dedicated fixed_nk solution

client_example/17_grouped_gemm_fastgelu/grouped_gemm_fastgelu.cpp

@@ -60,8 +60,6 @@ int main()
 
     std::vector<int> Ms, Ns, Ks, StrideAs, StrideBs, StrideEs;
 
-    int sum_of_m = 0;
-
     for(int i = 0; i < group_count; ++i)
     {
         Ms.push_back(256 + 256 * distrib(gen));
@@ -71,8 +69,6 @@ int main()
         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 =
@@ -106,10 +102,6 @@ int main()
 
     gemm_descs.reserve(group_count);
 
-    std::vector<ck::tensor_operation::device::GroupedGemmKernelArgument<>>
-        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) *
@@ -119,23 +111,11 @@ int main()
         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], StrideAs[i], StrideBs[i], StrideEs[i], {}});
+        gemm_descs.push_back({Ms[i], Ns[i], Ks[i], StrideAs[i], StrideBs[i], StrideEs[i], {}});
 
         p_a.push_back(a_dev_bufs[i].GetDeviceBuffer());
         p_b.push_back(b_dev_bufs[i].GetDeviceBuffer());
         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::DeviceGroupedGemm<ALayout,
@@ -182,20 +162,13 @@ int main()
         auto invoker_ptr = op_ptr->MakeInvokerPointer();
 
         SimpleDeviceMem gemm_desc_workspace(op_ptr->GetWorkSpaceSize(argument_ptr.get()));
-        // op_ptr->SetWorkSpacePointer(argument_ptr.get(), gemm_desc_workspace.GetDeviceBuffer());
+        op_ptr->SetWorkSpacePointer(argument_ptr.get(), gemm_desc_workspace.GetDeviceBuffer());
         std::string op_name = op_ptr->GetTypeString();
 
-        hipMemcpy(gemm_desc_workspace.GetDeviceBuffer(),
-                  grouped_gemm_kernel_args_.data(),
-                  op_ptr->GetWorkSpaceSize(argument_ptr.get()),
-                  hipMemcpyHostToDevice);
-
         if(op_ptr->IsSupportedArgument(argument_ptr.get()))
         {
 
-            float ave_time = invoker_ptr->Run(argument_ptr.get(),
-                                              gemm_desc_workspace.GetDeviceBuffer(),
-                                              StreamConfig{nullptr, true});
+            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)

example/15_grouped_gemm/CMakeLists.txt

@@ -7,6 +7,8 @@ add_example_executable(example_grouped_gemm_xdl_int8 grouped_gemm_xdl_int8.cpp)
 add_example_executable(example_grouped_gemm_multiple_d_dl_fp16 grouped_gemm_multiple_d_dl_fp16.cpp)
 add_example_executable(example_grouped_gemm_xdl_splitk_fp16 grouped_gemm_xdl_splitk_fp16.cpp)
 
+add_example_executable(example_grouped_gemm_xdl_fixed_nk_fp16 grouped_gemm_xdl_fixed_nk_fp16.cpp)
+
 
 add_dependencies(example_grouped_gemm_xdl
                  example_grouped_gemm_xdl_fp32
@@ -14,7 +16,9 @@ add_dependencies(example_grouped_gemm_xdl
                  example_grouped_gemm_xdl_bfp16
                  example_grouped_gemm_xdl_int8
                  example_grouped_gemm_multiple_d_dl_fp16
-                 example_grouped_gemm_xdl_splitk_fp16)
+                 example_grouped_gemm_xdl_splitk_fp16
+                 example_grouped_gemm_xdl_fixed_nk_fp16
+             )
 
 if(USE_BITINT_EXTENSION_INT4)
   add_example_executable(example_grouped_gemm_xdl_int4 grouped_gemm_xdl_int4.cpp)

example/15_grouped_gemm/grouped_gemm_xdl_fixed_nk_fp16.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 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 AccDataType      = F32;
+using CShuffleDataType = F16;
+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,   256,   128,    32,   8,   8,   32,   32,    4,    2,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,         1,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,             2,              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;
+    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],
+                              problem_size.stride_As[i],
+                              problem_size.stride_Bs[i],
+                              problem_size.stride_Cs[i],
+                              {}});
+
+        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_desc_workspace(gemm.GetWorkSpaceSize(&argument));
+
+    // gemm.SetWorkSpacePointer(&argument, gemm_desc_workspace.GetDeviceBuffer());
+
+    hip_check_error(hipMemcpy(gemm_desc_workspace.GetDeviceBuffer(),
+                              grouped_gemm_kernel_args_.data(),
+                              gemm.GetWorkSpaceSize(&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_desc_workspace.GetDeviceBuffer());
+
+    invoker.Run(argument, StreamConfig{nullptr, false});
+
+    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]);
+        }
+    }
+
+    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;
+    }
+
+    return pass;
+}
+
+// int main(int argc, char* argv[]) { return !run_grouped_gemm_example(argc, argv); }
+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 == 4)
+    {
+        config.do_verification = std::stoi(argv[1]);
+        config.init_method     = std::stoi(argv[2]);
+        config.time_kernel     = std::stoi(argv[3]);
+    }
+    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");
+        exit(0);
+    }
+
+    return !run_grouped_gemm(problem_size, config);
+}

example/15_grouped_gemm/grouped_gemm_xdl_fp16.cpp

@@ -10,7 +10,6 @@
 #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.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"
@@ -47,7 +46,7 @@ using AElementOp   = PassThrough;
 using BElementOp   = PassThrough;
 using CDEElementOp = PassThrough;
 
-static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::MNPadding;
+static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
 
 using DeviceGemmInstance = ck::tensor_operation::device::DeviceGroupedGemm_Xdl
     // clang-format off
@@ -58,268 +57,6 @@ using DeviceGemmInstance = ck::tensor_operation::device::DeviceGroupedGemm_Xdl
         < ALayout, BLayout, DsLayout, ELayout, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType,  AElementOp,  BElementOp, CDEElementOp,    GemmDefault,        1,   256,   256,   128,    32,   8,   8,   32,   32,    4,    2,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,         1,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,             2,              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;
+#include "run_grouped_gemm_example.inc"
 
-    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;
-    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],
-                              problem_size.stride_As[i],
-                              problem_size.stride_Bs[i],
-                              problem_size.stride_Cs[i],
-                              {}});
-
-        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_desc_workspace(gemm.GetWorkSpaceSize(&argument));
-
-    // gemm.SetWorkSpacePointer(&argument, gemm_desc_workspace.GetDeviceBuffer());
-
-    hip_check_error(hipMemcpy(gemm_desc_workspace.GetDeviceBuffer(),
-                              grouped_gemm_kernel_args_.data(),
-                              gemm.GetWorkSpaceSize(&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_desc_workspace.GetDeviceBuffer());
-
-    invoker.Run(argument, StreamConfig{nullptr, false});
-
-    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]);
-        }
-    }
-
-    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;
-    }
-
-    return pass;
-}
-
-// int main(int argc, char* argv[]) { return !run_grouped_gemm_example(argc, argv); }
-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 == 4)
-    {
-        config.do_verification = std::stoi(argv[1]);
-        config.init_method     = std::stoi(argv[2]);
-        config.time_kernel     = std::stoi(argv[3]);
-    }
-    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");
-        exit(0);
-    }
-
-    return !run_grouped_gemm(problem_size, config);
-}
+int main(int argc, char* argv[]) { return !run_grouped_gemm_example(argc, argv); }

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

@@ -20,24 +20,6 @@ struct GemmDesc
     std::vector<ck::index_t> stride_Ds_;
 };
 
-template <index_t NumDTensor = 0>
-struct GroupedGemmKernelArgument
-{
-    const void* p_a_grid;
-    const void* p_b_grid;
-    std::array<const void*, NumDTensor> p_ds_grid;
-    void* p_e_grid;
-
-    index_t M;
-    index_t N;
-    index_t K;
-
-    index_t StrideA;
-    index_t StrideB;
-    std::array<index_t, NumDTensor> StrideDs;
-    index_t StrideE;
-};
-
 template <typename ALayout,
           typename BLayout,
           typename DsLayout,
@@ -66,8 +48,6 @@ struct DeviceGroupedGemm : public BaseOperator
                         CElementwiseOperation c_element_op) = 0;
 
     virtual std::unique_ptr<BaseInvoker> MakeInvokerPointer() = 0;
-
-    virtual void SetDeviceKernelArgs(BaseArgument* p_arg, const void* kernel_args) const = 0;
 };
 
 } // namespace device

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

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include <iostream>
+#include <vector>
+
+#include "device_grouped_gemm.hpp"
+
+namespace ck {
+namespace tensor_operation {
+namespace device {
+
+template <index_t NumDTensor = 0>
+struct GroupedGemmKernelArgument
+{
+    const void* p_a_grid;
+    const void* p_b_grid;
+    std::array<const void*, NumDTensor> p_ds_grid;
+    void* p_e_grid;
+
+    index_t M;
+    index_t N;
+    index_t K;
+
+    index_t StrideA;
+    index_t StrideB;
+    std::array<index_t, NumDTensor> StrideDs;
+    index_t StrideE;
+};
+
+template <typename ALayout,
+          typename BLayout,
+          typename DsLayout,
+          typename ELayout,
+          typename ADataType,
+          typename BDataType,
+          typename DsDataType,
+          typename EDataType,
+          typename AElementwiseOperation,
+          typename BElementwiseOperation,
+          typename CElementwiseOperation>
+struct DeviceGroupedGemmFixedNK : DeviceGroupedGemm<ALayout,
+                                                    BLayout,
+                                                    DsLayout,
+                                                    ELayout,
+                                                    ADataType,
+                                                    BDataType,
+                                                    DsDataType,
+                                                    EDataType,
+                                                    AElementwiseOperation,
+                                                    BElementwiseOperation,
+                                                    CElementwiseOperation>
+{
+    virtual void SetDeviceKernelArgs(BaseArgument* p_arg, const void* kernel_args) const = 0;
+};
+
+} // namespace device
+} // namespace tensor_operation
+} // namespace ck

