Modify the kernel to 128x128x64, and use mfma_32x32x4

Add int4+scale based on Zhang, Jing pk_i4. Compile pass, function pass.

example/01_gemm/CMakeLists.txt

@@ -30,6 +30,7 @@ add_example_executable(example_gemm_xdl_fp8_v3 gemm_xdl_fp8_v3.cpp)
 add_example_dependencies(example_gemm_xdl example_gemm_xdl_fp8_v3)
 add_example_executable(example_gemm_xdl_fp16_fp8_v3 gemm_xdl_fp16_fp8_v3.cpp)
 add_example_executable(example_gemm_xdl_fp16_pk_i4_v3 gemm_xdl_fp16_pk_i4_v3.cpp)
+add_example_executable(example_gemm_xdl_fp16_pk_i4_v3_b_scale gemm_xdl_fp16_pk_i4_v3_b_scale.cpp)
 add_example_dependencies(example_gemm_xdl example_gemm_xdl_fp16_fp8_v3)
 add_example_executable(example_gemm_xdl_bf16_v3 gemm_xdl_bf16_v3.cpp)
 add_example_dependencies(example_gemm_xdl example_gemm_xdl_bf16_v3)

example/01_gemm/gemm_xdl_fp16_pk_i4_v3.cpp

@@ -23,7 +23,7 @@ static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecializa
 
 static constexpr bool PermuteB = true;
 
-static constexpr ck::index_t KPerBlock = 128;
+static constexpr ck::index_t KPerBlock = 64;
 
 // clang-format off
 using DeviceGemmV2Instance = 
@@ -43,28 +43,28 @@ using DeviceGemmV2Instance =
         2, 32, 32, 0,
         1, 1, S<1, 16, 1, 8>, 4,
 #else
-        128,
-        16, 64,
+        256,
+        128, 128,
         KPerBlock, 8, 32,
-        16,   16,
-        1,    2,
-        S<16, 8, 1>,  S<1, 0, 2>,  S<1, 0, 2>,
+        32,   32,
+        2,    2,
+        S<8, 32, 1>,  S<1, 0, 2>,  S<1, 0, 2>,
         2, 8, 8, 0,
-        S<4, 32, 1>,  S<1, 0, 2>,  S<1, 0, 2>,
+        S<2, 128, 1>,  S<1, 0, 2>,  S<1, 0, 2>,
         2, 32, 32, 0,
         1, 1, S<1, 16, 1, 8>, 4,
 #endif
-        ck::BlockGemmPipelineScheduler::Interwave, ck::BlockGemmPipelineVersion::v2, CDataType, CDataType, false, PermuteB>;
+        ck::BlockGemmPipelineScheduler::Intrawave, ck::BlockGemmPipelineVersion::v3, CDataType, CDataType, false, PermuteB>;
 
 // clang-format on
 
-    using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
-                                                                            BDataType,
-                                                                            CDataType,
-                                                                            AccDataType,
-                                                                            PassThrough,
-                                                                            PassThrough,
-                                                                            PassThrough>;
+using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
+                                                                        BDataType,
+                                                                        CDataType,
+                                                                        AccDataType,
+                                                                        PassThrough,
+                                                                        PassThrough,
+                                                                        PassThrough>;
 template <typename ProblemType>
 bool run_gemm(const ProblemType& problem_size, const ExecutionConfig& config)
 {

