CovCraft: LLM-Guided Intelligent Framework for Constraint-Based Testing of Deep Learning Compiler Pipelines

Abstract

Deep learning (DL) compilers such as Apache TVM translate high-level models into optimized low-level code through multi-stage compilation pipelines. While recent testing efforts have improved fuzzing of optimization stages, they still face two key challenges: (i) the lack of semantics-preserving test models, leading to low validity, and (ii) coarse-grained input generation that fails to trigger hard-to-reach compiler components. To address these limitations, we propose CovCraft, a unified testing framework that integrates constraint-driven model generation with large language model (LLM)-guided input adaptation. CovCraft constructs diverse and valid ONNX models via symbolic constraint encoding and SMT solving, and then iteratively refines inputs using LLM-guided prompts to target uncovered functions, enabling the activation of rarely executed code paths. We evaluate CovCraft on TVM and observe consistent improvements over state-of-the-art techniques: it increases branch and function coverage by 8.9% and 7.0%, respectively, and detects 8 bugs. Moreover, the LLM-guided component achieves an 83.75% success rate in covering designated target functions, demonstrating the effectiveness of combining constraint-based generation with adaptive LLM reasoning for DL compiler testing. The prototype implementation of CovCraft is publicly available at: https://github.com/duduhedangdang/CovCraft.