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

@@ -24,13 +24,6 @@ namespace device {
 
 template <typename GridwiseGemm,
           typename GemmDesc,
-          GemmSpecialization GemmSpec,
-          typename ALayout,
-          typename BLayout,
-          typename DsLayout,
-          typename ELayout,
-          typename Block2ETileMap,
-          typename GroupedGemmBlock2ETileMap,
           typename AElementwiseOperation,
           typename BElementwiseOperation,
           typename CDEElementwiseOperation,
@@ -41,7 +34,6 @@ __global__ void
 #endif
         kernel_grouped_gemm_xdl(const void CK_CONSTANT_ADDRESS_SPACE* gemm_descs_const,
                                 const index_t group_count,
-                                const index_t grid_size_grp,
                                 const AElementwiseOperation a_element_op,
                                 const BElementwiseOperation b_element_op,
                                 const CDEElementwiseOperation c_element_op)
@@ -55,7 +47,6 @@ __global__ void
     const auto gemm_desc_ptr =
         reinterpret_cast<const GemmDesc*>(cast_pointer_to_generic_address_space(gemm_descs_const));
 
-#if 0
     index_t left     = 0;
     index_t right    = group_count;
     index_t group_id = index_t((left + right) / 2);
@@ -73,14 +64,7 @@ __global__ void
         }
         group_id = index_t((left + right) / 2);
     }
-#endif
 
-    const index_t group_id = block_id / grid_size_grp;
-
-    if(group_id >= group_count)
-        return;
-
-#if 0
     GridwiseGemm::template Run<HasMainKBlockLoop>(
         gemm_desc_ptr[group_id].a_ptr_,
         gemm_desc_ptr[group_id].b_ptr_,
@@ -95,83 +79,6 @@ __global__ void
         gemm_desc_ptr[group_id].ds_grid_desc_mblock_mperblock_nblock_nperblock_,
         gemm_desc_ptr[group_id].e_grid_desc_mblock_mperblock_nblock_nperblock_,
         gemm_desc_ptr[group_id].block_2_etile_map_);
-#else
-    const index_t M = gemm_desc_ptr[group_id].M;
-    const index_t N = gemm_desc_ptr[group_id].N;
-    const index_t K = gemm_desc_ptr[group_id].K;
-
-    if(M == 0 || N == 0 || K == 0)
-        return;
-
-    const auto StrideA  = gemm_desc_ptr[group_id].StrideA;
-    const auto StrideB  = gemm_desc_ptr[group_id].StrideB;
-    const auto StrideDs = gemm_desc_ptr[group_id].StrideDs;
-    const auto StrideE  = gemm_desc_ptr[group_id].StrideE;
-
-#if 0
-    using Row = ck::tensor_layout::gemm::RowMajor;
-    using Col = ck::tensor_layout::gemm::ColumnMajor;
-
-    using ALayout  = Row;
-    using BLayout  = Col;
-    using DsLayout = ck::Tuple<>;
-    using ELayout  = Row;
-#endif
-
-    using DsDataType = ck::Tuple<>;
-
-    const auto e_grid_desc_m_n =
-        GridwiseGemm::template MakeEGridDescriptor_M_N<ELayout, GemmSpec>(M, N, StrideE);
-
-    const index_t BlockStart = group_id * grid_size_grp;
-
-    const auto local_b2e_tile_map = Block2ETileMap{e_grid_desc_m_n};
-
-    constexpr auto NumDTensor = 0;
-
-    using DsGridPointer = decltype(GridwiseGemm::MakeDsGridPointer());
-
-    DsGridPointer p_ds_grid_;
-
-    static_for<0, NumDTensor, 1>{}([&](auto i) {
-        using DDataType = remove_cvref_t<tuple_element_t<i.value, DsDataType>>;
-        // D pointer
-        p_ds_grid_(i) = static_cast<const DDataType*>(gemm_desc_ptr[group_id].p_ds_grid[i]);
-    });
-
-    auto m_loops = local_b2e_tile_map.CalculateMLoops();
-
-    index_t m_id = 0;
-    do
-    {
-        const auto block_2_etile_map =
-            GroupedGemmBlock2ETileMap(local_b2e_tile_map, BlockStart, m_id);
-
-        GridwiseGemm::
-            template Run<HasMainKBlockLoop, GemmSpec, ALayout, BLayout, DsLayout, ELayout>(
-                gemm_desc_ptr[group_id].p_a_grid,
-                gemm_desc_ptr[group_id].p_b_grid,
-                p_ds_grid_,
-                gemm_desc_ptr[group_id].p_e_grid,
-                p_shared,
-                a_element_op,
-                b_element_op,
-                c_element_op,
-                M,
-                N,
-                K,
-                StrideA,
-                StrideB,
-                StrideDs,
-                StrideE,
-                block_2_etile_map);
-
-        m_id += 1;
-
-    } while(m_id < m_loops);
-
-#endif
-
 #else
     ignore = gemm_descs_const;
     ignore = group_count;
@@ -374,162 +281,54 @@ struct DeviceGroupedGemm_Xdl : public DeviceGroupedGemm<ALayout,
     using EGridDesc_MBlock_MPerBlock_NBlock_NPerBlock  = remove_cvref_t<decltype(
         GridwiseGemm::MakeEGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(EGridDesc_M_N{}))>;
 
-    template <typename UnderlyingBlockToCTileMap>
-    struct OffsettedBlockToCTileMapMLoops
+    struct GroupedGemmBlock2ETileMap
     {
-        using underlying_type = UnderlyingBlockToCTileMap;
+        using Block2ETileMap =
+            remove_cvref_t<decltype(GridwiseGemm::MakeDefaultBlock2ETileMap(EGridDesc_M_N{}))>;
 
-        __host__ __device__
-        OffsettedBlockToCTileMapMLoops(UnderlyingBlockToCTileMap block_to_ctile_map,
-                                       index_t block_start,
-                                       index_t mblock_id_off = 0)
+        GroupedGemmBlock2ETileMap()
         {
-            block_to_ctile_map_ = block_to_ctile_map;
-            block_start_        = block_start;
-            mblock_id_off_      = mblock_id_off;
+            block_2_etile_map_ = GridwiseGemm::MakeDefaultBlock2ETileMap(EGridDesc_M_N{});
+            BlockStart_        = -1;
+        }
+
+        GroupedGemmBlock2ETileMap(const EGridDesc_M_N& e_grid_desc_m_n, ck::index_t BlockStart)
+        {
+            block_2_etile_map_ = GridwiseGemm::MakeDefaultBlock2ETileMap(e_grid_desc_m_n);
+            BlockStart_        = BlockStart;
         }
 
         template <typename TopIdx>
         __host__ __device__ constexpr auto CalculateBottomIndex(const TopIdx& idx_top) const
         {
-            auto idx_bot = block_to_ctile_map_.CalculateBottomIndex(
-                make_multi_index(idx_top[Number<0>{}] - block_start_));
-
-            return make_tuple(idx_bot[Number<0>{}] + mblock_id_off_, idx_bot[Number<1>{}]);
+            return block_2_etile_map_.CalculateBottomIndex(
+                make_multi_index(idx_top[I0] - BlockStart_));
         }
 
+        // it's actually E-Tile
         template <typename CTileIdx, typename CTileDim>
         __host__ __device__ bool ValidCTileIndex(const CTileIdx& c_tile_idx,
                                                  const CTileDim& c_tile_dim) const
         {
-            return block_to_ctile_map_.ValidCTileIndex(c_tile_idx, c_tile_dim);
-        }
-
-        template <typename CGridDesc_M_N>
-        __host__ bool CheckValidity(const CGridDesc_M_N& c_grid_desc_m_n) const
-        {
-            return block_to_ctile_map_.CheckValidity(c_grid_desc_m_n);
-        }
-
-        template <typename CGridDesc_M_N>
-        __host__ constexpr index_t CalculateGridSize(const CGridDesc_M_N& c_grid_desc_m_n) const
-        {
-            return block_to_ctile_map_.CalculateGridSize(c_grid_desc_m_n);
-        }
-
-        UnderlyingBlockToCTileMap block_to_ctile_map_;
-        index_t block_start_;
-        index_t mblock_id_off_;
-    };
-
-    template <index_t MPerBlock_, index_t NPerBlock_>
-    struct BlockToCTileMap_M00_N0_M01Adapt_MLoops
-    {
-        static constexpr auto I0 = Number<0>{};
-        static constexpr auto I1 = Number<1>{};
-
-        __host__ __device__ BlockToCTileMap_M00_N0_M01Adapt_MLoops() = default;
-
-        __host__ __device__ BlockToCTileMap_M00_N0_M01Adapt_MLoops(
-            const BlockToCTileMap_M00_N0_M01Adapt_MLoops&) = default;
-        __host__ __device__
-        BlockToCTileMap_M00_N0_M01Adapt_MLoops(BlockToCTileMap_M00_N0_M01Adapt_MLoops&&) = default;
-        __host__ __device__ BlockToCTileMap_M00_N0_M01Adapt_MLoops&
-        operator=(const BlockToCTileMap_M00_N0_M01Adapt_MLoops&) = default;
-        __host__ __device__ BlockToCTileMap_M00_N0_M01Adapt_MLoops&
-        operator=(BlockToCTileMap_M00_N0_M01Adapt_MLoops&&) = default;
-
-        __host__ __device__ BlockToCTileMap_M00_N0_M01Adapt_MLoops(index_t M,
-                                                                   index_t N,
-                                                                   index_t M01 = 8)
-            : M_(M), N_(N), M01_(M01)
-        {
-        }
-
-        template <typename CGridDesc_M_N>
-        __host__ __device__ BlockToCTileMap_M00_N0_M01Adapt_MLoops(
-            const CGridDesc_M_N& c_grid_desc_m_n, index_t M01 = 8)
-            : BlockToCTileMap_M00_N0_M01Adapt_MLoops(
-                  c_grid_desc_m_n.GetLength(I0), c_grid_desc_m_n.GetLength(I1), M01)
-        {
-        }
-
-        __host__ __device__ constexpr index_t CalculateMLoops() const
-        {
-            return math::integer_divide_ceil(M_, MPerBlock_);
-        }
-
-        __host__ static constexpr index_t CalculateGridSize(index_t /*M*/, index_t N)
-        {
-            const auto M0 = 1; // math::integer_divide_ceil(M, MPerBlock);
-            const auto N0 = math::integer_divide_ceil(N, NPerBlock);
-
-            return M0 * N0;
-        }
-
-        template <typename CGridDesc_M_N>
-        __host__ static constexpr index_t CalculateGridSize(const CGridDesc_M_N& c_grid_desc_m_n)
-        {
-            return CalculateGridSize(c_grid_desc_m_n.GetLength(I0), c_grid_desc_m_n.GetLength(I1));
-        }
-
-        template <typename CGridDesc_M_N>
-        __host__ bool CheckValidity(const CGridDesc_M_N& /* c_grid_desc_m_n */) const
-        {
-            return true;
-        }
-
-        template <typename TopIdx>
-        __host__ __device__ constexpr auto CalculateBottomIndex(const TopIdx& idx_top) const
-        {
-            auto block_1d_id = idx_top[I0];
-
-            const auto M0 = 1; // math::integer_divide_ceil(M_, MPerBlock_);
-            const auto N0 = math::integer_divide_ceil(N_, NPerBlock_);
-
-            block_1d_id = block_1d_id % (M0 * N0); // swallow batch index
-
-            index_t idx_N0 = block_1d_id % N0;
-            index_t idx_M0 = block_1d_id / N0;
-
-            const auto M01_adapt = (idx_M0 < M0 - M0 % M01_) ? M01_ : M0 % M01_;
-
-            index_t idx_M00          = idx_M0 / M01_;
-            index_t idx_M01          = idx_M0 % M01_;
-            index_t idx_N0_M01_local = idx_N0 + idx_M01 * N0;
-
-            return make_tuple(idx_N0_M01_local % M01_adapt + idx_M00 * M01_,
-                              idx_N0_M01_local / M01_adapt);
+            return block_2_etile_map_.ValidCTileIndex(c_tile_idx, c_tile_dim);
         }
 
