MegEngine/dnn/test/arm_common/conv_bias_multi_thread_benchmark.cpp

#include "test/arm_common/fixture.h"
#include "test/common/benchmarker.h"
#include "test/common/conv_bias.h"

using namespace megdnn;
using namespace test;
using namespace conv_bias;
#if MEGDNN_WITH_BENCHMARK
namespace {
void benchmark_impl(
        const param::ConvBias param,
        std::vector<std::pair<SmallVector<TensorShape>, float>>& shapes_and_computation,
        const std::string algo_name, size_t RUNS,
        TaskExecutorConfig&& multi_thread_config,
        TaskExecutorConfig&& single_thread_config, std::vector<DType>& data_type) {
    std::vector<float> multi_thread_times, single_thread_times;
    {
        auto multi_thread_hanle = create_cpu_handle(0, true, &multi_thread_config);
        auto benchmarker = Benchmarker<ConvBias>(multi_thread_hanle.get());
        benchmarker.set_times(RUNS)
                .set_display(false)
                .set_param(param)
                .set_dtype(0, data_type[0])
                .set_dtype(1, data_type[1])
                .set_dtype(2, data_type[2])
                .set_dtype(4, data_type[3])
                .set_before_exec_callback(
                        conv_bias::ConvBiasAlgoChecker<ConvBias>(algo_name.c_str()));
        for (auto shape : shapes_and_computation) {
            multi_thread_times.push_back(benchmarker.exec(shape.first) / RUNS);
        }
    }
    {
        auto single_thread_handle = create_cpu_handle(0, true, &single_thread_config);
        auto benchmarker = Benchmarker<ConvBias>(single_thread_handle.get());
        benchmarker.set_times(RUNS)
                .set_display(false)
                .set_param(param)
                .set_dtype(0, data_type[0])
                .set_dtype(1, data_type[1])
                .set_dtype(2, data_type[2])
                .set_dtype(4, data_type[3])
                .set_before_exec_callback(
                        conv_bias::ConvBiasAlgoChecker<ConvBias>(algo_name.c_str()));
        for (auto shape : shapes_and_computation) {
            single_thread_times.push_back(benchmarker.exec(shape.first) / RUNS);
        }
    }
    printf("Benchmark : Multi threads  %zu, ", multi_thread_config.nr_thread);
    printf("core_ids:");
    for (size_t i = 0; i < multi_thread_config.affinity_core_set.size(); i++) {
        printf("%zu ", multi_thread_config.affinity_core_set[i]);
    }
    printf(", Single thread core_id %zu\n", single_thread_config.affinity_core_set[0]);
    for (size_t i = 0; i < shapes_and_computation.size(); i++) {
        auto shapes = shapes_and_computation[i];
        printf("Bench case: ");
        for (auto&& shape : shapes.first) {
            printf("%s ", shape.to_string().c_str());
        }
        float computations = shapes.second;
        printf("%zu threads gflops: %f,\n single thread gflops: "
               "%f. spead up = %f, speedup/cores=%f\n",
               multi_thread_config.nr_thread, computations / multi_thread_times[i],
               computations / single_thread_times[i],
               single_thread_times[i] / multi_thread_times[i],
               single_thread_times[i] / multi_thread_times[i] /
                       multi_thread_config.nr_thread);
    }
}

void benchmark_with_contrast(
        const std::vector<conv_bias::TestArg>& args, const std::string algo_name,
        std::vector<DType>& data_type,
        const std::vector<conv_bias::TestArg>& args_contrast,
        const std::string algo_name_contrast, std::vector<DType>& data_type_contrast,
        size_t RUNS, TaskExecutorConfig&& single_thread_config) {
    auto single_thread_handle = create_cpu_handle(0, true, &single_thread_config);

    auto benchmarker = Benchmarker<ConvBias>(single_thread_handle.get());
    auto benchmarker_contrast = Benchmarker<ConvBias>(single_thread_handle.get());

    benchmarker.set_times(RUNS)
            .set_display(false)
            .set_dtype(0, data_type[0])
            .set_dtype(1, data_type[1])
            .set_dtype(2, data_type[2])
            .set_dtype(4, data_type[3])
            .set_before_exec_callback(
                    conv_bias::ConvBiasAlgoChecker<ConvBias>(algo_name.c_str()));
    benchmarker_contrast.set_times(RUNS)
            .set_display(false)
            .set_dtype(0, data_type_contrast[0])
            .set_dtype(1, data_type_contrast[1])
            .set_dtype(2, data_type_contrast[2])
            .set_dtype(4, data_type_contrast[3])
            .set_before_exec_callback(conv_bias::ConvBiasAlgoChecker<ConvBias>(
                    algo_name_contrast.c_str()));

    size_t arg_size = args.size(), arg_contrast_size = args_contrast.size();
    megdnn_assert(arg_size == arg_contrast_size);
    rep(i, arg_size) {
        TensorLayout dst_layout, dst_layout_contrast;
        auto opr = single_thread_handle.get()->create_operator<ConvBias>();

        auto&& arg = args[i];
        opr->param() = arg.param;
        opr->deduce_layout(
                {arg.src, data_type[0]}, {arg.filter, data_type[1]},
                {arg.bias, data_type[2]}, {}, dst_layout);
        float computation = (dst_layout.total_nr_elems() * arg.filter[1] *
                             arg.filter[2] * arg.filter[3] * arg.filter[4] * 2.0) /
                            (1024 * 1024 * 1024) * 1e3;
        benchmarker.set_param(arg.param);
        auto used = benchmarker.exec({arg.src, arg.filter, arg.bias, {}, {}}) / RUNS;

        auto&& arg_contrast = args_contrast[i];
        opr->param() = arg_contrast.param;
        opr->deduce_layout(
                {arg_contrast.src, data_type_contrast[0]},
                {arg_contrast.filter, data_type_contrast[1]},
                {arg_contrast.bias, data_type_contrast[2]}, {}, dst_layout_contrast);
        float computation_contrast =
                (dst_layout_contrast.total_nr_elems() * arg_contrast.filter[1] *
                 arg_contrast.filter[2] * arg_contrast.filter[3] *
                 arg_contrast.filter[4] * 2.0) /
                (1024 * 1024 * 1024) * 1e3;
        benchmarker_contrast.set_param(arg_contrast.param);
        auto used_contrast = benchmarker_contrast.exec(
                                     {arg_contrast.src,
                                      arg_contrast.filter,
                                      arg_contrast.bias,
                                      {},
                                      {}}) /
                             RUNS;

        printf("Bench case: \n");
        printf("padding: %u, stride: %u, nonline mode: %u\n", arg.param.pad_h,
               arg.param.stride_h, arg.param.nonlineMode);
        printf("%s %s %s\n", arg.src.to_string().c_str(),
               arg.filter.to_string().c_str(), arg.bias.to_string().c_str());
        printf("%s %s %s\n", arg_contrast.src.to_string().c_str(),
               arg_contrast.filter.to_string().c_str(),
               arg_contrast.bias.to_string().c_str());

        printf("%s: %f gflops;\n%s: %f gflops\n"
               "spead up = %f\n",
               algo_name.c_str(), computation / used, algo_name_contrast.c_str(),
               computation_contrast / used_contrast, used_contrast / used);
    }
}
}  // namespace

#if __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
TEST_F(ARM_COMMON_BENCHMARK_MULTI_THREADS, BENCHMARK_CONVBIAS_DIRECTF16) {
    constexpr size_t RUNS = 50;

    param::ConvBias param;
    param.nonlineMode = param::ConvBias::NonlineMode::RELU;
    param.pad_h = 1;
    param.pad_w = 1;
    param.stride_h = 1;
    param.stride_w = 1;
    param.sparse = param::ConvBias::Sparse::GROUP;

    std::vector<std::pair<SmallVector<TensorShape>, float>> shapes_and_computation;
    auto bench_case = [&](size_t N, size_t IC, size_t OC, size_t H, size_t W, size_t FS,
                          size_t group) {
        SmallVector<TensorShape> shapes{
                {N, IC, H, W},
                {group, OC / group, IC / group, FS, FS},
                {1, OC, 1, 1},
                {},
                {N, OC, H, W}};
        TensorShape dst{N, OC, H, W};
        float computations = ((IC / group) * FS * FS * dst.total_nr_elems() * 2 +
                              dst.total_nr_elems()) *
                             1e-6;
        shapes_and_computation.push_back(std::make_pair(shapes, computations));
    };

    bench_case(1, 32, 32, 200, 200, 3, 4);
    bench_case(1, 32, 32, 200, 200, 3, 32);
    bench_case(1, 32, 32, 128, 128, 3, 4);
    bench_case(1, 32, 32, 128, 128, 3, 32);
    bench_case(1, 32, 32, 100, 100, 3, 4);
    bench_case(1, 32, 32, 100, 100, 3, 32);
    bench_case(1, 32, 32, 80, 80, 3, 4);
    bench_case(1, 32, 32, 80, 80, 3, 32);

    std::string algo_name = "F16DIRECT";
    printf("Benchmark F16DIRECT_LARGE_GROUP algo\n");
    std::vector<DType> data_type = {
            dtype::Float16(), dtype::Float16(), dtype::Float16(), dtype::Float16()};
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {4, {4, 5, 6, 7}}, {1, {4}},
            data_type);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {4, {4, 5, 6, 7}}, {1, {7}},
            data_type);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {2, {4, 5}}, {1, {4}},
            data_type);
    shapes_and_computation.clear();

    algo_name = "F16DIRECT";
    printf("Benchmark F16DIRECT_SMALL_GROUP algo\n");
    bench_case(1, 32, 32, 200, 200, 3, 1);
    bench_case(1, 32, 32, 128, 128, 3, 1);
    bench_case(1, 32, 32, 100, 100, 3, 1);
    bench_case(1, 32, 32, 80, 80, 3, 1);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {4, {4, 5, 6, 7}}, {1, {4}},
            data_type);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {4, {4, 5, 6, 7}}, {1, {7}},
            data_type);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {2, {4, 5}}, {1, {4}},
            data_type);
}
TEST_F(ARM_COMMON_BENCHMARK_MULTI_THREADS, BENCHMARK_CONVBIAS_DIRECTF16_STR1) {
    constexpr size_t RUNS = 50;

    param::ConvBias param;
    param.nonlineMode = param::ConvBias::NonlineMode::RELU;
    param.pad_h = 1;
    param.pad_w = 1;
    param.stride_h = 1;
    param.stride_w = 1;
    param.sparse = param::ConvBias::Sparse::GROUP;

    std::vector<std::pair<SmallVector<TensorShape>, float>> shapes_and_computation;
    auto bench_case = [&](size_t N, size_t IC, size_t OC, size_t H, size_t W, size_t FS,
                          size_t group) {
        SmallVector<TensorShape> shapes{
                {N, IC, H, W},
                {group, OC / group, IC / group, FS, FS},
                {1, OC, 1, 1},
                {},
                {N, OC, H, W}};
        TensorShape dst{N, OC, H, W};
        float computations = ((IC / group) * FS * FS * dst.total_nr_elems() * 2 +
                              dst.total_nr_elems()) *
                             1e-6;
        shapes_and_computation.push_back(std::make_pair(shapes, computations));
    };

    bench_case(1, 32, 32, 200, 200, 3, 4);
    bench_case(1, 32, 32, 200, 200, 3, 32);
    bench_case(1, 32, 32, 128, 128, 3, 4);
    bench_case(1, 32, 32, 128, 128, 3, 32);
    bench_case(1, 32, 32, 100, 100, 3, 4);
    bench_case(1, 32, 32, 100, 100, 3, 32);
    bench_case(1, 32, 32, 80, 80, 3, 4);
    bench_case(1, 32, 32, 80, 80, 3, 32);

    std::string algo_name = "F16STRD1";
    printf("Benchmark F16STRD1_LARGE_GROUP algo\n");
    std::vector<DType> data_type = {
            dtype::Float16(), dtype::Float16(), dtype::Float16(), dtype::Float16()};
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {4, {4, 5, 6, 7}}, {1, {4}},
            data_type);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {4, {4, 5, 6, 7}}, {1, {7}},
            data_type);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {2, {4, 5}}, {1, {4}},
            data_type);
    shapes_and_computation.clear();

    algo_name = "F16STRD1";
    printf("Benchmark F16STRD1_SMALL_GROUP algo\n");
    bench_case(1, 32, 32, 200, 200, 3, 1);
    bench_case(1, 32, 32, 128, 128, 3, 1);
    bench_case(1, 32, 32, 100, 100, 3, 1);
    bench_case(1, 32, 32, 80, 80, 3, 1);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {4, {4, 5, 6, 7}}, {1, {4}},
            data_type);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {4, {4, 5, 6, 7}}, {1, {7}},
            data_type);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {2, {4, 5}}, {1, {4}},
            data_type);
}

