Merge branch 'develop' into ab_blk_copy_multi_d

CMakeLists.txt

 cmake_minimum_required(VERSION 3.14)
 
+# This has to be initialized before the project() command appears
+# Set the default of CMAKE_BUILD_TYPE to be release, unless user specifies with -D.  MSVC_IDE does not use CMAKE_BUILD_TYPE
+if( NOT MSVC_IDE AND NOT CMAKE_BUILD_TYPE )
+    set( CMAKE_BUILD_TYPE Release CACHE STRING "Choose the type of build, options are: None Debug Release RelWithDebInfo MinSizeRel." )
+endif()
+
+# Default installation path
+if(WIN32)
+    set(CMAKE_INSTALL_PREFIX "/opt/rocm/x86_64-w64-mingw32" CACHE PATH "")
+else()
+    set(CMAKE_INSTALL_PREFIX "/opt/rocm" CACHE PATH "")
+endif()
+
 set(version 1.1.0)
 # Check support for CUDA/HIP in Cmake
 project(composable_kernel VERSION ${version})

Dockerfile

@@ -73,15 +73,8 @@ RUN wget -qO /usr/local/bin/ninja.gz https://github.com/ninja-build/ninja/releas
 RUN gunzip /usr/local/bin/ninja.gz
 RUN chmod a+x /usr/local/bin/ninja
 RUN git clone https://github.com/nico/ninjatracing.git
-RUN apt purge --auto-remove -y cmake
-RUN apt update
-RUN apt install -y software-properties-common lsb-release
-RUN apt clean all
-RUN wget -O - https://apt.kitware.com/keys/kitware-archive-latest.asc 2>/dev/null | gpg --dearmor - | tee /etc/apt/trusted.gpg.d/kitware.gpg >/dev/null
-RUN apt-add-repository "deb https://apt.kitware.com/ubuntu/ $(lsb_release -cs) main"
-RUN apt install -y kitware-archive-keyring
-RUN rm /etc/apt/trusted.gpg.d/kitware.gpg
-RUN apt install -y cmake
+# Update the cmake to the latest version
+RUN pip install --upgrade cmake
 
 # Setup ubsan environment to printstacktrace
 RUN ln -s /usr/bin/llvm-symbolizer-3.8 /usr/local/bin/llvm-symbolizer

client_example/22_grouped_gemm/CMakeLists.txt

+add_executable(client_grouped_gemm_fixed_nk_fp16 grouped_gemm_fixed_nk_fp16.cpp)
+target_link_libraries(client_grouped_gemm_fixed_nk_fp16 PRIVATE composable_kernel::device_operations)
+
+add_executable(client_grouped_gemm_fixed_nk_fp8 grouped_gemm_fixed_nk_fp8.cpp)
+target_link_libraries(client_grouped_gemm_fixed_nk_fp8 PRIVATE composable_kernel::device_operations)
+
+add_executable(client_grouped_gemm_fixed_nk_i8 grouped_gemm_fixed_nk_i8.cpp)
+target_link_libraries(client_grouped_gemm_fixed_nk_i8 PRIVATE composable_kernel::device_operations)

client_example/22_grouped_gemm/grouped_gemm_fixed_nk_fp16.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#include <iomanip>
+#include <iostream>
+#include <vector>
+#include <random>
+
+#include "ck/ck.hpp"
+#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
+#include "ck/tensor_operation/gpu/device/device_grouped_gemm_fixed_nk.hpp"
+#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
+
+#include "ck/library/tensor_operation_instance/gpu/grouped_gemm_fixed_nk.hpp"
+
+using F16 = ck::half_t;
+using F32 = float;
+
+using Row = ck::tensor_layout::gemm::RowMajor;
+using Col = ck::tensor_layout::gemm::ColumnMajor;
+
+using PassThrough = ck::tensor_operation::element_wise::PassThrough;
+
+using ADataType  = F16;
+using BDataType  = F16;
+using DsDataType = ck::Tuple<>;
+using EDataType  = F16;
+
+using ALayout  = Row;
+using BLayout  = Row;
+using DsLayout = ck::Tuple<>;
+using ELayout  = Row;
+
+using AElementOp   = PassThrough;
+using BElementOp   = PassThrough;
+using CDEElementOp = PassThrough;
+
+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 main()
+{
+    std::vector<int> Ms, Ns, Ks, StrideAs, StrideBs, StrideEs;
+
+    int sum_of_m = 0;
+
+    // Ms = {167, 183, 177, 181, 153, 139, 156, 173, 163, 150, 204, 184, 168, 156, 168, 148};
+    Ms = {0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0};
+
+    int group_count = Ms.size();
+
+    for(int i = 0; i < group_count; ++i)
+    {
+        Ns.push_back(768);
+        Ks.push_back(4608);
+
+        StrideAs.push_back(std::is_same<Row, ALayout>::value ? Ks[i] : Ms[i]);
+        StrideBs.push_back(std::is_same<Row, BLayout>::value ? Ns[i] : Ks[i]);
+        StrideEs.push_back(std::is_same<Row, ELayout>::value ? Ns[i] : Ms[i]);
+
+        sum_of_m += Ms[i];
+    }
+
+    auto f_matrix_space_size =
+        [](std::size_t nRow, std::size_t nCol, std::size_t stride, auto layout) {
+            using Layout = decltype(layout);
+
+            if constexpr(std::is_same<Layout, ck::tensor_layout::gemm::RowMajor>::value)
+            {
+                return (nRow - 1) * stride + nCol;
+            }
+            else
+            {
+                return (nCol - 1) * stride + nRow;
+            }
+        };
+
+    std::vector<SimpleDeviceMem> a_dev_bufs, b_dev_bufs, e_dev_bufs;
+
+    a_dev_bufs.reserve(group_count);
+    b_dev_bufs.reserve(group_count);
+    e_dev_bufs.reserve(group_count);
+
+    std::vector<void*> p_e;
+
+    p_e.reserve(group_count);
+
+    std::vector<ck::tensor_operation::device::GemmDesc> gemm_descs;
+
+    gemm_descs.reserve(group_count);
+
+    std::vector<ck::tensor_operation::device::GroupedGemmKernelArgument<1>>
+        grouped_gemm_kernel_args_;
+    grouped_gemm_kernel_args_.reserve(group_count);
+
+    for(int i = 0; i < group_count; ++i)
+    {
+        a_dev_bufs.emplace_back(sizeof(ADataType) *
+                                f_matrix_space_size(Ms[i], Ks[i], StrideAs[i], ALayout{}));
+        b_dev_bufs.emplace_back(sizeof(BDataType) *
+                                f_matrix_space_size(Ks[i], Ns[i], StrideBs[i], BLayout{}));
+        e_dev_bufs.emplace_back(sizeof(EDataType) *
+                                f_matrix_space_size(Ms[i], Ns[i], StrideEs[i], ELayout{}));
+
+        gemm_descs.push_back({sum_of_m, Ns[i], Ks[i], 1, StrideBs[i], 1, {0}});
+
+        p_e.push_back(e_dev_bufs[i].GetDeviceBuffer());
+
+        grouped_gemm_kernel_args_.push_back({a_dev_bufs[i].GetDeviceBuffer(),
+                                             b_dev_bufs[i].GetDeviceBuffer(),
+                                             {},
+                                             e_dev_bufs[i].GetDeviceBuffer(),
+                                             Ms[i],
+                                             Ns[i],
+                                             Ks[i],
+                                             StrideAs[i],
+                                             StrideBs[i],
+                                             {},
+                                             StrideEs[i]});
+    }
+
+    using DeviceOp = ck::tensor_operation::device::DeviceGroupedGemmFixedNK<ALayout,
+                                                                            BLayout,
+                                                                            DsLayout,
+                                                                            ELayout,
+                                                                            ADataType,
+                                                                            BDataType,
+                                                                            DsDataType,
+                                                                            EDataType,
+                                                                            AElementOp,
+                                                                            BElementOp,
+                                                                            CDEElementOp>;
+
+    // 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;
+
+    const auto a_element_op   = AElementOp{};
+    const auto b_element_op   = BElementOp{};
+    const auto cde_element_op = CDEElementOp{};
+
+    std::string best_op_name;
+    bool found            = false;
+    int best_op_id        = -1;
+    float best_ave_time   = 0;
+    float best_tflops     = 0;
+    float best_gb_per_sec = 0;
+
+    // profile device operation instances
+    std::cout << "Run all instances and do timing" << std::endl;
+
+    std::vector<const void*> p_a = {}, p_b = {};
+    std::vector<std::array<const void*, 0>> p_ds = {};
+
+    for(int i = 0; i < op_ptrs.size(); ++i)
+    {
+        auto& op_ptr = op_ptrs[i];
+
+        auto argument_ptr = op_ptr->MakeArgumentPointer(
+            p_a, p_b, p_ds, p_e, gemm_descs, a_element_op, b_element_op, cde_element_op);
+
+        auto invoker_ptr = op_ptr->MakeInvokerPointer();
+
+        SimpleDeviceMem grouped_gemm_kernel_args_dev(
+            op_ptr->GetDeviceKernelArgSize(argument_ptr.get()));
+
+        SimpleDeviceMem grouped_gemm_workspace_dev(op_ptr->GetWorkSpaceSize(argument_ptr.get()));
+
+        std::string op_name = op_ptr->GetTypeString();
+
+        hipGetErrorString(hipMemcpy(grouped_gemm_kernel_args_dev.GetDeviceBuffer(),
+                                    grouped_gemm_kernel_args_.data(),
+                                    op_ptr->GetDeviceKernelArgSize(argument_ptr.get()),
+                                    hipMemcpyHostToDevice));
+
+        op_ptr->SetWorkSpacePointer(argument_ptr.get(),
+                                    grouped_gemm_workspace_dev.GetDeviceBuffer());
+
+        op_ptr->SetDeviceKernelArgs(argument_ptr.get(),
+                                    grouped_gemm_kernel_args_dev.GetDeviceBuffer());
+
+        op_ptr->SetKBatch(argument_ptr.get(), 32);
+
+        if(op_ptr->IsSupportedArgument(argument_ptr.get()))
+        {
+            float ave_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, true});
+
+            std::size_t flop = 0, num_btype = 0;
+            for(std::size_t j = 0; j < gemm_descs.size(); ++j)
+            {
+                flop += std::size_t(2) * Ms[j] * Ns[j] * Ks[j];
+
+                num_btype += sizeof(ADataType) * Ms[j] * Ks[j] + sizeof(BDataType) * Ks[j] * Ns[j] +
+                             sizeof(EDataType) * Ms[j] * Ns[j];
+            }
+
+            float tflops     = static_cast<float>(flop) / 1.E9 / ave_time;
+            float gb_per_sec = num_btype / 1.E6 / ave_time;
+
+            std::cout << "Perf: " << std::setw(10) << ave_time << " ms, " << tflops << " TFlops, "
+                      << gb_per_sec << " GB/s, " << op_name << std::endl;
+
+            if(tflops > best_tflops)
+            {
+                found           = true;
+                best_op_id      = i;
+                best_op_name    = op_name;
+                best_tflops     = tflops;
+                best_ave_time   = ave_time;
+                best_gb_per_sec = gb_per_sec;
+            }
+        }
+        else
+        {
+            std::cout << op_name << " does not support this problem" << std::endl;
+        }
+    }
+
+    std::cout << "Best Perf: " << best_ave_time << " ms, " << best_tflops << " TFlops, "
+              << best_gb_per_sec << " GB/s, " << best_op_name << std::endl;
+
+    return 0;
+}

client_example/22_grouped_gemm/grouped_gemm_fixed_nk_fp8.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#include <iomanip>
+#include <iostream>
+#include <vector>
+#include <random>
+
+#include "ck/ck.hpp"
+#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
+#include "ck/tensor_operation/gpu/device/device_grouped_gemm_fixed_nk.hpp"
+#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
+
+#include "ck/library/tensor_operation_instance/gpu/grouped_gemm_fixed_nk.hpp"
+
+using F8  = ck::f8_t;
+using F16 = ck::half_t;
+using F32 = float;
+
+using Row = ck::tensor_layout::gemm::RowMajor;
+using Col = ck::tensor_layout::gemm::ColumnMajor;
+
+using PassThrough = ck::tensor_operation::element_wise::PassThrough;
+
+using ADataType  = F16;
+using BDataType  = F8;
+using DsDataType = ck::Tuple<>;
+using EDataType  = F16;
+
+using ALayout  = Row;
+using BLayout  = Col;
+using DsLayout = ck::Tuple<>;
+using ELayout  = Row;
+
+using AElementOp   = PassThrough;
+using BElementOp   = PassThrough;
+using CDEElementOp = PassThrough;
+
+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 main()
+{
+    std::vector<int> Ms, Ns, Ks, StrideAs, StrideBs, StrideEs;
+
+    int sum_of_m = 0;
+
+    Ms = {167, 183, 177, 181, 153, 139, 156, 173, 163, 150, 204, 184, 168, 156, 168, 148};
+
+    int group_count = Ms.size();
+
+    for(int i = 0; i < group_count; ++i)
+    {
+        Ns.push_back(768);
+        Ks.push_back(4608);
+
+        StrideAs.push_back(std::is_same<Row, ALayout>::value ? Ks[i] : Ms[i]);
+        StrideBs.push_back(std::is_same<Row, BLayout>::value ? Ns[i] : Ks[i]);
+        StrideEs.push_back(std::is_same<Row, ELayout>::value ? Ns[i] : Ms[i]);
+
+        sum_of_m += Ms[i];
+    }
+
+    auto f_matrix_space_size =
+        [](std::size_t nRow, std::size_t nCol, std::size_t stride, auto layout) {
+            using Layout = decltype(layout);
+
+            if constexpr(std::is_same<Layout, ck::tensor_layout::gemm::RowMajor>::value)
+            {
+                return (nRow - 1) * stride + nCol;
+            }
+            else
+            {
+                return (nCol - 1) * stride + nRow;
+            }
+        };
+
+    std::vector<SimpleDeviceMem> a_dev_bufs, b_dev_bufs, e_dev_bufs;
+
+    a_dev_bufs.reserve(group_count);
+    b_dev_bufs.reserve(group_count);
+    e_dev_bufs.reserve(group_count);
+
+    std::vector<void*> p_e;
+
+    p_e.reserve(group_count);
+
+    std::vector<ck::tensor_operation::device::GemmDesc> gemm_descs;
+
+    gemm_descs.reserve(group_count);
+
+    std::vector<ck::tensor_operation::device::GroupedGemmKernelArgument<1>>
+        grouped_gemm_kernel_args_;
+    grouped_gemm_kernel_args_.reserve(group_count);
+
+    for(int i = 0; i < group_count; ++i)
+    {
+        a_dev_bufs.emplace_back(sizeof(ADataType) *
+                                f_matrix_space_size(Ms[i], Ks[i], StrideAs[i], ALayout{}));
+        b_dev_bufs.emplace_back(sizeof(BDataType) *
+                                f_matrix_space_size(Ks[i], Ns[i], StrideBs[i], BLayout{}));
+        e_dev_bufs.emplace_back(sizeof(EDataType) *
+                                f_matrix_space_size(Ms[i], Ns[i], StrideEs[i], ELayout{}));
+
+        gemm_descs.push_back({sum_of_m, Ns[i], Ks[i], 1, StrideBs[i], 1, {0}});
+
+        p_e.push_back(e_dev_bufs[i].GetDeviceBuffer());
+
+        grouped_gemm_kernel_args_.push_back({a_dev_bufs[i].GetDeviceBuffer(),
+                                             b_dev_bufs[i].GetDeviceBuffer(),
+                                             {},
+                                             e_dev_bufs[i].GetDeviceBuffer(),
+                                             Ms[i],
+                                             Ns[i],
+                                             Ks[i],
+                                             StrideAs[i],
+                                             StrideBs[i],
+                                             {},
+                                             StrideEs[i]});
+    }
+
+    using DeviceOp = ck::tensor_operation::device::DeviceGroupedGemmFixedNK<ALayout,
+                                                                            BLayout,
+                                                                            DsLayout,
+                                                                            ELayout,
+                                                                            ADataType,
+                                                                            BDataType,
+                                                                            DsDataType,
+                                                                            EDataType,
+                                                                            AElementOp,
+                                                                            BElementOp,
+                                                                            CDEElementOp>;
+
+    // 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;
+
+    const auto a_element_op   = AElementOp{};
+    const auto b_element_op   = BElementOp{};
+    const auto cde_element_op = CDEElementOp{};
+
+    std::string best_op_name;
+    bool found            = false;
+    int best_op_id        = -1;
+    float best_ave_time   = 0;
+    float best_tflops     = 0;
+    float best_gb_per_sec = 0;
+
+    // profile device operation instances
+    std::cout << "Run all instances and do timing" << std::endl;
+
+    std::vector<const void*> p_a = {}, p_b = {};
+    std::vector<std::array<const void*, 0>> p_ds = {};
+
+    for(int i = 0; i < op_ptrs.size(); ++i)
+    {
+        auto& op_ptr = op_ptrs[i];
+
+        auto argument_ptr = op_ptr->MakeArgumentPointer(
+            p_a, p_b, p_ds, p_e, gemm_descs, a_element_op, b_element_op, cde_element_op);
+
+        auto invoker_ptr = op_ptr->MakeInvokerPointer();
+
+        SimpleDeviceMem grouped_gemm_kernel_args_dev(
+            op_ptr->GetDeviceKernelArgSize(argument_ptr.get()));
+
+        SimpleDeviceMem grouped_gemm_workspace_dev(op_ptr->GetWorkSpaceSize(argument_ptr.get()));
+
+        std::string op_name = op_ptr->GetTypeString();
+
+        hipGetErrorString(hipMemcpy(grouped_gemm_kernel_args_dev.GetDeviceBuffer(),
+                                    grouped_gemm_kernel_args_.data(),
+                                    op_ptr->GetDeviceKernelArgSize(argument_ptr.get()),
+                                    hipMemcpyHostToDevice));
+
+        op_ptr->SetWorkSpacePointer(argument_ptr.get(),
+                                    grouped_gemm_workspace_dev.GetDeviceBuffer());
+
+        op_ptr->SetDeviceKernelArgs(argument_ptr.get(),
+                                    grouped_gemm_kernel_args_dev.GetDeviceBuffer());
+
+        op_ptr->SetKBatch(argument_ptr.get(), 16);
+
+        if(op_ptr->IsSupportedArgument(argument_ptr.get()))
+        {
+            float ave_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, true});
+
+            std::size_t flop = 0, num_btype = 0;
+            for(std::size_t j = 0; j < gemm_descs.size(); ++j)
+            {
+                flop += std::size_t(2) * Ms[j] * Ns[j] * Ks[j];
+
+                num_btype += sizeof(ADataType) * Ms[j] * Ks[j] + sizeof(BDataType) * Ks[j] * Ns[j] +
+                             sizeof(EDataType) * Ms[j] * Ns[j];
+            }
+
+            float tflops     = static_cast<float>(flop) / 1.E9 / ave_time;
+            float gb_per_sec = num_btype / 1.E6 / ave_time;
+
+            std::cout << "Perf: " << std::setw(10) << ave_time << " ms, " << tflops << " TFlops, "
+                      << gb_per_sec << " GB/s, " << op_name << std::endl;
+
+            if(tflops > best_tflops)
+            {
+                found           = true;
+                best_op_id      = i;
+                best_op_name    = op_name;
+                best_tflops     = tflops;
+                best_ave_time   = ave_time;
+                best_gb_per_sec = gb_per_sec;
+            }
+        }
+        else
+        {
+            std::cout << op_name << " does not support this problem" << std::endl;
+        }
+    }
+
+    std::cout << "Best Perf: " << best_ave_time << " ms, " << best_tflops << " TFlops, "
+              << best_gb_per_sec << " GB/s, " << best_op_name << std::endl;
+
+    return 0;
+}

