[Core] Support load and unload LoRA in api server (#6566)

Co-authored-by: Jee Jee Li <pandaleefree@gmail.com>

docs/requirements-docs.txt

@@ -11,6 +11,5 @@ pydantic >= 2.8
 torch
 py-cpuinfo
 transformers
-openai # Required by docs/source/serving/openai_compatible_server.md's vllm.entrypoints.openai.cli_args
 mistral_common >= 1.3.4
 openai # Required by docs/source/serving/openai_compatible_server.md's vllm.entrypoints.openai.cli_args
\ No newline at end of file

docs/source/models/lora.rst

@@ -107,3 +107,55 @@ The following is an example request
             "max_tokens": 7,
             "temperature": 0
         }' | jq
+
+
+Dynamically serving LoRA Adapters
+---------------------------------
+
+In addition to serving LoRA adapters at server startup, the vLLM server now supports dynamically loading and unloading
+LoRA adapters at runtime through dedicated API endpoints. This feature can be particularly useful when the flexibility
+to change models on-the-fly is needed.
+
+Note: Enabling this feature in production environments is risky as user may participate model adapter management.
+
+To enable dynamic LoRA loading and unloading, ensure that the environment variable `VLLM_ALLOW_RUNTIME_LORA_UPDATING`
+is set to `True`. When this option is enabled, the API server will log a warning to indicate that dynamic loading is active.
+
+.. code-block:: bash
+
+    export VLLM_ALLOW_RUNTIME_LORA_UPDATING=True
+
+
+Loading a LoRA Adapter:
+
+To dynamically load a LoRA adapter, send a POST request to the `/v1/load_lora_adapter` endpoint with the necessary
+details of the adapter to be loaded. The request payload should include the name and path to the LoRA adapter.
+
+Example request to load a LoRA adapter:
+
+.. code-block:: bash
+
+    curl -X POST http://localhost:8000/v1/load_lora_adapter \
+    -H "Content-Type: application/json" \
+    -d '{
+        "lora_name": "sql_adapter",
+        "lora_path": "/path/to/sql-lora-adapter"
+    }'
+
+Upon a successful request, the API will respond with a 200 OK status code. If an error occurs, such as if the adapter
+cannot be found or loaded, an appropriate error message will be returned.
+
+Unloading a LoRA Adapter:
+
+To unload a LoRA adapter that has been previously loaded, send a POST request to the `/v1/unload_lora_adapter` endpoint
+with the name or ID of the adapter to be unloaded.
+
+Example request to unload a LoRA adapter:
+
+.. code-block:: bash
+
+    curl -X POST http://localhost:8000/v1/unload_lora_adapter \
+    -H "Content-Type: application/json" \
+    -d '{
+        "lora_name": "sql_adapter"
+    }'

tests/entrypoints/llm/test_generate_multiple_loras.py

@@ -50,7 +50,7 @@ def zephyr_lora_files():
 @pytest.mark.skip_global_cleanup
 def test_multiple_lora_requests(llm: LLM, zephyr_lora_files):
     lora_request = [
-        LoRARequest(LORA_NAME, idx + 1, zephyr_lora_files)
+        LoRARequest(LORA_NAME + str(idx), idx + 1, zephyr_lora_files)
         for idx in range(len(PROMPTS))
     ]
     # Multiple SamplingParams should be matched with each prompt

