[BugFix] fix loading new adapter with added_tokens

python/sglang/srt/lora/lora_manager.py

@@ -71,7 +71,7 @@ def __init__(
         self.device: torch.device = next(self.base_model.parameters()).device
         self.tp_size: int = tp_size
         self.tp_rank: int = tp_rank
-        self.lora_added_tokens_size: Optional[int] = None
+        self.lora_added_tokens_size: int = 0
         self.enable_lora_overlap_loading: Optional[bool] = (
             server_args.enable_lora_overlap_loading
         )
@@ -133,6 +133,7 @@ def load_lora_adapter(self, lora_ref: LoRARef) -> LoRAUpdateOutput:
         try:
             # load configs
             new_adapter = LoRAConfig(lora_ref.lora_path)
+            self._maybe_update_added_tokens_size(new_adapter)
             self.validate_new_adapter(new_adapter, lora_ref)
             self.configs[lora_ref.lora_id] = new_adapter
 
@@ -418,24 +419,6 @@ def init_lora_shapes(
                 default=0,
             )
 
-        # Auto-infer self.lora_added_vocab_size from loaded LoRA configs
-        # This happens automatically without requiring user input
-        # if self.lora_added_vocab_size is None:
-        if self.lora_added_tokens_size is None:
-            inferred_extra_vocab_size = next(
-                (
-                    x.lora_added_tokens_size
-                    for x in self.configs.values()
-                    if x.lora_added_tokens_size > 0
-                ),
-                0,
-            )
-            if inferred_extra_vocab_size > 0:
-                logger.info(
-                    f"self.lora_added_tokens_size={inferred_extra_vocab_size} from LoRA adapters."
-                )
-            self.lora_added_tokens_size = inferred_extra_vocab_size
-
     def load_lora_weights(self, lora_ref: LoRARef):
         """
         Load the weights of a LoRA adapter to CPU memory and conducts post-loading validation.
@@ -490,6 +473,7 @@ def load_lora_adapter_from_tensors(
 
         try:
             new_adapter = LoRAConfig.from_dict(config_dict, added_tokens_config)
+            self._maybe_update_added_tokens_size(new_adapter)
             self.validate_new_adapter(new_adapter, lora_ref)
             self.configs[lora_ref.lora_id] = new_adapter
 
@@ -523,6 +507,19 @@ def init_memory_pool(self):
         # Initializing memory pool with base model
         self.fetch_new_loras({None})
 
+    def _maybe_update_added_tokens_size(self, new_config: LoRAConfig):
+        if new_config.lora_added_tokens_size == 0 or self.lora_added_tokens_size > 0:
+            return
+
+        self.lora_added_tokens_size = new_config.lora_added_tokens_size
+        logger.info(
+            f"self.lora_added_tokens_size={self.lora_added_tokens_size} from LoRA adapters."
+        )
+
+        # Some LoRA adapters are loaded before the memory pool is initialized
+        if hasattr(self, "memory_pool"):
+            self.init_memory_pool()
+
     def set_lora_module(self, module_name, module):
         lora_module = get_lora_layer(module, self.lora_backend)
         replace_submodule(self.base_model, module_name, lora_module)

test/registered/lora/test_lora_update.py

@@ -34,7 +34,7 @@
     popen_launch_server,
 )
 
-register_cuda_ci(est_time=487, suite="stage-b-test-large-1-gpu")
+register_cuda_ci(est_time=587, suite="stage-b-test-large-1-gpu")
 
 PROMPTS = [
     "SGL is a",
@@ -1482,6 +1482,105 @@ def test_dynamic_lora_update_server(self):
             mode=LoRAUpdateTestSessionMode.SERVER, test_cases=test_cases
         )
 
