Profiling in PyTorch (Part 3): Attention is all you profile
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This is part three in a series about reading profiling data from PyTorch—tools that show which parts of code take the longest to run.
This time they focus on the attention mechanism, which is an important part of modern AI models. They show how attention consists of a few basic operations (matrix multiplication, softmax, and masking), and how to make it faster by using in-place operations—small changes that save both time and memory. Finally, they present PyTorch's built-in SDPA function, which is already optimized for this.
In-place operations do not only save time (like we see in our case) but also memory (due to no extra copy) which is great for large tensors like logits!
Read the full story at Hugging Face →
Vibekollen prepared this summary with AI from the original publication. The content belongs to Hugging Face.
