[BugFix] gemma loading after quantization or LoRA.

[taeminlee]

In gemma, embed_token and lm_head are tied together. Therefore, in the VLLM implementation, only embed_token is used, not lm_head. This setup generally poses no issues when using Google’s gemma weights under normal circumstances.

However, during various processes such as quantization, LoRA, and fine-tuning, the tied key, lm_head, may sometimes get included and saved in the model. Given that embed_token and lm_head are essentially the same matrix, this inclusion results in duplication. Consequently, these duplicates are removed when called by the model.named_parameters function. This, however, leads to a problem during the load_weights process, as it searches for the non-existent lm_head, causing an error.

To address this issue, there are several possible solutions. From my perspective, the most straightforward and effective approach is to simply skip lm_head if it exists. This Pull Request includes the implementation of this solution. I sincerely hope for its consideration and acceptance.

FIX #3323, (link existing issues this PR will resolve)

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[simon-mo]

Thank you for the fix.