-        template <typename CTileIdx, typename CTileDim>
-        __host__ __device__ bool ValidCTileIndex(const CTileIdx& /* c_tile_idx */,
-                                                 const CTileDim& /* c_tile_dim */) const
+        __host__ bool CheckValidity(const EGridDesc_M_N& e_grid_desc_m_n) const
         {
-            return true; // always valid provided that user gets grid size from CalculateGridSize()
+            return block_2_etile_map_.CheckValidity(e_grid_desc_m_n);
         }
 
-        private:
-        index_t M_;
-        index_t N_;
-        index_t M01_;
+        Block2ETileMap block_2_etile_map_;
+        ck::index_t BlockStart_;
     };
 
-    using Block2ETileMap            = BlockToCTileMap_M00_N0_M01Adapt_MLoops<MPerBlock, NPerBlock>;
-    using GroupedGemmBlock2ETileMap = OffsettedBlockToCTileMapMLoops<Block2ETileMap>;
-
     struct GemmBiasTransKernelArg
     {
         // pointers
-        const void* a_ptr_;
-        const void* b_ptr_;
-        std::array<const void*, NumDTensor> ds_ptr_;
-        void* e_ptr_;
-
-        index_t M_, N_, K_;
-        index_t StrideA_, StrideB_;
-        std::array<index_t, NumDTensor> StrideDs_;
-        index_t StrideE_;
+        const ADataType* a_ptr_;
+        const BDataType* b_ptr_;
+        typename GridwiseGemm::DsGridPointer ds_ptr_;
+        EDataType* e_ptr_;
 
         // tensor descriptors for problem definiton
         AGridDesc_M_K a_grid_desc_m_k_;
@@ -545,7 +344,7 @@ struct DeviceGroupedGemm_Xdl : public DeviceGroupedGemm<ALayout,
         EGridDesc_MBlock_MPerBlock_NBlock_NPerBlock e_grid_desc_mblock_mperblock_nblock_nperblock_;
 
         // block-to-e-tile map
-        Block2ETileMap block_2_etile_map_;
+        GroupedGemmBlock2ETileMap block_2_etile_map_;
         ck::index_t BlockStart_, BlockEnd_;
     };
 
@@ -564,36 +363,18 @@ struct DeviceGroupedGemm_Xdl : public DeviceGroupedGemm<ALayout,
         {
             grid_size_ = 0;
 
-            grouped_gemm_kernel_args_dev = nullptr;
-
             group_count_ = ck::type_convert<ck::index_t>(gemm_descs.size());
 
-            if(!(group_count_ == ck::type_convert<ck::index_t>(p_As.size()) ||
-                 0 == ck::type_convert<ck::index_t>(p_As.size())))
-            {
-                throw std::runtime_error("wrong! group_count_ != p_As || 0 != p_As.size");
-            }
-
-            if(!(group_count_ == ck::type_convert<ck::index_t>(p_Bs.size()) ||
-                 0 == ck::type_convert<ck::index_t>(p_Bs.size())))
+            if(!(group_count_ == ck::type_convert<ck::index_t>(p_As.size()) &&
+                 group_count_ == ck::type_convert<ck::index_t>(p_Bs.size()) &&
+                 group_count_ == ck::type_convert<ck::index_t>(p_Es.size())))
             {
-                throw std::runtime_error("wrong! group_count_ != p_Bs || 0 != p_Bs.size");
-            }
-
-            if(!(group_count_ == ck::type_convert<ck::index_t>(p_Ds.size()) ||
-                 0 == ck::type_convert<ck::index_t>(p_Ds.size())))
-            {
-                throw std::runtime_error("wrong! group_count_ != p_Ds || 0 != p_Ds.size");
-            }
-
-            if(!(group_count_ == ck::type_convert<ck::index_t>(p_Es.size())))
-            {
-                throw std::runtime_error("wrong! group_count_ != p_Es");
+                throw std::runtime_error("wrong! group_count_ != p_As/b/c.size");
             }
 
             gemm_desc_kernel_arg_.reserve(group_count_);
 
-            index_t group_id = 0;
+            skipped_group_count_ = 0;
 
             for(std::size_t i = 0; i < gemm_descs.size(); i++)
             {
@@ -604,17 +385,23 @@ struct DeviceGroupedGemm_Xdl : public DeviceGroupedGemm<ALayout,
                 a_mtx_mraw_kraw_.emplace_back(M, K);
                 b_mtx_nraw_kraw_.emplace_back(N, K);
 
+                if(M == 0)
+                {
+                    skipped_group_count_++;
+                    continue;
+                }
+
                 const index_t StrideA = gemm_descs[i].stride_A_;
                 const index_t StrideB = gemm_descs[i].stride_B_;
                 const index_t StrideC = gemm_descs[i].stride_C_;
 
                 // pointer
-                std::array<const void*, NumDTensor> p_ds_grid;
+                typename GridwiseGemm::DsGridPointer p_ds_grid{};
 
                 static_for<0, NumDTensor, 1>{}([&](auto j) {
                     using DDataType = remove_cvref_t<tuple_element_t<j.value, DsDataType>>;
 
-                    p_ds_grid[j] = static_cast<const DDataType*>(p_Ds[i][j]);
+                    p_ds_grid(j) = static_cast<const DDataType*>(p_Ds[i][j]);
                 });
 
                 // tensor descriptors for problem definiton
@@ -623,16 +410,16 @@ struct DeviceGroupedGemm_Xdl : public DeviceGroupedGemm<ALayout,
 
                 DsGridDesc_M_N ds_grid_desc_m_n;
 
-                std::array<index_t, NumDTensor> StrideDs;
-
                 static_for<0, NumDTensor, 1>{}([&](auto j) {
                     using DLayout = remove_cvref_t<tuple_element_t<j.value, DsLayout>>;
 
-                    StrideDs[j]         = gemm_descs[i].stride_Ds_[j];
                     ds_grid_desc_m_n(j) = DeviceOp::MakeEGridDescriptor_M_N<DLayout>(
                         M, N, gemm_descs[i].stride_Ds_[j]);
                 });
 
+                const auto e_grid_desc_m_n =
+                    DeviceOp::MakeEGridDescriptor_M_N<ELayout>(M, N, StrideC);
+
                 // tensor descriptors for block/thread-wise copy
                 const auto a_grid_desc_ak0_m_ak1 =
                     GridwiseGemm::MakeDefaultAGridDescriptor_AK0_M_AK1(a_grid_desc_m_k);
@@ -640,26 +427,24 @@ struct DeviceGroupedGemm_Xdl : public DeviceGroupedGemm<ALayout,
                 const auto b_grid_desc_bk0_n_bk1 =
                     GridwiseGemm::MakeDefaultBGridDescriptor_BK0_N_BK1(b_grid_desc_n_k);
 
-                const auto e_grid_desc_m_n =
-                    DeviceOp::MakeEGridDescriptor_M_N<ELayout>(M, N, StrideC);
-
-                // block-to-e-tile map
-                const auto local_b2c_tile_map = Block2ETileMap{e_grid_desc_m_n};
-
-                const index_t grid_size_grp = local_b2c_tile_map.CalculateGridSize(e_grid_desc_m_n);
-
-                std::cout << "grp id: " << group_id << " grid_size: " << grid_size_grp << std::endl;
+                const index_t grid_size_grp =
+                    GroupedGemmBlock2ETileMap(e_grid_desc_m_n, 0)
+                        .block_2_etile_map_.CalculateGridSize(e_grid_desc_m_n);
 
                 const index_t BlockStart = grid_size_;
                 const index_t BlockEnd   = grid_size_ + grid_size_grp;
 
                 grid_size_ += grid_size_grp;
 
+                // block-to-e-tile map
+                const auto block_2_etile_map =
+                    GroupedGemmBlock2ETileMap(e_grid_desc_m_n, BlockStart);
+
                 if(GridwiseGemm::CheckValidity(a_grid_desc_m_k,
                                                b_grid_desc_n_k,
                                                ds_grid_desc_m_n,
                                                e_grid_desc_m_n,
-                                               local_b2c_tile_map))
+                                               block_2_etile_map))
                 {
                     // tensor descriptors for block/thread-wise copy
                     DsGridDesc_MBlock_MPerBlock_NBlock_NPerBlock
@@ -676,17 +461,10 @@ struct DeviceGroupedGemm_Xdl : public DeviceGroupedGemm<ALayout,
                             e_grid_desc_m_n);
 
                     gemm_desc_kernel_arg_.push_back(
-                        GemmBiasTransKernelArg{p_As.size() == 0 ? nullptr : p_As[i],
-                                               p_Bs.size() == 0 ? nullptr : p_Bs[i],
+                        GemmBiasTransKernelArg{static_cast<const ADataType*>(p_As[i]),
+                                               static_cast<const BDataType*>(p_Bs[i]),
                                                p_ds_grid,
-                                               p_Es[i],
-                                               M,
-                                               N,
-                                               K,
-                                               StrideA,
-                                               StrideB,
-                                               StrideDs,
-                                               StrideC,
+                                               static_cast<EDataType*>(p_Es[i]),
                                                a_grid_desc_m_k,
                                                b_grid_desc_n_k,
                                                ds_grid_desc_m_n,
@@ -695,17 +473,16 @@ struct DeviceGroupedGemm_Xdl : public DeviceGroupedGemm<ALayout,
                                                b_grid_desc_bk0_n_bk1,
                                                ds_grid_desc_mblock_mperblock_nblock_nperblock,
                                                e_grid_desc_mblock_mperblock_nblock_nperblock,
-                                               local_b2c_tile_map,
+                                               block_2_etile_map,
                                                BlockStart,
                                                BlockEnd});
                 }
-
-                group_id++;
             }
         }
 
