composable_kernel/tile_engine/ops/gemm_preshuffle/gemm_preshuffle_benchmark.hpp

#pragma once

#include "ck_tile/core.hpp"
#include "ck_tile/host.hpp"
#include "gemm_preshuffle_common.hpp"

//[TODO] Move parts of this File to commons
enum class Metric
{
    LATENCY   = 0,
    TFLOPS    = 1,
    BANDWIDTH = 2
};

inline constexpr auto get_metric_name(Metric m)
{
    switch(m)
    {
    case Metric::LATENCY: return "latency";
    case Metric::TFLOPS: return "tflops";
    case Metric::BANDWIDTH: return "bandwidth";
    default: throw std::invalid_argument("Unsupported metric type");
    }
}

struct KernelConfig
{
    std::tuple<int, int, int> tile_dims;
    std::tuple<int, int, int> warp_dims;
    std::tuple<int, int, int> warp_tile_dims;
    bool permuteN;
};

struct GemmProblem
{
    int split_k_;
    int m_, n_, k_;
    int stride_a_, stride_b_, stride_c_;

    std::string dtype_a_, dtype_b_, dtype_acc_, dtype_c_;
    std::string layout_a_, layout_b_, layout_c_;

    bool structured_sparsity_;

    friend std::ostream& operator<<(std::ostream& os, const GemmProblem& problem)
    {
        os << "{\n"
           << "   \"split_k\":" << problem.split_k_ << ",\n"
           << "   \"m\":" << problem.m_ << ",\n"
           << "   \"n\":" << problem.n_ << ",\n"
           << "   \"k\":" << problem.k_ << ",\n"
           << "   \"stride_a\":" << problem.stride_a_ << ",\n"
           << "   \"stride_b\":" << problem.stride_b_ << ",\n"
           << "   \"stride_c\":" << problem.stride_c_ << ",\n"
           << "   \"dtype_a\":\"" << problem.dtype_a_ << "\",\n"
           << "   \"dtype_b\":\"" << problem.dtype_b_ << "\",\n"
           << "   \"dtype_acc\":\"" << problem.dtype_acc_ << "\",\n"
           << "   \"dtype_c\":\"" << problem.dtype_c_ << "\",\n"
           << "   \"layout_a\":\"" << problem.layout_a_ << "\",\n"
           << "   \"layout_b\":\"" << problem.layout_b_ << "\",\n"
           << "   \"layout_c\":\"" << problem.layout_c_ << "\",\n"
           << "   \"structured_sparsity\":" << (problem.structured_sparsity_ ? "true" : "false")
           << "\n"
           << "}";
        return os;
    }
};

struct PerformanceResult
{
    double latency_;
    double tflops_;
    double bandwidth_;

    static bool compare(const PerformanceResult& a, const PerformanceResult& b, Metric m)
    {
        switch(m)
        {
        case Metric::LATENCY: return a.latency_ < b.latency_;
        case Metric::TFLOPS: return a.tflops_ > b.tflops_;
        case Metric::BANDWIDTH: return a.bandwidth_ > b.bandwidth_;
        default: throw std::invalid_argument("Unsupported metric type");
        }
    }

    friend std::ostream& operator<<(std::ostream& os, const PerformanceResult& result)
    {
        os << "{\n"
           << "   \"latency(ms)\": " << std::fixed << std::setprecision(2) << result.latency_
           << ",\n"
           << "   \"tflops(TFlops)\": " << result.tflops_ << ",\n"
           << "   \"bandwidth(GB/s)\": " << result.bandwidth_ << "\n"
           << "}";
        return os;
    }
};

struct KernelInstance
{
    std::string name_;
    GemmProblem problem_;
    PerformanceResult perf_result_;

    static bool compare(const KernelInstance& a, const KernelInstance& b, Metric m)
    {
        return PerformanceResult::compare(a.perf_result_, b.perf_result_, m);
    }

    friend std::ostream& operator<<(std::ostream& os, const KernelInstance& obj)
    {
        os << "{\n"
           << " \"name\": \"" << obj.name_ << "\",\n"
           << " \"problem\": " << obj.problem_ << ",\n"
           << " \"perf_result\": " << obj.perf_result_ << "\n"
           << "}";
        return os;
    }
};

struct Setting
{
    int n_warmup_;
    int n_repeat_;
    bool is_gpu_timer_;
    int verify_;
    int init_method_;
    bool log_;
    std::string csv_filename_;
    bool flush_cache_;
    int rotating_count_;
    bool json_output_;
};

