update example

example/09_convnd_fwd/convnd_fwd_common.hpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
+
+#include <cstdlib>
+#include <iostream>
+#include <numeric>
+#include <type_traits>
+
+#include "ck/ck.hpp"
+#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
+#include "ck/tensor_operation/gpu/device/device_convnd_fwd_nwc_kxc_nwk_xdl.hpp"
+#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
+
+#include "ck/library/utility/check_err.hpp"
+#include "ck/library/utility/convolution_parameter.hpp"
+#include "ck/library/utility/device_memory.hpp"
+#include "ck/library/utility/host_tensor.hpp"
+#include "ck/library/utility/host_tensor_generator.hpp"
+#include "ck/library/reference_tensor_operation/cpu/reference_conv_fwd.hpp"
+
+#include "parse_conv_parameter.hpp"
+
+template <ck::index_t NDimSpatial,
+          typename InDataType,
+          typename WeiDataType,
+          typename OutDataType,
+          typename AccDataType,
+          typename InLayout,
+          typename WeiLayout,
+          typename OutLayout,
+          typename InElementOp,
+          typename WeiElementOp,
+          typename OutElementOp,
+          typename DeviceConvNDFwdInstance,
+          typename ReferenceConvNDFwdInstance>
+int run_conv_fwd(const ck::tensor_operation::device::ConvParams& params,
+                 bool do_verification,
+                 int init_method,
+                 bool time_kernel)
+{
+    auto f_nchw_host_tensor_descriptor =
+        [](ck::index_t n, ck::index_t c, std::vector<ck::index_t> spatial_lengths) {
+            std::vector<std::size_t> nhwc_lengths{static_cast<std::size_t>(n),
+                                                  static_cast<std::size_t>(c)};
+            nhwc_lengths.insert(
+                nhwc_lengths.begin() + 1, spatial_lengths.begin(), spatial_lengths.end());
+
+            return transpose_host_tensor_descriptor_given_new2old(
+                HostTensorDescriptor(nhwc_lengths), std::vector<std::size_t>({0, 3, 1, 2}));
+        };
+
+    Tensor<InDataType> input(
+        f_nchw_host_tensor_descriptor(params.N_, params.C_, params.input_spatial_lengths_));
+    Tensor<InDataType> weights(
+        f_nchw_host_tensor_descriptor(params.K_, params.C_, params.filter_spatial_lengths_));
+    Tensor<InDataType> host_output(
+        f_nchw_host_tensor_descriptor(params.N_, params.K_, params.GetOutputSpatialLengths()));
+    Tensor<InDataType> device_output(
+        f_nchw_host_tensor_descriptor(params.N_, params.K_, params.GetOutputSpatialLengths()));
+
+    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;
+    default:
+        input.GenerateTensorValue(GeneratorTensor_3<InDataType>{0.0, 1.0});
+        weights.GenerateTensorValue(GeneratorTensor_3<WeiDataType>{-0.5, 0.5});
+    }
+
+    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     = DeviceConvNDFwdInstance{};
+    auto invoker  = conv.MakeInvoker();
+    auto argument = conv.MakeArgument(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_,
+                                      params.GetOutputSpatialLengths(),
+                                      params.conv_filter_strides_,
+                                      params.conv_filter_dilations_,
+                                      params.input_left_pads_,
+                                      params.input_right_pads_,
+                                      InElementOp{},
+                                      WeiElementOp{},
+                                      OutElementOp{});
+
+    if(!conv.IsSupportedArgument(argument))
+    {
+        throw std::runtime_error(
+            "wrong! device_conv with the specified compilation parameters does "
+            "not support this Conv problem");
+    }
+
+    float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
+
+    std::size_t flop      = params.GetFlops();
+    std::size_t num_btype = params.GetByte<InDataType, WeiDataType, OutDataType>();
+
+    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, "
+              << conv.GetTypeString() << std::endl;
+
+    if(do_verification)
+    {
+        auto ref_conv = ReferenceConvNDFwdInstance();
+
+        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());
+
+        return ck::utils::check_err(host_output.mData,
+                                    device_output.mData,
+                                    "Error: incorrect results!",
+                                    1e-5f,
+                                    1e-4f)
+                   ? 0
+                   : 1;
+    }
+
+    return 0;
+}

