Fix LoRA support for multimodal models (VLMs) by implementing a consistent pattern for skipping vision components (#11261)

python/sglang/srt/lora/lora_manager.py

@@ -418,10 +418,6 @@ class LoRAManager:
         replace_submodule(self.base_model, module_name, lora_module)
         return lora_module
 
-    def should_skip_lora_for_vision_model(self, module_name):
-        # TODO: support different vision models
-        return module_name.find("vision_model.model") != -1
-
     def init_lora_modules(self):
         # Look-up table that essentially maps (layer_index, module_name) to the corresponding LoRA module.
         self.lora_modules: List[Dict[str, BaseLayerWithLoRA]] = [
@@ -439,10 +435,6 @@ class LoRAManager:
             ) and not self.base_model.should_apply_lora(module_name):
                 continue
 
-            # Skip vision model
-            if self.should_skip_lora_for_vision_model(module_name):
-                continue
-
             # The module should be converted if it is included in target_names
             if module_name.split(".")[-1] in self.target_modules:
                 layer_id = get_layer_id(module_name)

python/sglang/srt/models/gemma3_mm.py

@@ -16,6 +16,7 @@
 # https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/models/gemma3_mm.py
 
 import logging
+import re
 from functools import lru_cache
 from typing import Dict, Iterable, List, Optional, Set, Tuple, TypedDict
 
@@ -154,6 +155,10 @@ class Gemma3ForConditionalGeneration(PreTrainedModel):
     embedding_modules = {}
     embedding_padding_modules = []
     supports_lora = True
+    # Pattern to match language model layers only (skip vision_tower and multi_modal_projector)
+    lora_pattern = re.compile(
+        r"^language_model\.model\.layers\.(\d+)\.(?:self_attn|mlp)\.(?:qkv_proj|o_proj|down_proj|gate_up_proj)"
+    )
 
     def __init__(
         self,
@@ -165,6 +170,13 @@ class Gemma3ForConditionalGeneration(PreTrainedModel):
         self.config = config
         self.quant_config = quant_config
 
+        # For LoRA compatibility: expose text_config attributes at top level
+        # This allows LoRA code to work without special multimodal handling
+        if not hasattr(config, "num_hidden_layers"):
+            config.num_hidden_layers = config.text_config.num_hidden_layers
+        if not hasattr(config, "hidden_size"):
+            config.hidden_size = config.text_config.hidden_size
+
         self.vision_tower = SiglipVisionModel(
             config=config.vision_config,
             quant_config=quant_config,
@@ -380,6 +392,10 @@ class Gemma3ForConditionalGeneration(PreTrainedModel):
 
         return hs
 
+    def should_apply_lora(self, module_name: str) -> bool:
+        """Skip vision tower and multi_modal_projector for LoRA."""
+        return bool(self.lora_pattern.match(module_name))
+
     def tie_weights(self):
         return self.language_model.tie_weights()
 

python/sglang/srt/models/mllama4.py

@@ -2,6 +2,7 @@ import json as json_lib
 import logging
 import math
 import os
+import re
 from collections.abc import Iterable
 from typing import List, Optional, Set, Tuple
 
@@ -422,6 +423,11 @@ class Llama4ForConditionalGeneration(nn.Module):
         "gate_up_proj": ["gate_proj", "up_proj"],
     }
 
+    # Pattern to match language model layers only (skip vision_model and multi_modal_projector)
+    lora_pattern = re.compile(
+        r"^language_model\.model\.layers\.(\d+)\.(?:self_attn|mlp)\.(?:qkv_proj|o_proj|down_proj|gate_up_proj)"
+    )
+
     def __init__(
         self,
         config: Llama4Config,
@@ -555,6 +561,10 @@ class Llama4ForConditionalGeneration(nn.Module):
 
         return projected_vision_flat
 
+    def should_apply_lora(self, module_name: str) -> bool:
+        """Skip vision model and multi_modal_projector for LoRA."""
+        return bool(self.lora_pattern.match(module_name))
+
     def forward(
         self,
         input_ids: torch.Tensor,