example/01_gemm/gemm_xdl_fp16_pk_i4_v3_b_scale.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#include "common.hpp"
+
+#include "ck/tensor_operation/gpu/device/impl/device_gemm_xdl_cshuffle_v3_b_scale.hpp"
+
+using ADataType        = ck::half_t;
+using BDataType        = ck::pk_i4_t;
+using BScaleDataType   = ck::half_t;
+using AccDataType      = float;
+using CShuffleDataType = ck::half_t;
+using CDataType        = ck::half_t;
+
+using ALayout = Row;
+using BLayout = Col;
+using CLayout = Row;
+
+using AElementOp = PassThrough;
+using BElementOp = PassThrough;
+using CElementOp = PassThrough;
+
+static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
+
+static constexpr bool PermuteB = true;
+
+static constexpr ck::index_t Scale_Block_N = 1;
+static constexpr ck::index_t Scale_Block_K = 64;
+
+static constexpr ck::index_t KPerBlock = 64;
+
+// clang-format off
+using DeviceGemmV2Instance = 
+    ck::tensor_operation::device::DeviceGemm_Xdl_CShuffleV3<
+        ALayout,   BLayout,  CLayout,   
+        ADataType, BDataType, BScaleDataType, CDataType, AccDataType, CShuffleDataType, 
+        AElementOp, BElementOp, CElementOp, GemmDefault, 
+#if 0
+        128, Scale_Block_N, Scale_Block_K,
+        16, 128,
+        KPerBlock, 8, 32,
+        16,   16,
+        1,    4,
+        S<16, 8, 1>,  S<1, 0, 2>,  S<1, 0, 2>,
+        2, 8, 8, 0,
+        S<4, 32, 1>,  S<1, 0, 2>,  S<1, 0, 2>,
+        2, 32, 32, 0,
+        1, 1, S<1, 16, 1, 8>, 4,
+#else
+        256, Scale_Block_N, Scale_Block_K,
+        128, 128,
+        KPerBlock, 8, 32,
+        32,   32,
+        2,    2,
+        S<8, 32, 1>,  S<1, 0, 2>,  S<1, 0, 2>,
+        2, 8, 8, 0,
+        S<2, 128, 1>,  S<1, 0, 2>,  S<1, 0, 2>,
+        2, 32, 32, 0,
+        1, 1, S<1, 16, 1, 8>, 4,
+#endif
+        ck::BlockGemmPipelineScheduler::Intrawave, ck::BlockGemmPipelineVersion::v3, CDataType, CDataType, false, PermuteB>;
+
+// clang-format on
+
+using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
+                                                                        AccDataType,
+                                                                        CDataType,
+                                                                        AccDataType,
+                                                                        PassThrough,
+                                                                        PassThrough,
+                                                                        PassThrough>;
+template <typename ProblemType>
+bool run_gemm(const ProblemType& problem_size, const ExecutionConfig& config)
+{
+    using namespace ck::literals;
+
+    auto M       = problem_size.M;
+    auto N       = problem_size.N;
+    auto K       = problem_size.K;
+    auto StrideA = problem_size.StrideA;
+    auto StrideB = problem_size.StrideB;
+    auto StrideC = problem_size.StrideC;
+    auto KBatch  = problem_size.KBatch;
+
+    auto f_host_tensor_descriptor =
+        [](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
+            if constexpr(std::is_same_v<decltype(layout), ck::tensor_layout::gemm::RowMajor>)
+            {
+                return HostTensorDescriptor({row, col}, {stride, 1_uz});
+            }
+            else
+            {
+                return HostTensorDescriptor({row, col}, {1_uz, stride});
+            }
+        };
+
+    auto f_get_default_stride =
+        [](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
+            if(stride == 0)