+    def test_lora_added_tokens_size(self):
+        """
+        Test that we correctly handle loading a new adapter that adds tokens to the vocabulary
+        """
+        added_tokens_model = "Qwen/Qwen3-0.6B"
+        no_added_tokens_adapter = "ethicalabs/Flwr-Qwen3-0.6B-Medical-PEFT"
+        added_tokens_adapter = "YoussefHosni/Qwen3-0.6b-2B-Token-arabic-LoRA-finetuned"
+
+        # Test loading adapter that adds tokens on startup
+        with LoRAUpdateTestSession(
+            testcase=self,
+            mode=LoRAUpdateTestSessionMode.SERVER,
+            model_path=added_tokens_model,
+            lora_paths=[
+                {
+                    "lora_name": "no_added_tokens_adapter",
+                    "lora_path": no_added_tokens_adapter,
+                },
+                {
+                    "lora_name": "added_tokens_adapter",
+                    "lora_path": added_tokens_adapter,
+                },
+            ],
+            max_loras_per_batch=2,
+            max_lora_rank=32,
+            lora_target_modules=["all"],
+            enable_lora=True,
+        ) as session:
+            # Verify both adapters are correctly loaded
+            response = requests.get(DEFAULT_URL_FOR_TEST + "/v1/models")
+            self.assertTrue(response.ok, response.text)
+
+            adapter_models = [m for m in response.json()["data"] if m.get("parent")]
+            self.assertEqual(
+                {m["id"] for m in adapter_models},
+                {"no_added_tokens_adapter", "added_tokens_adapter"},
+            )
+
+            # Make sure we can run forward with both adapters
+            result = session.forward(
+                prompts=[PROMPTS[0], PROMPTS[1]],
+                lora_paths=["no_added_tokens_adapter", "added_tokens_adapter"],
+            )
+            print(f"Got output from combined forward pass: {result}")
+
+        # Test dynamically loading adapter that adds tokens
+        with LoRAUpdateTestSession(
+            testcase=self,
+            mode=LoRAUpdateTestSessionMode.SERVER,
+            model_path=added_tokens_model,
+            lora_paths=[
+                {
+                    "lora_name": "no_added_tokens_adapter",
+                    "lora_path": no_added_tokens_adapter,
+                }
+            ],
+            max_loras_per_batch=2,
+            max_lora_rank=32,
+            lora_target_modules=["all"],
+            enable_lora=True,
+        ) as session:
+            # Verify adapter is correctly loaded
+            response = requests.get(DEFAULT_URL_FOR_TEST + "/v1/models")
+            self.assertTrue(response.ok, response.text)
+
+            adapter_models = [m for m in response.json()["data"] if m.get("parent")]
+            self.assertEqual(
+                {m["id"] for m in adapter_models}, {"no_added_tokens_adapter"}
+            )
+
+            # Run one forward request so that adapter weights are loaded into GPU memory
+            result = session.forward(
+                prompts=[PROMPTS[0]],
+                lora_paths=["no_added_tokens_adapter"],
+            )
+            print(f"Got output from no_added_tokens_adapter: {result}")
+
+            # Load adapter that adds tokens to vocab
+            session.load_lora_adapter(
+                lora_name="added_tokens_adapter", lora_path=added_tokens_adapter
+            )
+
+            # Verify both adapters are correctly loaded
+            response = requests.get(DEFAULT_URL_FOR_TEST + "/v1/models")
+            self.assertTrue(response.ok, response.text)
+
+            adapter_models = [m for m in response.json()["data"] if m.get("parent")]
+            self.assertEqual(
+                {m["id"] for m in adapter_models},
+                {"no_added_tokens_adapter", "added_tokens_adapter"},
+            )
+
+            # Make sure we can run forward with both adapters
+            result = session.forward(
+                prompts=[PROMPTS[0], PROMPTS[1]],
+                lora_paths=["no_added_tokens_adapter", "added_tokens_adapter"],
+            )
+            print(f"Got output from combined forward pass: {result}")
+
     def test_v1_models_endpoint_with_lora(self):
         """
         Test that /v1/models endpoint returns base model and loaded LoRA adapters.