Add FP64 XDL GEMM built-in function (#199)

* add intrin_mfma_f64_16x16x4f64

* add example

* gemm reference add double data type

* chang init data

* fix M N PerXdlops

* fix ifdef

* add comparsion config

* add conv fwd example

* format log out

* change rc matrix egister layout

* reorganize example

* reorganize example 2

* format,because merge develop

* fix call impl adding acc data type

* lost ;

* add compiler warning

* change example tunning parameters

* add test for fp64

* add instance

* add test/gemm/gemm_fp64.cpp

* fix get name issue

* remove some tunning parameter

* fix conflict

* format

* use integer value for GEMM test

* add acc data type

* remove typeid because fp16

* fix streamconfig etc bug from merging develop

* format

* remove test_gemm_xdl_fp64

* add AccDataType

* AccDataType problem

Co-authored-by: qinletao <letaoqin@amd.com>
Co-authored-by: Chao Liu <chao.liu2@amd.com>

[ROCm/composable_kernel commit: 3e6c2610ae]

example/01_gemm/CMakeLists.txt

@@ -4,3 +4,4 @@ add_example_executable(example_gemm_dl_int8 gemm_dl_int8.cpp)
 add_example_executable(example_gemm_xdl_fp16 gemm_xdl_fp16.cpp)
 add_example_executable(example_gemm_xdl_bf16 gemm_xdl_bf16.cpp)
 add_example_executable(example_gemm_xdl_int8 gemm_xdl_int8.cpp)
+add_example_executable(example_gemm_xdl_fp64 gemm_xdl_fp64.cpp)

example/01_gemm/gemm_dl_fp16.cpp

@@ -52,7 +52,7 @@ using DeviceGemmInstance = ck::tensor_operation::device::
 // clang-format on
 
 using ReferenceGemmInstance = ck::tensor_operation::host::
-    ReferenceGemm<ADataType, BDataType, CDataType, AElementOp, BElementOp, CElementOp>;
+    ReferenceGemm<ADataType, BDataType, CDataType, AccDataType, AElementOp, BElementOp, CElementOp>;
 
 int main(int argc, char* argv[])
 {

example/01_gemm/gemm_dl_fp32.cpp

@@ -51,7 +51,7 @@ using DeviceGemmInstance = ck::tensor_operation::device::
 // clang-format on
 
 using ReferenceGemmInstance = ck::tensor_operation::host::
-    ReferenceGemm<ADataType, BDataType, CDataType, AElementOp, BElementOp, CElementOp>;
+    ReferenceGemm<ADataType, BDataType, CDataType, AccDataType, AElementOp, BElementOp, CElementOp>;
 
 int main(int argc, char* argv[])
 {

example/01_gemm/gemm_dl_int8.cpp

@@ -49,7 +49,7 @@ using DeviceGemmInstance = ck::tensor_operation::device::
 // clang-format on
 
 using ReferenceGemmInstance = ck::tensor_operation::host::
-    ReferenceGemm<ADataType, BDataType, CDataType, AElementOp, BElementOp, CElementOp>;
+    ReferenceGemm<ADataType, BDataType, CDataType, AccDataType, AElementOp, BElementOp, CElementOp>;
 
 int main(int argc, char* argv[])
 {

example/01_gemm/gemm_xdl_bf16.cpp

@@ -84,7 +84,7 @@ using DeviceGemmInstance = ck::tensor_operation::device::DeviceGemm_Xdl_CShuffle
 // clang-format on
 
 using ReferenceGemmInstance = ck::tensor_operation::host::
-    ReferenceGemm<float, float, float, PassThrough, PassThrough, PassThrough>;
+    ReferenceGemm<float, float, float, float, PassThrough, PassThrough, PassThrough>;
 
 int main(int argc, char* argv[])
 {

example/01_gemm/gemm_xdl_fp16.cpp

@@ -52,7 +52,7 @@ using DeviceGemmInstance = ck::tensor_operation::device::DeviceGemm_Xdl_CShuffle
 // clang-format on
 