client_example/22_grouped_gemm/grouped_gemm_fixed_nk_i8.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#include <iomanip>
+#include <iostream>
+#include <vector>
+#include <random>
+
+#include "ck/ck.hpp"
+#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
+#include "ck/tensor_operation/gpu/device/device_grouped_gemm_fixed_nk.hpp"
+#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
+
+#include "ck/library/tensor_operation_instance/gpu/grouped_gemm_fixed_nk.hpp"
+
+using I8  = int8_t;
+using F16 = ck::half_t;
+using F32 = float;
+
+using Row = ck::tensor_layout::gemm::RowMajor;
+using Col = ck::tensor_layout::gemm::ColumnMajor;
+
+using PassThrough = ck::tensor_operation::element_wise::PassThrough;
+
+using ADataType  = F16;
+using BDataType  = I8;
+using DsDataType = ck::Tuple<>;
+using EDataType  = F16;
+
+using ALayout  = Row;
+using BLayout  = Row;
+using DsLayout = ck::Tuple<>;
+using ELayout  = Row;
+
+using AElementOp   = PassThrough;
+using BElementOp   = PassThrough;
+using CDEElementOp = PassThrough;
+
+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 main()
+{
+    std::vector<int> Ms, Ns, Ks, StrideAs, StrideBs, StrideEs;
+
+    int sum_of_m = 0;
+
+    Ms = {167, 183, 177, 181, 153, 139, 156, 173, 163, 150, 204, 184, 168, 156, 168, 148};
+
+    int group_count = Ms.size();
+
+    for(int i = 0; i < group_count; ++i)
+    {
+        Ns.push_back(768);
+        Ks.push_back(4608);
+
+        StrideAs.push_back(std::is_same<Row, ALayout>::value ? Ks[i] : Ms[i]);
+        StrideBs.push_back(std::is_same<Row, BLayout>::value ? Ns[i] : Ks[i]);
+        StrideEs.push_back(std::is_same<Row, ELayout>::value ? Ns[i] : Ms[i]);
+
+        sum_of_m += Ms[i];
+    }
+
+    auto f_matrix_space_size =
+        [](std::size_t nRow, std::size_t nCol, std::size_t stride, auto layout) {
+            using Layout = decltype(layout);
+
+            if constexpr(std::is_same<Layout, ck::tensor_layout::gemm::RowMajor>::value)
+            {
+                return (nRow - 1) * stride + nCol;
+            }
+            else
+            {
+                return (nCol - 1) * stride + nRow;
+            }
+        };
+
+    std::vector<SimpleDeviceMem> a_dev_bufs, b_dev_bufs, e_dev_bufs;
+
+    a_dev_bufs.reserve(group_count);
+    b_dev_bufs.reserve(group_count);
+    e_dev_bufs.reserve(group_count);
+
+    std::vector<void*> p_e;
+
+    p_e.reserve(group_count);
+
+    std::vector<ck::tensor_operation::device::GemmDesc> gemm_descs;
+
+    gemm_descs.reserve(group_count);
+
+    std::vector<ck::tensor_operation::device::GroupedGemmKernelArgument<1>>
+        grouped_gemm_kernel_args_;
+    grouped_gemm_kernel_args_.reserve(group_count);
+
+    for(int i = 0; i < group_count; ++i)
+    {
+        a_dev_bufs.emplace_back(sizeof(ADataType) *
+                                f_matrix_space_size(Ms[i], Ks[i], StrideAs[i], ALayout{}));
+        b_dev_bufs.emplace_back(sizeof(BDataType) *
+                                f_matrix_space_size(Ks[i], Ns[i], StrideBs[i], BLayout{}));
+        e_dev_bufs.emplace_back(sizeof(EDataType) *
+                                f_matrix_space_size(Ms[i], Ns[i], StrideEs[i], ELayout{}));
+
+        gemm_descs.push_back({sum_of_m, Ns[i], Ks[i], 1, StrideBs[i], 1, {0}});
+
+        p_e.push_back(e_dev_bufs[i].GetDeviceBuffer());
+
+        grouped_gemm_kernel_args_.push_back({a_dev_bufs[i].GetDeviceBuffer(),
+                                             b_dev_bufs[i].GetDeviceBuffer(),
+                                             {},
+                                             e_dev_bufs[i].GetDeviceBuffer(),
+                                             Ms[i],
+                                             Ns[i],
+                                             Ks[i],
+                                             StrideAs[i],
+                                             StrideBs[i],
+                                             {},
+                                             StrideEs[i]});
+    }
+
+    using DeviceOp = ck::tensor_operation::device::DeviceGroupedGemmFixedNK<ALayout,
+                                                                            BLayout,
+                                                                            DsLayout,
+                                                                            ELayout,
+                                                                            ADataType,
+                                                                            BDataType,
+                                                                            DsDataType,
+                                                                            EDataType,
+                                                                            AElementOp,
+                                                                            BElementOp,
+                                                                            CDEElementOp>;
+
+    // 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;
+
+    const auto a_element_op   = AElementOp{};
+    const auto b_element_op   = BElementOp{};
+    const auto cde_element_op = CDEElementOp{};
+
+    std::string best_op_name;
+    bool found            = false;
+    int best_op_id        = -1;
+    float best_ave_time   = 0;
+    float best_tflops     = 0;
+    float best_gb_per_sec = 0;
+
+    // profile device operation instances
+    std::cout << "Run all instances and do timing" << std::endl;
+
+    std::vector<const void*> p_a = {}, p_b = {};
+    std::vector<std::array<const void*, 0>> p_ds = {};
+
+    for(int i = 0; i < op_ptrs.size(); ++i)
+    {
+        auto& op_ptr = op_ptrs[i];
+
+        auto argument_ptr = op_ptr->MakeArgumentPointer(
+            p_a, p_b, p_ds, p_e, gemm_descs, a_element_op, b_element_op, cde_element_op);
+
+        auto invoker_ptr = op_ptr->MakeInvokerPointer();
+
+        SimpleDeviceMem grouped_gemm_kernel_args_dev(
+            op_ptr->GetDeviceKernelArgSize(argument_ptr.get()));
+
+        SimpleDeviceMem grouped_gemm_workspace_dev(op_ptr->GetWorkSpaceSize(argument_ptr.get()));
+
+        std::string op_name = op_ptr->GetTypeString();
+
+        hipGetErrorString(hipMemcpy(grouped_gemm_kernel_args_dev.GetDeviceBuffer(),
+                                    grouped_gemm_kernel_args_.data(),
+                                    op_ptr->GetDeviceKernelArgSize(argument_ptr.get()),
+                                    hipMemcpyHostToDevice));
+
+        op_ptr->SetWorkSpacePointer(argument_ptr.get(),
+                                    grouped_gemm_workspace_dev.GetDeviceBuffer());
+
+        op_ptr->SetDeviceKernelArgs(argument_ptr.get(),
+                                    grouped_gemm_kernel_args_dev.GetDeviceBuffer());
+
+        op_ptr->SetKBatch(argument_ptr.get(), 32);
+
+        if(op_ptr->IsSupportedArgument(argument_ptr.get()))
+        {
+            float ave_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, true});
+
+            std::size_t flop = 0, num_btype = 0;
+            for(std::size_t j = 0; j < gemm_descs.size(); ++j)
+            {
+                flop += std::size_t(2) * Ms[j] * Ns[j] * Ks[j];
+
+                num_btype += sizeof(ADataType) * Ms[j] * Ks[j] + sizeof(BDataType) * Ks[j] * Ns[j] +
+                             sizeof(EDataType) * Ms[j] * Ns[j];
+            }
+
+            float tflops     = static_cast<float>(flop) / 1.E9 / ave_time;
+            float gb_per_sec = num_btype / 1.E6 / ave_time;
+
+            std::cout << "Perf: " << std::setw(10) << ave_time << " ms, " << tflops << " TFlops, "
+                      << gb_per_sec << " GB/s, " << op_name << std::endl;
+
+            if(tflops > best_tflops)
+            {
+                found           = true;
+                best_op_id      = i;
+                best_op_name    = op_name;
+                best_tflops     = tflops;
+                best_ave_time   = ave_time;
+                best_gb_per_sec = gb_per_sec;
+            }
+        }
+        else
+        {
+            std::cout << op_name << " does not support this problem" << std::endl;
+        }
+    }
+
+    std::cout << "Best Perf: " << best_ave_time << " ms, " << best_tflops << " TFlops, "
+              << best_gb_per_sec << " GB/s, " << best_op_name << std::endl;
+
+    return 0;
+}

example/15_grouped_gemm/CMakeLists.txt

@@ -25,6 +25,11 @@ if(DTYPES MATCHES "int8" OR NOT DEFINED DTYPES)
   add_example_executable(example_grouped_gemm_xdl_int8 grouped_gemm_xdl_int8.cpp)
   add_dependencies(example_grouped_gemm_xdl example_grouped_gemm_xdl_int8)
 endif()
+if(DTYPES MATCHES "f8" OR NOT DEFINED DTYPES)
+  add_example_executable(example_grouped_gemm_xdl_fixed_nk_fp8 grouped_gemm_xdl_fixed_nk_fp8.cpp)
+  add_dependencies(example_grouped_gemm_xdl example_grouped_gemm_xdl_fixed_nk_fp8)
+endif()
+
 if(USE_BITINT_EXTENSION_INT4)
   add_example_executable(example_grouped_gemm_xdl_int4 grouped_gemm_xdl_int4.cpp)
   add_dependencies(example_grouped_gemm_xdl example_grouped_gemm_xdl_int4)

