Schema-Derived Constrained Decoding Enables Cross-Dialect MLIR Generation Without Retraining
A new method uses inference-time schema-derived constraints to generate Multi-Level Intermediate Representation (MLIR) code across different dialects without retraining language models. The researchers introduce four new NL-to-MLIR benchmarks and demonstrate that a 1.7B parameter model, using their constraint stack, can match or outperform much larger 15B-34B code language models on structurally constrained dialects, achieving 8-25x faster generation. The approach requires no new constraint-layer code when porting to new dialects dominated by structural constraints, and all benchmarks, code, and reproducibility tools are released.
Why it matters: This work shows that schema-derived priors can replace retraining for MLIR code generation across dialects, potentially reducing the need for per-dialect fine-tuning in ML compiler infrastructure.
Full story at: arXiv AI/ML ↗