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#ifndef DLRM_KERNEL_BASE_IMPL_HPP
#define DLRM_KERNEL_BASE_IMPL_HPP

#include <cmath>
#include <cstdlib>
#include <fstream>
#include <iostream>
#include <stdio.h>
#include <stdlib.h>
#include <tuple>

#include <hip/hip_ext.h>
#include <hip/hip_runtime.h>
#include <hip/hip_runtime_api.h>

#include <gtest/gtest.h>

#include <rocwmma/internal/constants.hpp>
#include <rocwmma/internal/utils.hpp>

#include "../common.hpp"
#include "./common.hpp"
#include "dlrm_kernel_base.hpp"
#include "performance.hpp"

// Library includes

#if ROCWMMA_VALIDATION_TESTS
#include "reference.hpp" // Vanilla CPU kernel
#endif // ROCWMMA_VALIDATION_TESTS

namespace rocwmma
{

    template <uint32_t TileSize, typename DataT>
    DlrmKernelBase<TileSize, DataT>::DlrmKernelBase()
    {
        reset();
    }
    template <uint32_t TileSize, typename DataT>
    DlrmKernelBase<TileSize, DataT>::~DlrmKernelBase()
    {
    }

    template <uint32_t TileSize, typename DataT>
    uint32_t DlrmKernelBase<TileSize, DataT>::ldsUsage() const
    {
        return 0;
    }

    template <uint32_t TileSize, typename DataT>
    dim3 DlrmKernelBase<TileSize, DataT>::gridDim() const
    {
        auto& device = DeviceInfo::instance();

        if(passDirection == DlrmDirection_t::Forward)
        {
            return dim3(ceil_div(mMPadded, TileSize * mTBlockX / device->warpSize()),
                        ceil_div(mM, TileSize),
                        mB);
        }
        else
        {
            return dim3(ceil_div(mMPadded, TileSize * mTBlockX / device->warpSize()),
                        ceil_div(mK, TileSize),
                        mB);
        }
    }

    template <uint32_t TileSize, typename DataT>
    dim3 DlrmKernelBase<TileSize, DataT>::blockDim() const
    {
        return dim3(mTBlockX);
    }

    template <uint32_t TileSize, typename DataT>
    bool DlrmKernelBase<TileSize, DataT>::checkDevice() const
    {
        auto& deviceInfo = DeviceInfo::instance();
        auto  deviceArch = deviceInfo->getGcnArch();

        // Arch
        auto isGfx908 = deviceArch == DeviceInfo::GFX908;
        auto isGfx11  = (deviceArch == DeviceInfo::GFX1100) || (deviceArch == DeviceInfo::GFX1101)
                       || (deviceArch == DeviceInfo::GFX1102)
                       || (deviceArch == DeviceInfo::GFX1150)
                       || (deviceArch == DeviceInfo::GFX1151);

        auto isGfx12 = (deviceArch == DeviceInfo::GFX1200) || (deviceArch == DeviceInfo::GFX1201);

        // Datatypes
        auto isF64 = std::is_same<DataT, float64_t>::value;

#if !ROCWMMA_TESTS_NO_HALF
        auto isH16 = std::is_same<DataT, hfloat16_t>::value;
#else
        auto isH16 = false;
#endif // !ROCWMMA_NO_HALF
        auto isF16  = std::is_same<DataT, float16_t>::value || isH16;
        auto isBF16 = (std::is_same<DataT, bfloat16_t>::value);
        auto isI8   = (std::is_same<DataT, int8_t>::value);

        // Block size
        auto is16x16 = (TileSize == 16);

        // No unsupported devices
        bool unsupportedDeviceCheck = !(deviceArch == DeviceInfo::UNSUPPORTED_ARCH);

        // gfx908 doesn't support f64
        bool gfx908F64Check = !(isGfx908 && isF64);

        // gfx11 only supports f16, i8 and bf16 inputs with block size 16
        bool gfx11Check = !(isGfx11 && ((!isF16 && !isBF16 && !isI8) || !is16x16));

        // gfx12 only supports f16, i8 and bf16 inputs with block size 16
        bool gfx12Check = !(isGfx12 && ((!isF16 && !isBF16 && !isI8) || !is16x16));

        return unsupportedDeviceCheck && gfx908F64Check && gfx11Check && gfx12Check;
    }

    template <uint32_t TileSize, typename DataT>
    bool DlrmKernelBase<TileSize, DataT>::checkSizes() const
    {
        return (mM >= TileSize && (mM % TileSize == 0) && mK >= TileSize && (mK % TileSize == 0)
                && (mTBlockX % TileSize == 0));
    }

    template <uint32_t TileSize, typename DataT>
    bool DlrmKernelBase<TileSize, DataT>::checkLds() const
    {
        return ldsUsage() <= DeviceInfo::instance()->sharedMemSize();
    }

