Merge pull request #827 from ROCmSoftwarePlatform/fpAintB_clear

fpAintB_gemm implementation

example/49_fpAintB_gemm/CMakeLists.txt

+if(GPU_TARGETS MATCHES "gfx1100" OR GPU_TARGETS MATCHES "gfx1101" OR GPU_TARGETS MATCHES "gfx1102")
+  add_custom_target(example_fpAintB_gemm_wmma)
+  add_example_executable(example_fp16int8_gemm_wmma fp16int8_gemm_wmma.cpp)
+  add_dependencies(example_fpAintB_gemm_wmma example_fp16int8_gemm_wmma)
+endif()

example/49_fpAintB_gemm/common.hpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include <cstdlib>
+#include <iostream>
+#include <initializer_list>
+#include <numeric>
+
+#include "ck/ck.hpp"
+#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
+#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
+#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
+#include "ck/utility/data_type.hpp"
+
+#include "ck/library/utility/check_err.hpp"
+#include "ck/library/utility/device_memory.hpp"
+#include "ck/library/utility/fill.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_fpAintB_gemm.hpp"
+
+struct ProblemSize final
+{
+    ck::index_t M = 3840;
+    ck::index_t N = 4096;
+    ck::index_t K = 4096;
+
+    ck::index_t StrideA = 4096;
+    ck::index_t StrideB = 4096;
+    ck::index_t StrideC = 4096;
+};
+
+struct ExecutionConfig final
+{
+    bool do_verification = true;
+    int init_method      = 1;
+    bool time_kernel     = false;
+};
+
+template <ck::index_t... Is>
+using S = ck::Sequence<Is...>;
+
+using Row = ck::tensor_layout::gemm::RowMajor;
+using Col = ck::tensor_layout::gemm::ColumnMajor;
+
+using PassThrough = ck::tensor_operation::element_wise::PassThrough;
+
+template <typename IntType>
+struct UnsignedWeightPreprocessor
+{
+};
+
+template <>
+struct UnsignedWeightPreprocessor<int8_t>
+{
+    using UnsignedWeight = Tensor<uint8_t>;
+    using SignedWeight   = Tensor<int8_t>;
+    static UnsignedWeight convert(SignedWeight const& Input)
+    {
+
+        UnsignedWeight Output = Input.template CopyAsType<uint8_t>();
+
+        auto f_kn = [&](auto k, auto n) {
+            const uint8_t adder = 128;
+            int8_t v_signed_weight;
+            uint8_t v_unsigned_weight;
+
+            ck::tensor_operation::element_wise::PassThrough{}(v_signed_weight, Input(k, n));
+            v_unsigned_weight = ck::type_convert<uint8_t>(v_signed_weight) + adder;
+            Output(k, n)      = v_unsigned_weight;
+        };
+
+        make_ParallelTensorFunctor(f_kn, Input.mDesc.GetLengths()[0], Input.mDesc.GetLengths()[1])(
+            std::thread::hardware_concurrency());
+
+        return Output;
+    }
+
+    UnsignedWeight operator()(SignedWeight const& Input) { return convert(Input); }
+};
+
+inline bool
+parse_cmd_args(int argc, char* argv[], ProblemSize& problem_size, ExecutionConfig& config)
+{
+    if(argc == 1)
+    {
+        // use default case
+    }
+    else 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 if(argc == 10)
+    {
+        config.do_verification = std::stoi(argv[1]);
+        config.init_method     = std::stoi(argv[2]);
+        config.time_kernel     = std::stoi(argv[3]);
+
+        problem_size.M = std::stoi(argv[4]);
+        problem_size.N = std::stoi(argv[5]);
+        problem_size.K = std::stoi(argv[6]);
+
+        problem_size.StrideA = std::stoi(argv[7]);
+        problem_size.StrideB = std::stoi(argv[8]);
+        problem_size.StrideC = std::stoi(argv[9]);
+    }
+    else
+    {
+        std::cerr << "arg1: verification (0=no, 1=yes)" << std::endl
+                  << "arg2: initialization (0=no init, 1=integer value, 2=decimal value)"
+                  << std::endl
+                  << "arg3: time kernel (0=no, 1=yes)" << std::endl
+                  << "arg4 to 9: M (256x), N(128x), K(32x), StrideA, StrideB, StrideC" << std::endl;
+        return false;
+    }
+
+    return true;
+}

example/49_fpAintB_gemm/fp16int8_gemm_wmma.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_fpAintB_gemm_wmma.hpp"
+
+// Implementation follows the paper:
+// Kim, Young Jin, Rawn Henry, Raffy Fahim, and Hany Hassan Awadalla. “Who Says Elephants Can’t Run:
+// Bringing Large Scale MoE Models into Cloud Scale Production.” arXiv, November 17, 2022.
+// https://doi.org/10.48550/arXiv.2211.10017. Assume weight (Matrix B) is add preprocess to
+// unsigned.
+
+// The DeviceOp is CDataType = ADataType * Dequant(BDataType) * ScaleDataType
+// The HostRef  is CDataType = ADataType * Dequant(QuantDataType) * ScaleDataType
+
+//TODO: Current implementation consume more VGPR than expected.
+
+using ADataType        = ck::half_t;
+using QuantDataType    = int8_t;
+using BDataType        = uint8_t;
+using ScaleDataType    = 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::MNKPadding;
+
+// clang-format off
+using DeviceGemmInstance = ck::tensor_operation::device::DeviceFpAintBGemm_Wmma_CShuffle
+         < ALayout,             
+           BLayout,             
+           CLayout,             
+           ADataType, 
+           BDataType,
+           ScaleDataType,
+           CDataType, 
+           AccDataType, 
+           CShuffleDataType,  
+           AElementOp,  
+           BElementOp,  
+           CElementOp,    
+           GemmDefault, 
+           1,           // Prefetch stage
+           128,         // BlockSize
+           64,          // MPerBlock
+           128,         // NPerBlock
+           64,          // KPerBlock
+           8,           // K1
+           16,          // MPerWmma
+           16,          // NPerWmma
+           2,           // M-Repeat // M-PerWmma / M-Repeat = M-Wave
+           4,           // N-Repeat // N-PerWmma / N-Repeat = N-Wave
+           S<4, 32, 1>,     
+           S<1, 0, 2>,     
+           S<1, 0, 2>,              
+           2,              
+           8,              
+           8,      
+           true,     
+           S<4, 32, 1>,     
+           S<1, 0, 2>,     
+           S<1, 0, 2>,             
+           2,              
+           8,              
+           8,      
+           true,           
+           1,           // C shuffle (M Repeat) Per store
+           1,           // C shuffle (N Repeat) Per store
+           S<1, 32, 1,  4>,               
+           8>;
+// clang-format on
+
+using ReferenceGemmInstance = ck::tensor_operation::host::ReferencefpAintBGemm<ADataType,
+                                                                               QuantDataType,
+                                                                               ScaleDataType,
+                                                                               CDataType,
+                                                                               AccDataType,
+                                                                               AElementOp,
+                                                                               BElementOp,
+                                                                               CElementOp>;
+
+#include "run_gemm_example.inc"
+
+int main(int argc, char* argv[]) { return !run_gemm_example(argc, argv); }

example/49_fpAintB_gemm/run_gemm_example.inc

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+bool run_gemm(const ProblemSize& problem_size, const ExecutionConfig& config)
+{
+#if defined(BUILD_INT4_EXAMPLE) && defined(CK_EXPERIMENTAL_BIT_INT_EXTENSION_INT4)
+    static_assert(sizeof(ck::int4_t) == sizeof(int8_t));
+#endif
+
+    using namespace ck::literals;
+
+    auto& [M, N, K, StrideA, StrideB, StrideC] = problem_size;
+
+    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});
+            }
+        };
+
+    Tensor<ADataType> a_m_k(f_host_tensor_descriptor(M, K, StrideA, ALayout{}));
+    Tensor<QuantDataType> quant_b_k_n(f_host_tensor_descriptor(K, N, StrideB, BLayout{}));
+    // assume scale tensor is [1, n]
+    Tensor<ScaleDataType> scale_k_n(f_host_tensor_descriptor(K, N, 0, Row{}));
+
+    switch(config.init_method)
+    {
+    case 0: break;
+    case 1:
+        ck::utils::FillUniformDistributionIntegerValue<ADataType>{-5.f, 5.f}(a_m_k);
+        ck::utils::FillUniformDistributionIntegerValue<QuantDataType>{-5.f, 5.f}(quant_b_k_n);
+        ck::utils::FillUniformDistributionIntegerValue<ScaleDataType>{-5.f, 5.f}(scale_k_n);
+        break;
+    case 2:
+        ck::utils::FillUniformDistribution<ADataType>{-1.f, 1.f}(a_m_k);
+        ck::utils::FillUniformDistribution<QuantDataType>{-1.f, 1.f}(quant_b_k_n);
+        ck::utils::FillUniformDistribution<ScaleDataType>{-1.f, 1.f}(scale_k_n);
+        break;
+    case 3:
+        ck::utils::FillUniformDistributionIntegerValue<ADataType>{1.f, 1.f}(a_m_k);
+        ck::utils::FillUniformDistributionIntegerValue<QuantDataType>{-5.f, 5.f}(quant_b_k_n);
+        ck::utils::FillUniformDistributionIntegerValue<ScaleDataType>{-5.f, 5.f}(scale_k_n);
+        break;
+    case 4:
+        ck::utils::FillUniformDistributionIntegerValue<ADataType>{1.f, 1.f}(a_m_k);
+        ck::utils::FillUniformDistributionIntegerValue<QuantDataType>{1.f, 1.f}(quant_b_k_n);
+        ck::utils::FillUniformDistributionIntegerValue<ScaleDataType>{2.f, 2.f}(scale_k_n);
+        break;
+    case 5:
+        ck::utils::FillUniformDistributionIntegerValue<ADataType>{-2.f, 2.f}(a_m_k);
+        ck::utils::FillUniformDistributionIntegerValue<QuantDataType>{-2.f, 2.f}(quant_b_k_n);
+        ck::utils::FillUniformDistributionIntegerValue<ScaleDataType>{-2.f, 2.f}(scale_k_n);
+        break;
+    default:
+        ck::utils::FillUniformDistribution<ADataType>{-1.f, 1.f}(a_m_k);
+        ck::utils::FillUniformDistribution<QuantDataType>{-1.f, 1.f}(quant_b_k_n);
+        ck::utils::FillUniformDistribution<ScaleDataType>{-1.f, 1.f}(scale_k_n);
+    }
+
+    UnsignedWeightPreprocessor<QuantDataType> preprocessor;
+    Tensor<BDataType> b_k_n = preprocessor(quant_b_k_n);
+
+    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 << "scale_k_n: " << scale_k_n.mDesc << std::endl;
+    std::cout << "c_m_n: " << c_m_n_host_result.mDesc << std::endl;
+
+#ifdef BUILD_INT4_EXAMPLE
+    DeviceMem a_m_k_device_buf(sizeof(KernelADataType) * a_m_k.mDesc.GetElementSpaceSize());
+    DeviceMem b_k_n_device_buf(sizeof(KernelBDataType) * b_k_n.mDesc.GetElementSpaceSize());
+    DeviceMem c_m_n_device_buf(sizeof(KernelCDataType) *
+                               c_m_n_device_result.mDesc.GetElementSpaceSize());
+
+    const Tensor<KernelADataType> a_m_k_converted(a_m_k);
+    const Tensor<KernelBDataType> b_k_n_converted(b_k_n);
+
+    a_m_k_device_buf.ToDevice(a_m_k_converted.mData.data());
+    b_k_n_device_buf.ToDevice(b_k_n_converted.mData.data());
+#else
+    DeviceMem a_m_k_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpaceSize());
+    DeviceMem b_k_n_device_buf(sizeof(BDataType) * b_k_n.mDesc.GetElementSpaceSize());
+    DeviceMem scale_k_n_device_buf(sizeof(ScaleDataType) * scale_k_n.mDesc.GetElementSpaceSize());
+    DeviceMem c_m_n_device_buf(sizeof(CDataType) * c_m_n_device_result.mDesc.GetElementSpaceSize());
+
+    a_m_k_device_buf.ToDevice(a_m_k.mData.data());
+    b_k_n_device_buf.ToDevice(b_k_n.mData.data());
+    scale_k_n_device_buf.ToDevice(scale_k_n.mData.data());
+#endif
+
+    auto a_element_op = AElementOp{};
+    auto b_element_op = BElementOp{};
+    auto c_element_op = CElementOp{};
+
+    // do GEMM
+    auto gemm     = DeviceGemmInstance{};
+    auto invoker  = gemm.MakeInvoker();
+    auto argument = gemm.MakeArgument(
+#ifdef BUILD_INT4_EXAMPLE
+        static_cast<KernelADataType*>(a_m_k_device_buf.GetDeviceBuffer()),
+        static_cast<KernelBDataType*>(b_k_n_device_buf.GetDeviceBuffer()),
+        static_cast<KernelCDataType*>(c_m_n_device_buf.GetDeviceBuffer()),
+#else
+        static_cast<ADataType*>(a_m_k_device_buf.GetDeviceBuffer()),
+        static_cast<BDataType*>(b_k_n_device_buf.GetDeviceBuffer()),
+        static_cast<ScaleDataType*>(scale_k_n_device_buf.GetDeviceBuffer()),
+        static_cast<CDataType*>(c_m_n_device_buf.GetDeviceBuffer()),
+#endif
+        M,
+        N,
+        K,
+        StrideA,
+        StrideB,
+        StrideC,
+        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;
+    }
+
+    float ave_time = invoker.Run(argument, StreamConfig{nullptr, config.time_kernel});
+
+    std::size_t flop = 2_uz * M * N * K;
+    std::size_t num_btype =
+        sizeof(ADataType) * M * K + sizeof(BDataType) * K * N + 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;
+
+    if(config.do_verification)
+    {
+        auto ref_gemm    = ReferenceGemmInstance{};
+        auto ref_invoker = ref_gemm.MakeInvoker();
+
+        auto ref_argument = ref_gemm.MakeArgument(a_m_k,
+                                                  quant_b_k_n,
+                                                  scale_k_n,
+                                                  c_m_n_host_result,
+                                                  a_element_op,
+                                                  b_element_op,
+                                                  c_element_op);
+
+        ref_invoker.Run(ref_argument);
+
+#ifdef BUILD_INT4_EXAMPLE
+        Tensor<CDataType> c_m_n_device_result_converted(c_m_n_host_result.mDesc);
+
+        c_m_n_device_buf.FromDevice(c_m_n_device_result_converted.mData.data());
+
+        c_m_n_device_result = c_m_n_device_result_converted.CopyAsType<CDataType>();
+
+        return ck::utils::check_err(c_m_n_device_result_converted, c_m_n_host_result);
+#else
+        c_m_n_device_buf.FromDevice(c_m_n_device_result.mData.data());
+
+        return ck::utils::check_err(c_m_n_device_result, c_m_n_host_result);
+#endif
+    }
+
+    return true;
+}
+
+bool run_gemm_example(int argc, char* argv[])
+{
+    ProblemSize problem_size;
+    ExecutionConfig config;
+
+    return !parse_cmd_args(argc, argv, problem_size, config) || run_gemm(problem_size, config);
+}

