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

#include <iostream>
#include <numeric>
#include <initializer_list>
#include <cstdlib>

#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_moe_gemm.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_gemm_multiple_d_xdl_cshuffle_v3.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/tensor_operation/gpu/element/unary_element_wise_operation.hpp"

#include "ck/library/utility/device_memory.hpp"
#include "ck/library/utility/host_tensor.hpp"
#include "ck/library/utility/host_tensor_generator.hpp"
#include "ck/library/utility/literals.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_moe_gemm2.hpp"
#include "ck/library/utility/check_err.hpp"

#include "ck/utility/blkgemmpipe_scheduler.hpp"

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

using F16  = ck::half_t;
// using BF16 = ck::bhalf_t;
using F8  = ck::f8_t;
using F32  = float;

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

using A0DataType       = F8;
using B0DataType       = F8;
using EDataType        = F16;
using AccDataType      = F32;
using CShuffleDataType = F32;
using D0DataType       = F32;
using D1DataType       = F32;
using D2DataType       = F32;
using DsDataType       = ck::Tuple<D0DataType, D1DataType, D2DataType>;

using A0Layout = Row;
using B0Layout = Col;
using ELayout  = Row;
using D0Layout = Row;
using D1Layout = Col;
using D2Layout = ELayout;
// using DsLayoutGate = ck::Tuple<D0Layout, D1Layout>;
using DsLayout = ck::Tuple<D0Layout, D1Layout, D2Layout>;

// d0: ascale, d1: bscale, d2:expert weight
struct MulABScaleExpertWeight
{
    template <typename E, typename C, typename D0, typename D1, typename D2>
    __host__ __device__ constexpr void
    operator()(E& e, const C& c, const D0& d0, const D1& d1, const D2& d2) const;
    //real kernel use
    template <>
    __host__ __device__ constexpr void operator()<EDataType, float, float, float, float>
                                                                           (EDataType& e,
                                                                            const float& c,
                                                                            const float& d0,
                                                                            const float& d1,
                                                                            const float& d2) const
    {
        e = ck::type_convert<EDataType>(c * d0 * d1 * d2);
    }
    // for reference
    template <>
    __host__ __device__ constexpr void operator()<float, float, float, float, float>
                                                                           (float& e,
                                                                            const float& c,
                                                                            const float& d0,
                                                                            const float& d1,
                                                                            const float& d2) const
    {
        e = ck::type_convert<EDataType>(c * d0 * d1 * d2);
    }
};

using CDEElementOp = MulABScaleExpertWeight;

void preShuffleBuffer(const B0DataType* src, B0DataType* dst, int N, int K, int NXdl)
{
    int KPack = 16 / sizeof(B0DataType);
    int NLane = NXdl;
    int KLane = 64 / NLane;

    int K0 = K / (KLane * KPack);
    // K -> K0 KLane KPack
    // N -> N0 NLane
    // N, K -> N0 K0 KLane NLane KPack
    int tempk;
    for(int n = 0; n < N; ++n)
    {
        for(int k = 0; k < K; ++k)
        {
            int n0 = n / NLane;
            int n1 = n % NLane;

            int k0 = k / (KLane * KPack);
            tempk  = k % (KLane * KPack);
            int k1 = tempk / KPack;
            int k2 = tempk % KPack;

            int outputIndex = n0 * KPack * NLane * KLane * K0 + k0 * KPack * NLane * KLane +
                              k1 * KPack * NLane + n1 * KPack + k2;

            dst[outputIndex] = src[n * K + k];
        }
    }
}
using PassThrough = ck::tensor_operation::element_wise::PassThrough;

using AElementOp   = PassThrough;
using BElementOp   = PassThrough;
using CDEElementOp = MulABScaleExpertWeight;