    template <uint32_t TileSize, typename DataT>
    void DlrmKernelBase<TileSize, DataT>::reset()
    {
        mM = mK = mB = 0;
        mMPadded = mKPadded = 0;
        mRepeats =
#if ROCWMMA_VALIDATION_TESTS
            1;
#else
            5;
#endif // ROCWMMA_VALIDATION_TESTS

        mRunFlag = true;

        mTotalGFlops = mMeasuredTFlopsPerSec = 0.0;
        mElapsedTimeMs                       = 0.0;
        mEfficiency                          = -1;

        passDirection = DlrmDirection_t::Forward;

        mValidationResult = false;
        mMaxRelativeError = 0.0;
    }

    template <uint32_t TileSize, typename DataT>
    HipResource* DlrmKernelBase<TileSize, DataT>::getResource()
    {
        return DataStorage::instance().get();
    }

    template <uint32_t TileSize, typename DataT>
    std::ostream& DlrmKernelBase<TileSize, DataT>::printHeader(std::ostream& stream) const
    {
        return stream << "TileSize, "
                      << "DataT, "
                      << "Direction, "
                      << "MatM, MatK, MatB, "
#if ROCWMMA_VALIDATION_TESTS
                      << "maxRelativeDiff, "
                      << "tolerance, "
#endif // ROCWMMA_VALIDATION_TESTS
                      << "elapsedMs, "
                      << "Problem Size(GFlops), "
                      << "TFlops/s, "
                      << "Efficiency(%)" << std::endl;
    }

    template <uint32_t TileSize, typename DataT>
    std::ostream& DlrmKernelBase<TileSize, DataT>::printKernel(std::ostream& stream) const
    {
        if(!mRunFlag)
        {
            return stream << TileSize << ", " << dataTypeToString<DataT>() << ", "
                          << (passDirection == DlrmDirection_t::Forward ? "Forwards" : "Backwards")
                          << ", " << mM << ", " << mK << ", " << mB << ", "

#if ROCWMMA_VALIDATION_TESTS
                          << "n/a, "
#endif // ROCWMMA_VALIDATION_TESTS
                          << "n/a, n/a, n/a, n/a, SKIPPED" << std::endl;
        }
        else
        {
            return stream << TileSize << ", " << dataTypeToString<DataT>() << ", "
                          << (passDirection == DlrmDirection_t::Forward ? "Forwards" : "Backwards")
                          << ", " << mM << ", " << mK << ", " << mB << ", "

#if ROCWMMA_VALIDATION_TESTS
                          << mMaxRelativeError << ", "
#endif // ROCWMMA_VALIDATION_TESTS
                          << mElapsedTimeMs << ", " << mTotalGFlops << ", " << mMeasuredTFlopsPerSec
                          << ", " << mEfficiency << ", "
#if ROCWMMA_VALIDATION_TESTS
                          << (mValidationResult ? "PASSED" : "FAILED")
#else
                          << "BENCH"
#endif // ROCWMMA_VALIDATION_TESTS
                          << std::endl;
        }
    }

    template <uint32_t TileSize, typename DataT>
    void DlrmKernelBase<TileSize, DataT>::setup(ProblemParams const& problem)
    {
        // Reset the flags in case of multiple runs
        mRunFlag = true;

        // Format incoming problem parameters
        std::tie(mTBlockX, mTBlockY)
            = std::tie(static_cast<uint32_t const&>(std::get<0>(problem.threadBlockSize)),
                       static_cast<uint32_t const&>(std::get<1>(problem.threadBlockSize)));
        std::tie(mM, mK, mB)
            = std::tie(static_cast<uint32_t const&>(std::get<0>(problem.problemSize)),
                       static_cast<uint32_t const&>(std::get<1>(problem.problemSize)),
                       static_cast<uint32_t const&>(std::get<2>(problem.problemSize)));

        mMPadded = ceil_div(mM, TileSize) * TileSize;
        mKPadded = ceil_div(mK, TileSize) * TileSize;

        // Determine whether to run forward or backward pass
        passDirection = problem.passDirection;

        mRunFlag &= checkDevice();
        mRunFlag &= checkSizes();
        mRunFlag &= checkLds();

        if(mRunFlag)
        {
            auto& dataInstance = DataStorage::instance();

            // Initialize matrix storage
            if(passDirection == DlrmDirection_t::Forward)
            {
                dataInstance->resizeFwdStorage(problem.problemSize);
            }
            else
            {
                dataInstance->resizeBwdStorage(problem.problemSize);
            }