tests/entrypoints/openai/test_serving_engine.py

+from http import HTTPStatus
+from unittest.mock import MagicMock
+
+import pytest
+
+from vllm.config import ModelConfig
+from vllm.engine.protocol import AsyncEngineClient
+from vllm.entrypoints.openai.protocol import (ErrorResponse,
+                                              LoadLoraAdapterRequest,
+                                              UnloadLoraAdapterRequest)
+from vllm.entrypoints.openai.serving_engine import OpenAIServing
+
+MODEL_NAME = "meta-llama/Llama-2-7b"
+LORA_LOADING_SUCCESS_MESSAGE = (
+    "Success: LoRA adapter '{lora_name}' added successfully.")
+LORA_UNLOADING_SUCCESS_MESSAGE = (
+    "Success: LoRA adapter '{lora_name}' removed successfully.")
+
+
+async def _async_serving_engine_init():
+    mock_engine_client = MagicMock(spec=AsyncEngineClient)
+    mock_model_config = MagicMock(spec=ModelConfig)
+    # Set the max_model_len attribute to avoid missing attribute
+    mock_model_config.max_model_len = 2048
+
+    serving_engine = OpenAIServing(mock_engine_client,
+                                   mock_model_config,
+                                   served_model_names=[MODEL_NAME],
+                                   lora_modules=None,
+                                   prompt_adapters=None,
+                                   request_logger=None)
+    return serving_engine
+
+
+@pytest.mark.asyncio
+async def test_load_lora_adapter_success():
+    serving_engine = await _async_serving_engine_init()
+    request = LoadLoraAdapterRequest(lora_name="adapter",
+                                     lora_path="/path/to/adapter2")
+    response = await serving_engine.load_lora_adapter(request)
+    assert response == LORA_LOADING_SUCCESS_MESSAGE.format(lora_name='adapter')
+    assert len(serving_engine.lora_requests) == 1
+    assert serving_engine.lora_requests[0].lora_name == "adapter"
+
+
+@pytest.mark.asyncio
+async def test_load_lora_adapter_missing_fields():
+    serving_engine = await _async_serving_engine_init()
+    request = LoadLoraAdapterRequest(lora_name="", lora_path="")
+    response = await serving_engine.load_lora_adapter(request)
+    assert isinstance(response, ErrorResponse)
+    assert response.type == "InvalidUserInput"
+    assert response.code == HTTPStatus.BAD_REQUEST
+
+
+@pytest.mark.asyncio
+async def test_load_lora_adapter_duplicate():
+    serving_engine = await _async_serving_engine_init()
+    request = LoadLoraAdapterRequest(lora_name="adapter1",
+                                     lora_path="/path/to/adapter1")
+    response = await serving_engine.load_lora_adapter(request)
+    assert response == LORA_LOADING_SUCCESS_MESSAGE.format(
+        lora_name='adapter1')
+    assert len(serving_engine.lora_requests) == 1
+
+    request = LoadLoraAdapterRequest(lora_name="adapter1",
+                                     lora_path="/path/to/adapter1")
+    response = await serving_engine.load_lora_adapter(request)
+    assert isinstance(response, ErrorResponse)
+    assert response.type == "InvalidUserInput"
+    assert response.code == HTTPStatus.BAD_REQUEST
+    assert len(serving_engine.lora_requests) == 1
+
+
+@pytest.mark.asyncio
+async def test_unload_lora_adapter_success():
+    serving_engine = await _async_serving_engine_init()
+    request = LoadLoraAdapterRequest(lora_name="adapter1",
+                                     lora_path="/path/to/adapter1")
+    response = await serving_engine.load_lora_adapter(request)
+    assert len(serving_engine.lora_requests) == 1
+
+    request = UnloadLoraAdapterRequest(lora_name="adapter1")
+    response = await serving_engine.unload_lora_adapter(request)
+    assert response == LORA_UNLOADING_SUCCESS_MESSAGE.format(
+        lora_name='adapter1')
+    assert len(serving_engine.lora_requests) == 0
+
+
+@pytest.mark.asyncio
+async def test_unload_lora_adapter_missing_fields():
+    serving_engine = await _async_serving_engine_init()
+    request = UnloadLoraAdapterRequest(lora_name="", lora_int_id=None)
+    response = await serving_engine.unload_lora_adapter(request)
+    assert isinstance(response, ErrorResponse)
+    assert response.type == "InvalidUserInput"
+    assert response.code == HTTPStatus.BAD_REQUEST
+
+
+@pytest.mark.asyncio
+async def test_unload_lora_adapter_not_found():
+    serving_engine = await _async_serving_engine_init()
+    request = UnloadLoraAdapterRequest(lora_name="nonexistent_adapter")
+    response = await serving_engine.unload_lora_adapter(request)
+    assert isinstance(response, ErrorResponse)
+    assert response.type == "InvalidUserInput"
+    assert response.code == HTTPStatus.BAD_REQUEST

vllm/entrypoints/openai/api_server.py

@@ -35,11 +35,13 @@ from vllm.entrypoints.openai.protocol import (ChatCompletionRequest,
                                               DetokenizeResponse,
                                               EmbeddingRequest,
                                               EmbeddingResponse, ErrorResponse,
+                                              LoadLoraAdapterRequest,
                                               TokenizeRequest,
-                                              TokenizeResponse)
-# yapf: enable
+                                              TokenizeResponse,
+                                              UnloadLoraAdapterRequest)
 from vllm.entrypoints.openai.rpc.client import AsyncEngineRPCClient
 from vllm.entrypoints.openai.rpc.server import run_rpc_server
+# yapf: enable
 from vllm.entrypoints.openai.serving_chat import OpenAIServingChat
 from vllm.entrypoints.openai.serving_completion import OpenAIServingCompletion
 from vllm.entrypoints.openai.serving_embedding import OpenAIServingEmbedding
@@ -343,6 +345,40 @@ if envs.VLLM_TORCH_PROFILER_DIR:
         return Response(status_code=200)
 
