Merge remote-tracking branch 'zjing14_ck/jizhan/mixed_prec_gemm_pk_i4' into...

Merge remote-tracking branch 'zjing14_ck/jizhan/mixed_prec_gemm_pk_i4' into zjing14_bf16xint4_gemm_contribution

CMakeLists.txt

@@ -585,7 +585,7 @@ if(NOT GPU_ARCHS AND USER_GPU_TARGETS)
    )
    add_subdirectory(example)
    if(BUILD_TESTING)
-	   add_subdirectory(test)
+       add_subdirectory(test)
    endif()
 endif()
 

cmake/EnableCompilerWarnings.cmake

@@ -66,7 +66,7 @@ else()
             -Wunreachable-code
             -Wunused
             -Wno-reserved-identifier
-	    -Werror
+            -Werror
             -Wno-option-ignored
             -Wsign-compare
             -Wno-extra-semi-stmt

example/01_gemm/CMakeLists.txt

@@ -29,6 +29,8 @@ add_example_dependencies(example_gemm_xdl example_gemm_xdl_fp16_v3)
 add_example_executable(example_gemm_xdl_fp8_v3 gemm_xdl_fp8_v3.cpp)
 add_example_dependencies(example_gemm_xdl example_gemm_xdl_fp8_v3)
 add_example_executable(example_gemm_xdl_fp16_fp8_v3 gemm_xdl_fp16_fp8_v3.cpp)
+add_example_executable(example_gemm_xdl_fp16_pk_i4_v3 gemm_xdl_fp16_pk_i4_v3.cpp)
+add_example_executable(example_gemm_xdl_bf16_pk_i4_v3 gemm_xdl_bf16_pk_i4_v3.cpp)
 add_example_dependencies(example_gemm_xdl example_gemm_xdl_fp16_fp8_v3)
 add_example_executable(example_gemm_xdl_bf16_v3 gemm_xdl_bf16_v3.cpp)
 add_example_dependencies(example_gemm_xdl example_gemm_xdl_bf16_v3)

example/01_gemm/common.hpp

@@ -287,3 +287,85 @@ bool parse_cmd_args<ProblemSizeSplitK>(int argc,
 
     return true;
 }
+
+template <typename DataType>
+inline __host__ __device__ constexpr double get_rtol()
+{
+    if constexpr(std::is_same_v<DataType, float>)
+    {
+        return 1e-3;
+    }
+    else if constexpr(std::is_same_v<DataType, double>)
+    {
+        return 1e-6;
+    }
+    else if constexpr(std::is_same_v<DataType, ck::half_t>)
+    {
+        return 1e-3;
+    }
+    else if constexpr(std::is_same_v<DataType, ck::bhalf_t>)
+    {
+        return 5e-2;
+    }
+    else if constexpr(std::is_same_v<DataType, int32_t>)
+    {
+        return 1e-1;
+    }
+    else if constexpr(std::is_same_v<DataType, int8_t>)
+    {
+        return 1e-1;
+    }
+    else if constexpr(std::is_same_v<DataType, ck::f8_t>)
+    {
+        return 1e-1; // 240 and 224 are acceptable
+    }
+    else if constexpr(std::is_same_v<DataType, ck::bf8_t>)
+    {
+        return 1.5e-1; // 57344 and 49152 are acceptable
+    }
+    else
+    {
+        return 1e-3;
+    }
+}
+
+template <typename DataType>
+inline __host__ __device__ constexpr double get_atol()
+{
+    if constexpr(std::is_same_v<DataType, float>)
+    {
+        return 1e-3;
+    }
+    else if constexpr(std::is_same_v<DataType, double>)
+    {
+        return 1e-6;
+    }
+    else if constexpr(std::is_same_v<DataType, ck::half_t>)
+    {
+        return 1e-3;
+    }
+    else if constexpr(std::is_same_v<DataType, ck::bhalf_t>)
+    {
+        return 5e-2;
+    }
+    else if constexpr(std::is_same_v<DataType, int32_t>)
+    {
+        return 1e-1;
+    }
+    else if constexpr(std::is_same_v<DataType, int8_t>)
+    {
+        return 1e-1;
+    }
+    else if constexpr(std::is_same_v<DataType, ck::f8_t>)
+    {
+        return 16.1; // 240 and 224 are acceptable
+    }
+    else if constexpr(std::is_same_v<DataType, ck::bf8_t>)
+    {
+        return 8192.1; // 57344 and 49152 are acceptable
+    }
+    else
+    {
+        return 1e-3;
+    }
+}

example/01_gemm/gemm_xdl_bf16_pk_i4_v3.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
+
+#include "common.hpp"
+
+#include "ck/tensor_operation/gpu/device/impl/device_gemm_xdl_cshuffle_v3.hpp"
+
+using ADataType        = ck::bhalf_t;
+using BDataType        = ck::pk_i4_t;
+using AccDataType      = float;
+using CShuffleDataType = ck::bhalf_t;
+using CDataType        = ck::bhalf_t;
+
+using ALayout = Row;
+using BLayout = Col;
+using CLayout = Row;
+
+using AElementOp = PassThrough;
+using BElementOp = PassThrough;
+using CElementOp = PassThrough;
+
+static constexpr auto GemmDefault      = ck::tensor_operation::device::GemmSpecialization::Default;
+static constexpr bool PermuteA         = false;
+static constexpr bool PermuteB         = true;
+static constexpr ck::index_t KPerBlock = 128;
+
+// clang-format off
+using DeviceGemmV2Instance = 
+    ck::tensor_operation::device::DeviceGemm_Xdl_CShuffleV3<
+        ALayout,   BLayout,  CLayout,   
+        ADataType, BDataType, CDataType, AccDataType, CShuffleDataType, 
+        AElementOp, BElementOp, CElementOp, GemmDefault, 
+        128,
+        16, 64,
+        KPerBlock, 8, 32,
+        16,   16,
+        1,    2,
+        S<16, 8, 1>,  S<1, 0, 2>,  S<1, 0, 2>,
+        2, 8, 8, 0,
+        S<4, 32, 1>,  S<1, 0, 2>,  S<1, 0, 2>,
+        2, 32, 32, 0,
+        1, 1, S<1, 16, 1, 8>, 4,
+        ck::BlockGemmPipelineScheduler::Interwave, ck::BlockGemmPipelineVersion::v2, ADataType, ADataType, PermuteA, PermuteB>;
+
+// clang-format on
+
+using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
+                                                                        BDataType,
+                                                                        CDataType,
+                                                                        AccDataType,
+                                                                        PassThrough,
+                                                                        PassThrough,
+                                                                        PassThrough>;
+template <typename ProblemType>
+bool run_gemm(const ProblemType& problem_size, const ExecutionConfig& config)
+{
+    using namespace ck::literals;
+
+    auto M       = problem_size.M;
+    auto N       = problem_size.N;
+    auto K       = problem_size.K;
+    auto StrideA = problem_size.StrideA;
+    auto StrideB = problem_size.StrideB;
+    auto StrideC = problem_size.StrideC;
+    auto KBatch  = problem_size.KBatch;
+
+    auto f_host_tensor_descriptor =
+        [](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
+            if constexpr(std::is_same_v<decltype(layout), ck::tensor_layout::gemm::RowMajor>)
+            {
+                return HostTensorDescriptor({row, col}, {stride, 1_uz});
+            }
+            else
+            {
+                return HostTensorDescriptor({row, col}, {1_uz, stride});
+            }
+        };
+
+    auto f_get_default_stride =
+        [](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
+            if(stride == 0)
+            {
+                // give a chance if stride is zero, return a default packed stride
+                if constexpr(std::is_same_v<decltype(layout), ck::tensor_layout::gemm::RowMajor>)
+                {
+                    return col;
+                }
+                else
+                {
+                    return row;
+                }
+            }
+            else
+                return stride;
+        };
+
+    StrideA = f_get_default_stride(M, K, StrideA, ALayout{});
+    StrideB = f_get_default_stride(K, N, StrideB, BLayout{});
+    StrideC = f_get_default_stride(M, N, StrideC, CLayout{});
+
+    Tensor<ADataType> a_m_k(f_host_tensor_descriptor(M, K, StrideA, ALayout{}));
+    Tensor<BDataType> b_k_n(f_host_tensor_descriptor(K, N, StrideB, BLayout{}));
+    Tensor<BDataType> b_k_n_permute(f_host_tensor_descriptor(K, N, StrideB, BLayout{}));
+
+    switch(config.init_method)
+    {
+    case 0:
+        a_m_k.GenerateTensorValue(GeneratorTensor_1<ADataType>{1});
+        b_k_n.GenerateTensorValue(GeneratorTensor_1<BDataType>{1});
+        break;
+    case 1:
+        a_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-2, 2});
+        b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-2, 2});
+        break;
+    case 2:
+        a_m_k.GenerateTensorValue(GeneratorTensor_1<ADataType>{1});
+        b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-2, 2});
+        break;
+    case 3:
+        a_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-2, 2});
+        b_k_n.GenerateTensorValue(GeneratorTensor_1<BDataType>{1});
+        break;
+    default:
+        a_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{0, 1.0});
+        b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-2, 2});
+    }
+
+    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 << "c_m_n: " << c_m_n_host_result.mDesc << std::endl;
+
+    DeviceMem a_m_k_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpaceSize());
+    DeviceMem b_k_n_device_buf(sizeof(BDataType) * b_k_n_permute.GetElementSpaceSize());
+    DeviceMem c_m_n_device_buf(sizeof(CDataType) * c_m_n_device_result.mDesc.GetElementSpaceSize());
+
+    // weight permute
+    if constexpr(PermuteB)
+    {
+        int K1 = KPerBlock;
+        int K0 = K / KPerBlock;
+
+        // int K0, N, K1
+        for(int j = 0; j < K0; j++)
+        {
+            for(int i = 0; i < N; i++)
+            {
+                for(int jj = 0; jj < K1; jj++)
+                {
+                    b_k_n_permute(j * N * K1 + i * K1 + jj) = b_k_n(i * K + (j * K1 + jj));
+                }
+            }
+        }
+    }
+    else
+    {
+        for(int i = 0; i < N; i++)
+        {
+            for(int j = 0; j < K; j++)
+            {
+                b_k_n_permute(i * K + j) = b_k_n(i * K + j);
+            }
+        }
+    }
+
+    a_m_k_device_buf.ToDevice(a_m_k.mData.data());
+    b_k_n_device_buf.ToDevice(b_k_n_permute.mData.data());
+    DeviceMem workspace;
+
+    auto a_element_op = AElementOp{};
+    auto b_element_op = BElementOp{};
+    auto c_element_op = CElementOp{};
+
+    // do GEMM
+    auto gemm      = DeviceGemmV2Instance{};
+    auto invoker   = gemm.MakeInvoker();
+    float ave_time = 0;
+
+    auto argument = gemm.MakeArgument(static_cast<ADataType*>(a_m_k_device_buf.GetDeviceBuffer()),
+                                      static_cast<BDataType*>(b_k_n_device_buf.GetDeviceBuffer()),
+                                      static_cast<CDataType*>(c_m_n_device_buf.GetDeviceBuffer()),
+                                      M,
+                                      N,
+                                      K,
+                                      StrideA,
+                                      StrideB,
+                                      StrideC,
+                                      KBatch,
+                                      a_element_op,
+                                      b_element_op,
+                                      c_element_op);
+
+    if(!gemm.IsSupportedArgument(argument))
+    {
+        std::cerr << gemm.GetTypeString() << " does not support this problem" << std::endl;
+
+        return true;
+    }
+
+    bool pass = true;
+    if(config.do_verification)
+    {
+        auto ref_gemm    = ReferenceGemmInstance{};
+        auto ref_invoker = ref_gemm.MakeInvoker();
+
+        auto ref_argument = ref_gemm.MakeArgument(
+            a_m_k, b_k_n, c_m_n_host_result, PassThrough{}, PassThrough{}, PassThrough{});
+
+        ref_invoker.Run(ref_argument);
+
+        ave_time = invoker.Run(argument, StreamConfig{nullptr, false, 0});
+        c_m_n_device_buf.FromDevice(c_m_n_device_result.mData.data());
+
+        pass &= ck::utils::check_err(c_m_n_device_result,
+                                     c_m_n_host_result,
+                                     "Error: Incorrect results!",
+                                     get_rtol<CDataType>(),
+                                     get_atol<CDataType>());
+    }
+
+    if(config.time_kernel)
+    {
+        ave_time =
+            invoker.Run(argument, StreamConfig{nullptr, config.time_kernel, 0, 20, 50, true, 50});
+
+        std::size_t flop = 2_uz * M * N * K;
+        std::size_t num_btype =
+            sizeof(ADataType) * M * K +
+            sizeof(BDataType) * K * N /
+                (ck::is_same_v<ck::remove_cvref_t<BDataType>, ck::pk_i4_t> ? 2 : 1) +
+            sizeof(CDataType) * M * N;
+
+        float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
+
+        float gb_per_sec = num_btype / 1.E6 / ave_time;
+
+        std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec
+                  << " GB/s, " << gemm.GetTypeString() << std::endl;
+    }
+    return pass;
+}
+
+bool run_gemm_splitk_example(int argc, char* argv[])
+{
+    ProblemSizeSplitK problem_size;
+    ExecutionConfig config;
+
+    return parse_cmd_args(argc, argv, problem_size, config) && run_gemm(problem_size, config);
+}
+
+int main(int argc, char* argv[]) { return !run_gemm_splitk_example(argc, argv); }

