    2,  S<1, 4, 32, 1>,   S<0, 3, 1, 2>,   S<0, 2, 1, 3>,              2,              4,              4,      true,  S<1, 4, 16, 2>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              4,              2,      true,           1,           1,   S<1, 32, 1, 4>,               4>,
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Tuple<BLayout>, F32,     F32,     F32,     F32, Tuple<F32>, PassThrough, Bilinear, PassThrough,  ConvSpec,   128,    64,   128,     4,  4,   32,   32,    2,    2,  S<1, 4, 16, 2>,   S<0, 3, 1, 2>,   S<0, 2, 1, 3>,              2,              4,              2,      true,  S<1, 4, 32, 1>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              4,              4,      true,           1,           1,   S<1, 32, 1, 4>,               4>,
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Tuple<BLayout>, F32,     F32,     F32,     F32, Tuple<F32>, PassThrough, Bilinear, PassThrough,  ConvSpec,    64,    64,    64,     4,  4,   32,   32,    2,    2,  S<1, 4, 16, 1>,   S<0, 3, 1, 2>,   S<0, 2, 1, 3>,              2,              4,              4,      true,  S<1, 4, 16, 1>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              4,              4,      true,           1,           1,   S<1, 16, 1, 4>,               4>,
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Tuple<BLayout>, F32,     F32,     F32,     F32, Tuple<F32>, PassThrough, Bilinear, PassThrough,  ConvSpec,   256,   128,    64,     4,  4,   32,   32,    2,    1,  S<1, 4, 32, 2>,   S<0, 3, 1, 2>,   S<0, 2, 1, 3>,              2,              4,              2,      true,  S<1, 4, 16, 4>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              4,              1,      true,           1,           1,   S<1, 32, 1, 4>,               4>,
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Tuple<BLayout>, F32,     F32,     F32,     F32, Tuple<F32>, PassThrough, Bilinear, PassThrough,  ConvSpec,   256,    64,   128,     4,  4,   32,   32,    1,    2,  S<1, 4, 16, 4>,   S<0, 3, 1, 2>,   S<0, 2, 1, 3>,              2,              4,              1,      true,  S<1, 4, 32, 2>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              4,              2,      true,           1,           1,   S<1, 32, 1, 4>,               4>,
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Tuple<BLayout>, F32,     F32,     F32,     F32, Tuple<F32>, PassThrough, Bilinear, PassThrough,  ConvSpec,   128,   128,    32,     4,  4,   32,   32,    2,    1,  S<1, 4, 32, 1>,   S<0, 3, 1, 2>,   S<0, 2, 1, 3>,              2,              4,              4,      true,  S<1, 4, 8,  4>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              4,              1,      true,           1,           1,   S<1, 32, 1, 4>,               4>,
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Tuple<BLayout>, F32,     F32,     F32,     F32, Tuple<F32>, PassThrough, Bilinear, PassThrough,  ConvSpec,   128,    32,   128,     4,  4,   32,   32,    1,    2,  S<1, 4, 8,  4>,   S<0, 3, 1, 2>,   S<0, 2, 1, 3>,              2,              4,              1,      true,  S<1, 4, 32, 1>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              4,              4,      true,           1,           1,   S<1, 32, 1, 4>,               4>,
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Tuple<BLayout>, F32,     F32,     F32,     F32, Tuple<F32>, PassThrough, Bilinear, PassThrough,  ConvSpec,    64,    64,    32,     4,  4,   32,   32,    2,    1,  S<1, 4, 16, 1>,   S<0, 3, 1, 2>,   S<0, 2, 1, 3>,              2,              4,              4,      true,  S<1, 4, 8,  2>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              4,              2,      true,           1,           1,   S<1, 16, 1, 4>,               4>,
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Tuple<BLayout>, F32,     F32,     F32,     F32, Tuple<F32>, PassThrough, Bilinear, PassThrough,  ConvSpec,    64,    32,    64,     4,  4,   32,   32,    1,    2,  S<1, 4, 8,  2>,   S<0, 3, 1, 2>,   S<0, 2, 1, 3>,              2,              4,              2,      true,  S<1, 4, 16, 1>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              4,              4,      true,           1,           1,   S<1, 16, 1, 4>,               4>
+    // clang-format on
+    >;
+
+template <ck::index_t NDimSpatial,
+          typename ALayout,
+          typename BLayout,
+          typename ELayout,
+          ConvolutionBackwardWeightSpecialization ConvSpec>
+using device_grouped_conv_bwd_weight_xdl_c_shuffle_f16_bilinear_instances = std::tuple<
+    // clang-format off
+        //#########################################|     Num| InLayout| WeiLayout| OutLayout| DsData| InData| WeiData| OutData| AccData| DsData|          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|
+        //#########################################|     Dim|         |          |          | Layout|   Type|    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|
+        //#########################################| 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|
+        //#########################################|        |         |          |          |       |       |        |        |        |       |            |            |            |                          |      |      |      |      |   |     |     |     |     |                |                 |               |               |               |               |          |                |               |               |              |               |               |          |            |            | NBlock_NPerBlock|                |
+        // generic instance
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Tuple<BLayout>, F16,     F16,     F16,     F32, Tuple<F16>, PassThrough, Bilinear, PassThrough,  ConvSpec,    64,    64,    64,     4,  8,   32,   32,    2,    2,  S<1, 4, 8,  2>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,              2,              2,              4,      true,  S<1, 4, 8,  2>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              2,              4,      true,           1,           1,   S<1, 16, 1, 4>,               2>,   
+        // instance for small conv.K
+        // for fp16 conv.K and conv.C must be divisible by 2
+        // since half_t atomic_add require scalar_per_x_vector % 2 == 0
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Tuple<BLayout>, F16,     F16,     F16,     F32, Tuple<F16>, PassThrough, Bilinear, PassThrough,  ConvSpec,   128,   128,    32,     4,  8,   32,   32,    2,    1,  S<1, 4, 16, 2>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,              2,              8,              4,      true,  S<1, 4, 4,  8>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              2,              1,      true,           1,           1,   S<1, 32, 1, 4>,               2>,  
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Tuple<BLayout>, F16,     F16,     F16,     F32, Tuple<F16>, PassThrough, Bilinear, PassThrough,  ConvSpec,    64,    32,    64,     4,  8,   32,   32,    1,    2,  S<1, 4, 4,  4>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,              2,              2,              2,      true,  S<1, 4, 8,  2>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              4,      true,           1,           1,   S<1, 16, 1, 4>,               8>,
+
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Tuple<BLayout>, F16,     F16,     F16,     F32, Tuple<F16>, PassThrough, Bilinear, PassThrough,  ConvSpec,   256,   256,   128,     4,  8,   32,   32,    4,    2,  S<1, 4, 32, 2>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,              2,              8,              4,      true,  S<1, 4, 16, 4>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              2,      true,           1,           1,   S<1, 32, 1, 8>,               8>,
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Tuple<BLayout>, F16,     F16,     F16,     F32, Tuple<F16>, PassThrough, Bilinear, PassThrough,  ConvSpec,   256,   128,   256,     4,  8,   32,   32,    2,    4,  S<1, 4, 16, 4>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,              2,              8,              2,      true,  S<1, 4, 32, 2>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              4,      true,           1,           1,   S<1, 32, 1, 8>,               8>,   
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Tuple<BLayout>, F16,     F16,     F16,     F32, Tuple<F16>, PassThrough, Bilinear, PassThrough,  ConvSpec,   128,   128,   128,     4,  8,   32,   32,    4,    2,  S<1, 4, 16, 2>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,              2,              8,              4,      true,  S<1, 4, 16, 2>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              4,      true,           1,           1,   S<1, 32, 1, 4>,               8>,   
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Tuple<BLayout>, F16,     F16,     F16,     F32, Tuple<F16>, PassThrough, Bilinear, PassThrough,  ConvSpec,   256,   128,   128,     4,  8,   32,   32,    2,    2,  S<1, 4, 16, 4>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,              2,              8,              2,      true,  S<1, 4, 16, 4>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              2,      true,           1,           1,   S<1, 32, 1, 4>,               8>,   
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Tuple<BLayout>, F16,     F16,     F16,     F32, Tuple<F16>, PassThrough, Bilinear, PassThrough,  ConvSpec,   128,   128,    64,     4,  8,   32,   32,    2,    2,  S<1, 4, 16, 2>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,              2,              8,              4,      true,  S<1, 4, 8,  4>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              2,      true,           1,           1,   S<1, 32, 1, 4>,               8>,   
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Tuple<BLayout>, F16,     F16,     F16,     F32, Tuple<F16>, PassThrough, Bilinear, PassThrough,  ConvSpec,   128,    64,   128,     4,  8,   32,   32,    2,    2,  S<1, 4, 8,  4>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,              2,              8,              2,      true,  S<1, 4, 16, 2>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              4,      true,           1,           1,   S<1, 32, 1, 4>,               8>,   
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Tuple<BLayout>, F16,     F16,     F16,     F32, Tuple<F16>, PassThrough, Bilinear, PassThrough,  ConvSpec,    64,    64,    64,     4,  8,   32,   32,    2,    2,  S<1, 4, 8,  2>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,              2,              8,              4,      true,  S<1, 4, 8,  2>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              4,      true,           1,           1,   S<1, 16, 1, 4>,               8>,   
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Tuple<BLayout>, F16,     F16,     F16,     F32, Tuple<F16>, PassThrough, Bilinear, PassThrough,  ConvSpec,   256,   128,    64,     4,  8,   32,   32,    2,    1,  S<1, 4, 16, 4>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,              2,              8,              2,      true,  S<1, 4, 8,  8>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              1,      true,           1,           1,   S<1, 32, 1, 4>,               8>,   
