//
//  ConvolutionFloatFactory.cpp
//  MNN
//
//  Created by MNN on 2018/07/16.
//  Copyright © 2018, Alibaba Group Holding Limited
//

#include "backend/cpu/compute/ConvolutionFloatFactory.h"
#include "backend/cpu/CPUConvolutionDepthwise.hpp"
#include "backend/cpu/CPURuntime.hpp"
#include "backend/cpu/compute/ConvOpt.h"
#include "backend/cpu/compute/Convolution1x1Strassen.hpp"
#include "backend/cpu/compute/ConvolutionGroup.hpp"
#include "backend/cpu/compute/ConvolutionIntFactory.hpp"

#include "backend/cpu/compute/ConvolutionWinogradBridge.hpp"
#include "backend/cpu/compute/DenseConvolutionTiledExecutor.hpp"
#ifdef MNN_USE_SPARSE_COMPUTE
#include "backend/cpu/compute/SparseConvolutionTiledExecutor.hpp"
#endif
#include "core/Macro.h"
#include "core/OpCommonUtils.hpp"
#include "backend/cpu/OneDNNConvolution.hpp"
#include "backend/cpu/compute/ConvInt8TiledExecutor.hpp"

#ifdef MNN_KLEIDIAI_ENABLED
#include "backend/cpu/kleidiai/KleidiAIConvInt8.hpp"
#include "backend/cpu/kleidiai/KleidiAIConvolution.hpp"
#include "backend/cpu/kleidiai/KleidiAIDenseConvolution.hpp"
#endif //MNN_KLEIDIAI_ENABLED

namespace MNN {

#ifdef MNN_KLEIDIAI_ENABLED
static Execution* _createKleidiAIUnit(const Tensor* input, const Tensor* output, Backend* backend, const Op* op,
                                      const float* originWeight, size_t originWeightSize, const float* bias,
                                      size_t biasSize, std::shared_ptr<ConvolutionCommon::Int8Common> weightQuantInfo,
                                      bool supportSparse, bool lowMemory) {
    auto cpuBackend = (CPUBackend*)backend;
    auto conv2d     = op->main_as_Convolution2D();
    auto common     = conv2d->common();

    bool fastWay = common->kernelY() == 1 && common->kernelX() == 1 && output->width() == input->width() &&
                   output->height() == input->height() && common->strideX() == 1 && common->strideY() == 1;

#ifdef MNN_LOW_MEMORY
    if (lowMemory && nullptr != weightQuantInfo.get() && originWeightSize == 0) {
        if (cpuBackend->memoryMode() == BackendConfig::Memory_Low) {
            do {
                if (!weightQuantInfo->canUseInt4) {
                    break;
                }
                auto convOp = op->main_as_Convolution2D();
                auto core   = static_cast<CPUBackend*>(backend)->functions();
                int oc      = convOp->common()->outputCount();
                int ic      = convOp->common()->inputCount();

                int blockNum   = 1;
                int dequantCnt = weightQuantInfo->alphaSize;
                if (weightQuantInfo->asymmetric) {
                    dequantCnt /= 2;
                }
                blockNum = dequantCnt / oc;

                bool bAsym     = weightQuantInfo->asymmetric;
                size_t blkSize = blockNum == 1 ? 0 : ic / blockNum;

                KleidiAI::AccelType accelType = KleidiAI::getQIntAccelType(4, bAsym, blkSize, core->bytes);

                KleidiAI& kai = KleidiAI::getInstance(*MNNGetCPUInfo());
                if (!kai.canAccelerate(accelType, convOp->common())) {
                    break;
                }

                if (!kai.isLoaded(accelType)) {
                    kai.setLoaded(accelType);
                    kai.printInfo(accelType);
                }

                return new KleidiAIConvInt8(backend, op, weightQuantInfo, true, kai, accelType, blockNum);
            } while (0);
        }

        // Have not supported the quantized weight.
        return nullptr;
    }
#else
    if (cpuBackend->memoryMode() == BackendConfig::Memory_Low) {
        if (MNNGetCPUInfo()->sme2 && !weightQuantInfo) {
            return new KleidiAIDenseConvolution(common, backend, originWeight, originWeightSize, bias, biasSize,
                                                weightQuantInfo);
        }