example/01_gemm/gemm_xdl_fp16_fp8_v3.cpp

 // SPDX-License-Identifier: MIT
-// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
+// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
 
 #include "common.hpp"
 
 #include "ck/tensor_operation/gpu/device/impl/device_gemm_xdl_cshuffle_v3.hpp"
 
-using ADataType        = ck::f8_t;
-using BDataType        = ck::half_t;
+using ADataType        = ck::half_t;
+using BDataType        = ck::f8_t;
 using AccDataType      = float;
 using CShuffleDataType = ck::half_t;
 using CDataType        = ck::half_t;
@@ -29,15 +29,15 @@ using DeviceGemmV2Instance =
         AElementOp, BElementOp, CElementOp, GemmDefault, 
         64,
         16, 16, 
-        64, 16, 8,
+        256, 8, 16,
         16,   16,
         1,    1, 
-        S<4, 16, 1>,  S<1, 0, 2>,  S<1, 0, 2>, 
-        2, 16, 16, 0,
-        S<8, 8, 1>,  S<1, 0, 2>,  S<1, 0, 2>, 
+        S<32, 2, 1>,  S<1, 0, 2>,  S<1, 0, 2>,
         2, 8, 8, 0,
+        S<16, 4, 1>,  S<1, 0, 2>,  S<1, 0, 2>,
+        2, 16, 16, 0,
         1, 1, S<1, 16, 1, 4>, 4,
-        ck::BlockGemmPipelineScheduler::Intrawave,ck::BlockGemmPipelineVersion::v1>;
+        ck::BlockGemmPipelineScheduler::Interwave, ck::BlockGemmPipelineVersion::v1>;
 // clang-format on
 
