The Output-Space Hypothesis: Enumerative Equivalence Checking for Tensor Programs
Tensor programs, as used in deep learning models, are a prime target for optimization, as small performance improvements can have a large impact across training or inference workloads. However, such optimizations are complicated and can produce subtle bugs. Traditionally, correctness is assumed when differential testing against a reference on random inputs fails to reveal bugs. However, the inputs to these programs are massive tensors, and finding bugs can require generating extremely low likelihood inputs with precise relationships among their values. We propose a novel way to find bugs more consistently by flipping the quantifiers. Rather than generating a single input and checking all ...
| Authors | Paul Biberstein, Joseph Devietti, Mayur Naik |
|---|---|
| Primary category | cs.PL |
| Published | 17 September 2026 |
| Last updated at source | 17 September 2026 |
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