static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::Default;
static constexpr ck::index_t MPerBlock = 64;
static constexpr ck::index_t BLOCKSIZE = 256;
static constexpr ck::index_t NPerBlock = 128;
static constexpr ck::index_t MNPerXDL = 32;
static constexpr ck::index_t KPerBlock = 256 / sizeof(A0DataType);
static constexpr ck::index_t MXDLPerWave = MPerBlock / 32; //todo fix this constraint
static constexpr ck::index_t CShuffleMXDLPerWave = MPerBlock / 32;
static constexpr ck::index_t CShuffleNLane = NPerBlock / 2;
static constexpr ck::index_t CShuffleMLane = BLOCKSIZE / CShuffleNLane;
static constexpr ck::index_t AK1 = 16 / sizeof(A0DataType);
static constexpr ck::index_t BK1 = 16 / sizeof(B0DataType);
static constexpr ck::index_t EVec = 2;
static constexpr ck::index_t D0Vec = 1;
static constexpr ck::index_t D1Vec = 1;
static constexpr ck::index_t D2Vec = 1;
using DeviceOpInstance = ck::tensor_operation::device::DeviceMoeGemm
    // clang-format off
///######|  ALayout|  BLayout| DsLayout| ELayout|      AData|      BData|     DsData|     EData|     AccData|         CShuffle|           A|           B|          CDE|           GEMM| 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|      Type|        Type|         DataType| Elementwise| Elementwise|  Elementwise| Spacialization|  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|               |      |      |      |      |    |    |     |     | Wave| Wave| Lengths_K0_M_K1|   ArrangeOrder|               |               |      PerVector|   PerVector_K1|          | Lengths_K0_N_K1|   ArrangeOrder|               |              |      PerVector|   PerVector_K1|          |  PerShuffle|  PerShuffle|         _NBlock_NWaveNPerXdl|   _NWaveNPerXdl|
///######|         |         |         |        |           |           |           |          |            |                 |            |            |             |               |      |      |      |      |    |    |     |     |     |     |                |               |               |               |               |               |          |                |               |               |              |               |               |          |            |            |                             |    S<C, D0, D1>|
///###### RCR
        // kernel 1: 256->32x128x128 
        // <      Row,      Col, DsLayout, ELayout, A0DataType, B0DataType, DsDataType, EDataType, AccDataType, CShuffleDataType,  AElementOp,  BElementOp, CDEElementOp,       GemmSpec,   256,   32,   128,    128,  16,  16,  32,   32,    1,    1,     S<8, 32, 1>,     S<1, 0, 2>,    S<1, 0, 2>,               2,             16,             16,          0,     S<8, 32, 1>,    S<1, 0, 2>,     S<1, 0, 2>,             2,              16,             16,          0,          1,           1,               S<1, 32, 1, 8>,      S<8, 8, 1>,  ck::BlockGemmPipelineScheduler::Interwave, ck::BlockGemmPipelineVersion::v1, EDataType>;
        // <      Row,      Col, DsLayout, ELayout, A0DataType, B0DataType, DsDataType, EDataType, AccDataType, CShuffleDataType,  AElementOp,  BElementOp, CDEElementOp,       GemmSpec,   256,   32,   128,    256,  16,  16,  32,   32,    1,    1,     S<16, 16, 1>,     S<1, 0, 2>,    S<1, 0, 2>,               2,             16,             16,          0,     S<16, 16, 1>,    S<1, 0, 2>,     S<1, 0, 2>,             2,              16,             16,          0,          1,           1,               S<1, 32, 1, 8>,      S<8, 8, 1>,  ck::BlockGemmPipelineScheduler::Intrawave, ck::BlockGemmPipelineVersion::v3, EDataType>;
        <      Row,      Col, DsLayout, ELayout, A0DataType, B0DataType, DsDataType, EDataType, AccDataType, CShuffleDataType,
               AElementOp,  BElementOp, CDEElementOp,       GemmSpec,   
               //threadnum, mblock, nblock, kblock
               BLOCKSIZE,   MPerBlock,   NPerBlock,    KPerBlock,
               // ak1, bk1
               AK1,   BK1,
               // mn_perxdl
               MNPerXDL,   MNPerXDL,
               // mn_xdlperwave 
               MXDLPerWave,    1,
               // a,b: loadtranfer cluster, cluster order, srcorder,VECDIM, srcpervec, dstpervec, lds_extra
            //    S<16, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 0,
            //    S<16, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 0,
               S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, AK1, AK1, 0,
               S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, AK1, AK1, 0,
               //    CShuffle|    CShuffle| CBlockTransferClusterLengths|  CBlockTransfer|
               //    MXdlPerWave| NXdlPerWave|         _MBlock_MWaveMPerXdl| ScalarPerVector|
                //  PerShuffle|  PerShuffle|         _NBlock_NWaveNPerXdl|   _NWaveNPerXdl|
               CShuffleMXDLPerWave,    1,   S<1, CShuffleMLane, 1, CShuffleNLane>, S<EVec, D0Vec, D1Vec, D2Vec>,
               ck::BlockGemmPipelineScheduler::Intrawave, ck::BlockGemmPipelineVersion::v1, false, A0DataType>;
        // kernel 2: 128->32x128x128
        //  <      Row,      Col, DsLayout, ELayout, A0DataType, B0DataType, DsDataType, EDataType, AccDataType, CShuffleDataType,  AElementOp,  BElementOp, CDEElementOp,       GemmSpec,   128,   32,   128,    128,  16,  16,  32,   32,    1,    2,     S<8, 16, 1>,     S<1, 0, 2>,    S<1, 0, 2>,               2,             16,             16,          0,     S<8, 16, 1>,    S<1, 0, 2>,     S<1, 0, 2>,             2,              16,             16,          0,          1,           1,               S<1, 16, 1, 8>,      S<8, 8, 1>,  ck::BlockGemmPipelineScheduler::Interwave, ck::BlockGemmPipelineVersion::v1, EDataType>;

