Integrate Muon optimizer (2725)

[Saurabh750]

Context

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• [ ] fix a bug
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Please link to any issues this PR addresses. #2725

Changelog

What are the changes made in this PR?

• Integrating Muon optimizer as a pytorch implementation in torchtune.
• Modify recipes accordingly.

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UX

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:x: 1 New Failure

As of commit 617d5e898ea74f8f8605a02c541d9e62ba7a5f97 with merge base b22a3ae68c9e254e7ec6afb6ae64b1b32e0f0cbe ():

NEW FAILURE - The following job has failed:

• Lint / lint (3.10) (gh) torchtune/modules/optim.py:11:1: F401 'torch.distributed as dist' imported but unused

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[krammnic]

Thanks for the first pass! Let's split single device and distributed versions of Muon in 2 separate files to improve readability. Speaking about plots: We will need some comparison performance plots against AdamW and general Wandb plots (loss) and results on evaluation recipe.

[krammnic]

I've added few comments, but it looks great! There are 2 things on which we might need to think though:

1. Can we reduce the amount of "muon checks"? Maybe some special wrapper similarly to a fused optimizer?
2. Maybe we need to implement it in a little bit cleaner way and support through builders?

[Saurabh750]

Based on above comments, 2 things came to my mind:

1. As @krammnic suggested, I can implement a fused optimizer - a wrapper around Muon where the 2nd optimizer for linear layers will be of the choice of the user. This will eliminate the muon checks and will be a cleaner way.
2. If we want to provide more flexibility while assigning optimizer, we can do it on parameter level. Eg: Inside config file:

optimizer:
  muon: [param1, param2]
  AdamW: []
muon:
  _component_: torchtune.modules.SingleDeviceMuon
  momentum: 0.95
  lr: 0.02
  weight_decay: 0
AdamW:
  _component_: bitsandbytes.optim.PagedAdamW
  lr: 1e-5

Muon will be assigned to only param1, param2 while AdamW will be assigned for all remaining ones. Please do let me know your views, I'll implement whatever is suitable!

[Saurabh750]

@joecummings @krammnic : I have added Muon to optim.py I tried finetuning on alpaca dataset using qwen2-0.5B and compared AdamW and Muon. Trained for 5, 10, 20 epochs for batchsize 5 and 10. For all the experiments, AdamW performed better than Muon. I am attaching snippet of one of the experiments with batch-size 10 and 20 epochs:

• Should I try running the same experiments on a different model? According to this, the results we are getting are on similar lines. AdamW performs better than Muon as the model was not pretrained using Muon.

There is a custom implementation of Adam in Muon class. I tried using the existing pytorch implementation with the view to use any pre-existing implementations of optimizer for linear layers. But this will not be possible due to load_state_dict() which supports only single optimizer to be stored. But, I believe load_state_dict() in OptimizerInBackward supports multiple optimizers. Please correct me if I am wrong.

I have tried reducing the muon checks, but still there is a muon check in the main file. Also, I have updated the get_lr() method for returning the lr of Muon only. Please suggest if this is correct.

[krammnic]

Then, something is wrong, I don't like the fact that we have worse performance, because loss might be fixable with some HPO. Will review your changes tomorrow, to maybe find a bottleneck...

[Saurabh750]

Hi @joecummings @krammnic , I have updated the Muon optimizer implementation. In below image: Green -> Muon optimizer for first 20 epochs with 5e-4 lr Blue -> Muon optimizer for epochs 20 to 40 with 5e-5 lr

For below image: Blue -> Muon optimizer for epochs 20 to 40 with 5e-5 lr Red -> AdamW optimizer for epochs 0 to 20 with 2e-5 lr

As suggested earlier, switching to another implementation and playing around with HPO helped. In the lr_scheduler, I am only returning the Muon learning rate. Please let me know if anything else needs attention.