         //  private:
         index_t group_count_;
+        index_t skipped_group_count_;
 
         AElementwiseOperation a_element_op_;
         BElementwiseOperation b_element_op_;
@@ -715,8 +492,6 @@ struct DeviceGroupedGemm_Xdl : public DeviceGroupedGemm<ALayout,
         std::vector<Tuple<index_t, index_t>> a_mtx_mraw_kraw_;
         std::vector<Tuple<index_t, index_t>> b_mtx_nraw_kraw_;
 
-        const void* grouped_gemm_kernel_args_dev;
-
         index_t grid_size_;
     };
 
@@ -729,12 +504,6 @@ struct DeviceGroupedGemm_Xdl : public DeviceGroupedGemm<ALayout,
         {
             bool has_main_k_block_loop = true;
 
-#if 1
-            std::vector<GroupedGemmKernelArgument<NumDTensor>> grouped_gemm_kernel_args;
-
-            grouped_gemm_kernel_args.reserve(arg.gemm_desc_kernel_arg_.size());
-#endif
-
             for(std::size_t i = 0; i < arg.gemm_desc_kernel_arg_.size(); i++)
             {
 #if DEBUG_LOG
@@ -777,81 +546,33 @@ struct DeviceGroupedGemm_Xdl : public DeviceGroupedGemm<ALayout,
                 {
                     throw std::runtime_error("wrong! not all gemm has_main_k_block_loop");
                 }
-
-#if 1
-                grouped_gemm_kernel_args.push_back(
-                    GroupedGemmKernelArgument<NumDTensor>{arg.gemm_desc_kernel_arg_[i].a_ptr_,
-                                                          arg.gemm_desc_kernel_arg_[i].b_ptr_,
-                                                          arg.gemm_desc_kernel_arg_[i].ds_ptr_,
-                                                          arg.gemm_desc_kernel_arg_[i].e_ptr_,
-                                                          arg.gemm_desc_kernel_arg_[i].M_,
-                                                          arg.gemm_desc_kernel_arg_[i].N_,
-                                                          arg.gemm_desc_kernel_arg_[i].K_,
-                                                          arg.gemm_desc_kernel_arg_[i].StrideA_,
-                                                          arg.gemm_desc_kernel_arg_[i].StrideB_,
-                                                          arg.gemm_desc_kernel_arg_[i].StrideDs_,
-                                                          arg.gemm_desc_kernel_arg_[i].StrideE_});
-#endif
             }
 
+            hipGetErrorString(hipMemcpyWithStream(arg.p_workspace_,
+                                                  arg.gemm_desc_kernel_arg_.data(),
+                                                  arg.gemm_desc_kernel_arg_.size() *
+                                                      sizeof(GemmBiasTransKernelArg),
+                                                  hipMemcpyHostToDevice,
+                                                  stream_config.stream_id_));
+
             float ave_time = 0;
 
             auto launch_kernel = [&](auto has_main_k_block_loop_) {
                 const auto kernel = kernel_grouped_gemm_xdl<GridwiseGemm,
-                                                            GroupedGemmKernelArgument<NumDTensor>,
-                                                            GemmSpec,
-                                                            ALayout,
-                                                            BLayout,
-                                                            DsLayout,
-                                                            ELayout,
-                                                            Block2ETileMap,
-                                                            GroupedGemmBlock2ETileMap,
+                                                            GemmBiasTransKernelArg,
                                                             AElementwiseOperation,
                                                             BElementwiseOperation,
                                                             CDEElementwiseOperation,
                                                             has_main_k_block_loop_>;
 
-                const index_t grid_size_grp = arg.gemm_desc_kernel_arg_[0].BlockEnd_ -
-                                              arg.gemm_desc_kernel_arg_[0].BlockStart_;
-
-                const void* kernel_args_dev = nullptr;
-
-                if(arg.grouped_gemm_kernel_args_dev != nullptr)
-                {
-                    kernel_args_dev = arg.grouped_gemm_kernel_args_dev;
-                }
-                else
-                {
-                    for(std::size_t i = 0; i < arg.gemm_desc_kernel_arg_.size(); i++)
-                    {
-                        if(arg.gemm_desc_kernel_arg_[i].a_ptr_ == nullptr ||
-                           arg.gemm_desc_kernel_arg_[i].b_ptr_ == nullptr ||
-                           arg.gemm_desc_kernel_arg_[i].e_ptr_ == nullptr)
-                        {
-                            throw std::runtime_error("wrong! p_a/b/c_grid is nullptr");
-                        }
-                    }
-
-                    hipGetErrorString(
-                        hipMemcpyWithStream(arg.p_workspace_,
-                                            grouped_gemm_kernel_args.data(),
-                                            grouped_gemm_kernel_args.size() *
-                                                sizeof(GroupedGemmKernelArgument<NumDTensor>),
-                                            hipMemcpyHostToDevice,
-                                            stream_config.stream_id_));
-
-                    kernel_args_dev = arg.p_workspace_;
-                }
-
                 return launch_and_time_kernel(
                     stream_config,
                     kernel,
                     dim3(arg.grid_size_),
                     dim3(BlockSize),
                     0,
-                    cast_pointer_to_constant_address_space(kernel_args_dev),
+                    cast_pointer_to_constant_address_space(arg.p_workspace_),
                     arg.gemm_desc_kernel_arg_.size(),
-                    grid_size_grp,
                     arg.a_element_op_,
                     arg.b_element_op_,
                     arg.c_element_op_);
@@ -879,7 +600,8 @@ struct DeviceGroupedGemm_Xdl : public DeviceGroupedGemm<ALayout,
 
     static bool IsSupportedArgument(const Argument& arg)
     {
-        if(ck::type_convert<ck::index_t>(arg.gemm_desc_kernel_arg_.size()) != arg.group_count_)
+        if((ck::type_convert<ck::index_t>(arg.gemm_desc_kernel_arg_.size()) +
+            arg.skipped_group_count_) != arg.group_count_)
         {
             return false;
         }
@@ -979,21 +701,9 @@ struct DeviceGroupedGemm_Xdl : public DeviceGroupedGemm<ALayout,
         return str.str();
     }
 
-    static void SetDeviceKernelArgs(Argument& arg, const void* kernel_args)
-    {
-        arg.grouped_gemm_kernel_args_dev = kernel_args;
-    }
-
-    // polymorphic
-    void SetDeviceKernelArgs(BaseArgument* p_arg, const void* kernel_args) const override
-    {
-        return SetDeviceKernelArgs(*dynamic_cast<Argument*>(p_arg), kernel_args);
-    }
-
     size_t GetWorkSpaceSize(const BaseArgument* p_arg) const override
     {
-        return dynamic_cast<const Argument*>(p_arg)->group_count_ *
-               sizeof(GroupedGemmKernelArgument<NumDTensor>);
+        return dynamic_cast<const Argument*>(p_arg)->group_count_ * sizeof(GemmBiasTransKernelArg);
     }
 };
 

