vinx13 commented on PR #77: URL: https://github.com/apache/tvm-rfcs/pull/77#issuecomment-1152992143
Thanks for the discussion. To provide more context, the A0 approach we discussed is TIR-Relax layout rewriting https://github.com/tlc-pack/relax/issues/162 (the general idea is to lift such transformation in TIR scheduling into the graph, and then cancels out redundant intermediate transformations by either proving fusing the pair of post-compute and pre-compute transformations produces an identity TIR function, or use high-level operator semantic). I think this is very similar to the [graph-level solution](https://discuss.tvm.apache.org/t/introducing-ty-nnp-backend-with-end2end-tensorir-integration/11807/4) mentioned by @wrongtest In general, both A0 and A1 are valid approaches. It is mainly about how we would like to handle the complexity in simplifications. -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
