// SPDX-License-Identifier: MIT
// Copyright (c) 2024-2025, Advanced Micro Devices, Inc. All rights reserved.
#pragma once

auto create_args(int argc, char* argv[])
{
    ck_tile::ArgParser arg_parser;
    arg_parser.insert("m", "3840", "m dimension")
        .insert("n", "4096", "n dimension")
        .insert("k", "2048", "k dimension")
        .insert("a_layout", "R", "A tensor data layout - Row by default")
        .insert("b_layout", "R", "B tensor data layout - Row by default")
        .insert("c_layout", "R", "C tensor data layout - Row by default")
        .insert("stride_a", "0", "Tensor A stride")
        .insert("stride_b", "0", "Tensor B stride")
        .insert("stride_c", "0", "Tensor C stride")
        .insert("v", "2", "0. No validation, 1. Validation on CPU, 2. Validation on GPU")
        .insert("prec", "fp16", "data type. fp16/bf16/fp8/bf8")
        .insert("warmup", "50", "number of iterations before benchmark the kernel")
        .insert("repeat", "100", "number of iterations to benchmark the kernel")
        .insert("timer", "gpu", "gpu:gpu timer, cpu:cpu timer")
        .insert("split_k", "1", "splitK value");

    bool result = arg_parser.parse(argc, argv);
    return std::make_tuple(result, arg_parser);
}

template <typename Layout>
static constexpr inline auto is_row_major(Layout layout_)
{
    return ck_tile::bool_constant<std::is_same_v<ck_tile::remove_cvref_t<decltype(layout_)>,
                                                 ck_tile::tensor_layout::gemm::RowMajor>>{};
}

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));
}

template <typename ALayout, typename BLayout, typename CLayout>
float invoke_gemm(ck_tile::DeviceMem& a_m_k_dev_buf,
                  ck_tile::DeviceMem& b_k_n_dev_buf,
                  ck_tile::DeviceMem& c_m_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,
                  ck_tile::index_t kbatch,
                  int n_warmup,
                  int n_repeat)
{
    ck_tile::GemmHostArgs args;
    args.a_ptr    = a_m_k_dev_buf.GetDeviceBuffer();
    args.b_ptr    = b_k_n_dev_buf.GetDeviceBuffer();
    args.c_ptr    = c_m_n_dev_buf.GetDeviceBuffer();
    args.k_batch  = kbatch;
    args.M        = M;
    args.N        = N;
    args.K        = K;
    args.stride_A = stride_A;
    args.stride_B = stride_B;
    args.stride_C = stride_C;

    using Row = ck_tile::tensor_layout::gemm::RowMajor;
    // TODO: Change datatypes in future to allow mixed precision gemms!
    gemm_traits traits{DataTypeTraits<ADataType>{}.name,
                       std::is_same_v<ALayout, Row>,
                       std::is_same_v<BLayout, Row>,
                       std::is_same_v<CLayout, Row>};

    float ave_time =
        gemm(traits, args, ck_tile::stream_config{nullptr, true, 1, n_warmup, n_repeat});

    std::size_t flop = std::size_t(2) * M * N * K;
    std::size_t num_byte =
        sizeof(ADataType) * M * K + sizeof(BDataType) * N * K + sizeof(CDataType) * M * N;
    float tflops     = static_cast<float>(flop) / 1.E9 / ave_time;
    float gb_per_sec = num_byte / 1.E6 / ave_time;

    std::cout << "Run Gemm kernel with M =" << M << " N =" << N << " K =" << K
              << " StrideA =" << stride_A << " StrideB =" << stride_B << " StrideC =" << stride_C
              << " : " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s, "
              << std::endl;

    return ave_time;
}

template <typename ALayout, typename BLayout, typename CLayout>
int run_gemm_example_with_layouts(int argc,
                                  char* argv[],
                                  const ALayout a_layout                  = ALayout{},
                                  const BLayout b_layout                  = BLayout{},
                                  [[maybe_unused]] const CLayout c_layout = CLayout{})
{
    auto [result, arg_parser] = create_args(argc, argv);
    if(!result)
        return -1;

    ck_tile::index_t M = arg_parser.get_int("m");
    ck_tile::index_t N = arg_parser.get_int("n");
    ck_tile::index_t K = arg_parser.get_int("k");

    ck_tile::index_t stride_A = arg_parser.get_int("stride_a");
    ck_tile::index_t stride_B = arg_parser.get_int("stride_b");
    ck_tile::index_t stride_C = arg_parser.get_int("stride_c");

    ck_tile::index_t kbatch = arg_parser.get_int("split_k");
    int n_warmup            = arg_parser.get_int("warmup");
    int n_repeat            = arg_parser.get_int("repeat");

    stride_A = ck_tile::get_default_stride(M, K, stride_A, is_row_major(a_layout));
    stride_B = ck_tile::get_default_stride(K, N, stride_B, is_row_major(b_layout));
    stride_C = ck_tile::get_default_stride(M, N, stride_C, is_row_major(CLayout{}));

    ck_tile::HostTensor<ADataType> a_m_k(
        ck_tile::host_tensor_descriptor(M, K, stride_A, is_row_major(a_layout)));
    ck_tile::HostTensor<BDataType> b_k_n(
        ck_tile::host_tensor_descriptor(K, N, stride_B, is_row_major(b_layout)));
    ck_tile::HostTensor<CDataType> c_m_n_dev_result(
        ck_tile::host_tensor_descriptor(M, N, stride_C, is_row_major(CLayout{})));