example/15_grouped_gemm/grouped_gemm_xdl_fixed_nk_fp8.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#include <iostream>
+#include <numeric>
+#include <initializer_list>
+#include <cstdlib>
+
+#include "ck/ck.hpp"
+#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
+#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
+#include "ck/tensor_operation/gpu/device/impl/device_grouped_gemm_xdl_fixed_nk.hpp"
+#include "ck/tensor_operation/gpu/device/device_grouped_gemm.hpp"
+#include "ck/tensor_operation/gpu/element/element_wise_operation.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/literals.hpp"
+#include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
+
+template <ck::index_t... Is>
+using S = ck::Sequence<Is...>;
+
+using F8  = ck::f8_t;
+using F16 = ck::half_t;
+using F32 = float;
+
+using Row = ck::tensor_layout::gemm::RowMajor;
+using Col = ck::tensor_layout::gemm::ColumnMajor;
+
+using PassThrough = ck::tensor_operation::element_wise::PassThrough;
+
+using ADataType        = F16;
+using BDataType        = F8;
+using AccDataType      = F32;
+using CShuffleDataType = F32;
+using DsDataType       = ck::Tuple<>;
+using EDataType        = F16;
+
+using ALayout  = Row;
+using BLayout  = Col;
+using DsLayout = ck::Tuple<>;
+using ELayout  = Row;
+
+using AElementOp   = PassThrough;
+using BElementOp   = PassThrough;
+using CDEElementOp = PassThrough;
+
+static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::MNPadding;
+
+using DeviceGemmInstance = ck::tensor_operation::device::DeviceGroupedGemm_Xdl_Fixed_NK
+    // clang-format off
+//######| ALayout| BLayout| DsLayout| ELayout|     AData|     BData|     AccData|         CShuffle|     DsData|     EData|           A|           B|          CDE|           GEMM| NumGemmK| Block|  MPer|  NPer|  KPer| AK1| BK1| MPer| NPer| MXdl| NXdl|  ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds|  BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds|    CShuffle|    CShuffle| CBlockTransferClusterLengths|  CBlockTransfer|
+//######|        |        |         |        |      Type|      Type|        Type|         DataType|       Type|      Type| Elementwise| Elementwise|  Elementwise| Spacialization| Prefetch|  Size| Block| Block| Block|    |    |  XDL|  XDL|  Per|  Per|   ThreadCluster|  ThreadCluster| SrcAccessOrder|   SrcVectorDim|      SrcScalar|      DstScalar| AddExtraM|   ThreadCluster|  ThreadCluster| SrcAccessOrder|  SrcVectorDim|      SrcScalar|      DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave|         _MBlock_MWaveMPerXdl| ScalarPerVector|
+//######|        |        |         |        |          |          |            |                 |           |          |   Operation|   Operation|    Operation|               |    Stage|      |      |      |      |    |    |     |     | Wave| Wave| Lengths_K0_M_K1|   ArrangeOrder|               |               |      PerVector|   PerVector_K1|          | Lengths_K0_N_K1|   ArrangeOrder|               |              |      PerVector|   PerVector_K1|          |  PerShuffle|  PerShuffle|         _NBlock_NWaveNPerXdl|   _NWaveNPerXdl|
+//######|        |        |         |        |          |          |            |                 |           |          |            |            |             |               |         |      |      |      |      |    |    |     |     |     |     |                |               |               |               |               |               |          |                |               |               |              |               |               |          |            |            |                             |                |
+        < ALayout, BLayout, DsLayout, ELayout, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType,  AElementOp,  BElementOp, CDEElementOp,    GemmDefault,        1,   256,    64,   128,    32,   8,   8,   32,   32,    1,    2,  S<1, 4, 64, 1>,  S<0, 2, 1, 3>,  S<0, 2, 1, 3>,              3,              8,              8,         1,  S<1, 4, 64, 1>,  S<0, 2, 1, 3>,  S<0, 2, 1, 3>,             3,              8,              8,         1,           1,           1,               S<1, 32, 1, 8>,              8>;
+// clang-format on
+
+struct ProblemSize final
+{
+    std::vector<ck::index_t> Ms;
+    std::vector<ck::index_t> Ns;
+    std::vector<ck::index_t> Ks;
+
+    std::vector<ck::index_t> stride_As;
+    std::vector<ck::index_t> stride_Bs;
+    std::vector<ck::index_t> stride_Cs;
+
+    ck::index_t group_count;
+};
+
+struct ExecutionConfig final
+{
+    bool do_verification = true;
+    int init_method      = 1;
+    int k_batch          = 1;
+    bool time_kernel     = false;
+};
+
+bool run_grouped_gemm(const ProblemSize& problem_size, const ExecutionConfig& config)
+{
+    auto group_count = problem_size.group_count;
+
+    // GEMM shape
+    std::vector<ck::tensor_operation::device::GemmDesc> gemm_descs;
+    std::vector<void*> p_Cs;
+
+    gemm_descs.reserve(group_count);
+
+    int sum_of_m = 0;
+
+    auto f_host_tensor_descriptor =
+        [](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
+            using namespace ck::literals;
+
+            if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value)
+            {
+                return HostTensorDescriptor({row, col}, {stride, 1_uz});
+            }
+            else
+            {
+                return HostTensorDescriptor({row, col}, {1_uz, stride});
+            }
+        };
+
+    std::vector<Tensor<ADataType>> a_tensors;
+    std::vector<Tensor<BDataType>> b_tensors;
+    std::vector<Tensor<EDataType>> c_host_tensors;
+    std::vector<Tensor<EDataType>> c_device_tensors;
+
+    a_tensors.reserve(group_count);
+    b_tensors.reserve(group_count);
+    c_host_tensors.reserve(group_count);
+    c_device_tensors.reserve(group_count);
+
+    using DeviceMemPtr = std::unique_ptr<DeviceMem>;
+
+    std::vector<DeviceMemPtr> a_tensors_device, b_tensors_device, c_tensors_device;
+
+    a_tensors_device.reserve(group_count);
+    b_tensors_device.reserve(group_count);
+    c_tensors_device.reserve(group_count);
+
+    std::size_t flop = 0, num_btype = 0;
+
+    for(int i = 0; i < group_count; i++)
+    {
+        sum_of_m += problem_size.Ms[i];
+        a_tensors.push_back(Tensor<ADataType>(f_host_tensor_descriptor(
+            problem_size.Ms[i], problem_size.Ks[i], problem_size.stride_As[i], ALayout{})));
+        b_tensors.push_back(Tensor<BDataType>(f_host_tensor_descriptor(
+            problem_size.Ks[i], problem_size.Ns[i], problem_size.stride_Bs[i], BLayout{})));
+        c_host_tensors.push_back(Tensor<EDataType>(f_host_tensor_descriptor(
+            problem_size.Ms[i], problem_size.Ns[i], problem_size.stride_Cs[i], ELayout{})));
+        c_device_tensors.push_back(Tensor<EDataType>(f_host_tensor_descriptor(
+            problem_size.Ms[i], problem_size.Ns[i], problem_size.stride_Cs[i], ELayout{})));
+        std::cout << "gemm[" << i << "] a_m_k: " << a_tensors[i].mDesc
+                  << " b_k_n: " << b_tensors[i].mDesc << " c_m_n: " << c_device_tensors[i].mDesc
+                  << std::endl;
+
+        flop += std::size_t(2) * problem_size.Ms[i] * problem_size.Ks[i] * problem_size.Ns[i];
+        num_btype += sizeof(ADataType) * a_tensors[i].mDesc.GetElementSize() +
+                     sizeof(BDataType) * b_tensors[i].mDesc.GetElementSize() +
+                     sizeof(EDataType) * c_device_tensors[i].mDesc.GetElementSize();
+
+        switch(config.init_method)
+        {
+        case 0: break;
+        case 1:
+            a_tensors[i].GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
+            b_tensors[i].GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
+            break;
+        case 2:
+            a_tensors[i].GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
+            b_tensors[i].GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
+            break;
+        default:
+            a_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<0>{});
+            b_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<1>{});
+        }
+    }
+
+    using GroupedGemmKernelArgument = ck::tensor_operation::device::GroupedGemmKernelArgument<>;
+
+    std::vector<GroupedGemmKernelArgument> grouped_gemm_kernel_args_;
+    grouped_gemm_kernel_args_.reserve(group_count);
+
+    for(int i = 0; i < group_count; i++)
+    {
+        a_tensors_device.emplace_back(
+            std::make_unique<DeviceMem>(sizeof(ADataType) * sum_of_m * problem_size.Ks[i]));
+
+        b_tensors_device.emplace_back(std::make_unique<DeviceMem>(
+            sizeof(BDataType) * problem_size.Ns[i] * problem_size.Ks[i]));
+
+        c_tensors_device.emplace_back(
+            std::make_unique<DeviceMem>(sizeof(EDataType) * sum_of_m * problem_size.Ns[i]));
+
+        a_tensors_device[i]->ToDevice(a_tensors[i].mData.data(),
+                                      a_tensors[i].mDesc.GetElementSpaceSize() * sizeof(ADataType));
+        b_tensors_device[i]->ToDevice(b_tensors[i].mData.data(),
+                                      b_tensors[i].mDesc.GetElementSpaceSize() * sizeof(BDataType));
+        c_tensors_device[i]->SetZero();
+
+        p_Cs.push_back(c_tensors_device[i]->GetDeviceBuffer());
+
+        gemm_descs.push_back({sum_of_m,
+                              problem_size.Ns[i],
+                              problem_size.Ks[i],
+                              1,
+                              problem_size.stride_Bs[i],
+                              1,
+                              {}});
+
+        grouped_gemm_kernel_args_.push_back({a_tensors_device[i]->GetDeviceBuffer(),
+                                             b_tensors_device[i]->GetDeviceBuffer(),
+                                             {},
+                                             c_tensors_device[i]->GetDeviceBuffer(),
+                                             problem_size.Ms[i],
+                                             problem_size.Ns[i],
+                                             problem_size.Ks[i],
+                                             problem_size.stride_As[i],
+                                             problem_size.stride_Bs[i],
+                                             {},
+                                             problem_size.stride_Cs[i]});
+    }
+
+    auto a_element_op = AElementOp{};
+    auto b_element_op = BElementOp{};
+    auto c_element_op = CDEElementOp{};
+
+    auto gemm    = DeviceGemmInstance{};
+    auto invoker = gemm.MakeInvoker();
+
+    std::vector<const void*> p_As                = {};
+    std::vector<const void*> p_Bs                = {};
+    std::vector<std::array<const void*, 0>> p_Ds = {};
+
+    // do GEMM
+    auto argument = gemm.MakeArgument(
+        p_As, p_Bs, p_Ds, p_Cs, gemm_descs, a_element_op, b_element_op, c_element_op);
+
+    DeviceMem gemm_arg_dev_mem(gemm.GetDeviceKernelArgSize(&argument));
+    DeviceMem gemm_workspace_dev(gemm.GetWorkSpaceSize(&argument));
+
+    gemm.SetWorkSpacePointer(&argument, gemm_workspace_dev.GetDeviceBuffer());
+
+    hip_check_error(hipMemcpy(gemm_arg_dev_mem.GetDeviceBuffer(),
+                              grouped_gemm_kernel_args_.data(),
+                              gemm.GetDeviceKernelArgSize(&argument),
+                              hipMemcpyHostToDevice));
+
+    if(!gemm.IsSupportedArgument(argument))
+    {
+        throw std::runtime_error(
+            "wrong! device_gemm with the specified compilation parameters does "
+            "not support this GEMM problem");
+    }
+
+    gemm.SetDeviceKernelArgs(argument, gemm_arg_dev_mem.GetDeviceBuffer());
+    gemm.SetKBatch(argument, config.k_batch);
+
+    invoker.Run(argument, StreamConfig{nullptr, false});
+
+    if(config.time_kernel)
+    {
+        float ave_time   = invoker.Run(argument, StreamConfig{nullptr, config.time_kernel});
+        float tflops     = static_cast<float>(flop) / 1.E9 / ave_time;
+        float gb_per_sec = num_btype / 1.E6 / ave_time;
+
+        std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec
+                  << " GB/s, " << gemm.GetTypeString() << std::endl;
+    }
+
+    bool pass = true;
+    if(config.do_verification)
+    {
+        using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
+                                                                                BDataType,
+                                                                                EDataType,
+                                                                                AccDataType,
+                                                                                AElementOp,
+                                                                                BElementOp,
+                                                                                CDEElementOp>;
+
+        for(std::size_t i = 0; i < gemm_descs.size(); i++)
+        {
+            c_tensors_device[i]->FromDevice(c_device_tensors[i].mData.data(),
+                                            c_device_tensors[i].mDesc.GetElementSize() *
+                                                sizeof(EDataType));
+            auto ref_gemm    = ReferenceGemmInstance{};
+            auto ref_invoker = ref_gemm.MakeInvoker();
+
+            auto ref_argument = ref_gemm.MakeArgument(a_tensors[i],
+                                                      b_tensors[i],
+                                                      c_host_tensors[i],
+                                                      a_element_op,
+                                                      b_element_op,
+                                                      c_element_op);
+
+            ref_invoker.Run(ref_argument);
+
+            pass &= ck::utils::check_err(c_device_tensors[i], c_host_tensors[i]);
+        }
+    }
+
+    return pass;
+}
+
+int main(int argc, char* argv[])
+{
+    ProblemSize problem_size;
+    ExecutionConfig config;
+
+    problem_size.group_count = 16;
+
+    problem_size.Ms = {
+        167, 183, 177, 181, 153, 139, 156, 173, 163, 150, 204, 184, 168, 156, 168, 148};
+
+    for(int i = 0; i < problem_size.group_count; i++)
+    {
+        problem_size.Ns.push_back(768);
+        problem_size.Ks.push_back(4608);
+
+        problem_size.stride_As.push_back(problem_size.Ks[i]);
+        problem_size.stride_Bs.push_back(problem_size.Ks[i]);
+        problem_size.stride_Cs.push_back(problem_size.Ns[i]);
+    }
+
+    if(argc == 5)
+    {
+        config.do_verification = std::stoi(argv[1]);
+        config.init_method     = std::stoi(argv[2]);
+        config.time_kernel     = std::stoi(argv[3]);
+        config.k_batch         = std::stoi(argv[4]);
+    }
+    else
+    {
+        printf("arg1: verification (0=no, 1=yes)\n");
+        printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
+        printf("arg3: time kernel (0=n0, 1=yes)\n");
+        printf("arg4: k_batch (> 0)\n");
+        exit(0);
+    }
+
+    return !run_grouped_gemm(problem_size, config);
+}

example/20_grouped_conv_bwd_weight/grouped_conv_bwd_weight_dl_fp16.cpp

@@ -3,7 +3,7 @@
 
 #include "common.hpp"
 
-#include "ck/tensor_operation/gpu/device/impl/device_grouped_conv_bwd_weight_gnwc_gkxc_gnwk_dl.hpp"
+#include "ck/tensor_operation/gpu/device/impl/device_grouped_conv_bwd_weight_dl.hpp"
 
 using InDataType  = F16;
 using WeiDataType = F16;
@@ -15,9 +15,20 @@ using WeiElementOp = PassThrough;
 using OutElementOp = PassThrough;
 
 template <ck::index_t NDimSpatial>
-using DeviceConvBwdWeightInstance =
-    ck::tensor_operation::device::DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Dl<
+using DeviceConvBwdWeightInstance = ck::tensor_operation::device::DeviceGroupedConvBwdWeight_Dl<
     NDimSpatial, // NDimSpatial
+    ck::tuple_element_t<NDimSpatial - 1,
+                        ck::Tuple<ck::tensor_layout::convolution::GNWC,
+                                  ck::tensor_layout::convolution::GNHWC,
+                                  ck::tensor_layout::convolution::GNDHWC>>, // InLayout
+    ck::tuple_element_t<NDimSpatial - 1,
+                        ck::Tuple<ck::tensor_layout::convolution::GKXC,
+                                  ck::tensor_layout::convolution::GKYXC,
+                                  ck::tensor_layout::convolution::GKZYXC>>, // WeiLayout
+    ck::tuple_element_t<NDimSpatial - 1,
+                        ck::Tuple<ck::tensor_layout::convolution::GNWK,
+                                  ck::tensor_layout::convolution::GNHWK,
+                                  ck::tensor_layout::convolution::GNDHWK>>, // OutLayout
     InDataType,                                                             // InDataType
     WeiDataType,                                                            // WeiDataType
     OutDataType,                                                            // OutDataType

example/20_grouped_conv_bwd_weight/run_grouped_conv_bwd_weight_example.inc

@@ -14,20 +14,8 @@ template <ck::index_t NDimSpatial>
 bool run_grouped_conv_bwd_weight(const ExecutionConfig& config,
                                  const ck::utils::conv::ConvParam& conv_param)
 {
-    ck::index_t split_k;
-    // Set split_k = 2 for xdl op, split_k = 1 for dl
     // Dl op doesn't support split_k > 1
-    // TODO: Add Dl op split_k > 1 support
-    if(!(ck::get_device_name() == "gfx906" || ck::get_device_name() == "gfx1030" ||
-         ck::get_device_name() == "gfx1100" || ck::get_device_name() == "gfx1101" ||
-         ck::get_device_name() == "gfx1102"))
-    {
-        split_k = 2;
-    }
-    else
-    {
-        split_k = 1;
-    }
+    constexpr ck::index_t split_k = 1;
 
     const auto in_g_n_c_wis_desc =
         ck::utils::conv::make_input_host_tensor_descriptor_g_n_c_wis_packed<

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

@@ -14,6 +14,7 @@
 #include "ck/tensor_operation/gpu/device/device_grouped_conv_bwd_weight.hpp"
 #include "ck/tensor_operation/gpu/device/convolution_backward_weight_specialization.hpp"
 #include "ck/tensor_operation/gpu/grid/gridwise_gemm_dl_v1r3.hpp"
+#include "ck/tensor_operation/gpu/device/matrix_padder.hpp"
 #include "ck/host_utility/device_prop.hpp"
 #include "ck/host_utility/kernel_launch.hpp"
 
@@ -72,6 +73,9 @@ __global__ void
             const Block2CTileMap block_2_ctile_map,
             const ComputePtrOffsetOfBatch compute_ptr_offset_of_batch)
 {
+#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx906__) || defined(__gfx1030__) ||           \
+    defined(__gfx90a__) || defined(__gfx908__) || defined(__gfx940__) || defined(__gfx1100__) || \
+    defined(__gfx1101__) || defined(__gfx1102__) || defined(__gfx941__) || defined(__gfx942__))
     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);
@@ -96,9 +100,27 @@ __global__ void
         block_2_ctile_map,
         integral_constant<bool, HasMainKBlockLoop>{},
         integral_constant<bool, HasDoubleTailKBlockLoop>{});
+#else
+    ignore = p_a_grid;
+    ignore = p_b_grid;
+    ignore = p_c_grid;
+    ignore = batch_count;
+    ignore = a_grid_desc_kbatch_k0_m0_m1_k1;
+    ignore = b_grid_desc_kbatch_k0_n0_n1_k1;
+    ignore = c_grid_desc_m0_m10_m11_n0_n10_n11;
+    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
 }
 
 template <ck::index_t NDimSpatial,
+          typename InLayout,
+          typename WeiLayout,
+          typename OutLayout,
           typename InDataType,
           typename WeiDataType,
           typename OutDataType,
@@ -134,21 +156,10 @@ template <ck::index_t NDimSpatial,
           typename CThreadTransferSrcDstAccessOrder,
           index_t CThreadTransferSrcDstVectorDim,
           index_t CThreadTransferDstScalarPerVector>
-struct DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Dl
-    : public DeviceGroupedConvBwdWeight<
-          NDimSpatial,
-          ck::tuple_element_t<NDimSpatial - 1,
-                              ck::Tuple<ck::tensor_layout::convolution::GNWC,
-                                        ck::tensor_layout::convolution::GNHWC,
-                                        ck::tensor_layout::convolution::GNDHWC>>,
-          ck::tuple_element_t<NDimSpatial - 1,
-                              ck::Tuple<ck::tensor_layout::convolution::GKXC,
-                                        ck::tensor_layout::convolution::GKYXC,
-                                        ck::tensor_layout::convolution::GKZYXC>>,
-          ck::tuple_element_t<NDimSpatial - 1,
-                              ck::Tuple<ck::tensor_layout::convolution::GNWK,
-                                        ck::tensor_layout::convolution::GNHWK,
-                                        ck::tensor_layout::convolution::GNDHWK>>,
+struct DeviceGroupedConvBwdWeight_Dl : public DeviceGroupedConvBwdWeight<NDimSpatial,
+                                                                         InLayout,
+                                                                         WeiLayout,
+                                                                         OutLayout,
                                                                          InDataType,
                                                                          WeiDataType,
                                                                          OutDataType,
@@ -156,7 +167,35 @@ struct DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Dl
                                                                          WeiElementwiseOperation,
                                                                          OutElementwiseOperation>
 {
-    using DeviceOp = DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Dl;
+    // 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;
     using BDataType = InDataType;
@@ -176,6 +215,8 @@ struct DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Dl
     static constexpr auto I4 = Number<4>{};
     static constexpr auto I5 = Number<5>{};
 
+    static constexpr auto spatial_offset = I3;
+
     static constexpr auto K1Number     = Number<K1>{};
     static constexpr auto GemmK1Number = K1Number;
 
