Scaled dot-product attention
HoldTechniques
An attention formulation that scales dot-product scores before softmax, commonly optimized in modern frameworks.
Why it's here
Placed in Hold: 1 article(s) of evidence from 1 source(s), led by research-stage coverage, with 0 in the last 30 days. Confidence 24%. Low accumulated evidence, so it defaults conservatively pending more signal.
Evidence (1)
- 3Hugging Face Blog·7/10/2026researchProfiling Attention in PyTorch
This Hugging Face blog post continues a profiling series by examining how attention appears in PyTorch profiler traces. It walks through naive causal attention and shows how discrete operations such as matmul, masking, softmax, and scaling are represented during execution.