Ensure layer math, parameter semantics, and model conversion/backends remain consistent so inference results don’t silently change. Apply these rules: - **Backward compatibility:** Do not change default parameter interpretation in a way that alters outputs for existing model files unless you provide a compatibility mechanism (e.g., versioned params) and tests. - **Convention correctness:** Follow the library’s established conventions for layer parameters (e.g., axis numbering). If multiple frameworks differ, translate explicitly at load/convert time. - **Numerical correctness:** Implement numerically sensitive formulas exactly (e.g., BatchNorm stability term belongs inside the sqrt when required). - **Conversion/runtime parity:** Conversion passes must only emit layers/operators that the runtime can execute. If the layer doesn’t exist yet, either implement it now or skip that conversion. - **Fast-path gating:** If an optimized path (packing/fast mode) isn’t fully implemented, disable it based on the controlling flag and **fall back** to the correct baseline implementation. Example pattern for fast-path gating: ```cpp int create_pipeline(const Option& opt) { if (opt.fast_gelu == 0) { support_packing = false; // disable incomplete fast/packing } return 0; } int forward_inplace(Mat& x, const Option& opt) const { if (opt.fast_gelu == 0) { // correct baseline implementation return GELU::forward(x, opt); } // otherwise run optimized path // ... return 0; } ``` These rules prevent silent output drift across AI inference workloads and keep model conversion reliable.