Support LoRA for Mistral3 (#17428)

Signed-off-by: mgoin <mgoin64@gmail.com>

vllm/model_executor/models/mistral3.py

@@ -18,6 +18,7 @@ from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import (ColumnParallelLinear,
                                                RowParallelLinear)
 from vllm.model_executor.layers.quantization import QuantizationConfig
+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 (MultiModalDataDict, MultiModalFieldConfig,
@@ -31,7 +32,8 @@ from vllm.multimodal.processing import (BaseMultiModalProcessor,
 from vllm.multimodal.profiling import BaseDummyInputsBuilder
 from vllm.sequence import IntermediateTensors
 
-from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP
+from .interfaces import (MultiModalEmbeddings, SupportsLoRA,
+                         SupportsMultiModal, SupportsPP)
 from .pixtral import PixtralHFEncoderInfo, PixtralHFVisionModel
 from .utils import (AutoWeightsLoader, flatten_bn, init_vllm_registered_model,
                     maybe_prefix, merge_multimodal_embeddings)
@@ -382,8 +384,8 @@ def init_vision_tower_for_llava(
     _build_mistral3_processor,
     info=_build_mistral3_info,
     dummy_inputs=Mistral3DummyInputsBuilder)
-class Mistral3ForConditionalGeneration(nn.Module, SupportsMultiModal,
-                                       SupportsPP):
+class Mistral3ForConditionalGeneration(nn.Module, SupportsLoRA,
+                                       SupportsMultiModal, SupportsPP):
 
     packed_modules_mapping = {
         "qkv_proj": ["q_proj", "k_proj", "v_proj"],
@@ -594,3 +596,12 @@ class Mistral3ForConditionalGeneration(nn.Module, SupportsMultiModal,
                                                    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")