#include <iostream>
#include <numeric>
#include <initializer_list>
#include <cstdlib>
#include <stdlib.h>

#include "check_err.hpp"
#include "config.hpp"
#include "device.hpp"
#include "host_tensor.hpp"
#include "host_tensor_generator.hpp"
#include "device_tensor.hpp"
#include "device_gemm_multiple_d_xdl_cshuffle.hpp"
#include "element_wise_operation.hpp"
#include "reference_gemm.hpp"
#include "gemm_specialization.hpp"

template <ck::index_t... Is>
using S = ck::Sequence<Is...>;

using F16 = ck::half_t;
using F32 = float;

using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;

using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using FastGelu    = ck::tensor_operation::element_wise::FastGelu;

struct AddAddFastGelu
{
    // https://paperswithcode.com/method/gelu
    // y = 0.5*x*(1+tanh(sqrt(2/pi)*(x+0.044715*x^3)))
    __host__ __device__ void
    operator()(ck::half_t& y, const float& x0, const ck::half_t& x1, const ck::half_t& x2) const
    {
#if 0
        const float x   = x0 + x1 + x2;
        const float u   = float(2) * x * (float(0.035677) * x * x + float(0.797885));
        const float emu = exp(-u);
        const float cdf = float(0.5) + float(0.5) * (float(2) / (float(1) + emu) - float(1));

        y = x * cdf;
#else
        const float x = x0 + x2;

        y = x;
#endif
    }
};

using ADataType   = F16;
using BDataType   = F16;
using AccDataType = F32;
using D0DataType  = F16;
using D1DataType  = F16;
using DsDataType  = ck::Tuple<D0DataType, D1DataType>;
using EDataType   = F16;

using ALayout     = Row;
using BLayout     = Col;
using ELayout     = Row;
using AccDataType = F32;

using AElementOp = PassThrough;
using BElementOp = PassThrough;
#if 0
using CDEElementOp = FastGelu;
#else
using CDEElementOp = AddAddFastGelu;
#endif

static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;

// clang-format off
using DeviceGemmInstance = ck::tensor_operation::device::DeviceGemmMultipleD_Xdl_CShuffle
//######| ALayout| BLayout| ELayout| AData| BData| AccData| CShuffle|     DsData| EData|           A|           B|           C|           GEMM| NumGemmK| Block|  MPer|  NPer|  KPer| AK1| BK1| MPer| NPer| MXdl| NXdl|  ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds|  BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds|    CShuffle|    CShuffle| CBlockTransferClusterLengths|  CBlockTransfer|
//######|        |        |        |  Type|  Type|    Type| DataType|       Type|  Type| Elementwise| Elementwise| Elementwise| Spacialization| Prefetch|  Size| Block| Block| Block|    |    |  XDL|  XDL|  Per|  Per|   ThreadCluster|  ThreadCluster| SrcAccessOrder|   SrcVectorDim|      SrcScalar|      DstScalar| AddExtraM|   ThreadCluster|  ThreadCluster| SrcAccessOrder|  SrcVectorDim|      SrcScalar|      DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave|         _MBlock_MWaveMPerXdl| ScalarPerVector|
//######|        |        |        |      |      |        |         |           |      |   Operation|   Operation|   Operation|               |    Stage|      |      |      |      |    |    |     |     | Wave| Wave| Lengths_K0_M_K1|   ArrangeOrder|               |               |      PerVector|   PerVector_K1|          | Lengths_K0_N_K1|   ArrangeOrder|               |              |      PerVector|   PerVector_K1|          |  PerShuffle|  PerShuffle|         _NBlock_NWaveNPerXdl|   _NWaveNPerXdl|
//######|        |        |        |      |      |        |         |           |      |            |            |            |               |         |      |      |      |      |    |    |     |     |     |     |                |               |               |               |               |               |          |                |               |               |              |               |               |          |            |            |                             |                |
        <     Row,     Col,     Row,   F16,   F16,     F32,     F32,  DsDataType,   F16,  AElementOp,  BElementOp,  CDEElementOp,    GemmDefault,        1,   256,   256,   128,    32,   8,   8,   32,   32,    4,    2,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,              2,              8,              8,         1,     S<4, 64, 1>,     S<1, 0, 2>,     S<1, 0, 2>,             2,              8,              8,         1,           1,           1,               S<1, 32, 1, 8>,               8>;
// clang-format on

int main(int argc, char* argv[])
{
    bool do_verification = true;
    int init_method      = 1;
    bool time_kernel     = false;

