The Next 700 ML-Enabled Compiler Optimizations
S. VenkataKeerthy Siddharth Jain Umesh Kalvakuntla Pranav Sai Gorantla Rajiv Shailesh Chitale Eugene Brevdo Albert Cohen Mircea Trofin Ramakrishna Upadrasta
There is a growing interest in enhancing compiler optimizations with ML
models, yet interactions between compilers and ML frameworks remain
challenging. Some optimizations require tightly coupled models and compiler
internals,raising issues with modularity, performance and framework
independence. Practical deployment and transparency for the end-user are also
important concerns. We propose ML-Compiler-Bridge to enable ML model
development within a traditional Python framework while making end-to-end
integration with an optimizing compiler possible and efficient. We evaluate it
on both research and production use cases, for training and inference, over
several optimization problems, multiple compilers and its versions, and gym
infrastructures.