Fix(gemma3_mm): Add robust weight loading for quantized VLM

vllm/model_executor/models/gemma3_mm.py

@@ -13,6 +13,7 @@
 from vllm.config import VllmConfig
 from vllm.logger import init_logger
 from vllm.model_executor.layers.layernorm import GemmaRMSNorm
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 from vllm.model_executor.models.module_mapping import MultiModelKeys
 from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.multimodal import MULTIMODAL_REGISTRY
@@ -35,9 +36,8 @@
 from .interfaces import (MultiModalEmbeddings, SupportsLoRA,
                          SupportsMultiModal, SupportsPP)
 from .siglip import SiglipVisionModel
-from .utils import (AutoWeightsLoader, WeightsMapper, flatten_bn,
-                    init_vllm_registered_model, maybe_prefix,
-                    merge_multimodal_embeddings)
+from .utils import (WeightsMapper, flatten_bn, init_vllm_registered_model,
+                    maybe_prefix, merge_multimodal_embeddings)
 
 logger = init_logger(__name__)
 
@@ -707,8 +707,41 @@ def compute_logits(
 
     def load_weights(self, weights: Iterable[tuple[str,
                                                    torch.Tensor]]) -> set[str]:
-        loader = AutoWeightsLoader(self)
-        return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)
+        # Custom weight loader for Gemma3 VLM to handle naming inconsistencies.
+        # This loader first applies the class-level hf_to_vllm_mapper and then
+        # applies a targeted fix for the "double prefix" issue within the
+        # vision model component.
+
+        params_dict = dict(self.named_parameters())
+        loaded_params: set[str] = set()
+
+        for original_name, loaded_weight in weights:
+            name = original_name
+
+            # Apply the standard class-level mapper first.
+            if self.hf_to_vllm_mapper is not None:
+                name = self.hf_to_vllm_mapper.map(name)
+
+            # Apply the targeted hotfix only if a vision weight is not found.
+            # This prevents regressions on other models.
+            if name not in params_dict and name.startswith("vision_model."):
+                potential_name = f"vision_model.{name}"
+                if potential_name in params_dict:
+                    name = potential_name
+
+            # Load the weight using the potentially corrected name.
+            if name not in params_dict:
+                # Silently skip any weights that are still not found.
+                loaded_params.add(original_name)
+                continue
+
+            param = params_dict[name]
+            weight_loader = getattr(param, "weight_loader",
+                                    default_weight_loader)
+            weight_loader(param, loaded_weight)
+            loaded_params.add(original_name)
+
+        return loaded_params
 
     def get_mm_mapping(self) -> MultiModelKeys:
         """