    // GEMM shape
    ck::index_t M = 3840;
    ck::index_t N = 4096;
    ck::index_t K = 4096;

    ck::index_t StrideA = 4096;
    ck::index_t StrideB = 4096;
    ck::index_t StrideE = 4096;

    if(argc == 1)
    {
        // use default case
    }
    else if(argc == 4)
    {
        do_verification = std::stoi(argv[1]);
        init_method     = std::stoi(argv[2]);
        time_kernel     = std::stoi(argv[3]);
    }
    else if(argc == 10)
    {
        do_verification = std::stoi(argv[1]);
        init_method     = std::stoi(argv[2]);
        time_kernel     = std::stoi(argv[3]);

        M = std::stoi(argv[4]);
        N = std::stoi(argv[5]);
        K = std::stoi(argv[6]);

        StrideA = std::stoi(argv[7]);
        StrideB = std::stoi(argv[8]);
        StrideE = std::stoi(argv[9]);
    }
    else
    {
        printf("arg1: verification (0=no, 1=yes)\n");
        printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
        printf("arg3: time kernel (0=n0, 1=yes)\n");
        printf("arg4 to 9: M (256x), N(128x), K(32x), StrideA, StrideB, StrideE\n");
        exit(0);
    }

    auto f_host_tensor_descriptor =
        [](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
            if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value)
            {
                return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
                                            std::vector<std::size_t>({stride, 1}));
            }
            else
            {
                return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
                                            std::vector<std::size_t>({1, stride}));
            }
        };

    Tensor<ADataType> a_m_k(f_host_tensor_descriptor(M, K, StrideA, ALayout{}));
    Tensor<BDataType> b_k_n(f_host_tensor_descriptor(K, N, StrideB, BLayout{}));
    Tensor<EDataType> d0_m_n(f_host_tensor_descriptor(M, N, 0, ELayout{}));
    Tensor<EDataType> d1_m_n(f_host_tensor_descriptor(M, N, StrideE, ELayout{}));
    Tensor<EDataType> e_m_n_host_result(f_host_tensor_descriptor(M, N, StrideE, ELayout{}));
    Tensor<EDataType> e_m_n_device_result(f_host_tensor_descriptor(M, N, StrideE, ELayout{}));

    std::cout << "a_m_k: " << a_m_k.mDesc << std::endl;
    std::cout << "b_k_n: " << b_k_n.mDesc << std::endl;
    std::cout << "d0_m_n: " << d0_m_n.mDesc << std::endl;
    std::cout << "d1_m_n: " << d1_m_n.mDesc << std::endl;
    std::cout << "e_m_n: " << e_m_n_host_result.mDesc << std::endl;

    switch(init_method)
    {
    case 0: break;
    case 1:
#if 0
        a_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
        b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
        d0_m_n.GenerateTensorValue(GeneratorTensor_2<EDataType>{-5, 5});
        d1_m_n.GenerateTensorValue(GeneratorTensor_2<EDataType>{-5, 5});
#else
        a_m_k.GenerateTensorValue(GeneratorTensor_1<ADataType>{1});
        b_k_n.GenerateTensorValue(GeneratorTensor_1<BDataType>{1});
        d0_m_n.GenerateTensorValue(GeneratorTensor_1<EDataType>{1});
        d1_m_n.GenerateTensorValue(GeneratorTensor_1<EDataType>{1});
#endif
        break;
    default:
        a_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
        b_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
#if 0
        d0_m_n.GenerateTensorValue(GeneratorTensor_3<EDataType>{0.0, 1.0});
        d1_m_n.GenerateTensorValue(GeneratorTensor_3<EDataType>{0.0, 1.0});
#else
        d0_m_n.GenerateTensorValue(GeneratorTensor_1<EDataType>{1});
        d1_m_n.GenerateTensorValue(GeneratorTensor_1<EDataType>{1});
#endif
    }

    std::cout << "a: " << a_m_k.mDesc.GetElementSpace() << std::endl;
    std::cout << "b: " << b_k_n.mDesc.GetElementSpace() << std::endl;
    std::cout << "d0: " << d0_m_n.mDesc.GetElementSpace() << std::endl;
    std::cout << "d1: " << d1_m_n.mDesc.GetElementSpace() << std::endl;
    std::cout << "e: " << e_m_n_device_result.mDesc.GetElementSpace() << std::endl;

