MNN/source/backend/cpu/CPUBackend.cpp

//
//  CPUBackend.cpp
//  MNN
//
//  Created by MNN on 2018/07/06.
//  Copyright © 2018, Alibaba Group Holding Limited
//

#include "CPUBackend.hpp"
#include <cmath>
#include <mutex>
#include "BufferAllocator.hpp"
#include "CPUConcat.hpp"
#include "CPUTensorConvert.hpp"
#include "CommonOptFunction.h"
#include "TensorUtils.hpp"
#include "ThreadPool.hpp"
#ifdef _OPENMP
#include <omp.h>
#endif // _OPENMP
#include "CPURuntime.hpp"

#define MAX_THREAD_NUMBER 32

//#define MNN_DUMP_MEMORY_USAGE
#define MNN_CPU_CHECK_NAN 1
namespace MNN {
#ifdef MNN_CODEGEN_REGISTER
void registerCPUOps();
#endif
static inline std::map<OpType, CPUBackend::Creator*>* getCreatorMap() {
    static std::once_flag of;
    static std::map<OpType, CPUBackend::Creator*>* ret = nullptr;
    std::call_once(of, [&]() { ret = new std::map<OpType, CPUBackend::Creator*>; });
    return ret;
}

bool CPUBackend::addCreator(OpType t, Creator* c) {
    auto map = getCreatorMap();
    if (map->find(t) != map->end()) {
        MNN_PRINT("Error: %d type has be added\n", t);
        return false;
    }
    map->insert(std::make_pair(t, c));
    return true;
}

CPUBackend::CPUBackend(int numberThread, BackendConfig::MemoryMode memory, BackendConfig::PowerMode power, size_t flags)
    : Backend(MNN_FORWARD_CPU), mThreadNumber(numberThread), mMemory(memory), mPower(power) {
    mThreadNumber = std::max(1, mThreadNumber);
    mThreadNumber = std::min(mThreadNumber, MAX_THREAD_NUMBER);
    mDynamicAllocator.reset(new BufferAllocator);
    mStaticAllocator.reset(new BufferAllocator);
    mCheckNAN = flags == MNN_CPU_CHECK_NAN;
#ifdef _OPENMP
    switch (power) {
        case BackendConfig::Power_Low:
            MNNSetCPUThreadsMode(MNN_CPU_MODE_LITTLE);
            break;
        case BackendConfig::Power_High:
            MNNSetCPUThreadsMode(MNN_CPU_MODE_POWER_FRI);
            break;
        default:
            break;
    }
#endif
#ifdef MNN_USE_THREAD_POOL
    mThreadNumber = ThreadPool::init(mThreadNumber);
    if (mThreadNumber > 1) {
        mTaskIndex = ThreadPool::acquireWorkIndex();
    } else {
        mTaskIndex = -1;
    }
    if (mTaskIndex >= 0 && mPower == BackendConfig::Power_High) {
        ThreadPool::active();
    }
#endif
}

CPUBackend::~CPUBackend() {
#ifdef MNN_USE_THREAD_POOL
    if (mTaskIndex >= 0 && mPower == BackendConfig::Power_High) {
        ThreadPool::deactive();
    }
    ThreadPool::releaseWorkIndex(mTaskIndex);
#endif
}

void CPUBackend::onExecuteBegin() const {
#ifdef MNN_DUMP_MEMORY_USAGE
    {
        auto dynamicMemoryInMB = mDynamicAllocator->totalSize() / 1024.0f / 1024.0f;
        FUNC_PRINT_ALL(dynamicMemoryInMB, f);
        auto staticMemoryInMB = mStaticAllocator->totalSize() / 1024.0f / 1024.0f;
        FUNC_PRINT_ALL(staticMemoryInMB, f);
    }
#endif
#ifdef MNN_USE_THREAD_POOL
    if (mTaskIndex >= 0 && mPower != BackendConfig::Power_High) {
        ThreadPool::active();
    }
#else
#ifdef _OPENMP
    omp_set_dynamic(0);
    omp_set_num_threads(mThreadNumber);
#endif
#endif
}
void CPUBackend::onExecuteEnd() const {
#ifdef MNN_USE_THREAD_POOL
    if (mTaskIndex >= 0 && mPower != BackendConfig::Power_High) {
        ThreadPool::deactive();
    }
#endif
}

bool CPUBackend::onAcquireBuffer(const MNN::Tensor* nativeTensorConst, StorageType storageType) {
    auto nativeTensor = (Tensor*)nativeTensorConst;
    auto& buffer      = nativeTensor->buffer();

    auto size = nativeTensor->size();

