Support limiting max loaded loras in CPU. (#8650)

docs/backend/lora.ipynb

@@ -33,6 +33,8 @@
     "\n",
     "* `max_loras_per_batch`: Maximum number of adaptors used by each batch. This argument can affect the amount of GPU memory reserved for multi-LoRA serving, so it should be set to a smaller value when memory is scarce. Defaults to be 8.\n",
     "\n",
+    "* `max_loaded_loras`: If specified, it limits the maximum number of LoRA adapters loaded in CPU memory at a time. The value must be greater than or equal to `max-loras-per-batch`.\n",
+    "\n",
     "* `lora_backend`: The backend of running GEMM kernels for Lora modules. It can be one of `triton` or `flashinfer`, and set to `triton` by default. For better performance and stability, we recommend using the Triton LoRA backend. In the future, faster backend built upon Cutlass or Cuda kernels will be added.\n",
     "\n",
     "* `max_lora_rank`: The maximum LoRA rank that should be supported. If not specified, it will be automatically inferred from the adapters provided in `--lora-paths`. This argument is needed when you expect to dynamically load adapters of larger LoRA rank after server startup.\n",

docs/backend/server_arguments.md

@@ -181,6 +181,7 @@ Please consult the documentation below and [server_args.py](https://github.com/s
 | `--lora-target-modules` | The union set of all target modules where LoRA should be applied (e.g., `q_proj`, `k_proj`, `gate_proj`). If not specified, it will be automatically inferred from the adapters provided in `--lora-paths`. This argument is needed when you expect to dynamically load adapters of different target modules after server startup. You can also set it to `all` to enable LoRA for all supported modules. However, enabling LoRA on additional modules introduces a minor performance overhead. If your application is performance-sensitive, we recommend only specifying the modules for which you plan to load adapters. | None |
 | `--lora-paths` | The list of LoRA adapters. You can provide a list of either path in str or renamed path in the format {name}={path}. | None |
 | `--max-loras-per-batch` | Maximum number of adapters for a running batch, include base-only request. | 8 |
+| `--max-loaded-loras` | If specified, it limits the maximum number of LoRA adapters loaded in CPU memory at a time. The value must be greater than or equal to `--max-loras-per-batch`. | None |
 | `--lora-backend` | Choose the kernel backend for multi-LoRA serving. | triton |
 
 ## Kernel backend

python/sglang/srt/lora/lora_registry.py

@@ -186,3 +186,10 @@ class LoRARegistry:
         self._registry[lora_ref.lora_name] = lora_ref
         self._counters[lora_ref.lora_id] = ConcurrentCounter()
         return lora_ref
+
+    @property
+    def num_registered_loras(self) -> int:
+        """
+        Returns the total number of LoRA adapters currently registered.
+        """
+        return len(self._registry)

python/sglang/srt/managers/io_struct.py

@@ -1097,7 +1097,7 @@ class UnloadLoRAAdapterReqInput:
 class LoRAUpdateResult:
     success: bool
     error_message: Optional[str] = None
-    loaded_adapters: Dict[str, LoRARef] = field(default_factory=dict)
+    loaded_adapters: Optional[Dict[str, LoRARef]] = None
 
 
 LoadLoRAAdapterReqOutput = UnloadLoRAAdapterReqOutput = LoRAUpdateResult

python/sglang/srt/managers/tokenizer_manager.py

@@ -1084,6 +1084,8 @@ class TokenizerManager:
         _: Optional[fastapi.Request] = None,
     ) -> LoadLoRAAdapterReqOutput:
         self.auto_create_handle_loop()
+
+        try:
             if not self.server_args.enable_lora:
                 raise ValueError(
                     "LoRA is not enabled. Please set `--enable-lora` to enable LoRA."
@@ -1101,6 +1103,17 @@ class TokenizerManager:
             )
 
             async with self.lora_update_lock:
+                if (
+                    self.server_args.max_loaded_loras is not None
+                    and self.lora_registry.num_registered_loras
+                    >= self.server_args.max_loaded_loras
+                ):
+                    raise ValueError(
+                        f"Cannot load LoRA adapter {obj.lora_name} at path {obj.lora_path}. "
+                        f"Maximum number of loaded LoRA adapters is {self.server_args.max_loaded_loras}. "
+                        "Please unload some LoRA adapters before loading new ones."
+                    )
+
                 # Generate new uniquely identifiable LoRARef object.
                 new_adapter = LoRARef(
                     lora_name=obj.lora_name,
@@ -1116,6 +1129,11 @@ class TokenizerManager:
                     await self.lora_registry.register(new_adapter)
 
                 return result
+        except ValueError as e:
+            return LoadLoRAAdapterReqOutput(
+                success=False,
+                error_message=str(e),
+            )
 
