5 papers
Tensor Algebraic Property Skeletons: Amplifying Property-Based Testing for AI Compilers
Yuxin Qiu, Ben Limpanukorn, Seongmin Lee +3
Deep learning (DL) compilers such as TVM and ONNX-MLIR lower tensor computation graphs into optimized executables for target backends. Testing these compilers has made substantial…
Finding Compiler-Platform Interaction Bugs in Deep Learning Pipelines via Cross-Layer Constraints
Yuxin Qiu, Jiyuan Wang, Ronak Badhe +3
The growing deployment of artificial intelligence (AI) necessitates robust deep learning (DL) compilers, such as TVM and ONNX-MLIR. These compilers take as input high-level AI mode…
Targeted Testing of Compiler Optimizations via Grammar-Level Composition Styles
Zitong Zhou, Ben Limpanukorn, Hong Jin Kang +5
Ensuring the correctness of compiler optimizations is critical, but existing fuzzers struggle to test optimizations effectively. First, most fuzzers use optimization pipelines (heu…
Structural Code Search using Natural Language Queries
Ben Limpanukorn, Yanjun Wang, Zach Patterson +5
Searching code is a common task that developers perform to understand APIs, learn common code patterns, and navigate code. Currently, developers most commonly search using keywords…
Fuzzing MLIR Compilers with Custom Mutation Synthesis
Ben Limpanukorn, Jiyuan Wang, Hong Jin Kang +2
Compiler technologies in deep learning and domain-specific hardware acceleration are increasingly adopting extensible compiler frameworks such as Multi-Level Intermediate Represent…