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Tuple<BLayout>, F16,     F16,     F16,     F32, Tuple<F16>, PassThrough, Bilinear, PassThrough,  ConvSpec,   256,    64,   128,     4,  8,   32,   32,    1,    2,  S<1, 4, 8,  8>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,              2,              8,              1,      true,  S<1, 4, 16, 4>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              2,      true,           1,           1,   S<1, 32, 1, 4>,               8>,   
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Tuple<BLayout>, F16,     F16,     F16,     F32, Tuple<F16>, PassThrough, Bilinear, PassThrough,  ConvSpec,   128,   128,    32,     4,  8,   32,   32,    2,    1,  S<1, 4, 16, 2>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,              2,              8,              4,      true,  S<1, 4, 4,  8>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              1,      true,           1,           1,   S<1, 32, 1, 4>,               8>,   
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Tuple<BLayout>, F16,     F16,     F16,     F32, Tuple<F16>, PassThrough, Bilinear, PassThrough,  ConvSpec,   128,    32,   128,     4,  8,   32,   32,    1,    2,  S<1, 4, 4,  8>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,              2,              8,              1,      true,  S<1, 4, 16, 2>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              4,      true,           1,           1,   S<1, 32, 1, 4>,               8>,   
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Tuple<BLayout>, F16,     F16,     F16,     F32, Tuple<F16>, PassThrough, Bilinear, PassThrough,  ConvSpec,    64,    64,    32,     4,  8,   32,   32,    2,    1,  S<1, 4, 8,  2>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,              2,              8,              4,      true,  S<1, 4, 4,  4>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              2,      true,           1,           1,   S<1, 16, 1, 4>,               8>,   
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Tuple<BLayout>, F16,     F16,     F16,     F32, Tuple<F16>, PassThrough, Bilinear, PassThrough,  ConvSpec,    64,    32,    64,     4,  8,   32,   32,    1,    2,  S<1, 4, 4,  4>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,              2,              8,              2,      true,  S<1, 4, 8,  2>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              4,      true,           1,           1,   S<1, 16, 1, 4>,               8>
+    // clang-format on
+    >;
+
+template <ck::index_t NDimSpatial,
+          typename ALayout,
+          typename BLayout,
+          typename ELayout,
+          ConvolutionBackwardWeightSpecialization ConvSpec>
+using device_grouped_conv_bwd_weight_xdl_c_shuffle_bf16_bilinear_instances = std::tuple<
+    // clang-format off
+        //#########################################|     Num| InLayout| WeiLayout| OutLayout| DsData| InData| WeiData| OutData| AccData| DsData|          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|
+        //#########################################|     Dim|         |          |          | Layout|   Type|    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|
+        //#########################################| 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|
+        //#########################################|        |         |          |          |       |       |        |        |        |       |            |            |            |                          |      |      |      |      |   |     |     |     |     |                |                 |               |               |               |               |          |                |               |               |              |               |               |          |            |            | NBlock_NPerBlock|                |
+        // generic instance
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,    ELayout, Tuple<BLayout>, BF16,     F32,    BF16,     F32, Tuple<F32>, PassThrough, Bilinear, PassThrough,   ConvSpec,    64,    64,    64,     4,  8,   32,   32,    2,    2,  S<1, 4, 8,  2>, S<0, 3, 1, 2>,   S<0, 2, 1, 3>,              2,              1,              4,      true,   S<1, 4, 8,  2>,   S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              1,              4,      true,           1,           1,   S<1, 16, 1, 4>,               1>,   
+        // instance for small conv.K
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,    ELayout, Tuple<BLayout>, BF16,     F32,    BF16,     F32, Tuple<F32>, PassThrough, Bilinear, PassThrough,   ConvSpec,   128,   128,    32,     4,  8,   32,   32,    2,    1,  S<1, 4, 16, 2>, S<0, 3, 1, 2>,   S<0, 2, 1, 3>,              2,              8,              4,      true,   S<1, 4, 4,  8>,   S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              1,              1,      true,           1,           1,   S<1, 32, 1, 4>,               1>, 
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,    ELayout, Tuple<BLayout>, BF16,     F32,    BF16,     F32, Tuple<F32>, PassThrough, Bilinear, PassThrough,   ConvSpec,    64,    32,    64,     4,  8,   32,   32,    1,    2,  S<1, 4, 4,  4>, S<0, 3, 1, 2>,   S<0, 2, 1, 3>,              2,              1,              2,      true,   S<1, 4, 8,  2>,   S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              4,      true,           1,           1,   S<1, 16, 1, 4>,               4>,
+
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,    ELayout, Tuple<BLayout>, BF16,     F32,    BF16,     F32, Tuple<F32>, PassThrough, Bilinear, PassThrough,   ConvSpec,   256,   256,   128,     4,  8,   32,   32,    4,    2,  S<1, 4, 32, 2>, S<0, 3, 1, 2>,   S<0, 2, 1, 3>,              2,              8,              4,      true,   S<1, 4, 16, 4>,   S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              2,      true,           1,           1,   S<1, 32, 1, 8>,               4>,
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,    ELayout, Tuple<BLayout>, BF16,     F32,    BF16,     F32, Tuple<F32>, PassThrough, Bilinear, PassThrough,   ConvSpec,   256,   128,   256,     4,  8,   32,   32,    2,    4,  S<1, 4, 16, 4>, S<0, 3, 1, 2>,   S<0, 2, 1, 3>,              2,              8,              2,      true,   S<1, 4, 32, 2>,   S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              4,      true,           1,           1,   S<1, 32, 1, 8>,               4>,   
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,    ELayout, Tuple<BLayout>, BF16,     F32,    BF16,     F32, Tuple<F32>, PassThrough, Bilinear, PassThrough,   ConvSpec,   128,   128,   128,     4,  8,   32,   32,    4,    2,  S<1, 4, 16, 2>, S<0, 3, 1, 2>,   S<0, 2, 1, 3>,              2,              8,              4,      true,   S<1, 4, 16, 2>,   S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              4,      true,           1,           1,   S<1, 32, 1, 4>,               4>,   
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,    ELayout, Tuple<BLayout>, BF16,     F32,    BF16,     F32, Tuple<F32>, PassThrough, Bilinear, PassThrough,   ConvSpec,   256,   128,   128,     4,  8,   32,   32,    2,    2,  S<1, 4, 16, 4>, S<0, 3, 1, 2>,   S<0, 2, 1, 3>,              2,              8,              2,      true,   S<1, 4, 16, 4>,   S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              2,      true,           1,           1,   S<1, 32, 1, 4>,               4>,   
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,    ELayout, Tuple<BLayout>, BF16,     F32,    BF16,     F32, Tuple<F32>, PassThrough, Bilinear, PassThrough,   ConvSpec,   128,   128,    64,     4,  8,   32,   32,    2,    2,  S<1, 4, 16, 2>, S<0, 3, 1, 2>,   S<0, 2, 1, 3>,              2,              8,              4,      true,   S<1, 4, 8,  4>,   S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              2,      true,           1,           1,   S<1, 32, 1, 4>,               4>,   
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,    ELayout, Tuple<BLayout>, BF16,     F32,    BF16,     F32, Tuple<F32>, PassThrough, Bilinear, PassThrough,   ConvSpec,   128,    64,   128,     4,  8,   32,   32,    2,    2,  S<1, 4, 8,  4>, S<0, 3, 1, 2>,   S<0, 2, 1, 3>,              2,              8,              2,      true,   S<1, 4, 16, 2>,   S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              4,      true,           1,           1,   S<1, 32, 1, 4>,               4>,   
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,    ELayout, Tuple<BLayout>, BF16,     F32,    BF16,     F32, Tuple<F32>, PassThrough, Bilinear, PassThrough,   ConvSpec,    64,    64,    64,     4,  8,   32,   32,    2,    2,  S<1, 4, 8,  2>, S<0, 3, 1, 2>,   S<0, 2, 1, 3>,              2,              8,              4,      true,   S<1, 4, 8,  2>,   S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              4,      true,           1,           1,   S<1, 16, 1, 4>,               4>,   
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,    ELayout, Tuple<BLayout>, BF16,     F32,    BF16,     F32, Tuple<F32>, PassThrough, Bilinear, PassThrough,   ConvSpec,   256,   128,    64,     4,  8,   32,   32,    2,    1,  S<1, 4, 16, 4>, S<0, 3, 1, 2>,   S<0, 2, 1, 3>,              2,              8,              2,      true,   S<1, 4, 8,  8>,   S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              1,      true,           1,           1,   S<1, 32, 1, 4>,               4>,   
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,    ELayout, Tuple<BLayout>, BF16,     F32,    BF16,     F32, Tuple<F32>, PassThrough, Bilinear, PassThrough,   ConvSpec,   256,    64,   128,     4,  8,   32,   32,    1,    2,  S<1, 4, 8,  8>, S<0, 3, 1, 2>,   S<0, 2, 1, 3>,              2,              8,              1,      true,   S<1, 4, 16, 4>,   S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              2,      true,           1,           1,   S<1, 32, 1, 4>,               4>,   
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,    ELayout, Tuple<BLayout>, BF16,     F32,    BF16,     F32, Tuple<F32>, PassThrough, Bilinear, PassThrough,   ConvSpec,   128,   128,    32,     4,  8,   32,   32,    2,    1,  S<1, 4, 16, 2>, S<0, 3, 1, 2>,   S<0, 2, 1, 3>,              2,              8,              4,      true,   S<1, 4, 4,  8>,   S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              1,      true,           1,           1,   S<1, 32, 1, 4>,               4>,   
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,    ELayout, Tuple<BLayout>, BF16,     F32,    BF16,     F32, Tuple<F32>, PassThrough, Bilinear, PassThrough,   ConvSpec,   128,    32,   128,     4,  8,   32,   32,    1,    2,  S<1, 4, 4,  8>, S<0, 3, 1, 2>,   S<0, 2, 1, 3>,              2,              8,              1,      true,   S<1, 4, 16, 2>,   S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              4,      true,           1,           1,   S<1, 32, 1, 4>,               4>,   
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,    ELayout, Tuple<BLayout>, BF16,     F32,    BF16,     F32, Tuple<F32>, PassThrough, Bilinear, PassThrough,   ConvSpec,    64,    64,    32,     4,  8,   32,   32,    2,    1,  S<1, 4, 8,  2>, S<0, 3, 1, 2>,   S<0, 2, 1, 3>,              2,              8,              4,      true,   S<1, 4, 4,  4>,   S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              2,      true,           1,           1,   S<1, 16, 1, 4>,               4>,   