// clang-format on

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

// tokens = 1
// topk = 1
// experts = 8
// per expert: 
    // GEMM shape
    ck::index_t N = 6144;
    ck::index_t K = 8192;
    ck::index_t experts = 8;
    ck::index_t sorted_tile_num = 9;
    ck::index_t valid_tile_num = 8;
    ck::index_t sorted_tile_size = MPerBlock;
    ck::index_t sorted_size = sorted_tile_num * sorted_tile_size;
    ck::index_t valid_size = valid_tile_num * sorted_tile_size;
    ck::index_t tokens = 64;

    if(argc == 1)
    {
        // use default case
    }
    else if(argc == 6)
    {
        do_verification = std::stoi(argv[1]);
        init_method     = std::stoi(argv[2]);
        time_kernel     = std::stoi(argv[3]);
        N = std::stoi(argv[4]);
        K = std::stoi(argv[5]);
    }
    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=no, 1=yes)\n");
        printf(
            "arg4 to 5: N, K\n");
        exit(0);
    }

    ck::index_t StrideA = K;
    ck::index_t StrideB = K;
    ck::index_t StrideE = N;
    constexpr ck::index_t NumDTensor = DsDataType::Size();
    constexpr auto StrideDs = std::array<ck::index_t, NumDTensor>{0, 0, 0};

    ck::index_t KBatch = 1;


    // const ck::index_t experts = 8;
    Tensor<ck::index_t> expert_ids(HostTensorDescriptor({experts}, {1}));
    Tensor<ck::index_t> sorted_token_ids(HostTensorDescriptor({sorted_size}, {1}));
    Tensor<ck::index_t> max_token_id(HostTensorDescriptor({1}));
    max_token_id.mData[0] = valid_size;
    for (int i = 0; i < sorted_tile_num; i++) {
        expert_ids.mData[i] = i;
    }
    int token_per_tile = tokens / sorted_tile_num;
    int tokenid = 0;
    // sorted_token_ids.mData[0] = 0;
    for (int i = 0; i < sorted_size; i++) {
        int tile_off = i % sorted_tile_size;
        if(tile_off < token_per_tile)
            sorted_token_ids.mData[i] = tokenid++;
        else
            sorted_token_ids.mData[i] = tokens;
    }

