Fix qwen3 model implementation in torchtune, closes #2866

[HayrapetyanZhirayr]

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What are the changes made in this PR?

• Change rope and rmsnorm order in qwen3 attention to match the default implementation.
  Related issue: #2866

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[pytorch-bot]

🔗 Helpful Links

🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/torchtune/2867

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[felipemello1]

hey @HayrapetyanZhirayr , thanks for opening the PR. It seems that we have one that also addressed it and we missed. The author is going to verify the output and may just merge it instead, if thats ok. Let just see if the generation improves so we can confirm: #2822

[leng-yue]

We're encountering an out-of-memory (OOM) error on a 2TB machine when attempting to load Qwen3-235B-A22B.

The issue seems to be that torchtune loads the full, unsharded checkpoint with self._checkpoint_client.load_base_checkpoint(). For a bf16 model of this size, the complete weights (~3.7TB) exceed the available memory. Loading the model in shards would likely resolve this.

Has anyone else encountered this?

[felipemello1]

@joecummings, our checkpoint expert, mind looking at this ckpt comment?

[nouranali]

I think it's working fine