mindspore-lite

MindSpore Lite云侧推理 Ascend 后端离线转换（ONNX → MindIR）与推理部署全流程。覆盖固定 shape、动态分档、纯动态 shape 的转换策略，以及 MindIR 推理验证与部署注意事项。

把开源算法模型适配到 MindSpore Lite 部署管线：按网络结构拆分导出 ONNX、ONNX Runtime 推理验证、ONNX→MindIR 转换、MindSpore Lite 推理实现，并交付文档与常见问题。用户想把某个开源模型迁移到 MSLite 部署时调用。

MindSpore Lite（Ascend）模型性能优化总攻略。做基线/profiling、融合算子改写、推理免拷贝、PTQ int8 量化、精度对齐与归档时调用。本文为总览与索引，细化策略见 references/。

Build configuration, CMake options, cross-compilation and packaging. Use when building MindSpore Lite, configuring CMake, cross-compiling for ARM/iOS/MCU, packaging release archives, or troubleshooting build errors.

Model conversion pipeline, parser development, optimization passes and quantization. Use when converting models to .ms, writing parser code, implementing optimizer passes, or configuring quantization.

Debugging, unit testing, benchmarking and performance analysis. Use when running gtest, benchmark tools, profiling latency or accuracy, diagnosing operator precision issues, delegate fallback, or memory leaks.

Device-side inference with LiteRT, NNACL and hardware delegates. Use for mobile/IoT inference, Android/iOS integration, NPU/GPU/CoreML delegates, Micro codegen for MCU, on-device training, or C/C++/Java/Python API usage with .ms models.

Operator and kernel development, NNACL, delegates, custom kernel registration. Use when adding operators, implementing NNACL kernels, writing delegate adapters (NPU/CoreML/Ascend), registering custom kernels, or modifying operator schema.

改完或 review MindSpore Lite 的 C++/Python/Shell/CMake 代码时用。先跑 pre-push（8 个 lint 全自动），再按 30 秒快速清单逐项过——三类硬指标（CCN≤19 / NLOC≤50 / 入参≤5）+ 指针/边界/逻辑/异常/接口契约等 lint 抓不到的语义问题 + 涉及外部数据时的安全编码。深度参考 clean-code-guidelines.md。