[Bugfix][Refactor] Unify model management in frontend (#11660)

Signed-off-by: Joe Runde <Joseph.Runde@ibm.com>

tests/entrypoints/openai/test_cli_args.py

@@ -4,7 +4,7 @@ import pytest
 
 from vllm.entrypoints.openai.cli_args import (make_arg_parser,
                                               validate_parsed_serve_args)
-from vllm.entrypoints.openai.serving_engine import LoRAModulePath
+from vllm.entrypoints.openai.serving_models import LoRAModulePath
 from vllm.utils import FlexibleArgumentParser
 
 from ...utils import VLLM_PATH

tests/entrypoints/openai/test_lora_lineage.py

@@ -55,7 +55,10 @@ def server_with_lora_modules_json(zephyr_lora_files):
         "64",
     ]
 
-    with RemoteOpenAIServer(MODEL_NAME, args) as remote_server:
+    # Enable the /v1/load_lora_adapter endpoint
+    envs = {"VLLM_ALLOW_RUNTIME_LORA_UPDATING": "True"}
+
+    with RemoteOpenAIServer(MODEL_NAME, args, env_dict=envs) as remote_server:
         yield remote_server
 
 
@@ -67,7 +70,7 @@ async def client_for_lora_lineage(server_with_lora_modules_json):
 
 
 @pytest.mark.asyncio
-async def test_check_lora_lineage(client_for_lora_lineage: openai.AsyncOpenAI,
+async def test_static_lora_lineage(client_for_lora_lineage: openai.AsyncOpenAI,
                                    zephyr_lora_files):
     models = await client_for_lora_lineage.models.list()
     models = models.data
@@ -81,3 +84,26 @@ async def test_check_lora_lineage(client_for_lora_lineage: openai.AsyncOpenAI,
     assert all(lora_model.parent == MODEL_NAME for lora_model in lora_models)
     assert lora_models[0].id == "zephyr-lora"
     assert lora_models[1].id == "zephyr-lora2"
+
+
+@pytest.mark.asyncio
+async def test_dynamic_lora_lineage(
+        client_for_lora_lineage: openai.AsyncOpenAI, zephyr_lora_files):
+
+    response = await client_for_lora_lineage.post("load_lora_adapter",
+                                                  cast_to=str,
+                                                  body={
+                                                      "lora_name":
+                                                      "zephyr-lora-3",
+                                                      "lora_path":
+                                                      zephyr_lora_files
+                                                  })
+    # Ensure adapter loads before querying /models
+    assert "success" in response
+
+    models = await client_for_lora_lineage.models.list()
+    models = models.data
+    dynamic_lora_model = models[-1]
+    assert dynamic_lora_model.root == zephyr_lora_files
+    assert dynamic_lora_model.parent == MODEL_NAME
+    assert dynamic_lora_model.id == "zephyr-lora-3"

tests/entrypoints/openai/test_serving_chat.py

@@ -8,7 +8,8 @@ from vllm.config import MultiModalConfig
 from vllm.engine.multiprocessing.client import MQLLMEngineClient
 from vllm.entrypoints.openai.protocol import ChatCompletionRequest
 from vllm.entrypoints.openai.serving_chat import OpenAIServingChat
-from vllm.entrypoints.openai.serving_engine import BaseModelPath
+from vllm.entrypoints.openai.serving_models import (BaseModelPath,
+                                                    OpenAIServingModels)
 from vllm.transformers_utils.tokenizer import get_tokenizer
 
 MODEL_NAME = "openai-community/gpt2"
@@ -50,14 +51,13 @@ async def _async_serving_chat_init():
     engine = MockEngine()
     model_config = await engine.get_model_config()
 
+    models = OpenAIServingModels(model_config, BASE_MODEL_PATHS)
     serving_completion = OpenAIServingChat(engine,
                                            model_config,
-                                           BASE_MODEL_PATHS,
+                                           models,
                                            response_role="assistant",
                                            chat_template=CHAT_TEMPLATE,
                                            chat_template_content_format="auto",
-                                           lora_modules=None,
-                                           prompt_adapters=None,
                                            request_logger=None)
     return serving_completion
 
@@ -72,14 +72,14 @@ def test_serving_chat_should_set_correct_max_tokens():
     mock_engine.get_tokenizer.return_value = get_tokenizer(MODEL_NAME)
     mock_engine.errored = False
 
