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