            // Initialize matrix data on device and transfer to host for validation
            if(passDirection == DlrmDirection_t::Forward)
            {
                MatrixUtil<row_major>::fillLaunchKernel(
                    dataInstance->deviceInput().get(), mM, mK, mB);
#if ROCWMMA_VALIDATION_TESTS
                dataInstance->copyDeviceToHostFwdInput();
#endif // ROCWMMA_VALIDATION_TESTS
            }
            else
            {
                uint gradSize = ((mM * (mM - 1)) / 2) + mK;
                MatrixUtil<row_major>::fillLaunchKernel(
                    dataInstance->deviceInput().get(), mM, mK, mB);
                MatrixUtil<row_major>::fillLaunchKernel(
                    dataInstance->deviceUpstreamGrad().get(), 1, gradSize, mB);
#if ROCWMMA_VALIDATION_TESTS
                dataInstance->copyDeviceToHostBwdInput();
#endif // ROCWMMA_VALIDATION_TESTS
            }
        }
    }

    template <uint32_t TileSize, typename DataT>
    void DlrmKernelBase<TileSize, DataT>::exec()
    {
        if(mRunFlag)
        {
            std::function<void()> dlrmKernel;
            if(passDirection == DlrmDirection_t::Forward)
            {
                if(mM == mMPadded && mK == mKPadded)
                {
                    uint inputBatchOffset  = mM * mK;
                    uint outputBatchOffset = ((mM * (mM - 1)) / 2) + mK;
                    uint accBatchOffset    = mM * mM;

                    dlrmKernel = [this, inputBatchOffset, outputBatchOffset, accBatchOffset]() {
                        auto& dataInstance = DataStorage::instance();
                        hipExtLaunchKernelGGL((this->kernelFwdImpl()),
                                              (this->gridDim()),
                                              (this->blockDim()),
                                              (this->ldsUsage()),
                                              0,
                                              nullptr,
                                              nullptr,
                                              0,
                                              dataInstance->deviceInput().get(),
                                              dataInstance->deviceOutput().get(),
                                              dataInstance->deviceAccFwd().get(),
                                              mM,
                                              mK,
                                              mB,
                                              inputBatchOffset,
                                              outputBatchOffset,
                                              accBatchOffset);
                    };
                }
            }
            else
            {
                if(mM == mMPadded && mK == mKPadded)
                {
                    auto& dataInstance = DataStorage::instance();

                    uint inputBatchOffset    = mM * mK;
                    uint upstreamBatchOffset = ((mM * (mM - 1)) / 2) + mK;
                    uint accBatchOffset      = mM * mM;

                    dlrmKernel = [this, inputBatchOffset, upstreamBatchOffset, accBatchOffset]() {
                        auto& dataInstance = DataStorage::instance();
                        auto  trilGridDim
                            = dim3(ceil_div(mM * mM, static_cast<uint32_t>(mTBlockX)), 1, mB);

                        hipEvent_t syncEvent;
                        CHECK_HIP_ERROR(hipEventCreate(&syncEvent));
                        hipExtLaunchKernelGGL((this->kernelTrilImpl()),
                                              trilGridDim,
                                              this->blockDim(),
                                              0,
                                              0,
                                              nullptr,
                                              nullptr,
                                              0,
                                              dataInstance->deviceUpstreamGrad().get(),
                                              dataInstance->deviceAccBwd().get(),
                                              mM,
                                              mK,
                                              mB,
                                              upstreamBatchOffset,
                                              accBatchOffset);
                        CHECK_HIP_ERROR(hipEventRecord(syncEvent));
                        CHECK_HIP_ERROR(hipEventSynchronize(syncEvent));

                        hipExtLaunchKernelGGL((this->kernelBwdImpl()),
                                              (this->gridDim()),
                                              (this->blockDim()),
                                              (this->ldsUsage()),
                                              0,
                                              nullptr,
                                              nullptr,
                                              0,
                                              dataInstance->deviceInput().get(),
                                              dataInstance->deviceUpstreamGrad().get(),
                                              dataInstance->deviceGrad().get(),
                                              dataInstance->deviceBottomMlpGrad().get(),
                                              dataInstance->deviceAccBwd().get(),
                                              mM,
                                              mK,
                                              mB,
                                              inputBatchOffset,
                                              upstreamBatchOffset,
                                              accBatchOffset);
                    };
                }
            }

            hipEvent_t startEvent, stopEvent;
            CHECK_HIP_ERROR(hipEventCreate(&startEvent));
            CHECK_HIP_ERROR(hipEventCreate(&stopEvent));

            CHECK_HIP_ERROR(hipEventRecord(startEvent));
            for(uint32_t i = 0; i < mRepeats; ++i)
            {
                dlrmKernel();
            }
            CHECK_HIP_ERROR(hipEventRecord(stopEvent));
            CHECK_HIP_ERROR(hipEventSynchronize(stopEvent));

            auto timeMs = 0.0f;
            CHECK_HIP_ERROR(hipEventElapsedTime(&timeMs, startEvent, stopEvent));