 using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,

example/01_gemm/gemm_xdl_fp16_pk_i4_v3.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
+
+#include "common.hpp"
+
+#include "ck/tensor_operation/gpu/device/impl/device_gemm_xdl_cshuffle_v3.hpp"
+
+using ADataType        = ck::half_t;
+using BDataType        = ck::pk_i4_t;
+using AccDataType      = float;
+using CShuffleDataType = ck::half_t;
+using CDataType        = ck::half_t;
+
+using ALayout = Row;
+using BLayout = Col;
+using CLayout = Row;
+
+using AElementOp = PassThrough;
+using BElementOp = PassThrough;
+using CElementOp = PassThrough;
+
+static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
+
+static constexpr bool PermuteA         = false;
+static constexpr bool PermuteB         = true;
+static constexpr ck::index_t KPerBlock = 128;
+
+// clang-format off
+using DeviceGemmV2Instance = 
+    ck::tensor_operation::device::DeviceGemm_Xdl_CShuffleV3<
+        ALayout,   BLayout,  CLayout,   
+        ADataType, BDataType, CDataType, AccDataType, CShuffleDataType, 
+        AElementOp, BElementOp, CElementOp, GemmDefault, 
+        128,
+        16, 128,
+        KPerBlock, 8, 32,
+        16,   16,
+        1,    4,
+        S<16, 8, 1>,  S<1, 0, 2>,  S<1, 0, 2>,
+        2, 8, 8, 0,
+        S<4, 32, 1>,  S<1, 0, 2>,  S<1, 0, 2>,
+        2, 32, 32, 0,
+        1, 1, S<1, 16, 1, 8>, 4,
+        ck::BlockGemmPipelineScheduler::Interwave, ck::BlockGemmPipelineVersion::v2, ADataType, ADataType, PermuteA, PermuteB>;
+
+// clang-format on
+
+using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
+                                                                        BDataType,
+                                                                        CDataType,
+                                                                        AccDataType,
+                                                                        PassThrough,
+                                                                        PassThrough,
+                                                                        PassThrough>;
+template <typename ProblemType>
+bool run_gemm(const ProblemType& problem_size, const ExecutionConfig& config)
+{
+    using namespace ck::literals;
+
+    auto M       = problem_size.M;
+    auto N       = problem_size.N;
+    auto K       = problem_size.K;
+    auto StrideA = problem_size.StrideA;
+    auto StrideB = problem_size.StrideB;
+    auto StrideC = problem_size.StrideC;
+    auto KBatch  = problem_size.KBatch;
+
+    auto f_host_tensor_descriptor =
+        [](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
+            if constexpr(std::is_same_v<decltype(layout), ck::tensor_layout::gemm::RowMajor>)
+            {
+                return HostTensorDescriptor({row, col}, {stride, 1_uz});
+            }
+            else
+            {
+                return HostTensorDescriptor({row, col}, {1_uz, stride});
+            }
+        };
+
+    auto f_get_default_stride =
+        [](std::size_t row, std::size_t col, ck::index_t stride, auto layout) {
+            if(stride == -1)
+            {
+                // give a chance if stride is -1, return a default packed stride
+                if constexpr(std::is_same_v<decltype(layout), ck::tensor_layout::gemm::RowMajor>)
+                {
+                    return static_cast<std::size_t>(col);
+                }
+                else
+                {
+                    return static_cast<std::size_t>(row);
+                }
+            }
+            else
+                return static_cast<std::size_t>(stride);
+        };
+
+    StrideA = f_get_default_stride(M, K, StrideA, ALayout{});
+    StrideB = f_get_default_stride(K, N, StrideB, BLayout{});
+    StrideC = f_get_default_stride(M, N, StrideC, CLayout{});
+
+    Tensor<ADataType> a_m_k(f_host_tensor_descriptor(M, K, StrideA, ALayout{}));
+    Tensor<BDataType> b_k_n(f_host_tensor_descriptor(K, N, StrideB, BLayout{}));
+    Tensor<BDataType> b_k_n_permute(f_host_tensor_descriptor(K, N, StrideB, BLayout{}));
+
+    switch(config.init_method)
+    {
+    case 0:
+        a_m_k.GenerateTensorValue(GeneratorTensor_1<ADataType>{1});
+        b_k_n.GenerateTensorValue(GeneratorTensor_1<BDataType>{1});
+        break;
+    case 1:
+        a_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-2, 2});
+        b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-2, 2});
+        break;
+    case 2:
+        a_m_k.GenerateTensorValue(GeneratorTensor_1<ADataType>{1});
+        b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-2, 2});
+        break;
+    case 3:
+        a_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-2, 2});
+        b_k_n.GenerateTensorValue(GeneratorTensor_1<BDataType>{1});
+        break;
+    default:
+        a_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
+        b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-2, 2});
+    }
+
+    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 << "c_m_n: " << c_m_n_host_result.mDesc << std::endl;
+
+    DeviceMem a_m_k_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpaceSize());
+    DeviceMem b_k_n_device_buf(sizeof(BDataType) * b_k_n_permute.mDesc.GetElementSpaceSize());
+    DeviceMem c_m_n_device_buf(sizeof(CDataType) * c_m_n_device_result.mDesc.GetElementSpaceSize());
+
+    // weight permute
+    if constexpr(PermuteB)
+    {
+        int K1 = KPerBlock;
+        int K0 = K / KPerBlock;
+
+        // int K0, N, K1
+        for(int j = 0; j < K0; j++)
+        {
+            for(int i = 0; i < N; i++)
+            {
+                for(int jj = 0; jj < K1; jj++)
+                {
+                    b_k_n_permute(j * N * K1 + i * K1 + jj) = b_k_n(i * K + (j * K1 + jj));
+                }
+            }
+        }
+    }
+    else
+    {
+        for(int i = 0; i < N; i++)
+        {
+            for(int j = 0; j < K; j++)
+            {
+                b_k_n_permute(i * K + j) = b_k_n(i * K + j);
+            }
+        }
+    }
+
+    // vector pk_i4x4 permute
+    for(int i = 0; i < N; i++)
+    {
+        for(int j = 0; j < K; j += 8)
+        {
+            int input[8];
+
+            for(int k = 0; k < 4; k++)
+            {
+                int i4x2         = b_k_n_permute(j + k * 2, i).data;
+                input[k * 2 + 0] = (i4x2 >> 4) & 0xf;
+                input[k * 2 + 1] = (i4x2 >> 0) & 0xf;
+            }
+
+            // permute 01234567->20643175
+            {
+                int hi   = input[2];
+                int lo   = input[0];
+                int i4x2 = (hi << 4) | lo;
+
+                b_k_n_permute(j + 0, i) = i4x2;
+            }
+
+            {
+                int hi   = input[6];
+                int lo   = input[4];
+                int i4x2 = (hi << 4) | lo;
+
+                b_k_n_permute(j + 2, i) = i4x2;
+            }
+
+            {
+                int hi   = input[3];
+                int lo   = input[1];
+                int i4x2 = (hi << 4) | lo;
+
+                b_k_n_permute(j + 4, i) = i4x2;
+            }
+
+            {
+                int hi   = input[7];
+                int lo   = input[5];
+                int i4x2 = (hi << 4) | lo;
+
+                b_k_n_permute(j + 6, i) = i4x2;
+            }
+        }
+    }
+
+    a_m_k_device_buf.ToDevice(a_m_k.mData.data());
+    b_k_n_device_buf.ToDevice(b_k_n_permute.mData.data());
+    DeviceMem workspace;
+
+    auto a_element_op = AElementOp{};
+    auto b_element_op = BElementOp{};
+    auto c_element_op = CElementOp{};
+
+    // do GEMM
+    auto gemm      = DeviceGemmV2Instance{};
+    auto invoker   = gemm.MakeInvoker();
+    float ave_time = 0;
+
+    auto argument = gemm.MakeArgument(static_cast<ADataType*>(a_m_k_device_buf.GetDeviceBuffer()),
+                                      static_cast<BDataType*>(b_k_n_device_buf.GetDeviceBuffer()),
+                                      static_cast<CDataType*>(c_m_n_device_buf.GetDeviceBuffer()),
+                                      M,
+                                      N,
+                                      K,
+                                      StrideA,
+                                      StrideB,
+                                      StrideC,
+                                      KBatch,
+                                      a_element_op,
+                                      b_element_op,
+                                      c_element_op);
+
+    if(!gemm.IsSupportedArgument(argument))
+    {
+        std::cerr << gemm.GetTypeString() << " does not support this problem" << std::endl;
+
+        return true;
+    }
+
+    bool pass = true;
+    if(config.do_verification)
+    {
+        auto ref_gemm    = ReferenceGemmInstance{};
+        auto ref_invoker = ref_gemm.MakeInvoker();
+
+        auto ref_argument = ref_gemm.MakeArgument(
+            a_m_k, b_k_n, c_m_n_host_result, PassThrough{}, PassThrough{}, PassThrough{});
+
+        ref_invoker.Run(ref_argument);
+
+        ave_time = invoker.Run(argument, StreamConfig{nullptr, false, 0});
+        c_m_n_device_buf.FromDevice(c_m_n_device_result.mData.data());
+
+        pass &= ck::utils::check_err(c_m_n_device_result,
+                                     c_m_n_host_result,
+                                     "Error: Incorrect results!",
+                                     get_rtol<CDataType>(),
+                                     get_atol<CDataType>());
+    }
+
+    if(config.time_kernel)
+    {
+        ave_time =
+            invoker.Run(argument, StreamConfig{nullptr, config.time_kernel, 0, 20, 50, true, 50});
+
+        std::size_t flop = 2_uz * M * N * K;
+        std::size_t num_btype =
+            sizeof(ADataType) * M * K +
+            sizeof(BDataType) * K * N /
+                (ck::is_same_v<ck::remove_cvref_t<BDataType>, ck::pk_i4_t> ? 2 : 1) +
+            sizeof(CDataType) * M * N;
+
+        float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
+
+        float gb_per_sec = num_btype / 1.E6 / ave_time;
+
+        std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec
+                  << " GB/s, " << gemm.GetTypeString() << std::endl;
+    }
+    return pass;
+}
+
+bool run_gemm_splitk_example(int argc, char* argv[])
+{
+    ProblemSizeSplitK problem_size;
+    ExecutionConfig config;
+
+    return parse_cmd_args(argc, argv, problem_size, config) && run_gemm(problem_size, config);
+}
+
+int main(int argc, char* argv[]) { return !run_gemm_splitk_example(argc, argv); }

example/01_gemm/gemm_xdl_fp16_v3.cpp

@@ -12,7 +12,7 @@ using CShuffleDataType = ck::half_t;
 using CDataType        = ck::half_t;
 
 using ALayout = Row;
-using BLayout = Row;
+using BLayout = Col;
 using CLayout = Row;
 
 using AElementOp = PassThrough;
@@ -27,17 +27,17 @@ using DeviceGemmV2Instance =
         ALayout,   BLayout,  CLayout,   
         ADataType,   BDataType,  CDataType,  AccDataType,  CShuffleDataType, 
         PassThrough, PassThrough, PassThrough, GemmDefault, 
-        256,
-        224, 256, 
-        64, 8, 2,
+        64,
+        16, 16, 
+        256, 8, 8,
         16,   16,
-        7,    8,
-        S<8, 32, 1>,  S<1, 0, 2>,  S<1, 0, 2>, 
+        1,    1,
+        S<32, 2, 1>,  S<1, 0, 2>,  S<1, 0, 2>, 
         2, 8, 8, 0,
-        S<8, 32, 1>,  S<0, 2, 1>,  S<0, 2, 1>, 
-        1, 8, 2, 0,
-        1, 2, S<1, 32, 1, 8>, 8,
-        ck::BlockGemmPipelineScheduler::Intrawave,ck::BlockGemmPipelineVersion::v3>;
+        S<32, 2, 1>,  S<1, 0, 2>,  S<1, 0, 2>, 
+        2, 8, 8, 0,
+        1, 1, S<1, 16, 1, 4>, 4,
+        ck::BlockGemmPipelineScheduler::Interwave, ck::BlockGemmPipelineVersion::v2>;
 // clang-format on
 
 using ReferenceGemmInstance = ck::tensor_operation::host::

example/01_gemm/run_gemm_example.inc

@@ -5,88 +5,6 @@
 
 #include "ck/tensor_operation/gpu/device/device_gemm_streamk.hpp"
 
-template <typename DataType>
-inline __host__ __device__ constexpr double get_rtol()
-{
-    if constexpr(std::is_same_v<DataType, float>)
-    {
-        return 1e-3;
-    }
-    else if constexpr(std::is_same_v<DataType, double>)
-    {
-        return 1e-6;
-    }
-    else if constexpr(std::is_same_v<DataType, ck::half_t>)
-    {
-        return 1e-3;
-    }
-    else if constexpr(std::is_same_v<DataType, ck::bhalf_t>)
-    {
-        return 5e-2;
-    }
-    else if constexpr(std::is_same_v<DataType, int32_t>)
-    {
-        return 1e-1;
-    }
-    else if constexpr(std::is_same_v<DataType, int8_t>)
-    {
-        return 1e-1;
-    }
-    else if constexpr(std::is_same_v<DataType, ck::f8_t>)
-    {
-        return 2e-1;
-    }
-    else if constexpr(std::is_same_v<DataType, ck::bf8_t>)
-    {
-        return 2e-1;
-    }
-    else
-    {
-        return 1e-3;
-    }
-}
-
-template <typename DataType>
-inline __host__ __device__ constexpr double get_atol()
-{
-    if constexpr(std::is_same_v<DataType, float>)
-    {
-        return 1e-3;
-    }
-    else if constexpr(std::is_same_v<DataType, double>)
-    {
-        return 1e-6;
-    }
-    else if constexpr(std::is_same_v<DataType, ck::half_t>)
-    {
-        return 1e-3;
-    }
-    else if constexpr(std::is_same_v<DataType, ck::bhalf_t>)
-    {
-        return 5e-2;
-    }
-    else if constexpr(std::is_same_v<DataType, int32_t>)
-    {
-        return 1e-1;
-    }
-    else if constexpr(std::is_same_v<DataType, int8_t>)
-    {
-        return 1e-1;
-    }
-    else if constexpr(std::is_same_v<DataType, ck::f8_t>)
-    {
-        return 2e-1;
-    }
-    else if constexpr(std::is_same_v<DataType, ck::bf8_t>)
-    {
-        return 2e-1;
-    }
-    else
-    {
-        return 1e-3;
-    }
-}
-
 template <typename ProblemType>
 bool run_gemm(const ProblemType& problem_size, const ExecutionConfig& config)
 {

