why does ds have a clip_grad_norm that's an alias to torch.nn.utils.clip_grad_norm_?

Is this a left-over from some older times? So it results in some weird code where you have a special method but it doesn't do anything special and is an alias to torch.nn.utils.clip_grad_norm_ instead.

As transformers is integrating various other engines, and some of those (e.g. fairscale) actually do have clip_grad_norm with a sig:

clip_grad_norm(self, max_norm: Union[float, int], norm_type: Union[float, int] = 2.0) -> torch.Tensor

ds having this method which has a different signature is awkward, so I had to code it as:

[...]
                        if hasattr(self.optimizer, "clip_grad_norm") and not self.args.deepspeed:
                            # Some optimizers (like the sharded optimizer) have a specific way to do gradient clipping
                            # deepspeed has clip_grad_norm aliased to torch.nn.utils.clip_grad_norm_
                            self.optimizer.clip_grad_norm(self.args.max_grad_norm)
                        else:
                            # Revert to normal clipping otherwise, handling Apex or full precision
                            torch.nn.utils.clip_grad_norm_(
                                amp.master_params(self.optimizer) if self.use_apex else model.parameters(),
                                self.args.max_grad_norm,
                            )

If this method isn't being overloaded, perhaps it could be removed?

On the other hand I can see that there is no standard on how each engine should make the signatures of functions with the same name/function identical, so you do have a total right to have it in a different way.

I thought I'd just ask if there is a special purpose behind this alias and we surely can leave the slightly strange code as it is.

Thank you!