example/09_convnd_fwd/convnd_fwd_xdl_bf16.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
+
+#include <cstdlib>
+#include <iostream>
+#include <numeric>
+#include <type_traits>
+
+#include "ck/ck.hpp"
+#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
+#include "ck/tensor_operation/gpu/device/device_convnd_fwd_nwc_kxc_nwk_xdl.hpp"
+#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
+
+#include "ck/library/utility/check_err.hpp"
+#include "ck/library/utility/convolution_parameter.hpp"
+#include "ck/library/utility/device_memory.hpp"
+#include "ck/library/utility/host_tensor.hpp"
+#include "ck/library/utility/host_tensor_generator.hpp"
+#include "ck/library/reference_tensor_operation/cpu/reference_conv_fwd.hpp"
+
+#include "parse_conv_parameter.hpp"
+
+using InDataType  = ck::bhalf_t;
+using WeiDataType = ck::bhalf_t;
+using OutDataType = ck::bhalf_t;
+using AccDataType = float;
+
+template <ck::index_t... Is>
+using S = ck::Sequence<Is...>;
+
+using InLayout  = ck::tensor_layout::convolution::NHWC;
+using WeiLayout = ck::tensor_layout::convolution::KYXC;
+using OutLayout = ck::tensor_layout::convolution::NHWK;
+
+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;
+
+template <ck::index_t NumDimSpatial>
+using DeviceConvNDFwdInstance = ck::tensor_operation::device::DeviceConvNdFwdNwcKxcNwk_Xdl<
+    InDataType,     //
+    WeiDataType,    //
+    OutDataType,    //
+    AccDataType,    //
+    InElementOp,    // Input Elementwise Operation
+    WeiElementOp,   // Weights Elementwise Operation
+    OutElementOp,   // Output Elementwise Operation
+    ConvFwdDefault, // ConvForwardSpecialization
+    NumDimSpatial,  // NumDimSpatial
+    256,            // BlockSize
+    128,            // MPerBlock
+    256,            // NPerBlock
+    4,              // K0PerBlock
+    8,              // K1
+    32,             // MPerXdl
+    32,             // NPerXdl
+    2,              // MXdlPerWave
+    4,              // NXdlPerWave
+    S<4, 64, 1>,    // ABlockTransferThreadClusterLengths_K0_M_K1
+    S<1, 0, 2>,     // ABlockTransferThreadClusterArrangeOrder
+    S<1, 0, 2>,     // ABlockTransferSrcAccessOrder
+    2,              // ABlockTransferSrcVectorDim
+    8,              // ABlockTransferSrcScalarPerVector
+    8,              // ABlockTransferDstScalarPerVector_K1
+    true,           // ABlockLdsAddExtraM
+    S<4, 64, 1>,    // BBlockTransferThreadClusterLengths_K0_N_K1
+    S<1, 0, 2>,     // BBlockTransferThreadClusterArrangeOrder
+    S<1, 0, 2>,     // BBlockTransferSrcAccessOrder
+    2,              // BBlockTransferSrcVectorDim
+    8,              // BBlockTransferSrcScalarPerVector
+    8,              // BBlockTransferDstScalarPerVector_K1
+    true,           // BBlockLdsAddExtraN
+    7,              // CThreadTransferSrcDstVectorDim
+    1>;             // CThreadTransferDstScalarPerVector
+
+template <ck::index_t NumDimSpatial>
+using ReferenceConvNDFwdInstance = ck::tensor_operation::host::ReferenceConvFwd<InDataType,
+                                                                                WeiDataType,
+                                                                                OutDataType,
+                                                                                InElementOp,
+                                                                                WeiElementOp,
+                                                                                OutElementOp,
+                                                                                NumDimSpatial>;
+
+int main(int argc, char* argv[])
+{
+    bool do_verification = true;
+    int init_method      = 1;
+    bool time_kernel     = true;
+    int num_dim_spatial  = 2;
+
+    ck::tensor_operation::device::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);
+    }
+
+    auto f_nchw_host_tensor_descriptor =
+        [](ck::index_t n, ck::index_t c, std::vector<ck::index_t> spatial_lengths) {
+            std::vector<std::size_t> nhwc_lengths{static_cast<std::size_t>(n),
+                                                  static_cast<std::size_t>(c)};
+            nhwc_lengths.insert(
+                nhwc_lengths.begin() + 1, spatial_lengths.begin(), spatial_lengths.end());
+
+            return transpose_host_tensor_descriptor_given_new2old(
+                HostTensorDescriptor(nhwc_lengths), std::vector<std::size_t>({0, 3, 1, 2}));
+        };
+
+    Tensor<InDataType> input(
+        f_nchw_host_tensor_descriptor(params.N_, params.C_, params.input_spatial_lengths_));
+    Tensor<InDataType> weights(
+        f_nchw_host_tensor_descriptor(params.K_, params.C_, params.filter_spatial_lengths_));
+    Tensor<InDataType> host_output(
+        f_nchw_host_tensor_descriptor(params.N_, params.K_, params.GetOutputSpatialLengths()));
+    Tensor<InDataType> device_output(
+        f_nchw_host_tensor_descriptor(params.N_, params.K_, params.GetOutputSpatialLengths()));
+
+    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;
+    default:
+        input.GenerateTensorValue(GeneratorTensor_3<InDataType>{0.0, 1.0});
+        weights.GenerateTensorValue(GeneratorTensor_3<WeiDataType>{-0.5, 0.5});
+    }
+
+    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 Conv
+    auto conv     = DeviceConvNDFwdInstance<2>{};
+    auto invoker  = conv.MakeInvoker();
+    auto argument = conv.MakeArgument(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_,
+                                      params.GetOutputSpatialLengths(),
+                                      params.conv_filter_strides_,
+                                      params.conv_filter_dilations_,
+                                      params.input_left_pads_,
+                                      params.input_right_pads_,
+                                      InElementOp{},
+                                      WeiElementOp{},
+                                      OutElementOp{});
+
+    if(!conv.IsSupportedArgument(argument))
+    {
+        throw std::runtime_error(
+            "wrong! device_conv with the specified compilation parameters does "
+            "not support this Conv problem");
+    }
+
+    float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
+
+    std::size_t flop      = params.GetFlops();
+    std::size_t num_btype = params.GetByte<InDataType, WeiDataType, OutDataType>();
+
+    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, "
+              << conv.GetTypeString() << 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());
+            return ck::utils::check_err(host_output.mData,
+                                        device_output.mData,
+                                        "Error: incorrect results!",
+                                        1e-5f,
+                                        1e-4f)
+                       ? 0
+                       : 1;
+        };
+
+        switch(num_dim_spatial)
+        {
+        case 1: {
+            auto ref_conv = ReferenceConvNDFwdInstance<1>();
+            return verify_f(ref_conv);
+        }
+        case 2: {
+            auto ref_conv = ReferenceConvNDFwdInstance<2>();
+            return verify_f(ref_conv);
+        }
+        case 3: {
+            auto ref_conv = ReferenceConvNDFwdInstance<3>();
+            return verify_f(ref_conv);
+        }
+        default: {
+            throw std::runtime_error("Unsupported number of spatial dimensions provided!");
+        }
+        }
+    }
+    return 0;
+}

