AI BriefingPyTorch Performance Optimization Guide
Key point
This is the first post in a guide series covering how to use torch.profiler to identify and optimize performance bottlenecks in PyTorch models.
Details
It emphasizes the importance of Profiling, a core part of model optimization, and teaches step by step how to read complex traces.
It begins by defining the concepts of GPU Kernels and CPU Scheduling.
Using the most basic operations, Matrix Multiplication and Bias Add, as examples, it explains how to set up torch.profiler and how to read trace tables and CPU/GPU lanes.
It guides readers on how to easily locate specific operations within a trace through code annotation using record_function.
This series will expand in the future to cover the optimization process for nn.Linear, MLPs, and LLMs (Large Language Models).
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