mirror of https://github.com/alibaba/MNN.git
205 lines
7.4 KiB
Markdown
205 lines
7.4 KiB
Markdown
# 自定义后端
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Backend是MNN对计算设备的抽象。MNN当前已经支持CPU、Vulkan、OpenCL、Metal等Backend，**只在计算设备暂未支持时新增Backend**，新增Op，请参阅[新增Op文档](customize_op)。
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## 声明
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所有新增Backend都需继承`Backend`类，并实现所有纯虚函数。
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```cpp
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class XPUBackend final : public Backend {
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XPUBackend(MNNForwardType type, MemoryMode mode);
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virtual ~XPUBackend();
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virtual Execution* onCreate(const std::vector<Tensor*>& inputs, const std::vector<Tensor*>& outputs, const MNN::Op* op) override;
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virtual void onExecuteBegin() const override;
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virtual void onExecuteEnd() const override;
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virtual bool onAcquireBuffer(const Tensor* tensor, StorageType storageType) override;
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virtual bool onReleaseBuffer(const Tensor* tensor, StorageType storageType) override;
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virtual bool onClearBuffer() override;
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virtual void onCopyBuffer(const Tensor* srcTensor, const Tensor* dstTensor) const override;
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}
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```
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## 构造与销毁
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Backend构造时，可以额外指定内存环境，在内存受限环境中，应避免非必要的内存使用。可以在构造函数中，完成对计算设备访问的必要初始化，如GPU下预加载shader等。
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```cpp
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/** backend memory mode */
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enum MemoryMode {
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/** use memory without limit. */
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NORMAL = 0,
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/** use memory thriftily. */
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LIMIT = 1
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};
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/**
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* @brief initializer.
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* @param type forward type.
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* @param mode memory mode.
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*/
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Backend(MNNForwardType type, MemoryMode mode = NORMAL);
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```
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## Execution创建
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Backend需要通过`onCreate`为op创建出exection实例：
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```cpp
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virtual Execution* onCreate(const std::vector<Tensor*>& inputs, const std::vector<Tensor*>& outputs, const MNN::Op* op) override;
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```
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可以在方法内根据op类型创建，但更建议提供注册接口：
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```cpp
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class XPUBackend final : public Backend {
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// ...
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class Creator {
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public:
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/**
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* @brief create execution for given input, op on metal backend.
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* @param inputs given input tensors.
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* @param op given op.
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* @param backend metal backend.
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* @return created execution if supported, NULL otherwise.
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*/
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virtual Execution *onCreate(const std::vector<Tensor *> &inputs, const MNN::Op *op,
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Backend *backend) const = 0;
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};
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/**
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* @brief register creator for given op type.
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* @param type given op type.
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* @param creator registering creator.
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*/
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static void addCreator(OpType type, Creator *creator);
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// ...
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};
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template <class T>
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class XPUCreatorRegister {
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public:
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/**
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* @brief initializer. register T creator for given op type.
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* @param type given op type.
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*/
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XPUCreatorRegister(OpType type) {
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T *test = new T;
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XPUBackend::addCreator(type, test);
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}
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};
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```
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这样，Op Execution中，就可以通过注册追加Op类型：
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```cpp
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class XPUPoolingCreator : public XPUBackend::Creator {
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public:
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virtual Execution *onCreate(const std::vector<Tensor *> &inputs, const MNN::Op *op, Backend *backend) const {
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return new XPUPooling(backend, op->main_as_Pool());
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}
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};
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static XPUCreatorRegister<XPUPoolingCreator> __reg(OpType_Pooling);
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```
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## 内存管理
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Backend通过`onAcquireBuffer`为tensor分配内存，通过`onReleaseBuffer`为tensor释放内存。内存有三种存储模式：`STATIC`内存不复用，一般用于op常量存储；`DYNAMIC`内存可复用，一般用于变量存储；`DYNAMIC_SEPERATE`内存在pipeline间可复用，一般用于pipeline常量存储。`_onAcquireBuffer_`_和_`_onReleaseBuffer_`_中可以不实际分配/释放内存，只记录内存用量变更，在_`_onAllocateBuffer_`_调用时，再根据用量计算出优化方案，一次性完成分配/释放。_
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```cpp
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/** backend buffer storage type */
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enum StorageType {
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/**
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use NOT reusable memory.
