Nice to Meet You: Synthesizing Practical MLIR Abstract Transformers

Abstract

This work introduces a novel approach for synthesizing practical Abstract Transformers tailored for the MLIR compiler infrastructure. By automating the generation of these crucial components, the system significantly reduces the manual engineering effort required to implement sound static analyses like data flow and shape inference. This methodology enhances the robustness and efficiency of optimization passes across complex, multi-level intermediate representations.

Report

Structured Report: Nice to Meet You: Synthesizing Practical MLIR Abstract Transformers

Key Highlights

• Automated Analysis Generation: The core innovation is the synthesis of MLIR Abstract Transformers, automating a highly complex and error-prone aspect of compiler development.
• Practicality Focus: The synthesized transformers are designed to be practical, meaning they handle the specific complexities and semantics of various operations and dialects found within the real-world MLIR ecosystem.
• Improved Static Analysis: This methodology directly supports and enhances static analysis passes, such as accurate shape inference, constant propagation, and bounds checking, which are critical for effective compiler optimization.
• Reduced Development Overhead: By automatically generating analysis logic, the technique lowers the maintenance burden and accelerates the development cycle for new MLIR optimization passes and compiler frontends.

Technical Details

• Target Components: The system focuses on Abstract Transformers, which are functions operating on abstract domains (e.g., intervals, sets, or symbolic shapes) within the framework of abstract interpretation.
• Synthesis Technique: While the specifics are not detailed, the synthesis likely employs techniques rooted in formal methods or constrained programming (e.g., SMT solvers or program synthesis from examples/specifications) to guarantee the soundness of the generated transformer relative to the underlying MLIR operation semantics.
• Integration with MLIR: The resulting analysis components integrate directly into the MLIR framework, allowing optimization passes written in C++ or other supported languages to query abstract properties efficiently.
• Handling Dialects: A key technical hurdle addressed is the need to generalize the synthesis process across various MLIR dialects, ensuring that the generated transformers correctly interpret the semantics of operations from domain-specific or hardware-specific representations (e.g., Linalg, Affine, or Vendor-specific hardware dialects).

Implications

• RISC-V Compiler Acceleration: The RISC-V ecosystem heavily relies on advanced compiler infrastructures, often utilizing MLIR, particularly for highly specialized domains like machine learning (ML) and High-Performance Computing (HPC). Synthesizing robust analysis components accelerates the creation of custom RISC-V toolchains and domain-specific compilers.
• Enabling Custom Hardware Optimization: When RISC-V silicon designers create custom accelerators (e.g., new vector instruction sets or matrix units), they need compiler passes that understand the resulting highly specialized MLIR dialects. Automated transformer synthesis minimizes the manual effort required to equip these new dialects with essential optimization capabilities (like dependence analysis and scheduling).
• Improved Code Quality: Better static analysis leads directly to more effective optimizations (e.g., better loop tiling, data layout decisions, and memory access patterns), resulting in faster and more power-efficient executable code critical for constrained RISC-V targets.
• Foundation for Verification: By synthesizing components based on formal specifications, the approach enhances the trustworthiness and verifiability of the analysis passes, which is paramount in safety-critical applications often targeted by RISC-V architecture.