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,    ELayout, Tuple<BLayout>, BF16,     F32,    BF16,     F32, Tuple<F32>, PassThrough, Bilinear, PassThrough,   ConvSpec,    64,    32,    64,     4,  8,   32,   32,    1,    2,  S<1, 4, 4,  4>, S<0, 3, 1, 2>,   S<0, 2, 1, 3>,              2,              8,              2,      true,   S<1, 4, 8,  2>,   S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              4,      true,           1,           1,   S<1, 16, 1, 4>,               4>
+    // clang-format on
+    >;
+
+template <ck::index_t NDimSpatial,
+          typename ALayout,
+          typename BLayout,
+          typename ELayout,
+          ConvolutionBackwardWeightSpecialization ConvSpec>
+using device_grouped_conv_bwd_weight_xdl_c_shuffle_f16_comp_bf8_f8_bilinear_instances = std::tuple<
+// clang-format off
+        //#########################################|     Num| InLayout| WeiLayout| OutLayout| DsData| InData| WeiData| OutData| AccData| DsData|          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|
+        //#########################################|     Dim|         |          |          | Layout|   Type|    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|
+        //#########################################| 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|
+        //#########################################|        |         |          |          |       |       |        |        |        |       |            |            |            |                          |      |      |      |      |   |     |     |     |     |                |                 |               |               |               |               |          |                |               |               |              |               |               |          |            |            | NBlock_NPerBlock|                |
+#if defined CK_ENABLE_FP8 && defined CK_ENABLE_BF8
+        // generic instance
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Tuple<BLayout>, F16,     F16,     F16,     F32, Tuple<F16>, PassThrough, Bilinear, PassThrough,  ConvSpec,    64,    64,    64,     4,  8,   32,   32,    2,    2,  S<1, 4, 8,  2>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,              2,              2,              4,      true,  S<1, 4, 8,  2>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              2,              4,      true,           1,           1,   S<1, 16, 1, 4>,               2,       BF8,     F8>,   
+        // instance for small conv.K
+        // for fp16 conv.K and conv.C must be divisible by 2
+        // since half_t atomic_add require scalar_per_x_vector % 2 == 0
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Tuple<BLayout>, F16,     F16,     F16,     F32, Tuple<F16>, PassThrough, Bilinear, PassThrough,  ConvSpec,   128,   128,    32,     4,  8,   32,   32,    2,    1,  S<1, 4, 16, 2>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,              2,              8,              4,      true,  S<1, 4, 4,  8>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              2,              1,      true,           1,           1,   S<1, 32, 1, 4>,               2,       BF8,     F8>,  
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Tuple<BLayout>, F16,     F16,     F16,     F32, Tuple<F16>, PassThrough, Bilinear, PassThrough,  ConvSpec,    64,    32,    64,     4,  8,   32,   32,    1,    2,  S<1, 4, 4,  4>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,              2,              2,              2,      true,  S<1, 4, 8,  2>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              4,      true,           1,           1,   S<1, 16, 1, 4>,               8,       BF8,     F8>,
+
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Tuple<BLayout>, F16,     F16,     F16,     F32, Tuple<F16>, PassThrough, Bilinear, PassThrough,  ConvSpec,   256,   256,   128,     4,  8,   32,   32,    4,    2,  S<1, 4, 32, 2>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,              2,              8,              4,      true,  S<1, 4, 16, 4>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              2,      true,           1,           1,   S<1, 32, 1, 8>,               8,       BF8,     F8>,
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Tuple<BLayout>, F16,     F16,     F16,     F32, Tuple<F16>, PassThrough, Bilinear, PassThrough,  ConvSpec,   256,   128,   256,     4,  8,   32,   32,    2,    4,  S<1, 4, 16, 4>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,              2,              8,              2,      true,  S<1, 4, 32, 2>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              4,      true,           1,           1,   S<1, 32, 1, 8>,               8,       BF8,     F8>,   
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Tuple<BLayout>, F16,     F16,     F16,     F32, Tuple<F16>, PassThrough, Bilinear, PassThrough,  ConvSpec,   128,   128,   128,     4,  8,   32,   32,    4,    2,  S<1, 4, 16, 2>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,              2,              8,              4,      true,  S<1, 4, 16, 2>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              4,      true,           1,           1,   S<1, 32, 1, 4>,               8,       BF8,     F8>,   
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Tuple<BLayout>, F16,     F16,     F16,     F32, Tuple<F16>, PassThrough, Bilinear, PassThrough,  ConvSpec,   256,   128,   128,     4,  8,   32,   32,    2,    2,  S<1, 4, 16, 4>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,              2,              8,              2,      true,  S<1, 4, 16, 4>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              2,      true,           1,           1,   S<1, 32, 1, 4>,               8,       BF8,     F8>,   
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Tuple<BLayout>, F16,     F16,     F16,     F32, Tuple<F16>, PassThrough, Bilinear, PassThrough,  ConvSpec,   128,   128,    64,     4,  8,   32,   32,    2,    2,  S<1, 4, 16, 2>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,              2,              8,              4,      true,  S<1, 4, 8,  4>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              2,      true,           1,           1,   S<1, 32, 1, 4>,               8,       BF8,     F8>,   
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Tuple<BLayout>, F16,     F16,     F16,     F32, Tuple<F16>, PassThrough, Bilinear, PassThrough,  ConvSpec,   128,    64,   128,     4,  8,   32,   32,    2,    2,  S<1, 4, 8,  4>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,              2,              8,              2,      true,  S<1, 4, 16, 2>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              4,      true,           1,           1,   S<1, 32, 1, 4>,               8,       BF8,     F8>,   
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Tuple<BLayout>, F16,     F16,     F16,     F32, Tuple<F16>, PassThrough, Bilinear, PassThrough,  ConvSpec,    64,    64,    64,     4,  8,   32,   32,    2,    2,  S<1, 4, 8,  2>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,              2,              8,              4,      true,  S<1, 4, 8,  2>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              4,      true,           1,           1,   S<1, 16, 1, 4>,               8,       BF8,     F8>,   
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Tuple<BLayout>, F16,     F16,     F16,     F32, Tuple<F16>, PassThrough, Bilinear, PassThrough,  ConvSpec,   256,   128,    64,     4,  8,   32,   32,    2,    1,  S<1, 4, 16, 4>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,              2,              8,              2,      true,  S<1, 4, 8,  8>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              1,      true,           1,           1,   S<1, 32, 1, 4>,               8,       BF8,     F8>,   
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Tuple<BLayout>, F16,     F16,     F16,     F32, Tuple<F16>, PassThrough, Bilinear, PassThrough,  ConvSpec,   256,    64,   128,     4,  8,   32,   32,    1,    2,  S<1, 4, 8,  8>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,              2,              8,              1,      true,  S<1, 4, 16, 4>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              2,      true,           1,           1,   S<1, 32, 1, 4>,               8,       BF8,     F8>,   
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Tuple<BLayout>, F16,     F16,     F16,     F32, Tuple<F16>, PassThrough, Bilinear, PassThrough,  ConvSpec,   128,   128,    32,     4,  8,   32,   32,    2,    1,  S<1, 4, 16, 2>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,              2,              8,              4,      true,  S<1, 4, 4,  8>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              1,      true,           1,           1,   S<1, 32, 1, 4>,               8,       BF8,     F8>,   
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Tuple<BLayout>, F16,     F16,     F16,     F32, Tuple<F16>, PassThrough, Bilinear, PassThrough,  ConvSpec,   128,    32,   128,     4,  8,   32,   32,    1,    2,  S<1, 4, 4,  8>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,              2,              8,              1,      true,  S<1, 4, 16, 2>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              4,      true,           1,           1,   S<1, 32, 1, 4>,               8,       BF8,     F8>,   
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Tuple<BLayout>, F16,     F16,     F16,     F32, Tuple<F16>, PassThrough, Bilinear, PassThrough,  ConvSpec,    64,    64,    32,     4,  8,   32,   32,    2,    1,  S<1, 4, 8,  2>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,              2,              8,              4,      true,  S<1, 4, 4,  4>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              2,      true,           1,           1,   S<1, 16, 1, 4>,               8,       BF8,     F8>,   
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Tuple<BLayout>, F16,     F16,     F16,     F32, Tuple<F16>, PassThrough, Bilinear, PassThrough,  ConvSpec,    64,    32,    64,     4,  8,   32,   32,    1,    2,  S<1, 4, 4,  4>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,              2,              8,              2,      true,  S<1, 4, 8,  2>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              4,      true,           1,           1,   S<1, 16, 1, 4>,               8,       BF8,     F8>
+#endif
+    // clang-format on
+    >;
+
+} // namespace instance
+} // namespace device
+} // namespace tensor_operation
+} // namespace ck

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

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2024, 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_multiple_d_xdl_cshuffle.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;
+using Scale       = ck::tensor_operation::element_wise::Scale;
+
+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>
+using device_grouped_conv_bwd_weight_xdl_c_shuffle_f32_scale_instances = std::tuple<
+    // clang-format off
