[Misc] Gemma3ForConditionalGeneration supports LoRA (#14797)

Signed-off-by: Jee Jee Li <[email protected]>

Author: jeejeelee

vllm/model_executor/models/gemma3_mm.py

@@ -12,6 +12,7 @@
 from vllm.logger import init_logger
 from vllm.model_executor.layers.layernorm import GemmaRMSNorm
 from vllm.model_executor.layers.sampler import SamplerOutput
+from vllm.model_executor.models.module_mapping import MultiModelKeys
 from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.multimodal import MULTIMODAL_REGISTRY
 from vllm.multimodal.inputs import MultiModalFieldConfig, MultiModalKwargs
@@ -23,7 +24,8 @@
 from vllm.multimodal.profiling import BaseDummyInputsBuilder, ProcessorInputs
 from vllm.sequence import IntermediateTensors
 
-from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP
+from .interfaces import (MultiModalEmbeddings, SupportsLoRA,
+                         SupportsMultiModal, SupportsPP)
 from .siglip import SiglipVisionModel
 from .utils import (AutoWeightsLoader, flatten_bn, init_vllm_registered_model,
                     maybe_prefix, merge_multimodal_embeddings)
@@ -371,8 +373,8 @@ def forward(self, vision_outputs: torch.Tensor):
 @MULTIMODAL_REGISTRY.register_processor(Gemma3MultiModalProcessor,
                                         info=Gemma3ProcessingInfo,
                                         dummy_inputs=Gemma3DummyInputsBuilder)
-class Gemma3ForConditionalGeneration(nn.Module, SupportsMultiModal,
-                                     SupportsPP):
+class Gemma3ForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP,
+                                     SupportsLoRA):
     packed_modules_mapping = {
         "qkv_proj": [
             "q_proj",
@@ -614,3 +616,12 @@ def load_weights(self, weights: Iterable[Tuple[str,
                                                    torch.Tensor]]) -> Set[str]:
         loader = AutoWeightsLoader(self)
         return loader.load_weights(weights)
+
+    def get_mm_mapping(self) -> MultiModelKeys:
+        """
+        Get the module prefix in multimodal models
+        """
+        return MultiModelKeys.from_string_field(
+            language_model="language_model",
+            connector="multi_modal_projector",
+            tower_model="vision_tower")