MLIR中的可组合和模块化代码生成：一种结构化和可重定向的张量构造方法
Composable and Modular Code Generation in MLIR: A Structured and Retargetable Approach to Tensor Compiler Construction
摘要：
尽管在软件基础设施上进行了大量投资，但机器学习系统、运行时和编译器并没有正确组合。我们提出了一个新的设计，旨在提供前所未有的模块化程度，可组合性和通用性。本文讨论了一种结构化的方法来构建特定于域的代码生成器的张量编译器，与提高编译器工程师和最终用户的生产力的既定目标。该方法利用了张量代数的自然结构。它一直是\MLIR中逐步降低路径设计的主要驱动力。提出的抽象和转换跨越数据结构和控制流的功能（SSA形式）和命令（副作用）语义。我们讨论了这个基础设施的编译器建设的影响，并提出了初步的实验结果。
英文摘要：
Despite significant investment in software infrastructure, machine learning systems, runtimes and compilers do not compose properly. We propose a new design aiming at providing unprecedented degrees of modularity, composability and genericity. This paper discusses a structured approach to the construction of domain-specific code generators for tensor compilers, with the stated goal of improving the productivity of both compiler engineers and end-users. The approach leverages the natural structure of tensor algebra. It has been the main driver for the design of progressive lowering paths in \MLIR. The proposed abstractions and transformations span data structures and control flow with both functional (SSA form) and imperative (side-effecting) semantics. We discuss the implications of this infrastructure on compiler construction and present preliminary experimental results.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
