PluS: Highly Efficient and Expandable ML Compiler with Pluggable Graph Schedules

Machine learning (ML) compilers are effective solutions for deploying diverse Deep Neural Network (DNN) workloads on various hardware platforms automatically. However, there is a notable lag in existing ML compilers when it comes to supporting emerging optimization techniques like recent attention optimizations. These compilers lack the requisite flexibility to support expert-driven subgraph optimizations timely, resulting in suboptimal performance compared to manually optimized libraries. Conversely, template-based compilers lack the ability to abstractly express subgraphs, thereby reducing their adaptability to subtle changes in model architectures.

In this paper, we present PluS, an end-to-end ML compiler that facilitates the deployment of expert-optimized subgraph implementations while still preserving compiler flexibility. We rethink the encapsulation of ML compiler and decouple the burdensome embedded graph transformation process. PluS provides a lightweight loop-centric subgraph abstraction for experts to manage a flexible pattern warehouse, and employs a pattern identification approach for subgraph generation. As a result, PluS can deploy efficient subgraph implementations with minimal manual efforts, making it outperform the state-of-the-art rule-based embedded compilers (up to 4.04× speedup) on popular ML models.