MoonMath AI has released a bf16 forward attention kernel for AMD’s MI300X GPU that solves the common performance bottleneck in transformer workloads. Many teams struggle to achieve peak attention speed without writing low‑level assembly, which is error‑prone and hard to maintain. The new kernel is written entirely in HIP, keeping the code readable while still delivering measurable gains.
The kernel outperforms AMD’s own AITER v3 implementation across every tested shape and rounding mode, with geomean speedups of 1.18× (RTNE), 1.15× (RTNA) and 1.08× (RTZ). In real‑world use, integrating the kernel into SGLang accelerated Wan2.1 video diffusion by 1.23× without any quality loss.
Key technical advantages that address typical pain points are:
– Memory placement that keeps K in LDS (double‑buffered), V hot in L1, and Q plus accumulators in registers, reducing data movement stalls.
– A one‑instruction asm wrapper technique that lets developers pick the exact opcode while the compiler handles register allocation, avoiding the trade‑off between intrinsics and raw assembly.
– An eight‑wave, two‑group pipeline that overlaps matrix‑core work with softmax and memory prefetch, ensuring the compute units never idle.
– Support for any sequence length, cross‑attention, and bf16 inputs with deterministic numerics matching AITER’s per‑mode rounding.
By adopting this MIT‑licensed HIP kernel, teams can obtain higher throughput on MI300X hardware without sacrificing code maintainability or numerical fidelity, directly translating to faster training and inference cycles for large language models and diffusion systems.
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