 using ReferenceGemmInstance = ck::tensor_operation::host::
-    ReferenceGemm<ADataType, BDataType, CDataType, AElementOp, BElementOp, CElementOp>;
+    ReferenceGemm<ADataType, BDataType, CDataType, AccDataType, AElementOp, BElementOp, CElementOp>;
 
 int main(int argc, char* argv[])
 {

example/01_gemm/gemm_xdl_fp64.cpp

@@ -0,0 +1,240 @@
+#include <iostream>
+#include <numeric>
+#include <initializer_list>
+#include <cstdlib>
+#include <stdlib.h>
+#include <half.hpp>
+
+#include "check_err.hpp"
+#include "config.hpp"
+#include "device.hpp"
+#include "host_tensor.hpp"
+#include "host_tensor_generator.hpp"
+#include "device_tensor.hpp"
+#include "device_gemm_xdl.hpp"
+#include "device_gemm_xdl_cshuffle.hpp"
+#include "element_wise_operation.hpp"
+#include "reference_gemm.hpp"
+#include "gemm_specialization.hpp"
+
+template <ck::index_t... Is>
+using S = ck::Sequence<Is...>;
+
+using F64 = double;
+using F32 = float;
+using F16 = ck::half_t;
+
+using ADataType   = double;
+using BDataType   = double;
+using CDataType   = double;
+using AccDataType = double;
+
+using Row = ck::tensor_layout::gemm::RowMajor;
+using Col = ck::tensor_layout::gemm::ColumnMajor;
+
+using PassThrough = ck::tensor_operation::element_wise::PassThrough;
+
+using ALayout = ck::tensor_layout::gemm::RowMajor;
+using BLayout = ck::tensor_layout::gemm::ColumnMajor;
+using CLayout = ck::tensor_layout::gemm::RowMajor;
+
+using AElementOp = ck::tensor_operation::element_wise::PassThrough;
+using BElementOp = ck::tensor_operation::element_wise::PassThrough;
+using CElementOp = ck::tensor_operation::element_wise::PassThrough;
+
+static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
+
+// clang-format off
+using DeviceGemmInstance = ck::tensor_operation::device::DeviceGemmXdl
+//##########| AData| BData| CData| AccData| ALayout| BLayout| CLayout|           A|           B|           C|          GEMM| Block|  MPer|  NPer| K0Per| K1| MPer| NPer| MXdl| NXdl|  ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds|  BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CThreadTransfer| CThreadTransfer|
+//##########|  Type|  Type|  Type|    Type|        |        |        | Elementwise| Elementwise| Elementwise|Spacialization|  Size| Block| Block| Block|   |  XDL|  XDL|  Per|  Per|   ThreadCluster|  ThreadCluster| SrcAccessOrder|   SrcVectorDim|      SrcScalar|      DstScalar| AddExtraM|   ThreadCluster|  ThreadCluster| SrcAccessOrder|  SrcVectorDim|      SrcScalar|      DstScalar| AddExtraN| SrcDstVectorDim|       DstScalar|
+//##########|      |      |      |        |        |        |        |   Operation|   Operation|   Operation|              |      |      |      |      |   |     |     | Wave| Wave| Lengths_K0_M_K1|   ArrangeOrder|               |               |      PerVector|   PerVector_K1|          | Lengths_K0_N_K1|   ArrangeOrder|               |              |      PerVector|   PerVector_K1|          |                |       PerVector|
+//##########|      |      |      |        |        |        |        |            |            |            |              |      |      |      |      |   |     |     |     |     |                |               |               |               |               |               |          |                |               |               |              |               |               |          |                |                |
+#if 0
+             <  F64,   F64,   F64,     F64,     Row,     Col,     Row, PassThrough, PassThrough, PassThrough,   GemmDefault,   64,    32,    32,     4,  1,   16,   16,    2,    2,     S<4, 16, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              1,              1,      true,     S<4, 16, 1>,     S<1, 0, 2>,     S<1, 0, 2>,             2,              1,              1,      true,               7,               1>;
+#else
+             <  F64,   F64,   F64,     F64,     Row,     Col,     Row, PassThrough, PassThrough, PassThrough,   GemmDefault,  256,   128,   128,     4,  2,   16,   16,    4,    4,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              2,              2,      true,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,             2,              2,              2,      true,               7,               1>;
+#endif
+    // clang-format on
+
+    using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
+                                                                            BDataType,
+                                                                            CDataType,
+                                                                            AccDataType,
+                                                                            AElementOp,
+                                                                            BElementOp,
