Operator Fusion
Operator Fusion — An inference/compiler optimization that combines compatible graph operations into a larger kernel to reduce intermediate memory traffic and launch overhead.
Why Fuse Operators?
A model graph may contain operations that would otherwise write an intermediate tensor to memory and immediately read it again. Fusion can keep intermediate values inside one generated or specialized kernel.
The benefit often comes from less memory traffic and fewer kernel launches rather than fewer mathematical operations.
Boundary
Fusion opportunities depend on graph structure, shapes, precision and runtime/backend support. A converted model can therefore perform differently across inference engines even when numerical output is equivalent.
Aggressive fusion should still be validated for numerical tolerance.