Portable Paged Attention: From Triton to Helion

Recently, the PyTorch team released Helion, a new domain-specific PyTorch-based language for developing high-performing, portable kernels. With extensive autotuning and broader backend support, Helion promises to advance performance portability beyond Triton.

To test this, we took on the challenge of writing one of AI's most performance-critical kernels in Helion: Paged Attention, the core of vLLM.

Building on our expertise creating the state-of-the-art portable Triton attention backend in vLLM, we developed the experimental Helion attention backend for vLLM.

In this talk, we will present the differences between paged attention in Triton and Helion, what changes to the algorithm of the kernel were necessary to achieve matching performance, and how Helion enables optimizations that were not possible with Triton. Then, we will share lessons we learned along the way and extensive benchmark results that show that Helion kernels can, in many scenarios, even outperform our optimized Triton kernels. Our early results indicate the experimental Helion attention backend can reduce the latency by up to 50% and improve the end-to-end throughput vs. Triton by up to 10%.