  Dynamic shape computations have become critical in modern machine learning
workloads, especially in emerging large language models. The success of these
models has driven demand for deploying them to a diverse set of backend
environments. In this paper, we present Relax, a compiler abstraction for
optimizing end-to-end dynamic machine learning workloads. Relax introduces
first-class symbolic shape annotations to track dynamic shape computations
globally across the program. It also introduces a cross-level abstraction that
encapsulates computational graphs, loop-level tensor programs, and library
calls in a single representation to enable cross-level optimizations. We build
an end-to-end compilation framework using the proposed approach to optimize
dynamic shape models. Experimental results on large language models show that
Relax delivers performance competitive with state-of-the-art hand-optimized
systems across platforms and enables deployment of emerging dynamic models to a
broader set of environments, including mobile phones, embedded devices, and web
browsers.