        // Do nothing and fallback.
        return nullptr;
    }
#endif

    // This is different with original impl. It's a corresponding impl for strassen,
    // which is called when built without MNN_REDUCE_SIZE. But for KleidiAI,
    // need not to care about this.
    if (fastWay && cpuBackend->functions()->matmulBytes == 0) {
        auto bytes     = cpuBackend->functions()->bytes;
        auto accelType = (bytes == 2) ? KleidiAI::AccelType::FP16 : KleidiAI::AccelType::FP32;
        KleidiAI& kai  = KleidiAI::getInstance(*MNNGetCPUInfo());
        if (kai.canAccelerate(accelType)) {
            return new KleidiAIConvolution(common, backend, originWeight, originWeightSize, bias, biasSize);
        }
    }

    if (MNNGetCPUInfo()->sme2 && !weightQuantInfo) {
        return new KleidiAIDenseConvolution(common, backend, originWeight, originWeightSize, bias, biasSize,
                                            weightQuantInfo);
    }

    return nullptr;
}
#endif //MNN_KLEIDIAI_ENABLED

static Execution* _createUnit(const Tensor* input, const Tensor* output, Backend* backend,
                              const Op* op, const float* originWeight, size_t originWeightSize, const float* bias, size_t biasSize, std::shared_ptr<ConvolutionCommon::Int8Common> weightQuantInfo, bool supportSparse, bool lowMemory) {
    auto cpuBackend = (CPUBackend*)backend;
    auto conv2d = op->main_as_Convolution2D();
    auto common = conv2d->common();
#ifdef MNN_USE_ONEDNN
    return OneDNN::createConvolution(common, backend, originWeight, originWeightSize, bias, biasSize);
#endif

#ifdef MNN_USE_SPARSE_COMPUTE
    if (conv2d->sparseParameter() && nullptr != weightQuantInfo.get()) {
        if (supportSparse && weightQuantInfo->quan->index() != nullptr) {
            return new SparseConvolutionTiledExecutor(common, backend, weightQuantInfo->quan,
                                                      conv2d->sparseParameter(), bias, biasSize);
        }
    }
#endif

#ifdef MNN_KLEIDIAI_ENABLED
    if (cpuBackend->getRuntime()->hint().enableKleidiAI) {
        auto execution = _createKleidiAIUnit(input, output, backend, op, originWeight, originWeightSize, bias, biasSize,
                                             weightQuantInfo, supportSparse, lowMemory);

        if (execution) {
            return execution;
        }
    }
#endif //MNN_KLEIDIAI_ENABLED

    bool fastWay = common->kernelY() == 1 && common->kernelX() == 1
        && output->width() == input->width() && output->height() == input->height()
        && common->strideX() == 1 && common->strideY() == 1;
#ifdef MNN_LOW_MEMORY
    if (lowMemory && nullptr != weightQuantInfo.get() && originWeightSize == 0) {
        if (cpuBackend->memoryMode() == BackendConfig::Memory_Low) {
            return new DenseConvInt8TiledExecutor(backend, op, weightQuantInfo, true);
        } else {
            return new DenseConvolutionTiledExecutor(common, backend, originWeight, originWeightSize, bias, biasSize, weightQuantInfo);
        }
    }
#else
    if (cpuBackend->memoryMode() == BackendConfig::Memory_Low) {
        return new DenseConvolutionTiledExecutor(common, backend, originWeight, originWeightSize, bias, biasSize, weightQuantInfo);
    }
#endif

#ifndef MNN_REDUCE_SIZE
    if (fastWay && cpuBackend->functions()->matmulBytes == 0) {
        return new Convolution1x1Strassen(common, backend, originWeight, originWeightSize, bias, biasSize);
    }
#endif

    if (cpuBackend->getRuntime()->hint().winogradMemoryUsed == 0 || (!ConvolutionWinogradBridge::canUseWinograd(common))) {
        return new DenseConvolutionTiledExecutor(common, backend, originWeight, originWeightSize, bias, biasSize, nullptr);
    }
    PerfConfig convPerfconfig = DenseConvolutionTiledExecutor::bestTileConvolutionConfig(common, input, output, cpuBackend->threadNumber(), backend);
    auto winogradConfig = ConvolutionWinogradBridge::bestWinogradUnit(common, input, output, cpuBackend->threadNumber(), backend, convPerfconfig);
    if (winogradConfig.unit <= 1) {
        return new DenseConvolutionTiledExecutor(common, backend, originWeight, originWeightSize, bias, biasSize, nullptr);
    }
    return ConvolutionWinogradBridge::createWinogradImpl(common, input, output, backend, originWeight, originWeightSize, bias, biasSize,
                                   winogradConfig);
}