TEST_F(ARM_COMMON_BENCHMARK_MULTI_THREADS, BENCHMARK_CHANNEL_WISE_FP16_NCHW88) {
    constexpr size_t RUNS = 50;

    std::string algo_name = "F16_CHANNEL_WISE_NCHW88";
    printf("Benchmarker F16_CHANNEL_WISE_NCHW88 algo\n");
    std::vector<DType> data_type = {
            dtype::Float16(), dtype::Float16(), dtype::Float16(), dtype::Float16()};

    auto bench_case = [&](size_t N, size_t IC, size_t H, size_t W, size_t FS, size_t P,
                          size_t S) {
        param::ConvBias param;
        param.nonlineMode = param::ConvBias::NonlineMode::RELU;
        param.pad_h = P;
        param.pad_w = P;
        param.stride_h = S;
        param.stride_w = S;
        param.sparse = param::ConvBias::Sparse::GROUP;
        param.format = param::ConvBias::Format::NCHW88;

        size_t group = IC;
        size_t OC = IC;
        SmallVector<TensorShape> shapes{
                {N, IC, H, W, 8},
                {group, 1, 1, FS, FS, 8},
                {1, OC, 1, 1, 8},
                {},
                {N, OC, (H + 2 * P - FS) / S + 1, (W + 2 * P - FS) / S + 1, 8}};
        TensorShape dst{N, OC, (H + 2 * P - FS) / S + 1, (W + 2 * P - FS) / S + 1, 8};
        float computations = ((IC / group) * FS * FS * dst.total_nr_elems() * 2 +
                              dst.total_nr_elems()) *
                             1e-6;
        std::vector<std::pair<SmallVector<TensorShape>, float>> shape_arg = {
                std::make_pair(shapes, computations)};

        benchmark_impl(
                param, shape_arg, algo_name, RUNS, {4, {4, 5, 6, 7}}, {1, {7}},
                data_type);
    };

    bench_case(1, 64, 100, 100, 5, 2, 1);
    bench_case(1, 64, 56, 56, 5, 2, 1);
    bench_case(1, 64, 28, 28, 5, 2, 1);
    bench_case(1, 64, 100, 100, 5, 2, 2);
    bench_case(1, 64, 56, 56, 5, 2, 2);
    bench_case(1, 64, 28, 28, 5, 2, 2);

    bench_case(1, 64, 100, 100, 3, 1, 1);
    bench_case(1, 64, 56, 56, 3, 1, 1);
    bench_case(1, 64, 28, 28, 3, 1, 1);
    bench_case(1, 64, 100, 100, 3, 1, 2);
    bench_case(1, 64, 56, 56, 3, 1, 2);
    bench_case(1, 64, 28, 28, 3, 1, 2);

    bench_case(1, 64, 100, 100, 2, 0, 1);
    bench_case(1, 64, 56, 56, 2, 0, 1);
    bench_case(1, 64, 28, 28, 2, 0, 1);
    bench_case(1, 64, 100, 100, 2, 0, 2);
    bench_case(1, 64, 56, 56, 2, 0, 2);
    bench_case(1, 64, 28, 28, 2, 0, 2);
}

TEST_F(ARM_COMMON_BENCHMARK_MULTI_THREADS, BENCHMARK_CONVBIAS_FP16_NCHW88) {
    constexpr size_t RUNS = 40;
    std::vector<DType> data_type = {
            dtype::Float16(), dtype::Float16(), dtype::Float16(), dtype::Float16()};
    auto bench_case = [&](size_t N, size_t IC, size_t OC, size_t H, size_t W, size_t FS,
                          size_t group, size_t P, size_t S) {
        param::ConvBias param;
        param.nonlineMode = param::ConvBias::NonlineMode::RELU;
        param.pad_h = P;
        param.pad_w = P;
        param.stride_h = S;
        param.stride_w = S;
        param.sparse = param::ConvBias::Sparse::DENSE;
        param.format = param::ConvBias::Format::NCHW88;
        auto OH = (H + 2 * P - FS) / static_cast<size_t>(S) + 1;
        auto OW = (W + 2 * P - FS) / static_cast<size_t>(S) + 1;
        TensorShape src = {N, IC / 8, H, W, 8};
        TensorShape filter = {OC / 8, IC / 8, FS, FS, 8, 8};
        if (group > 1) {
            filter = {group, OC / group / 8, IC / group / 8, FS, FS, 8, 8};
            param.sparse = param::ConvBias::Sparse::GROUP;
        }
        TensorShape bias = {1, OC / 8, 1, 1, 8};
        TensorShape dst = {N, OC / 8, OH, OW, 8};

        SmallVector<TensorShape> shapes{src, filter, bias, {}, dst};
        float computations = (((IC / group) * FS * FS + 1) * dst.total_nr_elems() * 2 +
                              dst.total_nr_elems()) *
                             1e-6;
        std::vector<std::pair<SmallVector<TensorShape>, float>> shape_arg = {
                std::make_pair(shapes, computations)};
        benchmark_impl(
                param, shape_arg, ".+", RUNS, {4, {4, 5, 6, 7}}, {1, {7}}, data_type);
    };
    bench_case(1, 64, 64, 28, 28, 3, 1, 1, 1);
    bench_case(1, 64, 64, 28, 28, 5, 1, 2, 1);
    bench_case(1, 64, 64, 28, 28, 7, 1, 3, 1);

    bench_case(1, 64, 64, 28, 28, 3, 1, 1, 2);
    bench_case(1, 64, 64, 28, 28, 5, 1, 2, 2);
    bench_case(1, 64, 64, 28, 28, 7, 1, 3, 2);

    bench_case(1, 64, 64, 28, 28, 3, 2, 1, 1);
    bench_case(1, 64, 64, 28, 28, 3, 4, 1, 1);
    bench_case(1, 64, 64, 28, 28, 3, 8, 1, 1);

    bench_case(1, 16, 16, 28, 28, 3, 1, 1, 1);
    bench_case(1, 32, 32, 28, 28, 3, 1, 1, 1);
    bench_case(1, 128, 128, 28, 28, 3, 1, 1, 1);
    bench_case(1, 256, 256, 28, 28, 3, 1, 1, 1);

    bench_case(1, 64, 64, 7, 7, 3, 1, 1, 1);
    bench_case(1, 64, 64, 14, 14, 3, 1, 1, 1);
    bench_case(1, 64, 64, 56, 56, 3, 1, 1, 1);
    bench_case(1, 64, 64, 112, 112, 3, 1, 1, 1);
}

#endif
TEST_F(ARM_COMMON_BENCHMARK_MULTI_THREADS, BENCHMARK_CONVBIAS_DIRECT_INT8x8x16) {
    constexpr size_t RUNS = 50;

    param::ConvBias param;
    param.nonlineMode = param::ConvBias::NonlineMode::IDENTITY;
    param.pad_h = 1;
    param.pad_w = 1;
    param.stride_h = 1;
    param.stride_w = 1;
    param.sparse = param::ConvBias::Sparse::GROUP;

    std::vector<std::pair<SmallVector<TensorShape>, float>> shapes_and_computation;
    auto bench_case = [&](size_t N, size_t IC, size_t OC, size_t H, size_t W, size_t FS,
                          size_t group) {
        SmallVector<TensorShape> shapes{
                {N, IC, H, W},
                {group, OC / group, IC / group, FS, FS},
                {},
                {},
                {N, OC, H, W}};
        TensorShape dst{N, OC, H, W};
        float computations = ((IC / group) * FS * FS * dst.total_nr_elems() * 2 +
                              dst.total_nr_elems()) *
                             1e-6;
        shapes_and_computation.push_back(std::make_pair(shapes, computations));
    };

    bench_case(1, 32, 32, 200, 200, 3, 4);
    bench_case(1, 32, 32, 200, 200, 3, 32);
    bench_case(1, 32, 32, 128, 128, 3, 4);
    bench_case(1, 32, 32, 128, 128, 3, 32);
    bench_case(1, 32, 32, 100, 100, 3, 4);
    bench_case(1, 32, 32, 100, 100, 3, 32);
    bench_case(1, 32, 32, 80, 80, 3, 4);
    bench_case(1, 32, 32, 80, 80, 3, 32);

    std::string algo_name = "I8816DIRECT";
    printf("Benchmark I8816DIRECT_LARGE_GROUP algo\n");
    std::vector<DType> data_type = {
            dtype::Int8(), dtype::Int8(), dtype::Int16(), dtype::Int16()};
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {4, {4, 5, 6, 7}}, {1, {4}},
            data_type);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {4, {4, 5, 6, 7}}, {1, {7}},
            data_type);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {2, {4, 5}}, {1, {4}},
            data_type);
    shapes_and_computation.clear();

    algo_name = "I8816DIRECT";
    printf("Benchmark I8816DIRECT_SMALL_GROUP algo\n");
    bench_case(1, 32, 32, 200, 200, 3, 1);
    bench_case(1, 32, 32, 128, 128, 3, 1);
    bench_case(1, 32, 32, 100, 100, 3, 1);
    bench_case(1, 32, 32, 80, 80, 3, 1);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {4, {4, 5, 6, 7}}, {1, {4}},
            data_type);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {4, {4, 5, 6, 7}}, {1, {7}},
            data_type);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {2, {4, 5}}, {1, {4}},
            data_type);
}
TEST_F(ARM_COMMON_BENCHMARK_MULTI_THREADS, BENCHMARK_CONVBIAS_DIRECT_INT8x8x16_STR2) {
    constexpr size_t RUNS = 50;
    param::ConvBias param;
    param.nonlineMode = param::ConvBias::NonlineMode::IDENTITY;
    param.pad_h = 1;
    param.pad_w = 1;
    param.stride_h = 2;
    param.stride_w = 2;
    param.sparse = param::ConvBias::Sparse::GROUP;

    std::vector<std::pair<SmallVector<TensorShape>, float>> shapes_and_computation;
    auto bench_case = [&](size_t N, size_t IC, size_t OC, size_t H, size_t W, size_t FS,
                          size_t group, size_t P, size_t S) {
        SmallVector<TensorShape> shapes{
                {N, IC, H, W},
                {group, OC / group, IC / group, FS, FS},
                {},
                {},
                {N, OC, (H + 2 * P - FS) / S + 1, (W + 2 * P - FS) / S + 1}};
        TensorShape dst{N, OC, (H + 2 * P - FS) / S + 1, (W + 2 * P - FS) / S + 1};
        float computations = ((IC / group) * FS * FS * dst.total_nr_elems() * 2 +
                              dst.total_nr_elems()) *
                             1e-6;
        shapes_and_computation.push_back(std::make_pair(shapes, computations));
    };

    bench_case(1, 32, 32, 200, 200, 3, 4, 1, 2);
    bench_case(1, 32, 32, 200, 200, 3, 32, 1, 2);
    bench_case(1, 32, 32, 128, 128, 3, 4, 1, 2);
    bench_case(1, 32, 32, 128, 128, 3, 32, 1, 2);
    bench_case(1, 32, 32, 100, 100, 3, 4, 1, 2);
    bench_case(1, 32, 32, 100, 100, 3, 32, 1, 2);
    bench_case(1, 32, 32, 80, 80, 3, 4, 1, 2);
    bench_case(1, 32, 32, 80, 80, 3, 32, 1, 2);

    std::string algo_name = "I8816STRD2";
    printf("Benchmark I8816STRD2_LARGE_GROUP algo\n");
    std::vector<DType> data_type = {
            dtype::Int8(), dtype::Int8(), dtype::Int16(), dtype::Int16()};
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {4, {4, 5, 6, 7}}, {1, {4}},
            data_type);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {4, {4, 5, 6, 7}}, {1, {7}},
            data_type);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {2, {4, 5}}, {1, {4}},
            data_type);
    shapes_and_computation.clear();

    algo_name = "I8816STRD2";
    printf("Benchmark I8816STRD2_SMALL_GROUP algo\n");
    bench_case(1, 32, 32, 200, 200, 3, 1, 1, 2);
    bench_case(1, 32, 32, 128, 128, 3, 1, 1, 2);
    bench_case(1, 32, 32, 100, 100, 3, 1, 1, 2);
    bench_case(1, 32, 32, 80, 80, 3, 1, 1, 2);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {4, {4, 5, 6, 7}}, {1, {4}},
            data_type);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {4, {4, 5, 6, 7}}, {1, {7}},
            data_type);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {2, {4, 5}}, {1, {4}},
            data_type);
}
TEST_F(ARM_COMMON_BENCHMARK_MULTI_THREADS, BENCHMARK_CONVBIAS_INT8_INT8_INT8_STRIDE1) {
    constexpr size_t RUNS = 50;

