Custom Operator Integration into GE Documentation【免费下载链接】geGEGraph Engine是面向昇腾的图编译器和执行器提供了计算图优化、多流并行、内存复用和模型下沉等技术手段加速模型执行效率减少模型内存占用。 GE 提供对 PyTorch、TensorFlow 前端的友好接入能力并同时支持 onnx、pb 等主流模型格式的解析与编译。项目地址: https://gitcode.com/cann/geThis directory contains documentation for two mechanisms for integrating custom operators into GE.Directory StructureDirectoryMechanismRegistration MethodApplicable Scenariocustom_op_v2/BaseCustomOp REG_AUTO_MAPPING_OPVirtual function inheritance factory registrationExternal custom operators (arbitrary kernel binary)custom_op_v1/IMPL_OP OpImplRegisterV2Chained function pointersStandard AscendC developed Aicore operators (framework fully managed)Core DifferencesDimensionV1 (IMPL_OP)V2 (BaseCustomOp)ExecutionFramework embedded (engine schedules tiling kernel launch)User-defined (EagerExecuteOp::Execute())Online CompilationFramework embedded (FE engine handles on behalf)User-defined (CompilableOp::Compile())SerializationFramework embedded (FE engine automatically serializes)User-defined (PortableOp::Serialize/Deserialize)Address RefreshFramework embedded (DavinciModel automatically handles)User-defined (ArgsUpdater::UpdateHostArgs())kernel LanguageAscend C / TBE (framework compiles)Arbitrary (user compiles or RTC)Both are complementary rather than replacements: V1 is suitable for standard AICore operators (framework fully managed, less development effort), V2 is suitable for non-standard kernels (user fully controlled, high flexibility).【免费下载链接】geGEGraph Engine是面向昇腾的图编译器和执行器提供了计算图优化、多流并行、内存复用和模型下沉等技术手段加速模型执行效率减少模型内存占用。 GE 提供对 PyTorch、TensorFlow 前端的友好接入能力并同时支持 onnx、pb 等主流模型格式的解析与编译。项目地址: https://gitcode.com/cann/ge创作声明：本文部分内容由AI辅助生成（AIGC），仅供参考