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

#pragma once

#include <iomanip>
#include <iostream>
#include <typeinfo>

#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.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/library/tensor_operation_instance/gpu/gemm_multiply_multiply.hpp"

#include "ck/library/utility/check_err.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"

namespace ck {
namespace profiler {

template <typename ADataType,
          typename BDataType,
          typename ComputeDataType,
          typename AccDataType,
          typename D0DataType,
          typename D1DataType,
          typename EDataType,
          typename ALayout,
          typename BLayout,
          typename D0Layout,
          typename D1Layout,
          typename ELayout>
bool profile_gemm_multiply_multiply_impl(int do_verification,
                                         int init_method,
                                         bool do_log,
                                         bool time_kernel,
                                         int M,
                                         int N,
                                         int K,
                                         int StrideA,
                                         int StrideB,
                                         int StrideD0,
                                         int StrideD1,
                                         int StrideE,
                                         int KBatch,
                                         int n_warmup,
                                         int n_iter,
                                         uint64_t rotating = 0)
{
    bool pass = true;

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

            if(is_same<decltype(layout), tensor_layout::gemm::RowMajor>::value)
            {
                return HostTensorDescriptor({row, col}, {stride, 1_uz});
            }
            else
            {
                return HostTensorDescriptor({row, col}, {1_uz, 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<D0DataType> d0_m_n(f_host_tensor_descriptor(M, N, StrideD0, D0Layout{}));
    Tensor<D1DataType> d1_m_n(f_host_tensor_descriptor(M, N, StrideD1, D1Layout{}));
    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{}));

    int total_gemm_needed =
        a_m_k.GetElementSpaceSizeInBytes() + b_k_n.GetElementSpaceSizeInBytes() +
        d0_m_n.GetElementSpaceSizeInBytes() + d1_m_n.GetElementSpaceSizeInBytes();
    int rotating_count = std::max(
        1,
        std::min(n_iter,
                 static_cast<int>(std::ceil(static_cast<double>(rotating) / total_gemm_needed))));

    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_device_result.mDesc << std::endl;
    std::cout << "rotating count: " << rotating_count << std::endl;

    switch(init_method)
    {
    case 0: break;
    case 1:
        a_m_k.GenerateTensorValue(GeneratorTensor_2<ADataType>{-1, 2});
        b_k_n.GenerateTensorValue(GeneratorTensor_2<BDataType>{-1, 2});
        d0_m_n.GenerateTensorValue(GeneratorTensor_2<D0DataType>{-5, 5});
        d1_m_n.GenerateTensorValue(GeneratorTensor_2<D1DataType>{-1, 1});
        break;
    default:
        a_m_k.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
        b_k_n.GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
        d0_m_n.GenerateTensorValue(GeneratorTensor_3<D0DataType>{0.0, 1.0});
        d1_m_n.GenerateTensorValue(GeneratorTensor_3<D1DataType>{0.0, 1.0});
    }

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

    using AElementOp = PassThrough;
    using BElementOp = PassThrough;
    using CElementOp = MultiplyMultiply;

    const auto a_element_op = AElementOp{};
    const auto b_element_op = BElementOp{};
    const auto c_element_op = CElementOp{};

    DeviceMem a_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpaceSize());
    DeviceMem b_device_buf(sizeof(BDataType) * b_k_n.mDesc.GetElementSpaceSize());
    DeviceMem d0_device_buf(sizeof(D0DataType) * d0_m_n.mDesc.GetElementSpaceSize());
    DeviceMem d1_device_buf(sizeof(D1DataType) * d1_m_n.mDesc.GetElementSpaceSize());
    DeviceMem c_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());
    d0_device_buf.ToDevice(d0_m_n.mData.data());
    d1_device_buf.ToDevice(d1_m_n.mData.data());

    using DeviceOp =
        ck::tensor_operation::device::DeviceGemmMultipleDSplitK<ALayout,
                                                                BLayout,
                                                                ck::Tuple<D0Layout, D1Layout>,
                                                                ELayout,
                                                                ADataType,
                                                                BDataType,
                                                                ck::Tuple<D0DataType, D1DataType>,
                                                                EDataType,
                                                                AElementOp,
                                                                BElementOp,
                                                                CElementOp>;

    // get device op instances
    const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
        DeviceOp>::GetInstances();

    std::cout << "found " << op_ptrs.size() << " instances" << std::endl;

    // Run reference GEMM
    if(do_verification)
    {
        Tensor<AccDataType> c_m_n({M, N});

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

        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, PassThrough{}, PassThrough{}, PassThrough{});

        ref_invoker.Run(ref_argument);

        for(int m = 0; m < M; ++m)
        {
            for(int n = 0; n < N; ++n)
            {
                c_element_op(e_m_n_host_result(m, n), c_m_n(m, n), d0_m_n(m, n), d1_m_n(m, n));
            }
        }
    }

    std::string best_op_name;
    float best_ave_time   = 0;
    float best_tflops     = 0;
    float best_gb_per_sec = 0;
    float best_kbatch     = 0;