    param::ConvBias param;
    param.nonlineMode = param::ConvBias::NonlineMode::RELU;
    param.pad_h = 1;
    param.pad_w = 1;
    param.stride_h = 1;
    param.stride_w = 1;
    param.sparse = param::ConvBias::Sparse::GROUP;

    std::vector<std::pair<SmallVector<TensorShape>, float>> shapes_and_computation;
    auto bench_case = [&](size_t N, size_t IC, size_t OC, size_t H, size_t W, size_t FS,
                          size_t group, size_t P, size_t S) {
        SmallVector<TensorShape> shapes{
                {N, IC, H, W},
                {group, OC / group, IC / group, FS, FS},
                {1, OC, 1, 1},
                {},
                {N, OC, (H + 2 * P - FS) / S + 1, (W + 2 * P - FS) / S + 1}};
        TensorShape dst{N, OC, H, W};
        float computations = ((IC / group) * FS * FS * dst.total_nr_elems() * 2 +
                              dst.total_nr_elems()) *
                             1e-6;
        shapes_and_computation.push_back(std::make_pair(shapes, computations));
    };

    bench_case(1, 32, 32, 200, 200, 3, 4, 1, 1);
    bench_case(1, 32, 32, 200, 200, 3, 32, 1, 1);
    bench_case(1, 32, 32, 128, 128, 3, 4, 1, 1);
    bench_case(1, 32, 32, 128, 128, 3, 32, 1, 1);
    bench_case(1, 32, 32, 100, 100, 3, 4, 1, 1);
    bench_case(1, 32, 32, 100, 100, 3, 32, 1, 1);
    bench_case(1, 32, 32, 80, 80, 3, 4, 1, 1);
    bench_case(1, 32, 32, 80, 80, 3, 32, 1, 1);

    std::string algo_name = "S8STRD1";
    printf("Benchmark S8STRD1_LARGE_GROUP algo\n");
    std::vector<DType> data_type = {
            dtype::QuantizedS8(2.5f), dtype::QuantizedS8(2.5f),
            dtype::QuantizedS32(6.25f), dtype::QuantizedS8(60.25f)};
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {4, {4, 5, 6, 7}}, {1, {4}},
            data_type);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {4, {4, 5, 6, 7}}, {1, {7}},
            data_type);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {2, {4, 5}}, {1, {4}},
            data_type);
    shapes_and_computation.clear();

    algo_name = "S8STRD1";
    printf("Benchmark S8STRD1_SMALL_GROUP algo\n");
    bench_case(1, 32, 32, 200, 200, 3, 1, 1, 1);
    bench_case(1, 32, 32, 128, 128, 3, 1, 1, 1);
    bench_case(1, 32, 32, 100, 100, 3, 1, 1, 1);
    bench_case(1, 32, 32, 80, 80, 3, 1, 1, 1);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {4, {4, 5, 6, 7}}, {1, {4}},
            data_type);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {4, {4, 5, 6, 7}}, {1, {7}},
            data_type);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {2, {4, 5}}, {1, {4}},
            data_type);
}

TEST_F(ARM_COMMON_BENCHMARK_MULTI_THREADS, BENCHMARK_CONVBIAS_INT8_NCHW44) {
    constexpr size_t RUNS = 40;
    std::vector<DType> data_type = {
            dtype::QuantizedS8(2.5f), dtype::QuantizedS8(2.5f),
            dtype::QuantizedS32(6.25f), dtype::QuantizedS8(60.25f)};
    auto bench_case = [&](size_t N, size_t IC, size_t OC, size_t H, size_t W, size_t FS,
                          size_t group, size_t P, size_t S, bool is_nchw = false) {
        param::ConvBias param;
        param.nonlineMode = param::ConvBias::NonlineMode::RELU;
        param.pad_h = P;
        param.pad_w = P;
        param.stride_h = S;
        param.stride_w = S;
        param.sparse = param::ConvBias::Sparse::DENSE;
        param.format = param::ConvBias::Format::NCHW44;
        auto OH = (H + 2 * P - FS) / static_cast<size_t>(S) + 1;
        auto OW = (W + 2 * P - FS) / static_cast<size_t>(S) + 1;
        TensorShape src = {N, IC / 4, H, W, 4};
        TensorShape filter = {OC / 4, IC / 4, FS, FS, 4, 4};
        if (group > 1) {
            filter = {group, OC / group / 4, IC / group / 4, FS, FS, 4, 4};
            param.sparse = param::ConvBias::Sparse::GROUP;
        }
        if (is_nchw) {
            src = {N, IC, H, W};
            filter = {OC / 4, FS, FS, IC, 4};
        }
        TensorShape bias = {1, OC / 4, 1, 1, 4};
        TensorShape dst = {N, OC / 4, OH, OW, 4};

        SmallVector<TensorShape> shapes{src, filter, bias, {}, dst};
        float computations = (((IC / group) * FS * FS + 1) * dst.total_nr_elems() * 2 +
                              dst.total_nr_elems()) *
                             1e-6;
        std::vector<std::pair<SmallVector<TensorShape>, float>> shape_arg = {
                std::make_pair(shapes, computations)};
        benchmark_impl(
                param, shape_arg, ".+", RUNS, {4, {4, 5, 6, 7}}, {1, {7}}, data_type);
    };
    bench_case(1, 2, 64, 160, 160, 1, 1, 0, 1, true);
    bench_case(1, 3, 64, 224, 224, 7, 1, 3, 2, true);
    bench_case(1, 64, 64, 56, 56, 3, 1, 1, 1);
    bench_case(1, 128, 128, 28, 28, 3, 1, 1, 1);
    bench_case(1, 256, 256, 14, 14, 3, 1, 1, 1);
    bench_case(1, 512, 512, 7, 7, 3, 1, 1, 1);

    bench_case(1, 64, 64, 56, 56, 3, 4, 1, 1);
    bench_case(1, 128, 128, 28, 28, 3, 4, 1, 1);
    bench_case(1, 256, 256, 14, 14, 3, 4, 1, 1);
    bench_case(1, 512, 512, 7, 7, 3, 4, 1, 1);

    bench_case(1, 4, 64, 224, 224, 7, 1, 1, 2);
    bench_case(1, 256, 128, 56, 56, 3, 1, 1, 2);
    bench_case(1, 512, 256, 28, 28, 3, 1, 1, 2);
    bench_case(1, 4, 32, 224, 224, 3, 1, 1, 2);

    bench_case(1, 256, 128, 56, 56, 3, 4, 1, 2);
    bench_case(1, 512, 256, 28, 28, 3, 4, 1, 2);
}

#if MGB_ENABLE_DOT
TEST_F(ARM_COMMON_BENCHMARK_MULTI_THREADS, BENCHMARK_CONVBIAS_INT8_NCHW44_DOT) {
    constexpr size_t RUNS = 40;
    std::vector<DType> data_type = {
            dtype::QuantizedS8(2.5f), dtype::QuantizedS8(2.5f),
            dtype::QuantizedS32(6.25f), dtype::QuantizedS8(60.25f)};
    auto bench_case = [&](size_t N, size_t IC, size_t OC, size_t H, size_t W, size_t FS,
                          size_t group, size_t P, size_t S, bool is_nchw = false) {
        param::ConvBias param;
        param.nonlineMode = param::ConvBias::NonlineMode::RELU;
        param.pad_h = P;
        param.pad_w = P;
        param.stride_h = S;
        param.stride_w = S;
        param.sparse = param::ConvBias::Sparse::DENSE;
        param.format = param::ConvBias::Format::NCHW44_DOT;
        auto OH = (H + 2 * P - FS) / static_cast<size_t>(S) + 1;
        auto OW = (W + 2 * P - FS) / static_cast<size_t>(S) + 1;
        TensorShape src = {N, IC / 4, H, W, 4};
        TensorShape filter = {OC / 4, IC / 4, FS, FS, 4, 4};
        if (group > 1) {
            filter = {group, OC / group / 4, IC / group / 4, FS, FS, 4, 4};
            param.sparse = param::ConvBias::Sparse::GROUP;
        }
        if (is_nchw) {
            src = {N, IC, H, W};
            filter = {OC / 4, FS, FS, IC, 4};
        }
        TensorShape bias = {1, OC / 4, 1, 1, 4};
        TensorShape dst = {N, OC / 4, OH, OW, 4};

        SmallVector<TensorShape> shapes{src, filter, bias, {}, dst};
        float computations = (((IC / group) * FS * FS + 1) * dst.total_nr_elems() * 2 +
                              dst.total_nr_elems()) *
                             1e-6;
        std::vector<std::pair<SmallVector<TensorShape>, float>> shape_arg = {
                std::make_pair(shapes, computations)};
        benchmark_impl(
                param, shape_arg, ".+", RUNS, {4, {4, 5, 6, 7}}, {1, {7}}, data_type);
    };
    bench_case(1, 64, 64, 56, 56, 3, 1, 1, 1);
    bench_case(1, 128, 128, 28, 28, 3, 1, 1, 1);
    bench_case(1, 256, 256, 14, 14, 3, 1, 1, 1);
    bench_case(1, 512, 512, 7, 7, 3, 1, 1, 1);

    bench_case(1, 64, 64, 56, 56, 3, 4, 1, 1);
    bench_case(1, 128, 128, 28, 28, 3, 4, 1, 1);
    bench_case(1, 256, 256, 14, 14, 3, 4, 1, 1);
    bench_case(1, 512, 512, 7, 7, 3, 4, 1, 1);
}

TEST_F(ARM_COMMON_BENCHMARK_MULTI_THREADS, BENCHMARK_CONVBIAS_INT8_NCHW44_DOT_S2) {
    constexpr size_t RUNS = 40;
    std::vector<DType> data_type = {
            dtype::QuantizedS8(2.5f), dtype::QuantizedS8(2.5f),
            dtype::QuantizedS32(6.25f), dtype::QuantizedS8(60.25f)};
    auto bench_case = [&](size_t N, size_t IC, size_t OC, size_t H, size_t W, size_t FS,
                          size_t group, size_t P, size_t S, bool is_nchw = false) {
        param::ConvBias param;
        param.nonlineMode = param::ConvBias::NonlineMode::RELU;
        param.pad_h = P;
        param.pad_w = P;
        param.stride_h = S;
        param.stride_w = S;
        param.sparse = param::ConvBias::Sparse::DENSE;
        param.format = param::ConvBias::Format::NCHW44_DOT;
        auto OH = (H + 2 * P - FS) / static_cast<size_t>(S) + 1;
        auto OW = (W + 2 * P - FS) / static_cast<size_t>(S) + 1;
        TensorShape src = {N, IC / 4, H, W, 4};
        TensorShape filter = {OC / 4, IC / 4, FS, FS, 4, 4};
        if (group > 1) {
            filter = {group, OC / group / 4, IC / group / 4, FS, FS, 4, 4};
            param.sparse = param::ConvBias::Sparse::GROUP;
        }
        if (is_nchw) {
            src = {N, IC, H, W};
            filter = {OC / 4, FS, FS, IC, 4};
        }
        TensorShape bias = {1, OC / 4, 1, 1, 4};
        TensorShape dst = {N, OC / 4, OH, OW, 4};

        SmallVector<TensorShape> shapes{src, filter, bias, {}, dst};
        float computations = (((IC / group) * FS * FS + 1) * dst.total_nr_elems() * 2 +
                              dst.total_nr_elems()) *
                             1e-6;
        std::vector<std::pair<SmallVector<TensorShape>, float>> shape_arg = {
                std::make_pair(shapes, computations)};
        benchmark_impl(
                param, shape_arg, ".+", RUNS, {4, {4, 5, 6, 7}}, {1, {7}}, data_type);
    };
    bench_case(1, 64, 64, 56, 56, 3, 1, 1, 2);
    bench_case(1, 64, 64, 128, 128, 3, 1, 1, 2);
    bench_case(1, 64, 64, 256, 256, 3, 1, 1, 2);
    bench_case(1, 64, 64, 156, 156, 3, 1, 1, 2);
    bench_case(1, 128, 128, 28, 28, 3, 1, 1, 2);
    bench_case(1, 256, 256, 14, 14, 3, 1, 1, 2);
    bench_case(1, 512, 512, 7, 7, 3, 1, 1, 2);

    bench_case(1, 64, 64, 56, 56, 3, 4, 1, 2);
    bench_case(1, 128, 128, 28, 28, 3, 4, 1, 2);
    bench_case(1, 256, 256, 14, 14, 3, 4, 1, 2);
    bench_case(1, 512, 512, 7, 7, 3, 4, 1, 2);
}