+                                                                            CElementOp>;
+
+template <typename DataType>
+std::ostream& show_2d_matrix(std::ostream& os, Tensor<DataType>& matrix)
+{
+    os << "[" << std::endl;
+    for(int x = 0; x < matrix.mDesc.GetLengths()[0]; x++)
+    {
+        os << "[";
+        for(int y = 0; y < matrix.mDesc.GetLengths()[1]; y++)
+        {
+            os << std::setw(4) << static_cast<float>(matrix(x, y));
+        }
+        os << "]" << std::endl;
+    }
+    os << "]";
+    return os;
+}
+
+int main(int argc, char* argv[])
+{
+    bool do_verification = 0;
+    int init_method      = 0;
+    bool time_kernel     = false;
+
+    // GEMM shape
+    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;
+
+    if(argc == 4)
+    {
+        do_verification = std::stoi(argv[1]);
+        init_method     = std::stoi(argv[2]);
+        time_kernel     = std::stoi(argv[3]);
+    }
+    else if(argc == 10)
+    {
+        do_verification = std::stoi(argv[1]);
+        init_method     = std::stoi(argv[2]);
+        time_kernel     = std::stoi(argv[3]);
+
+        M = std::stoi(argv[4]);
+        N = std::stoi(argv[5]);
+        K = std::stoi(argv[6]);
+
+        StrideA = std::stoi(argv[7]);
+        StrideB = std::stoi(argv[8]);
+        StrideC = std::stoi(argv[9]);
+    }
+    else
+    {
+        printf("arg1: verification (0=no, 1=yes)\n");
+        printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
+        printf("arg3: run kernel # of times (>1)\n");
+        printf("arg4 to 9: M (256x), N(128x), K(32x), StrideA, StrideB, StrideC\n");
+        exit(0);
+    }
+
+    auto f_host_tensor_descriptor =
+        [](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
+            if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value)
+            {
+                return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
+                                            std::vector<std::size_t>({stride, 1}));
+            }
+            else
+            {
+                return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
+                                            std::vector<std::size_t>({1, stride}));
+            }
+        };
+
+    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<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 << "data type: " << typeid(ADataType{}).name() << std::endl;
+    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;
+
+    switch(init_method)
+    {
+    case 0: break;
+    case 1:
+        a_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
+        b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
+        break;
+    case 2:
+        a_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
+        b_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
+        break;
+    default:
+        a_m_k.GenerateTensorValue(GeneratorTensor_1<ADataType>{1});
+        b_k_n.GenerateTensorValue(GeneratorTensor_1<BDataType>{1});
+    }
+
+    DeviceMem a_m_k_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpace());
+    DeviceMem b_k_n_device_buf(sizeof(BDataType) * b_k_n.mDesc.GetElementSpace());
+    DeviceMem c_m_n_device_buf(sizeof(CDataType) * c_m_n_device_result.mDesc.GetElementSpace());
+
+    a_m_k_device_buf.ToDevice(a_m_k.mData.data());
+    b_k_n_device_buf.ToDevice(b_k_n.mData.data());
+
+    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(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,
+                                      a_element_op,
+                                      b_element_op,
+                                      c_element_op);
+
+    if(!gemm.IsSupportedArgument(argument))
+    {
+        throw std::runtime_error(
+            "wrong! device_gemm with the specified compilation parameters does "
+            "not support this GEMM problem");
+    }
+
+    float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
+
+    std::size_t flop = std::size_t(2) * 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;
+
+    c_m_n_device_buf.FromDevice(c_m_n_device_result.mData.data());
+
+    if(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, a_element_op, b_element_op, c_element_op);
+
+        ref_invoker.Run(ref_argument);
+
+#if 0
+        {
+            show_2d_matrix(std::cout << "a : ", a_m_k) << std::endl;
+            show_2d_matrix(std::cout << "b: ", b_k_n) << std::endl;
+            show_2d_matrix(std::cout << "c_device: ", c_m_n_device_result) << std::endl;
+            show_2d_matrix(std::cout << "c_host  :", c_m_n_host_result) << std::endl;
+        }
+#endif
+        ck::utils::check_err(c_m_n_device_result.mData, c_m_n_host_result.mData);
+    }
+
+    return 0;
+}