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

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include "ck/utility/common_header.hpp"
+#include "ck/tensor_operation/gpu/thread/threadwise_tensor_slice_transfer.hpp"
+#include "ck/tensor_operation/gpu/warp/wmma_gemm.hpp"
+#include "ck/tensor_description/tensor_adaptor.hpp"
+#include "ck/tensor_operation/gpu/element/unary_element_wise_operation.hpp"
+
+#define CK_MNK_LOOP
+
+namespace ck {
+
+template <index_t BlockSize,
+          typename ADataType,
+          typename BDataType,
+          typename ScaleDataType,
+          typename FloatAcc,
+          typename ABlockDesc,
+          typename BBlockDesc,
+          typename ScaleBlockDesc,
+          index_t MPerBlock,
+          index_t NPerBlock,
+          index_t KPerBlock,
+          index_t MPerWMMA,
+          index_t NPerWMMA,
+          index_t MRepeat,
+          index_t NRepeat,
+          index_t KPack,
+          bool AEnableLds = true,
+          bool BEnableLds = true,
+          bool TransposeC = false>
+/* Option: Read from LDS, big buffer hold all threads required data
+ * Source
+ * A: K0PerBlock x MPerBlock x K1
+ * B: K0PerBlock x NPerBlock x K1
+ * Destination
+ * C, non-transpose
+ * thread level: MRepeat x NRepeat x MAccVgprs
+ * block  level: MRepeat x MWave x MSubGroup x NRepeat x NWave x NThreadPerSubGroup x MAccVgprs
+ * KPACK == WMMA_K = 16
+ *
+ * Option: Read from VMEM, small buffer hold each thread own required data (Skip LDS)
+ * Source:
+ * A(if skip LDS): MRepeat x KPack
+ * B(if skip LDS): NRepeat x KPack
+ * Destination
+ * C, non-transpose
+ * block level: MRepeat x MWave x MSubGroup x NRepeat x NWave x NThreadPerSubGroup x MAccVgprs
+ */
+struct Blockwise_fpAintB_GemmWMMA
+{
+    static constexpr auto I0    = Number<0>{};
+    static constexpr auto I1    = Number<1>{};
+    static constexpr auto I2    = Number<2>{};
+    static constexpr auto I3    = Number<3>{};
+    static constexpr auto I4    = Number<4>{};
+    static constexpr auto I5    = Number<5>{};
+    static constexpr auto WmmaK = Number<16>{};
+
+    using ThisThreadBlock = ThisThreadBlock<BlockSize>;
+
+    // Hardcode of WaveSize, since current HIP Runtime(5.4.0-10984) could not return correct one.
+    static constexpr index_t WaveSize = 32;
+
+    // When use LDS, each Row(16 consecutive lanes) read whole data from source buffer
+    // When not use LDS, each Row read half of whole data from source buffer, exchange the data via
+    // permutation
+    static constexpr index_t A_KRow = AEnableLds ? 1 : 2;
+    static constexpr index_t B_KRow = BEnableLds ? 1 : 2;
+    static constexpr index_t A_K1   = ABlockDesc{}.GetLength(I5);
+    static constexpr index_t B_K1   = BBlockDesc{}.GetLength(I5);
+
+    // As Float DataType
+    static constexpr auto wmma_gemm =
+        WmmaGemm<ADataType, ADataType, FloatAcc, MPerWMMA, NPerWMMA, KPack, TransposeC>{};
+
+    static constexpr index_t MWaves = MPerBlock / (MRepeat * MPerWMMA);
+    static constexpr index_t NWaves = NPerBlock / (NRepeat * NPerWMMA);
+
+    StaticBufferTupleOfVector<AddressSpaceEnum::Vgpr,
+                              FloatAcc,
+                              MRepeat * NRepeat,
+                              wmma_gemm.GetRegSizePerWmma(),
+                              true>
+        c_thread_buf_;
+
+    __host__ __device__ constexpr auto& GetCThreadBuffer() { return c_thread_buf_; }
+
+    __device__ static auto GetWaveIdx()
+    {
+        const index_t thread_id = ThisThreadBlock::GetThreadId();
+
+        constexpr auto threadid_to_wave_idx_adaptor = make_single_stage_tensor_adaptor(
+            make_tuple(make_merge_transform(make_tuple(MWaves, NWaves, WaveSize))),
+            make_tuple(Sequence<0, 1, 2>{}),
+            make_tuple(Sequence<0>{}));
+
+        return threadid_to_wave_idx_adaptor.CalculateBottomIndex(make_multi_index(thread_id));
+    }
+
+    // Default, Block buffer in LDS, thread level offset enabled
+    __device__ static auto CalculateAThreadOriginDataIndex()
+    {
+        if constexpr(AEnableLds)
+        {
+            const auto wave_idx   = GetWaveIdx();
+            const auto waveId_m   = wave_idx[I0];
+            const auto WMMA_a_idx = wmma_gemm.CalculateAThreadOriginDataIndex();
+
+            //  |KRepeat   |MRepeat|MWave    |KRow  |MLane  |KPack
+            return make_tuple(0, 0, waveId_m, 0, WMMA_a_idx, 0);
+        }
+        else
+        {
+            return make_tuple(0, 0, 0, 0, 0, 0);
+        }
+    }
+
+    __device__ static auto CalculateBThreadOriginDataIndex()
+    {
+        if constexpr(BEnableLds)
+        {
+            const auto wave_idx   = GetWaveIdx();
+            const auto waveId_n   = wave_idx[I1];
+            const auto WMMA_b_idx = wmma_gemm.CalculateBThreadOriginDataIndex();
+
+            //  |KRepeat   |NRepeat|Nwave     |KRow  |NLane  |KPack
+            return make_tuple(0, 0, waveId_n, 0, WMMA_b_idx, 0);
+        }
+        else
+        {
+            return make_tuple(0, 0, 0, 0, 0, 0);
+        }
+    }
+
+    template <index_t m0, index_t n0>
+    __device__ static auto CalculateCThreadOriginDataIndex(Number<m0>, Number<n0>)
+    {
+        const auto wave_idx = GetWaveIdx();
+
+        const auto waveId_m = wave_idx[I0];
+        const auto waveId_n = wave_idx[I1];
+
+        const auto blk_idx = wmma_gemm.GetBeginOfThreadBlk();
+
+        constexpr auto mrepeat_mwave_mperWMMA_to_m_adaptor = make_single_stage_tensor_adaptor(
+            make_tuple(make_unmerge_transform(make_tuple(MRepeat, MWaves, MPerWMMA))),
+            make_tuple(Sequence<0>{}),
+            make_tuple(Sequence<0, 1, 2>{}));
+
+        constexpr auto nrepeat_nwave_nperWMMA_to_n_adaptor = make_single_stage_tensor_adaptor(
+            make_tuple(make_unmerge_transform(make_tuple(NRepeat, NWaves, NPerWMMA))),
+            make_tuple(Sequence<0>{}),
+            make_tuple(Sequence<0, 1, 2>{}));
+
+        const index_t c_thread_m = mrepeat_mwave_mperWMMA_to_m_adaptor.CalculateBottomIndex(
+            make_tuple(m0, waveId_m, blk_idx[I0]))[I0];
+        const index_t c_thread_n = nrepeat_nwave_nperWMMA_to_n_adaptor.CalculateBottomIndex(
+            make_tuple(n0, waveId_n, blk_idx[I1]))[I0];
+
+        return make_tuple(c_thread_m, c_thread_n);
+    }
+
+    template <index_t m0, index_t n0>
+    __device__ static auto CalculateCThreadOriginDataIndex7D(Number<m0>, Number<n0>)
+    {
+        const auto wave_idx = GetWaveIdx();
+
+        const auto waveId_m = wave_idx[I0];
+        const auto waveId_n = wave_idx[I1];
+
+        const auto blk_idx = wmma_gemm.GetBeginOfThreadBlk3D();
+
+        return make_tuple(
+            Number<m0>{}, waveId_m, blk_idx[I0], Number<n0>{}, waveId_n, blk_idx[I1], blk_idx[I2]);
+    }
+
+    using Tuple6 = decltype(CalculateAThreadOriginDataIndex());
+    __host__ __device__
+    Blockwise_fpAintB_GemmWMMA(Tuple6 a_origin = CalculateAThreadOriginDataIndex(),
+                               Tuple6 b_origin = CalculateBThreadOriginDataIndex())
+        : a_thread_copy_(a_origin), b_thread_copy_(b_origin), scale_thread_copy_(b_origin)
+    {
+        static_assert(ABlockDesc::IsKnownAtCompileTime() && BBlockDesc::IsKnownAtCompileTime(),
+                      "wrong! Desc should be known at compile-time");
+
+        static_assert(ThisThreadBlock::GetNumOfThread() == MWaves * NWaves * WaveSize,
+                      "ThisThreadBlock::GetNumOfThread() != MWaves * NWaves * WaveSize\n");
+
+        static_assert(MPerBlock % (MPerWMMA * MRepeat) == 0 &&
+                          NPerBlock % (NPerWMMA * NRepeat) == 0,
+                      "wrong!");
+    }
+
+    // transposed WMMA output C' = B' * A'
+    __host__ __device__ static constexpr auto
+    GetCThreadDescriptor_MRepeat_MWave_MThreadPerSubGroup_NRepeat_NWave_NSubGroup_NAccVgprs()
+    {
+        constexpr auto c_msubgroup_nthreadpersubgroup_maccvgprs_tblk_lens =
+            wmma_gemm.GetCMSubGroupNThreadPerSubGroupMAccVgprsThreadBlkLengths();
+
+        constexpr auto NAccVgprs = c_msubgroup_nthreadpersubgroup_maccvgprs_tblk_lens[I2];
+
+        return make_naive_tensor_descriptor_packed(
+            //        |MRepeat            |MWave |MSubGroup |NRepeat           |NWave
+            //        |NThreadPerSubGroup |MAccVgprs
+            make_tuple(Number<MRepeat>{}, I1, I1, Number<NRepeat>{}, I1, I1, NAccVgprs));
+    }
+
+    // Thread level, register decriptor. Vector-write
+    __host__ __device__ static constexpr auto
+    GetCThreadDescriptor_MRepeat_MWave_MSubGroup_NRepeat_NWave_NThreadPerSubGroup_MAccVgprs()
+    {
+        constexpr auto c_msubgroup_nthreadpersubgroup_maccvgprs_tblk_lens =
+            wmma_gemm.GetCMSubGroupNThreadPerSubGroupMAccVgprsThreadBlkLengths();
+
+        constexpr auto MAccVgprs = c_msubgroup_nthreadpersubgroup_maccvgprs_tblk_lens[I2];
+        constexpr auto AccStride = c_msubgroup_nthreadpersubgroup_maccvgprs_tblk_lens[I3];
+        return make_naive_tensor_descriptor(
+            //        |MRepeat           |MWave |MSubGroup |NRepeat           |NWave
+            //        |NThreadPerSubGroup |MAccVgprs
+            make_tuple(Number<MRepeat>{}, I1, I1, Number<NRepeat>{}, I1, I1, MAccVgprs),
+            make_tuple(Number<NRepeat>{} * MAccVgprs * AccStride,
+                       Number<NRepeat>{} * MAccVgprs * AccStride,
+                       Number<NRepeat>{} * MAccVgprs * AccStride,
+                       MAccVgprs * AccStride,
+                       MAccVgprs * AccStride,
+                       MAccVgprs * AccStride,
+                       AccStride));
+    }
+
+    template <typename CGridDesc_M_N>
+    __host__ __device__ static constexpr auto
+    MakeCGridDescriptor_MBlockxRepeat_MWave_MSubGroup_NBlockxRepeat_NWave_NThreadPerSubGroup_MAccVgprs(
+        const CGridDesc_M_N& c_grid_desc_m_n)
+    {
+        const auto M = c_grid_desc_m_n.GetLength(I0);
+        const auto N = c_grid_desc_m_n.GetLength(I1);
+
+        const auto c_grid_desc_mblockxrepeat_mwave_mperwmma_nblockxrepeat_nwave_nperwmma =
+            transform_tensor_descriptor(
+                c_grid_desc_m_n,
+                make_tuple(
+                    make_unmerge_transform(make_tuple(M / (MWaves * MPerWMMA), MWaves, MPerWMMA)),
+                    make_unmerge_transform(make_tuple(N / (NWaves * NPerWMMA), NWaves, NPerWMMA))),
+                make_tuple(Sequence<0>{}, Sequence<1>{}),
+                make_tuple(Sequence<0, 1, 2>{}, Sequence<3, 4, 5>{}));
+
+        return wmma_gemm
+            .MakeCDesc_MBlockxRepeat_MWave_MSubGroup_NBlockxRepeat_NWave_NThreadPerSubGroup_MAccVgprs(
+                c_grid_desc_mblockxrepeat_mwave_mperwmma_nblockxrepeat_nwave_nperwmma);
+    }
+
+    // transposed WMMA output C' = B' * A'
+    __host__ __device__ static constexpr auto
+    GetCBlockDescriptor_MRepeat_MWave_MThreadPerSubGroup_NRepeat_NWave_NSubGroup_NAccVgprs()
+    {
+        constexpr auto c_block_desc_mrepeat_mwave_mperwmma_nrepeat_nwave_nperwmma =
+            make_naive_tensor_descriptor_packed(make_tuple(Number<MRepeat>{},
+                                                           Number<MWaves>{},
+                                                           Number<MPerWMMA>{},
+                                                           Number<NRepeat>{},
+                                                           Number<NWaves>{},
+                                                           Number<NPerWMMA>{}));
+
+        return wmma_gemm
+            .MakeCDesc_MBlockxRepeat_MWave_MThreadPerSubGroup_NBlockxRepeat_NWave_NSubGroup_NAccVgprs(
+                c_block_desc_mrepeat_mwave_mperwmma_nrepeat_nwave_nperwmma);
+    }
+
+    // Provide dimension size
+    __host__ __device__ static constexpr auto
+    GetCBlockDescriptor_MRepeat_MWave_MSubGroup_NRepeat_NWave_NThreadPerSubGroup_MAccVgprs()
+    {
+        constexpr auto c_block_desc_mrepeat_mwave_mperwmma_nrepeat_nwave_nperwmma =
+            make_naive_tensor_descriptor_packed(make_tuple(Number<MRepeat>{},
+                                                           Number<MWaves>{},
+                                                           Number<MPerWMMA>{},
+                                                           Number<NRepeat>{},
+                                                           Number<NWaves>{},
+                                                           Number<NPerWMMA>{}));
+
+        return wmma_gemm
+            .MakeCDesc_MBlockxRepeat_MWave_MSubGroup_NBlockxRepeat_NWave_NThreadPerSubGroup_MAccVgprs(
+                c_block_desc_mrepeat_mwave_mperwmma_nrepeat_nwave_nperwmma);
+    }
+
+    // Describe how data allocated in thread copy src buffer
+    // M0_M1_M2 = MRepeat_MWave_MPerWmma, N0_N1_N2 = NRepeat_NWave_NPerWmma
+    static constexpr ABlockDesc a_block_desc_k0_m0_m1_m2_k1;
+    static constexpr BBlockDesc b_block_desc_k0_n0_n1_n2_k1;
+    static constexpr ScaleBlockDesc scale_block_desc_1_n0_n1_n2_1;
+
+    template <typename ABlockBuffer,
+              typename BBlockBuffer,
+              typename ScaleBlockBuffer,
+              typename CThreadBuffer>
+    __device__ void Run(const ABlockBuffer& a_block_buf,
+                        const BBlockBuffer& b_block_buf,
+                        const ScaleBlockBuffer& scale_block_buf,
+                        CThreadBuffer& c_thread_buf) const
+    {
+        auto a_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ADataType>(
+            a_thread_desc_.GetElementSpaceSize());
+        auto b_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, BDataType>(
+            b_thread_desc_.GetElementSpaceSize());
+        auto scale_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ScaleDataType>(
+            scale_thread_desc_.GetElementSpaceSize());
+        // auto converted_b_thread_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ADataType>(
+        // b_thread_desc_.GetElementSpaceSize());
+        tensor_operation::element_wise::FastNumericArrayConverter<BDataType, ADataType, WmmaK>
+            fast_numeric_converter;
+
+        // basic intrinsic to determine loopover direction
+        if constexpr(MRepeat < NRepeat)
+        {
+            static_for<0, KPerBlock / WmmaK, 1>{}(
+                [&](auto k) { // k=0,1,2 instead of k=0,kpack*1, ...
+                    static_for<0, MRepeat, 1>{}([&](auto m0) {
+                        // read A
+                        a_thread_copy_.Run(
+                            a_block_desc_k0_m0_m1_m2_k1,
+                            make_tuple(Number<k * WmmaK / A_K1 / A_KRow>{}, m0, I0, I0, I0, I0),
+                            a_block_buf,
+                            a_thread_desc_,
+                            make_tuple(I0, m0, I0, I0, I0, I0),
+                            a_thread_buf);
+
+                        static_for<0, NRepeat, 1>{}([&](auto n0) {
+                            // read B
+                            b_thread_copy_.Run(
+                                b_block_desc_k0_n0_n1_n2_k1,
+                                make_tuple(Number<k * WmmaK / B_K1 / B_KRow>{}, n0, I0, I0, I0, I0),
+                                b_block_buf,
+                                b_thread_desc_,
+                                make_tuple(I0, n0, I0, I0, I0, I0),
+                                b_thread_buf);
+                            // read weight scale
+                            scale_thread_copy_.Run(
+                                scale_block_desc_1_n0_n1_n2_1,
+                                make_tuple(Number<k * WmmaK / B_K1 / B_KRow>{}, n0, I0, I0, I0, I0),
+                                scale_block_buf,
+                                scale_thread_desc_,
+                                make_tuple(I0, n0, I0, I0, I0, I0),
+                                scale_thread_buf);
+
+                            vector_type<BDataType, WmmaK> b_int_vec;
+                            vector_type<ADataType, WmmaK> b_thread_vec;
+
+                            static_for<0, WmmaK, 1>{}([&](auto i) {
+                                b_int_vec.template AsType<BDataType>()(i) =
+                                    b_thread_buf[Number<b_thread_desc_.CalculateOffset(
+                                        make_tuple(i / B_K1 / B_KRow,
+                                                   n0,
+                                                   0,
+                                                   (i / B_K1) % B_KRow,
+                                                   0,
+                                                   i % B_K1))>{}];
+                            });
+
+                            // convert B from uint8 to fp16, multiply scale
+                            b_thread_vec = fast_numeric_converter(b_int_vec);
+                            static_for<0, WmmaK, 1>{}([&](auto i) {
+                                b_thread_vec.template AsType<ADataType>()(i) =
+                                    scale_thread_buf[n0] *
+                                    b_thread_vec.template AsType<ADataType>()(i);
+                            });
+
+                            vector_type<ADataType, WmmaK> a_thread_vec;
+
+                            static_for<0, WmmaK, 1>{}([&](auto i) {
+                                a_thread_vec.template AsType<ADataType>()(i) =
+                                    a_thread_buf[Number<a_thread_desc_.CalculateOffset(
+                                        make_tuple(i / A_K1 / A_KRow,
+                                                   m0,
+                                                   0,
+                                                   (i / A_K1) % A_KRow,
+                                                   0,
+                                                   i % A_K1))>{}];
+                            });
+
+                            using wmma_input_type_a = typename vector_type<ADataType, WmmaK>::type;
+                            using wmma_input_type_b = typename vector_type<ADataType, WmmaK>::type;
+
+                            constexpr index_t c_offset =
+                                c_thread_desc_.CalculateOffset(make_tuple(m0, n0, 0));
+
+                            wmma_gemm.template Run(
+                                a_thread_vec.template AsType<wmma_input_type_a>()(Number<0>{}),
+                                b_thread_vec.template AsType<wmma_input_type_b>()(Number<0>{}),
+                                c_thread_buf.GetVectorTypeReference(Number<c_offset>{}));
+                        });
+                    });
+                });
+        }
+        else
+        {
+            static_for<0, KPerBlock / WmmaK, 1>{}([&](auto k) { // k=0,1,2 instead of
+                                                                // k=0,kpack*1, ..
+                static_for<0, NRepeat, 1>{}([&](auto n0) {
+                    // read weight scale
+                    scale_thread_copy_.Run(scale_block_desc_1_n0_n1_n2_1,
+                                           make_tuple(I0, n0, I0, I0, I0, I0),
+                                           scale_block_buf,
+                                           scale_thread_desc_,
+                                           make_tuple(I0, n0, I0, I0, I0, I0),
+                                           scale_thread_buf);
+
+                    // read B
+                    b_thread_copy_.Run(
+                        b_block_desc_k0_n0_n1_n2_k1,
+                        make_tuple(Number<k * WmmaK / B_K1 / B_KRow>{}, n0, I0, I0, I0, I0),
+                        b_block_buf,
+                        b_thread_desc_,
+                        make_tuple(I0, n0, I0, I0, I0, I0),
+                        b_thread_buf);
+
+                    vector_type<BDataType, WmmaK> b_int_vec;
+                    vector_type<ADataType, WmmaK> b_thread_vec;
+
+                    static_for<0, WmmaK, 1>{}([&](auto i) {
+                        b_int_vec.template AsType<BDataType>()(i) =
+                            b_thread_buf[Number<b_thread_desc_.CalculateOffset(make_tuple(
+                                i / B_K1 / B_KRow, n0, 0, (i / B_K1) % B_KRow, 0, i % B_K1))>{}];
+                    });
+
+                    // convert B from uint8 to fp16, multiply scale
+                    b_thread_vec = fast_numeric_converter(b_int_vec);
+                    static_for<0, WmmaK, 1>{}([&](auto i) {
+                        b_thread_vec.template AsType<ADataType>()(i) =
+                            scale_thread_buf[n0] * b_thread_vec.template AsType<ADataType>()(i);
+                    });
+
+                    static_for<0, MRepeat, 1>{}([&](auto m0) {
+                        // read A
+                        a_thread_copy_.Run(
+                            a_block_desc_k0_m0_m1_m2_k1,
+                            make_tuple(Number<k * WmmaK / A_K1 / A_KRow>{}, m0, I0, I0, I0, I0),
+                            a_block_buf,
+                            a_thread_desc_,
+                            make_tuple(I0, m0, I0, I0, I0, I0),
+                            a_thread_buf);
+
+                        vector_type<ADataType, WmmaK> a_thread_vec;
+
+                        static_for<0, WmmaK, 1>{}([&](auto i) {
+                            a_thread_vec.template AsType<ADataType>()(i) =
+                                a_thread_buf[Number<a_thread_desc_.CalculateOffset(
+                                    make_tuple(i / A_K1 / A_KRow,
+                                               m0,
+                                               0,
+                                               (i / A_K1) % A_KRow,
+                                               0,
+                                               i % A_K1))>{}];
+                        });
+
+                        using wmma_input_type_a = typename vector_type<ADataType, WmmaK>::type;
+                        using wmma_input_type_b = typename vector_type<ADataType, WmmaK>::type;
+
+                        constexpr index_t c_offset =
+                            c_thread_desc_.CalculateOffset(make_tuple(m0, n0, 0));
+
+                        wmma_gemm.template Run(
+                            a_thread_vec.template AsType<wmma_input_type_a>()(Number<0>{}),
+                            b_thread_vec.template AsType<wmma_input_type_b>()(Number<0>{}),
+                            c_thread_buf.GetVectorTypeReference(Number<c_offset>{}));
+                    });
+                });
+            });
+        }
+    }
+
+    protected:
+    static constexpr auto a_thread_desc_ =
+        make_naive_tensor_descriptor(make_tuple(Number<WmmaK / A_K1 / A_KRow>{},
+                                                Number<MRepeat>{},
+                                                I1,
+                                                Number<A_KRow>{},
+                                                I1,
+                                                Number<A_K1>{}),
+                                     make_tuple(Number<A_K1 * A_KRow>{},
+                                                Number<WmmaK>{},
+                                                Number<A_K1 * A_KRow>{},
+                                                Number<A_K1>{},
+                                                Number<A_K1>{},
+                                                Number<1>{}));
+
+    static constexpr auto b_thread_desc_ =
+        make_naive_tensor_descriptor(make_tuple(Number<WmmaK / B_K1 / B_KRow>{},
+                                                Number<NRepeat>{},
+                                                I1,
+                                                Number<B_KRow>{},
+                                                I1,
+                                                Number<B_K1>{}),
+                                     make_tuple(Number<B_K1 * B_KRow>{},
+                                                Number<WmmaK>{},
+                                                Number<B_K1 * B_KRow>{},
+                                                Number<B_K1>{},
+                                                Number<B_K1>{},
+                                                Number<1>{}));
+
+    static constexpr auto scale_thread_desc_ = make_naive_tensor_descriptor(
+        make_tuple(
+            Number<WmmaK / B_K1 / B_KRow>{}, Number<NRepeat>{}, I1, Number<B_KRow>{}, I1, I1),
+        make_tuple(I0, I1, I0, I0, I0, I0));
+
+    // C[M, N, NumRegWMMA]
+    static constexpr auto c_thread_desc_ = make_naive_tensor_descriptor_packed(
+        make_tuple(Number<MRepeat>{}, Number<NRepeat>{}, wmma_gemm.GetRegSizePerWmma()));
+
+    template <bool EnableLds>
+    struct AThreadCopySelector;
+
+    template <>
+    struct AThreadCopySelector<true>
+    {
+        using type =
+            ThreadwiseTensorSliceTransfer_v4<ADataType,
+                                             ADataType,
+                                             decltype(a_block_desc_k0_m0_m1_m2_k1),
+                                             decltype(a_thread_desc_),
+                                             Sequence<WmmaK / A_K1 / A_KRow, 1, 1, A_KRow, 1, A_K1>,
+                                             Sequence<0, 1, 2, 3, 4, 5>,
+                                             5,
+                                             A_K1,
+                                             A_K1>;
+    };
+
+    template <>
+    struct AThreadCopySelector<false>
+    {
+        using type = ThreadwiseTensorSliceTransfer_StaticToStatic_InterRow<
+            ADataType,
+            ADataType,
+            decltype(a_block_desc_k0_m0_m1_m2_k1),
+            decltype(a_thread_desc_),
+            tensor_operation::element_wise::PassThrough,
+            Sequence<WmmaK / A_K1 / A_KRow, 1, 1, 1, 1, A_K1>,
+            Sequence<0, 1, 2, 3, 4, 5>,
+            5,
+            A_K1,
+            0x76543210,
+            0xfedcba98,
+            TransposeC ? false : true>;
+    };
+
+    template <bool EnableLds>
+    struct BThreadCopySelector;
+
+    template <>
+    struct BThreadCopySelector<true>
+    {
+        using type =
+            ThreadwiseTensorSliceTransfer_v4<BDataType,
+                                             BDataType,
+                                             decltype(b_block_desc_k0_n0_n1_n2_k1),
+                                             decltype(b_thread_desc_),
+                                             Sequence<WmmaK / B_K1 / B_KRow, 1, 1, B_KRow, 1, B_K1>,
+                                             Sequence<0, 1, 2, 3, 4, 5>,
+                                             5,
+                                             B_K1,
+                                             B_K1>;
+    };
+
+    template <>
+    struct BThreadCopySelector<false>
+    {
+        using type = ThreadwiseTensorSliceTransfer_StaticToStatic_InterRow<
+            BDataType,
+            BDataType,
+            decltype(b_block_desc_k0_n0_n1_n2_k1),
+            decltype(b_thread_desc_),
+            tensor_operation::element_wise::PassThrough,
+            Sequence<WmmaK / B_K1 / B_KRow, 1, 1, 1, 1, B_K1>,
+            Sequence<0, 1, 2, 3, 4, 5>,
+            5,
+            B_K1,
+            0x76543210,
+            0xfedcba98,
+            TransposeC ? true : false>;
+    };
+
+    template <bool EnableLds>
+    struct ScaleThreadCopySelector;
+
+    template <>
+    struct ScaleThreadCopySelector<true>
+    {
+        using type =
+            ThreadwiseTensorSliceTransfer_v4<ScaleDataType,
+                                             ScaleDataType,
+                                             decltype(scale_block_desc_1_n0_n1_n2_1),
+                                             decltype(scale_thread_desc_),
+                                             Sequence<WmmaK / B_K1 / B_KRow, 1, 1, B_KRow, 1, 1>,
+                                             Sequence<0, 1, 2, 3, 4, 5>,
+                                             5,
+                                             1,
+                                             1>;
+    };
+
+    template <>
+    struct ScaleThreadCopySelector<false>
+    {
+        using type = ThreadwiseTensorSliceTransfer_StaticToStatic<
+            ScaleDataType,
+            ScaleDataType,
+            decltype(scale_block_desc_1_n0_n1_n2_1),
+            decltype(scale_thread_desc_),
+            tensor_operation::element_wise::PassThrough,
+            Sequence<WmmaK / B_K1 / B_KRow, 1, 1, 1, 1, B_K1>,
+            Sequence<0, 1, 2, 3, 4, 5>,
+            5,
+            1>;
+    };
+
+    typename AThreadCopySelector<AEnableLds>::type a_thread_copy_;
+    typename BThreadCopySelector<BEnableLds>::type b_thread_copy_;
+    typename ScaleThreadCopySelector<BEnableLds>::type scale_thread_copy_;
+};
+
+} // namespace ck