#endif
TEST_F(ARM_COMMON_BENCHMARK_MULTI_THREADS, BENCHMARK_CONVBIAS_FLOAT_NCHW44) {
    constexpr size_t RUNS = 40;
    std::vector<DType> data_type = {
            dtype::Float32(), dtype::Float32(), dtype::Float32(), dtype::Float32()};
    auto bench_case = [&](size_t N, size_t IC, size_t OC, size_t H, size_t W, size_t FS,
                          size_t group, size_t P, size_t S, bool is_nchw = false) {
        param::ConvBias param;
        param.nonlineMode = param::ConvBias::NonlineMode::RELU;
        param.pad_h = P;
        param.pad_w = P;
        param.stride_h = S;
        param.stride_w = S;
        param.sparse = param::ConvBias::Sparse::DENSE;
        param.format = param::ConvBias::Format::NCHW44;
        auto OH = (H + 2 * P - FS) / static_cast<size_t>(S) + 1;
        auto OW = (W + 2 * P - FS) / static_cast<size_t>(S) + 1;
        TensorShape src = {N, IC / 4, H, W, 4};
        TensorShape filter = {OC / 4, IC / 4, FS, FS, 4, 4};
        if (group > 1) {
            filter = {group, OC / group / 4, IC / group / 4, FS, FS, 4, 4};
            param.sparse = param::ConvBias::Sparse::GROUP;
        }
        if (is_nchw) {
            src = {N, IC, H, W};
            filter = {OC / 4, FS, FS, IC, 4};
        }
        TensorShape bias = {1, OC / 4, 1, 1, 4};
        TensorShape dst = {N, OC / 4, OH, OW, 4};

        SmallVector<TensorShape> shapes{src, filter, bias, {}, dst};
        float computations = (((IC / group) * FS * FS + 1) * dst.total_nr_elems() * 2 +
                              dst.total_nr_elems()) *
                             1e-6;
        std::vector<std::pair<SmallVector<TensorShape>, float>> shape_arg = {
                std::make_pair(shapes, computations)};
        benchmark_impl(
                param, shape_arg, ".+", RUNS, {4, {4, 5, 6, 7}}, {1, {7}}, data_type);
    };
    bench_case(1, 64, 64, 56, 56, 3, 1, 1, 2);
    bench_case(1, 128, 128, 28, 28, 3, 1, 1, 2);
    bench_case(1, 256, 256, 14, 14, 3, 1, 1, 2);
    bench_case(1, 512, 512, 7, 7, 3, 1, 1, 2);

    bench_case(1, 64, 64, 56, 56, 3, 4, 1, 2);
    bench_case(1, 128, 128, 28, 28, 3, 4, 1, 2);
    bench_case(1, 256, 256, 14, 14, 3, 4, 1, 2);
    bench_case(1, 512, 512, 7, 7, 3, 4, 1, 2);

    bench_case(1, 64, 64, 56 * 2, 56 * 2, 3, 4, 1, 2);
    bench_case(1, 128, 128, 28 * 2, 28 * 2, 3, 4, 1, 2);
    bench_case(1, 256, 256, 14 * 2, 14 * 2, 3, 4, 1, 2);
    bench_case(1, 512, 512, 7 * 2, 7 * 2, 3, 4, 1, 2);
}

TEST_F(ARM_COMMON_BENCHMARK_MULTI_THREADS, BENCHMARK_CONVBIAS_INT8_INT8_INT8_STRIDE2) {
    constexpr size_t RUNS = 50;

    param::ConvBias param;
    param.nonlineMode = param::ConvBias::NonlineMode::RELU;
    param.pad_h = 1;
    param.pad_w = 1;
    param.stride_h = 2;
    param.stride_w = 2;
    param.sparse = param::ConvBias::Sparse::GROUP;

    std::vector<std::pair<SmallVector<TensorShape>, float>> shapes_and_computation;
    auto bench_case = [&](size_t N, size_t IC, size_t OC, size_t H, size_t W, size_t FS,
                          size_t group, size_t P, size_t S) {
        SmallVector<TensorShape> shapes{
                {N, IC, H, W},
                {group, OC / group, IC / group, FS, FS},
                {1, OC, 1, 1},
                {},
                {N, OC, (H + 2 * P - FS) / S + 1, (W + 2 * P - FS) / S + 1}};
        TensorShape dst{N, OC, H, W};
        float computations = ((IC / group) * FS * FS * dst.total_nr_elems() * 2 +
                              dst.total_nr_elems()) *
                             1e-6;
        shapes_and_computation.push_back(std::make_pair(shapes, computations));
    };

    bench_case(1, 32, 32, 200, 200, 3, 4, 1, 2);
    bench_case(1, 32, 32, 200, 200, 3, 32, 1, 2);
    bench_case(1, 32, 32, 128, 128, 3, 4, 1, 2);
    bench_case(1, 32, 32, 128, 128, 3, 32, 1, 2);
    bench_case(1, 32, 32, 100, 100, 3, 4, 1, 2);
    bench_case(1, 32, 32, 100, 100, 3, 32, 1, 2);
    bench_case(1, 32, 32, 80, 80, 3, 4, 1, 2);
    bench_case(1, 32, 32, 80, 80, 3, 32, 1, 2);

    std::string algo_name = "S8STRD2";
    printf("Benchmark S8STRD2_LARGE_GROUP algo\n");
    std::vector<DType> data_type = {
            dtype::QuantizedS8(2.5f), dtype::QuantizedS8(2.5f),
            dtype::QuantizedS32(6.25f), dtype::QuantizedS8(60.25f)};
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {4, {4, 5, 6, 7}}, {1, {4}},
            data_type);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {4, {4, 5, 6, 7}}, {1, {7}},
            data_type);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {2, {4, 5}}, {1, {4}},
            data_type);
    shapes_and_computation.clear();

    algo_name = "S8STRD2";
    printf("Benchmark S8STRD2_SMALL_GROUP algo\n");
    bench_case(1, 32, 32, 200, 200, 3, 1, 1, 2);
    bench_case(1, 32, 32, 128, 128, 3, 1, 1, 2);
    bench_case(1, 32, 32, 100, 100, 3, 1, 1, 2);
    bench_case(1, 32, 32, 80, 80, 3, 1, 1, 2);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {4, {4, 5, 6, 7}}, {1, {4}},
            data_type);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {4, {4, 5, 6, 7}}, {1, {7}},
            data_type);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {2, {4, 5}}, {1, {4}},
            data_type);
}
#if MGB_ENABLE_DOT
TEST_F(ARM_COMMON_BENCHMARK_MULTI_THREADS,
       BENCHMARK_CONVBIAS_INT8_INT8_INT8_STRIDE1_WITHDOTPROD) {
    constexpr size_t RUNS = 50;

    param::ConvBias param;
    param.nonlineMode = param::ConvBias::NonlineMode::RELU;
    param.pad_h = 1;
    param.pad_w = 1;
    param.stride_h = 1;
    param.stride_w = 1;
    param.sparse = param::ConvBias::Sparse::GROUP;

    std::vector<std::pair<SmallVector<TensorShape>, float>> shapes_and_computation;
    auto bench_case = [&](size_t N, size_t IC, size_t OC, size_t H, size_t W, size_t FS,
                          size_t group, size_t P, size_t S) {
        SmallVector<TensorShape> shapes{
                {N, IC, H, W},
                {group, OC / group, IC / group, FS, FS},
                {1, OC, 1, 1},
                {},
                {N, OC, (H + 2 * P - FS) / S + 1, (W + 2 * P - FS) / S + 1}};
        TensorShape dst{N, OC, H, W};
        float computations = ((IC / group) * FS * FS * dst.total_nr_elems() * 2 +
                              dst.total_nr_elems()) *
                             1e-6;
        shapes_and_computation.push_back(std::make_pair(shapes, computations));
    };

    bench_case(1, 32, 32, 200, 200, 3, 4, 1, 1);
    bench_case(1, 32, 32, 200, 200, 3, 32, 1, 1);
    bench_case(1, 32, 32, 128, 128, 3, 4, 1, 1);
    bench_case(1, 32, 32, 128, 128, 3, 32, 1, 1);
    bench_case(1, 32, 32, 100, 100, 3, 4, 1, 1);
    bench_case(1, 32, 32, 100, 100, 3, 32, 1, 1);
    bench_case(1, 32, 32, 80, 80, 3, 4, 1, 1);
    bench_case(1, 32, 32, 80, 80, 3, 32, 1, 1);

    std::string algo_name = "ARMDOTS8STRD1";
    printf("Benchmark ARMDOTS8STRD1_LARGE_GROUP algo\n");
    std::vector<DType> data_type = {
            dtype::QuantizedS8(2.5f), dtype::QuantizedS8(2.5f),
            dtype::QuantizedS32(6.25f), dtype::QuantizedS8(60.25f)};
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {4, {4, 5, 6, 7}}, {1, {4}},
            data_type);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {4, {4, 5, 6, 7}}, {1, {7}},
            data_type);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {2, {4, 5}}, {1, {4}},
            data_type);
    shapes_and_computation.clear();

    algo_name = "ARMDOTS8STRD1";
    printf("Benchmark ARMDOTS8STRD1_SMALL_GROUP algo\n");
    bench_case(1, 32, 32, 200, 200, 3, 1, 1, 1);
    bench_case(1, 32, 32, 128, 128, 3, 1, 1, 1);
    bench_case(1, 32, 32, 100, 100, 3, 1, 1, 1);
    bench_case(1, 32, 32, 80, 80, 3, 1, 1, 1);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {4, {4, 5, 6, 7}}, {1, {4}},
            data_type);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {4, {4, 5, 6, 7}}, {1, {7}},
            data_type);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {2, {4, 5}}, {1, {4}},
            data_type);
}
TEST_F(ARM_COMMON_BENCHMARK_MULTI_THREADS,
       BENCHMARK_CONVBIAS_INT8_INT8_INT8_STRIDE2_WITHDOTPROD) {
    constexpr size_t RUNS = 50;

    param::ConvBias param;
    param.nonlineMode = param::ConvBias::NonlineMode::RELU;
    param.pad_h = 1;
    param.pad_w = 1;
    param.stride_h = 2;
    param.stride_w = 2;
    param.sparse = param::ConvBias::Sparse::GROUP;

    std::vector<std::pair<SmallVector<TensorShape>, float>> shapes_and_computation;
    auto bench_case = [&](size_t N, size_t IC, size_t OC, size_t H, size_t W, size_t FS,
                          size_t group, size_t P, size_t S) {
        SmallVector<TensorShape> shapes{
                {N, IC, H, W},
                {group, OC / group, IC / group, FS, FS},
                {1, OC, 1, 1},
                {},
                {N, OC, (H + 2 * P - FS) / S + 1, (W + 2 * P - FS) / S + 1}};
        TensorShape dst{N, OC, H, W};
        float computations = ((IC / group) * FS * FS * dst.total_nr_elems() * 2 +
                              dst.total_nr_elems()) *
                             1e-6;
        shapes_and_computation.push_back(std::make_pair(shapes, computations));
    };

    bench_case(1, 32, 32, 200, 200, 3, 4, 1, 2);
    bench_case(1, 32, 32, 200, 200, 3, 32, 1, 2);
    bench_case(1, 32, 32, 128, 128, 3, 4, 1, 2);
    bench_case(1, 32, 32, 128, 128, 3, 32, 1, 2);
    bench_case(1, 32, 32, 100, 100, 3, 4, 1, 2);
    bench_case(1, 32, 32, 100, 100, 3, 32, 1, 2);
    bench_case(1, 32, 32, 80, 80, 3, 4, 1, 2);
    bench_case(1, 32, 32, 80, 80, 3, 32, 1, 2);

    std::string algo_name = "ARMDOTS8STRD2";
    printf("Benchmark ARMDOTS8STRD2_LARGE_GROUP algo\n");
    std::vector<DType> data_type = {
            dtype::QuantizedS8(2.5f), dtype::QuantizedS8(2.5f),
            dtype::QuantizedS32(6.25f), dtype::QuantizedS8(60.25f)};
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {4, {4, 5, 6, 7}}, {1, {4}},
            data_type);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {4, {4, 5, 6, 7}}, {1, {7}},
            data_type);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {2, {4, 5}}, {1, {4}},
            data_type);
    shapes_and_computation.clear();

    algo_name = "ARMDOTS8STRD2";
    printf("Benchmark ARMDOTS8STRD2_SMALL_GROUP algo\n");
    bench_case(1, 32, 32, 200, 200, 3, 1, 1, 2);
    bench_case(1, 32, 32, 128, 128, 3, 1, 1, 2);
    bench_case(1, 32, 32, 100, 100, 3, 1, 1, 2);
    bench_case(1, 32, 32, 80, 80, 3, 1, 1, 2);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {4, {4, 5, 6, 7}}, {1, {4}},
            data_type);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {4, {4, 5, 6, 7}}, {1, {7}},
            data_type);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {2, {4, 5}}, {1, {4}},
            data_type);
}
#endif