example/01_gemm/run_gemm_example_streamk_v2.inc

@@ -3,88 +3,6 @@
 
 #pragma once
 
-template <typename DataType>
-inline __host__ __device__ constexpr double get_rtol()
-{
-    if constexpr(std::is_same_v<DataType, float>)
-    {
-        return 1e-3;
-    }
-    else if constexpr(std::is_same_v<DataType, double>)
-    {
-        return 1e-6;
-    }
-    else if constexpr(std::is_same_v<DataType, ck::half_t>)
-    {
-        return 1e-3;
-    }
-    else if constexpr(std::is_same_v<DataType, ck::bhalf_t>)
-    {
-        return 5e-2;
-    }
-    else if constexpr(std::is_same_v<DataType, int32_t>)
-    {
-        return 1e-1;
-    }
-    else if constexpr(std::is_same_v<DataType, int8_t>)
-    {
-        return 1e-1;
-    }
-    else if constexpr(std::is_same_v<DataType, ck::f8_t>)
-    {
-        return 1e-1; // 240 and 224 are acceptable
-    }
-    else if constexpr(std::is_same_v<DataType, ck::bf8_t>)
-    {
-        return 1.5e-1; // 57344 and 49152 are acceptable
-    }
-    else
-    {
-        return 1e-3;
-    }
-}
-
-template <typename DataType>
-inline __host__ __device__ constexpr double get_atol()
-{
-    if constexpr(std::is_same_v<DataType, float>)
-    {
-        return 1e-3;
-    }
-    else if constexpr(std::is_same_v<DataType, double>)
-    {
-        return 1e-6;
-    }
-    else if constexpr(std::is_same_v<DataType, ck::half_t>)
-    {
-        return 1e-3;
-    }
-    else if constexpr(std::is_same_v<DataType, ck::bhalf_t>)
-    {
-        return 5e-2;
-    }
-    else if constexpr(std::is_same_v<DataType, int32_t>)
-    {
-        return 1e-1;
-    }
-    else if constexpr(std::is_same_v<DataType, int8_t>)
-    {
-        return 1e-1;
-    }
-    else if constexpr(std::is_same_v<DataType, ck::f8_t>)
-    {
-        return 16.1; // 240 and 224 are acceptable
-    }
-    else if constexpr(std::is_same_v<DataType, ck::bf8_t>)
-    {
-        return 8192.1; // 57344 and 49152 are acceptable
-    }
-    else
-    {
-        return 1e-3;
-    }
-}
-
 template <typename ProblemType>
 bool run_gemm(const ProblemType& problem_size, const ExecutionConfig& config)
 {

example/01_gemm/run_gemm_example_v2.inc

@@ -3,88 +3,6 @@
 
 #pragma once
 
-template <typename DataType>
-inline __host__ __device__ constexpr double get_rtol()
-{
-    if constexpr(std::is_same_v<DataType, float>)
-    {
-        return 1e-3;
-    }
-    else if constexpr(std::is_same_v<DataType, double>)
-    {
-        return 1e-6;
-    }
-    else if constexpr(std::is_same_v<DataType, ck::half_t>)
-    {
-        return 1e-3;
-    }
-    else if constexpr(std::is_same_v<DataType, ck::bhalf_t>)
-    {
-        return 5e-2;
-    }
-    else if constexpr(std::is_same_v<DataType, int32_t>)
-    {
-        return 1e-1;
-    }
-    else if constexpr(std::is_same_v<DataType, int8_t>)
-    {
-        return 1e-1;
-    }
-    else if constexpr(std::is_same_v<DataType, ck::f8_t>)
-    {
-        return 1e-1; // 240 and 224 are acceptable
-    }
-    else if constexpr(std::is_same_v<DataType, ck::bf8_t>)
-    {
-        return 1.5e-1; // 57344 and 49152 are acceptable
-    }
-    else
-    {
-        return 1e-3;
-    }
-}
-
-template <typename DataType>
-inline __host__ __device__ constexpr double get_atol()
-{
-    if constexpr(std::is_same_v<DataType, float>)
-    {
-        return 1e-3;
-    }
-    else if constexpr(std::is_same_v<DataType, double>)
-    {
-        return 1e-6;
-    }
-    else if constexpr(std::is_same_v<DataType, ck::half_t>)
-    {
-        return 1e-3;
-    }
-    else if constexpr(std::is_same_v<DataType, ck::bhalf_t>)
-    {
-        return 5e-2;
-    }
-    else if constexpr(std::is_same_v<DataType, int32_t>)
-    {
-        return 1e-1;
-    }
-    else if constexpr(std::is_same_v<DataType, int8_t>)
-    {
-        return 1e-1;
-    }
-    else if constexpr(std::is_same_v<DataType, ck::f8_t>)
-    {
-        return 16.1; // 240 and 224 are acceptable
-    }
-    else if constexpr(std::is_same_v<DataType, ck::bf8_t>)
-    {
-        return 8192.1; // 57344 and 49152 are acceptable
-    }
-    else
-    {
-        return 1e-3;
-    }
-}
-
 template <typename ProblemType>
 bool run_gemm(const ProblemType& problem_size, const ExecutionConfig& config)
 {

include/ck/library/utility/host_tensor.hpp

@@ -266,18 +266,18 @@ struct Tensor
     using Data       = std::vector<T>;
 
     template <typename X>
-    Tensor(std::initializer_list<X> lens) : mDesc(lens), mData(mDesc.GetElementSpaceSize())
+    Tensor(std::initializer_list<X> lens) : mDesc(lens), mData(GetElementSpaceSize())
     {
     }
 
     template <typename X, typename Y>
     Tensor(std::initializer_list<X> lens, std::initializer_list<Y> strides)
-        : mDesc(lens, strides), mData(mDesc.GetElementSpaceSize())
+        : mDesc(lens, strides), mData(GetElementSpaceSize())
     {
     }
 
     template <typename Lengths>
-    Tensor(const Lengths& lens) : mDesc(lens), mData(mDesc.GetElementSpaceSize())
+    Tensor(const Lengths& lens) : mDesc(lens), mData(GetElementSpaceSize())
     {
     }
 
@@ -287,7 +287,7 @@ struct Tensor
     {
     }
 
-    Tensor(const Descriptor& desc) : mDesc(desc), mData(mDesc.GetElementSpaceSize()) {}
+    Tensor(const Descriptor& desc) : mDesc(desc), mData(GetElementSpaceSize()) {}
 
     template <typename OutT>
     Tensor<OutT> CopyAsType() const
@@ -322,7 +322,17 @@ struct Tensor
 
     std::size_t GetElementSize() const { return mDesc.GetElementSize(); }
 
-    std::size_t GetElementSpaceSize() const { return mDesc.GetElementSpaceSize(); }
+    std::size_t GetElementSpaceSize() const
+    {
+        if constexpr(ck::is_same_v<ck::remove_cvref_t<T>, ck::pk_i4_t>)
+        {
+            return (mDesc.GetElementSpaceSize() + 1) / 2;
+        }
+        else
+        {
+            return mDesc.GetElementSpaceSize();
+        }
+    }
 
     std::size_t GetElementSpaceSizeInBytes() const { return sizeof(T) * GetElementSpaceSize(); }
 
@@ -469,29 +479,64 @@ struct Tensor
     template <typename... Is>
     std::size_t GetOffsetFromMultiIndex(Is... is) const
     {
-        return mDesc.GetOffsetFromMultiIndex(is...);
+        if constexpr(ck::is_same_v<ck::remove_cvref_t<T>, ck::pk_i4_t>)
+        {
+            return mDesc.GetOffsetFromMultiIndex(is...) / 2;
+        }
+        else
+        {
+            return mDesc.GetOffsetFromMultiIndex(is...);
+        }
     }
 
     template <typename... Is>
     T& operator()(Is... is)
     {
-        return mData[mDesc.GetOffsetFromMultiIndex(is...)];
+        if constexpr(ck::is_same_v<ck::remove_cvref_t<T>, ck::pk_i4_t>)
+        {
+            return mData[mDesc.GetOffsetFromMultiIndex(is...) / 2];
+        }
+        else
+        {
+            return mData[mDesc.GetOffsetFromMultiIndex(is...)];
+        }
     }
 
     template <typename... Is>
     const T& operator()(Is... is) const
     {
-        return mData[mDesc.GetOffsetFromMultiIndex(is...)];
+        if constexpr(ck::is_same_v<ck::remove_cvref_t<T>, ck::pk_i4_t>)
+        {
+            return mData[mDesc.GetOffsetFromMultiIndex(is...) / 2];
+        }
+        else
+        {
+            return mData[mDesc.GetOffsetFromMultiIndex(is...)];
+        }
     }
 
     T& operator()(std::vector<std::size_t> idx)
     {
-        return mData[mDesc.GetOffsetFromMultiIndex(idx)];
+        if constexpr(ck::is_same_v<ck::remove_cvref_t<T>, ck::pk_i4_t>)
+        {
+            return mData[mDesc.GetOffsetFromMultiIndex(idx) / 2];
+        }
+        else
+        {
+            return mData[mDesc.GetOffsetFromMultiIndex(idx)];
+        }
     }
 
     const T& operator()(std::vector<std::size_t> idx) const
     {
-        return mData[mDesc.GetOffsetFromMultiIndex(idx)];
+        if constexpr(ck::is_same_v<ck::remove_cvref_t<T>, ck::pk_i4_t>)
+        {
+            return mData[mDesc.GetOffsetFromMultiIndex(idx) / 2];
+        }
+        else
+        {
+            return mData[mDesc.GetOffsetFromMultiIndex(idx)];
+        }
     }
 
     typename Data::iterator begin() { return mData.begin(); }

include/ck/library/utility/host_tensor_generator.hpp

@@ -81,6 +81,20 @@ struct GeneratorTensor_1<int8_t>
     }
 };
 
+template <>
+struct GeneratorTensor_1<ck::pk_i4_t>
+{
+    int8_t value = 1;
+
+    template <typename... Is>
+    ck::pk_i4_t operator()(Is...)
+    {
+        int t         = value + 8;
+        ck::pk_i4_t r = ((t << 4) + t) & 0xff;
+        return r;
+    }
+};
+
 template <typename T>
 struct GeneratorTensor_2
 {
@@ -121,6 +135,22 @@ struct GeneratorTensor_2<int8_t>
     }
 };
 