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

@@ -362,11 +362,11 @@ struct BlockwiseGemmWMMA
         }
         else
         {
-
             static_for<0, NRepeat, 1>{}([&](auto n0) {
                 static_for<0, MRepeat, 1>{}([&](auto m0) {
                     static_for<0, KPerBlock / WmmaK, 1>{}([&](auto k) { // k=0,1,2 instead of
-                                                                        // k=0,kpack*1, ... read B
+                                                                        // k=0,kpack*1, ..
+                        // read B
                         b_thread_copy_.Run(
                             b_block_desc_k0_n0_n1_n2_k1,
                             make_tuple(Number<k * WmmaK / B_K1 / B_KRow>{}, n0, I0, I0, I0, I0),

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

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include "ck/tensor_operation/gpu/device/device_base.hpp"
+
+namespace ck {
+namespace tensor_operation {
+namespace device {
+
+// Dequantization of input tensor could not be decoupled from gridwisegemm pipeline
+// As input tensor thread buffer declared inside blockwise-gemm pipeline.
+
+template <typename ALayout,
+          typename BLayout,
+          typename CLayout,
+          typename ADataType,
+          typename BDataType,
+          typename CDataType,
+          typename AElementwiseOperation,
+          typename BElementwiseOperation,
+          typename CElementwiseOperation>
+struct DeviceGemm_dequantB : public BaseOperator
+{
+    virtual std::unique_ptr<BaseArgument>
+    MakeArgumentPointer(const void* p_a,
+                        const void* p_b,
+                        const void* p_scale,
+                        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,
+                        AElementwiseOperation a_element_op,
+                        BElementwiseOperation b_element_op,
+                        CElementwiseOperation c_element_op) = 0;
+
+    virtual std::unique_ptr<BaseInvoker> MakeInvokerPointer() = 0;
+};
+
+} // namespace device
+} // namespace tensor_operation
+} // namespace ck

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

+// 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_gemm_dequantB.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_fpAintB_gemm_wmma.hpp"
+#include "ck/host_utility/device_prop.hpp"
+#include "ck/host_utility/kernel_launch.hpp"
+
+namespace ck {
+namespace tensor_operation {
+namespace device {
+
+// 1. DequantB(K, N) = int2fp(B(K, N)) * scale(1, N)
+// 2. C(M, N) = A(M, K) * DequantB(K, N)
+
+template <typename ALayout,
+          typename BLayout,
+          typename CLayout,
+          typename ADataType,
+          typename BDataType,
+          typename ScaleDataType,
+          typename CDataType,
+          typename AccDataType,
+          typename CShuffleDataType,
+          typename AElementwiseOperation,
+          typename BElementwiseOperation,
+          typename CElementwiseOperation,
+          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 K1,
+          ck::index_t MPerWmma,
+          ck::index_t NPerWmma,
+          ck::index_t MRepeat,
+          ck::index_t NRepeat,
+          typename ABlockTransferThreadClusterLengths_K0_M_K1,
+          typename ABlockTransferThreadClusterArrangeOrder,
+          typename ABlockTransferSrcAccessOrder,
+          ck::index_t ABlockTransferSrcVectorDim,
+          ck::index_t ABlockTransferSrcScalarPerVector,
+          ck::index_t ABlockTransferDstScalarPerVector_K1,
+          bool ABlockLdsAddExtraM,
+          typename BBlockTransferThreadClusterLengths_K0_N_K1,
+          typename BBlockTransferThreadClusterArrangeOrder,
+          typename BBlockTransferSrcAccessOrder,
+          ck::index_t BBlockTransferSrcVectorDim,
+          ck::index_t BBlockTransferSrcScalarPerVector,
+          ck::index_t BBlockTransferDstScalarPerVector_K1,
+          bool BBlockLdsAddExtraN,
+          index_t CShuffleMRepeatPerShuffle,
+          index_t CShuffleNRepeatPerShuffle,
+          typename CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
+          index_t CShuffleBlockTransferScalarPerVector_NPerBlock,
+          ck::LoopScheduler LoopSched     = make_default_loop_scheduler(),
+          ck::PipelineVersion PipelineVer = ck::PipelineVersion::dequant_v1>
+struct DeviceFpAintBGemm_Wmma_CShuffle : public DeviceGemm_dequantB<ALayout,
+                                                                    BLayout,
+                                                                    CLayout,
+                                                                    ADataType,
+                                                                    BDataType,
+                                                                    CDataType,
+                                                                    AElementwiseOperation,
+                                                                    BElementwiseOperation,
+                                                                    CElementwiseOperation>
+{
+    static constexpr auto I0 = Number<0>{};
+    static constexpr auto I1 = Number<1>{};
+    static constexpr auto I2 = Number<2>{};
+    static constexpr auto I3 = Number<3>{};
+    static constexpr auto I4 = Number<4>{};
+    static constexpr auto I5 = Number<5>{};
+    static constexpr auto I6 = Number<6>{};
+    // K1 = Max Vector Access Pixels
+    static constexpr auto K1Number = Number<K1>{};
+
+    static constexpr auto MWaves = MPerBlock / (MRepeat * MPerWmma);
+    static constexpr auto NWaves = NPerBlock / (NRepeat * NPerWmma);
+    static constexpr auto WmmaK  = 16;
+
+    static constexpr auto AEnableLds_auto = NWaves == 1 ? false : true;
+    static constexpr auto BEnableLds_auto = MWaves == 1 ? false : true;
+
+    // If true, LDS is used unconditionally
+    // LDS bypass feature not checked.
+    static constexpr auto AEnableLds_manu = true;
+    static constexpr auto BEnableLds_manu = true;
+
+    static constexpr auto AEnableLds = AEnableLds_auto || AEnableLds_manu || (NumPrefetch > 1);
+    static constexpr auto BEnableLds = BEnableLds_auto || BEnableLds_manu || (NumPrefetch > 1);
+
+    static constexpr auto matrix_padder =
+        MatrixPadder<GemmSpec, index_t, index_t, index_t>{MPerBlock, NPerBlock, KPerBlock};
+
+    using DeviceOp = DeviceFpAintBGemm_Wmma_CShuffle;
+
+    // Describe how data read from Global memory
+    static auto MakeAGridDescriptor(index_t MRaw, index_t KRaw, index_t StrideA)
+    {
+        const auto a_grid_desc_m_k = [&]() {
+            if constexpr(is_same<tensor_layout::gemm::RowMajor, ALayout>::value)
+            {
+                const auto a_grid_desc_mraw_kraw =
+                    make_naive_tensor_descriptor(make_tuple(MRaw, KRaw), make_tuple(StrideA, I1));
+
+                return matrix_padder.PadADescriptor_M_K(a_grid_desc_mraw_kraw);
+            }
+            else if constexpr(is_same<tensor_layout::gemm::ColumnMajor, ALayout>::value)
+            {
+                const auto a_grid_desc_mraw_kraw =
+                    make_naive_tensor_descriptor(make_tuple(MRaw, KRaw), make_tuple(I1, StrideA));
+
+                return matrix_padder.PadADescriptor_M_K(a_grid_desc_mraw_kraw);
+            }
+        }();
+
+        const auto M = a_grid_desc_m_k.GetLength(I0);
+        const auto K = a_grid_desc_m_k.GetLength(I1);
+        assert(K % K1 == 0);
+
+        if constexpr(AEnableLds)
+        {
+            const index_t K0 = K / K1;
+
+            return transform_tensor_descriptor(
+                a_grid_desc_m_k,
+                make_tuple(make_unmerge_transform(make_tuple(K0, K1Number)),
+                           make_pass_through_transform(M)),
+                make_tuple(Sequence<1>{}, Sequence<0>{}),
+                make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
+        }
+        else
+        {
+            constexpr auto A_KRow      = 2;
+            constexpr auto A_K0PerWmma = WmmaK / A_KRow / K1Number;
+            const auto A_KWmma         = K / WmmaK;
+
+            const auto M0 = M / MPerBlock;
+            // 0   1     0         1                2        3             4        5          6
+            // M - K <-> A_KWmma - MBlock*MRepeat - MWaves - A_K0PerWmma - A_KRow - MPerWmma - A_K1
+            return transform_tensor_descriptor(
+                a_grid_desc_m_k,
+                make_tuple(make_unmerge_transform(make_tuple(
+                               A_KWmma, Number<A_K0PerWmma>{}, Number<A_KRow>{}, K1Number)),
+                           make_unmerge_transform(
+                               make_tuple(M0 * MRepeat, Number<MWaves>{}, Number<MPerWmma>{}))),
+                make_tuple(Sequence<1>{}, Sequence<0>{}),
+                make_tuple(Sequence<0, 3, 4, 6>{}, Sequence<1, 2, 5>{}));
+        }
+    }
+
+    static auto MakeBGridDescriptor(index_t KRaw, index_t NRaw, index_t StrideB)
+    {
+        const auto b_grid_desc_n_k = [&]() {
+            if constexpr(is_same<tensor_layout::gemm::RowMajor, BLayout>::value)
+            {
+                const auto b_grid_desc_nraw_kraw =
+                    make_naive_tensor_descriptor(make_tuple(NRaw, KRaw), make_tuple(I1, StrideB));
+
+                return matrix_padder.PadBDescriptor_N_K(b_grid_desc_nraw_kraw);
+            }
+            else if constexpr(is_same_v<tensor_layout::gemm::ColumnMajor, BLayout>)
+            {
+                const auto b_grid_desc_nraw_kraw =
+                    make_naive_tensor_descriptor(make_tuple(NRaw, KRaw), make_tuple(StrideB, I1));
+
+                return matrix_padder.PadBDescriptor_N_K(b_grid_desc_nraw_kraw);
+            }
+        }();
+
+        const auto N = b_grid_desc_n_k.GetLength(I0);
+        const auto K = b_grid_desc_n_k.GetLength(I1);
+        assert(K % K1 == 0);
+
+        if constexpr(BEnableLds)
+        {
+            const index_t K0 = K / K1;
+
+            return transform_tensor_descriptor(
+                b_grid_desc_n_k,
+                make_tuple(make_unmerge_transform(make_tuple(K0, K1Number)),
+                           make_pass_through_transform(N)),
+                make_tuple(Sequence<1>{}, Sequence<0>{}),
+                make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
+        }
+        else
+        {
+            constexpr auto B_KRow      = 2;
+            constexpr auto B_K0PerWmma = WmmaK / B_KRow / K1Number;
+            const auto B_KWmma         = K / WmmaK;
+
+            const auto N0 = N / NPerBlock;
+            // 0   1     0         1                2        3             4        5          6
+            // M - K <-> A_KWmma - MBlock*MRepeat - MWaves - A_K0PerWmma - A_KRow - MPerWmma - A_K1
+            return transform_tensor_descriptor(
+                b_grid_desc_n_k,
+                make_tuple(make_unmerge_transform(make_tuple(
+                               B_KWmma, Number<B_K0PerWmma>{}, Number<B_KRow>{}, K1Number)),
+                           make_unmerge_transform(
+                               make_tuple(N0 * NRepeat, Number<NWaves>{}, Number<NPerWmma>{}))),
+                make_tuple(Sequence<1>{}, Sequence<0>{}),
+                make_tuple(Sequence<0, 3, 4, 6>{}, Sequence<1, 2, 5>{}));
+        }
+    }
+
+    static auto MakeScaleGridDescriptor(index_t KRaw, index_t NRaw, index_t StrideB = 0)
+    {
+        // assume Scale is [1, N]
+        const auto scale_grid_desc_n_k = [&]() {
+            const auto scale_grid_desc_nraw_kraw =
+                make_naive_tensor_descriptor(make_tuple(NRaw, KRaw), make_tuple(I1, StrideB));
+
+            return matrix_padder.PadBDescriptor_N_K(scale_grid_desc_nraw_kraw);
+        }();
+
+        const auto N = scale_grid_desc_n_k.GetLength(I0);
+        const auto K = scale_grid_desc_n_k.GetLength(I1);
+        // When K = 1, it might be scale tensor.
+        assert(K % K1 == 0 && K != 1);
+
+        if constexpr(BEnableLds)
+        {
+            const index_t K0 = K / K1;
+
+            return transform_tensor_descriptor(
+                scale_grid_desc_n_k,
+                make_tuple(make_unmerge_transform(make_tuple(K0, 1)), // Reduce K1 = 1
+                           make_pass_through_transform(N)),
+                make_tuple(Sequence<1>{}, Sequence<0>{}),
+                make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
+        }
+        else
+        {
+            constexpr auto B_KRow      = 2;
+            constexpr auto B_K0PerWmma = WmmaK / B_KRow / K1Number;
+            const auto B_KWmma         = K / WmmaK;
+
+            const auto N0 = N / NPerBlock;
+            // 0   1     0         1                2        3             4        5          6
+            // M - K <-> A_KWmma - MBlock*MRepeat - MWaves - A_K0PerWmma - A_KRow - MPerWmma - A_K1
+            return transform_tensor_descriptor(
+                scale_grid_desc_n_k,
+                make_tuple(make_unmerge_transform(make_tuple(
+                               B_KWmma, Number<B_K0PerWmma>{}, Number<B_KRow>{}, K1Number)),
+                           make_unmerge_transform(
+                               make_tuple(N0 * NRepeat, Number<NWaves>{}, Number<NPerWmma>{}))),
+                make_tuple(Sequence<1>{}, Sequence<0>{}),
+                make_tuple(Sequence<0, 3, 4, 6>{}, Sequence<1, 2, 5>{}));
+        }
+    }
+
+    static auto MakeCGridDescriptor_M_N(index_t MRaw, index_t NRaw, index_t StrideC)
+    {
+        const auto c_grid_desc_mraw_nraw = [&]() {
+            if constexpr(is_same<tensor_layout::gemm::RowMajor, CLayout>::value)
+            {
+                return make_naive_tensor_descriptor(make_tuple(MRaw, NRaw),
+                                                    make_tuple(StrideC, I1));
+            }
+            else if constexpr(is_same<tensor_layout::gemm::ColumnMajor, CLayout>::value)
+            {
+                return make_naive_tensor_descriptor(make_tuple(MRaw, NRaw),
+                                                    make_tuple(I1, StrideC));
+            }
+        }();
+
+        return matrix_padder.PadCDescriptor_M_N(c_grid_desc_mraw_nraw);
+    }
+
+    // Gridwise descriptor, mapping to whole given provblem.
+    using AGridDesc     = decltype(MakeAGridDescriptor(1, 1, 1));
+    using BGridDesc     = decltype(MakeBGridDescriptor(1, 1, 1));
+    using ScaleGridDesc = decltype(MakeScaleGridDescriptor(1, 1, 0));
+    using CGridDesc_M_N = decltype(MakeCGridDescriptor_M_N(1, 1, 1));
+
+    // GridwiseGemm
+    using GridwiseGemm = GridwiseFpAintBGemm_Wmma<
+        BlockSize,
+        ADataType,
+        BDataType,
+        ScaleDataType,
+        AccDataType,
+        CShuffleDataType,
+        CDataType,
+        InMemoryDataOperationEnum::Set,
+        AGridDesc,
+        BGridDesc,
+        ScaleGridDesc,
+        CGridDesc_M_N,
+        AElementwiseOperation,
+        BElementwiseOperation,
+        CElementwiseOperation,
+        MPerBlock,
+        NPerBlock,
+        KPerBlock,
+        MPerWmma,
+        NPerWmma,
+        K1,
+        MRepeat,
+        NRepeat,
+        ABlockTransferThreadClusterLengths_K0_M_K1,
+        ABlockTransferThreadClusterArrangeOrder,
+        ABlockTransferSrcAccessOrder,
+        ABlockTransferSrcVectorDim,
+        ABlockTransferSrcScalarPerVector,
+        ABlockTransferDstScalarPerVector_K1,
+        false, // AThreadTransferSrcResetCoordinateAfterRun,
+        AEnableLds,
+        ABlockLdsAddExtraM,
+        BBlockTransferThreadClusterLengths_K0_N_K1,
+        BBlockTransferThreadClusterArrangeOrder,
+        BBlockTransferSrcAccessOrder,
+        BBlockTransferSrcVectorDim,
+        BBlockTransferSrcScalarPerVector,
+        BBlockTransferDstScalarPerVector_K1,
+        false, // BThreadTransferSrcResetCoordinateAfterRun,
+        BEnableLds,
+        BBlockLdsAddExtraN,
+        CShuffleMRepeatPerShuffle,
+        CShuffleNRepeatPerShuffle,
+        CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
+        CShuffleBlockTransferScalarPerVector_NPerBlock,
+        NumPrefetch,
+        LoopSched,
+        PipelineVer>;
+
+    // Argument
+    struct Argument : public BaseArgument
+    {
+        Argument(const ADataType* p_a_grid,
+                 const BDataType* p_b_grid,
+                 const ScaleDataType* p_scale_grid,
+                 CDataType* p_c_grid,
+                 index_t M,
+                 index_t N,
+                 index_t K,
+                 index_t StrideA,
+                 index_t StrideB,
+                 index_t StrideC,
+                 index_t M01,
+                 index_t N01,
+                 AElementwiseOperation a_element_op,
+                 BElementwiseOperation b_element_op,
+                 CElementwiseOperation c_element_op)
+            : p_a_grid_{p_a_grid},
+              p_b_grid_{p_b_grid},
+              p_scale_grid_{p_scale_grid},
+              p_c_grid_{p_c_grid},
+              a_grid_desc_{},
+              b_grid_desc_{},
+              scale_grid_desc_{},
+              c_grid_desc_m_n_{},
+              c_grid_desc_mblock_mperblock_nblock_nperblock{},
+              block_2_ctile_map_{},
+              M01_{M01},
+              N01_{N01},
+              a_element_op_{a_element_op},
+              b_element_op_{b_element_op},
+              c_element_op_{c_element_op},
+              MRaw_{M},
+              NRaw_{N},
+              KRaw_{K}
+        {
+            a_grid_desc_     = DeviceOp::MakeAGridDescriptor(M, K, StrideA);
+            b_grid_desc_     = DeviceOp::MakeBGridDescriptor(K, N, StrideB);
+            scale_grid_desc_ = DeviceOp::MakeScaleGridDescriptor(K, N, 0);
+            c_grid_desc_m_n_ = DeviceOp::MakeCGridDescriptor_M_N(M, N, StrideC);
+
+            block_2_ctile_map_ =
+                GridwiseGemm::MakeDefaultBlock2CTileMap(c_grid_desc_m_n_, M01, N01);
+
+            if(GridwiseGemm::CheckValidity(
+                   a_grid_desc_, b_grid_desc_, c_grid_desc_m_n_, block_2_ctile_map_))
+            {
+                c_grid_desc_mblock_mperblock_nblock_nperblock =
+                    GridwiseGemm::MakeCGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(
+                        c_grid_desc_m_n_);
+            }
+        }
+
+        //  private:
+        const ADataType* p_a_grid_;
+        const BDataType* p_b_grid_;
+        const ScaleDataType* p_scale_grid_;
+        CDataType* p_c_grid_;
+        AGridDesc a_grid_desc_;
+        BGridDesc b_grid_desc_;
+        ScaleGridDesc scale_grid_desc_;
+        CGridDesc_M_N c_grid_desc_m_n_;
+        typename GridwiseGemm::CGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock
+            c_grid_desc_mblock_mperblock_nblock_nperblock;
+        typename GridwiseGemm::DefaultBlock2CTileMap block_2_ctile_map_;
+        index_t M01_;
+        index_t N01_;
+        AElementwiseOperation a_element_op_;
+        BElementwiseOperation b_element_op_;
+        CElementwiseOperation c_element_op_;
+        // for checking vector load/store
+        index_t MRaw_;
+        index_t NRaw_;
+        index_t KRaw_;
+    };
+
+    // Invoker
+    struct Invoker : public BaseInvoker
+    {
+        using Argument = DeviceOp::Argument;
+
+        float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
+        {
+            if(!GridwiseGemm::CheckValidity(arg.a_grid_desc_,
+                                            arg.b_grid_desc_,
+                                            arg.c_grid_desc_m_n_,
+                                            arg.block_2_ctile_map_))
+            {
+                throw std::runtime_error(
+                    "wrong! GridwiseGemm_k0mk1_k0nk1_m0nm1_wmma_v1r1 has invalid setting");
+            }
+
+            const index_t grid_size =
+                arg.block_2_ctile_map_.CalculateGridSize(arg.c_grid_desc_m_n_);
+
+            const auto K = [&]() {
+                if constexpr(AEnableLds)
+                {
+                    return arg.a_grid_desc_.GetLength(I0) * arg.a_grid_desc_.GetLength(I2);
+                }
+                else
+                {
+                    return arg.a_grid_desc_.GetLength(I0) * arg.a_grid_desc_.GetLength(I3) *
+                           arg.a_grid_desc_.GetLength(I4) * arg.a_grid_desc_.GetLength(I6);
+                }
+            }();
+            auto launch_kernel = [&](auto has_main_k_block_loop) {
+                const auto kernel = kernel_fpAintB_gemm_wmma<
+                    GridwiseGemm,
+                    ADataType,
+                    BDataType,
+                    ScaleDataType,
+                    CDataType,
+                    remove_reference_t<DeviceOp::AGridDesc>,
+                    remove_reference_t<DeviceOp::BGridDesc>,
+                    remove_reference_t<DeviceOp::ScaleGridDesc>,
+                    remove_reference_t<
+                        typename GridwiseGemm::CGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock>,
+                    AElementwiseOperation,
+                    BElementwiseOperation,
+                    CElementwiseOperation,
+                    remove_reference_t<typename GridwiseGemm::DefaultBlock2CTileMap>,
+                    has_main_k_block_loop>;
+
+                return launch_and_time_kernel(stream_config,
+                                              kernel,
+                                              dim3(grid_size),
+                                              dim3(BlockSize),
+                                              0,
+                                              arg.p_a_grid_,
+                                              arg.p_b_grid_,
+                                              arg.p_scale_grid_,
+                                              arg.p_c_grid_,
+                                              arg.a_grid_desc_,
+                                              arg.b_grid_desc_,
+                                              arg.scale_grid_desc_,
+                                              arg.c_grid_desc_mblock_mperblock_nblock_nperblock,
+                                              arg.a_element_op_,
+                                              arg.b_element_op_,
+                                              arg.c_element_op_,
+                                              arg.block_2_ctile_map_);
+            };
+
+            if(GridwiseGemm::CalculateHasMainKBlockLoop(K))
+            {
+                return launch_kernel(integral_constant<bool, true>{});
+            }
+            else
+            {
+                return launch_kernel(integral_constant<bool, false>{});
+            }
+        }
+
+        // polymorphic
+        float Run(const BaseArgument* p_arg,
+                  const StreamConfig& stream_config = StreamConfig{}) override
+        {
+            return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
+        }
+    };
+
+    static constexpr bool IsValidCompilationParameter()
+    {
+        // TODO: properly implement this check
+        return true;
+    }
+
+    static bool IsSupportedArgument(const Argument& arg)
+    {
+        if(ck::get_device_name() == "gfx1100" || ck::get_device_name() == "gfx1101" ||
+           ck::get_device_name() == "gfx1102")
+        {
+            if constexpr(!(is_same_v<AccDataType, float> || is_same_v<AccDataType, ck::half_t> ||
+                           is_same_v<AccDataType, int32_t>))
+            {
+                printf("DeviceOp err: AccDataType");
+                return false;
+            }
+        }
+        else
+        {
+            printf("DeviceOp err: Arch");
+            return false;
+        }
+
+        // check vector load/store
+        {
+            using Row = ck::tensor_layout::gemm::RowMajor;
+            using Col = ck::tensor_layout::gemm::ColumnMajor;
+
+            // check vector load of A
+            if constexpr(is_same_v<ALayout, Row> && ABlockTransferSrcVectorDim == 2)
+            {
+                if(arg.KRaw_ % ABlockTransferSrcScalarPerVector != 0)
+                {
+                    return false;
+                }
+            }
+            else if constexpr(is_same_v<ALayout, Col> && ABlockTransferSrcVectorDim == 1)
+            {
+                // FIXME: not rigorous
+                if(arg.MRaw_ % ABlockTransferSrcScalarPerVector != 0)
+                {
+                    return false;
+                }
+            }
+            else
+            {
+                return false;
+            }
+
+            // check vector laod of B
+            if constexpr(is_same_v<BLayout, Col> && BBlockTransferSrcVectorDim == 2)
+            {
+                if(arg.KRaw_ % BBlockTransferSrcScalarPerVector != 0)
+                {
+                    return false;
+                }
+            }
+            else if constexpr(is_same_v<BLayout, Row> && BBlockTransferSrcVectorDim == 1)
+            {
+                // FIXME: not rigorous
+                if(arg.NRaw_ % BBlockTransferSrcScalarPerVector != 0)
+                {
+                    return false;
+                }
+            }
+            else
+            {
+                return false;
+            }
+
+            // check vector store of C
+            // only support RowMajor for now
+            if constexpr(is_same_v<CLayout, Row>)
+            {
+                if(arg.NRaw_ % CShuffleBlockTransferScalarPerVector_NPerBlock != 0)
+                {
+                    return false;
+                }
+            }
+            else
+            {
+                return false;
+            }
+        }
+
+        return GridwiseGemm::CheckValidity(
+            arg.a_grid_desc_, arg.b_grid_desc_, arg.c_grid_desc_m_n_, arg.block_2_ctile_map_);
+    }
+
+    // polymorphic
+    bool IsSupportedArgument(const BaseArgument* p_arg) override
+    {
+        return IsSupportedArgument(*dynamic_cast<const Argument*>(p_arg));
+    }
+
+    static auto MakeArgument(const ADataType* p_a,
+                             const BDataType* p_b,
+                             const ScaleDataType* p_scale,
+                             CDataType* p_c,
+                             index_t M,
+                             index_t N,
+                             index_t K,
+                             index_t StrideA,
+                             index_t StrideB,
+                             index_t StrideC,
+                             AElementwiseOperation a_element_op,
+                             BElementwiseOperation b_element_op,
+                             CElementwiseOperation c_element_op)
+    {
+        return Argument{p_a,
+                        p_b,
+                        p_scale,
+                        p_c,
+                        M,
+                        N,
+                        K,
+                        StrideA,
+                        StrideB,
+                        StrideC,
+                        1,
+                        1,
+                        a_element_op,
+                        b_element_op,
+                        c_element_op};
+    }
+
+    static auto MakeInvoker() { return Invoker{}; }
+
+    // polymorphic
+    std::unique_ptr<BaseArgument> MakeArgumentPointer(const void* p_a,
+                                                      const void* p_b,
+                                                      const void* p_scale,
+                                                      void* p_c,
+                                                      index_t M,
+                                                      index_t N,
+                                                      index_t K,
+                                                      index_t StrideA,
+                                                      index_t StrideB,
+                                                      index_t StrideC,
+                                                      AElementwiseOperation a_element_op,
+                                                      BElementwiseOperation b_element_op,
+                                                      CElementwiseOperation c_element_op) override
+    {
+        return std::make_unique<Argument>(static_cast<const ADataType*>(p_a),
+                                          static_cast<const BDataType*>(p_b),
+                                          static_cast<const ScaleDataType*>(p_scale),
+                                          static_cast<CDataType*>(p_c),
+                                          M,
+                                          N,
+                                          K,
+                                          StrideA,
+                                          StrideB,
+                                          StrideC,
+                                          1,
+                                          1,
+                                          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();
+
+        std::map<LoopScheduler, std::string> LoopSchedToString{
+            {LoopScheduler::Default, "Default"}, {LoopScheduler::Interwave, "Interwave"}};
+
+        std::map<PipelineVersion, std::string> PipelineVersionToString{
+            {PipelineVersion::v1, "v1"},
+            {PipelineVersion::v2, "v2"},
+            {PipelineVersion::dequant_v1, "dequant_v1"}};
+
+        // clang-format off
+        str << "DeviceFpAintBGemm_Wmma_CShuffle"
+            << "<"
+            << BlockSize << ", "
+            << MPerBlock << ", "
+            << NPerBlock << ", "
+            << KPerBlock << ", "
+            << K1 << ", "
+            << MPerWmma << ", "
+            << NPerWmma << ", "
+            << MRepeat << ", "
+            << NRepeat
+            << ">"
+            << " AEnableLds: "
+            << AEnableLds << ", "
+            << "BEnableLds: "
+            << BEnableLds << ", "
+            << "NumPrefetch: "
+            << NumPrefetch << ", "
+            << "LoopScheduler: "
+            << LoopSchedToString[LoopSched] << ", "
+            << "PipelineVersion: "
+            << PipelineVersionToString[PipelineVer];
+        // clang-format on
+
+        return str.str();
+    }
+};
+
+} // namespace device
+} // namespace tensor_operation
+} // namespace ck

