[Model] Enable LoRA support for Pixtral (#31724)

Signed-off-by: <>
Signed-off-by: 赵策 <alcor@zhaocedeMacBook-Air.local>
Signed-off-by: 赵策 <alcor@mac.mynetworksettings.com>
Co-authored-by: 赵策 <alcor@mac.mynetworksettings.com>

docs/models/supported_models.md

@@ -724,7 +724,7 @@ These models primarily accept the [`LLM.generate`](./generative_models.md#llmgen
 | `PaliGemmaForConditionalGeneration` | PaliGemma, PaliGemma 2 | T + I<sup>E</sup> | `google/paligemma-3b-pt-224`, `google/paligemma-3b-mix-224`, `google/paligemma2-3b-ft-docci-448`, etc. | ✅︎ | ✅︎ |
 | `Phi3VForCausalLM` | Phi-3-Vision, Phi-3.5-Vision | T + I<sup>E+</sup> | `microsoft/Phi-3-vision-128k-instruct`, `microsoft/Phi-3.5-vision-instruct`, etc. | | ✅︎ |
 | `Phi4MMForCausalLM` | Phi-4-multimodal | T + I<sup>+</sup> / T + A<sup>+</sup> / I<sup>+</sup> + A<sup>+</sup> | `microsoft/Phi-4-multimodal-instruct`, etc. | ✅︎ | ✅︎ |
-| `PixtralForConditionalGeneration` | Ministral 3 (Mistral format), Mistral 3 (Mistral format), Mistral Large 3 (Mistral format), Pixtral (Mistral format) | T + I<sup>+</sup> | `mistralai/Ministral-3-3B-Instruct-2512`, `mistralai/Mistral-Small-3.1-24B-Instruct-2503`, `mistralai/Mistral-Large-3-675B-Instruct-2512` `mistralai/Pixtral-12B-2409` etc. | | ✅︎ |
+| `PixtralForConditionalGeneration` | Ministral 3 (Mistral format), Mistral 3 (Mistral format), Mistral Large 3 (Mistral format), Pixtral (Mistral format) | T + I<sup>+</sup> | `mistralai/Ministral-3-3B-Instruct-2512`, `mistralai/Mistral-Small-3.1-24B-Instruct-2503`, `mistralai/Mistral-Large-3-675B-Instruct-2512` `mistralai/Pixtral-12B-2409` etc. | ✅︎ | ✅︎ |
 | `QwenVLForConditionalGeneration`<sup>^</sup> | Qwen-VL | T + I<sup>E+</sup> | `Qwen/Qwen-VL`, `Qwen/Qwen-VL-Chat`, etc. | ✅︎ | ✅︎ |
 | `Qwen2AudioForConditionalGeneration` | Qwen2-Audio | T + A<sup>+</sup> | `Qwen/Qwen2-Audio-7B-Instruct` | | ✅︎ |
 | `Qwen2VLForConditionalGeneration` | QVQ, Qwen2-VL | T + I<sup>E+</sup> + V<sup>E+</sup> | `Qwen/QVQ-72B-Preview`, `Qwen/Qwen2-VL-7B-Instruct`, `Qwen/Qwen2-VL-72B-Instruct`, etc. | ✅︎ | ✅︎ |

vllm/model_executor/models/pixtral.py

@@ -63,7 +63,13 @@ from vllm.tokenizers import cached_tokenizer_from_config
 from vllm.tokenizers.mistral import MistralTokenizer
 from vllm.utils.tensor_schema import TensorSchema, TensorShape
 
-from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP
+from .interfaces import (
+    MultiModalEmbeddings,
+    SupportsLoRA,
+    SupportsMultiModal,
+    SupportsPP,
+)
+from .module_mapping import MultiModelKeys
 from .utils import init_vllm_registered_model, maybe_prefix
 from .vision import (
     VisionEncoderInfo,
@@ -365,7 +371,9 @@ class PixtralMultiModalProcessor(BaseMultiModalProcessor[PixtralProcessingInfo])
     info=PixtralProcessingInfo,
     dummy_inputs=PixtralDummyInputsBuilder,
 )
-class PixtralForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP):
+class PixtralForConditionalGeneration(
+    nn.Module, SupportsLoRA, SupportsMultiModal, SupportsPP
+):
     @classmethod
     def get_placeholder_str(cls, modality: str, i: int) -> str | None:
         if modality.startswith("image"):
@@ -581,6 +589,25 @@ class PixtralForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP)
         # Now we call the language model load with the generator
         self.language_model.load_weights(llm_weights_generator())
 
+    def get_mm_mapping(self) -> MultiModelKeys:
+        return MultiModelKeys.from_string_field(
+            language_model="language_model",
+            connector="vision_language_adapter",
+            tower_model="vision_encoder",
+        )
+
+    def get_num_mm_encoder_tokens(self, num_image_tokens: int) -> int:
+        if getattr(self, "patch_merger", None) is None:
+            return num_image_tokens
+        merge_size = self.vision_args.spatial_merge_size
+        return num_image_tokens * (merge_size**2)
+
+    def get_num_mm_connector_tokens(self, num_vision_tokens: int) -> int:
+        if getattr(self, "patch_merger", None) is None:
+            return num_vision_tokens
+        merge_size = self.vision_args.spatial_merge_size
+        return num_vision_tokens // (merge_size**2)
+
 
 # Vision encoder
 @dataclass