Title: Programming and profiling pytorch applications

Aishwariya Chakraborty aishwariya.chakraborty1@ibm.com 
Manas Jha manas.j@ibm.com 
Priyanka Naik priyanka.naik@ibm.com 

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Topics:

• GenAI workload profiling — tools and techniques

• nvidia-smi
• Nsys
• pytorch profiler
• Usage and exercises

• torch.compile

• The what and the why
• torch dynamo and AOT Autograd
• FX graphs, Graph breaks, Dynamic shapes
• Different compilation backends

• Basic usage example and demonstration of various steps
• Performance improvement using torch compile

Pre-requisites/background:

• Pytorch basics
• Pytorch setup on laptop/local machine

Expected outcomes
Participants will

• Observe and measure resource utilization of execution with ML models
• Be able to analyze performance of models using torch profiler
• Understand the eager and compile modes of model execution
• Understand the framework and usage of torch.compile

Reference material

1. https://docs.pytorch.org/docs/2.7/torch.compiler.html 

2. https://docs.pytorch.org/docs/stable/profiler.html