Relax is a compiler abstraction that optimizes machine learning models with dynamic shapes. It uses symbolic shape annotations to track dynamic shape computations and enables cross-level optimizations. The forward deduction method is used to infer annotations based on input components. Experimental results show competitive performance across different hardware backends.
Introducing Relax: A Compiler Abstraction for Optimizing Dynamic Machine Learning Workloads
Optimizing machine learning models with dynamic shapes is crucial for better performance and flexibility. Dynamic shapes refer to the ability of a model to handle input data with varying dimensions during runtime. This is particularly important in production settings where batch sizes can vary.
However, optimizing models with dynamic shapes poses challenges as traditional optimizations rely on static shape analysis. The missing information from dynamic dimensions can significantly affect the optimizations that can be performed. Current machine learning compilers often lose shape and additional information between abstraction layers, making incremental optimizations difficult.
Researchers have developed Relax, a compiler abstraction that optimizes end-to-end dynamic machine learning workloads. It uses symbolic shape annotations to track dynamic shape computations globally across the program. Relax also provides a cross-level abstraction that encapsulates computational graphs, tensor programs, and library calls, enabling cross-level optimizations.
Relax adopts a forward deduction method to deduce the annotation of an expression based on its input components. This method is simple and local, allowing for annotations to be obtained during compiler passes. When shapes cannot be inferred automatically, forward deduction can use user-inserted match cast results to continue inferring later annotations.
All optimizations in Relax are performed as composable dynamic shape-aware transformations. This allows for incremental optimization and partial lowering of computation using different approaches. It considers analysis from other levels and incorporates further optimizations assuming dynamic shape relations.
Experimental results show that Relax compiles and optimizes dynamic shape models onto diverse hardware backends, delivering competitive performance compared to platform-specific solutions. It also supports a broad range of devices and environments, including mobile phones, embedded devices, and web browsers through WebAssembly and WebGPU.
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List of Useful Links:
- AI Lab in Telegram @aiscrumbot – free consultation
- This AI Paper Introduces Relax: A Compiler Abstraction for Optimizing End-to-End Dynamic Machine Learning Workloads
- MarkTechPost
- Twitter – @itinaicom
