Distributed machine learning training and inference is common today because today's large models require more memory and compute than can be provided by a single GPU. Distributed models are generally produced by programmers who take a sequential model specification and apply several distribution strategies to distribute state and computation across GPUs. Unfortunately, bugs can be introduced in the process, and a distributed model implementation's outputs might differ from the sequential model's outputs. In this paper, we describe an approach to statically identify such bugs by checking model refinement, that is, can the sequential model's outputs be reconstructed from the distributed model's outputs? Our approach, implemented in Entangle, uses iterative rewriting to prove model refinement. Our approach can scale to today's large models and deployments: we evaluate it using GPT and Llama-3. Further, it provides actionable outputs that aids in bug localization.

Programmable network devices process packets using limited time and space. Consequently, much effort has been spent making network programs run as efficiently as possible. One promising line of work focuses on specializing the implementation of a network program to a particular---presumed constant---control-plane configuration. However, while some parts of the control plane configurations are constant for long periods of time, others change frequently, and in bursts (e.g., due to routing table updates).