The MLIR Transform Dialect. Your compiler is more powerful than you think
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Abstract (arXiv)
To take full advantage of a specific hardware target, performance engineers need to gain control on compilers in order to leverage their domain knowledge about the program and hardware. Yet, modern compilers are poorly controlled, usually by configuring a sequence of coarse-grained monolithic black-box passes, or by means of predefined compiler annotations/pragmas. These can be effective, but often do not let users precisely optimize their varying compute loads. As a consequence, performance engineers have to resort to implementing custom passes for a specific optimization heuristic, requiring compiler engineering expert knowledge. In this paper, we present a technique that provides fine-grained control of general-purpose compilers by introducing the Transform dialect, a controllable IR-based transformation system implemented in MLIR. The Transform dialect empowers performance engineers to optimize their various compute loads by composing and reusing existing - but currently hidden - compiler features without the need to implement new passes or even rebuilding the compiler. We demonstrate in five case studies that the Transform dialect enables precise, safe composition of compiler transformations and allows for straightforward integration with state-of-the-art search methods.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Sep 2024.
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Citing papers per year: 2025: 2; 2026: 1.
3 papers.
- 2026Nova: An End-to-End MLIR Compiler for Deep Learning15 Jul 2026
- 2025XTC, A Research Platform for Optimizing AI Workload Operators18 Dec 2025
- 2025Using MLIR Transform to Design Sliced Convolution Algorithm22 Nov 2025