TEST_F(ARM_COMMON_BENCHMARK_MULTI_THREADS,
       BENCHMARK_CONVBIAS_QUINT8_QUINT8_QUINT8_STRIDE1) {
    constexpr size_t RUNS = 50;

    param::ConvBias param;
    param.nonlineMode = param::ConvBias::NonlineMode::RELU;
    param.pad_h = 1;
    param.pad_w = 1;
    param.stride_h = 1;
    param.stride_w = 1;
    param.sparse = param::ConvBias::Sparse::GROUP;

    std::vector<std::pair<SmallVector<TensorShape>, float>> shapes_and_computation;
    auto bench_case = [&](size_t N, size_t IC, size_t OC, size_t H, size_t W, size_t FS,
                          size_t group, size_t P, size_t S) {
        SmallVector<TensorShape> shapes{
                {N, IC, H, W},
                {group, OC / group, IC / group, FS, FS},
                {1, OC, 1, 1},
                {},
                {N, OC, (H + 2 * P - FS) / S + 1, (W + 2 * P - FS) / S + 1}};
        TensorShape dst{N, OC, H, W};
        float computations = ((IC / group) * FS * FS * dst.total_nr_elems() * 2 +
                              dst.total_nr_elems()) *
                             1e-6;
        shapes_and_computation.push_back(std::make_pair(shapes, computations));
    };

    bench_case(1, 32, 32, 200, 200, 3, 4, 1, 1);
    bench_case(1, 32, 32, 200, 200, 3, 32, 1, 1);
    bench_case(1, 32, 32, 128, 128, 3, 4, 1, 1);
    bench_case(1, 32, 32, 128, 128, 3, 32, 1, 1);
    bench_case(1, 32, 32, 100, 100, 3, 4, 1, 1);
    bench_case(1, 32, 32, 100, 100, 3, 32, 1, 1);
    bench_case(1, 32, 32, 80, 80, 3, 4, 1, 1);
    bench_case(1, 32, 32, 80, 80, 3, 32, 1, 1);

    std::string algo_name = "QU8STRD1";
    printf("Benchmark QU8STRD1_LARGE_GROUP algo\n");
    std::vector<DType> data_type = {
            dtype::Quantized8Asymm(0.2f, 100), dtype::Quantized8Asymm(0.2f, 120),
            dtype::QuantizedS32(0.04f), dtype::Quantized8Asymm(1.4f, 110)};
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {4, {4, 5, 6, 7}}, {1, {4}},
            data_type);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {4, {4, 5, 6, 7}}, {1, {7}},
            data_type);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {2, {4, 5}}, {1, {4}},
            data_type);
    shapes_and_computation.clear();

    algo_name = "QU8STRD1";
    printf("Benchmark QU8STRD1_SMALL_GROUP algo\n");
    bench_case(1, 32, 32, 200, 200, 3, 1, 1, 1);
    bench_case(1, 32, 32, 128, 128, 3, 1, 1, 1);
    bench_case(1, 32, 32, 100, 100, 3, 1, 1, 1);
    bench_case(1, 32, 32, 80, 80, 3, 1, 1, 1);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {4, {4, 5, 6, 7}}, {1, {4}},
            data_type);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {4, {4, 5, 6, 7}}, {1, {7}},
            data_type);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {2, {4, 5}}, {1, {4}},
            data_type);
}
TEST_F(ARM_COMMON_BENCHMARK_MULTI_THREADS,
       BENCHMARK_CONVBIAS_QUINT8_QUINT8_QUINT8_STRIDE2) {
    constexpr size_t RUNS = 50;

    param::ConvBias param;
    param.nonlineMode = param::ConvBias::NonlineMode::RELU;
    param.pad_h = 1;
    param.pad_w = 1;
    param.stride_h = 2;
    param.stride_w = 2;
    param.sparse = param::ConvBias::Sparse::GROUP;

    std::vector<std::pair<SmallVector<TensorShape>, float>> shapes_and_computation;
    auto bench_case = [&](size_t N, size_t IC, size_t OC, size_t H, size_t W, size_t FS,
                          size_t group, size_t P, size_t S) {
        SmallVector<TensorShape> shapes{
                {N, IC, H, W},
                {group, OC / group, IC / group, FS, FS},
                {1, OC, 1, 1},
                {},
                {N, OC, (H + 2 * P - FS) / S + 1, (W + 2 * P - FS) / S + 1}};
        TensorShape dst{N, OC, H, W};
        float computations = ((IC / group) * FS * FS * dst.total_nr_elems() * 2 +
                              dst.total_nr_elems()) *
                             1e-6;
        shapes_and_computation.push_back(std::make_pair(shapes, computations));
    };

    bench_case(1, 32, 32, 200, 200, 3, 4, 1, 2);
    bench_case(1, 32, 32, 200, 200, 3, 32, 1, 2);
    bench_case(1, 32, 32, 128, 128, 3, 4, 1, 2);
    bench_case(1, 32, 32, 128, 128, 3, 32, 1, 2);
    bench_case(1, 32, 32, 100, 100, 3, 4, 1, 2);
    bench_case(1, 32, 32, 100, 100, 3, 32, 1, 2);
    bench_case(1, 32, 32, 80, 80, 3, 4, 1, 2);
    bench_case(1, 32, 32, 80, 80, 3, 32, 1, 2);

    std::string algo_name = "QU8STRD2";
    printf("Benchmark QU8STRD2_LARGE_GROUP algo\n");
    std::vector<DType> data_type = {
            dtype::Quantized8Asymm(0.2f, 100), dtype::Quantized8Asymm(0.2f, 120),
            dtype::QuantizedS32(0.04f), dtype::Quantized8Asymm(1.4f, 110)};
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {4, {4, 5, 6, 7}}, {1, {4}},
            data_type);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {4, {4, 5, 6, 7}}, {1, {7}},
            data_type);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {2, {4, 5}}, {1, {4}},
            data_type);
    shapes_and_computation.clear();

    algo_name = "QU8STRD2";
    printf("Benchmark QU8STRD2_SMALL_GROUP algo\n");
    bench_case(1, 32, 32, 200, 200, 3, 1, 1, 2);
    bench_case(1, 32, 32, 128, 128, 3, 1, 1, 2);
    bench_case(1, 32, 32, 100, 100, 3, 1, 1, 2);
    bench_case(1, 32, 32, 80, 80, 3, 1, 1, 2);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {4, {4, 5, 6, 7}}, {1, {4}},
            data_type);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {4, {4, 5, 6, 7}}, {1, {7}},
            data_type);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {2, {4, 5}}, {1, {4}},
            data_type);
}
#if MGB_ENABLE_DOT
TEST_F(ARM_COMMON_BENCHMARK_MULTI_THREADS,
       BENCHMARK_CONVBIAS_QUINT8_QUINT8_QUINT8_STRIDE1_WITHDOTPROD) {
    constexpr size_t RUNS = 50;

    param::ConvBias param;
    param.nonlineMode = param::ConvBias::NonlineMode::RELU;
    param.pad_h = 1;
    param.pad_w = 1;
    param.stride_h = 1;
    param.stride_w = 1;
    param.sparse = param::ConvBias::Sparse::GROUP;

    std::vector<std::pair<SmallVector<TensorShape>, float>> shapes_and_computation;
    auto bench_case = [&](size_t N, size_t IC, size_t OC, size_t H, size_t W, size_t FS,
                          size_t group, size_t P, size_t S) {
        SmallVector<TensorShape> shapes{
                {N, IC, H, W},
                {group, OC / group, IC / group, FS, FS},
                {1, OC, 1, 1},
                {},
                {N, OC, (H + 2 * P - FS) / S + 1, (W + 2 * P - FS) / S + 1}};
        TensorShape dst{N, OC, (H + 2 * P - FS) / S + 1, (W + 2 * P - FS) / S + 1};
        float computations = ((IC / group) * FS * FS * dst.total_nr_elems() * 2 +
                              dst.total_nr_elems()) *
                             1e-6;
        shapes_and_computation.push_back(std::make_pair(shapes, computations));
    };

    bench_case(1, 32, 32, 200, 200, 3, 4, 1, 1);
    bench_case(1, 32, 32, 200, 200, 3, 32, 1, 1);
    bench_case(1, 32, 32, 128, 128, 3, 4, 1, 1);
    bench_case(1, 32, 32, 128, 128, 3, 32, 1, 1);
    bench_case(1, 32, 32, 100, 100, 3, 4, 1, 1);
    bench_case(1, 32, 32, 100, 100, 3, 32, 1, 1);
    bench_case(1, 32, 32, 80, 80, 3, 4, 1, 1);
    bench_case(1, 32, 32, 80, 80, 3, 32, 1, 1);

    std::string algo_name = "ARMDOTU8STRD1";
    printf("Benchmark ARMDOTU8STRD1_LARGE_GROUP algo\n");
    std::vector<DType> data_type = {
            dtype::Quantized8Asymm(0.2f, 100), dtype::Quantized8Asymm(0.2f, 120),
            dtype::QuantizedS32(0.04f), dtype::Quantized8Asymm(1.4f, 110)};
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {4, {4, 5, 6, 7}}, {1, {4}},
            data_type);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {4, {4, 5, 6, 7}}, {1, {7}},
            data_type);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {2, {4, 5}}, {1, {4}},
            data_type);
    shapes_and_computation.clear();

    algo_name = "ARMDOTU8STRD1";
    printf("Benchmark ARMDOTS8STRD1_SMALL_GROUP algo\n");
    bench_case(1, 32, 32, 200, 200, 3, 1, 1, 1);
    bench_case(1, 32, 32, 128, 128, 3, 1, 1, 1);
    bench_case(1, 32, 32, 100, 100, 3, 1, 1, 1);
    bench_case(1, 32, 32, 80, 80, 3, 1, 1, 1);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {4, {4, 5, 6, 7}}, {1, {4}},
            data_type);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {4, {4, 5, 6, 7}}, {1, {7}},
            data_type);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {2, {4, 5}}, {1, {4}},
            data_type);
}
TEST_F(ARM_COMMON_BENCHMARK_MULTI_THREADS,
       BENCHMARK_CONVBIAS_QUINT8_QUINT8_QUINT8_STRIDE2_WITHDOTPROD) {
    constexpr size_t RUNS = 50;

    param::ConvBias param;
    param.nonlineMode = param::ConvBias::NonlineMode::RELU;
    param.pad_h = 1;
    param.pad_w = 1;
    param.stride_h = 2;
    param.stride_w = 2;
    param.sparse = param::ConvBias::Sparse::GROUP;

    std::vector<std::pair<SmallVector<TensorShape>, float>> shapes_and_computation;
    auto bench_case = [&](size_t N, size_t IC, size_t OC, size_t H, size_t W, size_t FS,
                          size_t group, size_t P, size_t S) {
        SmallVector<TensorShape> shapes{
                {N, IC, H, W},
                {group, OC / group, IC / group, FS, FS},
                {1, OC, 1, 1},
                {},
                {N, OC, (H + 2 * P - FS) / S + 1, (W + 2 * P - FS) / S + 1}};
        TensorShape dst{N, OC, (H + 2 * P - FS) / S + 1, (W + 2 * P - FS) / S + 1};
        float computations = ((IC / group) * FS * FS * dst.total_nr_elems() * 2 +
                              dst.total_nr_elems()) *
                             1e-6;
        shapes_and_computation.push_back(std::make_pair(shapes, computations));
    };

    bench_case(1, 32, 32, 200, 200, 5, 4, 1, 2);
    bench_case(1, 32, 32, 200, 200, 5, 32, 1, 2);
    bench_case(1, 32, 32, 128, 128, 5, 4, 1, 2);
    bench_case(1, 32, 32, 128, 128, 5, 32, 1, 2);
    bench_case(1, 32, 32, 100, 100, 5, 4, 1, 2);
    bench_case(1, 32, 32, 100, 100, 5, 32, 1, 2);
    bench_case(1, 32, 32, 80, 80, 5, 4, 1, 2);
    bench_case(1, 32, 32, 80, 80, 5, 32, 1, 2);

    std::string algo_name = "ARMDOTU8STRD2";
    printf("Benchmark ARMDOTU8STRD2_LARGE_GROUP algo\n");
    std::vector<DType> data_type = {
            dtype::Quantized8Asymm(0.2f, 100), dtype::Quantized8Asymm(0.2f, 120),
            dtype::QuantizedS32(0.04f), dtype::Quantized8Asymm(1.4f, 110)};
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {4, {4, 5, 6, 7}}, {1, {4}},
            data_type);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {4, {4, 5, 6, 7}}, {1, {7}},
            data_type);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {2, {4, 5}}, {1, {4}},
            data_type);
    shapes_and_computation.clear();

    algo_name = "ARMDOTU8STRD2";
    printf("Benchmark ARMDOTU8STRD2_SMALL_GROUP algo\n");
    bench_case(1, 32, 32, 200, 200, 5, 1, 1, 2);
    bench_case(1, 32, 32, 128, 128, 5, 1, 1, 2);
    bench_case(1, 32, 32, 100, 100, 5, 1, 1, 2);
    bench_case(1, 32, 32, 80, 80, 5, 1, 1, 2);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {4, {4, 5, 6, 7}}, {1, {4}},
            data_type);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {4, {4, 5, 6, 7}}, {1, {7}},
            data_type);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {2, {4, 5}}, {1, {4}},
            data_type);
}
#endif

