Profiling in PyTorch is a powerful tool for identifying computational bottlenecks and optimizing the performance of deep learning models. In this section, focus is on the attention mechanism, which is the core component of Transformer models and advanced AI languages.
Using torch.profiler you can record and visualize execution time, memory consumption, and the number of calls of attention operators. The obtained results can be examined in TensorBoard or as a detailed table to identify weaknesses and improve model performance significantly by adjusting batch size, mixed precision, or architectural changes.