Relax introduces a powerful compiler abstraction specifically designed to optimize machine learning workloads with dynamic shapes, crucial for today's large language models.

• Provides cross-level abstraction that bridges computational graphs, tensor programs, and external libraries
• Enables universal deployment across diverse backend environments
• Offers optimized performance for dynamic shape computations in modern ML systems
• Addresses critical challenges in scaling and deploying LLMs efficiently

This engineering breakthrough matters because it potentially solves one of the most significant challenges in deploying large language models: efficiently handling dynamic computations across different hardware and software environments.

Relax: Composable Abstractions for End-to-End Dynamic Machine Learning