// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.

#pragma once

#include <iomanip>

#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/device_gemm_multiple_d.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"

#include "ck/library/tensor_operation_instance/gpu/gemm_bilinear.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 AccDataType,
          typename DDataType,
          typename EDataType,
          typename ALayout,
          typename BLayout,
          typename DLayout,
          typename ELayout>
bool profile_gemm_bilinear_impl(int do_verification,
                                int init_method,
                                bool /*do_log*/,
                                bool time_kernel,
                                int M,
                                int N,
                                int K,
                                int StrideA,
                                int StrideB,
                                int StrideD,
                                int StrideE,
                                float alpha,
                                float beta)
{
    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<DDataType> d_m_n(f_host_tensor_descriptor(M, N, StrideD, DLayout{}));
    Tensor<EDataType> e_m_n_device_result(f_host_tensor_descriptor(M, N, StrideE, ELayout{}));
    Tensor<EDataType> e_m_n_host_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 << "d_m_n: " << d_m_n.mDesc << std::endl;
    std::cout << "e_m_n: " << e_m_n_device_result.mDesc << std::endl;

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

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

    using AElementOp   = PassThrough;
    using BElementOp   = PassThrough;
    using CDEElementOp = Bilinear;

    const auto a_element_op   = AElementOp{};
    const auto b_element_op   = BElementOp{};
    const auto cde_element_op = CDEElementOp{alpha, beta};

    using DeviceOp = ck::tensor_operation::device::DeviceGemmMultipleD<
        ALayout,
        BLayout,
        ck::Tuple<DLayout>,
        ELayout,
        ADataType,
        BDataType,
        ck::Tuple<DDataType>,
        EDataType,
        ck::tensor_operation::element_wise::PassThrough,
        ck::tensor_operation::element_wise::PassThrough,
        ck::tensor_operation::element_wise::Bilinear>;

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

        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), d_m_n(m, n));
            }
        }
    }

    DeviceMem a_device_buf(sizeof(ADataType) * a_m_k.mDesc.GetElementSpaceSize());
    DeviceMem b_device_buf(sizeof(BDataType) * b_k_n.mDesc.GetElementSpaceSize());
    DeviceMem d_m_n_device_buf(sizeof(DDataType) * d_m_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());
    d_m_n_device_buf.ToDevice(d_m_n.mData.data());

    std::string best_op_name;
    float best_ave_time   = 0;
    float best_tflops     = 0;
    float best_gb_per_sec = 0;
    int num_kernel        = 0;

    bool pass = true;

    // profile device operation instances
    for(auto& op_ptr : op_ptrs)
    {
        auto argument_ptr = op_ptr->MakeArgumentPointer(
            a_device_buf.GetDeviceBuffer(),
            b_device_buf.GetDeviceBuffer(),
            std::array<const void*, 1>{d_m_n_device_buf.GetDeviceBuffer()},
            e_device_buf.GetDeviceBuffer(),
            M,
            N,
            K,
            StrideA,
            StrideB,
            std::array<ck::index_t, 1>{StrideD},
            StrideE,
            a_element_op,
            b_element_op,
            cde_element_op);

        auto invoker_ptr = op_ptr->MakeInvokerPointer();

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

        if(op_ptr->IsSupportedArgument(argument_ptr.get()))
        {
            num_kernel++;
            // re-init E to zero before profiling a kernel
            e_device_buf.SetZero();

            float ave_time =
                invoker_ptr->Run(argument_ptr.get(), 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: " << std::setw(10) << ave_time << " ms, " << tflops << " TFlops, "
                      << gb_per_sec << " GB/s, " << op_name << std::endl;

            if(tflops > best_tflops)
            {
                best_op_name    = op_name;
                best_tflops     = tflops;
                best_ave_time   = ave_time;
                best_gb_per_sec = gb_per_sec;
            }

            if(do_verification)
            {
                e_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);
            }
        }
        else
        {
            std::cout << op_name << " does not support this problem" << std::endl;
        }
    }

    if(num_kernel == 0)
    {
        std::cout << "Error: No kernel is applicable" << std::endl;
        return false;
    }

    std::cout << "Best Perf: " << best_ave_time << " ms, " << best_tflops << " TFlops, "
              << best_gb_per_sec << " GB/s, " << best_op_name << std::endl;

    return pass;
}

} // namespace profiler
} // namespace ck
