[Verl] Add entropy loss to cross_entropy_loss and fused_linear_cross_entropy_loss

[hongpeng-guo]

Summary

In RLHF workflows, such as verl, the actor forward function usually generates both losses of cross_entropy_loss (-log_probs) and entropy_loss, the later was used to encourage the policy to be not over-deterministic.

There is a real needs for a kernel that will generates both the two losses, without materializing the huge logits tensor. Liger-kernel's fused_linear_cross_entropy_loss already works well to generate the cross_entropy_loss, but only calculating the second part of the loss, i.e., the entropy loss.

This PR adds the entropy loss option to the existing FLCE loss, and work as one important step to support verl.

1. Adding the entropy calculation in the second pass of online softmax in cross_entropy.py::liger_cross_entropy_kernel, both the loss and its gradient subject to input are calculated and stored;
2. Propagate the changes to relevant modules in fused_linear_cross_entropy.py,
3. Propagate relavent changes to other functional modules in PyTorch interface.

Testing Done

Made existing unit tests working; Adding new unittest WIP.

• Hardware Type:
• run make test to ensure correctness
• run make checkstyle to ensure code style
• run make test-convergence to ensure convergence

[Tcc0403]

Please add a unit test with return_entropy_loss. You can write a new pytorch implementation like CrossEntropyWithZLoss, or return_entropy_loss functionality on top of it.

[hongpeng-guo]

Update: Met some numerical unstable issue, inverstigating

[qingquansong]

Thanks for the efforts! Let's try to test thoroughly on both accuracy (numerical stability) and speed (including old ones) before checking in. Considering we may fused more and more losses such as the existing Z loss and the added entropy loss, api outputs kind of diverged and also make the loss quite heavy with multiple branches coupling together (like label smoothing, target weights, etc) We probably need to refactor a bit to make it cleaner to dev later. cc @ByronHsu @shivam15s @Tcc0403 @shimizust

[qingquansong]

can probably put an equation in the PR description and also a simple one in a comment here to demonstrate how the entropy_loss is calculated (especially on the reuse of m and d computed in the first pass online softmax

[qingquansong]

It seems you had done the implementation of weight provided case already above? dX_entropy_block = dX_entropy_block / sum_non_ignore_weight Did I misunderstand anything? If this is not the right equation for the weighted case, please use dX_entropy_block = dX_entropy_block / n_non_ignore above and also list a comment above as an TODO item.

[hongpeng-guo]

Thanks for catching this. I think this is a bug in my program. I just fixed it. But it seems the numerical problem is still there. Maybe we need to take a deeper look.

[hongpeng-guo]

BTW, it seems the CI stops running for this PR for some reason.