+            {
+                // give a chance if stride is zero, return a default packed stride
+                if constexpr(std::is_same_v<decltype(layout), ck::tensor_layout::gemm::RowMajor>)
+                {
+                    return col;
+                }
+                else
+                {
+                    return row;
+                }
+            }
+            else
+                return stride;
+        };
+
+    ck::index_t Scale_Stride_BN = (K + Scale_Block_K - 1) / Scale_Block_K;
+
+    StrideA = f_get_default_stride(M, K, StrideA, ALayout{});
+    StrideB = f_get_default_stride(K, N, StrideB, BLayout{});
+    StrideC = f_get_default_stride(M, N, StrideC, CLayout{});
+
+    Tensor<ADataType> a_m_k(f_host_tensor_descriptor(M, K, StrideA, ALayout{}));
+    Tensor<BDataType> b_k_n(f_host_tensor_descriptor(K, N, StrideB, BLayout{}));
+    Tensor<BDataType> b_k_n_permute(f_host_tensor_descriptor(K, N, StrideB, BLayout{}));
+    Tensor<BScaleDataType> b1_k_n(f_host_tensor_descriptor((K + Scale_Block_K - 1) / Scale_Block_K,
+                                                           (N + Scale_Block_N - 1) / Scale_Block_N,
+                                                           Scale_Stride_BN,
+                                                           BLayout{}));
+
+    switch(config.init_method)
+    {
+    case 0:
+        a_m_k.GenerateTensorValue(GeneratorTensor_1<ADataType>{1});
+        b_k_n.GenerateTensorValue(GeneratorTensor_1<BDataType>{1});
+        b1_k_n.GenerateTensorValue(GeneratorTensor_1<BScaleDataType>{1});
+        break;
+    case 1:
+        a_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-2, 2});
+        b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-2, 2});
+        b1_k_n.GenerateTensorValue(GeneratorTensor_2<BScaleDataType>{0, 1});
+        break;
+    case 2:
+        a_m_k.GenerateTensorValue(GeneratorTensor_1<ADataType>{1});
+        b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-2, 2});
+        b1_k_n.GenerateTensorValue(GeneratorTensor_1<BScaleDataType>{1});
+        break;
+    case 3:
+        a_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-2, 2});
+        b_k_n.GenerateTensorValue(GeneratorTensor_1<BDataType>{1});
+        b1_k_n.GenerateTensorValue(GeneratorTensor_1<BScaleDataType>{1});
+        break;
+    default:
+        a_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.5, 0.5});
+        b_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
+        b1_k_n.GenerateTensorValue(GeneratorTensor_3<BScaleDataType>{0, 1.0});
+    }
+
+    Tensor<CDataType> c_m_n_host_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
+    Tensor<CDataType> c_m_n_device_result(f_host_tensor_descriptor(M, N, StrideC, CLayout{}));
+
+    std::cout << "a_m_k: " << a_m_k.mDesc << std::endl;
+    std::cout << "b_k_n: " << b_k_n.mDesc << std::endl;
+    std::cout << "b1_k_n: " << b1_k_n.mDesc << std::endl;
+    std::cout << "c_m_n: " << c_m_n_host_result.mDesc << std::endl;
+
+    DeviceMem a_m_k_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpaceSize());
+    DeviceMem b_k_n_device_buf(sizeof(BDataType) * b_k_n_permute.mDesc.GetElementSpaceSize());
+    DeviceMem b1_scale_device_buf(sizeof(BScaleDataType) * b1_k_n.mDesc.GetElementSpaceSize());
+    DeviceMem c_m_n_device_buf(sizeof(CDataType) * c_m_n_device_result.mDesc.GetElementSpaceSize());
+
+    // weight permute
+    if constexpr(PermuteB)
+    {
+        int K1 = KPerBlock;
+        int K0 = K / KPerBlock;