    // profile device GEMM instances
    for(auto& op_ptr : op_ptrs)
    {
        // Seems like when performance measurement has bug when spiltK is large
        // std::vector<int> kbatch_list = {1, 2, 4, 8, 16, 19, 32, 38};
        std::vector<int> kbatch_list = {1, 2, 4, 8, 16};

        if(KBatch > 0)
        {
            kbatch_list = {KBatch};
        }

        for(std::size_t i = 0; i < kbatch_list.size(); i++)
        {
            auto kbatch_curr = kbatch_list[i];

            auto argument_ptr = op_ptr->MakeArgumentPointer(
                static_cast<ADataType*>(a_device_buf.GetDeviceBuffer()),
                static_cast<BDataType*>(b_device_buf.GetDeviceBuffer()),
                std::array<const void*, 2>{d0_device_buf.GetDeviceBuffer(),
                                           d1_device_buf.GetDeviceBuffer()},
                static_cast<EDataType*>(c_device_buf.GetDeviceBuffer()),
                M,
                N,
                K,
                StrideA,
                StrideB,
                std::array<ck::index_t, 2>{StrideD0, StrideD1},
                StrideE,
                kbatch_curr,
                a_element_op,
                b_element_op,
                c_element_op);

            auto invoker_ptr = op_ptr->MakeInvokerPointer();

            if(op_ptr->IsSupportedArgument(argument_ptr.get()))
            {

                // re-init C to zero before profiling next kernel
                c_device_buf.SetZero();

                invoker_ptr->Run(argument_ptr.get(),
                                 StreamConfig{nullptr, false, 0, n_warmup, n_iter});

                if(do_verification)
                {
                    c_device_buf.FromDevice(e_m_n_device_result.mData.data());

                    pass = pass & ck::utils::check_err(e_m_n_device_result, e_m_n_host_result);

                    if(do_log)
                    {
                        LogRangeAsType<float>(std::cout << "a : ", a_m_k.mData, ",") << std::endl;
                        LogRangeAsType<float>(std::cout << "b: ", b_k_n.mData, ",") << std::endl;
                        LogRangeAsType<float>(
                            std::cout << "c_host  : ", e_m_n_host_result.mData, ",")
                            << std::endl;
                        LogRangeAsType<float>(
                            std::cout << "c_device: ", e_m_n_device_result.mData, ",")
                            << std::endl;
                    }
                }

                std::string op_name = op_ptr->GetTypeString();

                hipStream_t stream;
                hip_check_error(hipStreamCreate(&stream));

                float ave_time = invoker_ptr->Run(argument_ptr.get(),
                                                  StreamConfig{stream,
                                                               time_kernel,
                                                               0,
                                                               n_warmup,
                                                               n_iter,
                                                               rotating_count > 1,
                                                               rotating_count});

                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: " << std::setw(10) << ave_time << " ms, " << tflops
                          << " TFlops, " << gb_per_sec << " GB/s, " << op_name << ", KBatch "
                          << kbatch_curr << std::endl;

#if defined CK_ENABLE_FP8 || defined CK_ENABLE_INT8
                // set softer tolerances for fp8
                if constexpr((is_same_v<ADataType, f8_t> || is_same_v<BDataType, f8_t> ||
                              is_same_v<EDataType, f8_t>) ||
                             (is_same_v<ADataType, int8_t> || is_same_v<BDataType, int8_t> ||
                              is_same_v<EDataType, int8_t>))
                {
                    std::string msg = "Error: Incorrect results!";
                    double rtol     = 1e-1;
                    double atol     = 1e-1;
                    pass            = pass & ck::utils::check_err(
                                      e_m_n_device_result, e_m_n_host_result, msg, rtol, atol);
                }
                else
                {
#endif
                    pass = pass & ck::utils::check_err(e_m_n_device_result, e_m_n_host_result);
#if defined CK_ENABLE_FP8 || defined CK_ENABLE_INT8
                }
#endif

                if(tflops > best_tflops && ave_time > 1e-10)
                {
                    best_op_name    = op_name;
                    best_tflops     = tflops;
                    best_ave_time   = ave_time;
                    best_gb_per_sec = gb_per_sec;
                    best_kbatch     = kbatch_curr;
                }
            }
            else
            {
                std::cout << op_ptr->GetTypeString() << " does not support this problem"
                          << std::endl;
            }
        }
    }

    if constexpr(is_same<EDataType, float>::value)
    {
        std::cout << "Best Perf for datatype = f32";
    }
    else if constexpr(is_same<EDataType, half_t>::value)
    {
        std::cout << "Best Perf for datatype = f16";
    }
    else if constexpr(is_same<EDataType, bhalf_t>::value)
    {
        std::cout << "Best Perf for datatype = bf16";
    }
    else if constexpr(is_same<EDataType, int8_t>::value)
    {
        std::cout << "Best Perf for datatype = int8";
    }

    if constexpr(is_same<ALayout, tensor_layout::gemm::RowMajor>::value)
    {
        std::cout << " ALayout =  RowMajor";
    }
    else if constexpr(is_same<ALayout, tensor_layout::gemm::ColumnMajor>::value)
    {
        std::cout << " ALayout =  ColumnMajor";
    }

    if constexpr(is_same<BLayout, tensor_layout::gemm::RowMajor>::value)
    {
        std::cout << " BLayout =  RowMajor";
    }
    else if constexpr(is_same<BLayout, tensor_layout::gemm::ColumnMajor>::value)
    {
        std::cout << " BLayout =  ColumnMajor";
    }

    std::cout << " M = " << M << " N = " << N << " K = " << K << " StrideA = " << StrideA
              << " StrideB = " << StrideB << " StrideE = " << StrideE << " KBatch = " << best_kbatch
              << " : " << best_ave_time << " ms, " << best_tflops << " TFlops, " << best_gb_per_sec
              << " GB/s, " << best_op_name << std::endl;

    return pass;
}

} // namespace profiler
} // namespace ck
