Multi-head-self-attention (MHSA) mechanisms achieve state-of-the-art (SOTA) performance across natural language processing and vision tasks. However, their quadratic dependence on sequence lengths has bottlenecked inference speeds. To circumvent this bottleneck, researchers have proposed various sparse-MHSA models, where a subset of full attention is computed. Despite their promise, current sparse libraries and compilers do not support high-performance implementations for diverse sparse-MHSA patterns due to the underlying sparse formats they operate on. These formats are either too specialised, failing to cover a wide-range of sparse patterns, or too general, incurring high metadata overhead when computing on the moderately sparse (10-50% non-zeros) matrices present in sparse-MHSA. We bridge this gap, achieving both generality and performance, by proposing a novel sparse format: affine-compressed-sparse-row (ACSR) and supporting code-generation scheme, SPLAT, that generates highperformance implementations for diverse sparse-MHSA patterns on GPUs. Core to our proposed format and code generation algorithm is the observation that common sparse-MHSA patterns have uniquely regular geometric properties. These properties, which can be analyzed just-in-time, expose novel optimizations and tiling strategies that SPLAT exploits to generate high-performance implementations for diverse patterns. To demonstrate SPLAT’s efficacy, we use it to generate code for various sparse-MHSA models, achieving speedups of up-to 2.05x and 4.05x over hand-written kernels written in Triton and TVM respectively on A100 GPUs in single-precision.

@article{paperbot3192,
  title = {SPLAT: A Framework for Optimised GPU Code-Generation for SParse reguLar ATtention},
  author = {Ahan Gupta and Yueming Yuan and Devansh Jain and Yuhao Ge and David Aponte and Yanqi Zhou and Charith Mendis},
  journal = {Proceedings of the ACM on Programming Languages},
  volume = {9},
  number = {OOPSLA1},
  year = {2025},
  doi = {10.1145/3720503}
}