 
+if envs.VLLM_ALLOW_RUNTIME_LORA_UPDATING:
+    logger.warning(
+        "Lora dynamic loading & unloading is enabled in the API server. "
+        "This should ONLY be used for local development!")
+
+    @router.post("/v1/load_lora_adapter")
+    async def load_lora_adapter(request: LoadLoraAdapterRequest):
+        response = await openai_serving_chat.load_lora_adapter(request)
+        if isinstance(response, ErrorResponse):
+            return JSONResponse(content=response.model_dump(),
+                                status_code=response.code)
+
+        response = await openai_serving_completion.load_lora_adapter(request)
+        if isinstance(response, ErrorResponse):
+            return JSONResponse(content=response.model_dump(),
+                                status_code=response.code)
+
+        return Response(status_code=200, content=response)
+
+    @router.post("/v1/unload_lora_adapter")
+    async def unload_lora_adapter(request: UnloadLoraAdapterRequest):
+        response = await openai_serving_chat.unload_lora_adapter(request)
+        if isinstance(response, ErrorResponse):
+            return JSONResponse(content=response.model_dump(),
+                                status_code=response.code)
+
+        response = await openai_serving_completion.unload_lora_adapter(request)
+        if isinstance(response, ErrorResponse):
+            return JSONResponse(content=response.model_dump(),
+                                status_code=response.code)
+
+        return Response(status_code=200, content=response)
+
+
 def build_app(args: Namespace) -> FastAPI:
     app = FastAPI(lifespan=lifespan)
     app.include_router(router)

vllm/entrypoints/openai/protocol.py

@@ -878,3 +878,13 @@ class DetokenizeRequest(OpenAIBaseModel):
 
 class DetokenizeResponse(OpenAIBaseModel):
     prompt: str
+
+
+class LoadLoraAdapterRequest(BaseModel):
+    lora_name: str
+    lora_path: str
+
+
+class UnloadLoraAdapterRequest(BaseModel):
+    lora_name: str
+    lora_int_id: Optional[int] = Field(default=None)

vllm/entrypoints/openai/serving_engine.py

@@ -16,11 +16,13 @@ from vllm.entrypoints.openai.protocol import (ChatCompletionRequest,
                                               CompletionRequest,
                                               DetokenizeRequest,
                                               EmbeddingRequest, ErrorResponse,
+                                              LoadLoraAdapterRequest,
                                               ModelCard, ModelList,
                                               ModelPermission,
                                               TokenizeChatRequest,
                                               TokenizeCompletionRequest,
-                                              TokenizeRequest)
+                                              TokenizeRequest,
+                                              UnloadLoraAdapterRequest)
 # yapf: enable
 from vllm.inputs.parse import parse_and_batch_prompt
 from vllm.logger import init_logger
@@ -32,6 +34,7 @@ from vllm.prompt_adapter.request import PromptAdapterRequest
 from vllm.sampling_params import LogitsProcessor, SamplingParams
 from vllm.sequence import Logprob
 from vllm.transformers_utils.tokenizer import AnyTokenizer
+from vllm.utils import AtomicCounter
 
 logger = init_logger(__name__)
 
@@ -78,6 +81,7 @@ class OpenAIServing:
 
         self.served_model_names = served_model_names
 
+        self.lora_id_counter = AtomicCounter(0)
         self.lora_requests = []
         if lora_modules is not None:
             self.lora_requests = [
@@ -403,3 +407,76 @@ class OpenAIServing:
         if logprob.decoded_token is not None:
             return logprob.decoded_token
         return tokenizer.decode(token_id)
+
+    async def _check_load_lora_adapter_request(
+            self, request: LoadLoraAdapterRequest) -> Optional[ErrorResponse]:
+        # Check if both 'lora_name' and 'lora_path' are provided
+        if not request.lora_name or not request.lora_path:
+            return self.create_error_response(
+                message="Both 'lora_name' and 'lora_path' must be provided.",
+                err_type="InvalidUserInput",
+                status_code=HTTPStatus.BAD_REQUEST)
+
+        # Check if the lora adapter with the given name already exists
+        if any(lora_request.lora_name == request.lora_name
+               for lora_request in self.lora_requests):
+            return self.create_error_response(
+                message=
+                f"The lora adapter '{request.lora_name}' has already been"
+                "loaded.",
+                err_type="InvalidUserInput",
+                status_code=HTTPStatus.BAD_REQUEST)
+
+        return None
+
+    async def _check_unload_lora_adapter_request(
+            self,
+            request: UnloadLoraAdapterRequest) -> Optional[ErrorResponse]:
+        # Check if either 'lora_name' or 'lora_int_id' is provided
+        if not request.lora_name and not request.lora_int_id:
+            return self.create_error_response(
+                message=
+                "either 'lora_name' and 'lora_int_id' needs to be provided.",
+                err_type="InvalidUserInput",
+                status_code=HTTPStatus.BAD_REQUEST)
+
+        # Check if the lora adapter with the given name exists
+        if not any(lora_request.lora_name == request.lora_name
+                   for lora_request in self.lora_requests):
+            return self.create_error_response(
+                message=
+                f"The lora adapter '{request.lora_name}' cannot be found.",
+                err_type="InvalidUserInput",
+                status_code=HTTPStatus.BAD_REQUEST)
+
+        return None
+
+    async def load_lora_adapter(
+            self,
+            request: LoadLoraAdapterRequest) -> Union[ErrorResponse, str]:
+        error_check_ret = await self._check_load_lora_adapter_request(request)
+        if error_check_ret is not None:
+            return error_check_ret
+
+        lora_name, lora_path = request.lora_name, request.lora_path
+        unique_id = self.lora_id_counter.inc(1)
+        self.lora_requests.append(
+            LoRARequest(lora_name=lora_name,
+                        lora_int_id=unique_id,
+                        lora_path=lora_path))
+        return f"Success: LoRA adapter '{lora_name}' added successfully."
+
+    async def unload_lora_adapter(
+            self,
+            request: UnloadLoraAdapterRequest) -> Union[ErrorResponse, str]:
+        error_check_ret = await self._check_unload_lora_adapter_request(request
+                                                                        )
+        if error_check_ret is not None:
+            return error_check_ret
+
+        lora_name = request.lora_name
+        self.lora_requests = [
+            lora_request for lora_request in self.lora_requests
+            if lora_request.lora_name != lora_name
+        ]
+        return f"Success: LoRA adapter '{lora_name}' removed successfully."

