arXiv:2606.27396v1 Announce Type: cross Abstract: Test-input generation for tensor kernels is folkloric. Most projects pick a representative shape and dtype, run a fixed-shape allclose-style check, and ship. We make the choices explicit and measure them. Using the gpuemu op-schema-aware seeded fuzzer (arXiv:2606.20128), we evaluate seven test-generation strategies across a 26-op corpus (16 correct controls and 10 LLM-style buggy variants seeded with documented transcription patterns) on an RTX 3060 GPU instance. Strategies vary the shape candidate set, the dtype mix, and the input value distri
This research is emerging as the complexity of AI models and their underlying tensor program architecture increases, making robust testing critical for reliability and performance.
Sophisticated readers should care because effective bug detection in tensor programs directly impacts the safety, reliability, and efficiency of AI systems, especially large language models.
The explicit evaluation of test-generation strategies provides a data-driven approach to improving the tooling and methodology for ensuring AI kernel correctness, moving beyond 'folkloric' practices.
- · AI developers
- · GPU manufacturers
- · MLOps platforms
- · Software testing tools
- · AI production with latent bugs
- · Manual testing methodologies
Improved reliability and performance of AI models due to better detection of kernel bugs.
Faster development cycles for new AI hardware and software as testing becomes more efficient and effective.
Enhanced trust in AI systems, leading to broader adoption in critical applications, but also raising the bar for regulatory compliance on AI safety and reliability.
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Read at arXiv cs.LG