+
+        // int K0, N, K1
+        for(int j = 0; j < K0; j++)
+        {
+            for(int i = 0; i < N; i++)
+            {
+                for(int jj = 0; jj < K1; jj++)
+                {
+                    b_k_n_permute(j * N * K1 + i * K1 + jj) = b_k_n(i * K + (j * K1 + jj));
+                }
+            }
+        }
+    }
+    else
+    {
+        for(int i = 0; i < N; i++)
+        {
+            for(int j = 0; j < K; j++)
+            {
+                b_k_n_permute(i * K + j) = b_k_n(i * K + j);
+            }
+        }
+    }
+
+    // vector pk_i4x4 permute
+    for(int i = 0; i < N; i++)
+    {
+        for(int j = 0; j < K; j += 8)
+        {
+            int input[8];
+
+            for(int k = 0; k < 4; k++)
+            {
+                int i4x2         = b_k_n_permute(j + k * 2, i);
+                input[k * 2 + 0] = (i4x2 >> 4) & 0xf;
+                input[k * 2 + 1] = (i4x2 >> 0) & 0xf;
+            }
+
+            // permute 01234567->20643175
+            {
+                int hi   = input[2];
+                int lo   = input[0];
+                int i4x2 = (hi << 4) | lo;
+
+                b_k_n_permute(j + 0, i) = i4x2;
+            }
+
+            {
+                int hi   = input[6];
+                int lo   = input[4];
+                int i4x2 = (hi << 4) | lo;
+
+                b_k_n_permute(j + 2, i) = i4x2;
+            }
+
+            {
+                int hi   = input[3];
+                int lo   = input[1];
+                int i4x2 = (hi << 4) | lo;
+
+                b_k_n_permute(j + 4, i) = i4x2;
+            }
+
+            {
+                int hi   = input[7];
+                int lo   = input[5];
+                int i4x2 = (hi << 4) | lo;
+
+                b_k_n_permute(j + 6, i) = i4x2;
+            }
+        }
+    }
+
+    a_m_k_device_buf.ToDevice(a_m_k.mData.data());
+    b_k_n_device_buf.ToDevice(b_k_n_permute.mData.data());
+    b1_scale_device_buf.ToDevice(b1_k_n.mData.data());
+    DeviceMem workspace;
+
+    auto a_element_op = AElementOp{};
+    auto b_element_op = BElementOp{};
+    auto c_element_op = CElementOp{};
+
+    // do GEMM
+    auto gemm      = DeviceGemmV2Instance{};
+    auto invoker   = gemm.MakeInvoker();
+    float ave_time = 0;
+
+    auto argument =
+        gemm.MakeArgument(static_cast<ADataType*>(a_m_k_device_buf.GetDeviceBuffer()),
+                          static_cast<BDataType*>(b_k_n_device_buf.GetDeviceBuffer()),
+                          static_cast<CDataType*>(c_m_n_device_buf.GetDeviceBuffer()),
+                          M,
+                          N,
+                          K,
+                          StrideA,
+                          StrideB,
+                          StrideC,
+                          static_cast<BScaleDataType*>(b1_scale_device_buf.GetDeviceBuffer()),
+                          KBatch,
+                          a_element_op,
+                          b_element_op,
+                          c_element_op);
+
+    if(!gemm.IsSupportedArgument(argument))
+    {
+        std::cerr << gemm.GetTypeString() << " does not support this problem" << std::endl;
+
+        return true;
+    }
+
+    bool pass = true;
+    if(config.do_verification)
+    {
+        Tensor<float> b_k_n_pre({K, N});
+
+        float v_b = 0;
+        for(int n = 0; n < N; n++)
+        {
+            for(int k = 0; k < K; k++)
+            {
+                ck::pk_i4_t i4x2 = b_k_n(k, n);
+                int8_t i4        = 0;
