A Beginner's Guide to Profiling in PyTorch with torch.profiler
By
Aritra Roy Gosthipaty, Sayak Paul, Sergio Paniego, Rémi Ouazan Reboul, Pedro Cuenca
Summary
A beginner-friendly guide to using PyTorch's torch.profiler for performance optimization. The article explains why profiling is essential for understanding and improving model performance (whether for LLM token throughput, inference speed, or training loop efficiency), acknowledges the steep learning curve of profiling tools, and aims to demystify the process for newcomers. It's part of a series on profiling in PyTorch.
Source
Twitter / XA Beginner's Guide to Profiling in PyTorch with torch.profilerhuggingface.coKey quotes
· 3 pulledWhat you cannot profile, you cannot optimize.
Whether you are trying to squeeze more tokens per second out of a Large Language Model (LLM), shave milliseconds off inference, or just understand why your training loop runs slower than the spec sheet promises, the path eventually runs through profiling.
The catch is that profiling has a steep on-ramp. The traces are dense walls of colored rectangles. The events carry intimidating names. Most tutorials assume you can already read them.
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