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Despite significant investment in software infrastructure, machine learningsystems, runtimes and compilers do not compose properly. We propose a newdesign aiming at providing unprecedented degrees of modularity, composabilityand genericity. This paper discusses a structured approach to the constructionof domain-specific code generators for tensor compilers, with the stated goalof improving the productivity of both compiler engineers and end-users. Theapproach leverages the natural structure of tensor algebra. It has been themain driver for the design of progressive lowering paths in \MLIR. The proposedabstractions and transformations span data structures and control flow withboth functional (SSA form) and imperative (side-effecting) semantics. Wediscuss the implications of this infrastructure on compiler construction andpresent preliminary experimental results.

Composable and Modular Code Generation in MLIR: A Structured and Retargetable Approach to Tensor Compiler Construction