example/01_gemm/gemm_xdl_int8.cpp

@@ -78,8 +78,13 @@ using DeviceGemmInstance = ck::tensor_operation::device::DeviceGemm_Xdl_CShuffle
      16>;                        // index_t CShuffleBlockTransferScalarPerVector_NPerBlock
 // clang-format on
 
-using ReferenceGemmInstance = ck::tensor_operation::host::
-    ReferenceGemm<ADataType, BDataType, CDataType, PassThrough, PassThrough, PassThrough>;
+using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
+                                                                        BDataType,
+                                                                        CDataType,
+                                                                        AccDataType,
+                                                                        PassThrough,
+                                                                        PassThrough,
+                                                                        PassThrough>;
 
 int main(int argc, char* argv[])
 {

example/09_convnd_fwd/CMakeLists.txt

@@ -1,6 +1,8 @@
 add_example_executable(example_convnd_fwd_xdl_fp32 convnd_fwd_xdl_fp32.cpp)
 add_example_executable(example_convnd_fwd_xdl_int8 convnd_fwd_xdl_int8.cpp)
 add_example_executable(example_convnd_fwd_xdl_fp16 convnd_fwd_xdl_fp16.cpp)
+add_example_executable(example_convnd_fwd_xdl_fp64 convnd_fwd_xdl_fp64.cpp)
+target_link_libraries(example_convnd_fwd_xdl_fp64 PRIVATE conv_util)
 target_link_libraries(example_convnd_fwd_xdl_fp32 PRIVATE conv_util)
 target_link_libraries(example_convnd_fwd_xdl_int8 PRIVATE conv_util)
 target_link_libraries(example_convnd_fwd_xdl_fp16 PRIVATE conv_util)

