Profiling in PyTorch (Part 3): Attention is all you profile
Profiling in PyTorch (Part 3): Attention is all you profile Profiling in PyTorch (Part 3): Attention is all you profile Published July 10, 2026 Update on GitHub Upvote 50 Aritra Roy Gosthipaty ariG23498 Sergio Paniego sergiopaniego Sayak Paul sayakpaul Rémi Ouazan Reboul ror This is the third post of Profiling in PyTorch, a series where we slowly build the skill of reading profiler traces and use it to drive optimization: Profiling in PyTorch (Part 1): A Beginner's Guide to torch.profiler Profiling in PyTorch (Part 2): From nn.Linear to a Fused MLP Profiling in PyTorch (Part 3): Attention is all you profile (current) The series "Profiling in PyTorch" is meant to make you comfortable reading profiler traces and tables. In Part 1 we profiled basic math operations like addition and multiplication.
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- Profiling in PyTorch (Part 3): Attention is all you profile Published July 10, 2026 Update on GitHub Upvote 50 Aritra Roy Gosthipaty ariG23498 Sergio Paniego sergiopaniego Sayak Paul sayakpaul Rémi Ouazan Reboul ror This is the third post of Profiling in PyTorch, a series where we slowly build the skill of reading profiler traces and use it to drive optimization: Profiling in PyTorch (Part 1): A Beginner's Guide to torch.profiler Profiling in PyTorch (Part 2): From nn.Linear to a Fused MLP Profiling in PyTorch (Part 3): Attention is all you profile (current) The series "Profiling in PyTorch" is meant to make you comfortable reading profiler traces and tables.
- In Part 1 we profiled basic math operations like addition and multiplication.
- We saw how the profiler table uncovers hotspots, and how the profiler trace shows the order in which an algorithm runs over time.
- In Part 2 we wrapped that addition and multiplication into a torch linear layer.
- We then stacked several linear layers on top of each other (a multilayer perceptron) and profiled that.
- Along the way we also profiled fused and hand-tuned kernels.