+template <>
+struct GeneratorTensor_2<ck::pk_i4_t>
+{
+    int min_value = 0;
+    int max_value = 1;
+
+    template <typename... Is>
+    ck::pk_i4_t operator()(Is...)
+    {
+        int hi        = std::rand() % (max_value - min_value) + min_value + 8;
+        int lo        = std::rand() % (max_value - min_value) + min_value + 8;
+        ck::pk_i4_t r = ((hi << 4) + lo) & 0xff;
+        return r;
+    }
+};
+
 #if defined CK_ENABLE_FP8
 template <>
 struct GeneratorTensor_2<ck::f8_t>

include/ck/tensor/static_tensor.hpp

@@ -167,7 +167,7 @@ struct StaticTensorTupleOfVectorBuffer
     // Idx is for S, not X. Idx should be aligned with X
     template <typename X,
               typename Idx,
-              typename enable_if<has_same_scalar_type<S, X>::value &&
+              typename enable_if<(has_same_scalar_type<S, X>::value || !is_native_type<S>()) &&
                                      is_known_at_compile_time<Idx>::value && Idx::Size() == ndim_,
                                  bool>::type = false>
     __host__ __device__ constexpr X GetAsType(Idx) const
@@ -201,7 +201,7 @@ struct StaticTensorTupleOfVectorBuffer
     // Idx is for S, not X. Idx should be aligned with X
     template <typename X,
               typename Idx,
-              typename enable_if<has_same_scalar_type<S, X>::value &&
+              typename enable_if<(has_same_scalar_type<S, X>::value || !is_native_type<S>()) &&
                                      is_known_at_compile_time<Idx>::value && Idx::Size() == ndim_,
                                  bool>::type = false>
     __host__ __device__ constexpr void SetAsType(Idx, X x)

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

@@ -36,6 +36,10 @@ struct DeviceGemmV2 : public BaseOperator
                         CElementwiseOperation c_element_op) = 0;
 
     virtual std::unique_ptr<BaseInvoker> MakeInvokerPointer() = 0;
+
+    virtual bool GetPermuteA()         = 0;
+    virtual bool GetPermuteB()         = 0;
+    virtual ck::index_t GetKPerBlock() = 0;
 };
 
 template <typename ALayout,

include/ck/tensor_operation/gpu/device/impl/device_gemm_xdl_cshuffle_v3.hpp

@@ -64,7 +64,9 @@ template <typename ALayout,
           BlockGemmPipelineScheduler BlkGemmPipeSched = BlockGemmPipelineScheduler::Intrawave,
           BlockGemmPipelineVersion BlkGemmPipelineVer = BlockGemmPipelineVersion::v1,
           typename ComputeTypeA                       = CDataType,
-          typename ComputeTypeB                       = ComputeTypeA>
+          typename ComputeTypeB                       = ComputeTypeA,
+          bool PermuteA                               = false,
+          bool PermuteB                               = false>
 struct DeviceGemm_Xdl_CShuffleV3 : public DeviceGemmV2<ALayout,
                                                        BLayout,
                                                        CLayout,
@@ -122,7 +124,9 @@ struct DeviceGemm_Xdl_CShuffleV3 : public DeviceGemmV2<ALayout,
         BlkGemmPipeSched,
         BlkGemmPipelineVer,
         ComputeTypeA,
-        ComputeTypeB>;
+        ComputeTypeB,
+        PermuteA,
+        PermuteB>;
 
     using Argument = typename GridwiseGemm::Argument;
 
@@ -633,6 +637,11 @@ struct DeviceGemm_Xdl_CShuffleV3 : public DeviceGemmV2<ALayout,
         return IsSupportedArgument(*dynamic_cast<const Argument*>(p_arg));
     }
 
+    index_t GetKPerBlock() override { return KPerBlock; }
+
+    bool GetPermuteA() override { return PermuteA; }
+    bool GetPermuteB() override { return PermuteB; }
+
     static auto MakeArgument(const ADataType* p_a,
                              const BDataType* p_b,
                              CDataType* p_c,

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

@@ -7,12 +7,177 @@
 #include "ck/utility/math.hpp"
 #include "ck/utility/math_v2.hpp"
 #include "ck/utility/type_convert.hpp"
+#include "ck/utility/amd_inline_asm.hpp"
 #include <cassert>
 
 namespace ck {
+
+// Fast int4x4 to half8_t data type conversion based on paper
+// [Who Says Elephants Can't Run: Bringing Large Scale MoE Models into Cloud Scale Production]
+// (https://arxiv.org/abs/2211.10017) and implementation:
+// https://github.com/NVIDIA/FasterTransformer/blob/main/src/fastertransformer/cutlass_extensions/include/cutlass_extensions/interleaved_numeric_conversion.h
+__host__ __device__ inline half4_t pki4_to_half4(int q)
+{
+    const int LO = 0x000f000f;
+    const int HI = 0x00f000f0;
+    const int EX = 0x64006400;
+
+    // Extract the two int4 at low bit and create two fp16 number.
+    int lo = amd_assembly_and_or_b32(q, LO, EX);
+    // Extract the two int4 at hight bit and create two fp16 number.
+    int hi = amd_assembly_and_or_b32(q, HI, EX);
+
+    const int SUB = 0xE408E408; // half2 {-1032, -1032}
+    const int MUL = 0x2c002c00; // half2 {1 / 16, 1 / 16}
+    const int ADD = 0xd480d480; // half2 {-72, -72}
+
+    vector_type<half_t, 4> res;
+
+    // for two fp16 from lowbit, subtract 1032 to get correct fp16 value
+    res.template AsType<half2_t>()(Number<0>{}) =
+        amd_assembly_pk_add_f16(bit_cast<half2_t>(lo), bit_cast<half2_t>(SUB));
+
+    // for two fp16 from highbit, divide 16 and subtract 72 to get correct fp16 value
+    res.template AsType<half2_t>()(Number<1>{}) = amd_assembly_pk_fma_f16(
+        bit_cast<half2_t>(hi), bit_cast<half2_t>(MUL), bit_cast<half2_t>(ADD));
+
+    return res.template AsType<half4_t>()[Number<0>{}];
+}
+
+__host__ __device__ inline half2_t pki4_to_half2(pk_i4_t q)
+{
+#if 1
+    uint8_t x_u8 = ck::bit_cast<uint8_t>(q);
+    uint32_t i4s = ((x_u8 & 0x0f) << 16) | ((x_u8 & 0xf0) >> 4);
+
+    const int EX  = 0x64006400;
+    const int SUB = 0xE408E408; //-8
+
+    int lo = i4s | EX;
+
+    return amd_assembly_pk_add_f16(bit_cast<half2_t>(lo), bit_cast<half2_t>(SUB));
+#else
+    uint8_t x_u8 = ck::bit_cast<uint8_t>(q);
+
+    vector_type<half_t, 2> res;
+
+    half_t x_h = (x_u8 & 0x0f) - 8;
+    half_t x_l = ((x_u8 & 0xf0) >> 4) - 8;
+
+    res.template AsType<half_t>()(Number<0>{}) = x_l;
+    res.template AsType<half_t>()(Number<1>{}) = x_h;
+
+    return res.template AsType<half2_t>()[Number<0>{}];
+#endif
+}
+
+__host__ __device__ inline bhalf4_t pki4_to_bhalf4(int q)
+{
+    uint32_t i8s = (q & 0xf) | ((q & 0xf0) << 4) | ((q & 0xf00) << 8) | ((q & 0xf000) << 12);
+
+    static constexpr uint32_t fp32_base = 0x4B000000;
+
+    float fp32_intermediates[4];
+
+    uint32_t* fp32_intermediates_casted = reinterpret_cast<uint32_t*>(fp32_intermediates);
+
+    fp32_intermediates_casted[0] = __byte_perm(i8s, fp32_base, 0x7650);
+    fp32_intermediates_casted[1] = __byte_perm(i8s, fp32_base, 0x7651);
+    fp32_intermediates_casted[2] = __byte_perm(i8s, fp32_base, 0x7652);
+    fp32_intermediates_casted[3] = __byte_perm(i8s, fp32_base, 0x7653);
+
+    fp32_intermediates[0] -= 8388616.f;
+    fp32_intermediates[1] -= 8388616.f;
+    fp32_intermediates[2] -= 8388616.f;
+    fp32_intermediates[3] -= 8388616.f;
+
+    vector_type<bhalf_t, 4> res;
+    res.template AsType<bhalf2_t>()(Number<0>{}) = bit_cast<bhalf2_t>(
+        __byte_perm(fp32_intermediates_casted[1], fp32_intermediates_casted[0], 0x7632));
+    res.template AsType<bhalf2_t>()(Number<1>{}) = bit_cast<bhalf2_t>(
+        __byte_perm(fp32_intermediates_casted[3], fp32_intermediates_casted[2], 0x7632));
+
+    return res.template AsType<bhalf4_t>()[Number<0>{}];
+}
+
+__host__ __device__ inline bhalf2_t pki4_to_bhalf2(pk_i4_t q)
+{
+    uint8_t x_u8 = ck::bit_cast<uint8_t>(q);
+
+    float x_h = ((x_u8 & 0x0f) >> 0) - 8.f;
+    float x_l = ((x_u8 & 0xf0) >> 4) - 8.f;
+
+    vector_type<bhalf_t, 2> res;
+
+    res.template AsType<bhalf_t>()(Number<0>{}) = type_convert<bhalf_t>(x_l);
+    res.template AsType<bhalf_t>()(Number<1>{}) = type_convert<bhalf_t>(x_h);
+
+    return res.template AsType<bhalf2_t>()[Number<0>{}];
+}
+
 namespace tensor_operation {
 namespace element_wise {
 