@@ -195,12 +236,12 @@ struct DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Dl
 
     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<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<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,
@@ -209,90 +250,102 @@ struct DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Dl
     {
         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 InLeftPadW    = input_left_pads[0];
-        const index_t InRightPadW   = input_right_pads[0];
-        const index_t ConvStrideW   = conv_filter_strides[0];
-        const index_t ConvDilationW = conv_filter_dilations[0];
+        const index_t N             = a_g_n_c_wis_lengths[I1];
+        const index_t K             = b_g_k_c_xs_lengths[I1];
+        const index_t C             = a_g_n_c_wis_lengths[I2];
+        const index_t Wi            = a_g_n_c_wis_lengths[spatial_offset];
+        const index_t Wo            = e_g_n_k_wos_lengths[spatial_offset];
+        const index_t X             = b_g_k_c_xs_lengths[spatial_offset];
+        const index_t InLeftPadW    = input_left_pads[I0];
+        const index_t InRightPadW   = input_right_pads[I0];
+        const index_t ConvStrideW   = conv_filter_strides[I0];
+        const index_t ConvDilationW = conv_filter_dilations[I0];
+
+        const auto InNStride  = a_g_n_c_wis_strides[I1];
+        const auto InCStride  = a_g_n_c_wis_strides[I2];
+        const auto InWStride  = a_g_n_c_wis_strides[spatial_offset];
+        const auto WeiKStride = b_g_k_c_xs_strides[I1];
+        const auto WeiCStride = b_g_k_c_xs_strides[I2];
+        const auto OutKStride = e_g_n_k_wos_strides[I2];
+        const auto OutWStride = e_g_n_k_wos_strides[spatial_offset];
 
         const index_t GemmKTotal = N * Wo;
-        const index_t GemmM      = K;
-        const index_t GemmN      = C * X;
-
         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_gemmktotal_gemmm_grid_desc = make_naive_tensor_descriptor(
+                make_tuple(N * Wo, K), make_tuple(OutWStride, OutKStride));
 
-            const auto out_gemmkpad_gemmm_grid_desc = transform_tensor_descriptor(
+            const auto out_gemmkpad_gemmmpad_grid_desc =
+                ck::tensor_operation::device::PadTensorDescriptor(
                     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>{}));
+                    make_tuple(GemmK1Number * K0PerBlock * GemmKBatch, MPerBlock),
+                    Sequence<true, true>{});
 
             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)),
+                out_gemmkpad_gemmmpad_grid_desc,
+                make_tuple(
+                    make_unmerge_transform(make_tuple(GemmKBatch, GemmK0, GemmK1Number)),
+                    make_pass_through_transform(out_gemmkpad_gemmmpad_grid_desc.GetLength(I1))),
                 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_gemmktotal_gemmn_grid_desc = make_naive_tensor_descriptor(
+                make_tuple(N * Wi, C), make_tuple(InWStride, InCStride));
 
-            const auto in_gemmkpad_gemmn_grid_desc = transform_tensor_descriptor(
+            const auto in_gemmkpad_gemmnpad_grid_desc =
+                ck::tensor_operation::device::PadTensorDescriptor(
                     in_gemmktotal_gemmn_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>{}));
+                    make_tuple(GemmK1Number * K0PerBlock * GemmKBatch, NPerBlock),
+                    Sequence<true, true>{});
 
             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(GemmM)),
+                in_gemmkpad_gemmnpad_grid_desc,
+                make_tuple(
+                    make_unmerge_transform(make_tuple(GemmKBatch, GemmK0, GemmK1Number)),
+                    make_pass_through_transform(in_gemmkpad_gemmnpad_grid_desc.GetLength(I1))),
                 make_tuple(Sequence<0>{}, Sequence<1>{}),
                 make_tuple(Sequence<0, 1, 3>{}, Sequence<2>{}));
 
             // C: weights tensor
-            const auto wei_gemmm_gemmn_grid_desc =
-                make_naive_tensor_descriptor_packed(make_tuple(K, X * C));
+            const auto wei_gemmm_gemmn_grid_desc = make_naive_tensor_descriptor(
+                make_tuple(K, X * C), make_tuple(WeiKStride, WeiCStride));
+
+            const auto wei_gemmmpad_gemmnpad_grid_desc =
+                ck::tensor_operation::device::PadTensorDescriptor(wei_gemmm_gemmn_grid_desc,
+                                                                  make_tuple(MPerBlock, NPerBlock),
+                                                                  Sequence<true, true>{});
 
             return make_tuple(out_gemmkbatch_gemmk0_gemmm_gemmk1_grid_desc,
                               in_gemmkbatch_gemmk0_gemmn_gemmk1_grid_desc,
-                              wei_gemmm_gemmn_grid_desc);
+                              wei_gemmmpad_gemmnpad_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));
+            const auto out_gemmktotal_gemmm_grid_desc = make_naive_tensor_descriptor(
+                make_tuple(N * Wo, K), make_tuple(OutWStride, OutKStride));
+            const auto in_n_wi_c_grid_desc = make_naive_tensor_descriptor(
+                make_tuple(N, Wi, C), make_tuple(InNStride, InWStride, InCStride));
 
             // A: output tensor
-            const auto out_gemmkpad_gemmm_grid_desc = transform_tensor_descriptor(
+            const auto out_gemmkpad_gemmmpad_grid_desc =
+                ck::tensor_operation::device::PadTensorDescriptor(
                     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>{}));
+                    make_tuple(GemmK1Number * K0PerBlock * GemmKBatch, MPerBlock),
+                    Sequence<true, true>{});
 
             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)),
+                out_gemmkpad_gemmmpad_grid_desc,
+                make_tuple(
+                    make_unmerge_transform(make_tuple(GemmKBatch, GemmK0, GemmK1Number)),
+                    make_pass_through_transform(out_gemmkpad_gemmmpad_grid_desc.GetLength(I1))),
                 make_tuple(Sequence<0>{}, Sequence<1>{}),
                 make_tuple(Sequence<0, 1, 3>{}, Sequence<2>{}));
 
@@ -321,38 +374,43 @@ struct DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Dl
                                             make_tuple(Sequence<1, 3>{}, Sequence<0, 2>{}),
                                             make_tuple(Sequence<1>{}, Sequence<0>{}));
 
-            const auto in_gemmkpad_gemmn_grid_desc = transform_tensor_descriptor(
+            const auto in_gemmkpad_gemmnpad_grid_desc =
+                ck::tensor_operation::device::PadTensorDescriptor(
                     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>{}));
+                    make_tuple(GemmK1Number * K0PerBlock * GemmKBatch, NPerBlock),
+                    Sequence<true, true>{});
 
             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)),
+                in_gemmkpad_gemmnpad_grid_desc,
+                make_tuple(
+                    make_unmerge_transform(make_tuple(GemmKBatch, GemmK0, GemmK1Number)),
+                    make_pass_through_transform(in_gemmkpad_gemmnpad_grid_desc.GetLength(I1))),
                 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));
+            const auto wei_gemmm_gemmn_grid_desc = make_naive_tensor_descriptor(
+                make_tuple(K, X * C), make_tuple(WeiKStride, WeiCStride));
+
+            const auto wei_gemmmpad_gemmnpad_grid_desc =
+                ck::tensor_operation::device::PadTensorDescriptor(wei_gemmm_gemmn_grid_desc,
+                                                                  make_tuple(MPerBlock, NPerBlock),
+                                                                  Sequence<true, true>{});
 
             return make_tuple(out_gemmkbatch_gemmk0_gemmm_gemmk1_grid_desc,
                               in_gemmkbatch_gemmk0_gemmn_gemmk1_grid_desc,
-                              wei_gemmm_gemmn_grid_desc);
+                              wei_gemmmpad_gemmnpad_grid_desc);
         }
 
     } // function end
     template <ck::index_t NDim, typename ck::enable_if<NDim == 2, bool>::type = false>
     static auto MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N(
-        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<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<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,
@@ -361,103 +419,111 @@ struct DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Dl
     {
         using namespace ck;
 
-        const index_t Hi = input_spatial_lengths[0];
-        const index_t Wi = input_spatial_lengths[1];
-
-        const index_t Ho = output_spatial_lengths[0];
-        const index_t Wo = output_spatial_lengths[1];
-
-        const index_t Y = filter_spatial_lengths[0];
-        const index_t X = filter_spatial_lengths[1];
-
-        const index_t InLeftPadH = input_left_pads[0];
-        const index_t InLeftPadW = input_left_pads[1];
-
-        const index_t InRightPadH = input_right_pads[0];
-        const index_t InRightPadW = input_right_pads[1];
-
-        const index_t ConvStrideH = conv_filter_strides[0];
-        const index_t ConvStrideW = conv_filter_strides[1];
-
-        const index_t ConvDilationH = conv_filter_dilations[0];
-        const index_t ConvDilationW = conv_filter_dilations[1];
+        const index_t N  = a_g_n_c_wis_lengths[I1];
+        const index_t K  = b_g_k_c_xs_lengths[I1];
+        const index_t C  = a_g_n_c_wis_lengths[I2];
+        const index_t Hi = a_g_n_c_wis_lengths[spatial_offset];
+        const index_t Wi = a_g_n_c_wis_lengths[spatial_offset + I1];
+        const index_t Ho = e_g_n_k_wos_lengths[spatial_offset];
+        const index_t Wo = e_g_n_k_wos_lengths[spatial_offset + I1];
+        const index_t Y  = b_g_k_c_xs_lengths[spatial_offset];
+        const index_t X  = b_g_k_c_xs_lengths[spatial_offset + I1];
+
+        const index_t InLeftPadH    = input_left_pads[I0];
+        const index_t InLeftPadW    = input_left_pads[I1];
+        const index_t InRightPadH   = input_right_pads[I0];
+        const index_t InRightPadW   = input_right_pads[I1];
+        const index_t ConvStrideH   = conv_filter_strides[I0];
+        const index_t ConvStrideW   = conv_filter_strides[I1];
+        const index_t ConvDilationH = conv_filter_dilations[I0];
+        const index_t ConvDilationW = conv_filter_dilations[I1];
+
+        const auto InNStride  = a_g_n_c_wis_strides[I1];
+        const auto InCStride  = a_g_n_c_wis_strides[I2];
+        const auto InHStride  = a_g_n_c_wis_strides[spatial_offset];
+        const auto InWStride  = a_g_n_c_wis_strides[spatial_offset + I1];
+        const auto WeiKStride = b_g_k_c_xs_strides[I1];
+        const auto WeiCStride = b_g_k_c_xs_strides[I2];
+        const auto OutKStride = e_g_n_k_wos_strides[I2];
+        const auto OutWStride = e_g_n_k_wos_strides[spatial_offset + I1];
 
         const index_t GemmKTotal = N * Ho * Wo;
-        const index_t GemmM      = K;
-        const index_t GemmN      = C * X * Y;
-
         const index_t GemmKBatch = batch_k;
         const index_t GemmK0 =
             math::integer_divide_ceil(GemmKTotal, GemmK1Number * K0PerBlock * GemmKBatch) *
             K0PerBlock;
-        const 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 * Ho * Wo, K));
+            const auto out_gemmktotal_gemmm_grid_desc = make_naive_tensor_descriptor(
+                make_tuple(N * Ho * Wo, K), make_tuple(OutWStride, OutKStride));
 
-            const auto out_gemmkpad_gemmm_grid_desc = transform_tensor_descriptor(
+            const auto out_gemmkpad_gemmmpad_grid_desc =
+                ck::tensor_operation::device::PadTensorDescriptor(
                     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>{}));
+                    make_tuple(GemmK1Number * K0PerBlock * GemmKBatch, MPerBlock),
+                    Sequence<true, true>{});
 
             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)),
+                out_gemmkpad_gemmmpad_grid_desc,
+                make_tuple(
+                    make_unmerge_transform(make_tuple(GemmKBatch, GemmK0, GemmK1Number)),
+                    make_pass_through_transform(out_gemmkpad_gemmmpad_grid_desc.GetLength(I1))),
                 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 * Hi * Wi, C));
+            const auto in_gemmktotal_gemmn_grid_desc = make_naive_tensor_descriptor(
+                make_tuple(N * Hi * Wi, C), make_tuple(InWStride, InCStride));
 
-            const auto in_gemmkpad_gemmn_grid_desc = transform_tensor_descriptor(
+            const auto in_gemmkpad_gemmnpad_grid_desc =
+                ck::tensor_operation::device::PadTensorDescriptor(
                     in_gemmktotal_gemmn_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>{}));
+                    make_tuple(GemmK1Number * K0PerBlock * GemmKBatch, NPerBlock),
+                    Sequence<true, true>{});
 
             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(GemmM)),
+                in_gemmkpad_gemmnpad_grid_desc,
+                make_tuple(
+                    make_unmerge_transform(make_tuple(GemmKBatch, GemmK0, GemmK1Number)),
+                    make_pass_through_transform(in_gemmkpad_gemmnpad_grid_desc.GetLength(I1))),
                 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, Y * X * C));
+            const auto wei_gemmm_gemmn_grid_desc = make_naive_tensor_descriptor(
+                make_tuple(K, Y * X * C), make_tuple(WeiKStride, WeiCStride));
+
+            const auto wei_gemmmpad_gemmnpad_grid_desc =
+                ck::tensor_operation::device::PadTensorDescriptor(wei_gemmm_gemmn_grid_desc,
+                                                                  make_tuple(MPerBlock, NPerBlock),
+                                                                  Sequence<true, true>{});
 
             return make_tuple(out_gemmkbatch_gemmk0_gemmm_gemmk1_grid_desc,
                               in_gemmkbatch_gemmk0_gemmn_gemmk1_grid_desc,
-                              wei_gemmm_gemmn_grid_desc);
+                              wei_gemmmpad_gemmnpad_grid_desc);
         }
         else
         {
-            const auto out_gemmktotal_gemmm_grid_desc =
-                make_naive_tensor_descriptor_packed(make_tuple(N * Ho * Wo, K));
-            const auto in_n_hi_wi_c_grid_desc =
-                make_naive_tensor_descriptor_packed(make_tuple(N, Hi, Wi, C));
+            const auto out_gemmktotal_gemmm_grid_desc = make_naive_tensor_descriptor(
+                make_tuple(N * Ho * Wo, K), make_tuple(OutWStride, OutKStride));
+            const auto in_n_hi_wi_c_grid_desc = make_naive_tensor_descriptor(
+                make_tuple(N, Hi, Wi, C), make_tuple(InNStride, InHStride, InWStride, InCStride));
 
             // A: output tensor
-            const auto out_gemmkpad_gemmm_grid_desc = transform_tensor_descriptor(
+            const auto out_gemmkpad_gemmmpad_grid_desc =
+                ck::tensor_operation::device::PadTensorDescriptor(
                     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>{}));
+                    make_tuple(GemmK1Number * K0PerBlock * GemmKBatch, MPerBlock),
+                    Sequence<true, true>{});
 
             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)),
+                out_gemmkpad_gemmmpad_grid_desc,
+                make_tuple(
+                    make_unmerge_transform(make_tuple(GemmKBatch, GemmK0, GemmK1Number)),
+                    make_pass_through_transform(out_gemmkpad_gemmmpad_grid_desc.GetLength(I1))),
                 make_tuple(Sequence<0>{}, Sequence<1>{}),
                 make_tuple(Sequence<0, 1, 3>{}, Sequence<2>{}));
 
@@ -488,39 +554,44 @@ struct DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Dl
                                             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(
+            const auto in_gemmkpad_gemmnpad_grid_desc =
+                ck::tensor_operation::device::PadTensorDescriptor(
                     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>{}));
+                    make_tuple(GemmK1Number * K0PerBlock * GemmKBatch, NPerBlock),
+                    Sequence<true, true>{});
 
             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)),
+                in_gemmkpad_gemmnpad_grid_desc,
+                make_tuple(
+                    make_unmerge_transform(make_tuple(GemmKBatch, GemmK0, GemmK1Number)),
+                    make_pass_through_transform(in_gemmkpad_gemmnpad_grid_desc.GetLength(I1))),
                 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, Y * X * C));
+            const auto wei_gemmm_gemmn_grid_desc = make_naive_tensor_descriptor(
+                make_tuple(K, Y * X * C), make_tuple(WeiKStride, WeiCStride));
+
+            const auto wei_gemmmpad_gemmnpad_grid_desc =
+                ck::tensor_operation::device::PadTensorDescriptor(wei_gemmm_gemmn_grid_desc,
+                                                                  make_tuple(MPerBlock, NPerBlock),
+                                                                  Sequence<true, true>{});
 
             return make_tuple(out_gemmkbatch_gemmk0_gemmm_gemmk1_grid_desc,
                               in_gemmkbatch_gemmk0_gemmn_gemmk1_grid_desc,
-                              wei_gemmm_gemmn_grid_desc);
+                              wei_gemmmpad_gemmnpad_grid_desc);
         }
 