     async def unload_lora_adapter(
         self,
@@ -1123,6 +1141,8 @@ class TokenizerManager:
         _: Optional[fastapi.Request] = None,
     ) -> UnloadLoRAAdapterReqOutput:
         self.auto_create_handle_loop()
+
+        try:
             if not self.server_args.enable_lora:
                 raise ValueError(
                     "LoRA is not enabled. Please set `--enable-lora` to enable LoRA."
@@ -1154,6 +1174,8 @@ class TokenizerManager:
                 result = (await self.update_lora_adapter_communicator(obj))[0]
 
                 return result
+        except ValueError as e:
+            return UnloadLoRAAdapterReqOutput(success=False, rror_message=str(e))
 
     async def get_weights_by_name(
         self, obj: GetWeightsByNameReqInput, request: Optional[fastapi.Request] = None

python/sglang/srt/server_args.py

@@ -149,6 +149,7 @@ class ServerArgs:
     max_lora_rank: Optional[int] = None
     lora_target_modules: Optional[Union[set[str], List[str]]] = None
     lora_paths: Optional[Union[dict[str, str], dict[str, LoRARef], List[str]]] = None
+    max_loaded_loras: Optional[int] = None
     max_loras_per_batch: int = 8
     lora_backend: str = "triton"
 
@@ -1237,6 +1238,12 @@ class ServerArgs:
             default=8,
             help="Maximum number of adapters for a running batch, include base-only request.",
         )
+        parser.add_argument(
+            "--max-loaded-loras",
+            type=int,
+            default=ServerArgs.max_loaded_loras,
+            help="If specified, it limits the maximum number of LoRA adapters loaded in CPU memory at a time. The value must be greater than or equal to `--max-loras-per-batch`.",
+        )
         parser.add_argument(
             "--lora-backend",
             type=str,
@@ -2008,6 +2015,19 @@ class ServerArgs:
                 self.max_lora_rank and self.lora_target_modules
             ), "When no initial --lora-paths is provided, you need to specify both --max-lora-rank and --lora-target-modules for LoRA initialization."
 
+            # Validate max_loaded_loras
+            if self.max_loaded_loras is not None:
+                assert self.max_loaded_loras >= self.max_loras_per_batch, (
+                    "max_loaded_loras should be greater than or equal to max_loras_per_batch. "
+                    f"max_loaded_loras={self.max_loaded_loras}, max_loras_per_batch={self.max_loras_per_batch}"
+                )
+                assert (
+                    not self.lora_paths or len(self.lora_paths) <= self.max_loaded_loras
+                ), (
+                    "The number of LoRA paths should not exceed max_loaded_loras. "
+                    f"max_loaded_loras={self.max_loaded_loras}, lora_paths={len(self.lora_paths)}"
+                )
+
     def validate_disagg_tp_size(self, prefill_tp: int, decode_tp: int):
         larger_tp = max(decode_tp, prefill_tp)
         smaller_tp = min(decode_tp, prefill_tp)

python/sglang/test/runners.py

@@ -514,6 +514,7 @@ class SRTRunner:
         max_lora_rank: Optional[int] = None,
         lora_target_modules: Optional[List[str]] = None,
         enable_lora: Optional[bool] = None,
+        max_loaded_loras: Optional[int] = None,
     ):
         self.model_type = model_type
         self.is_generation = model_type == "generation"
@@ -556,6 +557,7 @@ class SRTRunner:
             max_lora_rank=max_lora_rank,
             lora_target_modules=lora_target_modules,
             enable_lora=enable_lora,
+            max_loaded_loras=max_loaded_loras,
             **spec_kwargs,
         )
 

test/srt/models/lora/test_lora_update.py

@@ -70,6 +70,7 @@ class TestCase:
     max_lora_rank: Optional[int] = None
     lora_target_modules: Optional[List] = None
     max_new_tokens: int = 32
+    max_loaded_loras: Optional[int] = None
 