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

@@ -6,6 +6,7 @@
 #include "ck/utility/data_type.hpp"
 #include "ck/utility/math.hpp"
 #include "ck/utility/math_v2.hpp"
+#include "ck/utility/get_id.hpp"
 
 namespace ck {
 namespace tensor_operation {
@@ -68,6 +69,12 @@ struct PassThrough
         y = x;
     }
 
+    template <>
+    __host__ __device__ void operator()<uint8_t, uint8_t>(uint8_t& y, const uint8_t& x) const
+    {
+        y = x;
+    }
+
     template <>
     __host__ __device__ void operator()<int8_t, int32_t>(int8_t& y, const int32_t& x) const
     {
@@ -371,6 +378,76 @@ struct Swish
     float beta_ = 1.0f;
 };
 
+// support fastconvert of int8 to fp16
+
+template <typename InputDataType, typename OutputDataType, index_t RegPackNumber>
+struct FastNumericArrayConverter
+{
+};
+
+template <>
+struct FastNumericArrayConverter<uint8_t, ck::half_t, 4>
+{
+    using InputArray  = vector_type<uint8_t, 4>;
+    using OutputArray = vector_type<ck::half_t, 4>;
+
+    __device__ static OutputArray convert(InputArray const& Input)
+    {
+        OutputArray Output;
+
+        uint32_t* half_2       = reinterpret_cast<uint32_t*>(&Output);
+        uint32_t const uint8_4 = reinterpret_cast<uint32_t const&>(Input);
+
+        static constexpr uint32_t byte_selector_01 = 0x05010500;
+        static constexpr uint32_t byte_selector_23 = 0x05030502;
+        static constexpr uint32_t fp16_adder       = 0x64646464;
+        half_2[0] = __builtin_amdgcn_perm(fp16_adder, uint8_4, byte_selector_01);
+        half_2[1] = __builtin_amdgcn_perm(fp16_adder, uint8_4, byte_selector_23);
+
+        static constexpr uint32_t I8s_TO_F16s_MAGIC_NUM = 0x64806480;
+        asm volatile("v_pk_add_f16 %0, %1, %2 neg_lo:[0,1] neg_hi:[0,1]\n"
+                     : "=v"(half_2[0])
+                     : "v"(half_2[0]), "s"(I8s_TO_F16s_MAGIC_NUM));
+        asm volatile("v_pk_add_f16 %0, %1, %2 neg_lo:[0,1] neg_hi:[0,1]\n"
+                     : "=v"(half_2[1])
+                     : "v"(half_2[1]), "s"(I8s_TO_F16s_MAGIC_NUM));
+
+        return Output;
+    }
+
+    __device__ OutputArray operator()(InputArray const& Input) { return convert(Input); }
+};
+
+template <index_t N>
+struct FastNumericArrayConverter<uint8_t, ck::half_t, N>
+{
+    static constexpr int VEC_WIDTH = 4;
+    static_assert(!(N % VEC_WIDTH), "N must be multiple of 4.");
+
+    using InputArray  = vector_type<uint8_t, N>;
+    using OutputArray = vector_type<ck::half_t, N>;
+
+    __device__ static OutputArray convert(InputArray const& Input)
+    {
+        FastNumericArrayConverter<uint8_t, ck::half_t, 4> converter;
+
+        OutputArray Output;
+
+        using Vec_InputArray  = vector_type<uint8_t, 4>;
+        using Vec_OutputArray = vector_type<ck::half_t, 4>;
+
+        Vec_OutputArray* half_4_ptr       = reinterpret_cast<Vec_OutputArray*>(&Output);
+        Vec_InputArray const* uint8_4_ptr = reinterpret_cast<Vec_InputArray const*>(&Input);
+
+        static_for<0, N / VEC_WIDTH, 1>{}(
+            [&](auto i) { half_4_ptr[i] = converter(uint8_4_ptr[i]); });
+
+        return Output;
+    }
+
+    __device__ OutputArray operator()(InputArray const& Input) { return convert(Input); }
+};
+
 } // namespace element_wise
 } // namespace tensor_operation
 } // namespace ck