example/09_convnd_fwd/convnd_fwd_xdl_fp64.cpp

@@ -0,0 +1,344 @@
+#include <cstdlib>
+#include <iostream>
+#include <numeric>
+#include <type_traits>
+
+#include "check_err.hpp"
+#include "config.hpp"
+#include "conv_util.hpp"
+#include "device.hpp"
+#include "device_tensor.hpp"
+#include "device_convnd_fwd_xdl_nhwc_kyxc_nhwk.hpp"
+#include "element_wise_operation.hpp"
+#include "host_tensor.hpp"
+#include "host_tensor_generator.hpp"
+#include "reference_conv_fwd.hpp"
+#include "tensor_layout.hpp"
+
+namespace {
+
+using InDataType  = double;
+using WeiDataType = double;
+using OutDataType = double;
+using AccDataType = double;
+
+template <ck::index_t... Is>
+using S = ck::Sequence<Is...>;
+
+using InElementOp  = ck::tensor_operation::element_wise::PassThrough;
+using WeiElementOp = ck::tensor_operation::element_wise::PassThrough;
+using OutElementOp = ck::tensor_operation::element_wise::PassThrough;
+
+static constexpr auto ConvFwdDefault =
+    ck::tensor_operation::device::ConvolutionForwardSpecialization::Default;
+
+using DeviceConvFwdBasePtr =
+    ck::tensor_operation::device::DeviceConvFwdPtr<InElementOp, WeiElementOp, OutElementOp>;
+
+template <ck::index_t NumDimSpatial>
+using DeviceConvNDFwdInstance = ck::tensor_operation::device::
+    DeviceConvNDFwdXdl_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K<
+        // clang-format off
+        InDataType,         // 
+        WeiDataType,        //
+        OutDataType,        //
+        AccDataType,        // 
+        InElementOp,        // Input Elementwise Operation
+        WeiElementOp,       // Weights Elementwise Operation
+        OutElementOp,       // Output Elementwise Operation
+        ConvFwdDefault,     // ConvForwardSpecialization
+        NumDimSpatial,      // NumDimSpatial
+        256,                // BlockSize
+        128,                // MPerBlock
+        128,                // NPerBlock
+        4,                  // K0PerBlock
+        2,                  // K1
+        16,                 // MPerXDL
+        16,                 // NPerXDL
+        4,                  // MXdlPerWave
+        4,                  // NXdlPerWave
+        S<4, 64, 1>,        // ABlockTransferThreadClusterLengths_K0_M_K1
+        S<1, 0, 2>,         // ABlockTransferThreadClusterArrangeOrder
+        S<1, 0, 2>,         // ABlockTransferSrcAccessOrder
+        2,                  // ABlockTransferSrcVectorDim
+        2,                  // ABlockTransferSrcScalarPerVector
+        2,                  // ABlockTransferDstScalarPerVector_K1
+        true,               // ABlockLdsAddExtraM
+        S<4, 64, 1>,        // BBlockTransferThreadClusterLengths_K0_N_K1
+        S<1, 0, 2>,         // BBlockTransferThreadClusterArrangeOrder
+        S<1, 0, 2>,         // BBlockTransferSrcAccessOrder
+        2,                  // BBlockTransferSrcVectorDim
+        2,                  // BBlockTransferSrcScalarPerVector
+        2,                  // BBlockTransferDstScalarPerVector_K1
+        true,               // BBlockTransferAddExtraN
+        7,                  // CThreadTransferSrcDstVectorDim
+        1>;                 // CThreadTransferDstScalarPerVector
+// clang-format on
+
+template <ck::index_t NumDimSpatial>
+using ReferenceConvNDFwdInstance = ck::tensor_operation::host::ReferenceConvFwd<InDataType,
+                                                                                WeiDataType,
+                                                                                OutDataType,
+                                                                                InElementOp,
+                                                                                WeiElementOp,
+                                                                                OutElementOp,
+                                                                                NumDimSpatial>;
+
+DeviceConvFwdBasePtr get_conv_instance(int num_dim_spatial)
+{
+    switch(num_dim_spatial)
+    {
+    case 3: {
+        return std::make_unique<DeviceConvNDFwdInstance<3>>();
+    }
+    case 2: {
+        return std::make_unique<DeviceConvNDFwdInstance<2>>();
+    }
+    case 1: {
+        return std::make_unique<DeviceConvNDFwdInstance<1>>();
+    }
+    default: {
+        throw std::runtime_error("Unsupported number of spatial dimensions provided!");
+    }
+    }
+}
+
+void print_use_msg()
+{
+    std::cout << "arg1: verification (0=no, 1=yes)\n"
+              << "arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n"
+              << "arg3: run kernel # of times (>1)\n"
+              << "arg4: N spatial dimensions (default 2)\n"
+              << "Following arguments (depending on number of spatial dims):\n"
+              << " N, K, C, \n"