TEST_F(ARM_COMMON_BENCHMARK_MULTI_THREADS, BENCHMARK_CONVBIAS_WINOGRAD_F32) {
    constexpr size_t RUNS = 50;

    param::ConvBias param;
    param.nonlineMode = param::ConvBias::NonlineMode::RELU;
    param.pad_h = 1;
    param.pad_w = 1;
    param.stride_h = 1;
    param.stride_w = 1;
    param.sparse = param::ConvBias::Sparse::GROUP;

    std::vector<std::pair<SmallVector<TensorShape>, float>> shapes_and_computation;
    auto bench_case = [&](size_t N, size_t IC, size_t OC, size_t H, size_t W, size_t FS,
                          size_t group) {
        SmallVector<TensorShape> shapes{
                {N, IC, H, W},
                {group, OC / group, IC / group, FS, FS},
                {1, OC, 1, 1},
                {},
                {N, OC, H, W}};
        TensorShape dst{N, OC, H, W};
        float computations = ((IC / group) * FS * FS * dst.total_nr_elems() * 2 +
                              dst.total_nr_elems()) *
                             1e-6;
        shapes_and_computation.push_back(std::make_pair(shapes, computations));
    };

    bench_case(1, 32, 32, 200, 200, 3, 4);
    bench_case(1, 32, 32, 200, 200, 3, 1);
    bench_case(1, 32, 32, 128, 128, 3, 4);
    bench_case(1, 32, 32, 128, 128, 3, 1);
    bench_case(1, 32, 32, 100, 100, 3, 4);
    bench_case(1, 32, 32, 100, 100, 3, 1);
    bench_case(1, 32, 32, 80, 80, 3, 4);

    bench_case(1, 512, 512, 14, 14, 3, 1);
    bench_case(1, 512, 256, 14, 14, 3, 1);
    bench_case(1, 512, 128, 14, 14, 3, 1);
    bench_case(1, 512, 64, 14, 14, 3, 1);

    bench_case(1, 512, 512, 7, 7, 3, 1);
    bench_case(1, 512, 256, 7, 7, 3, 1);
    bench_case(1, 512, 128, 7, 7, 3, 1);
    bench_case(1, 512, 64, 7, 7, 3, 1);

    std::string algo_name;
#if MEGDNN_AARCH64
    algo_name = "WINOGRAD:AARCH64_F32_MK4_4x16:4:2";
#else
    algo_name = "WINOGRAD:ARMV7_F32_MK4_4x8:4:2";
#endif
    std::vector<DType> data_type = {
            dtype::Float32(), dtype::Float32(), dtype::Float32(), dtype::Float32()};
    printf("Benchmark WINOGRAD_F32_MK4 algo\n");
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {4, {4, 5, 6, 7}}, {1, {4}},
            data_type);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {4, {4, 5, 6, 7}}, {1, {7}},
            data_type);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {2, {4, 5}}, {1, {4}},
            data_type);
}

TEST_F(ARM_COMMON_BENCHMARK_MULTI_THREADS, BENCHMARK_CONVBIAS_WINOGRAD_INT8) {
    constexpr size_t RUNS = 50;

    param::ConvBias param;
    param.nonlineMode = param::ConvBias::NonlineMode::RELU;
    param.pad_h = 1;
    param.pad_w = 1;
    param.stride_h = 1;
    param.stride_w = 1;
    param.sparse = param::ConvBias::Sparse::GROUP;

    std::vector<std::pair<SmallVector<TensorShape>, float>> shapes_and_computation;
    auto bench_case = [&](size_t N, size_t IC, size_t OC, size_t H, size_t W, size_t FS,
                          size_t group) {
        SmallVector<TensorShape> shapes{
                {N, IC, H, W},
                {group, OC / group, IC / group, FS, FS},
                {1, OC, 1, 1},
                {},
                {N, OC, H, W}};
        TensorShape dst{N, OC, H, W};
        float computations = ((IC / group) * FS * FS * dst.total_nr_elems() * 2 +
                              dst.total_nr_elems()) *
                             1e-6;
        shapes_and_computation.push_back(std::make_pair(shapes, computations));
    };

    bench_case(1, 32, 32, 200, 200, 3, 4);
    bench_case(1, 32, 32, 200, 200, 3, 1);
    bench_case(1, 32, 32, 128, 128, 3, 4);
    bench_case(1, 32, 32, 128, 128, 3, 1);
    bench_case(1, 32, 32, 100, 100, 3, 4);
    bench_case(1, 32, 32, 100, 100, 3, 1);
    bench_case(1, 32, 32, 80, 80, 3, 4);

    bench_case(1, 512, 512, 14, 14, 3, 1);
    bench_case(1, 512, 256, 14, 14, 3, 1);
    bench_case(1, 512, 128, 14, 14, 3, 1);
    bench_case(1, 512, 64, 14, 14, 3, 1);

    bench_case(1, 512, 512, 7, 7, 3, 1);
    bench_case(1, 512, 256, 7, 7, 3, 1);
    bench_case(1, 512, 128, 7, 7, 3, 1);
    bench_case(1, 512, 64, 7, 7, 3, 1);

    std::string algo_name;
#if MEGDNN_AARCH64
    algo_name = "WINOGRAD:AARCH64_INT16X16X32_MK8_8X8:8:2:32";
#else
    algo_name = "WINOGRAD:ARMV7_INT16X16X32_MK8_4X8:8:2:32";
#endif

    std::vector<DType> data_type = {
            dtype::QuantizedS8(2.5f), dtype::QuantizedS8(2.5f),
            dtype::QuantizedS32(6.25f), dtype::QuantizedS8(60.25f)};
    printf("Benchmark WINOGRAD_IN8_MK8 algo\n");
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {4, {4, 5, 6, 7}}, {1, {4}},
            data_type);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {4, {4, 5, 6, 7}}, {1, {7}},
            data_type);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {2, {4, 5}}, {1, {4}},
            data_type);
}

TEST_F(ARM_COMMON_BENCHMARK_MULTI_THREADS,
       BENCHMARK_CONVBIAS_WINOGRAD_NCHW44_INT8_MK8) {
    constexpr size_t RUNS = 50;

    param::ConvBias param;
    param.nonlineMode = param::ConvBias::NonlineMode::RELU;
    param.pad_h = 1;
    param.pad_w = 1;
    param.stride_h = 1;
    param.stride_w = 1;
    param.sparse = param::ConvBias::Sparse::DENSE;
    param.format = param::ConvBias::Format::NCHW44;

    std::vector<std::pair<SmallVector<TensorShape>, float>> shapes_and_computation;
    auto bench_case = [&](size_t N, size_t IC, size_t OC, size_t H, size_t W, size_t FS,
                          size_t group) {
        SmallVector<TensorShape> shapes{
                {N, IC / 4, H, W, 4},
                {OC / 4, IC / 4, FS, FS, 4, 4},
                {1, OC / 4, 1, 1, 4},
                {},
                {N, OC / 4, H, W, 4}};
        TensorShape dst{N, OC, H, W};
        float computations = ((IC / group) * FS * FS * dst.total_nr_elems() * 2 +
                              dst.total_nr_elems()) *
                             1e-6;
        shapes_and_computation.push_back(std::make_pair(shapes, computations));
    };

    bench_case(1, 32, 32, 200, 200, 3, 1);
    bench_case(1, 32, 32, 128, 128, 3, 1);
    bench_case(1, 32, 32, 100, 100, 3, 1);

    bench_case(1, 512, 512, 14, 14, 3, 1);
    bench_case(1, 512, 256, 14, 14, 3, 1);
    bench_case(1, 512, 128, 14, 14, 3, 1);
    bench_case(1, 512, 64, 14, 14, 3, 1);

    bench_case(1, 512, 512, 7, 7, 3, 1);
    bench_case(1, 512, 256, 7, 7, 3, 1);
    bench_case(1, 512, 128, 7, 7, 3, 1);
    bench_case(1, 512, 64, 7, 7, 3, 1);

    std::string algo_name;
#if MEGDNN_AARCH64
    algo_name = "WINOGRAD_NCHW44:AARCH64_INT16X16X32_MK8_8X8:8:2:32";
#else
    algo_name = "WINOGRAD_NCHW44:ARMV7_INT16X16X32_MK8_4X8:8:2:32";
#endif

    std::vector<DType> data_type = {
            dtype::QuantizedS8(2.5f), dtype::QuantizedS8(2.5f),
            dtype::QuantizedS32(6.25f), dtype::QuantizedS8(60.25f)};
    printf("Benchmark WINOGRAD_INT8_MK8 algo\n");
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {4, {4, 5, 6, 7}}, {1, {4}},
            data_type);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {4, {4, 5, 6, 7}}, {1, {7}},
            data_type);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {2, {4, 5}}, {1, {4}},
            data_type);
}

TEST_F(ARM_COMMON_BENCHMARK_MULTI_THREADS,
       BENCHMARK_CONVBIAS_WINOGRAD_NCHW44_INT8_COMP_F32) {
    constexpr size_t RUNS = 50;

    param::ConvBias param;
    param.nonlineMode = param::ConvBias::NonlineMode::RELU;
    param.pad_h = 1;
    param.pad_w = 1;
    param.stride_h = 1;
    param.stride_w = 1;
    param.sparse = param::ConvBias::Sparse::DENSE;  // GROUP;
    param.format = param::ConvBias::Format::NCHW44;

    std::vector<std::pair<SmallVector<TensorShape>, float>> shapes_and_computation;
    auto bench_case = [&](size_t N, size_t IC, size_t OC, size_t H, size_t W, size_t FS,
                          size_t group) {
        SmallVector<TensorShape> shapes{
                {N, IC / 4, H, W, 4},
                {OC / 4, IC / 4, FS, FS, 4, 4},
                {1, OC / 4, 1, 1, 4},
                {},
                {N, OC / 4, H, W, 4}};
        TensorShape dst{N, OC, H, W};
        float computations = ((IC / group) * FS * FS * dst.total_nr_elems() * 2 +
                              dst.total_nr_elems()) *
                             1e-6;
        shapes_and_computation.push_back(std::make_pair(shapes, computations));
    };

    bench_case(1, 32, 32, 200, 200, 3, 1);
    bench_case(1, 32, 32, 128, 128, 3, 1);
    bench_case(1, 32, 32, 100, 100, 3, 1);

    bench_case(1, 512, 512, 14, 14, 3, 1);
    bench_case(1, 512, 256, 14, 14, 3, 1);
    bench_case(1, 512, 128, 14, 14, 3, 1);
    bench_case(1, 512, 64, 14, 14, 3, 1);

    bench_case(1, 512, 512, 7, 7, 3, 1);
    bench_case(1, 512, 256, 7, 7, 3, 1);
    bench_case(1, 512, 128, 7, 7, 3, 1);
    bench_case(1, 512, 64, 7, 7, 3, 1);

    std::string algo_name;
#if MEGDNN_AARCH64
    algo_name = "WINOGRAD_NCHW44:AARCH64_F32_MK4_4x16:4:2:32";
#else
    algo_name = "WINOGRAD_NCHW44:ARMV7_F32_MK4_4x8:4:2:32";
#endif

    std::vector<DType> data_type = {
            dtype::QuantizedS8(2.5f), dtype::QuantizedS8(2.5f),
            dtype::QuantizedS32(6.25f), dtype::QuantizedS8(60.25f)};
    printf("Benchmark WINOGRAD_INT8_NCHW44_MK4_COMP_F32 algo\n");
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {4, {4, 5, 6, 7}}, {1, {4}},
            data_type);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {4, {4, 5, 6, 7}}, {1, {7}},
            data_type);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {2, {4, 5}}, {1, {4}},
            data_type);
}

