🤖 AI Summary
This work addresses the challenge of reliably tracing the provenance of tensors and operators through graph rewrites—particularly non-injective transformations—in AI compilers. The authors propose a lightweight, generative provenance method grounded in observational semantics, which infers origins by analyzing the behavioral effects of graph transformations rather than relying on identifier propagation. For the first time, they introduce coalgebraic modeling and bisimulation to this domain, guaranteeing provenance consistency even after intermediate nodes are eliminated. The approach requires no invasive compiler modifications and naturally supports non-injective rewrites. Evaluated within COVAN, a prototype AI compiler, the method demonstrates stable, low-overhead provenance tracking throughout an end-to-end compilation pipeline.
📝 Abstract
AI compilers aggressively rewrite computation graphs through normalization, lowering, and optimization, making it difficult to track the provenance of tensors and operators across compilation. Reliable provenance is essential for attaching platform-specific postprocessing, debugging compiler behavior, and validating transformations, yet existing solutions are either invasive or ad hoc under non-injective graph rewrites.
We present a lightweight, generative approach to provenance tracking based on observational semantics. Instead of propagating identifiers through compiler passes, we observe graph transformations and reason about provenance in terms of observable computational actions. We formalize this approach using a coalgebraic model and bisimulation, which preserves provenance even when intermediate nodes are eliminated. Furthermore, we implement this approach in a prototype AI compiler COVAN, demonstrating stable provenance across compilation pipelines with minimal engineering overhead.