+              << " <filter spatial dimensions>, (ie Y, X for 2D)\n"
+              << " <input image spatial dimensions>, (ie Hi, Wi for 2D)\n"
+              << " <strides>, (ie Sy, Sx for 2D)\n"
+              << " <dilations>, (ie Dy, Dx for 2D)\n"
+              << " <left padding>, (ie LeftPy, LeftPx for 2D)\n"
+              << " <right padding>, (ie RightPy, RightPx for 2D)\n"
+              << std::endl;
+}
+
+ck::utils::conv::ConvParams parse_conv_params(int num_dim_spatial, int argc, char* argv[])
+{
+    // (N, K, C) + num_dim_spatial * 6 (filter, input, strides, dilations, pad left, pad right)
+    int conv_args     = 3 + num_dim_spatial * 6;
+    int cmdline_nargs = conv_args + 5;
+    if(cmdline_nargs != argc)
+    {
+        print_use_msg();
+        exit(0);
+    }
+
+    ck::utils::conv::ConvParams params;
+    int arg_idx = 5;
+
+    params.num_dim_spatial_ = num_dim_spatial;
+    params.N_               = std::stoi(argv[arg_idx++]);
+    params.K_               = std::stoi(argv[arg_idx++]);
+    params.C_               = std::stoi(argv[arg_idx++]);
+
+    params.filter_spatial_lengths_.resize(num_dim_spatial);
+    for(int i = 0; i < num_dim_spatial; ++i)
+    {
+        params.filter_spatial_lengths_[i] = std::stoi(argv[arg_idx++]);
+    }
+    params.input_spatial_lengths_.resize(num_dim_spatial);
+    for(int i = 0; i < num_dim_spatial; ++i)
+    {
+        params.input_spatial_lengths_[i] = std::stoi(argv[arg_idx++]);
+    }
+    params.conv_filter_strides_.resize(num_dim_spatial);
+    for(int i = 0; i < num_dim_spatial; ++i)
+    {
+        params.conv_filter_strides_[i] = std::stoi(argv[arg_idx++]);
+    }
+    params.conv_filter_dilations_.resize(num_dim_spatial);
+    for(int i = 0; i < num_dim_spatial; ++i)
+    {
+        params.conv_filter_dilations_[i] = std::stoi(argv[arg_idx++]);
+    }
+    params.input_left_pads_.resize(num_dim_spatial);
+    for(int i = 0; i < num_dim_spatial; ++i)
+    {
+        params.input_left_pads_[i] = std::stoi(argv[arg_idx++]);
+    }
+    params.input_right_pads_.resize(num_dim_spatial);
+    for(int i = 0; i < num_dim_spatial; ++i)
+    {
+        params.input_right_pads_[i] = std::stoi(argv[arg_idx++]);
+    }
+
+    return params;
+}
+
+} // anonymous namespace
+
+int main(int argc, char* argv[])
+{
+    using namespace ck::utils::conv;
+
+    bool do_verification = 0;
+    int init_method      = 0;
+    bool time_kernel     = false;
+    int num_dim_spatial  = 2;
+
+    ck::utils::conv::ConvParams params;
+
+    if(argc >= 5)
+    {
+        do_verification = std::stoi(argv[1]);
+        init_method     = std::stoi(argv[2]);
+        time_kernel     = std::stoi(argv[3]);
+        num_dim_spatial = std::stoi(argv[4]);
+    }
+
+    if(argc >= 6)
+    {
+        params = parse_conv_params(num_dim_spatial, argc, argv);
+    }
+
+    std::vector<std::size_t> input_dims{static_cast<std::size_t>(params.N_),
+                                        static_cast<std::size_t>(params.C_)};
+    input_dims.insert(std::end(input_dims),
+                      std::begin(params.input_spatial_lengths_),
+                      std::end(params.input_spatial_lengths_));
+
+    std::vector<std::size_t> filter_dims{static_cast<std::size_t>(params.K_),
+                                         static_cast<std::size_t>(params.C_)};
+    filter_dims.insert(std::end(filter_dims),
+                       std::begin(params.filter_spatial_lengths_),
+                       std::end(params.filter_spatial_lengths_));
+
+    const std::vector<ck::index_t>& output_spatial_lengths = params.GetOutputSpatialLengths();
+    std::vector<std::size_t> output_dims{static_cast<std::size_t>(params.N_),
+                                         static_cast<std::size_t>(params.K_)};
+    output_dims.insert(std::end(output_dims),
+                       std::begin(output_spatial_lengths),
+                       std::end(output_spatial_lengths));
+
+    Tensor<InDataType> input(get_input_host_tensor_descriptor(input_dims, num_dim_spatial));
+    Tensor<WeiDataType> weights(get_filters_host_tensor_descriptor(filter_dims, num_dim_spatial));
+    Tensor<OutDataType> host_output(
+        get_output_host_tensor_descriptor(output_dims, num_dim_spatial));
+    Tensor<OutDataType> device_output(
+        get_output_host_tensor_descriptor(output_dims, num_dim_spatial));
+
+    std::cout << "input: " << input.mDesc << std::endl;
+    std::cout << "weights: " << weights.mDesc << std::endl;
+    std::cout << "output: " << host_output.mDesc << std::endl;
+
+    switch(init_method)
+    {
+    case 0: break;
+    case 1:
+        input.GenerateTensorValue(GeneratorTensor_2<InDataType>{-5, 5});
+        weights.GenerateTensorValue(GeneratorTensor_2<WeiDataType>{-5, 5});
+        break;