Execution* ConvolutionFloatFactory::create(const std::vector<Tensor*>& inputs, const std::vector<Tensor*>& outputs,
                                           const MNN::Op* op, Backend* backend) {
    auto conv2d = op->main_as_Convolution2D();
    if (inputs.size() > 1) {
        // Multi Input
        return new ConvolutionTiledExecutorMultiInput(conv2d->common(), backend);
    }
#ifdef MNN_LOW_MEMORY
    bool lowMemory = static_cast<CPUBackend*>(backend)->memoryMode() == BackendConfig::Memory_Low;
    if (lowMemory && static_cast<CPUBackend*>(backend)->functions()->bytes == 2 && static_cast<CPUBackend*>(backend)->int8Functions()->MNNGemmInt8AddBiasScale_Unit_FP16 == nullptr) {
        // Fall back to fp32
        return nullptr;
    }
#else
    bool lowMemory = false;
#endif

    const float* originWeight = nullptr;
    const float* originBias   = nullptr;
    int originWeightSize   = 0;
    int originBiasSize     = 0;
    std::shared_ptr<ConvolutionCommon::Int8Common> quanCommon;
    std::unique_ptr<Tensor> externalWeightTensor, externalBiasTensor;
    bool supportSparse = false;
    auto core = static_cast<CPUBackend*>(backend)->functions();
    int bytes = core->bytes;
#ifdef MNN_USE_SPARSE_COMPUTE
#ifdef MNN_USE_SSE
    const bool onlySSENotAVX = core->pack == 4; // no backend of only sse without avx2 or avx512
#else
    const bool onlySSENotAVX = false;
#endif
    supportSparse = !onlySSENotAVX && bytes == 4;
#endif
    if (nullptr != conv2d->quanParameter()) {
        bool forceFloat = false;
        if (!supportSparse && conv2d->quanParameter()->index() != nullptr) {
            // The weight is storage as float sparse, but the backend don't support sparse compute, expand it
            forceFloat = true;
        }
        quanCommon = ConvolutionCommon::load(op, backend, forceFloat, lowMemory);
        if (nullptr == quanCommon) {
            MNN_ERROR("Memory not Enough, can't extract IDST Convolution: %s \n", op->name()->c_str());
            return nullptr;
        }

        if (conv2d->quanParameter()->has_scaleInt()) {
            if (bytes < 4) {
                // From BF16 / FP16
                return nullptr;
            }
            return ConvolutionIntFactory::create(inputs[0], outputs[0], op, backend, quanCommon.get());
        }
        // Back to float
        originWeight     = quanCommon->weightFloat.get();
        originWeightSize = quanCommon->weightFloat.size();
    } else if (nullptr == conv2d->weight() || nullptr == conv2d->bias()) {
        MNN_ERROR("%s has no weight or bias. The model may be benchmark model, please revert the weight/bias firstly\n", op->name()->c_str());
        return nullptr;
    }
    auto common = conv2d->common();
    if (nullptr == originWeight && nullptr != op->main_as_Convolution2D()->weight()) {
        originWeight     = op->main_as_Convolution2D()->weight()->data();
        originWeightSize = op->main_as_Convolution2D()->weight()->size();
    }
    if (nullptr == originBias && op->main_as_Convolution2D()->bias()) {
        originBias     = op->main_as_Convolution2D()->bias()->data();
        originBiasSize = op->main_as_Convolution2D()->bias()->size();
    }

    int group            = common->group();
    if (common->inputCount() != inputs[0]->channel() && common->inputCount() > 0) {
        group = inputs[0]->channel()/ conv2d->common()->inputCount();
    }
    MNN_ASSERT(group > 0);
    if (1 == group) {
        return _createUnit(inputs[0], outputs[0], backend, op, originWeight, originWeightSize,
                           originBias, originBiasSize, quanCommon, supportSparse, lowMemory);
    }
    // TODO: Use Geometry to split
    // Split
    std::vector<std::shared_ptr<Execution>> subConvolution;
    auto groupOutputCount = common->outputCount() / group;
    auto groupWeightSize  = originWeightSize / group;
    std::shared_ptr<Tensor> emptyInput(Tensor::createDevice<float>(inputs[0]->shape(), Tensor::CAFFE_C4));
    std::shared_ptr<Tensor> emptyOutput(Tensor::createDevice<float>(outputs[0]->shape(), Tensor::CAFFE_C4));
    emptyInput->setLength(1, inputs[0]->channel() / group);
    emptyOutput->setLength(1, outputs[0]->channel() / group);
    for (int i = 0; i < group; ++i) {
        auto newConvolution =
            _createUnit(emptyInput.get(), emptyOutput.get(), backend, op, originWeight + groupWeightSize * i,
                        groupWeightSize, conv2d->bias()->data() + groupOutputCount * i, groupOutputCount, quanCommon, supportSparse, lowMemory);
        subConvolution.push_back(std::shared_ptr<Execution>(newConvolution));
    }
    return new ConvolutionGroup(backend, subConvolution);
}
} // namespace MNN