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

+#pragma once
+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include <iostream>
+#include <sstream>
+
+#include "ck/utility/common_header.hpp"
+#include "ck/tensor_description/tensor_descriptor.hpp"
+#include "ck/tensor_description/tensor_descriptor_helper.hpp"
+#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
+#include "ck/tensor_operation/gpu/device/device_grouped_gemm_fixed_nk.hpp"
+#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
+#include "ck/tensor_operation/gpu/device/matrix_padder.hpp"
+#include "ck/tensor_operation/gpu/grid/gridwise_gemm_multiple_d_xdl_cshuffle.hpp"
+#include "ck/host_utility/device_prop.hpp"
+#include "ck/host_utility/kernel_launch.hpp"
+
+namespace ck {
+namespace tensor_operation {
+namespace device {
+
+template <typename GridwiseGemm,
+          typename GemmDesc,
+          GemmSpecialization GemmSpec,
+          typename ALayout,
+          typename BLayout,
+          typename DsLayout,
+          typename ELayout,
+          typename Block2ETileMap,
+          typename GroupedGemmBlock2ETileMap,
+          typename AElementwiseOperation,
+          typename BElementwiseOperation,
+          typename CDEElementwiseOperation,
+          bool HasMainKBlockLoop>
+__global__ void
+#if CK_USE_LAUNCH_BOUNDS
+    __launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
+#endif
+        kernel_grouped_gemm_xdl_fixed_nk(const void CK_CONSTANT_ADDRESS_SPACE* gemm_descs_const,
+                                         const index_t group_count,
+                                         const index_t grid_size_grp,
+                                         const AElementwiseOperation a_element_op,
+                                         const BElementwiseOperation b_element_op,
+                                         const CDEElementwiseOperation c_element_op)
+{
+#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx908__) || defined(__gfx90a__) || \
+    defined(__gfx940__) || defined(__gfx941__) || defined(__gfx942__))
+    __shared__ char p_shared[GridwiseGemm::GetSharedMemoryNumberOfByte()];
+
+    const index_t block_id = get_block_1d_id();
+
+    const auto gemm_desc_ptr =
+        reinterpret_cast<const GemmDesc*>(cast_pointer_to_generic_address_space(gemm_descs_const));
+
+#if 0
+    index_t left     = 0;
+    index_t right    = group_count;
+    index_t group_id = index_t((left + right) / 2);
+    while((!(block_id >= gemm_desc_ptr[group_id].BlockStart_ &&
+             block_id < gemm_desc_ptr[group_id].BlockEnd_)) &&
+          left <= right)
+    {
+        if(block_id < gemm_desc_ptr[group_id].BlockStart_)
+        {
+            right = group_id;
+        }
+        else
+        {
+            left = group_id;
+        }
+        group_id = index_t((left + right) / 2);
+    }
+#endif
+
+    const index_t group_id = block_id / grid_size_grp;
+
+    if(group_id >= group_count)
+        return;
+
+#if 0
+    GridwiseGemm::template Run<HasMainKBlockLoop>(
+        gemm_desc_ptr[group_id].a_ptr_,
+        gemm_desc_ptr[group_id].b_ptr_,
+        gemm_desc_ptr[group_id].ds_ptr_,
+        gemm_desc_ptr[group_id].e_ptr_,
+        p_shared,
+        a_element_op,
+        b_element_op,
+        c_element_op,
+        gemm_desc_ptr[group_id].a_grid_desc_ak0_m_ak1_,
+        gemm_desc_ptr[group_id].b_grid_desc_bk0_n_bk1_,
+        gemm_desc_ptr[group_id].ds_grid_desc_mblock_mperblock_nblock_nperblock_,
+        gemm_desc_ptr[group_id].e_grid_desc_mblock_mperblock_nblock_nperblock_,
+        gemm_desc_ptr[group_id].block_2_etile_map_);
+#else
+    const index_t M = gemm_desc_ptr[group_id].M;
+    const index_t N = gemm_desc_ptr[group_id].N;
+    const index_t K = gemm_desc_ptr[group_id].K;
+
+    if(M == 0 || N == 0 || K == 0)
+        return;
+
+    const auto StrideA  = gemm_desc_ptr[group_id].StrideA;
+    const auto StrideB  = gemm_desc_ptr[group_id].StrideB;
+    const auto StrideDs = gemm_desc_ptr[group_id].StrideDs;
+    const auto StrideE  = gemm_desc_ptr[group_id].StrideE;
+
+#if 0
+    using Row = ck::tensor_layout::gemm::RowMajor;
+    using Col = ck::tensor_layout::gemm::ColumnMajor;
+
+    using ALayout  = Row;
+    using BLayout  = Col;
+    using DsLayout = ck::Tuple<>;
+    using ELayout  = Row;
+#endif
+
+    using DsDataType = ck::Tuple<>;
+
+    const auto e_grid_desc_m_n =
+        GridwiseGemm::template MakeEGridDescriptor_M_N<ELayout, GemmSpec>(M, N, StrideE);
+
+    const index_t BlockStart = group_id * grid_size_grp;
+
+    const auto local_b2e_tile_map = Block2ETileMap{e_grid_desc_m_n};
+
+    constexpr auto NumDTensor = 0;
+
+    using DsGridPointer = decltype(GridwiseGemm::MakeDsGridPointer());
+
+    DsGridPointer p_ds_grid_;
+
+    static_for<0, NumDTensor, 1>{}([&](auto i) {
+        using DDataType = remove_cvref_t<tuple_element_t<i.value, DsDataType>>;
+        // D pointer
+        p_ds_grid_(i) = static_cast<const DDataType*>(gemm_desc_ptr[group_id].p_ds_grid[i]);
+    });
+
+    auto m_loops = local_b2e_tile_map.CalculateMLoops();
+
+    index_t m_id = 0;
+    do
+    {
+        const auto block_2_etile_map =
+            GroupedGemmBlock2ETileMap(local_b2e_tile_map, BlockStart, m_id);
+
+        GridwiseGemm::
+            template Run<HasMainKBlockLoop, GemmSpec, ALayout, BLayout, DsLayout, ELayout>(
+                gemm_desc_ptr[group_id].p_a_grid,
+                gemm_desc_ptr[group_id].p_b_grid,
+                p_ds_grid_,
+                gemm_desc_ptr[group_id].p_e_grid,
+                p_shared,
+                a_element_op,
+                b_element_op,
+                c_element_op,
+                M,
+                N,
+                K,
+                StrideA,
+                StrideB,
+                StrideDs,
+                StrideE,
+                block_2_etile_map);
+
+        m_id += 1;
+
+    } while(m_id < m_loops);
+
+#endif
+
+#else
+    ignore = gemm_descs_const;
+    ignore = group_count;
+    ignore = a_element_op;
+    ignore = b_element_op;
+    ignore = c_element_op;
+#endif
+}
+
+template <typename ALayout,
+          typename BLayout,
+          typename DsLayout,
+          typename ELayout,
+          typename ADataType,
+          typename BDataType,
+          typename AccDataType,
+          typename CShuffleDataType,
+          typename DsDataType,
+          typename EDataType,
+          typename AElementwiseOperation,
+          typename BElementwiseOperation,
+          typename CDEElementwiseOperation,
+          GemmSpecialization GemmSpec,
+          ck::index_t NumPrefetch,
+          ck::index_t BlockSize,
+          ck::index_t MPerBlock,
+          ck::index_t NPerBlock,
+          ck::index_t KPerBlock,
+          ck::index_t AK1,
+          ck::index_t BK1,
+          ck::index_t MPerXDL,
+          ck::index_t NPerXDL,
+          ck::index_t MXdlPerWave,
+          ck::index_t NXdlPerWave,
+          typename ABlockTransferThreadClusterLengths_K0_M_K1,
+          typename ABlockTransferThreadClusterArrangeOrder,
+          typename ABlockTransferSrcAccessOrder,
+          ck::index_t ABlockTransferSrcVectorDim,
+          ck::index_t ABlockTransferSrcScalarPerVector,
+          ck::index_t ABlockTransferDstScalarPerVector_K1,
+          bool ABlockLdsExtraM,
+          typename BBlockTransferThreadClusterLengths_K0_N_K1,
+          typename BBlockTransferThreadClusterArrangeOrder,
+          typename BBlockTransferSrcAccessOrder,
+          ck::index_t BBlockTransferSrcVectorDim,
+          ck::index_t BBlockTransferSrcScalarPerVector,
+          ck::index_t BBlockTransferDstScalarPerVector_K1,
+          bool BBlockLdsExtraN,
+          index_t CShuffleMXdlPerWavePerShuffle,
+          index_t CShuffleNXdlPerWavePerShuffle,
+          typename CDEBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
+          index_t CDEBlockTransferScalarPerVector_NPerBlock,
+          LoopScheduler LoopSched = make_default_loop_scheduler()>
+struct DeviceGroupedGemm_Xdl_Fixed_NK : public DeviceGroupedGemmFixedNK<ALayout,
+                                                                        BLayout,
+                                                                        DsLayout,
+                                                                        ELayout,
+                                                                        ADataType,
+                                                                        BDataType,
+                                                                        DsDataType,
+                                                                        EDataType,
+                                                                        AElementwiseOperation,
+                                                                        BElementwiseOperation,
+                                                                        CDEElementwiseOperation>
+{
+    using DeviceOp = DeviceGroupedGemm_Xdl_Fixed_NK;
+
+    static constexpr index_t NumDTensor = DsDataType::Size();
+
+    static constexpr auto I0 = Number<0>{};
+    static constexpr auto I1 = Number<1>{};
+    static constexpr auto I2 = Number<2>{};
+
+    static constexpr auto matrix_padder =
+        MatrixPadder<GemmSpec, index_t, index_t, index_t>{MPerBlock, NPerBlock, KPerBlock};
+
+    static auto MakeAGridDescriptor_M_K(index_t MRaw, index_t KRaw, index_t StrideA)
+    {
+        const auto a_grid_desc_mraw_kraw = [&]() {
+            if constexpr(is_same_v<tensor_layout::gemm::RowMajor, ALayout>)
+            {
+                return make_naive_tensor_descriptor(make_tuple(MRaw, KRaw),
+                                                    make_tuple(StrideA, I1));
+            }
+            else if constexpr(is_same_v<tensor_layout::gemm::ColumnMajor, ALayout>)
+            {
+                return make_naive_tensor_descriptor(make_tuple(MRaw, KRaw),
+                                                    make_tuple(I1, StrideA));
+            }
+        }();
+
+        return matrix_padder.PadADescriptor_M_K(a_grid_desc_mraw_kraw);
+    }
+
+    static auto MakeBGridDescriptor_N_K(index_t KRaw, index_t NRaw, index_t StrideB)
+    {
+        const auto b_grid_desc_nraw_kraw = [&]() {
+            if constexpr(is_same<tensor_layout::gemm::RowMajor, BLayout>::value)
+            {
+                return make_naive_tensor_descriptor(make_tuple(NRaw, KRaw),
+                                                    make_tuple(I1, StrideB));
