graph optimization

**Graph optimization** is the **compiler-driven transformation of computation graphs to improve runtime efficiency without changing semantics** - it rewrites operator graphs through fusion, elimination, and layout tuning to produce faster executable plans. **What Is Graph optimization?** - **Definition**: Set of optimization passes over model IR before or during execution. - **Typical Passes**: Constant folding, dead code elimination, operator fusion, and layout conversion. - **Execution Targets**: Optimized graphs can be emitted for CPU, GPU, or specialized accelerators. - **Constraint**: Passes must preserve numerical correctness and model behavior guarantees. **Why Graph optimization Matters** - **Performance**: Graph-level rewrites can improve speed without manual kernel-level engineering. - **Portability**: Compiler passes adapt one model definition to multiple hardware backends. - **Maintainability**: Centralized optimizations reduce need for hand-tuned code in model logic. - **Deployment Efficiency**: Optimized graphs lower serving latency and training runtime costs. - **Scalability**: Automation enables optimization across large model portfolios. **How It Is Used in Practice** - **IR Inspection**: Analyze graph before and after optimization to verify expected transformations. - **Pass Configuration**: Enable relevant optimization levels for target workload and hardware. - **Correctness Testing**: Run numerical equivalence checks and performance benchmarks post-optimization. Graph optimization is **a central compiler capability for high-performance ML execution** - carefully validated graph rewrites convert generic model definitions into hardware-efficient runtime plans.