论文标题
MLIR中的合成和模块化代码生成：一种结构化且可重新定位的张量编译器结构的方法
Composable and Modular Code Generation in MLIR: A Structured and Retargetable Approach to Tensor Compiler Construction
论文作者
论文摘要
尽管在软件基础架构，机器学习系统，运行时和编译器上进行了大量投资。我们提出了一种新的设计，旨在提供前所未有的模块化，合成性和通用程度。本文讨论了一种结构化方法，用于为张量编译器构建特定于域的代码生成器，其既定目标是提高编译器工程师和最终用户的生产率。该方法利用张量代数的自然结构。它一直是\ mlir中进行性降低路径设计的主要驱动力。提出的抽象和转换涵盖了数据结构和控制流动性（SSA形式）和命令性（副作用）语义。我们讨论了该基础设施对编译器构建的含义，并提出了初步的实验结果。
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.