example/09_convnd_fwd/convnd_fwd_xdl_fp16.cpp

 // SPDX-License-Identifier: MIT
 // Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
 
-#include <cstdlib>
-#include <iostream>
-#include <numeric>
-#include <type_traits>
-
-#include "ck/ck.hpp"
-#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
-#include "ck/tensor_operation/gpu/device/device_convnd_fwd_nwc_kxc_nwk_xdl.hpp"
-#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
-
-#include "ck/library/utility/check_err.hpp"
-#include "ck/library/utility/convolution_parameter.hpp"
-#include "ck/library/utility/device_memory.hpp"
-#include "ck/library/utility/host_tensor.hpp"
-#include "ck/library/utility/host_tensor_generator.hpp"
-#include "ck/library/reference_tensor_operation/cpu/reference_conv_fwd.hpp"
-
-#include "parse_conv_parameter.hpp"
+#include "convnd_fwd_common.hpp"
 
 using InDataType  = ck::half_t;
 using WeiDataType = ck::half_t;
@@ -87,151 +70,84 @@ using ReferenceConvNDFwdInstance = ck::tensor_operation::host::ReferenceConvFwd<
 
 int main(int argc, char* argv[])
 {
+    print_helper_msg();
+
     bool do_verification = true;
     int init_method      = 1;
-    bool time_kernel     = true;
+    bool time_kernel     = false;
     int num_dim_spatial  = 2;
 
     ck::tensor_operation::device::ConvParams params;
 
-    if(argc >= 5)
+    if(argc == 1)
+    {
+        // use default
+    }
+    else if(argc == 4)
     {
         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)
+    else
     {
+        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]);
+
         params = parse_conv_params(num_dim_spatial, argc, argv);
     }
 