+        //#########################################|     Num| InLayout| WeiLayout| OutLayout| DsData| InData| WeiData| OutData| AccData| DsData|          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|
+        //#########################################|     Dim|         |          |          | Layout|   Type|    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|
+        //#########################################| 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|
+        //#########################################|        |         |          |          |       |       |        |        |        |       |            |            |            |                          |      |      |      |      |   |     |     |     |     |                |                 |               |               |               |               |          |                |               |               |              |               |               |          |            |            | NBlock_NPerBlock|                |
+        // generic instance
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Empty_Tuple, F32,     F32,     F32,     F32, Empty_Tuple, PassThrough, Scale, PassThrough,      ConvSpec,    64,    64,    64,     4,  4,   32,   32,    2,    2,  S<1, 4, 16, 1>,   S<0, 3, 1, 2>,   S<0, 2, 1, 3>,              2,              1,              4,      true,  S<1, 4, 16, 1>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              1,              4,      true,           1,           1,   S<1, 16, 1, 4>,               1>,
+        // instances for small conv.K and conv.C
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Empty_Tuple, F32,     F32,     F32,     F32, Empty_Tuple, PassThrough, Scale, PassThrough,      ConvSpec,   128,   128,    32,     4,  4,   32,   32,    2,    1,  S<1, 4, 32, 1>,   S<0, 3, 1, 2>,   S<0, 2, 1, 3>,              2,              4,              4,      true,  S<1, 4, 8,  4>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              1,              1,      true,           1,           1,   S<1, 32, 1, 4>,               1>,
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Empty_Tuple, F32,     F32,     F32,     F32, Empty_Tuple, PassThrough, Scale, PassThrough,      ConvSpec,    64,    32,    64,     4,  4,   32,   32,    1,    2,  S<1, 4, 8,  2>,   S<0, 3, 1, 2>,   S<0, 2, 1, 3>,              2,              1,              2,      true,  S<1, 4, 16, 1>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              4,              4,      true,           1,           1,   S<1, 16, 1, 4>,               4>,
+
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Empty_Tuple, F32,     F32,     F32,     F32, Empty_Tuple, PassThrough, Scale, PassThrough,      ConvSpec,   256,   256,   128,     4,  4,   32,   32,    4,    2,  S<1, 4, 64, 1>,   S<0, 3, 1, 2>,   S<0, 2, 1, 3>,              2,              4,              4,      true,  S<1, 4, 32, 2>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              4,              2,      true,           1,           1,   S<1, 32, 1, 8>,               4>,
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Empty_Tuple, F32,     F32,     F32,     F32, Empty_Tuple, PassThrough, Scale, PassThrough,      ConvSpec,   256,   128,   256,     4,  4,   32,   32,    2,    4,  S<1, 4, 32, 2>,   S<0, 3, 1, 2>,   S<0, 2, 1, 3>,              2,              4,              2,      true,  S<1, 4, 64, 1>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              4,              4,      true,           1,           1,   S<1, 32, 1, 8>,               4>,
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Empty_Tuple, F32,     F32,     F32,     F32, Empty_Tuple, PassThrough, Scale, PassThrough,      ConvSpec,   128,   128,   128,     4,  4,   32,   32,    4,    2,  S<1, 4, 32, 1>,   S<0, 3, 1, 2>,   S<0, 2, 1, 3>,              2,              4,              4,      true,  S<1, 4, 32, 1>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              4,              4,      true,           1,           1,   S<1, 32, 1, 4>,               4>,
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Empty_Tuple, F32,     F32,     F32,     F32, Empty_Tuple, PassThrough, Scale, PassThrough,      ConvSpec,   256,   128,   128,     4,  4,   32,   32,    2,    2,  S<1, 4, 32, 2>,   S<0, 3, 1, 2>,   S<0, 2, 1, 3>,              2,              4,              2,      true,  S<1, 4, 32, 2>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              4,              2,      true,           1,           1,   S<1, 32, 1, 4>,               4>,
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Empty_Tuple, F32,     F32,     F32,     F32, Empty_Tuple, PassThrough, Scale, PassThrough,      ConvSpec,   128,   128,    64,     4,  4,   32,   32,    2,    2,  S<1, 4, 32, 1>,   S<0, 3, 1, 2>,   S<0, 2, 1, 3>,              2,              4,              4,      true,  S<1, 4, 16, 2>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              4,              2,      true,           1,           1,   S<1, 32, 1, 4>,               4>,
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Empty_Tuple, F32,     F32,     F32,     F32, Empty_Tuple, PassThrough, Scale, PassThrough,      ConvSpec,   128,    64,   128,     4,  4,   32,   32,    2,    2,  S<1, 4, 16, 2>,   S<0, 3, 1, 2>,   S<0, 2, 1, 3>,              2,              4,              2,      true,  S<1, 4, 32, 1>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              4,              4,      true,           1,           1,   S<1, 32, 1, 4>,               4>,
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Empty_Tuple, F32,     F32,     F32,     F32, Empty_Tuple, PassThrough, Scale, PassThrough,      ConvSpec,    64,    64,    64,     4,  4,   32,   32,    2,    2,  S<1, 4, 16, 1>,   S<0, 3, 1, 2>,   S<0, 2, 1, 3>,              2,              4,              4,      true,  S<1, 4, 16, 1>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              4,              4,      true,           1,           1,   S<1, 16, 1, 4>,               4>,
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Empty_Tuple, F32,     F32,     F32,     F32, Empty_Tuple, PassThrough, Scale, PassThrough,      ConvSpec,   256,   128,    64,     4,  4,   32,   32,    2,    1,  S<1, 4, 32, 2>,   S<0, 3, 1, 2>,   S<0, 2, 1, 3>,              2,              4,              2,      true,  S<1, 4, 16, 4>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              4,              1,      true,           1,           1,   S<1, 32, 1, 4>,               4>,
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Empty_Tuple, F32,     F32,     F32,     F32, Empty_Tuple, PassThrough, Scale, PassThrough,      ConvSpec,   256,    64,   128,     4,  4,   32,   32,    1,    2,  S<1, 4, 16, 4>,   S<0, 3, 1, 2>,   S<0, 2, 1, 3>,              2,              4,              1,      true,  S<1, 4, 32, 2>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              4,              2,      true,           1,           1,   S<1, 32, 1, 4>,               4>,
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Empty_Tuple, F32,     F32,     F32,     F32, Empty_Tuple, PassThrough, Scale, PassThrough,      ConvSpec,   128,   128,    32,     4,  4,   32,   32,    2,    1,  S<1, 4, 32, 1>,   S<0, 3, 1, 2>,   S<0, 2, 1, 3>,              2,              4,              4,      true,  S<1, 4, 8,  4>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              4,              1,      true,           1,           1,   S<1, 32, 1, 4>,               4>,
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Empty_Tuple, F32,     F32,     F32,     F32, Empty_Tuple, PassThrough, Scale, PassThrough,      ConvSpec,   128,    32,   128,     4,  4,   32,   32,    1,    2,  S<1, 4, 8,  4>,   S<0, 3, 1, 2>,   S<0, 2, 1, 3>,              2,              4,              1,      true,  S<1, 4, 32, 1>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              4,              4,      true,           1,           1,   S<1, 32, 1, 4>,               4>,
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Empty_Tuple, F32,     F32,     F32,     F32, Empty_Tuple, PassThrough, Scale, PassThrough,      ConvSpec,    64,    64,    32,     4,  4,   32,   32,    2,    1,  S<1, 4, 16, 1>,   S<0, 3, 1, 2>,   S<0, 2, 1, 3>,              2,              4,              4,      true,  S<1, 4, 8,  2>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              4,              2,      true,           1,           1,   S<1, 16, 1, 4>,               4>,
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Empty_Tuple, F32,     F32,     F32,     F32, Empty_Tuple, PassThrough, Scale, PassThrough,      ConvSpec,    64,    32,    64,     4,  4,   32,   32,    1,    2,  S<1, 4, 8,  2>,   S<0, 3, 1, 2>,   S<0, 2, 1, 3>,              2,              4,              2,      true,  S<1, 4, 16, 1>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              4,              4,      true,           1,           1,   S<1, 16, 1, 4>,               4>
+    // clang-format on
+    >;
+
+template <ck::index_t NDimSpatial,
+          typename ALayout,
+          typename BLayout,
+          typename ELayout,
+          ConvolutionBackwardWeightSpecialization ConvSpec>
+using device_grouped_conv_bwd_weight_xdl_c_shuffle_f16_scale_instances = std::tuple<
+    // clang-format off
+        //#########################################|     Num| InLayout| WeiLayout| OutLayout| DsData| InData| WeiData| OutData| AccData| DsData|          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|
+        //#########################################|     Dim|         |          |          | Layout|   Type|    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|
+        //#########################################| 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|
+        //#########################################|        |         |          |          |       |       |        |        |        |       |            |            |            |                          |      |      |      |      |   |     |     |     |     |                |                 |               |               |               |               |          |                |               |               |              |               |               |          |            |            | NBlock_NPerBlock|                |
+        // generic instance
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Empty_Tuple, F16,     F16,     F16,     F32, Empty_Tuple, PassThrough, Scale, PassThrough,      ConvSpec,    64,    64,    64,     4,  8,   32,   32,    2,    2,  S<1, 4, 8,  2>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,              2,              2,              4,      true,  S<1, 4, 8,  2>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              2,              4,      true,           1,           1,   S<1, 16, 1, 4>,               2>,   
+        // instance for small conv.K
+        // for fp16 conv.K and conv.C must be divisible by 2
+        // since half_t atomic_add require scalar_per_x_vector % 2 == 0
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Empty_Tuple, F16,     F16,     F16,     F32, Empty_Tuple, PassThrough, Scale, PassThrough,      ConvSpec,   128,   128,    32,     4,  8,   32,   32,    2,    1,  S<1, 4, 16, 2>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,              2,              8,              4,      true,  S<1, 4, 4,  8>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              2,              1,      true,           1,           1,   S<1, 32, 1, 4>,               2>,  