+struct PassThroughPack8
+{
+    template <typename Y, typename X>
+    __host__ __device__ void operator()(Y& y, const X& x) const;
+
+    __host__ __device__ constexpr void operator()(ck::half8_t& y, const ck::pk_i4x4_t& x) const
+    {
+#if 1
+        vector_type<half_t, 8> result;
+
+        result.template AsType<half4_t>()(Number<0>{}) = pki4_to_half4(bit_cast<int>(x));
+        result.template AsType<half4_t>()(Number<1>{}) = pki4_to_half4(bit_cast<int>(x) >> 8);
+
+        y = result.template AsType<half8_t>()[Number<0>{}];
+#else
+        vector_type<half_t, 8> dst;
+        vector_type<pk_i4_t, 4> src{x};
+
+        dst.template AsType<half2_t>()(Number<0>{}) =
+            pki4_to_half2(src.template AsType<pk_i4_t>()[Number<0>{}]);
+        dst.template AsType<half2_t>()(Number<1>{}) =
+            pki4_to_half2(src.template AsType<pk_i4_t>()[Number<1>{}]);
+        dst.template AsType<half2_t>()(Number<2>{}) =
+            pki4_to_half2(src.template AsType<pk_i4_t>()[Number<2>{}]);
+        dst.template AsType<half2_t>()(Number<3>{}) =
+            pki4_to_half2(src.template AsType<pk_i4_t>()[Number<3>{}]);
+
+        y = dst.template AsType<half8_t>()[Number<0>{}];
+#endif
+    }
+
+    __host__ __device__ constexpr void operator()(ck::bhalf8_t& y, const ck::pk_i4x4_t& x) const
+    {
+#if 1
+        vector_type<bhalf_t, 8> result;
+
+        result.template AsType<bhalf4_t>()(Number<0>{}) = pki4_to_bhalf4(bit_cast<int>(x));
+        result.template AsType<bhalf4_t>()(Number<1>{}) = pki4_to_bhalf4(bit_cast<int>(x) >> 16);
+
+        y = result.template AsType<bhalf8_t>()[Number<0>{}];
+#else
+        vector_type<bhalf_t, 8> dst;
+        vector_type<pk_i4_t, 4> src{x};
+
+        dst.template AsType<bhalf2_t>()(Number<0>{}) =
+            pki4_to_bhalf2(src.template AsType<pk_i4_t>()[Number<0>{}]);
+        dst.template AsType<bhalf2_t>()(Number<1>{}) =
+            pki4_to_bhalf2(src.template AsType<pk_i4_t>()[Number<1>{}]);
+        dst.template AsType<bhalf2_t>()(Number<2>{}) =
+            pki4_to_bhalf2(src.template AsType<pk_i4_t>()[Number<2>{}]);
+        dst.template AsType<bhalf2_t>()(Number<3>{}) =
+            pki4_to_bhalf2(src.template AsType<pk_i4_t>()[Number<3>{}]);
+
+        y          = dst.template AsType<bhalf8_t>()[Number<0>{}];
+#endif
+    }
+
+    constexpr const static bool is_pack8_invocable = true;
+};
+
 #pragma clang diagnostic push
 #pragma clang diagnostic ignored "-Wnon-virtual-dtor"
 struct UnaryOpBase
@@ -49,6 +214,24 @@ struct PassThroughPack2
         auto t = type_convert<float2_t>(x);
         y      = type_convert<half2_t>(t);
     }
+
+    __host__ __device__ constexpr void operator()(ck::half2_t& y, const ck::pk_i4_t& x) const
+    {
+#if 1
+        uint8_t x_u8 = ck::bit_cast<uint8_t>(x);
+        uint8_t x_l  = (x_u8 & 0x0f) >> 0;
+        uint8_t x_h  = (x_u8 & 0xf0) >> 4;
+
+        auto l_f16 = ck::type_convert<ck::half_t>(x_l);
+        auto h_f16 = ck::type_convert<ck::half_t>(x_h);
+
+        y = {l_f16, h_f16};
+#else
+        uint32_t t = ck::bit_cast<uint8_t>(x);
+        y          = ck::bit_cast<half2_t>(t);
+#endif
+    }
+
     constexpr const static bool is_pack2_invocable = true;
 };
 
@@ -76,6 +259,12 @@ struct PassThrough final : public UnaryOpBase
     template <typename Y, typename X>
     __host__ __device__ void operator()(Y& y, const X& x) const;
 
+    template <>
+    __host__ __device__ void operator()<pk_i4_t, pk_i4_t>(pk_i4_t& y, const pk_i4_t& x) const
+    {
+        y = x;
+    }
+
     template <>
     __host__ __device__ void operator()<float, double>(float& y, const double& x) const
     {

include/ck/tensor_operation/gpu/grid/gridwise_gemm_xdl_cshuffle_v3.hpp

@@ -127,7 +127,9 @@ template <typename ALayout,
           BlockGemmPipelineScheduler BlkGemmPipeSched = BlockGemmPipelineScheduler::Intrawave,
           BlockGemmPipelineVersion BlkGemmPipelineVer = BlockGemmPipelineVersion::v4,
           typename ComputeTypeA                       = CDataType,
-          typename ComputeTypeB                       = ComputeTypeA>
+          typename ComputeTypeB                       = ComputeTypeA,
+          bool PermuteA                               = false,
+          bool PermuteB                               = false>
 struct GridwiseGemm_xdl_cshuffle_v3
 {
     static constexpr auto I0 = Number<0>{};
@@ -151,6 +153,20 @@ struct GridwiseGemm_xdl_cshuffle_v3
 
     using ThisThreadBlock = ThisThreadBlock<BlockSize>;
 
+    static constexpr index_t APackedSize = []() {
+        if constexpr(is_same_v<remove_cvref_t<ADataType>, pk_i4_t>)
+            return 2;
+        else
+            return 1;
+    }();
+
+    static constexpr index_t BPackedSize = []() {
+        if constexpr(is_same_v<remove_cvref_t<BDataType>, pk_i4_t>)
+            return 2;
+        else
+            return 1;
+    }();
+
     __host__ static auto CalculateGridSize(index_t M, index_t N, index_t KBatch)
     {
         return std::make_tuple(Block2CTileMap::CalculateGridSize(M, N), 1, KBatch);
@@ -319,6 +335,10 @@ struct GridwiseGemm_xdl_cshuffle_v3
 
         using GemmSpecialization = tensor_operation::device::GemmSpecialization;
 
+        static_assert(!(is_same_v<remove_cvref_t<ADataType>, pk_i4_t> &&
+                        GemmSpec != GemmSpecialization::Default),
+                      "pk_i4_t does not support padding");
+
         if constexpr(GemmSpec == GemmSpecialization::NKPadding ||
                      GemmSpec == GemmSpecialization::MNKPadding)
         {
@@ -373,15 +393,39 @@ struct GridwiseGemm_xdl_cshuffle_v3
         }
         else
         {
-            // not pad N or K
-            const auto b_grid_desc_bk0_n_bk1 = transform_tensor_descriptor(
-                b_grid_desc_nraw_kraw,
-                make_tuple(make_unmerge_transform(make_tuple(BK0, BK1Value)),
-                           make_pass_through_transform(N)),
-                make_tuple(Sequence<1>{}, Sequence<0>{}),
-                make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
-
-            return b_grid_desc_bk0_n_bk1;
+            if constexpr(!PermuteB)
+            {
+                // not pad N or K
+                const auto b_grid_desc_bk0_n_bk1 = transform_tensor_descriptor(
+                    b_grid_desc_nraw_kraw,
+                    make_tuple(make_unmerge_transform(make_tuple(BK0, BK1Value)),
+                               make_pass_through_transform(N)),
+                    make_tuple(Sequence<1>{}, Sequence<0>{}),
+                    make_tuple(Sequence<0, 2>{}, Sequence<1>{}));
+
+                return b_grid_desc_bk0_n_bk1;
+            }
+            else
+            {
+                // Pre-shuffled Weight
+                // BGlobal[K / KPerBlock, N, KPerBlock / K1, K1] -> BTile[K / K1, N, K1]
+                constexpr index_t BK01 = KPerBlock / BK1Value;
+                const index_t BK0_     = StrideB / BK1Value;
+                const index_t BK00     = BK0_ / BK01;
+
+                const auto b_grid_desc_bk00_n_bk01_bk1_permute =
+                    make_naive_tensor_descriptor_packed(make_tuple(BK00, N, BK01, BK1Value));
+
+                const auto b_grid_desc_bk0_n_bk1_permute = transform_tensor_descriptor(
+                    b_grid_desc_bk00_n_bk01_bk1_permute,
+                    make_tuple(make_merge_transform(make_tuple(BK00, BK01)),
+                               make_pass_through_transform(make_tuple(N)),
+                               make_pass_through_transform(BK1Value)),
+                    make_tuple(Sequence<0, 2>{}, Sequence<1>{}, Sequence<3>{}),
+                    make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}));
+
+                return b_grid_desc_bk0_n_bk1_permute;
+            }
         }
     }
 
@@ -572,7 +616,7 @@ struct GridwiseGemm_xdl_cshuffle_v3
         {
             if constexpr(is_same_v<tensor_layout::gemm::RowMajor, ALayout>)
             {
-                a_k_split_offset = blockIdx.z * karg.KRead;
+                a_k_split_offset = blockIdx.z * karg.KRead / APackedSize;
             }
             else if constexpr(is_same_v<tensor_layout::gemm::ColumnMajor, ALayout>)
             {
@@ -585,7 +629,15 @@ struct GridwiseGemm_xdl_cshuffle_v3
             }
             else if constexpr(is_same_v<tensor_layout::gemm::ColumnMajor, BLayout>)
             {
-                b_k_split_offset = blockIdx.z * karg.KRead;
+                if constexpr(!PermuteB)
+                {
+                    b_k_split_offset = blockIdx.z * karg.KRead / BPackedSize;
+                }
+                else
+                {
+                    const int k0_offset = karg.KRead * karg.N;
+                    b_k_split_offset    = blockIdx.z * k0_offset / BPackedSize;
+                }
             }
 
             if(blockIdx.z < static_cast<uint32_t>(karg.KBatch - 1))
@@ -625,9 +677,8 @@ struct GridwiseGemm_xdl_cshuffle_v3
         // in some cases.
         else if constexpr(is_same<tensor_layout::gemm::RowMajor, ALayout>::value)
         {
-            constexpr auto MLdsLayer        = 32 * 4 / KPerBlock / sizeof(ADataType) < 1
-                                                  ? 1
-                                                  : 32 * 4 / KPerBlock / sizeof(ADataType);
+            constexpr index_t LdsSize       = 32 * 4 / KPerBlock / sizeof(ADataType) / APackedSize;
+            constexpr auto MLdsLayer        = LdsSize < 1 ? 1 : LdsSize;
             constexpr auto a_lds_block_desc = make_naive_tensor_descriptor(
                 make_tuple(
                     AK0Number * Number<MLdsLayer>{}, Number<MPerBlock / MLdsLayer>{}, AK1Number),
@@ -761,10 +812,8 @@ struct GridwiseGemm_xdl_cshuffle_v3
         else if constexpr(is_same<tensor_layout::gemm::ColumnMajor, BLayout>::value)
         {
             // NLdsLayer * K0 as logical Bank
-            constexpr auto NLdsLayer = 32 * 4 / KPerBlock / sizeof(BDataType) < 1
-                                           ? 1
-                                           : 32 * 4 / KPerBlock / sizeof(BDataType);
-            ;
+            constexpr index_t LdsSize       = 32 * 4 / KPerBlock / sizeof(BDataType) / BPackedSize;
+            constexpr index_t NLdsLayer     = LdsSize < 1 ? 1 : LdsSize;
             constexpr auto b_lds_block_desc = make_naive_tensor_descriptor(
                 make_tuple(
                     BK0Number * Number<NLdsLayer>{}, Number<NPerBlock / NLdsLayer>{}, BK1Number),
@@ -946,8 +995,8 @@ struct GridwiseGemm_xdl_cshuffle_v3
         constexpr auto c_block_size =
             c_shuffle_block_desc_mblock_mperblock_nblock_nperblock.GetElementSpaceSize();
 