    Tensor<A0DataType> a0_m_k(HostTensorDescriptor({sorted_size, K}, {K, 1}));
    Tensor<B0DataType> b0_e_n_k(HostTensorDescriptor({experts, N, K}, {N*K, K, 1}));
    Tensor<B0DataType> b0_preshuffled(HostTensorDescriptor({experts, N, K}, {N*K, K, 1}));
    Tensor<D0DataType> d0_m_n(HostTensorDescriptor({sorted_size, N}, {StrideDs[0], 0}));
    Tensor<D1DataType> d1_e_n(HostTensorDescriptor({experts, N}, {1, StrideDs[1]}));
    Tensor<D2DataType> d2_e_n(HostTensorDescriptor({sorted_size, N}, {1, 0}));
    Tensor<EDataType> e_t_n_host_result(HostTensorDescriptor({tokens, N}, {N, 1}));
    Tensor<EDataType> e_t_n_device_result(HostTensorDescriptor({tokens, N}, {N, 1}));
    e_t_n_device_result.SetZero();
    std::cout << "a0_m_k: " << a0_m_k.mDesc << std::endl;
    std::cout << "b0_e_n_k: " << b0_e_n_k.mDesc << std::endl;
    std::cout << "d2_e_n: " << d2_e_n.mDesc << std::endl;
    std::cout << "d1_e_n: " << d1_e_n.mDesc << std::endl;
    std::cout << "d0_m_n: " << d0_m_n.mDesc << std::endl;
    std::cout << "e_t_n: " << e_t_n_host_result.mDesc << std::endl;

    switch(init_method)
    {
    case 0: break;
    case 1:
        a0_m_k.GenerateTensorValue(GeneratorTensor_2<A0DataType>{-2, 2});
        b0_e_n_k.GenerateTensorValue(GeneratorTensor_2<B0DataType>{0, 2});
        d0_m_n.GenerateTensorValue(GeneratorTensor_2<D0DataType>{-2, 2});
        d1_e_n.GenerateTensorValue(GeneratorTensor_2<D1DataType>{-2, 2});
        d2_e_n.GenerateTensorValue(GeneratorTensor_2<D2DataType>{-2, 2});
        break;
    case 2:
        a0_m_k.GenerateTensorValue(GeneratorTensor_1<A0DataType>{});
        b0_e_n_k.GenerateTensorValue(GeneratorTensor_1<B0DataType>{});
        d0_m_n.GenerateTensorValue(GeneratorTensor_1<D0DataType>{});
        d1_e_n.GenerateTensorValue(GeneratorTensor_1<D1DataType>{});
        d2_e_n.GenerateTensorValue(GeneratorTensor_1<D2DataType>{});
        break;
    default:
        a0_m_k.GenerateTensorValue(GeneratorTensor_3<A0DataType>{0.0, 1.0});
        b0_e_n_k.GenerateTensorValue(GeneratorTensor_3<B0DataType>{-0.5, 0.5});
        d0_m_n.GenerateTensorValue(GeneratorTensor_3<D0DataType>{0.0, 1.0});
        d1_e_n.GenerateTensorValue(GeneratorTensor_3<D1DataType>{0.0, 1.0});
        d2_e_n.GenerateTensorValue(GeneratorTensor_3<D2DataType>{0.0, 1.0});
    }
    DeviceMem sorted_token_ids_dev(sizeof(ck::index_t) * sorted_token_ids.mDesc.GetElementSpaceSize());
    DeviceMem expert_ids_dev(sizeof(ck::index_t) * expert_ids.mDesc.GetElementSpaceSize());
    DeviceMem max_token_id_dev(sizeof(ck::index_t) * max_token_id.mDesc.GetElementSpaceSize());
    DeviceMem a0_device_buf(sizeof(A0DataType) * a0_m_k.mDesc.GetElementSpaceSize());
    DeviceMem b0_device_buf(sizeof(B0DataType) * b0_e_n_k.mDesc.GetElementSpaceSize());
    DeviceMem d0_device_buf(sizeof(D0DataType) * d0_m_n.mDesc.GetElementSpaceSize());
    DeviceMem d1_device_buf(sizeof(D1DataType) * d1_e_n.mDesc.GetElementSpaceSize());
    DeviceMem d2_device_buf(sizeof(D2DataType) * d2_e_n.mDesc.GetElementSpaceSize());
    DeviceMem e_device_buf(sizeof(EDataType) * e_t_n_device_result.mDesc.GetElementSpaceSize());
    a0_m_k.savetxt("a.txt");
    expert_ids.savetxt("expert_ids.txt", "int");
    sorted_token_ids.savetxt("sorted_token_ids.txt", "int");
    d0_m_n.savetxt("d0_m_n.txt", "int");
    d1_e_n.savetxt("d1_e_n.txt", "int");
    d2_e_n.savetxt("d2_e_n.txt", "int");
    sorted_token_ids_dev.ToDevice(sorted_token_ids.mData.data());
    expert_ids_dev.ToDevice(expert_ids.mData.data());
    max_token_id_dev.ToDevice(max_token_id.mData.data());
    a0_device_buf.ToDevice(a0_m_k.mData.data());
    d0_device_buf.ToDevice(d0_m_n.mData.data());
    d1_device_buf.ToDevice(d1_e_n.mData.data());
    d2_device_buf.ToDevice(d2_e_n.mData.data());
    e_device_buf.ToDevice(e_t_n_device_result.mData.data());