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Empty_Tuple, F16,     F16,     F16,     F32, Empty_Tuple, PassThrough, Scale, PassThrough,      ConvSpec,    64,    32,    64,     4,  8,   32,   32,    1,    2,  S<1, 4, 4,  4>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,              2,              2,              2,      true,  S<1, 4, 8,  2>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              4,      true,           1,           1,   S<1, 16, 1, 4>,               8>,
+
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Empty_Tuple, F16,     F16,     F16,     F32, Empty_Tuple, PassThrough, Scale, PassThrough,      ConvSpec,   256,   256,   128,     4,  8,   32,   32,    4,    2,  S<1, 4, 32, 2>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,              2,              8,              4,      true,  S<1, 4, 16, 4>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              2,      true,           1,           1,   S<1, 32, 1, 8>,               8>,
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Empty_Tuple, F16,     F16,     F16,     F32, Empty_Tuple, PassThrough, Scale, PassThrough,      ConvSpec,   256,   128,   256,     4,  8,   32,   32,    2,    4,  S<1, 4, 16, 4>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,              2,              8,              2,      true,  S<1, 4, 32, 2>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              4,      true,           1,           1,   S<1, 32, 1, 8>,               8>,   
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Empty_Tuple, F16,     F16,     F16,     F32, Empty_Tuple, PassThrough, Scale, PassThrough,      ConvSpec,   128,   128,   128,     4,  8,   32,   32,    4,    2,  S<1, 4, 16, 2>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,              2,              8,              4,      true,  S<1, 4, 16, 2>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              4,      true,           1,           1,   S<1, 32, 1, 4>,               8>,   
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Empty_Tuple, F16,     F16,     F16,     F32, Empty_Tuple, PassThrough, Scale, PassThrough,      ConvSpec,   256,   128,   128,     4,  8,   32,   32,    2,    2,  S<1, 4, 16, 4>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,              2,              8,              2,      true,  S<1, 4, 16, 4>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              2,      true,           1,           1,   S<1, 32, 1, 4>,               8>,   
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Empty_Tuple, F16,     F16,     F16,     F32, Empty_Tuple, PassThrough, Scale, PassThrough,      ConvSpec,   128,   128,    64,     4,  8,   32,   32,    2,    2,  S<1, 4, 16, 2>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,              2,              8,              4,      true,  S<1, 4, 8,  4>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              2,      true,           1,           1,   S<1, 32, 1, 4>,               8>,   
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Empty_Tuple, F16,     F16,     F16,     F32, Empty_Tuple, PassThrough, Scale, PassThrough,      ConvSpec,   128,    64,   128,     4,  8,   32,   32,    2,    2,  S<1, 4, 8,  4>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,              2,              8,              2,      true,  S<1, 4, 16, 2>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              4,      true,           1,           1,   S<1, 32, 1, 4>,               8>,   
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Empty_Tuple, F16,     F16,     F16,     F32, Empty_Tuple, PassThrough, Scale, PassThrough,      ConvSpec,    64,    64,    64,     4,  8,   32,   32,    2,    2,  S<1, 4, 8,  2>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,              2,              8,              4,      true,  S<1, 4, 8,  2>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              4,      true,           1,           1,   S<1, 16, 1, 4>,               8>,   
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Empty_Tuple, F16,     F16,     F16,     F32, Empty_Tuple, PassThrough, Scale, PassThrough,      ConvSpec,   256,   128,    64,     4,  8,   32,   32,    2,    1,  S<1, 4, 16, 4>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,              2,              8,              2,      true,  S<1, 4, 8,  8>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              1,      true,           1,           1,   S<1, 32, 1, 4>,               8>,   
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Empty_Tuple, F16,     F16,     F16,     F32, Empty_Tuple, PassThrough, Scale, PassThrough,      ConvSpec,   256,    64,   128,     4,  8,   32,   32,    1,    2,  S<1, 4, 8,  8>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,              2,              8,              1,      true,  S<1, 4, 16, 4>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              2,      true,           1,           1,   S<1, 32, 1, 4>,               8>,   
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Empty_Tuple, F16,     F16,     F16,     F32, Empty_Tuple, PassThrough, Scale, PassThrough,      ConvSpec,   128,   128,    32,     4,  8,   32,   32,    2,    1,  S<1, 4, 16, 2>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,              2,              8,              4,      true,  S<1, 4, 4,  8>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              1,      true,           1,           1,   S<1, 32, 1, 4>,               8>,   
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Empty_Tuple, F16,     F16,     F16,     F32, Empty_Tuple, PassThrough, Scale, PassThrough,      ConvSpec,   128,    32,   128,     4,  8,   32,   32,    1,    2,  S<1, 4, 4,  8>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,              2,              8,              1,      true,  S<1, 4, 16, 2>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              4,      true,           1,           1,   S<1, 32, 1, 4>,               8>,   
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Empty_Tuple, F16,     F16,     F16,     F32, Empty_Tuple, PassThrough, Scale, PassThrough,      ConvSpec,    64,    64,    32,     4,  8,   32,   32,    2,    1,  S<1, 4, 8,  2>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,              2,              8,              4,      true,  S<1, 4, 4,  4>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              2,      true,           1,           1,   S<1, 16, 1, 4>,               8>,   
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Empty_Tuple, F16,     F16,     F16,     F32, Empty_Tuple, PassThrough, Scale, PassThrough,      ConvSpec,    64,    32,    64,     4,  8,   32,   32,    1,    2,  S<1, 4, 4,  4>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,              2,              8,              2,      true,  S<1, 4, 8,  2>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              4,      true,           1,           1,   S<1, 16, 1, 4>,               8>
+    // clang-format on
+    >;
+
+template <ck::index_t NDimSpatial,
+          typename ALayout,
+          typename BLayout,
+          typename ELayout,
+          ConvolutionBackwardWeightSpecialization ConvSpec>
+using device_grouped_conv_bwd_weight_xdl_c_shuffle_bf16_scale_instances = std::tuple<
+    // clang-format off
+        //#########################################|     Num| InLayout| WeiLayout| OutLayout| DsData| InData| WeiData| OutData| AccData| DsData|          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|
+        //#########################################|     Dim|         |          |          | Layout|   Type|    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|
+        //#########################################| 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|
+        //#########################################|        |         |          |          |       |       |        |        |        |       |            |            |            |                          |      |      |      |      |   |     |     |     |     |                |                 |               |               |               |               |          |                |               |               |              |               |               |          |            |            | NBlock_NPerBlock|                |
+        // generic instance
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,    ELayout, Empty_Tuple, BF16,     F32,    BF16,     F32, Empty_Tuple, PassThrough, Scale, PassThrough,       ConvSpec,    64,    64,    64,     4,  8,   32,   32,    2,    2,  S<1, 4, 8,  2>, S<0, 3, 1, 2>,   S<0, 2, 1, 3>,              2,              1,              4,      true,   S<1, 4, 8,  2>,   S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              1,              4,      true,           1,           1,   S<1, 16, 1, 4>,               1>,   
+        // instance for small conv.K
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,    ELayout, Empty_Tuple, BF16,     F32,    BF16,     F32, Empty_Tuple, PassThrough, Scale, PassThrough,       ConvSpec,   128,   128,    32,     4,  8,   32,   32,    2,    1,  S<1, 4, 16, 2>, S<0, 3, 1, 2>,   S<0, 2, 1, 3>,              2,              8,              4,      true,   S<1, 4, 4,  8>,   S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              1,              1,      true,           1,           1,   S<1, 32, 1, 4>,               1>, 
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,    ELayout, Empty_Tuple, BF16,     F32,    BF16,     F32, Empty_Tuple, PassThrough, Scale, PassThrough,       ConvSpec,    64,    32,    64,     4,  8,   32,   32,    1,    2,  S<1, 4, 4,  4>, S<0, 3, 1, 2>,   S<0, 2, 1, 3>,              2,              1,              2,      true,   S<1, 4, 8,  2>,   S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              4,      true,           1,           1,   S<1, 16, 1, 4>,               4>,
+
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,    ELayout, Empty_Tuple, BF16,     F32,    BF16,     F32, Empty_Tuple, PassThrough, Scale, PassThrough,       ConvSpec,   256,   256,   128,     4,  8,   32,   32,    4,    2,  S<1, 4, 32, 2>, S<0, 3, 1, 2>,   S<0, 2, 1, 3>,              2,              8,              4,      true,   S<1, 4, 16, 4>,   S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              2,      true,           1,           1,   S<1, 32, 1, 8>,               4>,
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,    ELayout, Empty_Tuple, BF16,     F32,    BF16,     F32, Empty_Tuple, PassThrough, Scale, PassThrough,       ConvSpec,   256,   128,   256,     4,  8,   32,   32,    2,    4,  S<1, 4, 16, 4>, S<0, 3, 1, 2>,   S<0, 2, 1, 3>,              2,              8,              2,      true,   S<1, 4, 32, 2>,   S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              4,      true,           1,           1,   S<1, 32, 1, 8>,               4>,   