-        return math::max((a_block_space_size_aligned * sizeof(ADataType) +
-                          b_block_space_size_aligned * sizeof(BDataType)),
+        return math::max((a_block_space_size_aligned * sizeof(ADataType) / APackedSize +
+                          b_block_space_size_aligned * sizeof(BDataType) / BPackedSize),
                          c_block_size * sizeof(CShuffleDataType));
     }
 
@@ -1312,8 +1361,9 @@ struct GridwiseGemm_xdl_cshuffle_v3
             static_cast<ADataType*>(p_shared), a_block_desc_ak0_m_ak1.GetElementSpaceSize());
 
         auto b_block_buf = make_dynamic_buffer<AddressSpaceEnum::Lds>(
-            static_cast<BDataType*>(p_shared) +
-                a_block_space_size_aligned * sizeof(ADataType) / sizeof(BDataType),
+            reinterpret_cast<BDataType*>(static_cast<char*>(p_shared) + a_block_space_size_aligned *
+                                                                            sizeof(ADataType) /
+                                                                            APackedSize),
             b_block_desc_bk0_n_bk1.GetElementSpaceSize());
 
         constexpr auto a_block_slice_copy_step = make_multi_index(KPerBlock / AK1Number, 0, 0);
@@ -1706,16 +1756,16 @@ struct GridwiseGemm_xdl_cshuffle_v3
             static_cast<ADataType*>(p_shared_0), a_block_desc_ak0_m_ak1.GetElementSpaceSize());
 
         auto b_block_buf_ping = make_dynamic_buffer<AddressSpaceEnum::Lds>(
-            static_cast<BDataType*>(p_shared_0) +
-                a_block_space_size_aligned * sizeof(ADataType) / sizeof(BDataType),
+            bit_cast<BDataType*>(static_cast<char*>(p_shared_0) +
+                                 a_block_space_size_aligned * sizeof(ADataType)),
             b_block_desc_bk0_n_bk1.GetElementSpaceSize());
 
         auto a_block_buf_pong = make_dynamic_buffer<AddressSpaceEnum::Lds>(
             static_cast<ADataType*>(p_shared_1), a_block_desc_ak0_m_ak1.GetElementSpaceSize());
 
         auto b_block_buf_pong = make_dynamic_buffer<AddressSpaceEnum::Lds>(
-            static_cast<BDataType*>(p_shared_1) +
-                a_block_space_size_aligned * sizeof(ADataType) / sizeof(BDataType),
+            bit_cast<BDataType*>(bit_cast<char*>(p_shared_1) +
+                                 a_block_space_size_aligned * sizeof(ADataType)),
             b_block_desc_bk0_n_bk1.GetElementSpaceSize());
 
         auto a_block_bufs = make_tuple(a_block_buf_ping, a_block_buf_pong);

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

@@ -1007,6 +1007,13 @@ struct ThreadwiseTensorSliceTransfer_v4
 
     using SrcCoordStep = decltype(make_tensor_coordinate_step(SrcDesc{}, Index{}));
 
+    static constexpr index_t PackedSize = []() {
+        if constexpr(is_same_v<remove_cvref_t<SrcData>, pk_i4_t>)
+            return 2;
+        else
+            return 1;
+    }();
+
     __device__ constexpr ThreadwiseTensorSliceTransfer_v4(const Index& src_ref_idx)
         : src_ref_coord_(make_tensor_coordinate(SrcDesc{}, src_ref_idx))
     {
@@ -1015,6 +1022,11 @@ struct ThreadwiseTensorSliceTransfer_v4
 
         static_assert(SliceLengths::At(Number<SrcVectorDim>{}) % SrcScalarPerVector == 0,
                       "wrong! Not divisible");
+
+        if constexpr(is_same_v<remove_cvref_t<SrcData>, pk_i4_t>)
+        {
+            static_assert(SrcScalarPerVector % PackedSize == 0, "pk data N cannot be 1");
+        }
     }
 
     template <typename SrcRefToOriginDisplacement,
@@ -1109,7 +1121,7 @@ struct ThreadwiseTensorSliceTransfer_v4
 
             move_tensor_coordinate(src_desc, src_data_coord, src_ref_to_data_disp_coord_step);
 
-            vector_type_maker_t<SrcData, SrcScalarPerVector> src_tmp_vector;
+            vector_type_maker_t<SrcData, SrcScalarPerVector / PackedSize> src_tmp_vector;
 
             using src_vector_t = typename decltype(src_tmp_vector)::type;
 
@@ -1120,7 +1132,8 @@ struct ThreadwiseTensorSliceTransfer_v4
             if constexpr(SrcBuffer::IsDynamicBuffer())
             {
                 src_tmp_vector.template AsType<src_vector_t>()(Number<0>{}) =
-                    src_buf.template Get<src_vector_t>(src_data_coord.GetOffset(), is_src_valid);
+                    src_buf.template Get<src_vector_t>(src_data_coord.GetOffset() / PackedSize,
+                                                       is_src_valid);
             }
             else if constexpr(SrcBuffer::IsStaticBuffer())
             {
@@ -1133,9 +1146,36 @@ struct ThreadwiseTensorSliceTransfer_v4
                 });
             }
 
-            if constexpr(is_same<remove_cvref_t<SrcData>, f8_t>::value &&
-                         is_same<remove_cvref_t<DstData>, half_t>::value &&
-                         SrcScalarPerVector % 2 == 0)
+            if constexpr(is_same<remove_cvref_t<SrcData>, pk_i4_t>::value)
+            {
+                // copy data from src_tmp_vector to dst_tmp_vector (data cast data from SrcData to
+                // DstData)
+                vector_type_maker_t<DstData, SrcScalarPerVector> dst_tmp_vector;
+
+                constexpr index_t pack_size = 8;
+
+                static_assert(SrcScalarPerVector % pack_size == 0, "");
+
+                using src_v_t = typename vector_type_maker_t<SrcData, pack_size / PackedSize>::type;
+                using dst_v_t = typename vector_type_maker_t<DstData, pack_size>::type;
+
+                static_for<0, SrcScalarPerVector / pack_size, 1>{}([&](auto i) {
+                    ck::tensor_operation::element_wise::PassThroughPack8{}(
+                        dst_tmp_vector.template AsType<dst_v_t>()(i),
+                        src_tmp_vector.template AsType<src_v_t>()[i]);
+                });
+
+                // copy data from dst_tmp_vector into dst_buf
+                static_for<0, SrcScalarPerVector, 1>{}([&](auto i) {
+                    constexpr index_t dst_offset = dst_desc.CalculateOffset(
+                        dst_origin_idx + data_to_origin_disp_idx + i * src_scalar_step_in_vector);
+
+                    dst_buf(Number<dst_offset>{}) = dst_tmp_vector.template AsType<DstData>()[i];
+                });
+            }
+            else if constexpr(is_same<remove_cvref_t<SrcData>, f8_t>::value &&
+                              is_same<remove_cvref_t<DstData>, half_t>::value &&
+                              SrcScalarPerVector % 2 == 0)
             {
                 // copy data from src_tmp_vector to dst_tmp_vector (data cast data from SrcData to
                 // DstData)

include/ck/tensor_operation/gpu/thread/threadwise_tensor_slice_transfer_v3r1.hpp

@@ -31,8 +31,8 @@ template <typename SliceLengths,
           typename DstDimAccessOrder,
           index_t SrcVectorDim,
           index_t DstVectorDim,
-          index_t SrcScalarPerVector,
-          index_t DstScalarPerVector,
+          index_t SrcScalarPerVector_,
+          index_t DstScalarPerVector_,
           index_t SrcScalarStrideInVector,
           index_t DstScalarStrideInVector,
           bool SrcResetCoordinateAfterRun, // control whether to move back src coordinate after each
@@ -55,6 +55,16 @@ struct ThreadwiseTensorSliceTransfer_v3r1
 
     static constexpr auto I0 = Number<0>{};
 
+    static constexpr index_t PackedSize = []() {
+        if constexpr(is_same_v<remove_cvref_t<SrcData>, pk_i4_t>)
+            return 2;
+        else
+            return 1;
+    }();
+
+    static constexpr auto SrcScalarPerVector = Number<SrcScalarPerVector_ / PackedSize>{};
+    static constexpr auto DstScalarPerVector = Number<DstScalarPerVector_ / PackedSize>{};
+
     __device__ constexpr ThreadwiseTensorSliceTransfer_v3r1(
         const SrcDesc& src_desc,
         const Index& src_slice_origin,
@@ -67,6 +77,17 @@ struct ThreadwiseTensorSliceTransfer_v3r1
           src_element_op_(src_element_op),
           dst_element_op_(dst_element_op)
     {
+        if constexpr(is_same_v<remove_cvref_t<SrcData>, pk_i4_t>)
+        {
+            static_assert(is_same_v<remove_cvref_t<SrcData>, remove_cvref_t<DstData>>,
+                          "SrcData != DstData");
+
+            static_assert(
+                SrcScalarPerVector_ % PackedSize == 0 && DstScalarPerVector_ % PackedSize == 0,
+                "SrcScalarPerVector_ and DstScalarPerVector_ cannot be 1 for packed data type");
+
+            static_assert(SrcVectorDim == DstVectorDim, "pk_i4_t does not support transpose");
+        }
     }
 