+    case 2:
+        input.GenerateTensorValue(GeneratorTensor_3<InDataType>{0.0, 1.0});
+        weights.GenerateTensorValue(GeneratorTensor_3<WeiDataType>{-0.5, 0.5});
+        break;
+    default:
+        input.GenerateTensorValue(GeneratorTensor_1<InDataType>{1});
+        weights.GenerateTensorValue(GeneratorTensor_1<WeiDataType>{1});
+    }
+
+    DeviceMem in_device_buf(sizeof(InDataType) * input.mDesc.GetElementSpace());
+    DeviceMem wei_device_buf(sizeof(WeiDataType) * weights.mDesc.GetElementSpace());
+    DeviceMem out_device_buf(sizeof(OutDataType) * device_output.mDesc.GetElementSpace());
+
+    in_device_buf.ToDevice(input.mData.data());
+    wei_device_buf.ToDevice(weights.mData.data());
+
+    // do GEMM
+    auto conv    = get_conv_instance(num_dim_spatial);
+    auto invoker = conv->MakeInvokerPointer();
+    auto argument =
+        conv->MakeArgumentPointer(static_cast<InDataType*>(in_device_buf.GetDeviceBuffer()),
+                                  static_cast<WeiDataType*>(wei_device_buf.GetDeviceBuffer()),
+                                  static_cast<OutDataType*>(out_device_buf.GetDeviceBuffer()),
+                                  params.N_,
+                                  params.K_,
+                                  params.C_,
+                                  params.input_spatial_lengths_,
+                                  params.filter_spatial_lengths_,
+                                  output_spatial_lengths,
+                                  params.conv_filter_strides_,
+                                  params.conv_filter_dilations_,
+                                  params.input_left_pads_,
+                                  params.input_right_pads_,
+                                  InElementOp{},
+                                  WeiElementOp{},
+                                  OutElementOp{});
+
+    if(!conv->IsSupportedArgument(argument.get()))
+    {
+        throw std::runtime_error(
+            "wrong! device_conv with the specified compilation parameters does "
+            "not support this Conv problem");
+    }
+
+    float ave_time = invoker->Run(argument.get(), StreamConfig{nullptr, time_kernel});
+
+    std::size_t flop = get_flops(
+        params.N_, params.C_, params.K_, params.filter_spatial_lengths_, output_spatial_lengths);
+    std::size_t num_btype =
+        get_btype<InDataType, WeiDataType, OutDataType>(params.N_,
+                                                        params.C_,
+                                                        params.K_,
+                                                        params.input_spatial_lengths_,
+                                                        params.filter_spatial_lengths_,
+                                                        output_spatial_lengths);
+
+    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"
+              << std::endl;
+
+    if(do_verification)
+    {
+        auto verify_f = [&input, &weights, &host_output, &params, &out_device_buf, &device_output](
+                            const auto& ref_conv) {
+            auto ref_invoker  = ref_conv.MakeInvoker();
+            auto ref_argument = ref_conv.MakeArgument(input,
+                                                      weights,
+                                                      host_output,
+                                                      params.conv_filter_strides_,
+                                                      params.conv_filter_dilations_,
+                                                      params.input_left_pads_,
+                                                      params.input_right_pads_,
+                                                      InElementOp{},
+                                                      WeiElementOp{},
+                                                      OutElementOp{});
+
+            ref_invoker.Run(ref_argument);
+            out_device_buf.FromDevice(device_output.mData.data());
+            ck::utils::check_err(
+                host_output.mData, device_output.mData, "Error: incorrect results!", 1e-5f, 1e-4f);
+        };
+
+        switch(num_dim_spatial)
+        {
+        case 3: {
+            auto ref_conv = ReferenceConvNDFwdInstance<3>();
+            verify_f(ref_conv);
+            break;
+        }
+        case 2: {
+            auto ref_conv = ReferenceConvNDFwdInstance<2>();
+            verify_f(ref_conv);
+            break;
+        }
+        case 1: {
+            auto ref_conv = ReferenceConvNDFwdInstance<1>();
+            verify_f(ref_conv);
+            break;
+        }
+        default: {
+            throw std::runtime_error("Unsupported number of spatial dimensions provided!");
+        }
+        }
+    }
+}

