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.