Equality Saturation for Deep Learning Compilers

随着研究日益被企业实验室垄断，最先进的深度学习(DL)模型的计算需求呈指数级增长，引发了社会和环境问题，这些实验室可以负担数百万美元的培训过程，而培训过程又会产生数万公斤二氧化碳。因此，提高数字地球的效率对于使研究民主化和保护地球至关重要。相等饱和是一种新的编译器优化技术，是一种很有前途的方法，它将动态链接库的运行时间减少高达70%，在某些情况下，这种改进比传统方法快300倍。然而，它在深度学习编译中的广泛应用受到其复杂性、可扩展性以及难以适应目前可用的异类DL框架的限制。通过将等式饱和度集成到MLIR编译器中，等式饱和度可以在不需要数万行手写优化代码的情况下广泛应用于各种框架，从而增加了其对开发人员的可访问性，并极大地提高了现代DL系统的效率。

Exponential growth in the computational demands of state-of-the-art Deep Learning (DL) models poses both societal andenvironmental concerns as research is increasingly monopolized by corporate labs which can afford multi-million dollartraining processes which in turn produce tens of thousands of kilograms of CO2. Improving the efficiency of DL istherefore paramount to democratizing research and protecting the planet. Equality Saturation, a novel compileroptimization technique, is one promising approach, reducing DL runtime by up to 70% and finding such improvements300x faster than traditional methods in some scenarios. However, its widespread application to Deep Learning Compilersis limited by its complexity, scalability and the difficulty in adapting it to the heterogeneous DL frameworks availabletoday. By integrating Equality Saturation into the MLIR compiler, Equality Saturation could be widely applied acrossframeworks without requiring tens of thousands of lines of handwritten optimization code, thereby increasing itsaccessibility to developers and greatly improving the efficiency of modern DL systems.