     __device__ void SetSrcSliceOrigin(const SrcDesc& src_desc, const Index& src_slice_origin_idx)
@@ -95,11 +116,11 @@ struct ThreadwiseTensorSliceTransfer_v3r1
         // scalar per access on each dim
         // TODO: don't use lambda_scalar_per_access
         constexpr auto src_scalar_per_access = generate_sequence(
-            detail::lambda_scalar_per_access<SrcVectorDim, SrcScalarPerVector>{}, Number<nDim>{});
+            detail::lambda_scalar_per_access<SrcVectorDim, SrcScalarPerVector_>{}, Number<nDim>{});
 
         constexpr auto src_access_lengths = SliceLengths{} / src_scalar_per_access;
 
-        static_assert(SliceLengths::At(SrcVectorDim) % SrcScalarPerVector == 0,
+        static_assert(SliceLengths::At(SrcVectorDim) % (SrcScalarPerVector_) == 0,
                       "SliceLengths[SrcVectorDim] must be divisible by SrcScalarPerVector");
 
         constexpr auto src_dim_access_order = SrcDimAccessOrder{};
@@ -180,9 +201,6 @@ struct ThreadwiseTensorSliceTransfer_v3r1
             using src_vector_type = vector_type_maker_t<SrcData, SrcScalarPerVector>;
             using src_vector_t    = typename src_vector_type::type;
 
-            auto src_vector_container =
-                src_vector_type{src_buf.template Get<src_vector_t>(src_coord_.GetOffset(), true)};
-
             using dst_vector_type = vector_type_maker_t<DstData, SrcScalarPerVector>;
             using dst_vector_t    = typename dst_vector_type::type;
             dst_vector_type op_r_v;
@@ -193,17 +211,22 @@ struct ThreadwiseTensorSliceTransfer_v3r1
                     if constexpr(decltype(src_element_op_)::is_pack8_invocable)
                         return math::min(8, SrcScalarPerVector);
                 }
-                if constexpr(is_detected<is_pack4_invocable_t, decltype(src_element_op_)>::value)
+                else if constexpr(is_detected<is_pack4_invocable_t,
+                                              decltype(src_element_op_)>::value)
                 {
                     if constexpr(decltype(src_element_op_)::is_pack4_invocable)
                         return math::min(4, SrcScalarPerVector);
                 }
-                if constexpr(is_detected<is_pack2_invocable_t, decltype(src_element_op_)>::value)
+                else if constexpr(is_detected<is_pack2_invocable_t,
+                                              decltype(src_element_op_)>::value)
                 {
                     if constexpr(decltype(src_element_op_)::is_pack2_invocable)
                         return math::min(2, SrcScalarPerVector);
                 }
-                return 1;
+                else
+                {
+                    return 1;
+                }
             };
 
             constexpr index_t elem_op_vec_len = get_elem_op_vec_len();
@@ -211,6 +234,9 @@ struct ThreadwiseTensorSliceTransfer_v3r1
             using src_elem_op_vec_t = typename vector_type<SrcData, elem_op_vec_len>::type;
             using dst_elem_op_vec_t = typename vector_type<DstData, elem_op_vec_len>::type;
 
+            auto src_vector_container = src_vector_type{
+                src_buf.template Get<src_vector_t>(src_coord_.GetOffset() / PackedSize, true)};
+
             static_for<0, SrcScalarPerVector / elem_op_vec_len, 1>{}([&](auto idx) {
                 // apply the src elementwise op and convert to DstData under the hood if needed
                 src_element_op_(op_r_v.template AsType<dst_elem_op_vec_t>()(idx),
@@ -276,10 +302,9 @@ struct ThreadwiseTensorSliceTransfer_v3r1
             dst_thread_scratch_(idx) = src_thread_scratch_tuple_[thread_scratch_id][idx];
         });
 #else
-
         // OOB Check
         constexpr auto src_scalar_per_access = generate_sequence(
-            detail::lambda_scalar_per_access<SrcVectorDim, SrcScalarPerVector>{}, Number<nDim>{});
+            detail::lambda_scalar_per_access<SrcVectorDim, SrcScalarPerVector_>{}, Number<nDim>{});
 
         constexpr auto src_access_lengths = SliceLengths{} / src_scalar_per_access;
 
@@ -350,6 +375,8 @@ struct ThreadwiseTensorSliceTransfer_v3r1
                       (is_same<f8_t, remove_cvref_t<DstData>>::value &&
                        SrcScalarPerVector % 4 == 0 && DstScalarPerVector % 4 == 0)))
         {
+            static_assert(is_same_v<remove_cvref_t<SrcData>, pk_i4_t>,
+                          "transpose is not allowed for pk_i4_t");
             // each transpose does
             // DstScalarPerVector # of src vectors in src_thread_scratch_
             // SrcScalarPerVector # of dst vectors in dst_thread_scratch_
@@ -410,7 +437,12 @@ struct ThreadwiseTensorSliceTransfer_v3r1
         }
         else
         {
-            static_ford<SliceLengths>{}([&](auto idx) {
+            constexpr auto packed_per_access = generate_sequence(
+                detail::lambda_scalar_per_access<SrcVectorDim, PackedSize>{}, Number<nDim>{});
+
+            constexpr auto packed_access_lengths = SliceLengths{} / packed_per_access;
+
+            static_ford<decltype(packed_access_lengths)>{}([&](auto idx) {
                 dst_thread_scratch_(idx) = src_thread_scratch_tuple_[thread_scratch_id][idx];
             });
         }
@@ -438,7 +470,7 @@ struct ThreadwiseTensorSliceTransfer_v3r1
         // src scalar per access on each dim
         // TODO: don't use this
         constexpr auto dst_scalar_per_access = generate_sequence(
-            detail::lambda_scalar_per_access<DstVectorDim, DstScalarPerVector>{}, Number<nDim>{});
+            detail::lambda_scalar_per_access<DstVectorDim, DstScalarPerVector_>{}, Number<nDim>{});
 
         constexpr auto dst_access_lengths = SliceLengths{} / dst_scalar_per_access;
 
@@ -526,13 +558,11 @@ struct ThreadwiseTensorSliceTransfer_v3r1
 
                 // apply DstElementwiseOperation
                 dst_element_op_(dst_v, dst_vector_container.template AsType<DstData>()[i]);
-
-                dst_vector_container.template AsType<DstData>()(i) = dst_v;
             });
 
             // copy data from dst_vector_container to dst_buf
             dst_buf.template Set<dst_vector_t>(
-                dst_coord_.GetOffset(),
+                dst_coord_.GetOffset() / PackedSize,
                 is_dst_valid,
                 dst_vector_container.template AsType<dst_vector_t>()[I0]);
 
@@ -586,7 +616,7 @@ struct ThreadwiseTensorSliceTransfer_v3r1
         // scalar per access on each dim
         // TODO: don't use lambda_scalar_per_access
         constexpr auto src_scalar_per_access = generate_sequence(
-            detail::lambda_scalar_per_access<SrcVectorDim, SrcScalarPerVector>{}, Number<nDim>{});
+            detail::lambda_scalar_per_access<SrcVectorDim, SrcScalarPerVector_>{}, Number<nDim>{});
 
         constexpr auto src_access_lengths = SliceLengths{} / src_scalar_per_access;
 
@@ -644,7 +674,7 @@ struct ThreadwiseTensorSliceTransfer_v3r1
         // scalar per access on each dim
         // TODO: don't use lambda_scalar_per_access
         constexpr auto dst_scalar_per_access = generate_sequence(
-            detail::lambda_scalar_per_access<DstVectorDim, DstScalarPerVector>{}, Number<nDim>{});
+            detail::lambda_scalar_per_access<DstVectorDim, DstScalarPerVector_>{}, Number<nDim>{});
 
         constexpr auto dst_access_lengths = SliceLengths{} / dst_scalar_per_access;
 
@@ -730,7 +760,7 @@ struct ThreadwiseTensorSliceTransfer_v3r1
     __device__ static constexpr auto GetSrcThreadScratchDescriptor()
     {
         constexpr auto src_scalar_per_access = generate_sequence(
-            detail::lambda_scalar_per_access<SrcVectorDim, SrcScalarPerVector>{}, Number<nDim>{});
+            detail::lambda_scalar_per_access<SrcVectorDim, SrcScalarPerVector_>{}, Number<nDim>{});
 
         constexpr auto src_access_lengths = SliceLengths{} / src_scalar_per_access;
 
@@ -779,7 +809,7 @@ struct ThreadwiseTensorSliceTransfer_v3r1
     __device__ static constexpr auto GetSrcOOBThreadScratchDescriptor()
     {
         constexpr auto src_scalar_per_access = generate_sequence(
-            detail::lambda_scalar_per_access<SrcVectorDim, SrcScalarPerVector>{}, Number<nDim>{});
+            detail::lambda_scalar_per_access<SrcVectorDim, SrcScalarPerVector_>{}, Number<nDim>{});
 
         constexpr auto src_access_lengths = SliceLengths{} / src_scalar_per_access;
 
@@ -790,7 +820,7 @@ struct ThreadwiseTensorSliceTransfer_v3r1
     {
         // 1st stage of transforms
         constexpr auto dst_scalar_per_access = generate_sequence(
-            detail::lambda_scalar_per_access<DstVectorDim, DstScalarPerVector>{}, Number<nDim>{});
+            detail::lambda_scalar_per_access<DstVectorDim, DstScalarPerVector_>{}, Number<nDim>{});
 
         constexpr auto dst_access_lengths = SliceLengths{} / dst_scalar_per_access;