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- allocates memory when `onAcquireBuffer` is called.
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- releases memory when `onReleaseBuffer` is called or when the backend is deleted.
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- do NOTHING when `onClearBuffer` is called.
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*/
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STATIC,
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/**
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use reusable memory.
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- allocates or reuses memory when `onAcquireBuffer` is called. prefers reusing.
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- collects memory for reuse when `onReleaseBuffer` is called
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- releases memory when `onClearBuffer` is called or when the backend is deleted.
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*/
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DYNAMIC,
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/**
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use NOT reusable memory.
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- allocates memory when `onAcquireBuffer` is called.
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- do NOTHING when `onReleaseBuffer` is called.
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- releases memory when `onClearBuffer` is called or when the backend is deleted.
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*/
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DYNAMIC_SEPERATE
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};
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/**
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* @brief allocate buffer of tensor for given storage type.
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* @param tensor buffer provider.
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* @param storageType buffer storage type.
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* @return success or not.
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*/
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virtual bool onAcquireBuffer(const Tensor* tensor, StorageType storageType) = 0;
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/**
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* @brief release buffer of tensor for given storage type.
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* @param tensor buffer provider.
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* @param storageType buffer storage type.
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* @return success or not.
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*/
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virtual bool onReleaseBuffer(const Tensor* tensor, StorageType storageType) = 0;
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```
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在所有内存都分配完成后，backend会收到`onAllocateBuffer`回调：
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```cpp
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/**
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* @brief callback after all buffers needed by backend ops were allocated.
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* @return success or not. (result not used currently)
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*/
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virtual bool onAllocateBuffer() {
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return true;
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}
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```
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Backend在调用`onClearBuffer`时，需要释放所有`DYNAMIC`和`DYNAMIC_SEPERATE`存储模式的内存：
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```cpp
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/**
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* @brief clear all dynamic buffers.
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* @return success or not.
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*/
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virtual bool onClearBuffer() = 0;
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```
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此外，backend还需要负责tensor数据的拷贝：
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```cpp
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/**
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* @brief copy buffer from tensor to tensor.
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* @param srcTensor source buffer provider.
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* @param dstTensor dest buffer provider.
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*/
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virtual void onCopyBuffer(const Tensor* srcTensor, const Tensor* dstTensor) const = 0;
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```
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**拷贝可能在backend内部，也可能在backend与CPU backend之间。**
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**拷贝需要处理Tensor间的布局转换，相同布局时，可以直接拷贝数据；不同布局，如**`**NHWC**`**和**`**NC4HW4**`**，则一般需要做特殊转换。**
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## Pipeline回调
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Backend在pipeline执行的各个周期都会收到回调，`onResizeBegin`和`onResizeEnd`在调整内存分配前后调用（op的`onResize`会在此间调用）；`onExecuteBegin`和`onExecuteEnd`在op执行前后调用（op的`onExecute`会在此间调用）；`onWaitFinish`相对特殊，由用户主动调用，异步执行的pipeline需要同步等待完成。
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```cpp
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/**
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* @brief callback before resize ops.
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*/
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virtual void onResizeBegin();
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/**
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* @brief callback after resize ops.
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*/
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virtual ErrorCode onResizeEnd();
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/**
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* @brief callback before executing ops.
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*/
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virtual void onExecuteBegin() const = 0;
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/**
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* @brief callback after executing ops.
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*/
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virtual void onExecuteEnd() const = 0;
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```
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## 注册Backend
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最后，定义Backend Creator，注册方法中调用`MNNInsertExtraBackendCreator`就可以完成Backend的注册，这里的注册方法需要在BackendRegister.cpp中声明并调用：
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```cpp
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class XPUBackendCreator : public BackendCreator {
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virtual Backend *onCreate(const Backend::Info &info) const {
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return new MetalBackend;
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}
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};
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void registerCPUBackendCreator() {
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MNNInsertExtraBackendCreator(MNN_FORWARD_CPU, new CPUBackendCreator);
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};
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```
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使用cmake编译时，完成代码修改后，也需要相应修改CMakeLists.txt。 |