TEST_F(ARM_COMMON_BENCHMARK_MULTI_THREADS, BENCHMARK_CONVBIAS_IM2COL_FP32) {
    constexpr size_t RUNS = 50;

    param::ConvBias param;
    param.nonlineMode = param::ConvBias::NonlineMode::RELU;
    param.pad_h = 1;
    param.pad_w = 1;
    param.stride_h = 1;
    param.stride_w = 1;

    std::vector<std::pair<SmallVector<TensorShape>, float>> shapes_and_computation;
    auto bench_case = [&](size_t N, size_t IC, size_t OC, size_t H, size_t W, size_t FS,
                          size_t group) {
        SmallVector<TensorShape> shapes{
                {N, IC, H, W},
                {OC, IC / group, FS, FS},
                {1, OC, 1, 1},
                {},
                {N, OC, H, W}};
        TensorShape dst{N, OC, H, W};
        float computations = ((IC / group) * FS * FS * dst.total_nr_elems() * 2 +
                              dst.total_nr_elems()) *
                             1e-6;
        shapes_and_computation.push_back(std::make_pair(shapes, computations));
    };
    std::vector<DType> data_type = {
            dtype::Float32(), dtype::Float32(), dtype::Float32(), dtype::Float32()};
    bench_case(1, 32, 32, 300, 300, 3, 1);
    bench_case(1, 32, 32, 400, 400, 3, 1);
    bench_case(1, 32, 32, 100, 100, 3, 1);
    bench_case(1, 32, 32, 80, 80, 3, 1);
    bench_case(1, 32, 64, 200, 200, 3, 1);
    bench_case(1, 32, 64, 128, 128, 3, 1);
    bench_case(1, 32, 64, 100, 100, 3, 1);
    bench_case(1, 32, 64, 80, 80, 3, 1);
    bench_case(1, 32, 128, 200, 200, 3, 1);
    bench_case(1, 32, 128, 128, 128, 3, 1);
    bench_case(1, 32, 128, 100, 100, 3, 1);
    bench_case(1, 32, 128, 80, 80, 3, 1);

    bench_case(1, 64, 32, 7, 7, 3, 1);
    bench_case(1, 64, 64, 7, 7, 3, 1);
    bench_case(1, 64, 128, 7, 7, 3, 1);
    bench_case(1, 64, 256, 7, 7, 3, 1);
    bench_case(1, 64, 512, 7, 7, 3, 1);
    bench_case(1, 64, 1024, 7, 7, 3, 1);

    bench_case(1, 64, 32, 14, 14, 3, 1);
    bench_case(1, 64, 64, 14, 14, 3, 1);
    bench_case(1, 64, 128, 14, 14, 3, 1);
    bench_case(1, 64, 256, 14, 14, 3, 1);
    bench_case(1, 64, 512, 14, 14, 3, 1);

    bench_case(1, 64, 1024, 14, 14, 3, 1);
    bench_case(1, 128, 128, 14, 14, 3, 1);
    bench_case(1, 128, 256, 14, 14, 3, 1);
    bench_case(1, 512, 512, 14, 14, 3, 1);
    bench_case(1, 256, 512, 14, 14, 3, 1);
    bench_case(1, 512, 1024, 14, 14, 3, 1);
    bench_case(1, 1024, 1024, 14, 14, 3, 1);
    std::string algo_name = "IM2COLMATMUL:AARCH64_F32K8X12X1:96";
    printf("Benchmark IM2COLMATMUL:AARCH64_F32K8X12X1algo:96\n");
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {4, {4, 5, 6, 7}}, {1, {4}},
            data_type);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {4, {4, 5, 6, 7}}, {1, {7}},
            data_type);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {2, {4, 5}}, {1, {4}},
            data_type);
    algo_name = "IM2COLMATMUL:AARCH64_F32K8X12X1:192";
    printf("Benchmark IM2COLMATMUL:AARCH64_F32K8X12X1algo:192\n");
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {4, {4, 5, 6, 7}}, {1, {4}},
            data_type);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {4, {4, 5, 6, 7}}, {1, {7}},
            data_type);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {2, {4, 5}}, {1, {4}},
            data_type);
    algo_name = "IM2COLMATMUL:AARCH64_F32K8X12X1:384";
    printf("Benchmark IM2COLMATMUL:AARCH64_F32K8X12X1algo:384\n");
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {4, {4, 5, 6, 7}}, {1, {4}},
            data_type);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {4, {4, 5, 6, 7}}, {1, {7}},
            data_type);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {2, {4, 5}}, {1, {4}},
            data_type);
    shapes_and_computation.clear();
}

TEST_F(ARM_COMMON_BENCHMARK_MULTI_THREADS, BENCHMARK_CONVBIAS_IM2COL_NCHW44_VS_NCHW88) {
    constexpr size_t RUNS = 50;
    using NLMode = param::ConvBias::NonlineMode;

    std::vector<conv_bias::TestArg> args_nchw88, args_nchw44;
    auto bench_case = [&](size_t N, size_t IC, size_t OC, size_t H, size_t W, size_t FS,
                          size_t group) {
        param::ConvBias param_nchw88, param_nchw44;
        param_nchw88.format = param::ConvBias::Format::NCHW88;
        param_nchw44.format = param::ConvBias::Format::NCHW44;
        for (size_t pad : {1, 2, 4}) {
            for (size_t stride : {1, 2, 3}) {
                for (auto nlmode :
                     {NLMode::RELU, NLMode::IDENTITY, NLMode::SIGMOID,
                      NLMode::H_SWISH}) {
                    param_nchw88.nonlineMode = nlmode;
                    param_nchw88.pad_h = pad;
                    param_nchw88.pad_w = pad;
                    param_nchw88.stride_h = stride;
                    param_nchw88.stride_w = stride;

                    param_nchw44.nonlineMode = nlmode;
                    param_nchw44.pad_h = pad;
                    param_nchw44.pad_w = pad;
                    param_nchw44.stride_h = stride;
                    param_nchw44.stride_w = stride;

                    args_nchw88.emplace_back(
                            param_nchw88, TensorShape{N, IC / 8, H, W, 8},
                            TensorShape{OC / 8, IC / group / 8, FS, FS, 8, 8},
                            TensorShape{1, OC / 8, 1, 1, 8});
                    args_nchw44.emplace_back(
                            param_nchw44, TensorShape{N, IC / 4, H, W, 4},
                            TensorShape{OC / 4, IC / group / 4, FS, FS, 4, 4},
                            TensorShape{1, OC / 4, 1, 1, 4});
                }
            }
        }
    };
    std::vector<DType> data_type_fp16 = {
            dtype::Float16(), dtype::Float16(), dtype::Float16(), dtype::Float16()};
    std::vector<DType> data_type_fp32 = {
            dtype::Float32(), dtype::Float32(), dtype::Float32(), dtype::Float32()};
    bench_case(1, 32, 32, 300, 300, 3, 1);
    bench_case(1, 32, 32, 400, 400, 3, 1);
    bench_case(1, 32, 32, 100, 100, 3, 1);
    bench_case(1, 32, 32, 80, 80, 3, 1);
    bench_case(1, 32, 64, 200, 200, 3, 1);
    bench_case(1, 32, 64, 128, 128, 3, 1);
    bench_case(1, 32, 64, 100, 100, 3, 1);
    bench_case(1, 32, 64, 80, 80, 3, 1);
    bench_case(1, 32, 128, 200, 200, 3, 1);
    bench_case(1, 32, 128, 128, 128, 3, 1);
    bench_case(1, 32, 128, 100, 100, 3, 1);
    bench_case(1, 32, 128, 80, 80, 3, 1);

    bench_case(1, 64, 32, 7, 7, 3, 1);
    bench_case(1, 64, 64, 7, 7, 3, 1);
    bench_case(1, 64, 128, 7, 7, 3, 1);
    bench_case(1, 64, 256, 7, 7, 3, 1);
    bench_case(1, 64, 512, 7, 7, 3, 1);
    bench_case(1, 64, 1024, 7, 7, 3, 1);

    bench_case(1, 64, 32, 14, 14, 3, 1);
    bench_case(1, 64, 64, 14, 14, 3, 1);
    bench_case(1, 64, 128, 14, 14, 3, 1);
    bench_case(1, 64, 256, 14, 14, 3, 1);
    bench_case(1, 64, 512, 14, 14, 3, 1);

    bench_case(1, 64, 1024, 14, 14, 3, 1);
    bench_case(1, 128, 128, 14, 14, 3, 1);
    bench_case(1, 128, 256, 14, 14, 3, 1);
    bench_case(1, 512, 512, 14, 14, 3, 1);
    bench_case(1, 256, 512, 14, 14, 3, 1);
    bench_case(1, 512, 1024, 14, 14, 3, 1);
    bench_case(1, 1024, 1024, 14, 14, 3, 1);
    std::string algo_name_nchw88 = "IM2COLMATMUL:AARCH64_F16_MK8_16X12X1:96";
    std::string algo_name_nchw44 = "IM2COLMATMUL:AARCH64_F32_MK4_K8X12X1:96";

    benchmark_with_contrast(
            args_nchw88, algo_name_nchw88, data_type_fp16, args_nchw44,
            algo_name_nchw44, data_type_fp32, RUNS, {1, {4}});
}

TEST_F(ARM_COMMON_BENCHMARK_MULTI_THREADS,
       BENCHMARK_CHANNEL_WISE_INT8_INT8_INT8_STRIDE1) {
    constexpr size_t RUNS = 50;

    param::ConvBias param;
    param.nonlineMode = param::ConvBias::NonlineMode::RELU;
    param.pad_h = 1;
    param.pad_w = 1;
    param.stride_h = 1;
    param.stride_w = 1;
    param.sparse = param::ConvBias::Sparse::GROUP;
    param.format = param::ConvBias::Format::NCHW44;

    std::vector<std::pair<SmallVector<TensorShape>, float>> shapes_and_computation;
    auto bench_case = [&](size_t N, size_t IC, size_t H, size_t W, size_t FS,
                          size_t P) {
        size_t group = IC;
        size_t OC = IC;
        size_t S = 1;
        SmallVector<TensorShape> shapes{
                {N, IC, H, W, 4},
                {group, 1, 1, FS, FS, 4},
                {1, OC, 1, 1, 4},
                {},
                {N, OC, (H + 2 * P - FS) / S + 1, (W + 2 * P - FS) / S + 1, 4}};
        TensorShape dst{N, OC, (H + 2 * P - FS) / S + 1, (W + 2 * P - FS) / S + 1, 4};
        float computations = ((IC / group) * FS * FS * dst.total_nr_elems() * 2 +
                              dst.total_nr_elems()) *
                             1e-6;
        shapes_and_computation.push_back(std::make_pair(shapes, computations));
    };
    bench_case(1, 128, 200, 200, 3, 1);
    bench_case(1, 128, 128, 128, 3, 1);
    bench_case(1, 128, 100, 100, 3, 1);
    bench_case(1, 128, 80, 80, 3, 1);
    bench_case(1, 128, 56, 56, 3, 1);
    bench_case(1, 128, 28, 28, 3, 1);
    bench_case(1, 128, 14, 14, 3, 1);

    bench_case(1, 64, 200, 200, 3, 1);
    bench_case(1, 64, 128, 128, 3, 1);
    bench_case(1, 64, 100, 100, 3, 1);
    bench_case(1, 64, 80, 80, 3, 1);
    bench_case(1, 64, 56, 56, 3, 1);
    bench_case(1, 64, 28, 28, 3, 1);
    bench_case(1, 64, 14, 14, 3, 1);

    bench_case(1, 32, 200, 200, 3, 1);
    bench_case(1, 32, 128, 128, 3, 1);
    bench_case(1, 32, 100, 100, 3, 1);
    bench_case(1, 32, 80, 80, 3, 1);
    bench_case(1, 32, 56, 56, 3, 1);
    bench_case(1, 32, 28, 28, 3, 1);
    bench_case(1, 32, 14, 14, 3, 1);

    std::string algo_name = "S8_CHAN_WISE_STRD1_NCHW44";
    printf("Benchmarker S8_CHAN_WISE_STRD1_NCHW44 algo\n");
    std::vector<DType> data_type = {
            dtype::QuantizedS8(2.5f), dtype::QuantizedS8(2.5f),
            dtype::QuantizedS32(6.25f), dtype::QuantizedS8(60.25f)};
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {4, {4, 5, 6, 7}}, {1, {4}},
            data_type);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {4, {4, 5, 6, 7}}, {1, {7}},
            data_type);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {2, {4, 5}}, {1, {4}},
            data_type);
}