vllm/envs.py

@@ -61,6 +61,7 @@ if TYPE_CHECKING:
     VLLM_ALLOW_ENGINE_USE_RAY: bool = False
     VLLM_PLUGINS: Optional[List[str]] = None
     VLLM_TORCH_PROFILER_DIR: Optional[str] = None
+    VLLM_ALLOW_RUNTIME_LORA_UPDATING: bool = False
 
 
 def get_default_cache_root():
@@ -409,6 +410,12 @@ environment_variables: Dict[str, Callable[[], Any]] = {
     # If set, vLLM will use Triton implementations of AWQ.
     "VLLM_USE_TRITON_AWQ":
     lambda: bool(int(os.getenv("VLLM_USE_TRITON_AWQ", "0"))),
+
+    # If set, allow loading or unloading lora adapters in runtime,
+    "VLLM_ALLOW_RUNTIME_LORA_UPDATING":
+    lambda:
+    (os.environ.get("VLLM_ALLOW_RUNTIME_LORA_UPDATING", "0").strip().lower() in
+     ("1", "true")),
 }
 
 # end-env-vars-definition

vllm/lora/request.py

@@ -28,7 +28,6 @@ class LoRARequest(
     lora_path: str = ""
     lora_local_path: Optional[str] = msgspec.field(default=None)
     long_lora_max_len: Optional[int] = None
-    __hash__ = AdapterRequest.__hash__
 
     def __post_init__(self):
         if 'lora_local_path' in self.__struct_fields__:
@@ -75,3 +74,21 @@ class LoRARequest(
             DeprecationWarning,
             stacklevel=2)
         self.lora_path = value
+
+    def __eq__(self, value: object) -> bool:
+        """
+        Overrides the equality method to compare LoRARequest
+        instances based on lora_name. This allows for identification
+        and comparison lora adapter across engines.
+        """
+        return isinstance(value,
+                          self.__class__) and self.lora_name == value.lora_name
+
+    def __hash__(self) -> int:
+        """
+        Overrides the hash method to hash LoRARequest instances
+        based on lora_name. This ensures that LoRARequest instances
+        can be used in hash-based collections such as sets and dictionaries,
+        identified by their names across engines.
+        """
+        return hash(self.lora_name)

vllm/utils.py

@@ -1224,3 +1224,28 @@ async def _run_task_with_lock(task: Callable, lock: asyncio.Lock, *args,
 def supports_dynamo() -> bool:
     base_torch_version = Version(Version(torch.__version__).base_version)
     return base_torch_version >= Version("2.4.0")
+
+
+class AtomicCounter:
+    """An atomic, thread-safe counter"""
+
+    def __init__(self, initial=0):
+        """Initialize a new atomic counter to given initial value"""
+        self._value = initial
+        self._lock = threading.Lock()
+
+    def inc(self, num=1):
+        """Atomically increment the counter by num and return the new value"""
+        with self._lock:
+            self._value += num
+            return self._value
+
+    def dec(self, num=1):
+        """Atomically decrement the counter by num and return the new value"""
+        with self._lock:
+            self._value -= num
+            return self._value
+
+    @property
+    def value(self):
+        return self._value