example/14_gemm_xdl_requant_relu_requant/gemm_xdl_requant_relu_requant_int8.cpp

@@ -100,8 +100,13 @@ using DeviceGemmInstance = ck::tensor_operation::device::DeviceGemm_Xdl_CShuffle
      16>;                        // index_t CShuffleBlockTransferScalarPerVector_NPerBlock>
 // clang-format on
 
-using ReferenceGemmInstance = ck::tensor_operation::host::
-    ReferenceGemm<ADataType, BDataType, CDataType, PassThrough, PassThrough, RequantReluRequant>;
+using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
+                                                                        BDataType,
+                                                                        CDataType,
+                                                                        float,
+                                                                        PassThrough,
+                                                                        PassThrough,
+                                                                        RequantReluRequant>;
 
 int main(int argc, char* argv[])
 {

example/15_grouped_gemm/grouped_gemm_xdl_fp16.cpp

@@ -56,7 +56,7 @@ using DeviceGemmInstance = ck::tensor_operation::device::DeviceGroupedGemmXdl
 // clang-format on
 
 using ReferenceGemmInstance = ck::tensor_operation::host::
-    ReferenceGemm<ADataType, BDataType, CDataType, AElementOp, BElementOp, CElementOp>;
+    ReferenceGemm<ADataType, BDataType, CDataType, AccDataType, AElementOp, BElementOp, CElementOp>;
 
 int main(int argc, char* argv[])
 {

example/16_gemm_reduce/gemm_reduce_xdl_max_fp16.cpp

@@ -32,6 +32,7 @@ using CDataType         = F16;
 using ReduceAccDataType = F32;
 using DDataType         = F64;
 using DPtrsGlobal       = ck::Tuple<DDataType*>;
+using AccDataType       = F32;
 
 using ALayout = ck::tensor_layout::gemm::RowMajor;
 using BLayout = ck::tensor_layout::gemm::ColumnMajor;
@@ -59,7 +60,7 @@ using DeviceGemmReduceInstance = ck::tensor_operation::device::DeviceGemmReduce_
 // clang-format on
 
 using ReferenceGemmInstance = ck::tensor_operation::host::
-    ReferenceGemm<ADataType, BDataType, CDataType, AElementOp, BElementOp, CElementOp>;
+    ReferenceGemm<ADataType, BDataType, CDataType, AccDataType, AElementOp, BElementOp, CElementOp>;
 
 int main(int argc, char* argv[])
 {

example/16_gemm_reduce/gemm_reduce_xdl_sum_squaresum_fp16.cpp

@@ -32,6 +32,7 @@ using CDataType         = F16;
 using ReduceAccDataType = F32;
 using DDataType         = F32;
 using DPtrsGlobal       = ck::Tuple<DDataType*, DDataType*>;
+using AccDataType       = F32;
 
 using ALayout = ck::tensor_layout::gemm::RowMajor;
 using BLayout = ck::tensor_layout::gemm::ColumnMajor;
@@ -68,7 +69,7 @@ using DeviceGemmReduceInstance = ck::tensor_operation::device::DeviceGemmReduce_
 // clang-format on
 
 using ReferenceGemmInstance = ck::tensor_operation::host::
-    ReferenceGemm<ADataType, BDataType, CDataType, AElementOp, BElementOp, CElementOp>;
+    ReferenceGemm<ADataType, BDataType, CDataType, AccDataType, AElementOp, BElementOp, CElementOp>;
 
 int main(int argc, char* argv[])
 {