FMHA
Fused Multi-Head Attention, which fuses steps of the attention computation such as QK^T, softmax, and multiplication by V into a single GPU kernel for execution, reducing memory reads and writes and improving inference throughput.
Timeline (last 90 days)
FlyDSL covers core operations such as dense and grouped GEMM, fused multi-head attention (FMHA/Attention), MXFP8/MXFP4 scaled GEMM, RMSNorm and TopK.
The fused implementation of FMHA can reduce intermediate memory reads and writes.
FMHA (Fused Multi-Head Attention) fuses multiple steps of attention (QK^T, softmax, multiplication with V) into a single GPU kernel for execution, avoiding repeated writes of intermediate results to video memory, thereby reducing bandwidth pressure and improving throughput, and is a key optimization for long-sequence inference.
All Facts (3)
FlyDSL covers core operations such as dense and grouped GEMM, fused multi-head attention (FMHA/Attention), MXFP8/MXFP4 scaled GEMM, RMSNorm and TopK.
50%UnverifiedThe fused implementation of FMHA can reduce intermediate memory reads and writes.
50%UnverifiedFMHA (Fused Multi-Head Attention) fuses multiple steps of attention (QK^T, softmax, multiplication with V) into a single GPU kernel for execution, avoiding repeated writes of intermediate results to video memory, thereby reducing bandwidth pressure and improving throughput, and is a key optimization for long-sequence inference.
50%