Thanks for this question!

Pytorch Symbolic is simplifying the definition of the neural network models. It is indeed creating a graph under the hood to do this. In this graph, every edge is an nn.Module.

torchdynamo looks great as a tool for optimizing existing models to perform better on the GPU by removing the CPU overhead entirely. Sometimes the improvement is really impressive.

Yes, torchdynamo does some kind of graph capture as well. It even modifies the byte-code to speed up the execution. But in the end it is a wrapper for an nn.Module that speeds it up. To speed up the model, you have to define it first.

So the two libraries are actually independent. You can use torchdynamo to speed up models created with Pytorch Symbolic. IMO it is a great combination.