[BugFix] fix 3vl dense model load quant weight

[shaopeng-666]

What this PR does / why we need it?

Fix Qwen3VL dense quant model load weights Error.

Does this PR introduce any user-facing change?

no

How was this patch tested?

The Qwen3VL quantized model service initialized successfully. Inference requests are processed correctly, and valid responses are returned.

• vLLM version: v0.13.0
• vLLM main: vllm-project/vllm@d682094

[github-actions]

👋 Hi! Thank you for contributing to the vLLM Ascend project. The following points will speed up your PR merge:‌‌

• A PR should do only one thing, smaller PRs enable faster reviews.
• Every PR should include unit tests and end-to-end tests ‌to ensure it works and is not broken by other future PRs.
• Write the commit message by fulfilling the PR description to help reviewer and future developers understand.

If CI fails, you can run linting and testing checks locally according Contributing and Testing.

[gemini-code-assist]

Code Review

This pull request adds a prefix mapping for the qwen3_vl_text model to fix a quantization weight loading issue. The change is a good step, but appears to be incomplete. To ensure correct quantization for packed modules, a corresponding entry for qwen3_vl_text is likely needed in packed_modules_model_mapping. I've added a comment with details on this potential issue.

[gemini-code-assist]

While adding the prefix mapping for qwen3_vl_text is correct, a corresponding entry for this model type appears to be missing from packed_modules_model_mapping in this file (around line 312). Other dense Qwen VL models, such as qwen2_5_vl, have entries for packed modules like qkv_proj and gate_up_proj. If qwen3_vl_text also uses such packed modules, omitting it from packed_modules_model_mapping will likely cause a KeyError during quantization setup. Please consider adding an entry for qwen3_vl_text to packed_modules_model_mapping, similar to the one for qwen2_5_vl, to ensure correct handling of packed modules.