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,    ELayout, Empty_Tuple, BF16,     F32,    BF16,     F32, Empty_Tuple, PassThrough, Scale, PassThrough,       ConvSpec,   128,   128,   128,     4,  8,   32,   32,    4,    2,  S<1, 4, 16, 2>, S<0, 3, 1, 2>,   S<0, 2, 1, 3>,              2,              8,              4,      true,   S<1, 4, 16, 2>,   S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              4,      true,           1,           1,   S<1, 32, 1, 4>,               4>,   
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,    ELayout, Empty_Tuple, BF16,     F32,    BF16,     F32, Empty_Tuple, PassThrough, Scale, PassThrough,       ConvSpec,   256,   128,   128,     4,  8,   32,   32,    2,    2,  S<1, 4, 16, 4>, S<0, 3, 1, 2>,   S<0, 2, 1, 3>,              2,              8,              2,      true,   S<1, 4, 16, 4>,   S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              2,      true,           1,           1,   S<1, 32, 1, 4>,               4>,   
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,    ELayout, Empty_Tuple, BF16,     F32,    BF16,     F32, Empty_Tuple, PassThrough, Scale, PassThrough,       ConvSpec,   128,   128,    64,     4,  8,   32,   32,    2,    2,  S<1, 4, 16, 2>, S<0, 3, 1, 2>,   S<0, 2, 1, 3>,              2,              8,              4,      true,   S<1, 4, 8,  4>,   S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              2,      true,           1,           1,   S<1, 32, 1, 4>,               4>,   
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,    ELayout, Empty_Tuple, BF16,     F32,    BF16,     F32, Empty_Tuple, PassThrough, Scale, PassThrough,       ConvSpec,   128,    64,   128,     4,  8,   32,   32,    2,    2,  S<1, 4, 8,  4>, S<0, 3, 1, 2>,   S<0, 2, 1, 3>,              2,              8,              2,      true,   S<1, 4, 16, 2>,   S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              4,      true,           1,           1,   S<1, 32, 1, 4>,               4>,   
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,    ELayout, Empty_Tuple, BF16,     F32,    BF16,     F32, Empty_Tuple, PassThrough, Scale, PassThrough,       ConvSpec,    64,    64,    64,     4,  8,   32,   32,    2,    2,  S<1, 4, 8,  2>, S<0, 3, 1, 2>,   S<0, 2, 1, 3>,              2,              8,              4,      true,   S<1, 4, 8,  2>,   S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              4,      true,           1,           1,   S<1, 16, 1, 4>,               4>,   
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,    ELayout, Empty_Tuple, BF16,     F32,    BF16,     F32, Empty_Tuple, PassThrough, Scale, PassThrough,       ConvSpec,   256,   128,    64,     4,  8,   32,   32,    2,    1,  S<1, 4, 16, 4>, S<0, 3, 1, 2>,   S<0, 2, 1, 3>,              2,              8,              2,      true,   S<1, 4, 8,  8>,   S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              1,      true,           1,           1,   S<1, 32, 1, 4>,               4>,   
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,    ELayout, Empty_Tuple, BF16,     F32,    BF16,     F32, Empty_Tuple, PassThrough, Scale, PassThrough,       ConvSpec,   256,    64,   128,     4,  8,   32,   32,    1,    2,  S<1, 4, 8,  8>, S<0, 3, 1, 2>,   S<0, 2, 1, 3>,              2,              8,              1,      true,   S<1, 4, 16, 4>,   S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              2,      true,           1,           1,   S<1, 32, 1, 4>,               4>,   
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,    ELayout, Empty_Tuple, BF16,     F32,    BF16,     F32, Empty_Tuple, PassThrough, Scale, PassThrough,       ConvSpec,   128,   128,    32,     4,  8,   32,   32,    2,    1,  S<1, 4, 16, 2>, S<0, 3, 1, 2>,   S<0, 2, 1, 3>,              2,              8,              4,      true,   S<1, 4, 4,  8>,   S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              1,      true,           1,           1,   S<1, 32, 1, 4>,               4>,   
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,    ELayout, Empty_Tuple, BF16,     F32,    BF16,     F32, Empty_Tuple, PassThrough, Scale, PassThrough,       ConvSpec,   128,    32,   128,     4,  8,   32,   32,    1,    2,  S<1, 4, 4,  8>, S<0, 3, 1, 2>,   S<0, 2, 1, 3>,              2,              8,              1,      true,   S<1, 4, 16, 2>,   S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              4,      true,           1,           1,   S<1, 32, 1, 4>,               4>,   
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,    ELayout, Empty_Tuple, BF16,     F32,    BF16,     F32, Empty_Tuple, PassThrough, Scale, PassThrough,       ConvSpec,    64,    64,    32,     4,  8,   32,   32,    2,    1,  S<1, 4, 8,  2>, S<0, 3, 1, 2>,   S<0, 2, 1, 3>,              2,              8,              4,      true,   S<1, 4, 4,  4>,   S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              2,      true,           1,           1,   S<1, 16, 1, 4>,               4>,   
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,    ELayout, Empty_Tuple, BF16,     F32,    BF16,     F32, Empty_Tuple, PassThrough, Scale, PassThrough,       ConvSpec,    64,    32,    64,     4,  8,   32,   32,    1,    2,  S<1, 4, 4,  4>, S<0, 3, 1, 2>,   S<0, 2, 1, 3>,              2,              8,              2,      true,   S<1, 4, 8,  2>,   S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              4,      true,           1,           1,   S<1, 16, 1, 4>,               4>
+    // clang-format on
+    >;
+
+template <ck::index_t NDimSpatial,
+          typename ALayout,
+          typename BLayout,
+          typename ELayout,
+          ConvolutionBackwardWeightSpecialization ConvSpec>
+using device_grouped_conv_bwd_weight_xdl_c_shuffle_f16_comp_bf8_f8_scale_instances = std::tuple<
+// clang-format off
+        //#########################################|     Num| InLayout| WeiLayout| OutLayout| DsData| InData| WeiData| OutData| AccData| DsData|          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|
+        //#########################################|     Dim|         |          |          | Layout|   Type|    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|
+        //#########################################| 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|
+        //#########################################|        |         |          |          |       |       |        |        |        |       |            |            |            |                          |      |      |      |      |   |     |     |     |     |                |                 |               |               |               |               |          |                |               |               |              |               |               |          |            |            | NBlock_NPerBlock|                |
+#if defined CK_ENABLE_FP8 && defined CK_ENABLE_BF8
+        // generic instance
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Empty_Tuple, F16,     F16,     F16,     F32, Empty_Tuple, PassThrough, Scale, PassThrough,      ConvSpec,    64,    64,    64,     4,  8,   32,   32,    2,    2,  S<1, 4, 8,  2>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,              2,              2,              4,      true,  S<1, 4, 8,  2>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              2,              4,      true,           1,           1,   S<1, 16, 1, 4>,               2,       BF8,     F8>,   
+        // instance for small conv.K
+        // for fp16 conv.K and conv.C must be divisible by 2
+        // since half_t atomic_add require scalar_per_x_vector % 2 == 0
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Empty_Tuple, F16,     F16,     F16,     F32, Empty_Tuple, PassThrough, Scale, PassThrough,      ConvSpec,   128,   128,    32,     4,  8,   32,   32,    2,    1,  S<1, 4, 16, 2>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,              2,              8,              4,      true,  S<1, 4, 4,  8>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              2,              1,      true,           1,           1,   S<1, 32, 1, 4>,               2,       BF8,     F8>,  
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Empty_Tuple, F16,     F16,     F16,     F32, Empty_Tuple, PassThrough, Scale, PassThrough,      ConvSpec,    64,    32,    64,     4,  8,   32,   32,    1,    2,  S<1, 4, 4,  4>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,              2,              2,              2,      true,  S<1, 4, 8,  2>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              4,      true,           1,           1,   S<1, 16, 1, 4>,               8,       BF8,     F8>,
+
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Empty_Tuple, F16,     F16,     F16,     F32, Empty_Tuple, PassThrough, Scale, PassThrough,      ConvSpec,   256,   256,   128,     4,  8,   32,   32,    4,    2,  S<1, 4, 32, 2>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,              2,              8,              4,      true,  S<1, 4, 16, 4>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              2,      true,           1,           1,   S<1, 32, 1, 8>,               8,       BF8,     F8>,
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Empty_Tuple, F16,     F16,     F16,     F32, Empty_Tuple, PassThrough, Scale, PassThrough,      ConvSpec,   256,   128,   256,     4,  8,   32,   32,    2,    4,  S<1, 4, 16, 4>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,              2,              8,              2,      true,  S<1, 4, 32, 2>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              4,      true,           1,           1,   S<1, 32, 1, 8>,               8,       BF8,     F8>,   
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Empty_Tuple, F16,     F16,     F16,     F32, Empty_Tuple, PassThrough, Scale, PassThrough,      ConvSpec,   128,   128,   128,     4,  8,   32,   32,    4,    2,  S<1, 4, 16, 2>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,              2,              8,              4,      true,  S<1, 4, 16, 2>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              4,      true,           1,           1,   S<1, 32, 1, 4>,               8,       BF8,     F8>,   
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Empty_Tuple, F16,     F16,     F16,     F32, Empty_Tuple, PassThrough, Scale, PassThrough,      ConvSpec,   256,   128,   128,     4,  8,   32,   32,    2,    2,  S<1, 4, 16, 4>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,              2,              8,              2,      true,  S<1, 4, 16, 4>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              2,      true,           1,           1,   S<1, 32, 1, 4>,               8,       BF8,     F8>,   
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Empty_Tuple, F16,     F16,     F16,     F32, Empty_Tuple, PassThrough, Scale, PassThrough,      ConvSpec,   128,   128,    64,     4,  8,   32,   32,    2,    2,  S<1, 4, 16, 2>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,              2,              8,              4,      true,  S<1, 4, 8,  4>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              2,      true,           1,           1,   S<1, 32, 1, 4>,               8,       BF8,     F8>,   