            // Calculate efficiency
            auto& deviceInfo = DeviceInfo::instance();

            auto devicePeakGFlopsPerSec = deviceInfo->peakGFlopsPerSec<DataT>();
            auto outputSize = (passDirection == DlrmDirection_t::Forward) ? mM * mM : mM * mK;

            mElapsedTimeMs        = float64_t(timeMs);
            mTotalGFlops          = calculateGFlops(outputSize, mB, mK);
            mMeasuredTFlopsPerSec = calculateTFlopsPerSec(outputSize, mB, mK, mElapsedTimeMs)
                                    * static_cast<float64_t>(mRepeats);

            mEfficiency = round(mMeasuredTFlopsPerSec / devicePeakGFlopsPerSec * 100000.0);

            CHECK_HIP_ERROR(hipEventDestroy(startEvent));
            CHECK_HIP_ERROR(hipEventDestroy(stopEvent));

#if ROCWMMA_VALIDATION_TESTS

            // Run reference CPU kernel
            std::function<void()> cpuKernel;
            if(passDirection == DlrmDirection_t::Forward)
            {
                cpuKernel = [this]() {
                    auto& dataInstance = DataStorage::instance();
                    dlrm_fwd_CPU<DataT>(dataInstance->hostInput().get(),
                                        dataInstance->hostOutputRef().get(),
                                        mM,
                                        mK,
                                        mB);
                };
            }
            else
            {
                cpuKernel = [this]() {
                    auto& dataInstance = DataStorage::instance();
                    dlrm_bwd_CPU<DataT>(dataInstance->hostInput().get(),
                                        dataInstance->hostUpstreamGrad().get(),
                                        dataInstance->hostBottomMlpGradRef().get(),
                                        dataInstance->hostGradRef().get(),
                                        mM,
                                        mK,
                                        mB);
                };
            }
            cpuKernel();
#endif // ROCWMMA_VALIDATION_TESTS
        }
    }

    template <uint32_t TileSize, typename DataT>
    void DlrmKernelBase<TileSize, DataT>::validateResults()
    {
#if ROCWMMA_VALIDATION_TESTS
        if(mRunFlag)
        {
            auto& dataInstance = DataStorage::instance();
            if(passDirection == DlrmDirection_t::Forward)
            {
                uint batchSize = ((mM * (mM - 1)) / 2) + mK;
                auto reference = dataInstance->template allocDevice<DataT>(batchSize * mB);
                dataInstance->copyData(reference, dataInstance->hostOutputRef(), batchSize * mB);

                std::tie(mValidationResult, mMaxRelativeError)
                    = compareEqualLaunchKernel<DataT, DataT>(dataInstance->deviceOutput().get(),
                                                             reference.get(),
                                                             1,
                                                             batchSize,
                                                             mB,
                                                             10.0);

                EXPECT_TRUE(mValidationResult) << "Max relative error: " << mMaxRelativeError;
            }
            else
            {
                // Copy reference output gradient to device
                auto reference0 = dataInstance->template allocDevice<DataT>(mM * mK * mB);
                dataInstance->copyData(reference0, dataInstance->hostGradRef(), mM * mK * mB);

                std::tie(mValidationResult, mMaxRelativeError)
                    = compareEqualLaunchKernel<DataT, DataT>(
                        dataInstance->deviceGrad().get(), reference0.get(), mM, mK, mB);

                EXPECT_TRUE(mValidationResult) << "Max relative error: " << mMaxRelativeError;

                double maxRelativeError = mMaxRelativeError;

                // Copy reference bottom mlp gradient to device
                auto reference1 = dataInstance->template allocDevice<DataT>(mK * mB);
                dataInstance->copyData(reference1, dataInstance->hostBottomMlpGradRef(), mK * mB);

                std::tie(mValidationResult, mMaxRelativeError)
                    = compareEqualLaunchKernel<DataT, DataT>(
                        dataInstance->deviceBottomMlpGrad().get(), reference1.get(), 1, mK, mB);

                EXPECT_TRUE(mValidationResult) << "Max relative error: " << mMaxRelativeError;

                mMaxRelativeError
                    = (maxRelativeError > mMaxRelativeError) ? maxRelativeError : mMaxRelativeError;
            }
        }
#endif
    }

    template <uint32_t TileSize, typename DataT>
    void DlrmKernelBase<TileSize, DataT>::reportResults()
    {
        if(!KernelI::sHeaderPrinted)
        {
            printHeader();
            KernelI::sHeaderPrinted = true;
        }
        printKernel();
    }

    template <uint32_t TileSize, typename DataT>
    void DlrmKernelBase<TileSize, DataT>::tearDown()
    {
    }

} // namespace rocwmma

#endif // DLRM_KERNEL_BASE_IMPL_HPP
