CMLCompiler: A Unified Compiler for Classical Machine Learning

Xu Wen, Wanling Gao, An-Dong Li, Lei Wang, Zihan Jiang
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Abstract

Classical machine learning (CML) occupies nearly half of machine learning pipelines in production applications. Unfortunately, it fails to utilize the state-of-the-practice devices fully and performs poorly. Without a unified framework, the hybrid deployments of deep learning (DL) and CML also suffer from severe performance and portability issues. This paper presents the design of a unified compiler, called CMLCompiler, for CML inference. We propose two unified abstractions: operator representations and extended computational graphs. The CMLCompiler framework performs the conversion and graph optimization based on two unified abstractions, then outputs an optimized computational graph to DL compilers or frameworks. We implement CMLCompiler on TVM. The evaluation shows CMLCompiler's portability and superior performance. It achieves up to 4.38× speedup on CPU, 3.31× speedup on GPU, and 5.09× speedup on IoT devices, compared to the state-of-the-art solutions --- scikit-learn, intel sklearn, and hummingbird. Our performance of CML and DL mixed pipelines achieves up to 3.04x speedup compared with cross-framework implementations. The project documents and source code are available at https://www.computercouncil.org/cmlcompiler.
CMLCompiler:经典机器学习的统一编译器
经典机器学习(CML)占据了生产应用中近一半的机器学习管道。不幸的是,它不能充分利用最先进的设备,性能很差。如果没有统一的框架,深度学习(DL)和CML的混合部署也会遇到严重的性能和可移植性问题。本文设计了一个用于CML推理的统一编译器CMLCompiler。我们提出了两个统一的抽象:算子表示和扩展计算图。CMLCompiler框架基于两个统一的抽象执行转换和图形优化,然后将优化后的计算图输出到DL编译器或框架。我们在TVM上实现了CMLCompiler。评估结果表明CMLCompiler具有良好的可移植性和性能。与scikit-learn、intel sklearn和hummingbird等最先进的解决方案相比,它在CPU上实现了4.38倍的加速,在GPU上实现了3.31倍的加速,在物联网设备上实现了5.09倍的加速。与跨框架实现相比,我们的CML和DL混合管道的性能提高了3.04倍。项目文档和源代码可在https://www.computercouncil.org/cmlcompiler上获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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