+            }
+            else if constexpr(is_same<tensor_layout::gemm::ColumnMajor, BLayout>::value)
+            {
+                return make_naive_tensor_descriptor(make_tuple(NRaw, KRaw),
+                                                    make_tuple(StrideB, I1));
+            }
+        }();
+
+        return matrix_padder.PadBDescriptor_N_K(b_grid_desc_nraw_kraw);
+    }
+
+    template <typename ELay>
+    static auto MakeEGridDescriptor_M_N(index_t MRaw, index_t NRaw, index_t StrideE)
+    {
+        const auto e_grid_desc_mraw_nraw = [&]() {
+            if constexpr(is_same<tensor_layout::gemm::RowMajor, ELay>::value)
+            {
+                return make_naive_tensor_descriptor(make_tuple(MRaw, NRaw),
+                                                    make_tuple(StrideE, I1));
+            }
+            else if constexpr(is_same<tensor_layout::gemm::ColumnMajor, ELay>::value)
+            {
+                return make_naive_tensor_descriptor(make_tuple(MRaw, NRaw),
+                                                    make_tuple(I1, StrideE));
+            }
+        }();
+
+        return matrix_padder.PadCDescriptor_M_N(e_grid_desc_mraw_nraw);
+    }
+
+    static auto MakeDsGridDescriptor_M_N(const std::array<index_t, NumDTensor>& MRaws,
+                                         const std::array<index_t, NumDTensor>& NRaws,
+                                         const std::array<index_t, NumDTensor>& DsStride)
+    {
+        return generate_tuple(
+            [&](auto i) {
+                using DLayout = remove_cvref_t<tuple_element_t<i.value, DsLayout>>;
+
+                return DeviceOp::MakeEGridDescriptor_M_N<DLayout>(MRaws[i], NRaws[i], DsStride[i]);
+            },
+            Number<NumDTensor>{});
+    }
+
+    using AGridDesc_M_K  = decltype(MakeAGridDescriptor_M_K(1, 1, 1));
+    using BGridDesc_N_K  = decltype(MakeBGridDescriptor_N_K(1, 1, 1));
+    using DsGridDesc_M_N = remove_cvref_t<decltype(MakeDsGridDescriptor_M_N({}, {}, {}))>;
+    using EGridDesc_M_N  = decltype(MakeEGridDescriptor_M_N<ELayout>(1, 1, 1));
+
+    // GridwiseGemm
+    using GridwiseGemm = GridwiseGemmMultipleD_xdl_cshuffle<
+        ADataType, // TODO: distinguish A/B datatype
+        AccDataType,
+        CShuffleDataType,
+        DsDataType,
+        EDataType,
+        AElementwiseOperation,
+        BElementwiseOperation,
+        CDEElementwiseOperation,
+        InMemoryDataOperationEnum::Set,
+        NumPrefetch, // NumGemmKPrefetchStage
+        BlockSize,
+        MPerBlock,
+        NPerBlock,
+        KPerBlock,
+        AK1,
+        BK1,
+        MPerXDL,
+        NPerXDL,
+        MXdlPerWave,
+        NXdlPerWave,
+        ABlockTransferThreadClusterLengths_K0_M_K1,
+        ABlockTransferThreadClusterArrangeOrder,
+        ABlockTransferSrcAccessOrder,
+        ABlockTransferSrcVectorDim,
+        ABlockTransferSrcScalarPerVector,
+        ABlockTransferDstScalarPerVector_K1,
+        false, // AThreadTransferSrcResetCoordinateAfterRun,
+        ABlockLdsExtraM,
+        BBlockTransferThreadClusterLengths_K0_N_K1,
+        BBlockTransferThreadClusterArrangeOrder,
+        BBlockTransferSrcAccessOrder,
+        BBlockTransferSrcVectorDim,
+        BBlockTransferSrcScalarPerVector,
+        BBlockTransferDstScalarPerVector_K1,
+        false, // BThreadTransferSrcResetCoordinateAfterRun,
+        BBlockLdsExtraN,
+        CShuffleMXdlPerWavePerShuffle,
+        CShuffleNXdlPerWavePerShuffle,
+        CDEBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
+        CDEBlockTransferScalarPerVector_NPerBlock,
+        LoopSched>;
+
+    using AGridDesc_AK0_M_AK1                          = remove_cvref_t<decltype(
+        GridwiseGemm::MakeDefaultAGridDescriptor_AK0_M_AK1(AGridDesc_M_K{}))>;
+    using BGridDesc_BK0_N_BK1                          = remove_cvref_t<decltype(
+        GridwiseGemm::MakeDefaultBGridDescriptor_BK0_N_BK1(BGridDesc_N_K{}))>;
+    using DsGridDesc_MBlock_MPerBlock_NBlock_NPerBlock = remove_cvref_t<decltype(
+        GridwiseGemm::MakeDsGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(DsGridDesc_M_N{}))>;
+    using EGridDesc_MBlock_MPerBlock_NBlock_NPerBlock  = remove_cvref_t<decltype(
+        GridwiseGemm::MakeEGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(EGridDesc_M_N{}))>;
+
+    template <typename UnderlyingBlockToCTileMap>
+    struct OffsettedBlockToCTileMapMLoops
+    {
+        using underlying_type = UnderlyingBlockToCTileMap;
+
+        __host__ __device__
+        OffsettedBlockToCTileMapMLoops(UnderlyingBlockToCTileMap block_to_ctile_map,
+                                       index_t block_start,
+                                       index_t mblock_id_off = 0)
+        {
+            block_to_ctile_map_ = block_to_ctile_map;
+            block_start_        = block_start;
+            mblock_id_off_      = mblock_id_off;
+        }
+
+        template <typename TopIdx>
+        __host__ __device__ constexpr auto CalculateBottomIndex(const TopIdx& idx_top) const
+        {
+            auto idx_bot = block_to_ctile_map_.CalculateBottomIndex(
+                make_multi_index(idx_top[Number<0>{}] - block_start_));
+
+            return make_tuple(idx_bot[Number<0>{}] + mblock_id_off_, idx_bot[Number<1>{}]);
+        }
+
+        template <typename CTileIdx, typename CTileDim>
+        __host__ __device__ bool ValidCTileIndex(const CTileIdx& c_tile_idx,
+                                                 const CTileDim& c_tile_dim) const
+        {
+            return block_to_ctile_map_.ValidCTileIndex(c_tile_idx, c_tile_dim);
+        }
+
+        template <typename CGridDesc_M_N>
+        __host__ bool CheckValidity(const CGridDesc_M_N& c_grid_desc_m_n) const
+        {
+            return block_to_ctile_map_.CheckValidity(c_grid_desc_m_n);
+        }
+
+        template <typename CGridDesc_M_N>
+        __host__ constexpr index_t CalculateGridSize(const CGridDesc_M_N& c_grid_desc_m_n) const
+        {
+            return block_to_ctile_map_.CalculateGridSize(c_grid_desc_m_n);
+        }
+
+        UnderlyingBlockToCTileMap block_to_ctile_map_;
+        index_t block_start_;
+        index_t mblock_id_off_;
+    };
+
+    template <index_t MPerBlock_, index_t NPerBlock_>
+    struct BlockToCTileMap_M00_N0_M01Adapt_MLoops
+    {
+        static constexpr auto I0 = Number<0>{};
+        static constexpr auto I1 = Number<1>{};
+
+        __host__ __device__ BlockToCTileMap_M00_N0_M01Adapt_MLoops() = default;
+
+        __host__ __device__ BlockToCTileMap_M00_N0_M01Adapt_MLoops(
+            const BlockToCTileMap_M00_N0_M01Adapt_MLoops&) = default;
+        __host__ __device__
+        BlockToCTileMap_M00_N0_M01Adapt_MLoops(BlockToCTileMap_M00_N0_M01Adapt_MLoops&&) = default;
+        __host__ __device__ BlockToCTileMap_M00_N0_M01Adapt_MLoops&
+        operator=(const BlockToCTileMap_M00_N0_M01Adapt_MLoops&) = default;
+        __host__ __device__ BlockToCTileMap_M00_N0_M01Adapt_MLoops&
+        operator=(BlockToCTileMap_M00_N0_M01Adapt_MLoops&&) = default;
+
+        __host__ __device__ BlockToCTileMap_M00_N0_M01Adapt_MLoops(index_t M,
+                                                                   index_t N,
+                                                                   index_t M01 = 8)
+            : M_(M), N_(N), M01_(M01)
+        {
+        }
+
+        template <typename CGridDesc_M_N>
+        __host__ __device__ BlockToCTileMap_M00_N0_M01Adapt_MLoops(
+            const CGridDesc_M_N& c_grid_desc_m_n, index_t M01 = 8)
+            : BlockToCTileMap_M00_N0_M01Adapt_MLoops(
+                  c_grid_desc_m_n.GetLength(I0), c_grid_desc_m_n.GetLength(I1), M01)
+        {
+        }
+
+        __host__ __device__ constexpr index_t CalculateMLoops() const
+        {
+            return math::integer_divide_ceil(M_, MPerBlock_);
+        }
+
+        __host__ static constexpr index_t CalculateGridSize(index_t /*M*/, index_t N)
+        {
+            const auto M0 = 1; // math::integer_divide_ceil(M, MPerBlock);
+            const auto N0 = math::integer_divide_ceil(N, NPerBlock);
+
+            return M0 * N0;
+        }
+
+        template <typename CGridDesc_M_N>
+        __host__ static constexpr index_t CalculateGridSize(const CGridDesc_M_N& c_grid_desc_m_n)
+        {
+            return CalculateGridSize(c_grid_desc_m_n.GetLength(I0), c_grid_desc_m_n.GetLength(I1));
+        }
+
+        template <typename CGridDesc_M_N>
+        __host__ bool CheckValidity(const CGridDesc_M_N& /* c_grid_desc_m_n */) const
+        {
+            return true;
+        }
+
+        template <typename TopIdx>
+        __host__ __device__ constexpr auto CalculateBottomIndex(const TopIdx& idx_top) const
+        {
+            auto block_1d_id = idx_top[I0];
+
+            const auto M0 = 1; // math::integer_divide_ceil(M_, MPerBlock_);
+            const auto N0 = math::integer_divide_ceil(N_, NPerBlock_);
+
+            block_1d_id = block_1d_id % (M0 * N0); // swallow batch index
+
+            index_t idx_N0 = block_1d_id % N0;
+            index_t idx_M0 = block_1d_id / N0;
+
+            const auto M01_adapt = (idx_M0 < M0 - M0 % M01_) ? M01_ : M0 % M01_;
+
+            index_t idx_M00          = idx_M0 / M01_;
+            index_t idx_M01          = idx_M0 % M01_;
+            index_t idx_N0_M01_local = idx_N0 + idx_M01 * N0;
+
+            return make_tuple(idx_N0_M01_local % M01_adapt + idx_M00 * M01_,
+                              idx_N0_M01_local / M01_adapt);
+        }
+
+        template <typename CTileIdx, typename CTileDim>
+        __host__ __device__ bool ValidCTileIndex(const CTileIdx& /* c_tile_idx */,
+                                                 const CTileDim& /* c_tile_dim */) const
+        {
+            return true; // always valid provided that user gets grid size from CalculateGridSize()
+        }
+
+        private:
+        index_t M_;
+        index_t N_;
+        index_t M01_;
+    };
+
+    using Block2ETileMap            = BlockToCTileMap_M00_N0_M01Adapt_MLoops<MPerBlock, NPerBlock>;
+    using GroupedGemmBlock2ETileMap = OffsettedBlockToCTileMapMLoops<Block2ETileMap>;
+
+    struct GemmBiasTransKernelArg
+    {
+        // pointers
+        const void* a_ptr_;
+        const void* b_ptr_;
+        std::array<const void*, NumDTensor> ds_ptr_;
+        void* e_ptr_;
+
+        index_t M_, N_, K_;