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

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include "ck/utility/common_header.hpp"
+#include "ck/tensor_description/multi_index_transform_helper.hpp"
+#include "ck/tensor_description/tensor_descriptor.hpp"
+#include "ck/tensor_description/tensor_descriptor_helper.hpp"
+#include "ck/tensor_operation/gpu/grid/block_to_ctile_map.hpp"
+#include "ck/tensor_operation/gpu/grid/gridwise_gemm_pipeline_selector.hpp"
+#include "ck/tensor_operation/gpu/block/blockwise_fpAintB_gemm_wmma.hpp"
+#include "ck/tensor_operation/gpu/block/thread_group_tensor_slice_transfer_v4r1.hpp"
+#include "ck/tensor_operation/gpu/block/thread_group_tensor_slice_transfer_v6r1.hpp"
+#include "ck/tensor_operation/gpu/thread/threadwise_tensor_slice_transfer.hpp"
+#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
+
+namespace ck {
+
+template <typename GridwiseGemm,
+          typename ADataType,
+          typename BDataType,
+          typename ScaleDataType,
+          typename CDataType,
+          typename AGridDesc,
+          typename BGridDesc,
+          typename ScaleGridDesc,
+          typename CGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock,
+          typename AElementwiseOperation,
+          typename BElementwiseOperation,
+          typename CElementwiseOperation,
+          typename Block2CTileMap,
+          bool HasMainKBlockLoop>
+__global__ void
+#if CK_USE_LAUNCH_BOUNDS
+    __launch_bounds__(CK_MAX_THREAD_PER_BLOCK, CK_MIN_BLOCK_PER_CU)
+#endif
+        kernel_fpAintB_gemm_wmma(const ADataType* __restrict__ p_a_grid,
+                                 const BDataType* __restrict__ p_b_grid,
+                                 const ScaleDataType* __restrict__ p_scale_grid,
+                                 CDataType* __restrict__ p_c_grid,
+                                 const AGridDesc a_grid_desc,
+                                 const BGridDesc b_grid_desc,
+                                 const ScaleGridDesc scale_grid_desc,
+                                 const CGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock
+                                     c_grid_desc_mblock_mperblock_nblock_nperblock,
+                                 const AElementwiseOperation a_element_op,
+                                 const BElementwiseOperation b_element_op,
+                                 const CElementwiseOperation c_element_op,
+                                 const Block2CTileMap block_2_ctile_map)
+{
+#if(!defined(__HIP_DEVICE_COMPILE__) || defined(__gfx1100__) || defined(__gfx1101__) || \
+    defined(__gfx1102__))
+    __shared__ char p_shared[GridwiseGemm::SharedMemTrait::lds_size];
+
+    GridwiseGemm::template Run<HasMainKBlockLoop>(p_a_grid,
+                                                  p_b_grid,
+                                                  p_scale_grid,
+                                                  p_c_grid,
+                                                  p_shared,
+                                                  a_grid_desc,
+                                                  b_grid_desc,
+                                                  scale_grid_desc,
+                                                  c_grid_desc_mblock_mperblock_nblock_nperblock,
+                                                  a_element_op,
+                                                  b_element_op,
+                                                  c_element_op,
+                                                  block_2_ctile_map);
+#else
+    ignore = p_a_grid;
+    ignore = p_b_grid;
+    ignore = p_scale_grid;
+    ignore = p_c_grid;
+    ignore = a_grid_desc;
+    ignore = b_grid_desc;
+    ignore = scale_grid_desc;
+    ignore = c_grid_desc_mblock_mperblock_nblock_nperblock;
+    ignore = a_element_op;
+    ignore = b_element_op;
+    ignore = c_element_op;
+    ignore = block_2_ctile_map;
+#endif // end of if (defined(__gfx1100__))
+}
+
+template <index_t BlockSize,
+          typename ADataType,
+          typename BDataType,
+          typename ScaleDataType,
+          typename AccDataType,
+          typename CShuffleDataType,
+          typename CDataType,
+          InMemoryDataOperationEnum CGlobalMemoryDataOperation,
+          typename AGridDesc,
+          typename BGridDesc,
+          typename ScaleGridDesc,
+          typename CGridDesc_M_N,
+          typename AElementwiseOperation,
+          typename BElementwiseOperation,
+          typename CElementwiseOperation,
+          index_t MPerBlock,
+          index_t NPerBlock,
+          index_t KPerBlock,
+          index_t MPerWmma,
+          index_t NPerWmma,
+          index_t K1Value,
+          index_t MRepeat,
+          index_t NRepeat,
+          typename ABlockTransferThreadClusterLengths_K0_M_K1,
+          typename ABlockTransferThreadClusterArrangeOrder,
+          typename ABlockTransferSrcAccessOrder,
+          index_t ABlockTransferSrcVectorDim,
+          index_t ABlockTransferSrcScalarPerVector,
+          index_t ABlockTransferDstScalarPerVector_K1,
+          bool AThreadTransferSrcResetCoordinateAfterRun,
+          bool AEnableLds,
+          bool ABlockLdsExtraM,
+          typename BBlockTransferThreadClusterLengths_K0_N_K1,
+          typename BBlockTransferThreadClusterArrangeOrder,
+          typename BBlockTransferSrcAccessOrder,
+          index_t BBlockTransferSrcVectorDim,
+          index_t BBlockTransferSrcScalarPerVector,
+          index_t BBlockTransferDstScalarPerVector_K1,
+          bool BThreadTransferSrcResetCoordinateAfterRun,
+          bool BEnableLds,
+          bool BBlockLdsExtraN,
+          index_t CShuffleMRepeatPerShuffle,
+          index_t CShuffleNRepeatPerShuffle,
+          typename CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
+          index_t CShuffleBlockTransferScalarPerVector_NPerBlock,
+          index_t NumGemmKPrefetchStage = 1,
+          LoopScheduler LoopSched       = make_default_loop_scheduler(),
+          PipelineVersion PipelineVer   = PipelineVersion::dequant_v1>
+struct GridwiseFpAintBGemm_Wmma
+{
+    static constexpr auto I0 = Number<0>{};
+    static constexpr auto I1 = Number<1>{};
+    static constexpr auto I2 = Number<2>{};
+    static constexpr auto I3 = Number<3>{};
+    static constexpr auto I4 = Number<4>{};
+    static constexpr auto I5 = Number<5>{};
+    static constexpr auto I6 = Number<6>{};
+    static constexpr auto I7 = Number<7>{};
+
+    // FIX ME: To be deprecated
+    static constexpr auto K1 = Number<K1Value>{};
+
+    static constexpr auto MWaves = MPerBlock / (MRepeat * MPerWmma);
+    static constexpr auto NWaves = NPerBlock / (NRepeat * NPerWmma);
+    static constexpr auto WmmaK  = 16;
+
+    using ThisThreadBlock = ThisThreadBlock<BlockSize>;
+
+    using GridwiseGemmPipe =
+        remove_cvref_t<decltype(GridwiseGemmPipeline_Selector<PipelineVer,
+                                                              NumGemmKPrefetchStage,
+                                                              LoopSched,
+                                                              AEnableLds,
+                                                              BEnableLds>())>;
+
+    // Describe how data store to (LDS/VGPR) buffer from Global memory
+    __host__ __device__ static constexpr auto MakeABlockDescriptor()
+    {
+        constexpr auto a_block_desc = [&]() {
+            if constexpr(AEnableLds)
+            {
+                // K0->M->K1 Per Block
+                constexpr auto K0PerBlock    = KPerBlock / K1;
+                constexpr auto max_lds_align = K1;
+
+                if constexpr(ABlockLdsExtraM)
+                {
+                    return make_naive_tensor_descriptor(
+                        make_tuple(Number<K0PerBlock>{}, Number<MPerBlock>{}, K1),
+                        make_tuple(Number<MPerBlock + 1>{} * K1, K1, I1));
+                }
+                else
+                {
+                    return make_naive_tensor_descriptor_aligned(
+                        make_tuple(Number<K0PerBlock>{}, Number<MPerBlock>{}, K1), max_lds_align);
+                }
+            }
+            else
+            {
+                constexpr auto KWmmaPerblock = KPerBlock / WmmaK;
+                constexpr auto K0PerWmma     = WmmaK / 2 / K1;
+                // KWmma->MRepeat->MWave->K0PerWmma->KRow->MPerWmma->K1 Per Thread
+                return make_naive_tensor_descriptor(
+                    make_tuple(Number<KWmmaPerblock>{},
+                               Number<MRepeat>{},
+                               I1,
+                               Number<K0PerWmma>{},
+                               I1,
+                               I1,
+                               K1),
+                    make_tuple(Number<MRepeat>{} * Number<K0PerWmma>{} * K1,
+                               Number<K0PerWmma>{} * K1,
+                               Number<K0PerWmma>{} * K1,
+                               K1,
+                               K1,
+                               K1,
+                               I1));
+            }
+        }();
+
+        return a_block_desc;
+    }
+
+    __host__ __device__ static constexpr auto MakeBBlockDescriptor()
+    {
+        constexpr auto b_block_desc = [&]() {
+            if constexpr(BEnableLds)
+            {
+                // K0->N->K1 Per Block
+                constexpr auto K0PerBlock    = KPerBlock / K1;
+                constexpr auto max_lds_align = K1;
+
+                if constexpr(BBlockLdsExtraN)
+                {
+                    return make_naive_tensor_descriptor(
+                        make_tuple(Number<K0PerBlock>{}, Number<NPerBlock>{}, K1),
+                        make_tuple(Number<NPerBlock + 1>{} * K1, K1, I1));
+                }
+                else
+                {
+                    return make_naive_tensor_descriptor_aligned(
+                        make_tuple(Number<K0PerBlock>{}, Number<NPerBlock>{}, K1), max_lds_align);
+                }
+            }
+            else
+            {
+                constexpr auto KWmmaPerblock = KPerBlock / WmmaK;
+                constexpr auto K0PerWmma     = WmmaK / 2 / K1;
+                // KWmma->NRepeat->MWave->K0PerWmma->KRow->MPerWmma->K1 Per Thread
+                return make_naive_tensor_descriptor(
+                    make_tuple(Number<KWmmaPerblock>{},
+                               Number<NRepeat>{},
+                               I1,
+                               Number<K0PerWmma>{},
+                               I1,
+                               I1,
+                               K1),
+                    make_tuple(Number<NRepeat>{} * Number<K0PerWmma>{} * K1,
+                               Number<K0PerWmma>{} * K1,
+                               Number<K0PerWmma>{} * K1,
+                               K1,
+                               K1,
+                               K1,
+                               I1));
+            }
+        }();
+
+        return b_block_desc;
+    }
+
+    __host__ __device__ static constexpr auto MakeScaleBlockDescriptor()
+    {
+        // Scale [1, N], all K related dimension reduce to 1
+        constexpr auto scale_block_desc = [&]() {
+            if constexpr(BEnableLds)
+            {
+                // K0->N->K1 Per Block
+                constexpr auto K0PerBlock = KPerBlock / K1;
+
+                return make_naive_tensor_descriptor(
+                    make_tuple(Number<K0PerBlock>{}, Number<NPerBlock>{}, I1),
+                    make_tuple(I0, I1, I0));
+            }
+            else
+            {
+                constexpr auto KWmmaPerblock = KPerBlock / WmmaK;
+                constexpr auto K0PerWmma     = WmmaK / 2 / K1;
+                // KWmma->NRepeat->MWave->K0PerWmma->KRow->MPerWmma->K1 Per Thread
+                return make_naive_tensor_descriptor(make_tuple(Number<KWmmaPerblock>{},
+                                                               Number<NRepeat>{},
+                                                               I1,
+                                                               Number<K0PerWmma>{},
+                                                               I1,
+                                                               I1,
+                                                               I1),
+                                                    make_tuple(I0, I1, I0, I0, I0, I0, I0));
+            }
+        }();
+
+        return scale_block_desc;
+    }
+
+    __host__ __device__ static constexpr auto MakeABlockSliceCopyStep()
+    {
+        constexpr auto a_block_copy_step = [&]() {
+            if constexpr(AEnableLds)
+            {
+                constexpr auto K0PerBlock = KPerBlock / K1;
+
+                return make_multi_index(K0PerBlock, 0, 0);
+            }
+            else
+            {
+                constexpr auto KWmmaPerBlock = KPerBlock / WmmaK;
+
+                return make_multi_index(KWmmaPerBlock, 0, 0, 0, 0, 0, 0);
+            }
+        }();
+
+        return a_block_copy_step;
+    }
+
+    __host__ __device__ static constexpr auto MakeBBlockSliceCopyStep()
+    {
+        constexpr auto b_block_copy_step = [&]() {
+            if constexpr(BEnableLds)
+            {
+                constexpr auto K0PerBlock = KPerBlock / K1;
+
+                return make_multi_index(K0PerBlock, 0, 0);
+            }
+            else
+            {
+                constexpr auto KWmmaPerBlock = KPerBlock / WmmaK;
+
+                return make_multi_index(KWmmaPerBlock, 0, 0, 0, 0, 0, 0);
+            }
+        }();
+
+        return b_block_copy_step;
+    }
+
+    // Describe how data read from (LDS/VGPR) buffer
+    template <typename ABlockDesc_>
+    __host__ __device__ static constexpr auto MakeAWaveDescriptor(const ABlockDesc_&)
+    {
+
+        constexpr auto a_wave_desc = [&]() {
+            if constexpr(AEnableLds)
+            {
+                // AK0_M_AK1 -> AK0_MRepeat_Mwaves_AKRow_MPerWmma_AK1
+                constexpr auto A_K0   = ABlockDesc_{}.GetLength(I0);
+                constexpr auto A_K1   = ABlockDesc_{}.GetLength(I2);
+                constexpr auto A_KRow = I1;
+                return transform_tensor_descriptor(
+                    ABlockDesc_{},
+                    make_tuple(make_unmerge_transform(make_tuple(Number<A_K0>{}, A_KRow)),
+                               make_unmerge_transform(make_tuple(
+                                   Number<MRepeat>{}, Number<MWaves>{}, Number<MPerWmma>{})),
+                               make_pass_through_transform(Number<A_K1>{})),
+                    make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
+                    make_tuple(Sequence<0, 3>{}, Sequence<1, 2, 4>{}, Sequence<5>{}));
+            }
+            else
+            {
+                // KWmma_MRepeat_MWave_K0PerWmma_KRow_MPerWmma_K1 -> K0_MRepeat_Mwaves_MPerWmma_K1
+                constexpr auto KWmma     = ABlockDesc_{}.GetLength(I0);
+                constexpr auto K0PerWmma = ABlockDesc_{}.GetLength(I3);
+                constexpr auto A_KRow    = ABlockDesc_{}.GetLength(I4);
+                constexpr auto A_K1      = ABlockDesc_{}.GetLength(I6);
+
+                // Err: merge transform cause non-constexpr issue
+
+                // return transform_tensor_descriptor(
+                //     ABlockDesc_{},
+                //     make_tuple(make_merge_transform(make_tuple(Number<KWmma>{}, I1)),
+                //                make_pass_through_transform(Number<MRepeat>{}),
+                //                make_pass_through_transform(I1),
+                //                make_pass_through_transform(I1),
+                //                make_pass_through_transform(Number<A_K1>{})),
+                //     make_tuple(Sequence<0, 3>{},
+                //                Sequence<1>{},
+                //                Sequence<2>{},
+                //                Sequence<4>{},
+                //                Sequence<5>{}),
+                //     make_tuple(
+                //         Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{},
+                //         Sequence<4>{}));
+
+                // Workaround, Freeze transform
+                return make_naive_tensor_descriptor_packed(make_tuple(Number<KWmma * K0PerWmma>{},
+                                                                      Number<MRepeat>{},
+                                                                      I1,
+                                                                      Number<A_KRow>{},
+                                                                      I1,
+                                                                      Number<A_K1>{}));
+            }
+        }();
+
+        return a_wave_desc;
+    }
+
+    template <typename BBlockDesc_>
+    __host__ __device__ static constexpr auto MakeBWaveDescriptor(const BBlockDesc_&)
+    {
+        constexpr auto b_wave_desc = [&]() {
+            if constexpr(BEnableLds)
+            {
+                // BK0_N_BK1 -> BK0_NRepeat_Nwaves_NPerWmma_BK1
+                constexpr auto B_K0   = BBlockDesc_{}.GetLength(I0);
+                constexpr auto B_K1   = BBlockDesc_{}.GetLength(I2);
+                constexpr auto B_KRow = I1;
+                return transform_tensor_descriptor(
+                    BBlockDesc_{},
+                    make_tuple(make_unmerge_transform(make_tuple(Number<B_K0>{}, B_KRow)),
+                               make_unmerge_transform(make_tuple(
+                                   Number<NRepeat>{}, Number<NWaves>{}, Number<NPerWmma>{})),
+                               make_pass_through_transform(Number<B_K1>{})),
+                    make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
+                    make_tuple(Sequence<0, 3>{}, Sequence<1, 2, 4>{}, Sequence<5>{}));
+            }
+            else
+            {
+                // KWmma_MRepeat_MWave_K0PerWmma_KRow_MPerWmma_K1 -> K0_MRepeat_Mwaves_MPerWmma_K1
+                constexpr auto KWmma     = BBlockDesc_{}.GetLength(I0);
+                constexpr auto K0PerWmma = BBlockDesc_{}.GetLength(I3);
+                constexpr auto B_KRow    = BBlockDesc_{}.GetLength(I4);
+                constexpr auto B_K1      = BBlockDesc_{}.GetLength(I6);
+
+                // Workaround, Freeze transform
+                return make_naive_tensor_descriptor_packed(make_tuple(Number<KWmma * K0PerWmma>{},
+                                                                      Number<NRepeat>{},
+                                                                      I1,
+                                                                      Number<B_KRow>{},
+                                                                      I1,
+                                                                      Number<B_K1>{}));
+            }
+        }();
+
+        return b_wave_desc;
+    }
+
+    template <typename ScaleBlockDesc_>
+    __host__ __device__ static constexpr auto MakeScaleWaveDescriptor(const ScaleBlockDesc_&)
+    {
+        constexpr auto scale_wave_desc = [&]() {
+            if constexpr(BEnableLds)
+            {
+                // BK0_N_BK1 -> BK0_NRepeat_Nwaves_NPerWmma_BK1
+                constexpr auto B_K0   = ScaleBlockDesc_{}.GetLength(I0);
+                constexpr auto B_K1   = ScaleBlockDesc_{}.GetLength(I2);
+                constexpr auto B_KRow = I1;
+                return transform_tensor_descriptor(
+                    ScaleBlockDesc_{},
+                    make_tuple(make_unmerge_transform(make_tuple(Number<B_K0>{}, B_KRow)),
+                               make_unmerge_transform(make_tuple(
+                                   Number<NRepeat>{}, Number<NWaves>{}, Number<NPerWmma>{})),
+                               make_pass_through_transform(Number<B_K1>{})),
+                    make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}),
+                    make_tuple(Sequence<0, 3>{}, Sequence<1, 2, 4>{}, Sequence<5>{}));
+            }
+            else
+            {
+                // KWmma_MRepeat_MWave_K0PerWmma_KRow_MPerWmma_K1 -> K0_MRepeat_Mwaves_MPerWmma_K1
+                constexpr auto KWmma     = ScaleBlockDesc_{}.GetLength(I0);
+                constexpr auto K0PerWmma = ScaleBlockDesc_{}.GetLength(I3);
+                constexpr auto B_KRow    = ScaleBlockDesc_{}.GetLength(I4);
+                constexpr auto B_K1      = ScaleBlockDesc_{}.GetLength(I6);
+
+                // Workaround, Freeze transform
+                return make_naive_tensor_descriptor(make_tuple(Number<KWmma * K0PerWmma>{},
+                                                               Number<NRepeat>{},
+                                                               I1,
+                                                               Number<B_KRow>{},
+                                                               I1,
+                                                               Number<B_K1>{}),
+                                                    make_tuple(I0, I1, I0, I0, I0, I0));
+            }
+        }();
+
+        return scale_wave_desc;
+    }
+
+    __host__ __device__ static constexpr auto
+    // *Caution Here repeat is shuffle repeat
+    GetCShuffleBlockDescriptor_MShRepeat_MPerShRepeat_NShRepeat_NPerShRepeat()
+    {
+        constexpr auto c_shuffle_block_desc_mshrepeat_mpershrepeat_nshrepeat_npershrepeat =
+            make_naive_tensor_descriptor_packed(
+                make_tuple(I1,
+                           Number<CShuffleMRepeatPerShuffle * MWaves * MPerWmma>{},
+                           I1,
+                           Number<CShuffleNRepeatPerShuffle * NWaves * NPerWmma>{}));
+
+        return c_shuffle_block_desc_mshrepeat_mpershrepeat_nshrepeat_npershrepeat;
+    }
+
+    // block_id to matrix tile idx (m0, n0) mapping are controlled by {M01, N01}
+    template <typename Block2CTileMap>
+    __host__ __device__ static constexpr bool CheckValidity(const AGridDesc& a_grid_desc,
+                                                            const BGridDesc& b_grid_desc,
+                                                            const CGridDesc_M_N& c_grid_desc_m_n,
+                                                            const Block2CTileMap& block_2_ctile_map)
+    {
+        static_assert(is_known_at_compile_time<remove_cv_t<decltype(K1)>>::value,
+                      "wrong! K1 need to be known at compile-time");
+
+        static_assert((MPerBlock % (MPerWmma * MRepeat) == 0) &&
+                          (NPerBlock % (NRepeat * NPerWmma)) == 0,
+                      "Invalid tuning param!");
+
+        const auto GetAProblemsizeMK = [&]() {
+            if constexpr(AEnableLds)
+            {
+                return make_tuple(a_grid_desc.GetLength(I1),
+                                  a_grid_desc.GetLength(I0) * a_grid_desc.GetLength(I2));
+            }
+            else
+            {
+                return make_tuple(a_grid_desc.GetLength(I1) * a_grid_desc.GetLength(I2) *
+                                      a_grid_desc.GetLength(I5),
+                                  a_grid_desc.GetLength(I0) * a_grid_desc.GetLength(I3) *
+                                      a_grid_desc.GetLength(I4) * a_grid_desc.GetLength(I6));
+            }
+        };
+
+        const auto GetBProblemsizeNK = [&]() {
+            if constexpr(BEnableLds)
+            {
+                return make_tuple(b_grid_desc.GetLength(I1),
+                                  b_grid_desc.GetLength(I0) * b_grid_desc.GetLength(I2));
+            }
+            else
+            {
+                return make_tuple(b_grid_desc.GetLength(I1) * b_grid_desc.GetLength(I2) *
+                                      b_grid_desc.GetLength(I5),
+                                  b_grid_desc.GetLength(I0) * b_grid_desc.GetLength(I3) *
+                                      b_grid_desc.GetLength(I4) * b_grid_desc.GetLength(I6));
+            }
+        };
+
+        const auto M = GetAProblemsizeMK()[I0];
+        const auto N = GetBProblemsizeNK()[I0];
+        const auto K = GetAProblemsizeMK()[I1];
+
+        if(!(M == c_grid_desc_m_n.GetLength(I0) && N == c_grid_desc_m_n.GetLength(I1) &&
+             K == GetBProblemsizeNK()[I1]))
+        {
+            printf("A: MxK = %d x %d, B: NxK = %d x %d, C: MxN = %d x %d\n",
+                   GetAProblemsizeMK()[I0],
+                   GetAProblemsizeMK()[I1],
+                   GetBProblemsizeNK()[I0],
+                   GetBProblemsizeNK()[I1],
+                   c_grid_desc_m_n.GetLength(I0),
+                   c_grid_desc_m_n.GetLength(I1));
+            printf("GridwiseOp err: ProblemSize check");
+            return false;
+        }
+
+        if(!(M % MPerBlock == 0 && N % NPerBlock == 0 && K % KPerBlock == 0))
+        {
+            printf("GridwiseOp err: ProblemSize division");
+            return false;
+        }
+
+        // check gridwise gemm pipeline
+        const auto num_k_loop = K / KPerBlock;
+
+        if(!GridwiseGemmPipe::IsSupported(num_k_loop))
+        {
+            printf("GridwiseOp err: Pipeline not support this k_loop");
+            return false;
+        }
+
+        if(!block_2_ctile_map.CheckValidity(c_grid_desc_m_n))
+        {
+            return false;
+        }
+
+        // TODO: also check validity of all components (blockwise-copy, threadwise-copy, etc)
+        constexpr long_index_t TwoGB = (long_index_t{1} << 31);
+
+        if(!(a_grid_desc.GetElementSpaceSize() * sizeof(ADataType) <= TwoGB &&
+             b_grid_desc.GetElementSpaceSize() * sizeof(BDataType) <= TwoGB))
+        {
+            return false;
+        }
+        return true;
+    }
+
+    __host__ __device__ static constexpr bool CalculateHasMainKBlockLoop(index_t K)
+    {
+        const index_t num_loop = K / KPerBlock;
+
+        return GridwiseGemmPipe::CalculateHasMainLoop(num_loop);
+    }
+
+    __host__ __device__ static constexpr auto
+    MakeCGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(const CGridDesc_M_N& c_grid_desc_m_n)
+    {
+        const auto M = c_grid_desc_m_n.GetLength(I0);
+        const auto N = c_grid_desc_m_n.GetLength(I1);
+
+        const auto MBlock = M / MPerBlock;
+        const auto NBlock = N / NPerBlock;
+
+        const auto c_grid_desc_mblock_mperblock_nblock_nperblock = transform_tensor_descriptor(
+            c_grid_desc_m_n,
+            make_tuple(make_unmerge_transform(make_tuple(MBlock, Number<MPerBlock>{})),
+                       make_unmerge_transform(make_tuple(NBlock, Number<NPerBlock>{}))),
+            make_tuple(Sequence<0>{}, Sequence<1>{}),
+            make_tuple(Sequence<0, 1>{}, Sequence<2, 3>{}));
+
+        return c_grid_desc_mblock_mperblock_nblock_nperblock;
+    }
+
+    // return block_id to C matrix tile idx (m0, n0) mapping
+    __host__ __device__ static constexpr auto MakeDefaultBlock2CTileMap(
+        const CGridDesc_M_N& c_grid_desc_m_n, index_t /* M01 */, index_t /* N01 */)
+    {
+        return BlockToCTileMap_M00_N0_M01Adapt<MPerBlock, NPerBlock, CGridDesc_M_N>(
+            c_grid_desc_m_n);
+    }
+
+    using CGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock = remove_cvref_t<decltype(
+        MakeCGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock(CGridDesc_M_N{}))>;
+    using DefaultBlock2CTileMap =
+        remove_cvref_t<decltype(MakeDefaultBlock2CTileMap(CGridDesc_M_N{}, 1, 1))>;
+
+    struct SharedMemTrait
+    {
+        // LDS allocation for A and B: be careful of alignment
+
+        static constexpr auto max_lds_align = K1;
+
+        static constexpr auto a_block_space_size_aligned =
+            AEnableLds ? math::integer_least_multiple(MakeABlockDescriptor().GetElementSpaceSize(),
+                                                      max_lds_align)
+                       : 0;
+        static constexpr auto b_block_space_size_aligned =
+            BEnableLds ? math::integer_least_multiple(MakeBBlockDescriptor().GetElementSpaceSize(),
+                                                      max_lds_align)
+                       : 0;
+        static constexpr auto scale_block_space_size_aligned =
+            BEnableLds ? math::integer_least_multiple(
+                             MakeScaleBlockDescriptor().GetElementSpaceSize(), max_lds_align)
+                       : 0;
+
+        static constexpr auto a_block_space_offset = 0;
+        static constexpr auto b_block_space_offset =
+            (a_block_space_offset + a_block_space_size_aligned) * sizeof(ADataType) /
+            sizeof(BDataType);
+        static constexpr auto scale_block_space_offset =
+            (b_block_space_offset + b_block_space_size_aligned) * sizeof(BDataType) /
+            sizeof(ScaleDataType);
+
+        // LDS allocation for C shuffle in LDS
+        static constexpr auto c_shuffle_block_space_size =
+            GetCShuffleBlockDescriptor_MShRepeat_MPerShRepeat_NShRepeat_NPerShRepeat()
+                .GetElementSpaceSize();
+
+        static constexpr auto c_shuffle_block_space_offset = 0;
+
+        static constexpr auto lds_size =
+            math::max(c_shuffle_block_space_size * sizeof(CShuffleDataType),
+                      a_block_space_size_aligned * sizeof(ADataType) +
+                          b_block_space_size_aligned * sizeof(BDataType) +
+                          scale_block_space_size_aligned * sizeof(ScaleDataType));
+    };
+
+    template <bool HasMainKBlockLoop, typename Block2CTileMap = DefaultBlock2CTileMap>
+    __device__ static void Run(const ADataType* __restrict__ p_a_grid,
+                               const BDataType* __restrict__ p_b_grid,
+                               const ScaleDataType* __restrict__ p_scale_grid,
+                               CDataType* __restrict__ p_c_grid,
+                               void* __restrict__ p_shared,
+                               const AGridDesc& a_grid_desc,
+                               const BGridDesc& b_grid_desc,
+                               const ScaleGridDesc& scale_grid_desc,
+                               const CGridDescriptor_MBlock_MPerBlock_NBlock_NPerBlock&
+                                   c_grid_desc_mblock_mperblock_nblock_nperblock,
+                               const AElementwiseOperation& a_element_op,
+                               const BElementwiseOperation& b_element_op,
+                               const CElementwiseOperation& c_element_op,
+                               const Block2CTileMap& block_2_ctile_map)
+    {
+        // clang-format off
+/*******************************************************************************/
+// Memory buffer zone.
+        const auto a_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
+            p_a_grid, a_grid_desc.GetElementSpaceSize());
+        const auto b_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
+            p_b_grid, b_grid_desc.GetElementSpaceSize());
+        const auto scale_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
+            p_scale_grid, scale_grid_desc.GetElementSpaceSize());
+        auto c_grid_buf = make_dynamic_buffer<AddressSpaceEnum::Global>(
+            p_c_grid, c_grid_desc_mblock_mperblock_nblock_nperblock.GetElementSpaceSize());
+
+/*******************************************************************************/
