[Model] LoRA with lm_head and embed_tokens fully trained

[sergeykochetkov]

FIX #4186 #2816

Support lm_head and embed_tokens fully trained in LoRA.

We found that quality of our adapters significantly drops without fully-trained lm_head or lm_head trained in LoRA style.
This is functionality of peft modules_to_save=[lm_head, mebed_tokens] https://huggingface.co/docs/peft/v0.12.0/en/package_reference/#peft.LoraConfig.modules_to_save

The idea is to replace base_model VocabParallelEmbedding and ParallelLMHead by layers loaded from modules_to_save at inferencing LoRA

• dirty implementation
• tests for new functionality
• checking old functionality is working
• inference with fully trained lm_head performance measurement
• implement embed_tokens fully trained as well

PR moved to #11714

[github-actions]

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[sergeykochetkov]

/ready

[AlongWY]

should it unmarked as Draft ?

[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. @sergeykochetkov please rebase it. https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork

[Tostino]

Just wanted to throw out that this is something I am looking forward to.

I am attempting to use Qwen/Qwen2.5-14B as a base model, and load up two LoRA's with the OpenAI API. One of the LoRA's is just the Instruct model extracted as a LoRA from the base. The other model is a fine tune that I did off of the base, and used MergeKit to do a TIES merge with the base and instruct model, and then extracted an adapter from that merge.

Works great when I was testing with HF transformers, but was surprised when I was getting errors trying to use these adapters with vLLM.

[sergeykochetkov]

PR is recreated here