You can absolutely implement it (and even contribute it back to pytorch, if you wish). We didn’t implement it in the first release for the exact reason that @Jayant_Parashar mentioned, as explained in the tutorial: since most models have different-size layers (with a different number of units or channels), global structured pruning across layers does not have a straightforward definition, logically speaking. Does it make sense to compare the norm of a channel with N kernels of size LxL in a given layer with a channel with n<<N kernels of size lxl with l<<L in another layer?
I mean, the comparison is valid, and you can definitely perform it an implement it as a pruning technique, but is it really a good proxy for importance of those channels? or is it just a measure of the size of their associated weight tensors? It might make more sense to try to normalize these norms by the number of total parameters that go into the computation of each norm, or something like that. Perhaps if you find a good paper that implements global structured pruning, we can see how they do it there, and implement their version of it.