Advanced PyTorch Graph Manipulation: FX Graph Mode Quantization Coding tutorial - Part 3/3

If you need help with anything quantization or ML related (e.g. debugging code) feel free to book a 30 minute consultation session! https://calendly.com/oscar-savolainen I'm also available for long-term freelance work, e.g. for training / productionizing models, teaching AI concepts, etc. *Video Summary:* In this video, in part 3/3 of our coding tutorial on how to quantize a PyTorch ResNet with FX Graph Mode Quantization, we look at two advanced graph manipulation techniques. The first is how one can iteratively propagate a tensor through the graph, and access intermediary tensors in a way that is reminiscent of forward hooks. The second is how one can swap out nodes: we use the example of using BatchNorm layers into their preceding Convolution layers. *Timestamps:* 00:00 Intro 02:39 Iterating through the graph 11:17 Replacing nodes in the graph 18:42 Outro *Links:* Github for code: https://github.com/OscarSavolainen/Quantization-Tutorials Connect on LinkedIn: / oscar-savolainen-phd-b88277121