    // MNN_PRINT("Acquire size = %d\n", size);
    if (size <= 0) {
        MNN_ASSERT(false);
        return false;
    }
    switch (storageType) {
        case STATIC: {
            buffer.host = (uint8_t*)mStaticAllocator->alloc(size, true);
            break;
        }
        case DYNAMIC: {
            buffer.host = (uint8_t*)(mDynamicAllocator->alloc(size, false));
            break;
        }
        case DYNAMIC_SEPERATE: {
            buffer.host = (uint8_t*)(mDynamicAllocator->alloc(size, true));
            break;
        }
        default:
            break;
    }
    if (nullptr == buffer.host) {
        MNN_ERROR("Alloc buffer error for cpu backend\n");
        return false;
    }
    if (buffer.type.code == halide_type_handle) {
        ::memset(buffer.host, 0, size);
    }
    return true;
}

bool CPUBackend::onReleaseBuffer(const MNN::Tensor* nativeTensor, StorageType storageType) {
    if (nullptr == nativeTensor->buffer().host) {
        return false;
    }
    if (STATIC == storageType) {
        mStaticAllocator->free(nativeTensor->buffer().host, true);
        return true;
    }
    if (DYNAMIC_SEPERATE == storageType) {
        return true;
    }
    mDynamicAllocator->free(nativeTensor->buffer().host);
    return true;
}

/// get execution
Execution* CPUBackend::onCreate(const std::vector<Tensor*>& inputs, const std::vector<Tensor*>& outputs,
                                const MNN::Op* op) {
    auto map  = getCreatorMap();
    auto iter = map->find(op->type());
    if (iter == map->end()) {
        MNN_PRINT("Don't support type %d, %s\n", op->type(), op->name()->c_str());
        return nullptr;
    }
    auto exe = iter->second->onCreate(inputs, outputs, op, this);
    if (nullptr == exe) {
        MNN_PRINT("The Creator Don't support type %d, %s\n", op->type(), op->name()->c_str());
        return nullptr;
    }
    if (mCheckNAN) {
        class CheckNANExecution : public Execution {
        public:
            CheckNANExecution(Execution* exe) : Execution(exe->backend()) {
                mExecution.reset(exe);
                mValid = exe->valid();
            }
            virtual ~CheckNANExecution() {
                // Do nothing
            }
            virtual ErrorCode onResize(const std::vector<Tensor*>& inputs,
                                       const std::vector<Tensor*>& outputs) override {
                return mExecution->onResize(inputs, outputs);
            }

            virtual ErrorCode onReleaseCache() override {
                return mExecution->onReleaseCache();
            }

            virtual ErrorCode onExecute(const std::vector<Tensor*>& inputs,
                                        const std::vector<Tensor*>& outputs) override {
                for (auto tensor : inputs) {
                    if (halide_type_float != tensor->getType().code) {
                        return NO_ERROR;
                    }
                    auto size = tensor->elementSize();
                    auto ptr  = tensor->host<float>();
                    for (int i = 0; i < size; ++i) {
                        auto value = ptr[i];
                        if (std::isnan(value) || std::isinf(value)) {
                            return INVALID_VALUE;
                        }
                    }
                }
                auto code = mExecution->onExecute(inputs, outputs);
                if (NO_ERROR != code) {
                    return code;
                }
                for (auto tensor : outputs) {
                    if (halide_type_float != tensor->getType().code) {
                        return NO_ERROR;
                    }
                    auto size = tensor->elementSize();
                    auto ptr  = tensor->host<float>();
                    for (int i = 0; i < size; ++i) {
                        auto value = ptr[i];
                        if (std::isnan(value) || std::isinf(value)) {
                            return INVALID_VALUE;
                        }
                    }
                }
                return NO_ERROR;
            }

        private:
            std::unique_ptr<Execution> mExecution;
        };
        return new CheckNANExecution(exe);
    }
    return exe;
}

bool CPUBackend::onAllocateBuffer() {
    return true;
}

bool CPUBackend::onClearBuffer() {
    mDynamicAllocator->release();
    return true;
}

void CPUBackend::onCopyBuffer(const Tensor* srcTensor, const Tensor* dstTensor) const {
    auto& srcBuffer = srcTensor->buffer();
    auto& dstBuffer = dstTensor->buffer();

    MNN_ASSERT(srcBuffer.dimensions == dstBuffer.dimensions);
    MNN_ASSERT(srcBuffer.type == dstBuffer.type);
    if (srcTensor->getDimensionType() == dstTensor->getDimensionType()) {
        for (int i = 0; i < srcBuffer.dimensions; ++i) {
            MNN_ASSERT(srcBuffer.dim[i].extent <= dstBuffer.dim[i].extent);
        }
    }
    // Don't support cpu to gpu / gpu to cpu
    MNN_ASSERT(srcBuffer.host != nullptr && dstBuffer.host != nullptr);

    CPUTensorConverter::convert(srcTensor, dstTensor);
}

struct CPUBackendCreator : BackendCreator {
    Backend* onCreate(const Backend::Info& info) const override {
        auto power   = BackendConfig::Power_Normal;
        auto memory  = BackendConfig::Memory_Normal;
        size_t flags = 0;
        if (nullptr != info.user) {
            power  = info.user->power;
            memory = info.user->memory;
            flags  = info.user->flags;
        }
#ifdef MNN_CODEGEN_REGISTER
        static std::once_flag s_flag;
        std::call_once(s_flag, [&]() { registerCPUOps(); });
#endif
        return new CPUBackend(info.numThread, memory, power, flags);
    }
};

void registerCPUBackendCreator() {
    MNNInsertExtraBackendCreator(MNN_FORWARD_CPU, new CPUBackendCreator);
};
} // namespace MNN