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Empty_Tuple, F16,     F16,     F16,     F32, Empty_Tuple, PassThrough, Scale, PassThrough,      ConvSpec,   128,    64,   128,     4,  8,   32,   32,    2,    2,  S<1, 4, 8,  4>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,              2,              8,              2,      true,  S<1, 4, 16, 2>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              4,      true,           1,           1,   S<1, 32, 1, 4>,               8,       BF8,     F8>,   
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Empty_Tuple, F16,     F16,     F16,     F32, Empty_Tuple, PassThrough, Scale, PassThrough,      ConvSpec,    64,    64,    64,     4,  8,   32,   32,    2,    2,  S<1, 4, 8,  2>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,              2,              8,              4,      true,  S<1, 4, 8,  2>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              4,      true,           1,           1,   S<1, 16, 1, 4>,               8,       BF8,     F8>,   
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Empty_Tuple, F16,     F16,     F16,     F32, Empty_Tuple, PassThrough, Scale, PassThrough,      ConvSpec,   256,   128,    64,     4,  8,   32,   32,    2,    1,  S<1, 4, 16, 4>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,              2,              8,              2,      true,  S<1, 4, 8,  8>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              1,      true,           1,           1,   S<1, 32, 1, 4>,               8,       BF8,     F8>,   
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Empty_Tuple, F16,     F16,     F16,     F32, Empty_Tuple, PassThrough, Scale, PassThrough,      ConvSpec,   256,    64,   128,     4,  8,   32,   32,    1,    2,  S<1, 4, 8,  8>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,              2,              8,              1,      true,  S<1, 4, 16, 4>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              2,      true,           1,           1,   S<1, 32, 1, 4>,               8,       BF8,     F8>,   
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Empty_Tuple, F16,     F16,     F16,     F32, Empty_Tuple, PassThrough, Scale, PassThrough,      ConvSpec,   128,   128,    32,     4,  8,   32,   32,    2,    1,  S<1, 4, 16, 2>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,              2,              8,              4,      true,  S<1, 4, 4,  8>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              1,      true,           1,           1,   S<1, 32, 1, 4>,               8,       BF8,     F8>,   
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Empty_Tuple, F16,     F16,     F16,     F32, Empty_Tuple, PassThrough, Scale, PassThrough,      ConvSpec,   128,    32,   128,     4,  8,   32,   32,    1,    2,  S<1, 4, 4,  8>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,              2,              8,              1,      true,  S<1, 4, 16, 2>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              4,      true,           1,           1,   S<1, 32, 1, 4>,               8,       BF8,     F8>,   
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Empty_Tuple, F16,     F16,     F16,     F32, Empty_Tuple, PassThrough, Scale, PassThrough,      ConvSpec,    64,    64,    32,     4,  8,   32,   32,    2,    1,  S<1, 4, 8,  2>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,              2,              8,              4,      true,  S<1, 4, 4,  4>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              2,      true,           1,           1,   S<1, 16, 1, 4>,               8,       BF8,     F8>,   
+        DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle< NDimSpatial,  ALayout,   BLayout,   ELayout, Empty_Tuple, F16,     F16,     F16,     F32, Empty_Tuple, PassThrough, Scale, PassThrough,      ConvSpec,    64,    32,    64,     4,  8,   32,   32,    1,    2,  S<1, 4, 4,  4>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,              2,              8,              2,      true,  S<1, 4, 8,  2>,  S<0, 3, 1, 2>,  S<0, 2, 1, 3>,             2,              8,              4,      true,           1,           1,   S<1, 16, 1, 4>,               8,       BF8,     F8>
+#endif
+    // clang-format on
+    >;
+
+} // namespace instance
+} // namespace device
+} // namespace tensor_operation
+} // namespace ck

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

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include <cstdlib>
+
+#include "ck/ck.hpp"
+#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
+#include "ck/tensor_operation/gpu/device/device_grouped_conv_bwd_weight_multiple_d.hpp"
+#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
+
+#include "ck/library/tensor_operation_instance/device_operation_instance_factory.hpp"
+
+namespace ck {
+namespace tensor_operation {
+namespace device {
+namespace instance {
+
+#ifdef CK_USE_XDL
+#ifdef CK_ENABLE_BF16
+void add_device_grouped_conv3d_bwd_weight_xdl_bilinear_ndhwgc_gkzyxc_ndhwgk_bf16_f32_bf16_instances(
+    std::vector<std::unique_ptr<DeviceGroupedConvBwdWeightMultipleD<3,
+                                                                    NDHWGC,
+                                                                    GKZYXC,
+                                                                    NDHWGK,
+                                                                    Tuple<GKZYXC>,
+                                                                    BF16,
+                                                                    F32,
+                                                                    BF16,
+                                                                    Tuple<F32>,
+                                                                    PassThrough,
+                                                                    Bilinear,
+                                                                    PassThrough>>>& instances);
+#endif
+#ifdef CK_ENABLE_FP16
+void add_device_grouped_conv3d_bwd_weight_xdl_bilinear_ndhwgc_gkzyxc_ndhwgk_f16_instances(
+    std::vector<std::unique_ptr<DeviceGroupedConvBwdWeightMultipleD<3,
+                                                                    NDHWGC,
+                                                                    GKZYXC,
+                                                                    NDHWGK,
+                                                                    Tuple<GKZYXC>,
+                                                                    F16,
+                                                                    F16,
+                                                                    F16,
+                                                                    Tuple<F16>,
+                                                                    PassThrough,
+                                                                    Bilinear,
+                                                                    PassThrough>>>& instances);
+#endif
+#ifdef CK_ENABLE_FP32
+void add_device_grouped_conv3d_bwd_weight_xdl_bilinear_ndhwgc_gkzyxc_ndhwgk_f32_instances(
+    std::vector<std::unique_ptr<DeviceGroupedConvBwdWeightMultipleD<3,
+                                                                    NDHWGC,
+                                                                    GKZYXC,
+                                                                    NDHWGK,
+                                                                    Tuple<GKZYXC>,
+                                                                    F32,
+                                                                    F32,
+                                                                    F32,
+                                                                    Tuple<F32>,
+                                                                    PassThrough,
+                                                                    Bilinear,
+                                                                    PassThrough>>>& instances);
+#endif
+#if defined CK_ENABLE_FP16 && defined CK_ENABLE_FP8 && defined CK_ENABLE_BF8
+void add_device_grouped_conv3d_bwd_weight_xdl_bilinear_ndhwgc_gkzyxc_ndhwgk_f16_comp_bf8_f8_instances(
+    std::vector<std::unique_ptr<DeviceGroupedConvBwdWeightMultipleD<3,
+                                                                    NDHWGC,
+                                                                    GKZYXC,
+                                                                    NDHWGK,
+                                                                    Tuple<GKZYXC>,
+                                                                    F16,
+                                                                    F16,
+                                                                    F16,
+                                                                    Tuple<F16>,
+                                                                    PassThrough,
+                                                                    Bilinear,
+                                                                    PassThrough,
+                                                                    BF8,
+                                                                    F8>>>& instances);
+#endif
+#endif
+
+template <ck::index_t NumDimSpatial,
+          typename InLayout,
+          typename WeiLayout,
+          typename OutLayout,
+          typename DsLayout,
+          typename InDataType,
+          typename WeiDataType,
+          typename OutDataType,
+          typename DsDataType,
+          typename ComputeTypeA,
+          typename ComputeTypeB>
+struct DeviceOperationInstanceFactory<
+    ck::tensor_operation::device::DeviceGroupedConvBwdWeightMultipleD<
+        NumDimSpatial,
+        InLayout,
+        WeiLayout,
+        OutLayout,
+        DsLayout,
+        InDataType,
+        WeiDataType,
+        OutDataType,
+        DsDataType,
+        ck::tensor_operation::element_wise::PassThrough,
+        ck::tensor_operation::element_wise::Bilinear,
+        ck::tensor_operation::element_wise::PassThrough,
+        ComputeTypeA,
+        ComputeTypeB>>
+{
+    using DeviceOp =
+        DeviceGroupedConvBwdWeightMultipleD<NumDimSpatial,
+                                            InLayout,
+                                            WeiLayout,
+                                            OutLayout,
+                                            DsLayout,
+                                            InDataType,
+                                            WeiDataType,
+                                            OutDataType,
+                                            DsDataType,
+                                            ck::tensor_operation::element_wise::PassThrough,
+                                            ck::tensor_operation::element_wise::Bilinear,
+                                            ck::tensor_operation::element_wise::PassThrough,
+                                            ComputeTypeA,
+                                            ComputeTypeB>;
+
+    static auto GetInstances()
+    {
+        std::vector<std::unique_ptr<DeviceOp>> op_ptrs;
+
+#ifdef CK_USE_XDL
+        if constexpr(NumDimSpatial == 3)
+        {
+            if constexpr(is_same_v<InLayout, NDHWGC> && is_same_v<WeiLayout, GKZYXC> &&
+                         is_same_v<OutLayout, NDHWGK>)
+            {
+#ifdef CK_ENABLE_FP32