+        index_t StrideA_, StrideB_;
+        std::array<index_t, NumDTensor> StrideDs_;
+        index_t StrideE_;
+
+        // tensor descriptors for problem definiton
+        AGridDesc_M_K a_grid_desc_m_k_;
+        BGridDesc_N_K b_grid_desc_n_k_;
+        DsGridDesc_M_N ds_grid_desc_m_n_;
+        EGridDesc_M_N e_grid_desc_m_n_;
+
+        // tensor descriptors for block/thread-wise copy
+        AGridDesc_AK0_M_AK1 a_grid_desc_ak0_m_ak1_;
+        BGridDesc_BK0_N_BK1 b_grid_desc_bk0_n_bk1_;
+        DsGridDesc_MBlock_MPerBlock_NBlock_NPerBlock
+            ds_grid_desc_mblock_mperblock_nblock_nperblock_;
+        EGridDesc_MBlock_MPerBlock_NBlock_NPerBlock e_grid_desc_mblock_mperblock_nblock_nperblock_;
+
+        // block-to-e-tile map
+        Block2ETileMap block_2_etile_map_;
+        ck::index_t BlockStart_, BlockEnd_;
+    };
+
+    // Argument
+    struct Argument : public BaseArgument
+    {
+        Argument(std::vector<const void*>& p_As,
+                 std::vector<const void*>& p_Bs,
+                 std::vector<std::array<const void*, NumDTensor>>& p_Ds,
+                 std::vector<void*>& p_Es,
+                 std::vector<GemmDesc>& gemm_descs,
+                 AElementwiseOperation a_element_op,
+                 BElementwiseOperation b_element_op,
+                 CDEElementwiseOperation c_element_op)
+            : a_element_op_{a_element_op}, b_element_op_{b_element_op}, c_element_op_{c_element_op}
+        {
+            grid_size_ = 0;
+
+            grouped_gemm_kernel_args_dev = nullptr;
+
+            group_count_ = ck::type_convert<ck::index_t>(gemm_descs.size());
+
+            if(!(group_count_ == ck::type_convert<ck::index_t>(p_As.size()) ||
+                 0 == ck::type_convert<ck::index_t>(p_As.size())))
+            {
+                throw std::runtime_error("wrong! group_count_ != p_As || 0 != p_As.size");
+            }
+
+            if(!(group_count_ == ck::type_convert<ck::index_t>(p_Bs.size()) ||
+                 0 == ck::type_convert<ck::index_t>(p_Bs.size())))
+            {
+                throw std::runtime_error("wrong! group_count_ != p_Bs || 0 != p_Bs.size");
+            }
+
+            if(!(group_count_ == ck::type_convert<ck::index_t>(p_Ds.size()) ||
+                 0 == ck::type_convert<ck::index_t>(p_Ds.size())))
+            {
+                throw std::runtime_error("wrong! group_count_ != p_Ds || 0 != p_Ds.size");
+            }
+
+            if(!(group_count_ == ck::type_convert<ck::index_t>(p_Es.size())))
+            {
+                throw std::runtime_error("wrong! group_count_ != p_Es");
+            }
+
+            gemm_desc_kernel_arg_.reserve(group_count_);
+
+            index_t group_id = 0;
+
+            for(std::size_t i = 0; i < gemm_descs.size(); i++)
+            {
+                const index_t M = gemm_descs[i].M_;
+                const index_t N = gemm_descs[i].N_;
+                const index_t K = gemm_descs[i].K_;
+
+                a_mtx_mraw_kraw_.emplace_back(M, K);
+                b_mtx_nraw_kraw_.emplace_back(N, K);
+
+                const index_t StrideA = gemm_descs[i].stride_A_;
+                const index_t StrideB = gemm_descs[i].stride_B_;
+                const index_t StrideC = gemm_descs[i].stride_C_;
+
+                // pointer
+                std::array<const void*, NumDTensor> p_ds_grid;
+
+                static_for<0, NumDTensor, 1>{}([&](auto j) {
+                    using DDataType = remove_cvref_t<tuple_element_t<j.value, DsDataType>>;
+
+                    p_ds_grid[j] = static_cast<const DDataType*>(p_Ds[i][j]);
+                });
+
+                // tensor descriptors for problem definiton
+                const auto a_grid_desc_m_k = DeviceOp::MakeAGridDescriptor_M_K(M, K, StrideA);
+                const auto b_grid_desc_n_k = DeviceOp::MakeBGridDescriptor_N_K(K, N, StrideB);
+
+                DsGridDesc_M_N ds_grid_desc_m_n;
+
+                std::array<index_t, NumDTensor> StrideDs;
+
+                static_for<0, NumDTensor, 1>{}([&](auto j) {
+                    using DLayout = remove_cvref_t<tuple_element_t<j.value, DsLayout>>;
+
+                    StrideDs[j]         = gemm_descs[i].stride_Ds_[j];
+                    ds_grid_desc_m_n(j) = DeviceOp::MakeEGridDescriptor_M_N<DLayout>(
+                        M, N, gemm_descs[i].stride_Ds_[j]);
+                });
+
+                // tensor descriptors for block/thread-wise copy
+                const auto a_grid_desc_ak0_m_ak1 =
+                    GridwiseGemm::MakeDefaultAGridDescriptor_AK0_M_AK1(a_grid_desc_m_k);
+
+                const auto b_grid_desc_bk0_n_bk1 =
+                    GridwiseGemm::MakeDefaultBGridDescriptor_BK0_N_BK1(b_grid_desc_n_k);
+
+                const auto e_grid_desc_m_n =
+                    DeviceOp::MakeEGridDescriptor_M_N<ELayout>(M, N, StrideC);
+
+                // block-to-e-tile map
+                const auto local_b2c_tile_map = Block2ETileMap{e_grid_desc_m_n};
+
+                const index_t grid_size_grp = local_b2c_tile_map.CalculateGridSize(e_grid_desc_m_n);
+
+                std::cout << "grp id: " << group_id << " grid_size: " << grid_size_grp << std::endl;
+
+                const index_t BlockStart = grid_size_;
+                const index_t BlockEnd   = grid_size_ + grid_size_grp;
+
+                grid_size_ += grid_size_grp;
+
+                if(GridwiseGemm::CheckValidity(a_grid_desc_m_k,
+                                               b_grid_desc_n_k,
+                                               ds_grid_desc_m_n,
+                                               e_grid_desc_m_n,
+                                               local_b2c_tile_map))
+                {
+                    // tensor descriptors for block/thread-wise copy
+                    DsGridDesc_MBlock_MPerBlock_NBlock_NPerBlock
+                        ds_grid_desc_mblock_mperblock_nblock_nperblock;
+
+                    static_for<0, NumDTensor, 1>{}([&](auto j) {
+                        ds_grid_desc_mblock_mperblock_nblock_nperblock(j) =
+                            GridwiseGemm::MakeEGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(
+                                ds_grid_desc_m_n[j]);
+                    });
+
+                    const auto e_grid_desc_mblock_mperblock_nblock_nperblock =
+                        GridwiseGemm::MakeEGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(
+                            e_grid_desc_m_n);
+
+                    gemm_desc_kernel_arg_.push_back(
+                        GemmBiasTransKernelArg{p_As.size() == 0 ? nullptr : p_As[i],
+                                               p_Bs.size() == 0 ? nullptr : p_Bs[i],
+                                               p_ds_grid,
+                                               p_Es[i],
+                                               M,
+                                               N,
+                                               K,
+                                               StrideA,
+                                               StrideB,
+                                               StrideDs,
+                                               StrideC,
+                                               a_grid_desc_m_k,
+                                               b_grid_desc_n_k,
+                                               ds_grid_desc_m_n,
+                                               e_grid_desc_m_n,
+                                               a_grid_desc_ak0_m_ak1,
+                                               b_grid_desc_bk0_n_bk1,
+                                               ds_grid_desc_mblock_mperblock_nblock_nperblock,
+                                               e_grid_desc_mblock_mperblock_nblock_nperblock,
+                                               local_b2c_tile_map,
+                                               BlockStart,
+                                               BlockEnd});
+                }
+
+                group_id++;
+            }
+        }
+
+        //  private:
+        index_t group_count_;
+
+        AElementwiseOperation a_element_op_;
+        BElementwiseOperation b_element_op_;
+        CDEElementwiseOperation c_element_op_;
+
+        std::vector<GemmBiasTransKernelArg> gemm_desc_kernel_arg_;
+        std::vector<Tuple<index_t, index_t>> a_mtx_mraw_kraw_;
+        std::vector<Tuple<index_t, index_t>> b_mtx_nraw_kraw_;
+
+        const void* grouped_gemm_kernel_args_dev;
+
+        index_t grid_size_;
+    };
+
+    // Invoker
+    struct Invoker : public BaseInvoker
+    {
+        using Argument = DeviceOp::Argument;
+
+        float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
+        {
+            bool has_main_k_block_loop = true;
+
+#if 1
+            std::vector<GroupedGemmKernelArgument<NumDTensor>> grouped_gemm_kernel_args;
+
+            grouped_gemm_kernel_args.reserve(arg.gemm_desc_kernel_arg_.size());
+#endif
+
+            for(std::size_t i = 0; i < arg.gemm_desc_kernel_arg_.size(); i++)
+            {
+#if DEBUG_LOG
+                std::cout << "group: " << i << " arg.a_grid_desc_ak0_m_ak1_{"
+                          << arg.gemm_desc_kernel_arg_[i].a_grid_desc_ak0_m_ak1_.GetLength(I0)
+                          << ", "
+                          << arg.gemm_desc_kernel_arg_[i].a_grid_desc_ak0_m_ak1_.GetLength(I1)
+                          << ", "
+                          << arg.gemm_desc_kernel_arg_[i].a_grid_desc_ak0_m_ak1_.GetLength(I2)
+                          << "}";
+
+                std::cout << ", arg.b_grid_desc_bk0_n_bk1_{"
+                          << arg.gemm_desc_kernel_arg_[i].b_grid_desc_bk0_n_bk1_.GetLength(I0)
+                          << ", "
+                          << arg.gemm_desc_kernel_arg_[i].b_grid_desc_bk0_n_bk1_.GetLength(I1)
+                          << ", "
+                          << arg.gemm_desc_kernel_arg_[i].b_grid_desc_bk0_n_bk1_.GetLength(I2)
+                          << "}";
+
+                std::cout << ", arg.e_grid_desc_m_n_{ "
+                          << arg.gemm_desc_kernel_arg_[i].e_grid_desc_m_n_.GetLength(I0) << ", "
+                          << arg.gemm_desc_kernel_arg_[i].e_grid_desc_m_n_.GetLength(I1) << "}"
+                          << std::endl;
+#endif
+
+                if(!GridwiseGemm::CheckValidity(arg.gemm_desc_kernel_arg_[i].a_grid_desc_m_k_,
