[Fix] Fix several issues preventing gemma3n LoRA support. (#8776)

python/sglang/srt/lora/lora_manager.py

@@ -386,6 +386,13 @@ class LoRAManager:
         else:
             self.target_modules = set()
             for config in self.configs.values():
+                if not isinstance(config.target_modules, list):
+                    raise ValueError(
+                        f"SGLang currently only supports inferring LoRA target modules when a list of "
+                        "suffixes is provided in `target_modules` field of PEFT config. Please explicitly "
+                        "specify `--lora-target-modules` during server startup. You can specify `all` to "
+                        "enable all support modules types. "
+                    )
                 self.target_modules.update(config.target_modules)
 
         if max_lora_rank is not None:

python/sglang/srt/models/gemma3n_mm.py

@@ -492,5 +492,44 @@ class Gemma3nForConditionalGeneration(PreTrainedModel):
             loaded_params.add(name)
         return loaded_params
 
+    lora_pattern = re.compile(
+        r"^language_model\.layers\.(\d+)\.(?:self_attn|mlp)\.(?:qkv_proj|o_proj|down_proj|gate_up_proj)"
+    )
+
+    def should_apply_lora(self, module_name: str) -> bool:
+        return bool(self.lora_pattern.match(module_name))
+
+    def get_hidden_dim(self, module_name):
+        # return input_dim, output_dim
+        if module_name in ["q_proj", "qkv_proj"]:
+            return (
+                self.config.hidden_size,
+                self.config.head_dim * self.config.num_attention_heads,
+            )
+        elif module_name in ["o_proj"]:
+            return (
+                self.config.head_dim * self.config.num_attention_heads,
+                self.config.hidden_size,
+            )
+        elif module_name in ["kv_proj"]:
+            return (
+                self.config.hidden_size,
+                self.config.head_dim * self.config.num_key_value_heads,
+            )
+        elif module_name == "gate_up_proj":
+            assert len(set(self.config.intermediate_size)) == 1, (
+                "Currently SGLang requires uniform intermediate size for all layers. "
+                "Please file an issue if you need support for non-uniform intermediate sizes."
+            )
+            return self.config.hidden_size, self.config.intermediate_size[0]
+        elif module_name == "down_proj":
+            assert len(set(self.config.intermediate_size)) == 1, (
+                "Currently SGLang requires uniform intermediate size for all layers. "
+                "Please file an issue if you need support for non-uniform intermediate sizes."
+            )
+            return self.config.intermediate_size[0], self.config.hidden_size
+        else:
+            raise NotImplementedError()
+
 
 EntryClass = Gemma3nForConditionalGeneration

python/sglang/srt/server_args.py

@@ -1943,10 +1943,16 @@ class ServerArgs:
         if "Llama4" in model_arch:
             assert self.attention_backend == "fa3", "fa3 is required for Llama4 model"
 
-        if "Gemma2ForCausalLM" in model_arch:
+        if model_arch in [
+            "Gemma2ForCausalLM",
+            "Gemma3nForCausalLM",
+            "Gemma3nForConditionalGeneration",
+        ]:
             # FIXME: https://github.com/sgl-project/sglang/pull/7367 is not compatible with gemma2 model.
             # It failed at this test: https://github.com/sgl-project/sglang/actions/runs/16255155597/job/45890331952#step:4:736
-            logger.warning("Disable hybrid SWA memory for Gemma2ForCausalLM.")
+            logger.warning(
+                f"Disable hybrid SWA memory for {model_arch} as it is not yet supported."
+            )
             self.disable_hybrid_swa_memory = True
 
         # Check LoRA