+                if constexpr(is_same_v<InDataType, float> && is_same_v<WeiDataType, float> &&
+                             is_same_v<OutDataType, float> && is_same_v<ComputeTypeA, float> &&
+                             is_same_v<ComputeTypeB, float>)
+                {
+                    add_device_grouped_conv3d_bwd_weight_xdl_bilinear_ndhwgc_gkzyxc_ndhwgk_f32_instances(
+                        op_ptrs);
+                }
+#endif
+#ifdef CK_ENABLE_FP16
+                if constexpr(is_same_v<InDataType, half_t> && is_same_v<WeiDataType, half_t> &&
+                             is_same_v<OutDataType, half_t> && is_same_v<ComputeTypeA, half_t> &&
+                             is_same_v<ComputeTypeB, half_t>)
+                {
+                    add_device_grouped_conv3d_bwd_weight_xdl_bilinear_ndhwgc_gkzyxc_ndhwgk_f16_instances(
+                        op_ptrs);
+                }
+#endif
+#ifdef CK_ENABLE_BF16
+                if constexpr(is_same_v<InDataType, ck::bhalf_t> && is_same_v<WeiDataType, float> &&
+                             is_same_v<OutDataType, ck::bhalf_t> &&
+                             is_same_v<ComputeTypeA, ck::bhalf_t> &&
+                             is_same_v<ComputeTypeB, ck::bhalf_t>)
+                {
+                    add_device_grouped_conv3d_bwd_weight_xdl_bilinear_ndhwgc_gkzyxc_ndhwgk_bf16_f32_bf16_instances(
+                        op_ptrs);
+                }
+#endif
+#if defined CK_ENABLE_FP16 && defined CK_ENABLE_FP8 && defined CK_ENABLE_BF8
+                if constexpr(is_same_v<InDataType, half_t> && is_same_v<WeiDataType, half_t> &&
+                             is_same_v<OutDataType, half_t> && is_same_v<ComputeTypeA, bf8_t> &&
+                             is_same_v<ComputeTypeB, f8_t>)
+                {
+                    add_device_grouped_conv3d_bwd_weight_xdl_bilinear_ndhwgc_gkzyxc_ndhwgk_f16_comp_bf8_f8_instances(
+                        op_ptrs);
+                }
+#endif
+            }
+        }
+#endif
+        return op_ptrs;
+    }
+};
+
+} // namespace instance
+} // namespace device
+} // namespace tensor_operation
+} // namespace ck

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

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include <cstdlib>
+
+#include "ck/ck.hpp"
+#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
+#include "ck/tensor_operation/gpu/device/device_grouped_conv_bwd_weight_multiple_d.hpp"
+#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
+
+#include "ck/library/tensor_operation_instance/device_operation_instance_factory.hpp"
+
+namespace ck {
+namespace tensor_operation {
+namespace device {
+namespace instance {
+
+#ifdef CK_USE_XDL
+#ifdef CK_ENABLE_BF16
+void add_device_grouped_conv3d_bwd_weight_xdl_scale_ndhwgc_gkzyxc_ndhwgk_bf16_f32_bf16_instances(
+    std::vector<std::unique_ptr<DeviceGroupedConvBwdWeightMultipleD<3,
+                                                                    NDHWGC,
+                                                                    GKZYXC,
+                                                                    NDHWGK,
+                                                                    Tuple<>,
+                                                                    BF16,
+                                                                    F32,
+                                                                    BF16,
+                                                                    Tuple<>,
+                                                                    PassThrough,
+                                                                    Scale,
+                                                                    PassThrough>>>& instances);
+#endif
+#ifdef CK_ENABLE_FP16
+void add_device_grouped_conv3d_bwd_weight_xdl_scale_ndhwgc_gkzyxc_ndhwgk_f16_instances(
+    std::vector<std::unique_ptr<DeviceGroupedConvBwdWeightMultipleD<3,
+                                                                    NDHWGC,
+                                                                    GKZYXC,
+                                                                    NDHWGK,
+                                                                    Tuple<>,
+                                                                    F16,
+                                                                    F16,
+                                                                    F16,
+                                                                    Tuple<>,
+                                                                    PassThrough,
+                                                                    Scale,
+                                                                    PassThrough>>>& instances);
+#endif
+#ifdef CK_ENABLE_FP32
+void add_device_grouped_conv3d_bwd_weight_xdl_scale_ndhwgc_gkzyxc_ndhwgk_f32_instances(
+    std::vector<std::unique_ptr<DeviceGroupedConvBwdWeightMultipleD<3,
+                                                                    NDHWGC,
+                                                                    GKZYXC,
+                                                                    NDHWGK,
+                                                                    Tuple<>,
+                                                                    F32,
+                                                                    F32,
+                                                                    F32,
+                                                                    Tuple<>,
+                                                                    PassThrough,
+                                                                    Scale,
+                                                                    PassThrough>>>& instances);
+#endif
+#if defined CK_ENABLE_FP16 && defined CK_ENABLE_FP8 && defined CK_ENABLE_BF8
+void add_device_grouped_conv3d_bwd_weight_xdl_scale_ndhwgc_gkzyxc_ndhwgk_f16_comp_bf8_f8_instances(
+    std::vector<std::unique_ptr<DeviceGroupedConvBwdWeightMultipleD<3,
+                                                                    NDHWGC,
+                                                                    GKZYXC,
+                                                                    NDHWGK,
+                                                                    Tuple<>,
+                                                                    F16,
+                                                                    F16,
+                                                                    F16,
+                                                                    Tuple<>,
+                                                                    PassThrough,
+                                                                    Scale,
+                                                                    PassThrough,
+                                                                    BF8,
+                                                                    F8>>>& instances);
+#endif
+#endif
+
+template <ck::index_t NumDimSpatial,
+          typename InLayout,
+          typename WeiLayout,
+          typename OutLayout,
+          typename DsLayout,
+          typename InDataType,
+          typename WeiDataType,
+          typename OutDataType,
+          typename DsDataType,
+          typename ComputeTypeA,
+          typename ComputeTypeB>
+struct DeviceOperationInstanceFactory<
+    ck::tensor_operation::device::DeviceGroupedConvBwdWeightMultipleD<
+        NumDimSpatial,
+        InLayout,
+        WeiLayout,
+        OutLayout,
+        DsLayout,
+        InDataType,
+        WeiDataType,
+        OutDataType,
+        DsDataType,
+        ck::tensor_operation::element_wise::PassThrough,
+        ck::tensor_operation::element_wise::Scale,
+        ck::tensor_operation::element_wise::PassThrough,
+        ComputeTypeA,
+        ComputeTypeB>>
+{
+    using DeviceOp =
+        DeviceGroupedConvBwdWeightMultipleD<NumDimSpatial,
+                                            InLayout,
+                                            WeiLayout,
+                                            OutLayout,
+                                            DsLayout,
+                                            InDataType,
+                                            WeiDataType,
+                                            OutDataType,
+                                            DsDataType,
+                                            ck::tensor_operation::element_wise::PassThrough,
+                                            ck::tensor_operation::element_wise::Scale,
+                                            ck::tensor_operation::element_wise::PassThrough,
+                                            ComputeTypeA,
+                                            ComputeTypeB>;
+
+    static auto GetInstances()
+    {
+        std::vector<std::unique_ptr<DeviceOp>> op_ptrs;
+
+#ifdef CK_USE_XDL
+        if constexpr(NumDimSpatial == 3)
+        {
+            if constexpr(is_same_v<InLayout, NDHWGC> && is_same_v<WeiLayout, GKZYXC> &&
+                         is_same_v<OutLayout, NDHWGK>)
+            {
+#ifdef CK_ENABLE_FP32
+                if constexpr(is_same_v<InDataType, float> && is_same_v<WeiDataType, float> &&
+                             is_same_v<OutDataType, float> && is_same_v<ComputeTypeA, float> &&
+                             is_same_v<ComputeTypeB, float>)
+                {
+                    add_device_grouped_conv3d_bwd_weight_xdl_scale_ndhwgc_gkzyxc_ndhwgk_f32_instances(
+                        op_ptrs);
+                }
+#endif
+#ifdef CK_ENABLE_FP16
+                if constexpr(is_same_v<InDataType, half_t> && is_same_v<WeiDataType, half_t> &&
+                             is_same_v<OutDataType, half_t> && is_same_v<ComputeTypeA, half_t> &&
+                             is_same_v<ComputeTypeB, half_t>)
+                {
+                    add_device_grouped_conv3d_bwd_weight_xdl_scale_ndhwgc_gkzyxc_ndhwgk_f16_instances(
+                        op_ptrs);
+                }
+#endif
+#ifdef CK_ENABLE_BF16
+                if constexpr(is_same_v<InDataType, ck::bhalf_t> && is_same_v<WeiDataType, float> &&
+                             is_same_v<OutDataType, ck::bhalf_t> &&
+                             is_same_v<ComputeTypeA, ck::bhalf_t> &&
+                             is_same_v<ComputeTypeB, ck::bhalf_t>)
+                {
+                    add_device_grouped_conv3d_bwd_weight_xdl_scale_ndhwgc_gkzyxc_ndhwgk_bf16_f32_bf16_instances(
+                        op_ptrs);
+                }
+#endif
+#if defined CK_ENABLE_FP16 && defined CK_ENABLE_FP8 && defined CK_ENABLE_BF8
+                if constexpr(is_same_v<InDataType, half_t> && is_same_v<WeiDataType, half_t> &&
+                             is_same_v<OutDataType, half_t> && is_same_v<ComputeTypeA, bf8_t> &&
+                             is_same_v<ComputeTypeB, f8_t>)
+                {
+                    add_device_grouped_conv3d_bwd_weight_xdl_scale_ndhwgc_gkzyxc_ndhwgk_f16_comp_bf8_f8_instances(
+                        op_ptrs);
+                }
+#endif
+            }
+        }
+#endif
+        return op_ptrs;
+    }
+};
+
+} // namespace instance
+} // namespace device
+} // namespace tensor_operation
+} // namespace ck