    // TODO: add different init types
    ck_tile::FillUniformDistribution<ADataType>{-5.f, 5.f}(a_m_k);
    ck_tile::FillUniformDistribution<BDataType>{-5.f, 5.f}(b_k_n);

    ck_tile::DeviceMem a_m_k_dev_buf(a_m_k.get_element_space_size_in_bytes());
    ck_tile::DeviceMem b_k_n_dev_buf(b_k_n.get_element_space_size_in_bytes());
    ck_tile::DeviceMem c_m_n_dev_buf(c_m_n_dev_result.get_element_space_size_in_bytes());

    a_m_k_dev_buf.ToDevice(a_m_k.data());
    b_k_n_dev_buf.ToDevice(b_k_n.data());
    c_m_n_dev_buf.SetZero();
    c_m_n_dev_result.SetZero();

    invoke_gemm<ALayout, BLayout, CLayout>(a_m_k_dev_buf,
                                           b_k_n_dev_buf,
                                           c_m_n_dev_buf,
                                           M,
                                           N,
                                           K,
                                           stride_A,
                                           stride_B,
                                           stride_C,
                                           kbatch,
                                           n_warmup,
                                           n_repeat);

    c_m_n_dev_buf.FromDevice(c_m_n_dev_result.data());
    bool pass = true;

    if(arg_parser.get_int("v") == 1)
    {
        ck_tile::HostTensor<CDataType> c_m_n_host_ref(
            ck_tile::host_tensor_descriptor(M, N, stride_C, is_row_major(CLayout{})));
        c_m_n_host_ref.SetZero();

        ck_tile::reference_gemm<ADataType, BDataType, AccDataType, CDataType>(
            a_m_k, b_k_n, c_m_n_host_ref);
        const float max_accumulated_value =
            *std::max_element(c_m_n_host_ref.mData.begin(), c_m_n_host_ref.mData.end());
        const auto rtol_atol = calculate_rtol_atol(K, kbatch, max_accumulated_value);
        pass                 = ck_tile::check_err(c_m_n_dev_result,
                                  c_m_n_host_ref,
                                  "Error: Incorrect results!",
                                  rtol_atol.at(ck_tile::number<0>{}),
                                  rtol_atol.at(ck_tile::number<1>{}));

        std::cout << "Relative error threshold: " << rtol_atol.at(ck_tile::number<0>{})
                  << " Absolute error threshold: " << rtol_atol.at(ck_tile::number<1>{})
                  << std::endl;
        std::cout << "The CPU veification result is:" << (pass ? "correct" : "fail") << std::endl;
    }
    else if(arg_parser.get_int("v") == 2)
    {
        ck_tile::HostTensor<CDataType> c_m_n_gpu_ref(
            ck_tile::host_tensor_descriptor(M, N, stride_C, is_row_major(CLayout{})));
        ck_tile::DeviceMem c_m_n_gpu_buf_ref(c_m_n_gpu_ref.get_element_space_size_in_bytes());
        c_m_n_gpu_ref.SetZero();
        c_m_n_gpu_buf_ref.SetZero();

        ADataType* d_A;
        BDataType* d_B;
        CDataType* d_C;

        ck_tile::hip_check_error(hipMalloc(&d_A, M * K * sizeof(ADataType)));
        ck_tile::hip_check_error(hipMalloc(&d_B, N * K * sizeof(BDataType)));
        ck_tile::hip_check_error(hipMalloc(&d_C, M * N * sizeof(CDataType)));

        ck_tile::hip_check_error(hipMemcpy(d_A,
                                           a_m_k_dev_buf.GetDeviceBuffer(),
                                           M * K * sizeof(ADataType),
                                           hipMemcpyHostToDevice));
        ck_tile::hip_check_error(hipMemcpy(d_B,
                                           b_k_n_dev_buf.GetDeviceBuffer(),
                                           N * K * sizeof(BDataType),
                                           hipMemcpyHostToDevice));

        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);

        ck_tile::hip_check_error(hipMemcpy(c_m_n_gpu_buf_ref.GetDeviceBuffer(),
                                           d_C,
                                           M * N * sizeof(CDataType),
                                           hipMemcpyDeviceToHost));

        ck_tile::hip_check_error(hipFree(d_A));
        ck_tile::hip_check_error(hipFree(d_B));
        ck_tile::hip_check_error(hipFree(d_C));

        c_m_n_gpu_buf_ref.FromDevice(c_m_n_gpu_ref.data());
        const float max_accumulated_value =
            *std::max_element(c_m_n_gpu_ref.mData.begin(), c_m_n_gpu_ref.mData.end());
        const auto rtol_atol = calculate_rtol_atol(K, kbatch, max_accumulated_value);
        pass                 = ck_tile::check_err(c_m_n_dev_result,
                                  c_m_n_gpu_ref,
                                  "Error: Incorrect results!",
                                  rtol_atol.at(ck_tile::number<0>{}),
                                  rtol_atol.at(ck_tile::number<1>{}));

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

    return pass;
}
