I’m still looking a bit at types in the PyTorch frontend and wondered if there already is or it would be reasonable to create a “type propagation” operator complementing the type inference pass with the following semantics:
- You pass in a relay node without type info,
- it goes backward to nodes with checked input types and then propagates types to the previously untyped nodes.
The reason I want to something like this is that I’m thinking
- repeatedly running type inference while building the graph in the frontend is O(N^2) complexity (and I have the vague feeling it shows for large models, too),
- I’d like to rely more on Relay’s inference for type information at the nodes, so this would increase the effect.
What do you think?