+                if(k % 2 == 1)
+                    i4 = (i4x2 >> 0) & 0xf;
+                else
+                    i4 = (i4x2 >> 4) & 0xf;
+                i4  = i4 - 8;
+                v_b = ck::type_convert<float>(i4);
+
+                b_k_n_pre(k, n) =
+                    ck::type_convert<float>(v_b) *
+                    ck::type_convert<float>(b1_k_n(k / Scale_Block_K, n / Scale_Block_N));
+            }
+        }
+
+        auto ref_gemm    = ReferenceGemmInstance{};
+        auto ref_invoker = ref_gemm.MakeInvoker();
+
+        auto ref_argument = ref_gemm.MakeArgument(
+            a_m_k, b_k_n_pre, c_m_n_host_result, PassThrough{}, PassThrough{}, PassThrough{});
+
+        ref_invoker.Run(ref_argument);
+
+        ave_time = invoker.Run(argument, StreamConfig{nullptr, false, 0});
+        c_m_n_device_buf.FromDevice(c_m_n_device_result.mData.data());
+
+        pass &= ck::utils::check_err(c_m_n_device_result,
+                                     c_m_n_host_result,
+                                     "Error: Incorrect results!",
+                                     get_rtol<CDataType>(),
+                                     get_atol<CDataType>());
+
+#if 0
+        std::cout << "a_m_k: " << std::endl;
+        for(int i = 0; i < M; i++)
+        {
+            for(int j = 0; j < K; j++)
+            {
+                std::cout << ck::type_convert<float>(a_m_k(i, j)) << ",";
+            }
+            std::cout << std::endl;
+        }
+
+        std::cout << "b_k_n: " << std::endl;
+        for(int i = 0; i < N; i++)
+        {
+            for(int j = 0; j < K; j++)
+            {
+                ck::pk_i4_t i4x2 = b_k_n(j, i);
+                int8_t i4 = 0;
+                if( j % 2 == 1)
+                    i4 = (i4x2 >> 0) & 0xf;
+                else
+                    i4 = (i4x2 >> 4) & 0xf;
+                i4 = i4 - 8;
+                std::cout << ck::type_convert<float>(i4) << ",";
+            }
+            std::cout << std::endl;
+        }
+
+        std::cout << "c_m_n_device_result: " << std::endl;
+        for(int i = 0; i < M; i++)
+        {
+            for(int j = 0; j < N; j++)
+            {
+                std::cout << ck::type_convert<float>(c_m_n_device_result(i, j)) << ",";
+            }
+            std::cout << std::endl;
+        }
+
+        std::cout << "c_m_n_host_result: " << std::endl;
+        for(int i = 0; i < M; i++)
+        {
+            for(int j = 0; j < N; j++)
+            {
+                std::cout << ck::type_convert<float>(c_m_n_host_result(i, j)) << ",";
+            }
+            std::cout << std::endl;
+        }
+#endif
+    }
+
+    if(config.time_kernel)
+    {
+        ave_time =
+            invoker.Run(argument, StreamConfig{nullptr, config.time_kernel, 0, 20, 50, true, 50});
+
+        std::size_t flop = 2_uz * M * N * K;
+        std::size_t num_btype =
+            sizeof(ADataType) * M * K +
+            sizeof(BDataType) * K * N /
+                (ck::is_same_v<ck::remove_cvref_t<BDataType>, ck::pk_i4_t> ? 2 : 1) +
+            sizeof(CDataType) * M * N;
+
+        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;
+}
+
+bool run_gemm_splitk_example(int argc, char* argv[])
+{
+    ProblemSizeSplitK problem_size;
+    ExecutionConfig config;
+
+    return !parse_cmd_args(argc, argv, problem_size, config) || run_gemm(problem_size, config);
+}
+
+int main(int argc, char* argv[]) { return !run_gemm_splitk_example(argc, argv); }