+                                                arg.gemm_desc_kernel_arg_[i].b_grid_desc_n_k_,
+                                                arg.gemm_desc_kernel_arg_[i].ds_grid_desc_m_n_,
+                                                arg.gemm_desc_kernel_arg_[i].e_grid_desc_m_n_,
+                                                arg.gemm_desc_kernel_arg_[i].block_2_etile_map_))
+                {
+                    throw std::runtime_error(
+                        "wrong! GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v2r3 has invalid setting");
+                }
+
+                const auto K = arg.gemm_desc_kernel_arg_[i].a_grid_desc_ak0_m_ak1_.GetLength(I0) *
+                               arg.gemm_desc_kernel_arg_[i].a_grid_desc_ak0_m_ak1_.GetLength(I2);
+
+                if(GridwiseGemm::CalculateHasMainKBlockLoop(K) != has_main_k_block_loop)
+                {
+                    throw std::runtime_error("wrong! not all gemm has_main_k_block_loop");
+                }
+
+#if 1
+                grouped_gemm_kernel_args.push_back(
+                    GroupedGemmKernelArgument<NumDTensor>{arg.gemm_desc_kernel_arg_[i].a_ptr_,
+                                                          arg.gemm_desc_kernel_arg_[i].b_ptr_,
+                                                          arg.gemm_desc_kernel_arg_[i].ds_ptr_,
+                                                          arg.gemm_desc_kernel_arg_[i].e_ptr_,
+                                                          arg.gemm_desc_kernel_arg_[i].M_,
+                                                          arg.gemm_desc_kernel_arg_[i].N_,
+                                                          arg.gemm_desc_kernel_arg_[i].K_,
+                                                          arg.gemm_desc_kernel_arg_[i].StrideA_,
+                                                          arg.gemm_desc_kernel_arg_[i].StrideB_,
+                                                          arg.gemm_desc_kernel_arg_[i].StrideDs_,
+                                                          arg.gemm_desc_kernel_arg_[i].StrideE_});
+#endif
+            }
+
+            float ave_time = 0;
+
+            auto launch_kernel = [&](auto has_main_k_block_loop_) {
+                const auto kernel =
+                    kernel_grouped_gemm_xdl_fixed_nk<GridwiseGemm,
+                                                     GroupedGemmKernelArgument<NumDTensor>,
+                                                     GemmSpec,
+                                                     ALayout,
+                                                     BLayout,
+                                                     DsLayout,
+                                                     ELayout,
+                                                     Block2ETileMap,
+                                                     GroupedGemmBlock2ETileMap,
+                                                     AElementwiseOperation,
+                                                     BElementwiseOperation,
+                                                     CDEElementwiseOperation,
+                                                     has_main_k_block_loop_>;
+
+                const index_t grid_size_grp = arg.gemm_desc_kernel_arg_[0].BlockEnd_ -
+                                              arg.gemm_desc_kernel_arg_[0].BlockStart_;
+
+                const void* kernel_args_dev = nullptr;
+
+                if(arg.grouped_gemm_kernel_args_dev != nullptr)
+                {
+                    kernel_args_dev = arg.grouped_gemm_kernel_args_dev;
+                }
+                else
+                {
+                    for(std::size_t i = 0; i < arg.gemm_desc_kernel_arg_.size(); i++)
+                    {
+                        if(arg.gemm_desc_kernel_arg_[i].a_ptr_ == nullptr ||
+                           arg.gemm_desc_kernel_arg_[i].b_ptr_ == nullptr ||
+                           arg.gemm_desc_kernel_arg_[i].e_ptr_ == nullptr)
+                        {
+                            throw std::runtime_error("wrong! p_a/b/c_grid is nullptr");
+                        }
+                    }
+
+                    hipGetErrorString(
+                        hipMemcpyWithStream(arg.p_workspace_,
+                                            grouped_gemm_kernel_args.data(),
+                                            grouped_gemm_kernel_args.size() *
+                                                sizeof(GroupedGemmKernelArgument<NumDTensor>),
+                                            hipMemcpyHostToDevice,
+                                            stream_config.stream_id_));
+
+                    kernel_args_dev = arg.p_workspace_;
+                }
+
+                return launch_and_time_kernel(
+                    stream_config,
+                    kernel,
+                    dim3(arg.grid_size_),
+                    dim3(BlockSize),
+                    0,
+                    cast_pointer_to_constant_address_space(kernel_args_dev),
+                    arg.gemm_desc_kernel_arg_.size(),
+                    grid_size_grp,
+                    arg.a_element_op_,
+                    arg.b_element_op_,
+                    arg.c_element_op_);
+            };
+
+            if(has_main_k_block_loop)
+            {
+                ave_time = launch_kernel(integral_constant<bool, true>{});
+            }
+            else
+            {
+                ave_time = launch_kernel(integral_constant<bool, false>{});
+            }
+
+            return ave_time;
+        }
+
+        // polymorphic
+        float Run(const BaseArgument* p_arg,
+                  const StreamConfig& stream_config = StreamConfig{}) override
+        {
+            return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
+        }
+    };
+
+    static bool IsSupportedArgument(const Argument& arg)
+    {
+        if(ck::type_convert<ck::index_t>(arg.gemm_desc_kernel_arg_.size()) != arg.group_count_)
+        {
+            return false;
+        }
+
+        bool supported = true;
+
+        // If we use padding we do not support vector loads for dimensions not divisible by vector
+        // load size.
+        if constexpr(GemmSpec != GemmSpecialization::Default)
+        {
+            // [A|B]BlockTransferSrcVectorDim value define dimension in the block {K0,M,K1} layout,
+            // thus we have to adapt it to the {M,K} or {N,K} layout.
+            const auto a_raw_vector_dim = ABlockTransferSrcVectorDim != 1 ? 1 : 0;
+            const auto b_raw_vector_dim = BBlockTransferSrcVectorDim != 1 ? 1 : 0;
+
+            for(index_t i = 0; i < arg.group_count_; ++i)
+            {
+                const auto a_vector_dim = arg.a_mtx_mraw_kraw_[i].At(Number<a_raw_vector_dim>{});
+                const auto b_vector_dim = arg.b_mtx_nraw_kraw_[i].At(Number<b_raw_vector_dim>{});
+
+                supported = supported & (a_vector_dim % ABlockTransferSrcScalarPerVector == 0);
+                supported = supported & (b_vector_dim % BBlockTransferSrcScalarPerVector == 0);
+            }
+        }
+
+        return supported;
+    }
+
+    // polymorphic
+    bool IsSupportedArgument(const BaseArgument* p_arg) override
+    {
+        return IsSupportedArgument(*dynamic_cast<const Argument*>(p_arg));
+    }
+
+    static auto MakeArgument(std::vector<const void*>& p_As,
+                             std::vector<const void*>& p_Bs,
+                             std::vector<std::array<const void*, NumDTensor>>& p_Ds,
+                             std::vector<void*>& p_Es,
+                             std::vector<GemmDesc> gemm_descs,
+                             AElementwiseOperation a_element_op,
+                             BElementwiseOperation b_element_op,
+                             CDEElementwiseOperation c_element_op)
+    {
+        return Argument{
+            p_As, p_Bs, p_Ds, p_Es, gemm_descs, a_element_op, b_element_op, c_element_op};
+    }
+
+    static auto MakeInvoker() { return Invoker{}; }
+
+    // polymorphic
+    std::unique_ptr<BaseArgument>
+    MakeArgumentPointer(std::vector<const void*>& p_As,
+                        std::vector<const void*>& p_Bs,
+                        std::vector<std::array<const void*, NumDTensor>>& p_Ds,
+                        std::vector<void*>& p_Es,
+                        std::vector<GemmDesc>& gemm_descs,
+                        AElementwiseOperation a_element_op,
+                        BElementwiseOperation b_element_op,
+                        CDEElementwiseOperation c_element_op) override
+    {
+        return std::make_unique<Argument>(
+            p_As, p_Bs, p_Ds, p_Es, gemm_descs, a_element_op, b_element_op, c_element_op);
+    }
+
+    // polymorphic
+    std::unique_ptr<BaseInvoker> MakeInvokerPointer() override
+    {
+        return std::make_unique<Invoker>(Invoker{});
+    }
+
+    // polymorphic
+    std::string GetTypeString() const override
+    {
+        auto str = std::stringstream();
+
+        // clang-format off
+        str << "DeviceGroupedGemm_Xdl_Fixed_NK"
+            << "<"
+            << BlockSize << ", "
+            << MPerBlock << ", "
+            << NPerBlock << ", "
+            << KPerBlock << ", "
+            << AK1 << ", "
+            << BK1 << ", "
+            << MPerXDL << ", "
+            << NPerXDL << ", "
+            << MXdlPerWave << ", "
+            << NXdlPerWave << ", "
+            << ABlockTransferSrcScalarPerVector << ", "
+            << BBlockTransferSrcScalarPerVector << ", "
+            << CShuffleMXdlPerWavePerShuffle << ", "
+            << CShuffleNXdlPerWavePerShuffle << ", "
+            << getGemmSpecializationString(GemmSpec)
+            << ">";
+        // clang-format on
+
+        return str.str();
+    }
+
+    static void SetDeviceKernelArgs(Argument& arg, const void* kernel_args)
+    {
+        arg.grouped_gemm_kernel_args_dev = kernel_args;
+    }
+
+    // polymorphic
+    void SetDeviceKernelArgs(BaseArgument* p_arg, const void* kernel_args) const override
+    {
+        return SetDeviceKernelArgs(*dynamic_cast<Argument*>(p_arg), kernel_args);
+    }
+
+    size_t GetWorkSpaceSize(const BaseArgument* p_arg) const override
+    {
+        return dynamic_cast<const Argument*>(p_arg)->group_count_ *
+               sizeof(GroupedGemmKernelArgument<NumDTensor>);
+    }
+};
+
+} // namespace device
+} // namespace tensor_operation
+} // namespace ck