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


    // do GEMM
    auto device_op = DeviceOpInstance{};

    int NPerXdl = device_op.GetPreShuffleParameters();

    preShuffleBuffer(b0_e_n_k.mData.data(), b0_preshuffled.mData.data(), N * experts, K, NPerXdl);

    b0_device_buf.ToDevice(b0_preshuffled.mData.data());

    auto invoker = device_op.MakeInvoker();
    auto argument =
        device_op.MakeArgument(sorted_token_ids_dev.GetDeviceBuffer(),
                                expert_ids_dev.GetDeviceBuffer(),
                                max_token_id_dev.GetDeviceBuffer(),
                                a0_device_buf.GetDeviceBuffer(),
                               b0_device_buf.GetDeviceBuffer(),
                               std::array<const void*, NumDTensor>{d0_device_buf.GetDeviceBuffer(),
                                                                   d1_device_buf.GetDeviceBuffer(),
                                                                   d2_device_buf.GetDeviceBuffer()},
                               e_device_buf.GetDeviceBuffer(),
                               tokens,
                               sorted_size,
                               N,
                               K,
                               StrideA,
                               StrideB,
                               StrideDs,
                               StrideE,
                               KBatch,
                               a_element_op,
                               b_element_op,
                               cde_element_op);

    if(!device_op.IsSupportedArgument(argument))
    {
        throw std::runtime_error(
            "wrong! device_gemm with the specified compilation parameters does "
            "not support this GEMM problem");
    }
    if (time_kernel) {
        // not result correct here because output buf not setzero
        float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});

        std::size_t flop = std::size_t(2) * sorted_size * N * K;
        std::size_t num_btype =
            sizeof(A0DataType) * sorted_size * K + sizeof(B0DataType) * K * N * experts + sizeof(EDataType) * sorted_size * 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"
                << std::endl;
    }

    if(do_verification)
    {
        //gemm2 use atomic, so need to reinit outputs
        e_device_buf.ToDevice(e_t_n_device_result.mData.data());
        invoker.Run(argument, StreamConfig{nullptr, false, 0 ,0,1});

        Tensor<CShuffleDataType> c_t_n({tokens, N});

        using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceMoeGemm2<A0DataType,
                                                                                B0DataType,
                                                                                D0DataType,
                                                                                D1DataType,
                                                                                D2DataType,
                                                                                CShuffleDataType,
                                                                                AccDataType,
                                                                                PassThrough,
                                                                                PassThrough,
                                                                                CDEElementOp>;
        auto ref_moe_gemm           = ReferenceGemmInstance{};
        auto ref_invoker            = ref_moe_gemm.MakeInvoker();
        auto ref_argument = ref_moe_gemm.MakeArgument(
           sorted_token_ids, expert_ids, max_token_id, sorted_tile_size, a0_m_k, b0_e_n_k, d0_m_n, d1_e_n, d2_e_n, c_t_n, PassThrough{}, PassThrough{}, cde_element_op);

        ref_invoker.Run(ref_argument);

        for(int t = 0; t < tokens; ++t)
        {
            for(int n = 0; n < N; ++n)
            {
                e_t_n_host_result(t, n) = ck::type_convert<EDataType>(c_t_n(t, n));
            }
        }

        e_device_buf.FromDevice(e_t_n_device_result.mData.data());
        e_t_n_device_result.savetxt("out.txt");
        e_t_n_host_result.savetxt("ref.txt");

        return ck::utils::check_err(
                   e_t_n_device_result, e_t_n_host_result, "Error: Incorrect results!", 1e-3, 5e-2)
                   ? 0
                   : 1;
    }

    return 0;
}