TEST_F(ARM_COMMON_BENCHMARK_MULTI_THREADS,
       BENCHMARK_CHANNEL_WISE_INT8_INT8_INT16_STRIDE1) {
    constexpr size_t RUNS = 50;

    param::ConvBias param;
    param.nonlineMode = param::ConvBias::NonlineMode::IDENTITY;
    param.pad_h = 1;
    param.pad_w = 1;
    param.stride_h = 1;
    param.stride_w = 1;
    param.sparse = param::ConvBias::Sparse::GROUP;
    param.format = param::ConvBias::Format::NCHW44;

    std::vector<std::pair<SmallVector<TensorShape>, float>> shapes_and_computation;
    auto bench_case = [&](size_t N, size_t IC, size_t H, size_t W, size_t FS,
                          size_t P) {
        size_t group = IC;
        size_t OC = IC;
        size_t S = 1;
        SmallVector<TensorShape> shapes{
                {N, IC, H, W, 4},
                {group, 1, 1, FS, FS, 4},
                {1, OC, 1, 1, 4},
                {},
                {N, OC, (H + 2 * P - FS) / S + 1, (W + 2 * P - FS) / S + 1, 4}};
        TensorShape dst{N, OC, (H + 2 * P - FS) / S + 1, (W + 2 * P - FS) / S + 1, 4};
        float computations = ((IC / group) * FS * FS * dst.total_nr_elems() * 2 +
                              dst.total_nr_elems()) *
                             1e-6;
        shapes_and_computation.push_back(std::make_pair(shapes, computations));
    };
    bench_case(1, 128, 200, 200, 3, 1);
    bench_case(1, 128, 128, 128, 3, 1);
    bench_case(1, 128, 100, 100, 3, 1);
    bench_case(1, 128, 80, 80, 3, 1);
    bench_case(1, 128, 56, 56, 3, 1);
    bench_case(1, 128, 28, 28, 3, 1);
    bench_case(1, 128, 14, 14, 3, 1);

    bench_case(1, 64, 200, 200, 3, 1);
    bench_case(1, 64, 128, 128, 3, 1);
    bench_case(1, 64, 100, 100, 3, 1);
    bench_case(1, 64, 80, 80, 3, 1);
    bench_case(1, 64, 56, 56, 3, 1);
    bench_case(1, 64, 28, 28, 3, 1);
    bench_case(1, 64, 14, 14, 3, 1);

    bench_case(1, 32, 200, 200, 3, 1);
    bench_case(1, 32, 128, 128, 3, 1);
    bench_case(1, 32, 100, 100, 3, 1);
    bench_case(1, 32, 80, 80, 3, 1);
    bench_case(1, 32, 56, 56, 3, 1);
    bench_case(1, 32, 28, 28, 3, 1);
    bench_case(1, 32, 14, 14, 3, 1);

    std::string algo_name = "S8x8x16_CHAN_WISE_STRD1_STRD2_NCHW44";
    printf("Benchmarker S8x8x16_CHAN_WISE_STRD1_STRD2_NCHW44 algo\n");
    std::vector<DType> data_type = {
            dtype::Int8(), dtype::Int8(), dtype::Int16(), dtype::Int16()};
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {4, {4, 5, 6, 7}}, {1, {4}},
            data_type);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {4, {4, 5, 6, 7}}, {1, {7}},
            data_type);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {2, {4, 5}}, {1, {4}},
            data_type);
}

TEST_F(ARM_COMMON_BENCHMARK_MULTI_THREADS, BENCHMARK_IM2COL_NCHW44_INT8x8x32_STRIDE1) {
    constexpr size_t RUNS = 50;

    param::ConvBias param;
    param.nonlineMode = param::ConvBias::NonlineMode::IDENTITY;
    param.pad_h = 1;
    param.pad_w = 1;
    param.stride_h = 1;
    param.stride_w = 1;
    param.sparse = param::ConvBias::Sparse::DENSE;
    param.format = param::ConvBias::Format::NCHW44;

    std::vector<std::pair<SmallVector<TensorShape>, float>> shapes_and_computation;
    auto bench_case = [&](size_t N, size_t IC, size_t OC, size_t H, size_t W, size_t FS,
                          size_t group = 1) {
        SmallVector<TensorShape> shapes{
                {N, IC, H, W, 4},
                {OC, IC / group, FS, FS, 4, 4},
                {/*1, OC, 1, 1*/},
                {},
                {N, OC, H, W, 4}};
        TensorShape dst{N, OC, H, W, 4};
        float computations = ((4 * IC / group) * FS * FS * dst.total_nr_elems() * 2 +
                              dst.total_nr_elems()) *
                             1e-6;
        shapes_and_computation.push_back(std::make_pair(shapes, computations));
    };

    bench_case(1, 32, 32, 300, 300, 3, 1);
    bench_case(1, 32, 32, 400, 400, 3, 1);
    bench_case(1, 32, 32, 100, 100, 3, 1);
    bench_case(1, 32, 32, 80, 80, 3, 1);
    bench_case(1, 32, 64, 200, 200, 3, 1);
    bench_case(1, 32, 64, 128, 128, 3, 1);
    bench_case(1, 32, 64, 100, 100, 3, 1);
    bench_case(1, 32, 64, 80, 80, 3, 1);
    bench_case(1, 32, 128, 200, 200, 3, 1);
    bench_case(1, 32, 128, 128, 128, 3, 1);
    bench_case(1, 32, 128, 100, 100, 3, 1);
    bench_case(1, 32, 128, 80, 80, 3, 1);
#if 1
    bench_case(1, 64, 32, 7, 7, 3, 1);
    bench_case(1, 64, 64, 7, 7, 3, 1);
    bench_case(1, 64, 128, 7, 7, 3, 1);
    bench_case(1, 64, 256, 7, 7, 3, 1);
    bench_case(1, 64, 512, 7, 7, 3, 1);
    bench_case(1, 64, 1024, 7, 7, 3, 1);

    bench_case(1, 64, 32, 14, 14, 3, 1);
    bench_case(1, 64, 64, 14, 14, 3, 1);
    bench_case(1, 64, 128, 14, 14, 3, 1);
    bench_case(1, 64, 256, 14, 14, 3, 1);
    bench_case(1, 64, 512, 14, 14, 3, 1);

    bench_case(1, 64, 1024, 14, 14, 3, 1);
    bench_case(1, 128, 128, 14, 14, 3, 1);
    bench_case(1, 128, 256, 14, 14, 3, 1);
    bench_case(1, 512, 512, 14, 14, 3, 1);
    bench_case(1, 256, 512, 14, 14, 3, 1);
    bench_case(1, 512, 1024, 14, 14, 3, 1);
    bench_case(1, 1024, 1024, 14, 14, 3, 1);
#endif
    std::string algo_name = "IM2COLMATMUL:AARCH64_INT8X8X32_MK4_4X4X16:96";
    printf("Benchmarker  IM2COLMATMUL:AARCH64_INT8X8X32_MK4_4X4X16:96  algo\n");
    std::vector<DType> data_type = {
            dtype::QuantizedS8(2.5f),
            dtype::QuantizedS8(2.5f),
            dtype::QuantizedS32(6.25f),
            {}};
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {4, {4, 5, 6, 7}}, {1, {4}},
            data_type);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {4, {4, 5, 6, 7}}, {1, {7}},
            data_type);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {2, {4, 5}}, {1, {4}},
            data_type);

    algo_name = "IM2COLMATMUL:AARCH64_INT8X8X32_MK4_4X4X16:192";
    printf("Benchmarker  IM2COLMATMUL:AARCH64_INT8X8X32_MK4_4X4X16:192  "
           "algo\n");
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {4, {4, 5, 6, 7}}, {1, {4}},
            data_type);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {4, {4, 5, 6, 7}}, {1, {7}},
            data_type);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {2, {4, 5}}, {1, {4}},
            data_type);

    algo_name = "IM2COLMATMUL:AARCH64_INT8X8X32_MK4_4X4X16:384";
    printf("Benchmarker  IM2COLMATMUL:AARCH64_INT8X8X32_MK4_4X4X16:384  "
           "algo\n");
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {4, {4, 5, 6, 7}}, {1, {4}},
            data_type);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {4, {4, 5, 6, 7}}, {1, {7}},
            data_type);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {2, {4, 5}}, {1, {4}},
            data_type);
}

#endif

/*================== BENCHMARK MULTITHREAD CONV1X1 =====================*/
#if MEGDNN_WITH_BENCHMARK

namespace {
std::vector<std::pair<SmallVector<TensorShape>, float>>
get_conv1x1_multithread_benchmark_args() {
    std::vector<std::pair<SmallVector<TensorShape>, float>> shapes_and_computation;
    auto bench_case = [&](size_t IC, size_t OC, size_t H, size_t W) {
        SmallVector<TensorShape> shapes{
                {1, IC, H, W}, {OC, IC, 1, 1}, {1, OC, 1, 1}, {}, {1, OC, H, W}};
        TensorShape dst{1, OC, H, W};
        float computations =
                (IC * dst.total_nr_elems() * 2 + dst.total_nr_elems()) * 1e-6;
        shapes_and_computation.push_back(std::make_pair(shapes, computations));
    };
    bench_case(32, 32, 300, 300);
    bench_case(32, 32, 400, 400);
    bench_case(32, 32, 100, 100);
    bench_case(32, 32, 80, 80);
    bench_case(32, 64, 200, 200);
    bench_case(32, 64, 128, 128);
    bench_case(32, 64, 100, 100);
    bench_case(32, 64, 80, 80);
    bench_case(32, 128, 200, 200);
    bench_case(32, 128, 128, 128);
    bench_case(32, 128, 100, 100);
    bench_case(32, 128, 80, 80);

    bench_case(64, 32, 7, 7);
    bench_case(64, 64, 7, 7);
    bench_case(64, 128, 7, 7);
    bench_case(64, 256, 7, 7);
    bench_case(64, 512, 7, 7);
    bench_case(64, 1024, 7, 7);

    bench_case(64, 32, 14, 14);
    bench_case(64, 64, 14, 14);
    bench_case(64, 128, 14, 14);
    bench_case(64, 256, 14, 14);
    bench_case(64, 512, 14, 14);

    bench_case(64, 1024, 14, 14);
    bench_case(128, 128, 14, 14);
    bench_case(128, 256, 14, 14);
    bench_case(512, 512, 14, 14);
    bench_case(256, 512, 14, 14);
    bench_case(512, 1024, 14, 14);
    bench_case(1024, 1024, 14, 14);
    return shapes_and_computation;
}

void conv1x1_multithread_benchmark(
        const char* algo_name, DType stype, DType ftype, DType btype, DType dtype) {
    constexpr size_t RUNS = 50;
    std::vector<std::pair<SmallVector<TensorShape>, float>> shapes_and_computation =
            get_conv1x1_multithread_benchmark_args();

    std::vector<DType> data_type = {stype, ftype, btype, dtype};

    param::ConvBias param;
    param.nonlineMode = param::ConvBias::NonlineMode::RELU;
    param.pad_h = 0;
    param.pad_w = 0;
    param.stride_h = 1;
    param.stride_w = 1;

    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {4, {4, 5, 6, 7}}, {1, {4}},
            data_type);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {4, {4, 5, 6, 7}}, {1, {7}},
            data_type);
    benchmark_impl(
            param, shapes_and_computation, algo_name, RUNS, {2, {4, 5}}, {1, {4}},
            data_type);
    shapes_and_computation.clear();
}
}  // namespace

TEST_F(ARM_COMMON_BENCHMARK_MULTI_THREADS, BENCHMARK_CONVBIAS_CONV1X1_S1_FP32) {
#if MEGDNN_AARCH64
    conv1x1_multithread_benchmark(
            "CONV1x1:AARCH64_F32K8X12X1:8", dtype::Float32(), dtype::Float32(),
            dtype::Float32(), dtype::Float32());
#else
    conv1x1_multithread_benchmark(
            "CONV1x1:ARMV7_F32:8", dtype::Float32(), dtype::Float32(), dtype::Float32(),
            dtype::Float32());
#endif
}

TEST_F(ARM_COMMON_BENCHMARK_MULTI_THREADS,
       BENCHMARK_CONVBIAS_CONV1X1_S1_QUANTIZEDASYM) {
    dtype::Quantized8Asymm stype(0.2f, 100);
    dtype::Quantized8Asymm ftype(0.2f, 120);
    dtype::QuantizedS32 btype(0.04f);
    dtype::Quantized8Asymm dtype(1.4f, 110);
#if MEGDNN_AARCH64
#if MGB_ENABLE_DOT
    conv1x1_multithread_benchmark(
            "CONV1x1:AARCH64_QUINT8_K8X8X4_DOTPROD:8", stype, ftype, btype, dtype);
#else
    conv1x1_multithread_benchmark(
            "CONV1x1:AARCH64_QUINT8_K8X8X8:8", stype, ftype, btype, dtype);
#endif
#else
    conv1x1_multithread_benchmark(
            "CONV1x1:ARMV7_QUINT8_K4X8X8:8", stype, ftype, btype, dtype);
#endif
}

#endif

// vim: syntax=cpp.doxygen