Pytorch-lightning: Pass second-order closure to all optimizers (not just LBFGS)

[awaelchli]

🚀 Feature

I could be wrong, but I noticed the following in the code of lightning module's optimizer_step

        if on_tpu:
            xm.optimizer_step(optimizer)
        elif using_native_amp:
            self.trainer.scaler.step(optimizer)
        elif using_lbfgs:
            optimizer.step(second_order_closure)
        else:
            optimizer.step()

If someone uses a custom optimizer that needs the closure returning the loss multiple times, it won't work.

Pitch

Since all classes that inherit from torch.optim.Optimizer have the step method accept a closure (even if they don't need it), we could just do

        if on_tpu:
            xm.optimizer_step(optimizer)
        elif using_native_amp:
            self.trainer.scaler.step(optimizer)
        # elif using_lbfgs:
        #     optimizer.step(second_order_closure)
        else:
            optimizer.step(second_order_closure)

and drop the "using_lbfgs" argument?

Alternatives

The user has to override the optimizer_step themself.

[Borda]

it sounds as a good way to go to me...
cc: @williamFalcon

[williamFalcon]

we had that before but removed it for some reason. I can't remember it though. But i originally agreed.

Let's do this after 0.9.0 since we need to dig back to why it changed

[edenlightning]

@awaelchli want to take a look at this now? or better post v1?

[awaelchli]

@williamFalcon LBFGS not being compatible with native amp is the only exception I found in the code. maybe you mean that?
@edenlightning I don't think it's an essential for v1.0, but if you wish to have the final api without this argument "using_lbfgs" for optimizer step, then I could send a PR.