     } // function end
 
     template <ck::index_t NDim, typename ck::enable_if<NDim == 3, bool>::type = false>
     static auto MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N(
-        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<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<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,
@@ -529,110 +600,120 @@ struct DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Dl
     {
         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 InLeftPadD = input_left_pads[0];
-        const index_t InLeftPadH = input_left_pads[1];
-        const index_t InLeftPadW = input_left_pads[2];
-
-        const index_t InRightPadD = input_right_pads[0];
-        const index_t InRightPadH = input_right_pads[1];
-        const index_t InRightPadW = input_right_pads[2];
-
-        const index_t ConvStrideD = conv_filter_strides[0];
-        const index_t ConvStrideH = conv_filter_strides[1];
-        const index_t ConvStrideW = conv_filter_strides[2];
-
-        const index_t ConvDilationD = conv_filter_dilations[0];
-        const index_t ConvDilationH = conv_filter_dilations[1];
-        const index_t ConvDilationW = conv_filter_dilations[2];
+        const index_t N  = a_g_n_c_wis_lengths[I1];
+        const index_t K  = b_g_k_c_xs_lengths[I1];
+        const index_t C  = a_g_n_c_wis_lengths[I2];
+        const index_t Di = a_g_n_c_wis_lengths[spatial_offset + I0];
+        const index_t Hi = a_g_n_c_wis_lengths[spatial_offset + I1];
+        const index_t Wi = a_g_n_c_wis_lengths[spatial_offset + I2];
+        const index_t Do = e_g_n_k_wos_lengths[spatial_offset + I0];
+        const index_t Ho = e_g_n_k_wos_lengths[spatial_offset + I1];
+        const index_t Wo = e_g_n_k_wos_lengths[spatial_offset + I2];
+        const index_t Z  = b_g_k_c_xs_lengths[spatial_offset + I0];
+        const index_t Y  = b_g_k_c_xs_lengths[spatial_offset + I1];
+        const index_t X  = b_g_k_c_xs_lengths[spatial_offset + I2];
+
+        const index_t InLeftPadD    = input_left_pads[I0];
+        const index_t InLeftPadH    = input_left_pads[I1];
+        const index_t InLeftPadW    = input_left_pads[I2];
+        const index_t InRightPadD   = input_right_pads[I0];
+        const index_t InRightPadH   = input_right_pads[I1];
+        const index_t InRightPadW   = input_right_pads[I2];
+        const index_t ConvStrideD   = conv_filter_strides[I0];
+        const index_t ConvStrideH   = conv_filter_strides[I1];
+        const index_t ConvStrideW   = conv_filter_strides[I2];
+        const index_t ConvDilationD = conv_filter_dilations[I0];
+        const index_t ConvDilationH = conv_filter_dilations[I1];
+        const index_t ConvDilationW = conv_filter_dilations[I2];
+
+        const auto InNStride  = a_g_n_c_wis_strides[I1];
+        const auto InCStride  = a_g_n_c_wis_strides[I2];
+        const auto InDStride  = a_g_n_c_wis_strides[spatial_offset];
+        const auto InHStride  = a_g_n_c_wis_strides[spatial_offset + I1];
+        const auto InWStride  = a_g_n_c_wis_strides[spatial_offset + I2];
+        const auto WeiKStride = b_g_k_c_xs_strides[I1];
+        const auto WeiCStride = b_g_k_c_xs_strides[I2];
+        const auto OutKStride = e_g_n_k_wos_strides[I2];
+        const auto OutWStride = e_g_n_k_wos_strides[spatial_offset + I2];
 
         const index_t GemmKTotal = N * Do * Ho * Wo;
-        const index_t GemmM      = K;
-        const index_t GemmN      = C * Z * X * Y;
-
         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 * Do * Ho * Wo, K));
+            const auto out_gemmktotal_gemmm_grid_desc = make_naive_tensor_descriptor(
+                make_tuple(N * Do * Ho * Wo, K), make_tuple(OutWStride, OutKStride));
 
-            const auto out_gemmkpad_gemmm_grid_desc = transform_tensor_descriptor(
+            const auto out_gemmkpad_gemmmpad_grid_desc =
+                ck::tensor_operation::device::PadTensorDescriptor(
                     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>{}));
+                    make_tuple(GemmK1Number * K0PerBlock * GemmKBatch, MPerBlock),
+                    Sequence<true, true>{});
 
             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)),
+                out_gemmkpad_gemmmpad_grid_desc,
+                make_tuple(
+                    make_unmerge_transform(make_tuple(GemmKBatch, GemmK0, GemmK1Number)),
+                    make_pass_through_transform(out_gemmkpad_gemmmpad_grid_desc.GetLength(I1))),
                 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 * Di * Hi * Wi, C));
+            const auto in_gemmktotal_gemmn_grid_desc = make_naive_tensor_descriptor(
+                make_tuple(N * Di * Hi * Wi, C), make_tuple(InWStride, InCStride));
 
-            const auto in_gemmkpad_gemmn_grid_desc = transform_tensor_descriptor(
+            const auto in_gemmkpad_gemmnpad_grid_desc =
+                ck::tensor_operation::device::PadTensorDescriptor(
                     in_gemmktotal_gemmn_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>{}));
+                    make_tuple(GemmK1Number * K0PerBlock * GemmKBatch, NPerBlock),
+                    Sequence<true, true>{});
 
             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(GemmM)),
+                in_gemmkpad_gemmnpad_grid_desc,
+                make_tuple(
+                    make_unmerge_transform(make_tuple(GemmKBatch, GemmK0, GemmK1Number)),
+                    make_pass_through_transform(in_gemmkpad_gemmnpad_grid_desc.GetLength(I1))),
                 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, Z * Y * X * C));
+            const auto wei_gemmm_gemmn_grid_desc = make_naive_tensor_descriptor(
+                make_tuple(K, Z * Y * X * C), make_tuple(WeiKStride, WeiCStride));
+
+            const auto wei_gemmmpad_gemmnpad_grid_desc =
+                ck::tensor_operation::device::PadTensorDescriptor(wei_gemmm_gemmn_grid_desc,
+                                                                  make_tuple(MPerBlock, NPerBlock),
+                                                                  Sequence<true, true>{});
 
             return make_tuple(out_gemmkbatch_gemmk0_gemmm_gemmk1_grid_desc,
                               in_gemmkbatch_gemmk0_gemmn_gemmk1_grid_desc,
-                              wei_gemmm_gemmn_grid_desc);
+                              wei_gemmmpad_gemmnpad_grid_desc);
         }
         else
         {
-            const auto out_gemmktotal_gemmm_grid_desc =
-                make_naive_tensor_descriptor_packed(make_tuple(N * Do * Ho * Wo, K));
-            const auto in_n_di_hi_wi_c_grid_desc =
-                make_naive_tensor_descriptor_packed(make_tuple(N, Di, Hi, Wi, C));
+            const auto out_gemmktotal_gemmm_grid_desc = make_naive_tensor_descriptor(
+                make_tuple(N * Do * Ho * Wo, K), make_tuple(OutWStride, OutKStride));
+            const auto in_n_di_hi_wi_c_grid_desc = make_naive_tensor_descriptor(
+                make_tuple(N, Di, Hi, Wi, C),
+                make_tuple(InNStride, InDStride, InHStride, InWStride, InCStride));
 
             // A: output tensor
-            const auto out_gemmkpad_gemmm_grid_desc = transform_tensor_descriptor(
+            const auto out_gemmkpad_gemmmpad_grid_desc =
+                ck::tensor_operation::device::PadTensorDescriptor(
                     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>{}));
+                    make_tuple(GemmK1Number * K0PerBlock * GemmKBatch, MPerBlock),
+                    Sequence<true, true>{});
 
             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)),
+                out_gemmkpad_gemmmpad_grid_desc,
+                make_tuple(
+                    make_unmerge_transform(make_tuple(GemmKBatch, GemmK0, GemmK1Number)),
+                    make_pass_through_transform(out_gemmkpad_gemmmpad_grid_desc.GetLength(I1))),
                 make_tuple(Sequence<0>{}, Sequence<1>{}),
                 make_tuple(Sequence<0, 1, 3>{}, Sequence<2>{}));
 
@@ -672,27 +753,32 @@ struct DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Dl
                 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(
+            const auto in_gemmkpad_gemmnpad_grid_desc =
+                ck::tensor_operation::device::PadTensorDescriptor(
                     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>{}));
+                    make_tuple(GemmK1Number * K0PerBlock * GemmKBatch, NPerBlock),
+                    Sequence<true, true>{});
 
             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)),
+                in_gemmkpad_gemmnpad_grid_desc,
+                make_tuple(
+                    make_unmerge_transform(make_tuple(GemmKBatch, GemmK0, GemmK1Number)),
+                    make_pass_through_transform(in_gemmkpad_gemmnpad_grid_desc.GetLength(I1))),
                 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, Z * Y * X * C));
+            const auto wei_gemmm_gemmn_grid_desc = make_naive_tensor_descriptor(
+                make_tuple(K, Z * Y * X * C), make_tuple(WeiKStride, WeiCStride));
+
+            const auto wei_gemmmpad_gemmnpad_grid_desc =
+                ck::tensor_operation::device::PadTensorDescriptor(wei_gemmm_gemmn_grid_desc,
+                                                                  make_tuple(MPerBlock, NPerBlock),
+                                                                  Sequence<true, true>{});
 
             return make_tuple(out_gemmkbatch_gemmk0_gemmm_gemmk1_grid_desc,
                               in_gemmkbatch_gemmk0_gemmn_gemmk1_grid_desc,
-                              wei_gemmm_gemmn_grid_desc);
+                              wei_gemmmpad_gemmnpad_grid_desc);
         }
 
     } // function end
@@ -701,22 +787,22 @@ struct DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Dl
     static auto GetABCGridDesc()
     {
         return MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N<1>(
-            1, 1, 1, {1}, {1}, {1}, {1}, {1}, {1}, {1}, 1);
+            {1}, {1}, {1}, {1}, {1}, {1}, {1}, {1}, {1}, {1}, 1);
     }
 
     template <ck::index_t NDim, typename ck::enable_if<NDim == 2, bool>::type = false>
     static auto GetABCGridDesc()
     {
         return MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N<2>(
-            1, 1, 1, {1, 1}, {1, 1}, {1, 1}, {1, 1}, {1, 1}, {1, 1}, {1, 1}, 1);
+            {1, 1}, {1, 1}, {1, 1}, {1, 1}, {1, 1}, {1, 1}, {1, 1}, {1, 1}, {1, 1}, {1, 1}, 1);
     }
 
     template <ck::index_t NDim, typename ck::enable_if<NDim == 3, bool>::type = false>
     static auto GetABCGridDesc()
     {
-        return MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N<3>(1,
-                                                                  1,
-                                                                  1,
+        return MakeABCGridDescriptor_A_K0_M_K1_B_K0_N_K1_C_M_N<3>({1, 1, 1},
+                                                                  {1, 1, 1},
+                                                                  {1, 1, 1},
                                                                   {1, 1, 1},
                                                                   {1, 1, 1},
                                                                   {1, 1, 1},
@@ -785,11 +871,11 @@ struct DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Dl
                  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>& 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>& 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>& e_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,
@@ -809,38 +895,24 @@ struct DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Dl
               a_element_op_{out_element_op},
               b_element_op_{wei_element_op},
               c_element_op_{in_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]},
-              input_spatial_lengths_{},
-              filter_spatial_lengths_{},
-              output_spatial_lengths_{},
+              Conv_G_{a_g_n_c_wis_lengths[I0]},
+              Conv_K_{b_g_k_c_xs_lengths[I1]},
+              Conv_C_{a_g_n_c_wis_lengths[I2]},
+              filter_lengths_{b_g_k_c_xs_lengths},
               conv_filter_strides_{conv_filter_strides},
               conv_filter_dilations_{conv_filter_dilations},
               input_left_pads_{input_left_pads},
               input_right_pads_{input_right_pads},
               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),
-                      begin(input_spatial_lengths_));
-            std::copy(begin(b_g_k_c_xs_lengths) + spatial_offset,
-                      end(b_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),
-                      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_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,
                     conv_filter_strides,
                     conv_filter_dilations,
                     input_left_pads,
@@ -863,24 +935,9 @@ struct DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Dl
                 GridwiseGemm::MakeCBlockClusterAdaptor(c_grid_desc_m_n_, M01, N01, k_batch_);
 
             // A/B/C Batch Stride
-            compute_ptr_offset_of_batch_.BatchStrideA_ =
-                Conv_N_ * Conv_K_ *
-                std::accumulate(begin(output_spatial_lengths_),
-                                end(output_spatial_lengths_),
-                                index_t{1},
-                                std::multiplies<>{});
-            compute_ptr_offset_of_batch_.BatchStrideB_ =
-                Conv_N_ * Conv_C_ *
-                std::accumulate(begin(input_spatial_lengths_),
-                                end(input_spatial_lengths_),
-                                index_t{1},
-                                std::multiplies<>{});
-            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<>{});
+            compute_ptr_offset_of_batch_.BatchStrideA_ = e_g_n_k_wos_strides[I0];
+            compute_ptr_offset_of_batch_.BatchStrideB_ = a_g_n_c_wis_strides[I0];
+            compute_ptr_offset_of_batch_.BatchStrideC_ = b_g_k_c_xs_strides[I0];
         }
 
         const ADataType* p_a_grid_;
@@ -908,13 +965,10 @@ struct DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Dl
 
         // 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_;
+        std::array<ck::index_t, NDimSpatial + 3> filter_lengths_;
         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_;
@@ -1036,10 +1090,14 @@ struct DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Dl
 
     static bool IsSupportedArgument(const Argument& arg)
     {
-        // check device
-        if(!(ck::get_device_name() == "gfx906" || ck::get_device_name() == "gfx1030" ||
-             ck::get_device_name() == "gfx1100" || ck::get_device_name() == "gfx1101" ||
-             ck::get_device_name() == "gfx1102"))
+
+        // DL version only supports split_k equal to 1
+        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))))
         {
             return false;
         }
@@ -1050,8 +1108,9 @@ struct DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Dl
             // 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))
+                if(!(arg.filter_lengths_[spatial_offset + i] == 1 &&
+                     arg.conv_filter_strides_[i] == 1 && arg.input_left_pads_[i] == 0 &&
+                     arg.input_right_pads_[i] == 0))
                 {
                     return false;
                 }
@@ -1206,7 +1265,7 @@ struct DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Dl
         auto str = std::stringstream();
 
         // clang-format off
-        str << "DeviceGroupedConvBwdWeightGnwcGkxcGnwk_Dl"
+        str << "DeviceGroupedConvBwdWeight_Dl"
             << "<"
             << BlockSize << ", "
             << MPerBlock << ", "

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

@@ -193,6 +193,7 @@ template <typename ALayout,
           index_t CShuffleNXdlPerWavePerShuffle,
           typename CDEBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
           index_t CDEBlockTransferScalarPerVector_NPerBlock,
+          typename ComputeType    = ADataType,
           LoopScheduler LoopSched = make_default_loop_scheduler()>
 struct DeviceGroupedGemm_Xdl_Fixed_NK : public DeviceGroupedGemmFixedNK<ALayout,
                                                                         BLayout,
@@ -217,6 +218,8 @@ struct DeviceGroupedGemm_Xdl_Fixed_NK : public DeviceGroupedGemmFixedNK<ALayout,
     // GridwiseGemm
     using GridwiseGemm = GridwiseGemmMultipleD_xdl_splitk_cshuffle<
         ADataType, // TODO: distinguish A/B datatype
+        BDataType,
+        ComputeType,
         AccDataType,
         CShuffleDataType,
         DsDataType,

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

@@ -75,12 +75,24 @@ struct PassThrough
         y = type_convert<bhalf_t>(x);
     }
 
+    template <>
+    __host__ __device__ void operator()<float, half_t>(float& y, const half_t& x) const
+    {
+        y = type_convert<float>(x);
+    }
+
     template <>
     __host__ __device__ void operator()<int8_t, int8_t>(int8_t& y, const int8_t& x) const
     {
         y = x;
     }
 
+    template <>
+    __host__ __device__ void operator()<half_t, int8_t>(half_t& y, const int8_t& x) const
+    {
+        y = type_convert<half_t>(x);
+    }
+
     template <>
     __host__ __device__ void operator()<int8_t, int32_t>(int8_t& y, const int32_t& x) const
     {

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

@@ -29,7 +29,9 @@ namespace ck {
 //   E = cde_op(C, D0, D1, ...)
 // Assume:
 //   D0, D1, ... and E have the same layout
-template <typename ABDataType, // FIXME: don't assume A/B have same datatype
+template <typename ADataType,
+          typename BDataType,
+          typename ComputeType,
           typename AccDataType,
           typename CShuffleDataType,
           typename DsDataType,
@@ -96,17 +98,6 @@ struct GridwiseGemmMultipleD_xdl_splitk_cshuffle
     using GridwiseGemmPipe = remove_cvref_t<
         decltype(GridwiseGemmPipeline_Selector<PipelineVer, NumGemmKPrefetchStage, LoopSched>())>;
 
