[Model] Fix MiniCPM-V 4.6 vit_merger qkv weight loading

[tc-mb]

In MiniCPMV4_6 (vllm/model_executor/models/minicpmv4_6.py), the vit_merger.self_attn module uses vLLM's fused QKVParallelLinear (a single qkv_proj), while the HuggingFace checkpoint stores the projections as three separate tensors: q_proj, k_proj, and v_proj.

Although the top-level model already declares:

packed_modules_mapping = {
"qkv_proj": ["q_proj", "k_proj", "v_proj"],
...
}
AutoWeightsLoader does not correctly stack the three shards into qkv_proj for the vit_merger sub-tree. As a result, vit_merger.self_attn.qkv_proj is never populated with the pretrained weights — loading either raises missing/unexpected-key errors for vit_merger.self_attn.{q,k,v}_proj.*, or silently leaves qkv_proj at its random init, producing corrupted vision features.

Fix: add a dedicated load_weights method on MiniCPMV4_6ViTWindowAttentionSelfAttn that follows vLLM's standard stacked_params_mapping pattern — feeding q_proj / k_proj / v_proj into qkv_proj.weight_loader with shard_id="q"/"k"/"v" respectively, and falling back to default_weight_loader for the remaining parameters (e.g. out_proj).

[gemini-code-assist]

Code Review

This pull request implements a load_weights method in the MiniCPMV4_6 model to handle weight loading, specifically for stacked QKV projections. The review feedback identifies a potential crash due to missing key validation when encountering unexpected keys in the checkpoint and points out that the method should return original weight names instead of transformed ones to prevent the model loader from incorrectly reporting unexpected keys.