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.

翻译：随着利用机器学习模型增强编译器优化的兴趣日益增长，编译器与机器学习框架之间的交互仍然面临挑战。某些优化需要将模型与编译器内部实现紧密耦合，从而引发模块化、性能和框架独立性等问题。实际部署和面向最终用户的透明度也是重要考量。我们提出ML-Compiler-Bridge，使得在传统Python框架中进行机器学习模型开发成为可能，同时实现与优化编译器的端到端高效集成。我们在研究及生产场景中，针对多个编译问题（涵盖训练与推理）、多种编译器及其版本，以及gym基础设施上对其进行了评估。