-    // denorm test fix, required to work around fp16 mfma issue
-    // we convert fp16->fp32->bf16 and execute bf16 mfma instruction
-    // when mfma if fixed, remove this section and update
-    // ABDataTypeAdjusted -> ABDataType throughout this file
-#if CK_WORKAROUND_DENORM_FIX
-    using ABDataTypeAdjusted =
-        conditional_t<is_same_v<ABDataType, ck::half_t>, ck::bhalf_t, ABDataType>;
-#else
-    using ABDataTypeAdjusted = ABDataType;
-#endif
-
     __host__ __device__ static constexpr auto GetABlockDescriptor_KBatch_AK0PerBlock_MPerBlock_AK1()
     {
         // A matrix in LDS memory, dst of blockwise copy
@@ -196,7 +187,7 @@ struct GridwiseGemmMultipleD_xdl_splitk_cshuffle
             c_shuffle_block_desc_mblock_mperblock_nblock_nperblock.GetElementSpaceSize();
 
         return math::max((a_block_space_size_aligned + b_block_space_size_aligned) *
-                             sizeof(ABDataType),
+                             sizeof(ComputeType),
                          c_block_size * sizeof(CShuffleDataType));
     }
 
@@ -401,8 +392,8 @@ struct GridwiseGemmMultipleD_xdl_splitk_cshuffle
         // check tensor size: cannot be larger than 2GB each
         constexpr long_index_t TwoGB = (long_index_t{1} << 31);
 
-        if(!(a_grid_desc_kbatch_ak0_m_ak1.GetElementSpaceSize() * sizeof(ABDataType) <= TwoGB &&
-             b_grid_desc_kbatch_bk0_n_bk1.GetElementSpaceSize() * sizeof(ABDataType) <= TwoGB &&
+        if(!(a_grid_desc_kbatch_ak0_m_ak1.GetElementSpaceSize() * sizeof(ADataType) <= TwoGB &&
+             b_grid_desc_kbatch_bk0_n_bk1.GetElementSpaceSize() * sizeof(BDataType) <= TwoGB &&
              e_grid_desc_m_n.GetElementSpaceSize() * sizeof(EDataType) <= TwoGB))
         {
             return false;
@@ -470,8 +461,8 @@ struct GridwiseGemmMultipleD_xdl_splitk_cshuffle
               typename EGridDesc_MBlock_MPerBlock_NBlock_NPerBlock,
               typename CDEElementwiseOperation_,
               typename Block2ETileMap>
-    __device__ static void Run(const ABDataType* __restrict__ p_a_grid,
-                               const ABDataType* __restrict__ p_b_grid,
+    __device__ static void Run(const ADataType* __restrict__ p_a_grid,
+                               const BDataType* __restrict__ p_b_grid,
                                DsGridPointer p_ds_grid,
                                EDataType* __restrict__ p_e_grid,
                                void* __restrict__ p_shared,
@@ -538,8 +529,8 @@ struct GridwiseGemmMultipleD_xdl_splitk_cshuffle
                                                 Sequence<1, AK0PerBlock, MPerBlock, AK1>,
                                                 ABlockTransferThreadClusterLengths_KBatch_AK0_M_AK1,
                                                 ABlockTransferThreadClusterArrangeOrder,
-                                                ABDataType,
-                                                ABDataTypeAdjusted,
+                                                ADataType,
+                                                ComputeType,
                                                 decltype(a_grid_desc_kbatch_ak0_m_ak1),
                                                 decltype(a_block_desc_kbatch_ak0_m_ak1),
                                                 ABlockTransferSrcAccessOrder,
@@ -569,8 +560,8 @@ struct GridwiseGemmMultipleD_xdl_splitk_cshuffle
                                                 Sequence<1, BK0PerBlock, NPerBlock, BK1>,
                                                 BBlockTransferThreadClusterLengths_KBatch_BK0_N_BK1,
                                                 BBlockTransferThreadClusterArrangeOrder,
-                                                ABDataType,
-                                                ABDataTypeAdjusted,
+                                                BDataType,
+                                                ComputeType,
                                                 decltype(b_grid_desc_kbatch_bk0_n_bk1),
                                                 decltype(b_block_desc_kbatch_bk0_n_bk1),
                                                 BBlockTransferSrcAccessOrder,
@@ -606,11 +597,11 @@ struct GridwiseGemmMultipleD_xdl_splitk_cshuffle
         // sanity check
         constexpr index_t KPack =
             math::max(math::lcm(AK1, BK1),
-                      MfmaSelector<ABDataTypeAdjusted, MPerXdl, NPerXdl>::selected_mfma.k_per_blk);
+                      MfmaSelector<ComputeType, MPerXdl, NPerXdl>::selected_mfma.k_per_blk);
 
         auto blockwise_gemm = BlockwiseGemmXdlops_k0mk1_k0nk1_m0n0m1n1m2m3m4n2_Selector<
             BlockSize,
-            ABDataTypeAdjusted,
+            ComputeType,
             AccDataType,
             decltype(a_block_desc_ak0_m_ak1),
             decltype(b_block_desc_bk0_n_bk1),
@@ -683,11 +674,10 @@ struct GridwiseGemmMultipleD_xdl_splitk_cshuffle
             a_block_desc_ak0_m_ak1.GetElementSpaceSize(), max_lds_align);
 
         auto a_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
-            static_cast<ABDataTypeAdjusted*>(p_shared),
-            a_block_desc_ak0_m_ak1.GetElementSpaceSize());
+            static_cast<ComputeType*>(p_shared), a_block_desc_ak0_m_ak1.GetElementSpaceSize());
 
         auto b_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
-            static_cast<ABDataTypeAdjusted*>(p_shared) + a_block_space_size_aligned,
+            static_cast<ComputeType*>(p_shared) + a_block_space_size_aligned,
             b_block_desc_bk0_n_bk1.GetElementSpaceSize());
 
         constexpr auto a_block_slice_copy_step = make_multi_index(0, KPerBlock / AK1, 0, 0);
@@ -999,8 +989,8 @@ struct GridwiseGemmMultipleD_xdl_splitk_cshuffle
                                const index_t KBatch,
                                const Block2ETileMap& block_2_etile_map)
     {
-        const auto p_a_grid = reinterpret_cast<const ABDataType*>(p_a_grid_);
-        const auto p_b_grid = reinterpret_cast<const ABDataType*>(p_b_grid_);
+        const auto p_a_grid = reinterpret_cast<const ADataType*>(p_a_grid_);
+        const auto p_b_grid = reinterpret_cast<const BDataType*>(p_b_grid_);
         const auto p_e_grid = reinterpret_cast<EDataType*>(p_e_grid_);
 
         using DsGridDesc_M_N =

include/ck/utility/inner_product.hpp

@@ -72,6 +72,18 @@ inner_product<float4_t, float4_t, float>(const float4_t& a, const float4_t& b, f
                   c);
 }
 
+template <>
+__device__ void inner_product<bhalf_t, bhalf_t, float>(const bhalf_t& a, const bhalf_t& b, float& c)
+{
+    inner_product(type_convert<float>(a), type_convert<float>(b), c);
+}
+
+template <>
+__device__ void inner_product<half_t, half_t, float>(const half_t& a, const half_t& b, float& c)
+{
+    inner_product(type_convert<float>(a), type_convert<float>(b), c);
+}
+
 template <>
 __device__ void inner_product<half2_t, half2_t, float>(const half2_t& a, const half2_t& b, float& c)
 {

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

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2023, 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_dl.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;
+
+using Empty_Tuple = ck::Tuple<>;
+
+template <ck::index_t... Is>
+using S = ck::Sequence<Is...>;
+
+using PassThrough = ck::tensor_operation::element_wise::PassThrough;
+
+static constexpr auto ConvBwdWeightDefault =
+    ck::tensor_operation::device::ConvolutionBackwardWeightSpecialization::Default;
+
+static constexpr auto ConvBwdWeightFilter1x1Stride1Pad0 =
+    ck::tensor_operation::device::ConvolutionBackwardWeightSpecialization::Filter1x1Stride1Pad0;
+
+template <ck::index_t NDimSpatial,
+          typename ALayout,
+          typename BLayout,
+          typename ELayout,
+          ConvolutionBackwardWeightSpecialization ConvSpec>
+using device_grouped_conv_bwd_weight_dl_f32_instances = std::tuple<
+    // clang-format off
+        //############################|        Num| InLayout| WeiLayout| OutLayout| InData| WeiData| OutData| AccData|          In|         Wei|         Out|              ConvBackward| Block|  MPer|  NPer| K0Per| K1|  M1Per|  N1Per|   KPer|  M1N1Thread|  M1N1Thread|     ABlockTransfer|       ABlockTransfer| ABlockTransfer| ABlockTransfer|         ABlockTransfer|     ABlockTransfer|         ABlockTransfer|     BBlockTransfer|       BBlockTransfer| BBlockTransfer| BBlockTransfer|         BBlockTransfer|     BBlockTransfer|         BBlockTransfer|   CThreadTransfer| CThreadTransfer|    CThreadTransfer| 
+        //############################|        Dim|         |          |          |   Type|    Type|    Type|    Type| Elementwise| Elementwise| Elementwise|                    Weight|  Size| Block| Block| Block|   | Thread| Thread| Thread| ClusterM1Xs| ClusterN1Xs| ThreadSliceLengths| ThreadClusterLengths|  ThreadCluster| SrcAccessOrder| SrcVectorTensorLengths|    SrcVectorTensor| DstVectorTensorLengths| ThreadSliceLengths| ThreadClusterLengths|  ThreadCluster| SrcAccessOrder| SrcVectorTensorLengths|    SrcVectorTensor| DstVectorTensorLengths| SrcDstAccessOrder| SrcDstVectorDim| DstScalarPerVector|
+        //############################|    Spatial|         |          |          |       |        |        |        |   Operation|   Operation|   Operation|            Specialization|      |      |      |      |   |       |       |       |            |            |       _K0_M0_M1_K1|         _K0_M0_M1_K1|   ArrangeOrder|               |           _K0_M0_M1_K1| ContiguousDimOrder|           _K0_M0_M1_K1|       _K0_N0_N1_K1|         _K0_N0_N1_K1|   ArrangeOrder|               |           _K0_N0_N1_K1| ContiguousDimOrder|           _K0_N0_N1_K1|                  |                |                   |
+        //############################|           |         |          |          |       |        |        |        |            |            |            |                          |      |      |      |      |   |       |       |       |            |            |                   |                     |               |               |                       |                   |                       |                   |                     |               |               |                       |                   |                       |                  |                |                   |
+        // generic instance
+        DeviceGroupedConvBwdWeight_Dl< NDimSpatial,  ALayout,   BLayout,   ELayout,    F32,     F32,     F32,     F32, PassThrough, PassThrough, PassThrough,                  ConvSpec,   256,   128,   128,    16,  1,      4,      4,      1,     S<8, 2>,     S<8, 2>,   S<1, 8, 1, 1, 1>,   S<1, 2, 1, 128, 1>, S<0, 2, 3, 1, 4>, S<0, 2, 3, 1, 4>,   S<1, 1, 1, 1, 1>,   S<0, 2, 3, 1, 4>,       S<1, 1, 1, 1, 1>,   S<1, 1, 1, 8, 1>,   S<1, 16, 1, 16, 1>, S<0, 1, 4, 2, 3>, S<0, 1, 4, 2, 3>,   S<1, 1, 1, 1, 1>,   S<0, 1, 4, 2, 3>,       S<1, 1, 1, 1, 1>, S<0, 1, 2, 3, 4, 5>,             5,                   1>
+    // clang-format on
+    >;
+
+template <ck::index_t NDimSpatial,
+          typename ALayout,
+          typename BLayout,
+          typename ELayout,
+          ConvolutionBackwardWeightSpecialization ConvSpec>
+using device_grouped_conv_bwd_weight_dl_f16_instances = std::tuple<
+    // clang-format off
+        //############################|        Num| InLayout| WeiLayout| OutLayout| InData| WeiData| OutData| AccData|          In|         Wei|         Out|              ConvBackward| Block|  MPer|  NPer| K0Per| K1|  M1Per|  N1Per|   KPer|  M1N1Thread|  M1N1Thread|     ABlockTransfer|       ABlockTransfer| ABlockTransfer| ABlockTransfer|         ABlockTransfer|     ABlockTransfer|         ABlockTransfer|     BBlockTransfer|       BBlockTransfer| BBlockTransfer| BBlockTransfer|         BBlockTransfer|     BBlockTransfer|         BBlockTransfer|   CThreadTransfer| CThreadTransfer|    CThreadTransfer| 
+        //############################|        Dim|         |          |          |   Type|    Type|    Type|    Type| Elementwise| Elementwise| Elementwise|                    Weight|  Size| Block| Block| Block|   | Thread| Thread| Thread| ClusterM1Xs| ClusterN1Xs| ThreadSliceLengths| ThreadClusterLengths|  ThreadCluster| SrcAccessOrder| SrcVectorTensorLengths|    SrcVectorTensor| DstVectorTensorLengths| ThreadSliceLengths| ThreadClusterLengths|  ThreadCluster| SrcAccessOrder| SrcVectorTensorLengths|    SrcVectorTensor| DstVectorTensorLengths| SrcDstAccessOrder| SrcDstVectorDim| DstScalarPerVector|
+        //############################|    Spatial|         |          |          |       |        |        |        |   Operation|   Operation|   Operation|            Specialization|      |      |      |      |   |       |       |       |            |            |       _K0_M0_M1_K1|         _K0_M0_M1_K1|   ArrangeOrder|               |           _K0_M0_M1_K1| ContiguousDimOrder|           _K0_M0_M1_K1|       _K0_N0_N1_K1|         _K0_N0_N1_K1|   ArrangeOrder|               |           _K0_N0_N1_K1| ContiguousDimOrder|           _K0_N0_N1_K1|                  |                |                   |
+        //############################|           |         |          |          |       |        |        |        |            |            |            |                          |      |      |      |      |   |       |       |       |            |            |                   |                     |               |               |                       |                   |                       |                   |                     |               |               |                       |                   |                       |                  |                |                   |
+        // generic instance
+        DeviceGroupedConvBwdWeight_Dl< NDimSpatial,  ALayout,   BLayout,   ELayout,    F16,     F16,     F16,     F32, PassThrough, PassThrough, PassThrough,                  ConvSpec,   256,   128,   128,    16,  1,      4,      4,      1,     S<8, 2>,     S<8, 2>,   S<1, 8, 1, 1, 1>,   S<1, 2, 1, 128, 1>, S<0, 2, 3, 1, 4>, S<0, 2, 3, 1, 4>,   S<1, 1, 1, 1, 1>,   S<0, 2, 3, 1, 4>,       S<1, 1, 1, 1, 1>,   S<1, 1, 1, 8, 1>,   S<1, 16, 1, 16, 1>, S<0, 1, 4, 2, 3>, S<0, 1, 4, 2, 3>,   S<1, 1, 1, 1, 1>,   S<0, 1, 4, 2, 3>,       S<1, 1, 1, 1, 1>, S<0, 1, 2, 3, 4, 5>,             5,                   1>
+    // clang-format on
+    >;
+
+template <ck::index_t NDimSpatial,
+          typename ALayout,
+          typename BLayout,
+          typename ELayout,
+          ConvolutionBackwardWeightSpecialization ConvSpec>
+using device_grouped_conv_bwd_weight_dl_bf16_instances = std::tuple<
+    // clang-format off
+        //############################|        Num| InLayout| WeiLayout| OutLayout| InData| WeiData| OutData| AccData|          In|         Wei|         Out|              ConvBackward| Block|  MPer|  NPer| K0Per| K1|  M1Per|  N1Per|   KPer|  M1N1Thread|  M1N1Thread|     ABlockTransfer|       ABlockTransfer| ABlockTransfer| ABlockTransfer|         ABlockTransfer|     ABlockTransfer|         ABlockTransfer|     BBlockTransfer|       BBlockTransfer| BBlockTransfer| BBlockTransfer|         BBlockTransfer|     BBlockTransfer|         BBlockTransfer|   CThreadTransfer| CThreadTransfer|    CThreadTransfer| 
+        //############################|        Dim|         |          |          |   Type|    Type|    Type|    Type| Elementwise| Elementwise| Elementwise|                    Weight|  Size| Block| Block| Block|   | Thread| Thread| Thread| ClusterM1Xs| ClusterN1Xs| ThreadSliceLengths| ThreadClusterLengths|  ThreadCluster| SrcAccessOrder| SrcVectorTensorLengths|    SrcVectorTensor| DstVectorTensorLengths| ThreadSliceLengths| ThreadClusterLengths|  ThreadCluster| SrcAccessOrder| SrcVectorTensorLengths|    SrcVectorTensor| DstVectorTensorLengths| SrcDstAccessOrder| SrcDstVectorDim| DstScalarPerVector|
+        //############################|    Spatial|         |          |          |       |        |        |        |   Operation|   Operation|   Operation|            Specialization|      |      |      |      |   |       |       |       |            |            |       _K0_M0_M1_K1|         _K0_M0_M1_K1|   ArrangeOrder|               |           _K0_M0_M1_K1| ContiguousDimOrder|           _K0_M0_M1_K1|       _K0_N0_N1_K1|         _K0_N0_N1_K1|   ArrangeOrder|               |           _K0_N0_N1_K1| ContiguousDimOrder|           _K0_N0_N1_K1|                  |                |                   |
+        //############################|           |         |          |          |       |        |        |        |            |            |            |                          |      |      |      |      |   |       |       |       |            |            |                   |                     |               |               |                       |                   |                       |                   |                     |               |               |                       |                   |                       |                  |                |                   |
+        // generic instance
+        DeviceGroupedConvBwdWeight_Dl< NDimSpatial,  ALayout,   BLayout,   ELayout,   BF16,     F32,    BF16,     F32, PassThrough, PassThrough, PassThrough,                  ConvSpec,   256,   128,   128,    16,  1,      4,      4,      1,     S<8, 2>,     S<8, 2>,   S<1, 8, 1, 1, 1>,   S<1, 2, 1, 128, 1>, S<0, 2, 3, 1, 4>, S<0, 2, 3, 1, 4>,   S<1, 1, 1, 1, 1>,   S<0, 2, 3, 1, 4>,       S<1, 1, 1, 1, 1>,   S<1, 1, 1, 8, 1>,   S<1, 16, 1, 16, 1>, S<0, 1, 4, 2, 3>, S<0, 1, 4, 2, 3>,   S<1, 1, 1, 1, 1>,   S<0, 1, 4, 2, 3>,       S<1, 1, 1, 1, 1>, S<0, 1, 2, 3, 4, 5>,             5,                   1>
+    // clang-format on
+    >;
+
+} // namespace instance
+} // namespace device
+} // namespace tensor_operation
+} // namespace ck

library/include/ck/library/tensor_operation_instance/gpu/grouped_conv_fwd/device_grouped_conv_fwd_dl_instance.hpp

