[Bugfix] Fix Gemma3 weights loading

[Isotr0py]

Essential Elements of an Effective PR Description Checklist

• The purpose of the PR, such as "Fix some issue (link existing issues this PR will resolve)".
• The test plan, such as providing test command.
• The test results, such as pasting the results comparison before and after, or e2e results
• (Optional) The necessary documentation update, such as updating supported_models.md and examples for a new model.

Purpose

• Fix VLLM serve VLM quantized Gemma3 KeyError: 'vision_model.encoder.layers.0.self_attn.qkv_proj.weight' llm-compressor#1546 (comment)
• Fix(models/siglip): Add compatibility for Gemma models quantized by llm-compressor #19643 broke unquantized gemma3 weights loading by mistake, it should map vision_model.vision_model. exactly, my bad! 😢
• This PR corrects the name mapping for Gemma3.

Test Plan

python examples/offline_inference/vision_language.py -m gemma3

Test Result

• Gemma3 loading should work now.

(Optional) Documentation Update

[github-actions]

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[gemini-code-assist]

Summary of Changes

Hello @Isotr0py, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request provides a critical bugfix for Gemma3 model weight loading. It resolves an issue where unquantized Gemma3 models failed to load due to a misconfigured name mapping for the vision component, ensuring that these models can now be successfully initialized and used.

Highlights

• Bugfix: Corrected a regression introduced in PR #19643 that prevented the proper loading of unquantized Gemma3 model weights. The issue stemmed from an incorrect name mapping for the vision_model component.
• Model Loading: Adjusted the _get_name_mapping logic within the Gemma3ForConditionalGeneration class to correctly handle the vision_model.vision_model. mapping, specifically for llm-compressor quantized checkpoints.

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[gemini-code-assist]

Code Review

This pull request fixes a bug in Gemma3 weights loading by correcting the name mapping for llm-compressor quantized checkpoints. The changes involve moving the orig_to_new_substr mapping into its own dictionary within the hf_to_vllm_mapper in Gemma3ForConditionalGeneration. This ensures that the mapping for quantized checkpoints is correctly applied. The code is more readable and the fix addresses the weights loading issue.

[gemini-code-assist]

Moving the orig_to_new_substr mapping into its own dictionary improves readability and organization. This change also ensures that the mapping for quantized checkpoints is correctly applied, fixing the weights loading issue. However, it would be more readable if the orig_to_new_substr dictionary was defined before the orig_to_new_prefix dictionary.

        orig_to_new_substr={
            # mapping for llm-compressor quantized checkpoints
            "vision_model.vision_model.": "vision_model.",
        },
        orig_to_new_prefix={