// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, 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/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_gemm_multiple_d_xdl_cshuffle.hpp"
#include "ck/tensor_operation/gpu/element/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_gemm.hpp"
#include "ck/library/utility/check_err.hpp"

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

using I8  = int8_t;
using I32 = int32_t;
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;

using PassThrough  = ck::tensor_operation::element_wise::PassThrough;
using ActivationOp = ck::tensor_operation::element_wise::Relu;
using CDEElementOp = ck::tensor_operation::element_wise::Add_Activation_Mul_Clamp<ActivationOp>;

using ADataType        = I8;
using BDataType        = I8;
using AccDataType      = I32;
using CShuffleDataType = I32;
using BiasDataType     = I32;
using DsDataType       = ck::Tuple<BiasDataType>;
using EDataType        = I8;

using ALayout    = Row;
using BLayout    = Col;
using BiasLayout = Row;
using DsLayout   = ck::Tuple<BiasLayout>;
using ELayout    = Row;

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

// clang-format off
using DeviceGemmInstance = ck::tensor_operation::device::DeviceGemmMultipleD_Xdl_CShuffle<
     ALayout,
     BLayout,
     DsLayout,
     ELayout,
     ADataType,
     BDataType,
     AccDataType,
     CShuffleDataType,
     DsDataType,
     EDataType,
     PassThrough,                // AElementwiseOperation,
     PassThrough,                // BElementwiseOperation,
     CDEElementOp,               // CDEElementwiseOperation,
     GemmDefault,                // GemmSpecialization GemmSpec,
     1,                          // NumGemmKPrefetchStage,
     256,                        // BlockSize,
     256,                        // MPerBlock,
     128,                        // NPerBlock,
     64,                         // KPerBlock,
     16,                         // AK1,
     16,                         // BK1,
     32,                         // MPerXDL,
     32,                         // NPerXDL,
     4,                          // MXdlPerWave,
     2,                          // NXdlPerWave,
     S<4, 64, 1>,                // ABlockTransferThreadClusterLengths_AK0_M_AK1,
     S<1, 0, 2>,                 // ABlockTransferThreadClusterArrangeOrder,
     S<1, 0, 2>,                 // ABlockTransferSrcAccessOrder,
     2,                          // index_t ABlockTransferSrcVectorDim,
     16,                         // index_t ABlockTransferSrcScalarPerVector,
     16,                         // index_t ABlockTransferDstScalarPerVector_AK1,
     1,                          // bool ABlockLdsExtraM,
     S<4, 64, 1>,                // typename BBlockTransferThreadClusterLengths_BK0_N_BK1,
     S<1, 0, 2>,                 // typename BBlockTransferThreadClusterArrangeOrder,
     S<1, 0, 2>,                 // typename BBlockTransferSrcAccessOrder,
     2,                          // index_t BBlockTransferSrcVectorDim,
     8,                          // index_t BBlockTransferSrcScalarPerVector,
     8,                          // index_t BBlockTransferDstScalarPerVector_BK1,
     1,                          // bool BBlockLdsExtraN,
     1,                          // index_t CShuffleMXdlPerWavePerShuffle,
     1,                          // index_t CShuffleNXdlPerWavePerShuffle,
     S<1, 64, 1, 4>,             // typename CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock,
     8>;                         // index_t CShuffleBlockTransferScalarPerVector_NPerBlock>
// clang-format on

using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
                                                                        BDataType,
                                                                        AccDataType,
                                                                        AccDataType,
                                                                        PassThrough,
                                                                        PassThrough,
                                                                        PassThrough>;

int main()
{
    bool do_verification = true;
    bool time_kernel     = false;

    // GEMM shape
    ck::index_t M = 1024;
    ck::index_t N = 1024;
    ck::index_t K = 1024;

    ck::index_t StrideA    = 1024;
    ck::index_t StrideB    = 1024;
    ck::index_t StrideBias = 0;
    ck::index_t StrideE    = 1024;

    float requant_scale = 0.03;

    auto f_host_tensor_descriptor2d =
        [](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
            using namespace ck::literals;

            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_uz}));
            }
            else
            {
                return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
                                            std::vector<std::size_t>({1_uz, stride}));
            }
        };

    auto f_host_tensor_descriptor1d = [](std::size_t len, std::size_t stride) {
        return HostTensorDescriptor(std::vector<std::size_t>({len}),
                                    std::vector<std::size_t>({stride}));
    };

    Tensor<ADataType> a_m_k(f_host_tensor_descriptor2d(M, K, StrideA, ALayout{}));
    Tensor<BDataType> b_k_n(f_host_tensor_descriptor2d(K, N, StrideB, BLayout{}));
    Tensor<BiasDataType> bias_n(f_host_tensor_descriptor1d(N, 1));
    Tensor<EDataType> e_m_n_host_result(f_host_tensor_descriptor2d(M, N, StrideE, ELayout{}));
    Tensor<EDataType> e_m_n_device_result(f_host_tensor_descriptor2d(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 << "bias_n: " << bias_n.mDesc << std::endl;
    std::cout << "e_m_n: " << e_m_n_host_result.mDesc << std::endl;

    a_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-128, 127});
    b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-128, 127});
    bias_n.GenerateTensorValue(GeneratorTensor_2<BiasDataType>{-128, 127});

    DeviceMem a_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpaceSize());
    DeviceMem b_device_buf(sizeof(BDataType) * b_k_n.mDesc.GetElementSpaceSize());
    DeviceMem bias_device_buf(sizeof(BiasDataType) * bias_n.mDesc.GetElementSpaceSize());
    DeviceMem e_device_buf(sizeof(EDataType) * e_m_n_device_result.mDesc.GetElementSpaceSize());

    a_device_buf.ToDevice(a_m_k.mData.data());
    b_device_buf.ToDevice(b_k_n.mData.data());
    bias_device_buf.ToDevice(bias_n.mData.data());

    auto a_element_op   = PassThrough{};
    auto b_element_op   = PassThrough{};
    auto cde_element_op = CDEElementOp{requant_scale, ActivationOp{}};

    // do GEMM
    auto gemm     = DeviceGemmInstance{};
    auto invoker  = gemm.MakeInvoker();
    auto argument = gemm.MakeArgument(a_device_buf.GetDeviceBuffer(),
                                      b_device_buf.GetDeviceBuffer(),
                                      {bias_device_buf.GetDeviceBuffer()},
                                      e_device_buf.GetDeviceBuffer(),
                                      M,
                                      N,
                                      K,
                                      StrideA,
                                      StrideB,
                                      {StrideBias},
                                      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_device_buf.FromDevice(e_m_n_device_result.mData.data());

    if(do_verification)
    {
        Tensor<AccDataType> c_m_n(HostTensorDescriptor{M, N});

        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), bias_n(n));
            }
        }

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

    return 0;
}