@@ -55,8 +55,8 @@ using device_grouped_conv2d_fwd_dl_f16_instances = std::tuple<
            // ########################################|            |       |        |            |        |        |         |          |          |          |            |             |              |               |                  |      |      |      |      |   |           |           |       |              |              |                   |                     |               |               |                    |                   |                    |                   |                     |               |               |                    |                   |                    |                    |                |                   |
         // generic instances
         // TODO: Change to ScalarPerVector = 1 when inner_product<half_t, half_t, float> will be supported
-        DeviceGroupedConvFwdDlMultipleD_NHWC_KYXC_NHWK<           2,    F16,     F16,  DsDatatype,     F16,     F32, InLayout, WeiLayout,  DsLayout, OutLayout, PassThrough,  PassThrough,  CDEElementOp,       ConvSpec,    GemmMNKPadding,     8,    16,     4,     2,  2,          1,          2,      1,       S<4, 2>,       S<1, 1>,      S<2, 1, 2, 2>,        S<1, 1, 8, 1>,  S<1, 2, 0, 3>,  S<1, 2, 0, 3>,       S<1, 1, 1, 2>,      S<1, 2, 0, 3>,       S<1, 1, 1, 2>,      S<1, 1, 1, 2>,        S<2, 1, 4, 1>,  S<1, 2, 0, 3>,  S<1, 2, 0, 3>,       S<1, 1, 1, 1>,      S<1, 2, 0, 3>,       S<1, 1, 1, 2>, S<0, 1, 2, 3, 4, 5>,               5,                 2>,
-        DeviceGroupedConvFwdDlMultipleD_NHWC_KYXC_NHWK<           2,    F16,     F16,  DsDatatype,     F16,     F32, InLayout, WeiLayout,  DsLayout, OutLayout, PassThrough,  PassThrough,  CDEElementOp,       ConvSpec,    GemmMNKPadding,   256,   128,   128,    16,  2,          4,          4,      1,       S<8, 2>,       S<8, 2>,      S<8, 1, 1, 2>,      S<2, 1, 128, 1>,  S<1, 2, 0, 3>,  S<1, 2, 0, 3>,       S<1, 1, 1, 2>,      S<1, 2, 0, 3>,       S<1, 1, 1, 2>,      S<8, 1, 1, 2>,      S<2, 1, 128, 1>,  S<1, 2, 0, 3>,  S<1, 2, 0, 3>,       S<1, 1, 1, 1>,      S<1, 2, 0, 3>,       S<1, 1, 1, 2>, S<0, 1, 2, 3, 4, 5>,               5,                 2>,
+        DeviceGroupedConvFwdDlMultipleD_NHWC_KYXC_NHWK<           2,    F16,     F16,  DsDatatype,     F16,     F32, InLayout, WeiLayout,  DsLayout, OutLayout, PassThrough,  PassThrough,  CDEElementOp,       ConvSpec,    GemmMNKPadding,     8,    16,     4,     2,  1,          1,          2,      1,       S<4, 2>,       S<1, 1>,      S<2, 1, 2, 1>,        S<1, 1, 8, 1>,  S<1, 2, 0, 3>,  S<1, 2, 0, 3>,       S<1, 1, 1, 1>,      S<1, 2, 0, 3>,       S<1, 1, 1, 1>,      S<1, 1, 1, 1>,        S<2, 1, 4, 1>,  S<1, 2, 0, 3>,  S<1, 2, 0, 3>,       S<1, 1, 1, 1>,      S<1, 2, 0, 3>,       S<1, 1, 1, 1>, S<0, 1, 2, 3, 4, 5>,               5,                 1>,
+        DeviceGroupedConvFwdDlMultipleD_NHWC_KYXC_NHWK<           2,    F16,     F16,  DsDatatype,     F16,     F32, InLayout, WeiLayout,  DsLayout, OutLayout, PassThrough,  PassThrough,  CDEElementOp,       ConvSpec,    GemmMNKPadding,   256,   128,   128,    16,  1,          4,          4,      1,       S<8, 2>,       S<8, 2>,      S<8, 1, 1, 1>,      S<2, 1, 128, 1>,  S<1, 2, 0, 3>,  S<1, 2, 0, 3>,       S<1, 1, 1, 1>,      S<1, 2, 0, 3>,       S<1, 1, 1, 1>,      S<8, 1, 1, 1>,      S<2, 1, 128, 1>,  S<1, 2, 0, 3>,  S<1, 2, 0, 3>,       S<1, 1, 1, 1>,      S<1, 2, 0, 3>,       S<1, 1, 1, 1>, S<0, 1, 2, 3, 4, 5>,               5,                 1>,
 
         DeviceGroupedConvFwdDlMultipleD_NHWC_KYXC_NHWK<           2,    F16,     F16,  DsDatatype,     F16,     F32, InLayout, WeiLayout,  DsLayout, OutLayout, PassThrough,  PassThrough,  CDEElementOp,       ConvSpec,    GemmMNKPadding,   256,   128,   128,    16,  2,          4,          4,      1,       S<8, 2>,       S<8, 2>,      S<8, 1, 1, 2>,      S<2, 1, 128, 1>,  S<1, 2, 0, 3>,  S<1, 2, 0, 3>,       S<4, 1, 1, 2>,      S<1, 2, 0, 3>,       S<1, 1, 1, 2>,      S<8, 1, 1, 2>,      S<2, 1, 128, 1>,  S<1, 2, 0, 3>,  S<1, 2, 0, 3>,       S<4, 1, 1, 2>,      S<1, 2, 0, 3>,       S<1, 1, 1, 2>, S<0, 1, 2, 3, 4, 5>,               5,                 4>
     // clang-format on

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

@@ -17,6 +17,7 @@ namespace tensor_operation {
 namespace device {
 namespace instance {
 
+// xdl
 // conv1d backward weight
 void add_device_grouped_conv1d_bwd_weight_xdl_gnwc_gkxc_gnwk_bf16_f32_bf16_instances(
     std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<1,
@@ -200,6 +201,228 @@ void add_device_grouped_conv3d_bwd_weight_xdl_ndhwgc_gkzyxc_ndhwgk_f32_instances
                                                            PassThrough,
                                                            PassThrough>>>& instances);
 
+#ifdef DL_KERNELS
+// dl
+// conv1d backward weight
+void add_device_grouped_conv1d_bwd_weight_dl_gnwc_gkxc_gnwk_bf16_f32_bf16_instances(
+    std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<1,
+                                                           GNWC,
+                                                           GKXC,
+                                                           GNWK,
+                                                           BF16,
+                                                           F32,
+                                                           BF16,
+                                                           PassThrough,
+                                                           PassThrough,
+                                                           PassThrough>>>& instances);
+
+void add_device_grouped_conv1d_bwd_weight_dl_gnwc_gkxc_gnwk_f16_instances(
+    std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<1,
+                                                           GNWC,
+                                                           GKXC,
+                                                           GNWK,
+                                                           F16,
+                                                           F16,
+                                                           F16,
+                                                           PassThrough,
+                                                           PassThrough,
+                                                           PassThrough>>>& instances);
+
+void add_device_grouped_conv1d_bwd_weight_dl_gnwc_gkxc_gnwk_f32_instances(
+    std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<1,
+                                                           GNWC,
+                                                           GKXC,
+                                                           GNWK,
+                                                           F32,
+                                                           F32,
+                                                           F32,
+                                                           PassThrough,
+                                                           PassThrough,
+                                                           PassThrough>>>& instances);
+
+void add_device_grouped_conv1d_bwd_weight_dl_nwgc_gkxc_nwgk_bf16_f32_bf16_instances(
+    std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<1,
+                                                           NWGC,
+                                                           GKXC,
+                                                           NWGK,
+                                                           BF16,
+                                                           F32,
+                                                           BF16,
+                                                           PassThrough,
+                                                           PassThrough,
+                                                           PassThrough>>>& instances);
+
+void add_device_grouped_conv1d_bwd_weight_dl_nwgc_gkxc_nwgk_f16_instances(
+    std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<1,
+                                                           NWGC,
+                                                           GKXC,
+                                                           NWGK,
+                                                           F16,
+                                                           F16,
+                                                           F16,
+                                                           PassThrough,
+                                                           PassThrough,
+                                                           PassThrough>>>& instances);
+
+void add_device_grouped_conv1d_bwd_weight_dl_nwgc_gkxc_nwgk_f32_instances(
+    std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<1,
+                                                           NWGC,
+                                                           GKXC,
+                                                           NWGK,
+                                                           F32,
+                                                           F32,
+                                                           F32,
+                                                           PassThrough,
+                                                           PassThrough,
+                                                           PassThrough>>>& instances);
+
+// conv2d backward weight
+void add_device_grouped_conv2d_bwd_weight_dl_gnhwc_gkyxc_gnhwk_bf16_f32_bf16_instances(
+    std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<2,
+                                                           GNHWC,
+                                                           GKYXC,
+                                                           GNHWK,
+                                                           BF16,
+                                                           F32,
+                                                           BF16,
+                                                           PassThrough,
+                                                           PassThrough,
+                                                           PassThrough>>>& instances);
+
+void add_device_grouped_conv2d_bwd_weight_dl_gnhwc_gkyxc_gnhwk_f16_instances(
+    std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<2,
+                                                           GNHWC,
+                                                           GKYXC,
+                                                           GNHWK,
+                                                           F16,
+                                                           F16,
+                                                           F16,
+                                                           PassThrough,
+                                                           PassThrough,
+                                                           PassThrough>>>& instances);
+
+void add_device_grouped_conv2d_bwd_weight_dl_gnhwc_gkyxc_gnhwk_f32_instances(
+    std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<2,
+                                                           GNHWC,
+                                                           GKYXC,
+                                                           GNHWK,
+                                                           F32,
+                                                           F32,
+                                                           F32,
+                                                           PassThrough,
+                                                           PassThrough,
+                                                           PassThrough>>>& instances);
+
+void add_device_grouped_conv2d_bwd_weight_dl_nhwgc_gkyxc_nhwgk_bf16_f32_bf16_instances(
+    std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<2,
+                                                           NHWGC,
+                                                           GKYXC,
+                                                           NHWGK,
+                                                           BF16,
+                                                           F32,
+                                                           BF16,
+                                                           PassThrough,
+                                                           PassThrough,
+                                                           PassThrough>>>& instances);
+
+void add_device_grouped_conv2d_bwd_weight_dl_nhwgc_gkyxc_nhwgk_f16_instances(
+    std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<2,
+                                                           NHWGC,
+                                                           GKYXC,
+                                                           NHWGK,
+                                                           F16,
+                                                           F16,
+                                                           F16,
+                                                           PassThrough,
+                                                           PassThrough,
+                                                           PassThrough>>>& instances);
+
+void add_device_grouped_conv2d_bwd_weight_dl_nhwgc_gkyxc_nhwgk_f32_instances(
+    std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<2,
+                                                           NHWGC,
+                                                           GKYXC,
+                                                           NHWGK,
+                                                           F32,
+                                                           F32,
+                                                           F32,
+                                                           PassThrough,
+                                                           PassThrough,
+                                                           PassThrough>>>& instances);
+
+// conv3d backward weight
+void add_device_grouped_conv3d_bwd_weight_dl_gndhwc_gkzyxc_gndhwk_bf16_f32_bf16_instances(
+    std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<3,
+                                                           GNDHWC,
+                                                           GKZYXC,
+                                                           GNDHWK,
+                                                           BF16,
+                                                           F32,
+                                                           BF16,
+                                                           PassThrough,
+                                                           PassThrough,
+                                                           PassThrough>>>& instances);
+
+void add_device_grouped_conv3d_bwd_weight_dl_gndhwc_gkzyxc_gndhwk_f16_instances(
+    std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<3,
+                                                           GNDHWC,
+                                                           GKZYXC,
+                                                           GNDHWK,
+                                                           F16,
+                                                           F16,
+                                                           F16,
+                                                           PassThrough,
+                                                           PassThrough,
+                                                           PassThrough>>>& instances);
+
+void add_device_grouped_conv3d_bwd_weight_dl_gndhwc_gkzyxc_gndhwk_f32_instances(
+    std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<3,
+                                                           GNDHWC,
+                                                           GKZYXC,
+                                                           GNDHWK,
+                                                           F32,
+                                                           F32,
+                                                           F32,
+                                                           PassThrough,
+                                                           PassThrough,
+                                                           PassThrough>>>& instances);
+
+void add_device_grouped_conv3d_bwd_weight_dl_ndhwgc_gkzyxc_ndhwgk_bf16_f32_bf16_instances(
+    std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<3,
+                                                           NDHWGC,
+                                                           GKZYXC,
+                                                           NDHWGK,
+                                                           BF16,
+                                                           F32,
+                                                           BF16,
+                                                           PassThrough,
+                                                           PassThrough,
+                                                           PassThrough>>>& instances);
+
+void add_device_grouped_conv3d_bwd_weight_dl_ndhwgc_gkzyxc_ndhwgk_f16_instances(
+    std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<3,
+                                                           NDHWGC,
+                                                           GKZYXC,
+                                                           NDHWGK,
+                                                           F16,
+                                                           F16,
+                                                           F16,
+                                                           PassThrough,
+                                                           PassThrough,
+                                                           PassThrough>>>& instances);
+
+void add_device_grouped_conv3d_bwd_weight_dl_ndhwgc_gkzyxc_ndhwgk_f32_instances(
+    std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<3,
+                                                           NDHWGC,
+                                                           GKZYXC,
+                                                           NDHWGK,
+                                                           F32,
+                                                           F32,
+                                                           F32,
+                                                           PassThrough,
+                                                           PassThrough,
+                                                           PassThrough>>>& instances);
+#endif
+
 template <ck::index_t NumDimSpatial,
           typename InLayout,
           typename WeiLayout,
@@ -242,21 +465,54 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe
                 if constexpr(is_same_v<InDataType, float> && is_same_v<WeiDataType, float> &&
                              is_same_v<OutDataType, float>)
                 {
+#ifdef DL_KERNELS
+                    add_device_grouped_conv1d_bwd_weight_dl_gnwc_gkxc_gnwk_f32_instances(op_ptrs);
+#endif
                     add_device_grouped_conv1d_bwd_weight_xdl_gnwc_gkxc_gnwk_f32_instances(op_ptrs);
                 }
                 else if constexpr(is_same_v<InDataType, half_t> && is_same_v<WeiDataType, half_t> &&
                                   is_same_v<OutDataType, half_t>)
                 {
+#ifdef DL_KERNELS
+                    add_device_grouped_conv1d_bwd_weight_dl_gnwc_gkxc_gnwk_f16_instances(op_ptrs);
+#endif
                     add_device_grouped_conv1d_bwd_weight_xdl_gnwc_gkxc_gnwk_f16_instances(op_ptrs);
                 }
                 else if constexpr(is_same_v<InDataType, ck::bhalf_t> &&
                                   is_same_v<WeiDataType, float> &&
                                   is_same_v<OutDataType, ck::bhalf_t>)
                 {
+#ifdef DL_KERNELS
+                    add_device_grouped_conv1d_bwd_weight_dl_gnwc_gkxc_gnwk_bf16_f32_bf16_instances(
+                        op_ptrs);
+#endif
                     add_device_grouped_conv1d_bwd_weight_xdl_gnwc_gkxc_gnwk_bf16_f32_bf16_instances(
                         op_ptrs);
                 }
             }
+            else if constexpr(is_same_v<InLayout, NWGC> && is_same_v<WeiLayout, GKXC> &&
+                              is_same_v<OutLayout, NWGK>)
+            {
+#ifdef DL_KERNELS
+                if constexpr(is_same_v<InDataType, float> && is_same_v<WeiDataType, float> &&
+                             is_same_v<OutDataType, float>)
+                {
+                    add_device_grouped_conv1d_bwd_weight_dl_nwgc_gkxc_nwgk_f32_instances(op_ptrs);
+                }
+                else if constexpr(is_same_v<InDataType, half_t> && is_same_v<WeiDataType, half_t> &&
+                                  is_same_v<OutDataType, half_t>)
+                {
+                    add_device_grouped_conv1d_bwd_weight_dl_nwgc_gkxc_nwgk_f16_instances(op_ptrs);
+                }
+                else if constexpr(is_same_v<InDataType, ck::bhalf_t> &&
+                                  is_same_v<WeiDataType, float> &&
+                                  is_same_v<OutDataType, ck::bhalf_t>)
+                {
+                    add_device_grouped_conv1d_bwd_weight_dl_nwgc_gkxc_nwgk_bf16_f32_bf16_instances(
+                        op_ptrs);
+                }
+#endif
+            }
         }
         else if constexpr(NumDimSpatial == 2)
         {
@@ -266,12 +522,20 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe
                 if constexpr(is_same_v<InDataType, float> && is_same_v<WeiDataType, float> &&
                              is_same_v<OutDataType, float>)
                 {
+#ifdef DL_KERNELS
+                    add_device_grouped_conv2d_bwd_weight_dl_gnhwc_gkyxc_gnhwk_f32_instances(
+                        op_ptrs);
+#endif
                     add_device_grouped_conv2d_bwd_weight_xdl_gnhwc_gkyxc_gnhwk_f32_instances(
                         op_ptrs);
                 }
                 else if constexpr(is_same_v<InDataType, half_t> && is_same_v<WeiDataType, half_t> &&
                                   is_same_v<OutDataType, half_t>)
                 {
+#ifdef DL_KERNELS
+                    add_device_grouped_conv2d_bwd_weight_dl_gnhwc_gkyxc_gnhwk_f16_instances(
+                        op_ptrs);
+#endif
                     add_device_grouped_conv2d_bwd_weight_xdl_gnhwc_gkyxc_gnhwk_f16_instances(
                         op_ptrs);
                 }
@@ -279,6 +543,10 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe
                                   is_same_v<WeiDataType, float> &&
                                   is_same_v<OutDataType, ck::bhalf_t>)
                 {
+#ifdef DL_KERNELS
+                    add_device_grouped_conv2d_bwd_weight_dl_gnhwc_gkyxc_gnhwk_bf16_f32_bf16_instances(
+                        op_ptrs);
+#endif
                     add_device_grouped_conv2d_bwd_weight_xdl_gnhwc_gkyxc_gnhwk_bf16_f32_bf16_instances(
                         op_ptrs);
                 }
@@ -289,12 +557,20 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe
                 if constexpr(is_same_v<InDataType, float> && is_same_v<WeiDataType, float> &&
                              is_same_v<OutDataType, float>)
                 {
+#ifdef DL_KERNELS
+                    add_device_grouped_conv2d_bwd_weight_dl_nhwgc_gkyxc_nhwgk_f32_instances(
+                        op_ptrs);
+#endif
                     add_device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_f32_instances(
                         op_ptrs);
                 }
                 else if constexpr(is_same_v<InDataType, half_t> && is_same_v<WeiDataType, half_t> &&
                                   is_same_v<OutDataType, half_t>)
                 {
+#ifdef DL_KERNELS
+                    add_device_grouped_conv2d_bwd_weight_dl_nhwgc_gkyxc_nhwgk_f16_instances(
+                        op_ptrs);
+#endif
                     add_device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_f16_instances(
                         op_ptrs);
                 }
@@ -302,6 +578,10 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe
                                   is_same_v<WeiDataType, float> &&
                                   is_same_v<OutDataType, ck::bhalf_t>)
                 {
+#ifdef DL_KERNELS
+                    add_device_grouped_conv2d_bwd_weight_dl_nhwgc_gkyxc_nhwgk_bf16_f32_bf16_instances(
+                        op_ptrs);
+#endif
                     add_device_grouped_conv2d_bwd_weight_xdl_nhwgc_gkyxc_nhwgk_bf16_f32_bf16_instances(
                         op_ptrs);
                 }
@@ -315,12 +595,20 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe
                 if constexpr(is_same_v<InDataType, float> && is_same_v<WeiDataType, float> &&
                              is_same_v<OutDataType, float>)
                 {
+#ifdef DL_KERNELS
+                    add_device_grouped_conv3d_bwd_weight_dl_gndhwc_gkzyxc_gndhwk_f32_instances(
+                        op_ptrs);
+#endif
                     add_device_grouped_conv3d_bwd_weight_xdl_gndhwc_gkzyxc_gndhwk_f32_instances(
                         op_ptrs);
                 }
                 else if constexpr(is_same_v<InDataType, half_t> && is_same_v<WeiDataType, half_t> &&
                                   is_same_v<OutDataType, half_t>)
                 {
+#ifdef DL_KERNELS
+                    add_device_grouped_conv3d_bwd_weight_dl_gndhwc_gkzyxc_gndhwk_f16_instances(
+                        op_ptrs);
+#endif
                     add_device_grouped_conv3d_bwd_weight_xdl_gndhwc_gkzyxc_gndhwk_f16_instances(
                         op_ptrs);
                 }
@@ -328,6 +616,10 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe
                                   is_same_v<WeiDataType, float> &&
                                   is_same_v<OutDataType, ck::bhalf_t>)
                 {
+#ifdef DL_KERNELS
+                    add_device_grouped_conv3d_bwd_weight_dl_gndhwc_gkzyxc_gndhwk_bf16_f32_bf16_instances(
+                        op_ptrs);
+#endif
                     add_device_grouped_conv3d_bwd_weight_xdl_gndhwc_gkzyxc_gndhwk_bf16_f32_bf16_instances(
                         op_ptrs);
                 }
@@ -338,12 +630,20 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe
                 if constexpr(is_same_v<InDataType, float> && is_same_v<WeiDataType, float> &&
                              is_same_v<OutDataType, float>)
                 {
+#ifdef DL_KERNELS
+                    add_device_grouped_conv3d_bwd_weight_dl_ndhwgc_gkzyxc_ndhwgk_f32_instances(
+                        op_ptrs);
+#endif
                     add_device_grouped_conv3d_bwd_weight_xdl_ndhwgc_gkzyxc_ndhwgk_f32_instances(
                         op_ptrs);
                 }
                 else if constexpr(is_same_v<InDataType, half_t> && is_same_v<WeiDataType, half_t> &&
                                   is_same_v<OutDataType, half_t>)
                 {
+#ifdef DL_KERNELS
+                    add_device_grouped_conv3d_bwd_weight_dl_ndhwgc_gkzyxc_ndhwgk_f16_instances(
+                        op_ptrs);
+#endif
                     add_device_grouped_conv3d_bwd_weight_xdl_ndhwgc_gkzyxc_ndhwgk_f16_instances(
                         op_ptrs);
                 }
@@ -351,6 +651,10 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe
                                   is_same_v<WeiDataType, float> &&
                                   is_same_v<OutDataType, ck::bhalf_t>)
                 {
+#ifdef DL_KERNELS
+                    add_device_grouped_conv3d_bwd_weight_dl_ndhwgc_gkzyxc_ndhwgk_bf16_f32_bf16_instances(
+                        op_ptrs);
+#endif
                     add_device_grouped_conv3d_bwd_weight_xdl_ndhwgc_gkzyxc_ndhwgk_bf16_f32_bf16_instances(
                         op_ptrs);
                 }

