Jacques_Pienaar:

a common problem is that folks find a case where compositional form does poorly, special cases a transformation and then moves on and without such overarching examples it is easy to miss that the problem isn’t being addressed.


IMHO this is exactly the point.
And I think it is why some specific reusable components ( keras-nlp, keras-cv, tf-addons) that are serving e2e models, also in our selected models in model garden, could be one of the driver to understand what we are expecting from the compiler stack.
Just take a look at our current threshold in TF addons:
we need at least >50 citations to accept a feature related to a paper so it is not something that it is totally brand new.
If we need to have a custom c++ op to reach good enough performance for a new layer but then the codeowner disappear after one or two months or people require to use it in Colab/Google cloud TPU isn’t better to try to interact with these use cases directly with the compiler stack team to understand a little bit how to handle our end2end performance request and to better evaluate a solution that it is alternative to maintain a partial hw covered large custom op?
Just my 2¢