-    auto f_nchw_host_tensor_descriptor =
-        [](ck::index_t n, ck::index_t c, std::vector<ck::index_t> spatial_lengths) {
-            std::vector<std::size_t> nhwc_lengths{static_cast<std::size_t>(n),
-                                                  static_cast<std::size_t>(c)};
-            nhwc_lengths.insert(
-                nhwc_lengths.begin() + 1, spatial_lengths.begin(), spatial_lengths.end());
-
-            return transpose_host_tensor_descriptor_given_new2old(
-                HostTensorDescriptor(nhwc_lengths), std::vector<std::size_t>({0, 3, 1, 2}));
-        };
-
-    Tensor<InDataType> input(
-        f_nchw_host_tensor_descriptor(params.N_, params.C_, params.input_spatial_lengths_));
-    Tensor<InDataType> weights(
-        f_nchw_host_tensor_descriptor(params.K_, params.C_, params.filter_spatial_lengths_));
-    Tensor<InDataType> host_output(
-        f_nchw_host_tensor_descriptor(params.N_, params.K_, params.GetOutputSpatialLengths()));
-    Tensor<InDataType> device_output(
-        f_nchw_host_tensor_descriptor(params.N_, params.K_, params.GetOutputSpatialLengths()));
-
-    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)
+    if(num_dim_spatial == 1)
     {
-    case 0: break;
-    case 1:
-        input.GenerateTensorValue(GeneratorTensor_2<InDataType>{-5, 5});
-        weights.GenerateTensorValue(GeneratorTensor_2<WeiDataType>{-5, 5});
-        break;
-    default:
-        input.GenerateTensorValue(GeneratorTensor_3<InDataType>{0.0, 1.0});
-        weights.GenerateTensorValue(GeneratorTensor_3<WeiDataType>{-0.5, 0.5});
+        return run_conv_fwd<1,
+                            InDataType,
+                            WeiDataType,
+                            OutDataType,
+                            AccDataType,
+                            InLayout,
+                            WeiLayout,
+                            OutLayout,
+                            InElementOp,
+                            WeiElementOp,
+                            OutElementOp,
+                            DeviceConvNDFwdInstance<1>,
+                            ReferenceConvNDFwdInstance<1>>(
+            params, do_verification, init_method, time_kernel);
     }
-
-    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     = DeviceConvNDFwdInstance<2>{};
-    auto invoker  = conv.MakeInvoker();
-    auto argument = conv.MakeArgument(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_,
-                                      params.GetOutputSpatialLengths(),
-                                      params.conv_filter_strides_,
-                                      params.conv_filter_dilations_,
-                                      params.input_left_pads_,
-                                      params.input_right_pads_,
-                                      InElementOp{},
-                                      WeiElementOp{},
-                                      OutElementOp{});
-
-    if(!conv.IsSupportedArgument(argument))
+    else if(num_dim_spatial == 2)
     {
-        throw std::runtime_error(
-            "wrong! device_conv with the specified compilation parameters does "
-            "not support this Conv problem");
+        return run_conv_fwd<2,
+                            InDataType,
+                            WeiDataType,
+                            OutDataType,
+                            AccDataType,
+                            InLayout,
+                            WeiLayout,
+                            OutLayout,
+                            InElementOp,
+                            WeiElementOp,
+                            OutElementOp,
+                            DeviceConvNDFwdInstance<2>,
+                            ReferenceConvNDFwdInstance<2>>(
+            params, do_verification, init_method, time_kernel);
     }
-
-    float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
-
-    std::size_t flop      = params.GetFlops();
-    std::size_t num_btype = params.GetByte<InDataType, WeiDataType, OutDataType>();
-
-    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, "
-              << conv.GetTypeString() << std::endl;
-
-    if(do_verification)
+    else if(num_dim_spatial == 3)
     {
-        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());
-            return ck::utils::check_err(host_output.mData,
-                                        device_output.mData,
-                                        "Error: incorrect results!",
-                                        1e-5f,
-                                        1e-4f)
-                       ? 0
-                       : 1;
-        };
-
-        switch(num_dim_spatial)
-        {
-        case 1: {
-            auto ref_conv = ReferenceConvNDFwdInstance<1>();
-            return verify_f(ref_conv);
-        }
-        case 2: {
-            auto ref_conv = ReferenceConvNDFwdInstance<2>();
-            return verify_f(ref_conv);
-        }
-        case 3: {
-            auto ref_conv = ReferenceConvNDFwdInstance<3>();
-            return verify_f(ref_conv);
-        }
-        default: {
-            throw std::runtime_error("Unsupported number of spatial dimensions provided!");
-        }
-        }
+        return run_conv_fwd<3,
+                            InDataType,
+                            WeiDataType,
+                            OutDataType,
+                            AccDataType,
+                            InLayout,
+                            WeiLayout,
+                            OutLayout,
+                            InElementOp,
+                            WeiElementOp,
+                            OutElementOp,
+                            DeviceConvNDFwdInstance<3>,
+                            ReferenceConvNDFwdInstance<3>>(
+            params, do_verification, init_method, time_kernel);
     }
-    return 0;
 }

example/09_convnd_fwd/parse_conv_parameter.hpp

@@ -52,7 +52,7 @@ parse_conv_params(int num_dim_spatial, int arg_idx, char* const argv[])
     return params;
 }
 
-void print_use_msg()
+void print_helper_msg()
 {
     std::cout << "arg1: verification (0=no, 1=yes)\n"
               << "arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n"

library/src/utility/device_memory.cpp

@@ -2,6 +2,7 @@
 // Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
 
 #include "ck/device_utility/hip_check_error.hpp"
+
 #include "ck/library/utility/device_memory.hpp"
 
 DeviceMem::DeviceMem(std::size_t mem_size) : mMemSize(mem_size)