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

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include <vector>
+#include <memory>
+#include "ck/ck.hpp"
+#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
+#include "ck/tensor_operation/gpu/device/device_grouped_gemm_fixed_nk.hpp"
+
+#include "ck/library/tensor_operation_instance/device_operation_instance_factory.hpp"
+
+namespace ck {
+namespace tensor_operation {
+namespace device {
+namespace instance {
+
+// fp16_output
+void add_device_grouped_gemm_xdl_fixed_nk_f16_f16_f16_mk_kn_mn_instances(
+    std::vector<std::unique_ptr<DeviceGroupedGemmFixedNK<Row,
+                                                         Row,
+                                                         Empty_Tuple,
+                                                         Row,
+                                                         F16,
+                                                         F16,
+                                                         Empty_Tuple,
+                                                         F16,
+                                                         PassThrough,
+                                                         PassThrough,
+                                                         PassThrough>>>& instances);
+
+void add_device_grouped_gemm_xdl_fixed_nk_f16_f16_f16_mk_nk_mn_instances(
+    std::vector<std::unique_ptr<DeviceGroupedGemmFixedNK<Row,
+                                                         Col,
+                                                         Empty_Tuple,
+                                                         Row,
+                                                         F16,
+                                                         F16,
+                                                         Empty_Tuple,
+                                                         F16,
+                                                         PassThrough,
+                                                         PassThrough,
+                                                         PassThrough>>>& instances);
+
+// fp8_inputB
+void add_device_grouped_gemm_xdl_fixed_nk_f16_f8_f16_mk_kn_mn_instances(
+    std::vector<std::unique_ptr<DeviceGroupedGemmFixedNK<Row,
+                                                         Row,
+                                                         Empty_Tuple,
+                                                         Row,
+                                                         F16,
+                                                         F8,
+                                                         Empty_Tuple,
+                                                         F16,
+                                                         PassThrough,
+                                                         PassThrough,
+                                                         PassThrough>>>& instances);
+
+void add_device_grouped_gemm_xdl_fixed_nk_f16_f8_f16_mk_nk_mn_instances(
+    std::vector<std::unique_ptr<DeviceGroupedGemmFixedNK<Row,
+                                                         Col,
+                                                         Empty_Tuple,
+                                                         Row,
+                                                         F16,
+                                                         F8,
+                                                         Empty_Tuple,
+                                                         F16,
+                                                         PassThrough,
+                                                         PassThrough,
+                                                         PassThrough>>>& instances);
+
+// i8_inputB
+void add_device_grouped_gemm_xdl_fixed_nk_f16_i8_f16_mk_kn_mn_instances(
+    std::vector<std::unique_ptr<DeviceGroupedGemmFixedNK<Row,
+                                                         Row,
+                                                         Empty_Tuple,
+                                                         Row,
+                                                         F16,
+                                                         I8,
+                                                         Empty_Tuple,
+                                                         F16,
+                                                         PassThrough,
+                                                         PassThrough,
+                                                         PassThrough>>>& instances);
+
+void add_device_grouped_gemm_xdl_fixed_nk_f16_i8_f16_mk_nk_mn_instances(
+    std::vector<std::unique_ptr<DeviceGroupedGemmFixedNK<Row,
+                                                         Col,
+                                                         Empty_Tuple,
+                                                         Row,
+                                                         F16,
+                                                         I8,
+                                                         Empty_Tuple,
+                                                         F16,
+                                                         PassThrough,
+                                                         PassThrough,
+                                                         PassThrough>>>& instances);
+
+template <typename ALayout,
+          typename BLayout,
+          typename ELayout,
+          typename ADataType,
+          typename BDataType,
+          typename EDataType>
+struct DeviceOperationInstanceFactory<
+    ck::tensor_operation::device::DeviceGroupedGemmFixedNK<ALayout,
+                                                           BLayout,
+                                                           Empty_Tuple,
+                                                           ELayout,
+                                                           ADataType,
+                                                           BDataType,
+                                                           Empty_Tuple,
+                                                           EDataType,
+                                                           PassThrough,
+                                                           PassThrough,
+                                                           PassThrough>>
+{
+    using DeviceOp = DeviceGroupedGemmFixedNK<ALayout,
+                                              BLayout,
+                                              Empty_Tuple,
+                                              ELayout,
+                                              ADataType,
+                                              BDataType,
+                                              Empty_Tuple,
+                                              EDataType,
+                                              PassThrough,
+                                              PassThrough,
+                                              PassThrough>;
+
+    static auto GetInstances()
+    {
+        std::vector<std::unique_ptr<DeviceOp>> op_ptrs;
+
+        // fp16_output
+        if constexpr(is_same_v<ADataType, half_t> && is_same_v<BDataType, half_t> &&
+                     is_same_v<EDataType, half_t>)
+        {
+            if constexpr(is_same_v<ALayout, Row> && is_same_v<BLayout, Row> &&
+                         is_same_v<ELayout, Row>)
+            {
+                add_device_grouped_gemm_xdl_fixed_nk_f16_f16_f16_mk_kn_mn_instances(op_ptrs);
+            }
+            if constexpr(is_same_v<ALayout, Row> && is_same_v<BLayout, Col> &&
+                         is_same_v<ELayout, Row>)
+            {
+                add_device_grouped_gemm_xdl_fixed_nk_f16_f16_f16_mk_nk_mn_instances(op_ptrs);
+            }
+        }
+
+        // fp8_input
+        if constexpr(is_same_v<ADataType, half_t> && is_same_v<BDataType, f8_t> &&
+                     is_same_v<EDataType, half_t>)
+        {
+            if constexpr(is_same_v<ALayout, Row> && is_same_v<BLayout, Row> &&
+                         is_same_v<ELayout, Row>)
+            {
+                add_device_grouped_gemm_xdl_fixed_nk_f16_f8_f16_mk_kn_mn_instances(op_ptrs);
+            }
+            if constexpr(is_same_v<ALayout, Row> && is_same_v<BLayout, Col> &&
+                         is_same_v<ELayout, Row>)
+            {
+                add_device_grouped_gemm_xdl_fixed_nk_f16_f8_f16_mk_nk_mn_instances(op_ptrs);
+            }
+        }
+
+        // i8_input
+        if constexpr(is_same_v<ADataType, half_t> && is_same_v<BDataType, int8_t> &&
+                     is_same_v<EDataType, half_t>)
+        {
+            if constexpr(is_same_v<ALayout, Row> && is_same_v<BLayout, Row> &&
+                         is_same_v<ELayout, Row>)
+            {
+                add_device_grouped_gemm_xdl_fixed_nk_f16_i8_f16_mk_kn_mn_instances(op_ptrs);
+            }
+            if constexpr(is_same_v<ALayout, Row> && is_same_v<BLayout, Col> &&
+                         is_same_v<ELayout, Row>)
+            {
+                add_device_grouped_gemm_xdl_fixed_nk_f16_i8_f16_mk_nk_mn_instances(op_ptrs);
+            }
+        }
+
+        return op_ptrs;
+    }
+};
+
+} // namespace instance
+} // namespace device
+} // namespace tensor_operation
+} // namespace ck

library/src/tensor_operation_instance/gpu/grouped_conv1d_bwd_weight/CMakeLists.txt

-add_instance_library(device_grouped_conv1d_bwd_weight_instance
+set(GROUPED_CONV1D_BWD_WEIGHT
     device_grouped_conv1d_bwd_weight_xdl_gnwc_gkxc_gnwk_f16_instance.cpp
     device_grouped_conv1d_bwd_weight_xdl_gnwc_gkxc_gnwk_f32_instance.cpp
-    device_grouped_conv1d_bwd_weight_xdl_gnwc_gkxc_gnwk_bf16_instance.cpp
-)
+    device_grouped_conv1d_bwd_weight_xdl_gnwc_gkxc_gnwk_bf16_instance.cpp)
+
+if(DL_KERNELS)
+    list(APPEND GROUPED_CONV1D_BWD_WEIGHT
+        device_grouped_conv1d_bwd_weight_dl_gnwc_gkxc_gnwk_f16_instance.cpp
+        device_grouped_conv1d_bwd_weight_dl_gnwc_gkxc_gnwk_f32_instance.cpp
+        device_grouped_conv1d_bwd_weight_dl_gnwc_gkxc_gnwk_bf16_instance.cpp
+        device_grouped_conv1d_bwd_weight_dl_nwgc_gkxc_nwgk_f16_instance.cpp
+        device_grouped_conv1d_bwd_weight_dl_nwgc_gkxc_nwgk_f32_instance.cpp
+        device_grouped_conv1d_bwd_weight_dl_nwgc_gkxc_nwgk_bf16_instance.cpp)
+endif()
+
+add_instance_library(device_grouped_conv1d_bwd_weight_instance ${GROUPED_CONV1D_BWD_WEIGHT})