TechHuggingFaceabout 10 hours ago
Profiling PyTorch Attention Kernels
1 min read
A PyTorch profiling series examines attention implementations on an NVIDIA A100 GPU. Naive attention launches 5 GPU kernels per forward pass. Switching to in-place masked_fill_ removes a Memcpy kernel. The SDPA math backend surprisingly launches 20 kernels per forward, more than the naive version.
Level
Hype check
Tap to vote and see what everyone thinks.
Summary by ByteBrief
