Cons of using named tensors

vision
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I have not seen named tensors in any project I have viewed but they seem almost as good of a practice as writing tests. What is the reason they have not caught on? Are there any cons to using them besides taking the time to write them?

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  1. Implementation is incomplete, and I don’t think anyone works on it
  2. It is oddly non-polymorphic, i.e. lots of operations are just blocked when names are present. Not only ops with dim=int arguments, but also things like unsqueeze(). If these could re-synchronize names instead, it would be much easier to include code with named tensors into existing codebases.
  3. Related to above, they’re not compatible with many standard routines (first of all, stuff from nn namespace). For example, while F.linear/mm works, convolutions or rnns fail.
  4. Most of ML code doesn’t require dimension permutations, I guess people working with BCHW tensors don’t care about dimension names that much.
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