inline std::string get_rocm_version()
{
    std::ifstream version_file("/opt/rocm/.info/version");
    if(version_file.is_open())
    {
        std::string version;
        std::getline(version_file, version);
        return version;
    }
    return "Unknown";
}

template <typename ADataType, typename BDataType, typename AccDataType, typename CDataType>
auto calculate_rtol_atol(const ck_tile::index_t K,
                         const ck_tile::index_t kbatch,
                         const float max_accumulated_value)
{
    using ComputeType =
        std::conditional_t<sizeof(ADataType) < sizeof(BDataType), ADataType, BDataType>;
    // Calculate thresholds
    const auto rtol = ck_tile::get_relative_threshold<ComputeType, CDataType, AccDataType>(
        ck_tile::integer_divide_ceil(K, kbatch));
    const auto atol = ck_tile::get_absolute_threshold<ComputeType, CDataType, AccDataType>(
        max_accumulated_value / kbatch, ck_tile::integer_divide_ceil(K, kbatch));
    // Calculate error due to split_k accumulation
    const auto rtol_split_k =
        ck_tile::get_relative_threshold<CDataType, CDataType, CDataType>(kbatch);
    const auto atol_split_k = ck_tile::get_absolute_threshold<CDataType, CDataType, CDataType>(
        max_accumulated_value, kbatch);
    // Use higher threshold
    return ck_tile::make_tuple(std::max(rtol, rtol_split_k), std::max(atol, atol_split_k));
}

/// @brief Function to compare the results of the device and host computations
bool compare(std::string instanceName,
             ck_tile::index_t K,
             ck_tile::index_t kbatch,
             ck_tile::HostTensor<CDataType>& c_m_n_dev_result,
             ck_tile::HostTensor<CDataType>& c_m_n_ref)
{
    const float max_accumulated_value =
        *std::max_element(c_m_n_ref.mData.begin(), c_m_n_ref.mData.end());
    const auto rtol_atol = calculate_rtol_atol<ADataType, BDataType, AccDataType, CDataType>(
        K, kbatch, max_accumulated_value);
    bool pass = ck_tile::check_err(c_m_n_dev_result,
                                   c_m_n_ref,
                                   "Error: Incorrect results!",
                                   rtol_atol.at(ck_tile::number<0>{}),
                                   rtol_atol.at(ck_tile::number<1>{}));

    std::cout << "For " << instanceName << " Relative error threshold is "
              << rtol_atol.at(ck_tile::number<0>{}) << " Absolute error threshold is "
              << rtol_atol.at(ck_tile::number<1>{}) << std::endl;
    std::cout << "The verification result is:" << (pass ? "correct" : "fail") << std::endl;

    return pass;
}

/// @brief Function to get the kernel output with reference implementation on CPU/GPU
void gemm_host_reference(int verify,
                         ck_tile::HostTensor<ADataType>& a_m_k,
                         ck_tile::HostTensor<BDataType>& b_k_n,
                         ck_tile::HostTensor<CDataType>& c_m_n_ref,
                         ck_tile::DeviceMem& a_m_k_dev_buf,
                         ck_tile::DeviceMem& b_k_n_dev_buf,
                         ck_tile::index_t M,
                         ck_tile::index_t N,
                         ck_tile::index_t K,
                         ck_tile::index_t stride_A,
                         ck_tile::index_t stride_B,
                         ck_tile::index_t stride_C)
{
    if(verify == 1)
    {
        ck_tile::reference_gemm<ADataType, BDataType, AccDataType, CDataType>(
            a_m_k, b_k_n, c_m_n_ref);
    }
    else if(verify == 2)
    {
        a_m_k_dev_buf.ToDevice(a_m_k.data());
        b_k_n_dev_buf.ToDevice(b_k_n.data());

        ck_tile::DeviceMem c_m_n_gpu_buf_ref(c_m_n_ref.get_element_space_size_in_bytes());
        c_m_n_gpu_buf_ref.SetZero();

        ADataType* d_A = static_cast<ADataType*>(a_m_k_dev_buf.GetDeviceBuffer());
        BDataType* d_B = static_cast<BDataType*>(b_k_n_dev_buf.GetDeviceBuffer());
        CDataType* d_C = static_cast<CDataType*>(c_m_n_gpu_buf_ref.GetDeviceBuffer());

        ck_tile::reference_gemm_gpu<ADataType,
                                    BDataType,
                                    AccDataType,
                                    CDataType,
                                    ALayout,
                                    BLayout,
                                    CLayout>(d_A, d_B, d_C, M, N, K, stride_A, stride_B, stride_C);

        c_m_n_gpu_buf_ref.FromDevice(c_m_n_ref.data());
    }
}