include/ck/tensor_operation/gpu/block/blockwise_gemm_pipeline_xdlops_selector.hpp

@@ -4,8 +4,8 @@
 #pragma once
 
 #include "ck/tensor_operation/gpu/block/blockwise_gemm_pipeline_xdlops_v1.hpp"
-#include "ck/tensor_operation/gpu/block/blockwise_gemm_pipeline_xdlops_v2.hpp"
-#include "ck/tensor_operation/gpu/block/blockwise_gemm_pipeline_xdlops_v3.hpp"
+#include "ck/tensor_operation/gpu/block/blockwise_gemm_pipeline_xdlops_v2_b_scale.hpp"
+#include "ck/tensor_operation/gpu/block/blockwise_gemm_pipeline_xdlops_v3_b_scale.hpp"
 #include "ck/tensor_operation/gpu/block/blockwise_gemm_pipeline_xdlops_v4.hpp"
 #include "ck/tensor_operation/gpu/block/blockwise_gemm_pipeline_xdlops_v5.hpp"
 
@@ -68,49 +68,49 @@ constexpr auto BlockGemmPipeline_Selector()
     }
     else if constexpr(BlkGemmPipelineVer == BlockGemmPipelineVersion::v2)
     {
-        return BlockwiseGemmXdlops_pipeline_v2<BlkGemmPipeSche,
-                                               BlockSize,
-                                               ADataType,
-                                               BDataType,
-                                               ComputeDataType,
-                                               AccDataType,
-                                               ATileDesc,
-                                               BTileDesc,
-                                               AMmaTileDesc,
-                                               BMmaTileDesc,
-                                               ABlockTransferSrcScalarPerVector,
-                                               BBlockTransferSrcScalarPerVector,
-                                               MPerBlock,
-                                               NPerBlock,
-                                               KPerBlock,
-                                               MPerXDL,
-                                               NPerXDL,
-                                               MRepeat,
-                                               NRepeat,
-                                               KPack>{};
+        return BlockwiseGemmXdlops_pipeline_v2_b_scale<BlkGemmPipeSche,
+                                                       BlockSize,
+                                                       ADataType,
+                                                       BDataType,
+                                                       ComputeDataType,
+                                                       AccDataType,
+                                                       ATileDesc,
+                                                       BTileDesc,
+                                                       AMmaTileDesc,
+                                                       BMmaTileDesc,
+                                                       ABlockTransferSrcScalarPerVector,
+                                                       BBlockTransferSrcScalarPerVector,
+                                                       MPerBlock,
+                                                       NPerBlock,
+                                                       KPerBlock,
+                                                       MPerXDL,
+                                                       NPerXDL,
+                                                       MRepeat,
+                                                       NRepeat,
+                                                       KPack>{};
     }
     else if constexpr(BlkGemmPipelineVer == BlockGemmPipelineVersion::v3)
     {
-        return BlockwiseGemmXdlops_pipeline_v3<BlkGemmPipeSche,
-                                               BlockSize,
-                                               ADataType,
-                                               BDataType,
-                                               ComputeDataType,
-                                               AccDataType,
-                                               ATileDesc,
-                                               BTileDesc,
-                                               AMmaTileDesc,
-                                               BMmaTileDesc,
-                                               ABlockTransferSrcScalarPerVector,
-                                               BBlockTransferSrcScalarPerVector,
-                                               MPerBlock,
-                                               NPerBlock,
-                                               KPerBlock,
-                                               MPerXDL,
-                                               NPerXDL,
-                                               MRepeat,
-                                               NRepeat,
-                                               KPack>{};
+        return BlockwiseGemmXdlops_pipeline_v3_b_scale<BlkGemmPipeSche,
+                                                        BlockSize,
+                                                        ADataType,
+                                                        BDataType,
+                                                        ComputeDataType,
+                                                        AccDataType,
+                                                        ATileDesc,
+                                                        BTileDesc,
+                                                        AMmaTileDesc,
+                                                        BMmaTileDesc,
+                                                        ABlockTransferSrcScalarPerVector,
+                                                        BBlockTransferSrcScalarPerVector,
+                                                        MPerBlock,
+                                                        NPerBlock,
+                                                        KPerBlock,
+                                                        MPerXDL,
+                                                        NPerXDL,
+                                                        MRepeat,
+                                                        NRepeat,
+                                                        KPack>{};
     }
     else if constexpr(BlkGemmPipelineVer == BlockGemmPipelineVersion::v4)
     {

include/ck/tensor_operation/gpu/device/device_gemm_v2.hpp

@@ -76,6 +76,42 @@ struct DeviceGemmV2R1 : public BaseOperator
     virtual std::unique_ptr<BaseInvoker> MakeInvokerPointer() = 0;
 };
 
+template <typename ALayout,
+          typename BLayout,
+          typename CLayout,
+          typename ADataType,
+          typename BDataType,
+          typename BScaleType,
+          typename CDataType,
+          index_t ScaleBlockN,
+          index_t ScaleBlockK,
+          typename AElementwiseOperation,
+          typename BElementwiseOperation,
+          typename CElementwiseOperation>
+struct DeviceGemmV2BScale : public BaseOperator
+{
+    virtual std::unique_ptr<BaseArgument>
+    MakeArgumentPointer(const void* p_a,
+                        const void* p_b,
+                        void* p_c,
+                        ck::index_t M,
+                        ck::index_t N,
+                        ck::index_t K,
+                        ck::index_t StrideA,
+                        ck::index_t StrideB,
+                        ck::index_t StrideC,                        
+                        const void* p_b_scale,
+                        ck::index_t KSplit,
+                        AElementwiseOperation a_element_op,
+                        BElementwiseOperation b_element_op,
+                        CElementwiseOperation c_element_op) = 0;
+
+    virtual std::unique_ptr<BaseInvoker> MakeInvokerPointer() = 0;
+
+    virtual bool GetPermuteB()         = 0;
+    virtual ck::index_t GetKPerBlock() = 0;
+};
+
 } // namespace device
 } // namespace tensor_operation
 } // namespace ck