 
 def create_batch_data(adapters: Union[str, list]) -> List[tuple[str, str]]:
@@ -559,7 +560,43 @@ MAX_LORA_RANK_TESTS = [
         ],
     ),
 ]
-ALL_TESTS = BASIC_TESTS + TARGET_MODULE_TESTS + MAX_LORA_RANK_TESTS
+MAX_LOADED_LORAS_TESTS = [
+    TestCase(
+        description="Test max_loaded_loras limit",
+        base="meta-llama/Llama-3.1-8B-Instruct",
+        max_loras_per_batch=2,
+        max_loaded_loras=2,
+        all_adapters=[
+            "philschmid/code-llama-3-1-8b-text-to-sql-lora",
+            "Nutanix/Meta-Llama-3.1-8B-Instruct_lora_4_alpha_16",
+            "pbevan11/llama-3.1-8b-ocr-correction",
+        ],
+        initial_adapters=["philschmid/code-llama-3-1-8b-text-to-sql-lora"],
+        op_sequence=[
+            Operation(
+                type=OperationType.LOAD,
+                data="Nutanix/Meta-Llama-3.1-8B-Instruct_lora_4_alpha_16",
+            ),
+            Operation(
+                type=OperationType.LOAD,
+                data="pbevan11/llama-3.1-8b-ocr-correction",
+                expected_error="Maximum number of loaded LoRA adapters",
+            ),
+            Operation(
+                type=OperationType.UNLOAD,
+                data="Nutanix/Meta-Llama-3.1-8B-Instruct_lora_4_alpha_16",
+            ),
+            Operation(
+                type=OperationType.LOAD,
+                data="pbevan11/llama-3.1-8b-ocr-correction",
+            ),
+        ],
+    ),
+]
+
+ALL_TESTS = (
+    BASIC_TESTS + TARGET_MODULE_TESTS + MAX_LORA_RANK_TESTS + MAX_LOADED_LORAS_TESTS
+)
 
 
 class LoRAUpdateTestSessionMode(Enum):
@@ -579,6 +616,7 @@ class LoRAUpdateTestSessionBase:
         model_path: str,
         lora_paths: list[str],
         max_loras_per_batch: int,
+        max_loaded_loras: Optional[int] = None,
         max_lora_rank: Optional[int],
         enable_lora: Optional[bool] = None,
         lora_target_modules: Optional[List[str]] = None,
@@ -592,6 +630,7 @@ class LoRAUpdateTestSessionBase:
         self.max_lora_rank = max_lora_rank
         self.lora_target_modules = lora_target_modules
         self.max_loras_per_batch = max_loras_per_batch
+        self.max_loaded_loras = max_loaded_loras
         self.lora_backend = lora_backend
         self.disable_cuda_graph = disable_cuda_graph
         self.cuda_graph_max_bs = cuda_graph_max_bs
@@ -654,6 +693,7 @@ class LoRAUpdateEngineTestSession(LoRAUpdateTestSessionBase):
             torch_dtype=torch.float16,
             mem_fraction_static=MEM_FRACTION_STATIC,
             max_loras_per_batch=self.max_loras_per_batch,
+            max_loaded_loras=self.max_loaded_loras,
             disable_cuda_graph=self.disable_cuda_graph,
             cuda_graph_max_bs=self.cuda_graph_max_bs,
             disable_radix_cache=True,
@@ -774,6 +814,8 @@ class LoRAUpdateServerTestSession(LoRAUpdateTestSessionBase):
             other_args.extend(["--max-lora-rank", str(self.max_lora_rank)])
         if self.lora_target_modules is not None:
             other_args.extend(["--lora-target-modules"] + self.lora_target_modules)
+        if self.max_loaded_loras is not None:
+            other_args.extend(["--max-loaded-loras", str(self.max_loaded_loras)])
 
         # launch external server
         self.handle = popen_launch_server(
@@ -898,8 +940,9 @@ class TestLoRADynamicUpdate(CustomTestCase):
         mode: LoRAUpdateTestSessionMode,
         base: str,
         initial_adapters: List[str],
-        max_loras_per_batch: int,
         op_sequence: List[Operation],
+        max_loras_per_batch: int,
+        max_loaded_loras: Optional[int] = None,
         enable_lora: Optional[bool] = None,
         max_lora_rank: Optional[int] = None,
         lora_target_modules: Optional[List[str]] = None,
@@ -917,6 +960,7 @@ class TestLoRADynamicUpdate(CustomTestCase):
             model_path=base,
             lora_paths=initial_adapters,
             max_loras_per_batch=max_loras_per_batch,
+            max_loaded_loras=max_loaded_loras,
             max_lora_rank=max_lora_rank,
             lora_target_modules=lora_target_modules,
             enable_lora=enable_lora,
@@ -972,6 +1016,7 @@ class TestLoRADynamicUpdate(CustomTestCase):
                 enable_lora=test_case.enable_lora,
                 base=test_case.base,
                 max_loras_per_batch=test_case.max_loras_per_batch,
+                max_loaded_loras=test_case.max_loaded_loras,
                 op_sequence=test_case.op_sequence,
                 max_new_tokens=test_case.max_new_tokens,
                 max_lora_rank=test_case.max_lora_rank,
@@ -985,6 +1030,12 @@ class TestLoRADynamicUpdate(CustomTestCase):
                 if x.type == OperationType.FORWARD and x.expected_error is None
             ]
 
+            if not forward_ops:
+                print(
+                    f"No forward operations found in test case {case_idx}. Skipping static pass."
+                )
+                continue
+
             print("=" * 100)
             print(f"\n--- Running static pass with {len(forward_ops)} operations ---")
             static_output = self._run_operation_sequence(