+// BlockIdx.x -> [BlockId.m, BlockId.n]
+        const auto block_work_idx = block_2_ctile_map.CalculateBottomIndex(make_multi_index(get_block_1d_id()));
+        if(!block_2_ctile_map.ValidCTileIndex(
+               block_work_idx,
+               make_tuple(c_grid_desc_mblock_mperblock_nblock_nperblock.GetLength(I0),
+                          c_grid_desc_mblock_mperblock_nblock_nperblock.GetLength(I2))))
+        { return; }
+
+        // Store BlockId into SGPR
+        const index_t m_block_data_idx_on_grid = __builtin_amdgcn_readfirstlane(block_work_idx[I0] * MPerBlock);
+        const index_t n_block_data_idx_on_grid = __builtin_amdgcn_readfirstlane(block_work_idx[I1] * NPerBlock);
+
+/*******************************************************************************/
+// BlockLevel, A/B Matrix ThreadMapping in WMMA Source buffer, As Destinaion of BlockWise_Copy
+        const auto K = [&](){
+            if constexpr(AEnableLds){
+                return a_grid_desc.GetLength(I0) * a_grid_desc.GetLength(I2);
+            }
+            else{
+                return a_grid_desc.GetLength(I0) * a_grid_desc.GetLength(I3) 
+                        * a_grid_desc.GetLength(I4) * a_grid_desc.GetLength(I6);
+            }
+        }();
+
+        constexpr auto a_block_desc = MakeABlockDescriptor();
+        constexpr auto b_block_desc = MakeBBlockDescriptor();
+        constexpr auto scale_block_desc = MakeScaleBlockDescriptor();
+        
+        auto a_block_trait = [&](){
+            // A matrix blockwise copy
+            if constexpr(AEnableLds)
+            {
+                constexpr auto K0PerBlock = KPerBlock/ K1;
+                auto a_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
+                    static_cast<ADataType*>(p_shared), 
+                    SharedMemTrait::a_block_space_size_aligned);        
+
+                auto a_blockwise_copy =
+                    ThreadGroupTensorSliceTransfer_v4r1<ThisThreadBlock,
+/* typename SrcElementwiseOperation,              */    AElementwiseOperation,
+/* typename DstElementwiseOperation,              */    ck::tensor_operation::element_wise::PassThrough,
+/* InMemoryDataOperationEnum DstInMemOp,          */    InMemoryDataOperationEnum::Set,
+/* typename BlockSliceLengths,                    */    Sequence<K0PerBlock, MPerBlock, K1>,
+/* typename ThreadClusterLengths,                 */    ABlockTransferThreadClusterLengths_K0_M_K1,
+/* typename ThreadClusterArrangeOrder,            */    ABlockTransferThreadClusterArrangeOrder,
+/* typename SrcData,                              */    ADataType,
+/* typename DstData,                              */    ADataType,
+/* typename SrcDesc,                              */    decltype(a_grid_desc),
+/* typename DstDesc,                              */    decltype(a_block_desc),
+/* typename SrcDimAccessOrder,                    */    ABlockTransferSrcAccessOrder,
+/* typename DstDimAccessOrder,                    */    Sequence<0, 1, 2>,
+/* index_t SrcVectorDim,                          */    ABlockTransferSrcVectorDim,
+/* index_t DstVectorDim,                          */    2,
+/* index_t SrcScalarPerVector,                    */    ABlockTransferSrcScalarPerVector,
+/* index_t DstScalarPerVector,                    */    ABlockTransferDstScalarPerVector_K1,
+/* index_t SrcScalarStrideInVector,               */    1,
+/* index_t DstScalarStrideInVector,               */    1,
+/* bool ThreadTransferSrcResetCoordinateAfterRun, */    AThreadTransferSrcResetCoordinateAfterRun,
+/* bool ThreadTransferDstResetCoordinateAfterRun, */    true,
+                                                        NumGemmKPrefetchStage>(
+                a_grid_desc,
+                make_multi_index(0, m_block_data_idx_on_grid, 0),
+                a_element_op,
+                a_block_desc,
+                make_multi_index(0, 0, 0),
+                ck::tensor_operation::element_wise::PassThrough{});
+
+                return make_tuple(a_block_buf, a_blockwise_copy);
+            }
+            else
+            {
+                // Thread-wise copy
+                // KPerBlock/WmmaK -> MRepeat -> MWaves -> K0PerWmma -> KRow -> MPerWmma -> K1
+                constexpr auto KWmmaPerBlock = KPerBlock / WmmaK;
+                constexpr auto K0PerWmma     = WmmaK/2/K1Value;
+                auto a_block_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ADataType>(
+                    a_block_desc.GetElementSpaceSize());
+                
+                // Limitation: NumDim of Src and Dst descriptor should be identical
+                auto a_blockwise_copy =
+                    ThreadwiseTensorSliceTransfer_v2<ADataType,
+                                                     ADataType,
+                                                     decltype(a_grid_desc),
+                                                     decltype(a_block_desc),
+                                                     Sequence<Number<KWmmaPerBlock>{},
+                                                              Number<MRepeat>{},
+                                                              I1,
+                                                              Number<K0PerWmma>{},
+                                                              I1,
+                                                              I1,
+                                                              Number<K1Value>{}>,
+                                                     Sequence<0, 1, 2, 3, 4, 5, 6>,
+                                                     6,
+                                                     ABlockTransferSrcScalarPerVector,
+                                                     AThreadTransferSrcResetCoordinateAfterRun,
+                                                     true>(
+                    a_grid_desc,
+                    make_multi_index(0, 
+                                     m_block_data_idx_on_grid/(MWaves * MPerWmma), 
+                                     get_thread_local_1d_id() / 32,
+                                     0,
+                                     (get_thread_local_1d_id() % 32 )/ 16, 
+                                     get_thread_local_1d_id() % 16,
+                                     0));
+                                
+                return make_tuple(a_block_buf, a_blockwise_copy);
+            }
+        };
+
+        auto b_block_trait = [&](){
+            if constexpr(BEnableLds)
+            {
+                constexpr auto K0PerBlock = KPerBlock/ K1;
+                auto b_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
+                    static_cast<BDataType*>(p_shared) + SharedMemTrait::b_block_space_offset, 
+                    SharedMemTrait::b_block_space_size_aligned);
+
+                auto b_blockwise_copy =
+                    ThreadGroupTensorSliceTransfer_v4r1<ThisThreadBlock,
+                                                        BElementwiseOperation,
+                                                        ck::tensor_operation::element_wise::PassThrough,
+                                                        InMemoryDataOperationEnum::Set,
+                                                        Sequence<K0PerBlock, NPerBlock, K1>,
+                                                        BBlockTransferThreadClusterLengths_K0_N_K1,
+                                                        BBlockTransferThreadClusterArrangeOrder,
+                                                        BDataType,
+                                                        BDataType,
+                                                        decltype(b_grid_desc),
+                                                        decltype(b_block_desc),
+                                                        BBlockTransferSrcAccessOrder,
+                                                        Sequence<0, 1, 2>,
+                                                        BBlockTransferSrcVectorDim,
+                                                        2,
+                                                        BBlockTransferSrcScalarPerVector,
+                                                        BBlockTransferDstScalarPerVector_K1,
+                                                        1,
+                                                        1,
+                                                        BThreadTransferSrcResetCoordinateAfterRun,
+                                                        true,
+                                                        NumGemmKPrefetchStage>(
+                    b_grid_desc,
+                    make_multi_index(0, n_block_data_idx_on_grid, 0),
+                    b_element_op,
+                    b_block_desc,
+                    make_multi_index(0, 0, 0),
+                    ck::tensor_operation::element_wise::PassThrough{});
+
+                return make_tuple(b_block_buf, b_blockwise_copy);
+            }
+            else
+            {
+                // Thread-wise copy
+                // KPerBlock/WmmaK -> NRepeat -> NWaves -> WmmaK/K1 -> NPerWmma -> K1
+                constexpr auto KWmmaPerBlock = KPerBlock / WmmaK;
+                constexpr auto K0PerWmma     = WmmaK/2/K1Value;
+                auto b_block_buf = make_static_buffer<AddressSpaceEnum::Vgpr, BDataType>(
+                    b_block_desc.GetElementSpaceSize());
+                
+                // Limitation: NumDim of Src and Dst descriptor should be identical
+                auto b_blockwise_copy =
+                    ThreadwiseTensorSliceTransfer_v2<BDataType,
+                                                     BDataType,
+                                                     decltype(b_grid_desc),
+                                                     decltype(b_block_desc),
+                                                     Sequence<Number<KWmmaPerBlock>{},
+                                                              Number<NRepeat>{},
+                                                              I1,
+                                                              Number<K0PerWmma>{},
+                                                              I1,
+                                                              I1,
+                                                              Number<K1Value>{}>,
+                                                     Sequence<0, 1, 2, 3, 4, 5, 6>,
+                                                     6,
+                                                     BBlockTransferSrcScalarPerVector,
+                                                     BThreadTransferSrcResetCoordinateAfterRun,
+                                                     true>(
+                    b_grid_desc,
+                    make_multi_index(0, 
+                                     n_block_data_idx_on_grid/(NWaves * NPerWmma), 
+                                     get_thread_local_1d_id() / 32,
+                                     0,
+                                     (get_thread_local_1d_id() % 32 )/ 16, 
+                                     get_thread_local_1d_id() % 16,
+                                     0));
+                                
+                return make_tuple(b_block_buf, b_blockwise_copy);
+            }
+        };
+
+        auto scale_block_trait = [&](){
+            if constexpr(BEnableLds)
+            {
+                constexpr auto K0PerBlock = KPerBlock/ K1;
+
+                auto scale_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
+                    static_cast<ScaleDataType*>(p_shared) + SharedMemTrait::scale_block_space_offset, 
+                    SharedMemTrait::scale_block_space_size_aligned);
+                
+                auto scale_blockwise_copy =
+                    ThreadGroupTensorSliceTransfer_v4r1<ThisThreadBlock,
+                                                        BElementwiseOperation,
+                                                        ck::tensor_operation::element_wise::PassThrough,
+                                                        InMemoryDataOperationEnum::Set,
+                                                        // Reduce slice length K1 to 1
+                                                        Sequence<K0PerBlock, NPerBlock, I1>,
+                                                        BBlockTransferThreadClusterLengths_K0_N_K1,
+                                                        BBlockTransferThreadClusterArrangeOrder,
+                                                        ScaleDataType,
+                                                        ScaleDataType,
+                                                        decltype(scale_grid_desc),
+                                                        decltype(scale_block_desc),
+                                                        BBlockTransferSrcAccessOrder,
+                                                        Sequence<0, 1, 2>,
+                                                        BBlockTransferSrcVectorDim,
+                                                        2,
+                                                        1,
+                                                        1,
+                                                        1, // no effect
+                                                        1, // no effect
+                                                        BThreadTransferSrcResetCoordinateAfterRun,
+                                                        true,
+                                                        NumGemmKPrefetchStage>(
+                    scale_grid_desc,
+                    make_multi_index(0, n_block_data_idx_on_grid, 0),
+                    b_element_op,
+                    scale_block_desc,
+                    make_multi_index(0, 0, 0),
+                    ck::tensor_operation::element_wise::PassThrough{});
+                
+                return make_tuple(scale_block_buf, scale_blockwise_copy);
+            }
+            else
+            {
+                // Thread-wise copy
+                constexpr auto KWmmaPerBlock = KPerBlock / WmmaK;
+                constexpr auto K0PerWmma     = WmmaK/2/K1Value;
+                // KPerBlock/WmmaK -> NRepeat -> NWaves -> WmmaK/K1 -> NPerWmma -> K1
+                auto scale_block_buf = make_static_buffer<AddressSpaceEnum::Vgpr, ScaleDataType>(
+                    scale_block_desc.GetElementSpaceSize());
+                
+                auto scale_blockwise_copy =
+                    ThreadwiseTensorSliceTransfer_v2<ScaleDataType,
+                                                     ScaleDataType,
+                                                     decltype(scale_grid_desc),
+                                                     decltype(scale_block_desc),
+                                                     Sequence<Number<KWmmaPerBlock>{},
+                                                              Number<NRepeat>{},
+                                                              I1,
+                                                              Number<K0PerWmma>{},
+                                                              I1,
+                                                              I1,
+                                                              Number<K1Value>{}>,
+                                                     Sequence<0, 1, 2, 3, 4, 5, 6>,
+                                                     6,
+                                                     BBlockTransferSrcScalarPerVector,
+                                                     BThreadTransferSrcResetCoordinateAfterRun,
+                                                     true>(
+                    scale_grid_desc,
+                    make_multi_index(0, 
+                                     n_block_data_idx_on_grid/(NWaves * NPerWmma), 
+                                     get_thread_local_1d_id() / 32,
+                                     0,
+                                     (get_thread_local_1d_id() % 32 )/ 16, 
+                                     get_thread_local_1d_id() % 16,
+                                     0));
+
+                return make_tuple(scale_block_buf, scale_blockwise_copy);
+            }
+        };
+
+        auto a_block_buf       = a_block_trait()[I0];
+        auto a_blockwise_copy  = a_block_trait()[I1];
+
+        auto b_block_buf       = b_block_trait()[I0];
+        auto b_blockwise_copy  = b_block_trait()[I1];
+
+        auto scale_block_buf       = scale_block_trait()[I0];
+        auto scale_blockwise_copy  = scale_block_trait()[I1];
+/*******************************************************************************/
+        // GEMM
+        constexpr auto KPack = math::integer_least_multiple(K1, WmmaK);
+
+        auto blockwise_gemm =
+            Blockwise_fpAintB_GemmWMMA<BlockSize,
+                              ADataType,
+                              BDataType,
+                              ScaleDataType,
+                              AccDataType,
+                              decltype(MakeAWaveDescriptor(a_block_desc)),
+                              decltype(MakeBWaveDescriptor(b_block_desc)),
+                              decltype(MakeScaleWaveDescriptor(scale_block_desc)),
+                              MPerBlock,
+                              NPerBlock,
+                              KPerBlock,
+                              MPerWmma,
+                              NPerWmma,
+                              MRepeat,
+                              NRepeat,
+                              KPack,
+                              AEnableLds,
+                              BEnableLds>{};
+
+        // Prepare Register for C matrix
+        auto c_thread_buf = blockwise_gemm.GetCThreadBuffer();
+
+/*******************************************************************************/        
+        // Shift Per SUB_K
+        constexpr auto a_block_slice_copy_step = MakeABlockSliceCopyStep();
+        constexpr auto b_block_slice_copy_step = MakeBBlockSliceCopyStep();
+
+        // gridwise GEMM pipeline
+        const index_t KBlockMainLoop = __builtin_amdgcn_readfirstlane(K / KPerBlock);
+        GridwiseGemmPipe::template Run<HasMainKBlockLoop>(a_grid_desc,
+                                                          a_block_desc,
+                                                          a_blockwise_copy,
+                                                          a_grid_buf,
+                                                          a_block_buf,
+                                                          a_block_slice_copy_step,
+                                                          b_grid_desc,
+                                                          b_block_desc,
+                                                          b_blockwise_copy,
+                                                          b_grid_buf,
+                                                          b_block_buf,
+                                                          b_block_slice_copy_step,
+                                                          scale_grid_desc,
+                                                          scale_block_desc,
+                                                          scale_blockwise_copy,
+                                                          scale_grid_buf,
+                                                          scale_block_buf,
+                                                          blockwise_gemm,
+                                                          c_thread_buf,
+                                                          KBlockMainLoop);
+/*******************************************************************************/
+        // write out to C, implement shuffle
+        {
+            // C mapping in single thread.
+            constexpr auto c_thread_desc_mrepeat_mwave_msubgroup_nrepeat_nwave_nthreadpersubgroup_maccvgprs =  
+            blockwise_gemm.GetCThreadDescriptor_MRepeat_MWave_MSubGroup_NRepeat_NWave_NThreadPerSubGroup_MAccVgprs();
+
+            // C mapping in single block
+            constexpr auto c_block_desc_mrepeat_mwave_msubgroup_nrepeat_nwave_nthreadpersubgroup_maccvgprs_tmp =
+                blockwise_gemm.GetCBlockDescriptor_MRepeat_MWave_MSubGroup_NRepeat_NWave_NThreadPerSubGroup_MAccVgprs();
+
+            constexpr auto MWave              = c_block_desc_mrepeat_mwave_msubgroup_nrepeat_nwave_nthreadpersubgroup_maccvgprs_tmp.GetLength(I1);
+            constexpr auto MSubGroup          = c_block_desc_mrepeat_mwave_msubgroup_nrepeat_nwave_nthreadpersubgroup_maccvgprs_tmp.GetLength(I2);
+            constexpr auto NWave              = c_block_desc_mrepeat_mwave_msubgroup_nrepeat_nwave_nthreadpersubgroup_maccvgprs_tmp.GetLength(I4);
+            constexpr auto NThreadPerSubGroup = c_block_desc_mrepeat_mwave_msubgroup_nrepeat_nwave_nthreadpersubgroup_maccvgprs_tmp.GetLength(I5);
+            constexpr auto MAccVgprs          = c_block_desc_mrepeat_mwave_msubgroup_nrepeat_nwave_nthreadpersubgroup_maccvgprs_tmp.GetLength(I6);
+
+            // LDS descriptor, shuffle and write out in MRepeat x NRepeat times
+            constexpr auto c_shuffle_block_desc_mshrepeat_mpershrepeat_nshrepeat_npershrepeat =
+                GetCShuffleBlockDescriptor_MShRepeat_MPerShRepeat_NShRepeat_NPerShRepeat();
+
+            auto c_shuffle_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
+                static_cast<CShuffleDataType*>(p_shared) + SharedMemTrait::c_shuffle_block_space_offset, 
+                SharedMemTrait::c_shuffle_block_space_size);
+
+            constexpr auto c_block_desc_mrepeat_mwave_msubgroup_nrepeat_nwave_nthreadpersubgroup_maccvgprs = transform_tensor_descriptor(
+                c_shuffle_block_desc_mshrepeat_mpershrepeat_nshrepeat_npershrepeat,
+                make_tuple(
+                    make_freeze_transform(I0),
+                    make_unmerge_transform(make_tuple(
+                        Number<CShuffleMRepeatPerShuffle>{}, // MRepeat per shuffle repeat
+                        MWave,                               // MWave
+                        MSubGroup,                           // MSubGroup * MAccVgprs = MPerWmma
+                        MAccVgprs)),
+                    make_freeze_transform(I0),
+                    make_unmerge_transform(make_tuple(
+                        Number<CShuffleNRepeatPerShuffle>{}, // NRepeat per shuffle repeat
+                        NWave,                               // NWave
+                        NThreadPerSubGroup))),               // NThreadPerSubGroup = NPerWmma
+                make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}),
+                make_tuple(Sequence<>{}, Sequence<0, 1, 2, 6>{}, Sequence<>{}, Sequence<3, 4, 5>{}));
+
+            // calculate origin of thread output tensor on global memory
+            //     blockwise GEMM c matrix starting index
+            const auto c_thread_mtx_on_block = blockwise_gemm.CalculateCThreadOriginDataIndex(I0, I0);
+
+            const index_t m_thread_data_on_block = c_thread_mtx_on_block[I0];
+            const index_t n_thread_data_on_block = c_thread_mtx_on_block[I1];
+
+            const auto m_thread_data_on_block_to_mrepeat_mwave_msubgroup_maccvgprs_adaptor =
+            make_single_stage_tensor_adaptor(
+                make_tuple(make_merge_transform(make_tuple(MRepeat, MWave, MSubGroup, MAccVgprs))),
+                make_tuple(Sequence<0, 1, 2, 3>{}),
+                make_tuple(Sequence<0>{}));
+
+            const auto n_thread_data_on_block_to_nrepeat_nwave_nthreadpersubgroup_adaptor =
+            make_single_stage_tensor_adaptor(
+                make_tuple(make_merge_transform(make_tuple(NRepeat, NWave, NThreadPerSubGroup))),
+                make_tuple(Sequence<0, 1, 2>{}),
+                make_tuple(Sequence<0>{}));
+            
+            const auto m_thread_data_on_block_idx = m_thread_data_on_block_to_mrepeat_mwave_msubgroup_maccvgprs_adaptor.CalculateBottomIndex(
+                make_multi_index(m_thread_data_on_block));
+            
+            const auto n_thread_data_on_block_idx = n_thread_data_on_block_to_nrepeat_nwave_nthreadpersubgroup_adaptor.CalculateBottomIndex(
+                make_multi_index(n_thread_data_on_block));
+
+            // shuffle: threadwise copy C from VGPR to LDS
+            auto c_thread_copy_vgpr_to_lds =
+                ThreadwiseTensorSliceTransfer_v1r3<AccDataType,
+                                                   CShuffleDataType,
+                                                   decltype(c_thread_desc_mrepeat_mwave_msubgroup_nrepeat_nwave_nthreadpersubgroup_maccvgprs),
+                                                   decltype(c_block_desc_mrepeat_mwave_msubgroup_nrepeat_nwave_nthreadpersubgroup_maccvgprs),
+                                                   ck::tensor_operation::element_wise::PassThrough,
+                                                   Sequence<CShuffleMRepeatPerShuffle,
+                                                            I1,
+                                                            I1,
+                                                            CShuffleNRepeatPerShuffle,
+                                                            I1,
+                                                            I1,
+                                                            MAccVgprs>,
+                                                   Sequence<0, 1, 2, 3, 4, 5, 6>,
+                                                   6,
+                                                   1, // vector write pixel
+                                                   InMemoryDataOperationEnum::Set,
+                                                   1,
+                                                   true>{
+                    c_block_desc_mrepeat_mwave_msubgroup_nrepeat_nwave_nthreadpersubgroup_maccvgprs,
+                    make_multi_index(0,
+                                     m_thread_data_on_block_idx[I1],
+                                     m_thread_data_on_block_idx[I2],
+                                     0,
+                                     n_thread_data_on_block_idx[I1],
+                                     n_thread_data_on_block_idx[I2],
+                                     m_thread_data_on_block_idx[I3]),
+                    ck::tensor_operation::element_wise::PassThrough{}};
+
+            // shuffle: blockwise copy C from LDS to global
+            auto c_shuffle_block_copy_lds_to_global = ThreadGroupTensorSliceTransfer_v6r1<
+                ThisThreadBlock,            // ThreadGroup
+                CElementwiseOperation,      // ElementwiseOperation,
+                CGlobalMemoryDataOperation, // DstInMemOp,
+                Sequence<1,
+                         CShuffleMRepeatPerShuffle * MWave * MPerWmma,
+                         1,
+                         CShuffleNRepeatPerShuffle * NWave * NPerWmma>, // BlockSliceLengths,
+                CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
+                Sequence<0, 1, 2, 3>, // typename ThreadClusterArrangeOrder,
+                CShuffleDataType,        // typename SrcData,
+                CDataType,               // typename DstData,
+                decltype(c_shuffle_block_desc_mshrepeat_mpershrepeat_nshrepeat_npershrepeat),
+                decltype(c_grid_desc_mblock_mperblock_nblock_nperblock),
+                Sequence<0, 1, 2, 3>,                           // typename DimAccessOrder,
+                3,                                              // index_t VectorDim,
+                CShuffleBlockTransferScalarPerVector_NPerBlock, // index_t ScalarPerVector,
+                true,  // bool ThreadTransferSrcResetCoordinateAfterRun,
+                false> // bool ThreadTransferDstResetCoordinateAfterRun>
+                {c_shuffle_block_desc_mshrepeat_mpershrepeat_nshrepeat_npershrepeat,
+                 make_multi_index(0, 0, 0, 0),
+                 c_grid_desc_mblock_mperblock_nblock_nperblock,
+                 make_multi_index(block_work_idx[I0], 0, block_work_idx[I1], 0),
+                 c_element_op};
+
+            // space filling curve for local reg & global memory
+            // space filling curve for threadwise C in VGPR
+            constexpr auto sfc_c_vgpr =
+                SpaceFillingCurve<Sequence<MRepeat, 1, 1, NRepeat, 1, 1, MAccVgprs>,
+                                  Sequence<0, 1, 2, 3, 4, 5, 6>,
+                                  Sequence<CShuffleMRepeatPerShuffle,
+                                           1,
+                                           1,
+                                           CShuffleNRepeatPerShuffle,
+                                           1,
+                                           1,
+                                           MAccVgprs>>{};
+
+            // space filling curve for shuffled blockwise C in global mem
+            constexpr auto sfc_c_global =
+                SpaceFillingCurve<Sequence<1, MPerBlock, 1, NPerBlock>,
+                                  Sequence<0, 2, 1, 3>,
+                                  Sequence<1,
+                                           CShuffleMRepeatPerShuffle * MWave * MPerWmma,
+                                           1,
+                                           CShuffleNRepeatPerShuffle * NWave * NPerWmma>>{};
+
+            constexpr index_t num_access = sfc_c_vgpr.GetNumOfAccess();
+
+            static_assert(num_access == sfc_c_global.GetNumOfAccess(), "wrong!");
+
+            static_for<0, num_access, 1>{}([&](auto access_id) {
+                // make sure it's safe to write to LDS
+                block_sync_lds();
+
+                // each thread write its data from VGPR to LDS
+                c_thread_copy_vgpr_to_lds.Run(c_thread_desc_mrepeat_mwave_msubgroup_nrepeat_nwave_nthreadpersubgroup_maccvgprs,
+                                              sfc_c_vgpr.GetIndexTupleOfNumber(access_id),
+                                              c_thread_buf,
+                                              c_block_desc_mrepeat_mwave_msubgroup_nrepeat_nwave_nthreadpersubgroup_maccvgprs,
+                                              c_shuffle_block_buf);
+
+                // make sure it's safe to read from LDS
+                block_sync_lds();
+
+                // each block copy its data from LDS to global
+                c_shuffle_block_copy_lds_to_global.Run(
+                    c_shuffle_block_desc_mshrepeat_mpershrepeat_nshrepeat_npershrepeat,
+                    c_shuffle_block_buf,
+                    c_grid_desc_mblock_mperblock_nblock_nperblock,
+                    c_grid_buf);
+
+                if constexpr(access_id < num_access - 1)
+                {
+                    constexpr auto c_global_step = sfc_c_global.GetForwardStep(access_id);
+
+                    // move on C
+                    c_shuffle_block_copy_lds_to_global.MoveDstSliceWindow(
+                        c_grid_desc_mblock_mperblock_nblock_nperblock, c_global_step);
+                }
+            });
+        }
+        // clang-format on
+    }
+};
+
+} // namespace ck

