add documentation example for LoRA overlap loading and cleanup unused function (#17464)

python/sglang/srt/lora/backend/lora_registry.py

@@ -1,4 +1,5 @@
 import logging
+from typing import Type
 
 from sglang.srt.lora.backend.base_backend import BaseLoRABackend
 
@@ -50,7 +51,7 @@ def create_flashinfer_backend():
     )
 
 
-def get_backend_from_name(name: str) -> BaseLoRABackend:
+def get_backend_from_name(name: str) -> Type[BaseLoRABackend]:
     """
     Get corresponding backend class from backend's name
     """

python/sglang/srt/lora/lora_manager.py

@@ -55,13 +55,13 @@ class LoRAManager:
         max_loras_per_batch: int,
         load_config: LoadConfig,
         dtype: torch.dtype,
+        server_args: ServerArgs,
         lora_backend: str = "triton",
         tp_size: int = 1,
         tp_rank: int = 0,
         max_lora_rank: Optional[int] = None,
         target_modules: Optional[Iterable[str]] = None,
         lora_paths: Optional[List[LoRARef]] = None,
-        server_args: Optional[ServerArgs] = None,
     ):
         self.base_model: torch.nn.Module = base_model
         self.base_hf_config: AutoConfig = base_hf_config

python/sglang/srt/managers/tp_worker.py

@@ -195,10 +195,6 @@ class BaseTpWorker(ABC):
         )
         return result
 
-    def can_run_lora_batch(self, lora_ids: list[str]) -> bool:
-        lora_ids_set = set(lora_ids) if isinstance(lora_ids, list) else lora_ids
-        return self.model_runner.lora_manager.validate_lora_batch(lora_ids_set)
-
     def forward_batch_embedding(self, model_worker_batch: ModelWorkerBatch):
         forward_batch = ForwardBatch.init_new(model_worker_batch, self.model_runner)
         logits_output = self.model_runner.forward(forward_batch).logits_output

python/sglang/srt/model_executor/model_runner.py

@@ -1424,13 +1424,13 @@ class ModelRunner(ModelRunnerKVCacheMixin):
             max_loras_per_batch=self.server_args.max_loras_per_batch,
             load_config=self.load_config,
             dtype=self.dtype,
+            server_args=self.server_args,
             lora_backend=self.server_args.lora_backend,
             tp_size=self.tp_size,
             tp_rank=self.tp_rank,
             max_lora_rank=self.server_args.max_lora_rank,
             target_modules=self.server_args.lora_target_modules,
             lora_paths=self.server_args.lora_paths,
-            server_args=self.server_args,
         )
 
     def load_lora_adapter(self, lora_ref: LoRARef):