Profiling in PyTorch (Part 3): Attention is all you profile
This post profiles attention mechanisms in PyTorch, comparing naive attention, in-place optimization, and Scaled Dot Product Attention (SDPA) backends. It reveals that the math backend of SDPA is slower than naive attention due to Tensor Core underutilization, mask reconstruction, and safe softmax overhead.
PyTorch
NVIDIA
Hugging Face blog29.07 · 23:03