    DeviceMem a_m_k_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpace());
    DeviceMem b_k_n_device_buf(sizeof(BDataType) * b_k_n.mDesc.GetElementSpace());
#if 1
    DeviceMem d0_m_n_device_buf(sizeof(EDataType) * d0_m_n.mDesc.GetElementSpace());
#else
    DeviceMem d0_m_n_device_buf(sizeof(EDataType) * d1_m_n.mDesc.GetElementSpace());
#endif
    DeviceMem d1_m_n_device_buf(sizeof(EDataType) * d1_m_n.mDesc.GetElementSpace());
    DeviceMem e_m_n_device_buf(sizeof(EDataType) * e_m_n_device_result.mDesc.GetElementSpace());

    a_m_k_device_buf.ToDevice(a_m_k.mData.data());
    b_k_n_device_buf.ToDevice(b_k_n.mData.data());

    auto a_element_op   = AElementOp{};
    auto b_element_op   = BElementOp{};
    auto cde_element_op = CDEElementOp{};

    // do GEMM
    auto gemm    = DeviceGemmInstance{};
    auto invoker = gemm.MakeInvoker();
    auto argument =
        gemm.MakeArgument(a_m_k_device_buf.GetDeviceBuffer(),
                          b_k_n_device_buf.GetDeviceBuffer(),
                          std::array<const void*, 2>{{d0_m_n_device_buf.GetDeviceBuffer(),
                                                      d1_m_n_device_buf.GetDeviceBuffer()}},
                          e_m_n_device_buf.GetDeviceBuffer(),
                          M,
                          N,
                          K,
                          StrideA,
                          StrideB,
                          std::array<ck::index_t, 2>{{0, StrideE}},
                          StrideE,
                          a_element_op,
                          b_element_op,
                          cde_element_op);

    if(!gemm.IsSupportedArgument(argument))
    {
        throw std::runtime_error(
            "wrong! device_gemm with the specified compilation parameters does "
            "not support this GEMM problem");
    }

    float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});

    std::size_t flop = std::size_t(2) * M * N * K;
    std::size_t num_btype =
        sizeof(ADataType) * M * K + sizeof(BDataType) * K * N + sizeof(EDataType) * M * N;

    float tflops = static_cast<float>(flop) / 1.E9 / ave_time;

    float gb_per_sec = num_btype / 1.E6 / ave_time;

    std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s, "
              << gemm.GetTypeString() << std::endl;

    e_m_n_device_buf.FromDevice(e_m_n_device_result.mData.data());

    if(do_verification)
    {
#if 1
        Tensor<AccDataType> c_m_n(f_host_tensor_descriptor(M, N, StrideE, ELayout{}));

        using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
                                                                                BDataType,
                                                                                AccDataType,
                                                                                AccDataType,
                                                                                AElementOp,
                                                                                BElementOp,
                                                                                PassThrough>;
        auto ref_gemm               = ReferenceGemmInstance{};
        auto ref_invoker            = ref_gemm.MakeInvoker();

        auto ref_argument =
            ref_gemm.MakeArgument(a_m_k, b_k_n, c_m_n, a_element_op, b_element_op, PassThrough{});

        ref_invoker.Run(ref_argument);

        for(int m = 0; m < M; ++m)
        {
            for(int n = 0; n < N; ++n)
            {
                cde_element_op(e_m_n_host_result(m, n), c_m_n(m, n), d0_m_n(m, n), d1_m_n(m, n));
            }
        }
#else
        using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
                                                                                BDataType,
                                                                                EDataType,
                                                                                AccDataType,
                                                                                AElementOp,
                                                                                BElementOp,
                                                                                CDEElementOp>;
        auto ref_gemm = ReferenceGemmInstance{};
        auto ref_invoker = ref_gemm.MakeInvoker();

        auto ref_argument = ref_gemm.MakeArgument(
            a_m_k, b_k_n, e_m_n_host_result, a_element_op, b_element_op, CDEElementOp{});

        ref_invoker.Run(ref_argument);

#endif

        return ck::utils::check_err(e_m_n_device_result.mData, e_m_n_host_result.mData) ? 0 : 1;
    }

    return 0;
}