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

@@ -12,6 +12,7 @@ enum struct PipelineVersion
 {
     v1,
     v2,
+    dequant_v1,
 };
 
 template <PipelineVersion PipelineVer,
@@ -36,6 +37,10 @@ constexpr auto GridwiseGemmPipeline_Selector()
     {
         return GridwiseGemmPipeline_v2{};
     }
+    else if constexpr(PipelineVer == PipelineVersion::dequant_v1)
+    {
+        return GridwiseGemmPipeline_v1_dequant<NumPrefetch, AEnableLds, BEnableLds>{};
+    }
     else
     {
         std::cerr << "GridwiseGemmPipeline configuration is not available" << std::endl;

include/ck/tensor_operation/gpu/grid/gridwise_gemm_pipeline_v1.hpp

@@ -550,6 +550,225 @@ struct GridwiseGemmPipeline_v1<1, false, false>
     }
 };
 
+template <index_t NumPrefetch, bool AEnableLds, bool BEnableLds>
+struct GridwiseGemmPipeline_v1_dequant;
+
+template <>
+struct GridwiseGemmPipeline_v1_dequant<1, true, true>
+{
+    static constexpr auto I0 = Number<0>{};
+    static constexpr auto I1 = Number<1>{};
+
+    __host__ __device__ static constexpr bool IsSupported(index_t /* num_loop */) { return true; }
+
+    __host__ __device__ static constexpr bool CalculateHasMainLoop(index_t num_loop)
+    {
+        return num_loop > 1;
+    }
+
+    template <bool HasMainLoop,
+              typename AGridDesc,
+              typename ABlockDesc,
+              typename ABlockTransfer,
+              typename AGridBuffer,
+              typename ABlockBuffer,
+              typename ABlockTransferStep,
+              typename BGridDesc,
+              typename BBlockDesc,
+              typename BBlockTransfer,
+              typename BGridBuffer,
+              typename BBlockBuffer,
+              typename BBlockTransferStep,
+              typename ScaleGridDesc,
+              typename ScaleBlockDesc,
+              typename ScaleBlockTransfer,
+              typename ScaleGridBuffer,
+              typename ScaleBlockBuffer,
+              typename BlockwiseGemm,
+              typename CThreadBuffer>
+    __device__ static void Run(const AGridDesc& a_grid_desc,
+                               const ABlockDesc& a_block_desc,
+                               ABlockTransfer& a_blockwise_copy,
+                               const AGridBuffer& a_grid_buf,
+                               ABlockBuffer& a_block_buf,
+                               const ABlockTransferStep& a_block_copy_step,
+                               const BGridDesc& b_grid_desc,
+                               const BBlockDesc& b_block_desc,
+                               BBlockTransfer& b_blockwise_copy,
+                               const BGridBuffer& b_grid_buf,
+                               BBlockBuffer& b_block_buf,
+                               const BBlockTransferStep& b_block_copy_step,
+                               const ScaleGridDesc& scale_grid_desc,
+                               const ScaleBlockDesc& scale_block_desc,
+                               ScaleBlockTransfer& scale_blockwise_copy,
+                               const ScaleGridBuffer& scale_grid_buf,
+                               ScaleBlockBuffer& scale_block_buf,
+                               const BlockwiseGemm& blockwise_gemm,
+                               CThreadBuffer& c_thread_buf,
+                               index_t num_loop)
+    {
+        // preload data into LDS
+        a_blockwise_copy.RunRead(a_grid_desc, a_grid_buf);
+        b_blockwise_copy.RunRead(b_grid_desc, b_grid_buf);
+        scale_blockwise_copy.RunRead(scale_grid_desc, scale_grid_buf);
+
+        a_blockwise_copy.MoveSrcSliceWindow(a_grid_desc, a_block_copy_step);
+        b_blockwise_copy.MoveSrcSliceWindow(b_grid_desc, b_block_copy_step);
+
+        // Initialize C
+        c_thread_buf.Clear();
+
+        a_blockwise_copy.RunWrite(a_block_desc, a_block_buf);
+        b_blockwise_copy.RunWrite(b_block_desc, b_block_buf);
+        scale_blockwise_copy.RunWrite(scale_block_desc, scale_block_buf);
+
+        // main body
+        if constexpr(HasMainLoop)
+        {
+            index_t i = 0;
+
+            do
+            {
+                a_blockwise_copy.RunRead(a_grid_desc, a_grid_buf);
+
+                block_sync_lds();
+
+                b_blockwise_copy.RunRead(b_grid_desc, b_grid_buf);
+
+                blockwise_gemm.Run(a_block_buf, b_block_buf, scale_block_buf, c_thread_buf);
+
+                block_sync_lds();
+
+                a_blockwise_copy.MoveSrcSliceWindow(a_grid_desc, a_block_copy_step);
+                b_blockwise_copy.MoveSrcSliceWindow(b_grid_desc, b_block_copy_step);
+
+                a_blockwise_copy.RunWrite(a_block_desc, a_block_buf);
+                b_blockwise_copy.RunWrite(b_block_desc, b_block_buf);
+
+                ++i;
+            } while(i < (num_loop - 1));
+        }
+
+        // tail
+        {
+            block_sync_lds();
+
+            blockwise_gemm.Run(a_block_buf, b_block_buf, scale_block_buf, c_thread_buf);
+        }
+    }
+};
+
+template <>
+struct GridwiseGemmPipeline_v1_dequant<1, true, false>
+{
+    static constexpr auto I0 = Number<0>{};
+    static constexpr auto I1 = Number<1>{};
+
+    __host__ __device__ static constexpr bool IsSupported(index_t /* num_loop */) { return true; }
+
+    __host__ __device__ static constexpr bool CalculateHasMainLoop(index_t num_loop)
+    {
+        return num_loop > 1;
+    }
+
+    template <bool HasMainLoop,
+              typename AGridDesc,
+              typename ABlockDesc,
+              typename ABlockTransfer,
+              typename AGridBuffer,
+              typename ABlockBuffer,
+              typename ABlockTransferStep,
+              typename BGridDesc,
+              typename BBlockDesc,
+              typename BBlockTransfer,
+              typename BGridBuffer,
+              typename BBlockBuffer,
+              typename BBlockTransferStep,
+              typename ScaleGridDesc,
+              typename ScaleBlockDesc,
+              typename ScaleBlockTransfer,
+              typename ScaleGridBuffer,
+              typename ScaleBlockBuffer,
+              typename BlockwiseGemm,
+              typename CThreadBuffer>
+    __device__ static void Run(const AGridDesc& a_grid_desc,
+                               const ABlockDesc& a_block_desc,
+                               ABlockTransfer& a_blockwise_copy,
+                               const AGridBuffer& a_grid_buf,
+                               ABlockBuffer& a_block_buf,
+                               const ABlockTransferStep& a_block_copy_step,
+                               const BGridDesc& b_grid_desc,
+                               const BBlockDesc& b_block_desc,
+                               BBlockTransfer& b_blockwise_copy,
+                               const BGridBuffer& b_grid_buf,
+                               BBlockBuffer& b_block_buf,
+                               const BBlockTransferStep& b_block_copy_step,
+                               const ScaleGridDesc& scale_grid_desc,
+                               const ScaleBlockDesc& scale_block_desc,
+                               ScaleBlockTransfer& scale_blockwise_copy,
+                               const ScaleGridBuffer& scale_grid_buf,
+                               ScaleBlockBuffer& scale_block_buf,
+                               const BlockwiseGemm& blockwise_gemm,
+                               CThreadBuffer& c_thread_buf,
+                               index_t num_loop)
+    {
+        constexpr auto b_block_origin_idx = make_tuple(I0, I0, I0, I0, I0, I0, I0);
+        auto b_block_buf_switch           = b_block_buf;
+
+        // preload data into LDS
+        a_blockwise_copy.RunRead(a_grid_desc, a_grid_buf);
+        b_blockwise_copy.Run(
+            b_grid_desc, b_grid_buf, b_block_desc, b_block_origin_idx, b_block_buf);
+        scale_blockwise_copy.Run(
+            scale_grid_desc, scale_grid_buf, scale_block_desc, b_block_origin_idx, scale_block_buf);
+
+        a_blockwise_copy.MoveSrcSliceWindow(a_grid_desc, a_block_copy_step);
+        b_blockwise_copy.MoveSrcSliceWindow(b_grid_desc, b_block_copy_step);
+
+        // Initialize C
+        c_thread_buf.Clear();
+
+        a_blockwise_copy.RunWrite(a_block_desc, a_block_buf);
+
+        // main body
+        if constexpr(HasMainLoop)
+        {
+            index_t i = 0;
+
+            do
+            {
+                b_blockwise_copy.Run(
+                    b_grid_desc, b_grid_buf, b_block_desc, b_block_origin_idx, b_block_buf_switch);
+
+                block_sync_lds();
+
+                a_blockwise_copy.RunRead(a_grid_desc, a_grid_buf);
+
+                blockwise_gemm.Run(a_block_buf, b_block_buf, scale_block_buf, c_thread_buf);
+
+                block_sync_lds();
+
+                a_blockwise_copy.MoveSrcSliceWindow(a_grid_desc, a_block_copy_step);
+                b_blockwise_copy.MoveSrcSliceWindow(b_grid_desc, b_block_copy_step);
+
+                a_blockwise_copy.RunWrite(a_block_desc, a_block_buf);
+
+                b_block_buf = b_block_buf_switch;
+                ++i;
+            } while(i < (num_loop - 1));
+        }
+
+        // tail
+        {
+            block_sync_lds();
+
+            blockwise_gemm.Run(a_block_buf, b_block_buf, scale_block_buf, c_thread_buf);
+
+            block_sync_lds();
+        }
+    }
+};
+
 template <index_t NumPrefetch>
 struct GridwiseGemmPipelineInterwave_v1;
 

include/ck/tensor_operation/gpu/thread/threadwise_tensor_slice_transfer.hpp

@@ -1417,10 +1417,7 @@ struct ThreadwiseTensorSliceTransfer_StaticToStatic_InterRow
                                                     1,
                                                     0);
                 v_theother_row = type_convert_sp<SrcData>(temp);
-                // if (get_thread_local_1d_id() == 0){
-                //                 printf("src_offset:%d, dst_offset for this row: %d, dst_offset
-                //                 for the other row: %d \n",
-                //                         src_offset, dst_offset, dst_offset+DstScalarPerVector);}
+
                 if(get_thread_local_1d_id() % 32 < 16)
                 {
                     // apply type convert

include/ck/utility/amd_buffer_addressing.hpp

@@ -312,7 +312,8 @@ __device__ typename vector_type<T, N>::type amd_buffer_load_impl(int32x4_t src_w
             (is_same<T, half_t>::value && (N == 1 || N == 2 || N == 4 || N == 8)) ||
             (is_same<T, bhalf_t>::value && (N == 1 || N == 2 || N == 4 || N == 8)) ||
             (is_same<T, int32_t>::value && (N == 1 || N == 2 || N == 4 || N == 8)) ||
-            (is_same<T, int8_t>::value && (N == 1 || N == 2 || N == 4 || N == 8 || N == 16)),
+            (is_same<T, int8_t>::value && (N == 1 || N == 2 || N == 4 || N == 8 || N == 16)) ||
+            (is_same<T, uint8_t>::value && (N == 1 || N == 2 || N == 4 || N == 8 || N == 16)),
         "wrong! not implemented");
 
     if constexpr(is_same<T, double>::value)
@@ -614,6 +615,114 @@ __device__ typename vector_type<T, N>::type amd_buffer_load_impl(int32x4_t src_w
                                                               static_cast<index_t>(coherence));
 
             return bit_cast<int8x16_t>(tmp);
+#endif
+        }
+    }
+    else if constexpr(is_same<T, uint8_t>::value)
+    {
+        if constexpr(N == 1)
+        {
+            return llvm_amdgcn_raw_buffer_load_i8(src_wave_buffer_resource,
+                                                  src_thread_addr_offset,
+                                                  src_wave_addr_offset,
+                                                  static_cast<index_t>(coherence));
+        }
+        else if constexpr(N == 2)
+        {
+#if !CK_WORKAROUND_SWDEV_XXXXXX_INT8_BUFFER_LOAD_STORE_ISSUE
+            return llvm_amdgcn_raw_buffer_load_i8x2(src_wave_buffer_resource,
+                                                    src_thread_addr_offset,
+                                                    src_wave_addr_offset,
+                                                    static_cast<index_t>(coherence));
+#else
+            int16_t tmp = llvm_amdgcn_raw_buffer_load_i16(src_wave_buffer_resource,
+                                                          src_thread_addr_offset,
+                                                          src_wave_addr_offset,
+                                                          static_cast<index_t>(coherence));
+
+            return bit_cast<uint8x2_t>(tmp);
+#endif
+        }
+        else if constexpr(N == 4)
+        {
+#if !CK_WORKAROUND_SWDEV_XXXXXX_INT8_BUFFER_LOAD_STORE_ISSUE
+            return llvm_amdgcn_raw_buffer_load_i8x4(src_wave_buffer_resource,
+                                                    src_thread_addr_offset,
+                                                    src_wave_addr_offset,
+                                                    static_cast<index_t>(coherence));
+#else
+            int32_t tmp = llvm_amdgcn_raw_buffer_load_i32(src_wave_buffer_resource,
+                                                          src_thread_addr_offset,
+                                                          src_wave_addr_offset,
+                                                          static_cast<index_t>(coherence));
+
+            return bit_cast<uint8x4_t>(tmp);
+#endif
+        }
+        else if constexpr(N == 8)
+        {
+#if !CK_WORKAROUND_SWDEV_XXXXXX_INT8_BUFFER_LOAD_STORE_ISSUE
+            vector_type<uint8_t, 8> tmp;
+
+            tmp.AsType<uint8x4_t>()(Number<0>{}) =
+                llvm_amdgcn_raw_buffer_load_i8x4(src_wave_buffer_resource,
+                                                 src_thread_addr_offset,
+                                                 src_wave_addr_offset,
+                                                 static_cast<index_t>(coherence));
+
+            tmp.AsType<uint8x4_t>()(Number<1>{}) =
+                llvm_amdgcn_raw_buffer_load_i8x4(src_wave_buffer_resource,
+                                                 src_thread_addr_offset,
+                                                 src_wave_addr_offset + 4 * sizeof(int8_t),
+                                                 static_cast<index_t>(coherence));
+
+            return tmp.AsType<uint8x8_t>()(Number<0>{});
+#else
+            int32x2_t tmp = llvm_amdgcn_raw_buffer_load_i32x2(src_wave_buffer_resource,
+                                                              src_thread_addr_offset,
+                                                              src_wave_addr_offset,
+                                                              static_cast<index_t>(coherence));
+
+            return bit_cast<uint8x8_t>(tmp);
+#endif
+        }
+        else if constexpr(N == 16)
+        {
+#if !CK_WORKAROUND_SWDEV_XXXXXX_INT8_BUFFER_LOAD_STORE_ISSUE
+            vector_type<uint8_t, 16> tmp;
+
+            tmp.AsType<uint8x4_t>()(Number<0>{}) =
+                llvm_amdgcn_raw_buffer_load_i8x4(src_wave_buffer_resource,
+                                                 src_thread_addr_offset,
+                                                 src_wave_addr_offset,
+                                                 static_cast<index_t>(coherence));
+
+            tmp.AsType<uint8x4_t>()(Number<1>{}) =
+                llvm_amdgcn_raw_buffer_load_i8x4(src_wave_buffer_resource,
+                                                 src_thread_addr_offset,
+                                                 src_wave_addr_offset + 4 * sizeof(int8_t),
+                                                 static_cast<index_t>(coherence));
+
+            tmp.AsType<uint8x4_t>()(Number<2>{}) =
+                llvm_amdgcn_raw_buffer_load_i8x4(src_wave_buffer_resource,
+                                                 src_thread_addr_offset,
+                                                 src_wave_addr_offset + 8 * sizeof(int8_t),
+                                                 static_cast<index_t>(coherence));
+
+            tmp.AsType<uint8x4_t>()(Number<3>{}) =
+                llvm_amdgcn_raw_buffer_load_i8x4(src_wave_buffer_resource,
+                                                 src_thread_addr_offset,
+                                                 src_wave_addr_offset + 12 * sizeof(int8_t),
+                                                 static_cast<index_t>(coherence));
+
+            return tmp.AsType<uint8x16_t>()(Number<0>{});
+#else
+            int32x4_t tmp = llvm_amdgcn_raw_buffer_load_i32x4(src_wave_buffer_resource,
+                                                              src_thread_addr_offset,
+                                                              src_wave_addr_offset,
+                                                              static_cast<index_t>(coherence));
+
+            return bit_cast<uint8x16_t>(tmp);
 #endif
         }
     }

include/ck/utility/data_type.hpp

@@ -133,6 +133,13 @@ struct scalar_type<int8_t>
     static constexpr index_t vector_size = 1;
 };
 
+template <>
+struct scalar_type<uint8_t>
+{
+    using type                           = uint8_t;
+    static constexpr index_t vector_size = 1;
+};
+
 #ifdef CK_EXPERIMENTAL_BIT_INT_EXTENSION_INT4
 template <>
 struct scalar_type<int4_t>
@@ -944,6 +951,15 @@ using int8x16_t = typename vector_type<int8_t, 16>::type;
 using int8x32_t = typename vector_type<int8_t, 32>::type;
 using int8x64_t = typename vector_type<int8_t, 64>::type;
 
+// u8
+// i8
+using uint8x2_t  = typename vector_type<uint8_t, 2>::type;
+using uint8x4_t  = typename vector_type<uint8_t, 4>::type;
+using uint8x8_t  = typename vector_type<uint8_t, 8>::type;
+using uint8x16_t = typename vector_type<uint8_t, 16>::type;
+using uint8x32_t = typename vector_type<uint8_t, 32>::type;
+using uint8x64_t = typename vector_type<uint8_t, 64>::type;
+
 // Convert X to Y
 template <typename Y, typename X>
 __host__ __device__ constexpr Y type_convert(X x)
@@ -1090,6 +1106,16 @@ inline __host__ __device__ constexpr bhalf_t type_convert<bhalf_t, int8_t>(int8_
     return type_convert<bhalf_t>(x_fp32);
 }
 
+// convert int8 to fp16 via fp32
+template <>
+inline __host__ __device__ constexpr half_t type_convert<half_t, int8_t>(int8_t x)
+{
+    // TODO: replace it with fast_converter
+    float x_fp32 = static_cast<float>(x);
+
+    return type_convert<half_t>(x_fp32);
+}
+
 // Declare a template function for bf16 conversion using RTN
 template <typename Y, typename X>
 __host__ __device__ constexpr Y bf16_convert_rtn(X x);

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

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+
+#pragma once
+
+#include <iostream>
+#include <sstream>
+
+#include "ck/tensor_operation/gpu/element/unary_element_wise_operation.hpp"
+#include "ck/tensor_operation/gpu/device/device_base.hpp"
+#include "ck/library/utility/host_tensor.hpp"
+
+namespace ck {
+namespace tensor_operation {
+namespace host {
+
+template <typename ADataType,
+          typename BDataType,
+          typename ScaleDataType,
+          typename CDataType,
+          typename AccDataType,
+          typename AElementwiseOperation,
+          typename BElementwiseOperation,
+          typename CElementwiseOperation>
+struct ReferencefpAintBGemm : public device::BaseOperator
+{
+    // Argument
+    struct Argument : public device::BaseArgument
+    {
+        Argument(const Tensor<ADataType>& a_m_k,
+                 const Tensor<BDataType>& b_k_n,
+                 const Tensor<ScaleDataType>& scale_k_n,
+                 Tensor<CDataType>& c_m_n,
+                 AElementwiseOperation a_element_op,
+                 BElementwiseOperation b_element_op,
+                 CElementwiseOperation c_element_op)
+            : a_m_k_{a_m_k},
+              b_k_n_{b_k_n},
+              scale_k_n_{scale_k_n},
+              c_m_n_{c_m_n},
+              a_element_op_{a_element_op},
+              b_element_op_{b_element_op},
+              c_element_op_{c_element_op}
+        {
+        }
+
+        const Tensor<ADataType>& a_m_k_;
+        const Tensor<BDataType>& b_k_n_;
+        const Tensor<ScaleDataType>& scale_k_n_;
+        Tensor<CDataType>& c_m_n_;
+
+        AElementwiseOperation a_element_op_;
+        BElementwiseOperation b_element_op_;
+        CElementwiseOperation c_element_op_;
+    };
+
+    // Invoker
+    struct Invoker : public device::BaseInvoker
+    {
+        using Argument = ReferencefpAintBGemm::Argument;
+
+        float Run(const Argument& arg)
+        {
+            auto f_mk_kn_mn = [&](auto m, auto n) {
+                const int K = arg.a_m_k_.mDesc.GetLengths()[1];
+
+                AccDataType v_acc = 0;
+
+                for(int k = 0; k < K; ++k)
+                {
+                    ADataType v_a;
+                    BDataType v_b;
+                    ScaleDataType v_scale;
+                    ADataType v_converted_b;
+
+                    // use PassThrough instead of ConvertBF16RTN for reference calculation
+                    if constexpr(is_same_v<AElementwiseOperation,
+                                           ck::tensor_operation::element_wise::ConvertBF16RTN>)
+                    {
+                        ck::tensor_operation::element_wise::PassThrough{}(v_a, arg.a_m_k_(m, k));
+                    }
+                    else
+                    {
+                        arg.a_element_op_(v_a, arg.a_m_k_(m, k));
+                    }
+
+                    // same for B matrix
+                    if constexpr(is_same_v<BElementwiseOperation,
+                                           ck::tensor_operation::element_wise::ConvertBF16RTN>)
+                    {
+                        ck::tensor_operation::element_wise::PassThrough{}(v_b, arg.b_k_n_(k, n));
+                    }
+                    else
+                    {
+                        arg.b_element_op_(v_b, arg.b_k_n_(k, n));
+                    }
+
+                    // same for scale matrix
+                    if constexpr(is_same_v<BElementwiseOperation,
+                                           ck::tensor_operation::element_wise::ConvertBF16RTN>)
+                    {
+                        ck::tensor_operation::element_wise::PassThrough{}(v_scale,
+                                                                          arg.scale_k_n_(k, n));
+                    }
+                    else
+                    {
+                        arg.b_element_op_(v_scale, arg.scale_k_n_(k, n));
+                    }
+
+                    v_converted_b = type_convert<ADataType>(v_b) * v_scale;
+                    v_acc += ck::type_convert<AccDataType>(v_a) *
+                             ck::type_convert<AccDataType>(v_converted_b);
+                }
+
+                AccDataType v_c;
+
+                arg.c_element_op_(v_c, v_acc);
+
+                arg.c_m_n_(m, n) = ck::type_convert<CDataType>(v_c);
+            };
+
+            make_ParallelTensorFunctor(
+                f_mk_kn_mn, arg.c_m_n_.mDesc.GetLengths()[0], arg.c_m_n_.mDesc.GetLengths()[1])(
+                std::thread::hardware_concurrency());
+
+            return 0;
+        }
+
+        float Run(const device::BaseArgument* p_arg,
+                  const StreamConfig& /* stream_config */ = StreamConfig{}) override
+        {
+            return Run(*dynamic_cast<const Argument*>(p_arg));
+        }
+    };
+
+    static constexpr bool IsValidCompilationParameter()
+    {
+        // TODO: properly implement this check
+        return true;
+    }
+
+    bool IsSupportedArgument(const device::BaseArgument*) override { return true; }
+
+    static auto MakeArgument(const Tensor<ADataType>& a_m_k,
+                             const Tensor<BDataType>& b_k_n,
+                             const Tensor<ScaleDataType>& scale_k_n,
+                             Tensor<CDataType>& c_m_n,
+                             AElementwiseOperation a_element_op,
+                             BElementwiseOperation b_element_op,
+                             CElementwiseOperation c_element_op)
+    {
+        return Argument{a_m_k, b_k_n, scale_k_n, c_m_n, a_element_op, b_element_op, c_element_op};
+    }
+
+    static auto MakeInvoker() { return Invoker{}; }
+
+    virtual std::unique_ptr<device::BaseInvoker> MakeInvokerPointer()
+    {
+        return std::make_unique<Invoker>(Invoker{});
+    }
+
+    std::string GetTypeString() const override
+    {
+        auto str = std::stringstream();
+
+        // clang-format off
+        str << "ReferenceGemm"
+            << std::endl;
+        // clang-format on
+
+        return str.str();
+    }
+};
+
+} // namespace host
+} // namespace tensor_operation
+} // namespace ck

library/src/tensor_operation_instance/gpu/gemm/device_gemm_wmma_f16_f16_f16_km_kn_mn_instance.cpp

@@ -29,9 +29,8 @@ using PassThrough = ck::tensor_operation::element_wise::PassThrough;
 static constexpr auto GemmMNKPadding = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
 
 // Compilation parameters for a[k, m] * b[k, n] = c[m, n]
-using device_gemm_wmma_f16_f16_f16_km_kn_mn_instances =
-    std::tuple<
-        // clang-format off
+using device_gemm_wmma_f16_f16_f16_km_kn_mn_instances = std::tuple<
+    // clang-format off
         //######################| ALayout| BLayout| CLayout| AData| BData| CData| AccData| CShuffle|           A|           B|           C|          GEMM| NumPrefetch| Block|  MPer|  NPer|  KPer| K1| MPer| NPer|      M|       N|  ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds|  BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CShuffleBlockTransfer| CShuffleBlockTransfer|
         //######################|        |        |        |  Type|  Type|  Type|    Type| DataType| Elementwise| Elementwise| Elementwise|Specialization|            |  Size| Block| Block| Block|   | WMMA| WMMA| Repeat|  Repeat|   ThreadCluster|  ThreadCluster| SrcAccessOrder|   SrcVectorDim|      SrcScalar|      DstScalar| AddExtraM|   ThreadCluster|  ThreadCluster| SrcAccessOrder|  SrcVectorDim|      SrcScalar|      DstScalar| AddExtraN|  MRepeat|  MRepeat|        ClusterLengths|       ScalarPerVector|
         //######################|        |        |        |      |      |      |        |         |   Operation|   Operation|   Operation|              |            |      |      |      |      |   |     |     |       |        | Lengths_K0_M_K1|   ArrangeOrder|               |               |      PerVector|   PerVector_K1|          | Lengths_K0_N_K1|   ArrangeOrder|               |              |      PerVector|   PerVector_K1|          | PerStore| PerStore|      MBlock_MPerBlock|                      |
@@ -62,8 +61,8 @@ using device_gemm_wmma_f16_f16_f16_km_kn_mn_instances =
         // 1 Wave
         DeviceGemmWmma_CShuffle<      Col,     Row,     Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough, GemmMNKPadding,           1,    32,    16,    32,    64,  8,   16,   16,      1,       2,     S<2, 16, 1>,     S<0, 2, 1>,     S<0, 2, 1>,              1,              1,              8,      true,     S<2, 16, 1>,     S<0, 2, 1>,     S<0, 2, 1>,              1,              1,              8,      true,        1,        1,       S<1, 16, 1,  2>,                      8>,
         DeviceGemmWmma_CShuffle<      Col,     Row,     Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough, GemmMNKPadding,           1,    32,    16,    16,    64,  8,   16,   16,      1,       1,     S<2, 16, 1>,     S<0, 2, 1>,     S<0, 2, 1>,              1,              1,              8,      true,     S<2, 16, 1>,     S<0, 2, 1>,     S<0, 2, 1>,              1,              1,              8,      true,        1,        1,       S<1, 16, 1,  2>,                      8>
-        // clang-format on
-        >;
+    // clang-format on
+    >;
 
 void add_device_gemm_wmma_f16_f16_f16_km_kn_mn_instances(
     std::vector<std::unique_ptr<

library/src/tensor_operation_instance/gpu/gemm/device_gemm_wmma_f16_f16_f16_km_nk_mn_instance.cpp

@@ -29,9 +29,8 @@ using PassThrough = ck::tensor_operation::element_wise::PassThrough;
 static constexpr auto GemmMNKPadding = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
 
 // Compilation parameters for a[k, m] * b[n, k] = c[m, n]
-using device_gemm_wmma_f16_f16_f16_km_nk_mn_instances =
-    std::tuple<
-        // clang-format off
+using device_gemm_wmma_f16_f16_f16_km_nk_mn_instances = std::tuple<
+    // clang-format off
         //######################| ALayout| BLayout| CLayout| AData| BData| CData| AccData| CShuffle|           A|           B|           C|          GEMM| NumPrefetch| Block|  MPer|  NPer|  KPer| K1| MPer| NPer|      M|       N|  ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds|  BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CShuffleBlockTransfer| CShuffleBlockTransfer|
         //######################|        |        |        |  Type|  Type|  Type|    Type| DataType| Elementwise| Elementwise| Elementwise|Specialization|            |  Size| Block| Block| Block|   | WMMA| WMMA| Repeat|  Repeat|   ThreadCluster|  ThreadCluster| SrcAccessOrder|   SrcVectorDim|      SrcScalar|      DstScalar| AddExtraM|   ThreadCluster|  ThreadCluster| SrcAccessOrder|  SrcVectorDim|      SrcScalar|      DstScalar| AddExtraN|  MRepeat|  MRepeat|        ClusterLengths|       ScalarPerVector|
         //######################|        |        |        |      |      |      |        |         |   Operation|   Operation|   Operation|              |            |      |      |      |      |   |     |     |       |        | Lengths_K0_M_K1|   ArrangeOrder|               |               |      PerVector|   PerVector_K1|          | Lengths_K0_N_K1|   ArrangeOrder|               |              |      PerVector|   PerVector_K1|          | PerStore| PerStore|      MBlock_MPerBlock|                      |
@@ -62,8 +61,8 @@ using device_gemm_wmma_f16_f16_f16_km_nk_mn_instances =
         // 1 Wave
         DeviceGemmWmma_CShuffle<      Col,     Col,     Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough, GemmMNKPadding,           1,    32,    16,    32,    64,  8,   16,   16,      1,       2,     S<2, 16, 1>,     S<0, 2, 1>,     S<0, 2, 1>,              1,              1,              8,      true,     S<2, 16, 1>,     S<1, 0, 2>,     S<1, 0, 2>,             2,              8,              8,      true,        1,        1,       S<1, 16, 1,  2>,                      8>,
         DeviceGemmWmma_CShuffle<      Col,     Col,     Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough, GemmMNKPadding,           1,    32,    16,    16,    64,  8,   16,   16,      1,       1,     S<2, 16, 1>,     S<0, 2, 1>,     S<0, 2, 1>,              1,              1,              8,      true,     S<2, 16, 1>,     S<1, 0, 2>,     S<1, 0, 2>,             2,              8,              8,      true,        1,        1,       S<1, 16, 1,  2>,                      8>
-        // clang-format on
-        >;
+    // clang-format on
+    >;
 
 void add_device_gemm_wmma_f16_f16_f16_km_nk_mn_instances(
     std::vector<std::unique_ptr<

library/src/tensor_operation_instance/gpu/gemm/device_gemm_wmma_f16_f16_f16_mk_kn_mn_instance.cpp

@@ -29,9 +29,8 @@ using PassThrough = ck::tensor_operation::element_wise::PassThrough;
 static constexpr auto GemmMNKPadding = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
 
 // Compilation parameters for a[m, k] * b[k, n] = c[m, n]
-using device_gemm_wmma_f16_f16_f16_mk_kn_mn_instances =
-    std::tuple<
-        // clang-format off
+using device_gemm_wmma_f16_f16_f16_mk_kn_mn_instances = std::tuple<
+    // clang-format off
         //######################| ALayout| BLayout| CLayout| AData| BData| CData| AccData| CShuffle|           A|           B|           C|          GEMM| NumPrefetch| Block|  MPer|  NPer|  KPer| K1| MPer| NPer|      M|       N|  ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds|  BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CShuffleBlockTransfer| CShuffleBlockTransfer|
         //######################|        |        |        |  Type|  Type|  Type|    Type| DataType| Elementwise| Elementwise| Elementwise|Specialization|            |  Size| Block| Block| Block|   | WMMA| WMMA| Repeat|  Repeat|   ThreadCluster|  ThreadCluster| SrcAccessOrder|   SrcVectorDim|      SrcScalar|      DstScalar| AddExtraM|   ThreadCluster|  ThreadCluster| SrcAccessOrder|  SrcVectorDim|      SrcScalar|      DstScalar| AddExtraN|  MRepeat|  MRepeat|        ClusterLengths|       ScalarPerVector|
         //######################|        |        |        |      |      |      |        |         |   Operation|   Operation|   Operation|              |            |      |      |      |      |   |     |     |       |        | Lengths_K0_M_K1|   ArrangeOrder|               |               |      PerVector|   PerVector_K1|          | Lengths_K0_N_K1|   ArrangeOrder|               |              |      PerVector|   PerVector_K1|          | PerStore| PerStore|      MBlock_MPerBlock|                      |
@@ -143,7 +142,7 @@ using device_gemm_wmma_f16_f16_f16_mk_kn_mn_instances =
         DeviceGemmWmma_CShuffle<      Row,     Row,     Row,   F16,   F16,   F16,     F32,      F16, PassThrough, PassThrough, PassThrough, GemmMNKPadding,           1,    32,    16,    16,    64,  8,   16,   16,      1,       1,     S<2, 16, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,      true,     S<2, 16, 1>,     S<0, 2, 1>,     S<0, 2, 1>,             1,              8,              8,      true,        1,        1,       S<1, 16, 1,  2>,                      8>
 #endif
     // clang-format on
-        >;
+    >;
 
 void add_device_gemm_wmma_f16_f16_f16_mk_kn_mn_instances(
     std::vector<std::unique_ptr<