+    models = OpenAIServingModels(base_model_paths=BASE_MODEL_PATHS,
+                                 model_config=MockModelConfig())
     serving_chat = OpenAIServingChat(mock_engine,
                                      MockModelConfig(),
-                                     BASE_MODEL_PATHS,
+                                     models,
                                      response_role="assistant",
                                      chat_template=CHAT_TEMPLATE,
                                      chat_template_content_format="auto",
-                                     lora_modules=None,
-                                     prompt_adapters=None,
                                      request_logger=None)
     req = ChatCompletionRequest(
         model=MODEL_NAME,
@@ -115,14 +115,14 @@ def test_serving_chat_could_load_correct_generation_config():
     mock_engine.errored = False
 
     # Initialize the serving chat
+    models = OpenAIServingModels(base_model_paths=BASE_MODEL_PATHS,
+                                 model_config=mock_model_config)
     serving_chat = OpenAIServingChat(mock_engine,
                                      mock_model_config,
-                                     BASE_MODEL_PATHS,
+                                     models,
                                      response_role="assistant",
                                      chat_template=CHAT_TEMPLATE,
                                      chat_template_content_format="auto",
-                                     lora_modules=None,
-                                     prompt_adapters=None,
                                      request_logger=None)
     req = ChatCompletionRequest(
         model=MODEL_NAME,

tests/entrypoints/openai/test_serving_models.py

@@ -4,11 +4,11 @@ from unittest.mock import MagicMock
 import pytest
 
 from vllm.config import ModelConfig
-from vllm.engine.protocol import EngineClient
 from vllm.entrypoints.openai.protocol import (ErrorResponse,
                                               LoadLoraAdapterRequest,
                                               UnloadLoraAdapterRequest)
-from vllm.entrypoints.openai.serving_engine import BaseModelPath, OpenAIServing
+from vllm.entrypoints.openai.serving_models import (BaseModelPath,
+                                                    OpenAIServingModels)
 from vllm.lora.request import LoRARequest
 
 MODEL_NAME = "meta-llama/Llama-2-7b"
@@ -19,47 +19,45 @@ LORA_UNLOADING_SUCCESS_MESSAGE = (
     "Success: LoRA adapter '{lora_name}' removed successfully.")
 
 
-async def _async_serving_engine_init():
+async def _async_serving_models_init() -> OpenAIServingModels:
-    mock_engine_client = MagicMock(spec=EngineClient)
     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,
+    serving_models = OpenAIServingModels(base_model_paths=BASE_MODEL_PATHS,
-                                   mock_model_config,
+                                         model_config=mock_model_config,
-                                   BASE_MODEL_PATHS,
                                          lora_modules=None,
-                                   prompt_adapters=None,
+                                         prompt_adapters=None)
-                                   request_logger=None)
+
-    return serving_engine
+    return serving_models
 
 
 @pytest.mark.asyncio
 async def test_serving_model_name():
-    serving_engine = await _async_serving_engine_init()
+    serving_models = await _async_serving_models_init()
-    assert serving_engine._get_model_name(None) == MODEL_NAME
+    assert serving_models.model_name(None) == MODEL_NAME
     request = LoRARequest(lora_name="adapter",
                           lora_path="/path/to/adapter2",
                           lora_int_id=1)
-    assert serving_engine._get_model_name(request) == request.lora_name
+    assert serving_models.model_name(request) == request.lora_name
 
 
 @pytest.mark.asyncio
 async def test_load_lora_adapter_success():
-    serving_engine = await _async_serving_engine_init()
+    serving_models = await _async_serving_models_init()
     request = LoadLoraAdapterRequest(lora_name="adapter",
                                      lora_path="/path/to/adapter2")
-    response = await serving_engine.load_lora_adapter(request)
+    response = await serving_models.load_lora_adapter(request)
     assert response == LORA_LOADING_SUCCESS_MESSAGE.format(lora_name='adapter')
-    assert len(serving_engine.lora_requests) == 1
+    assert len(serving_models.lora_requests) == 1
-    assert serving_engine.lora_requests[0].lora_name == "adapter"
+    assert serving_models.lora_requests[0].lora_name == "adapter"
 
 
 @pytest.mark.asyncio
 async def test_load_lora_adapter_missing_fields():
-    serving_engine = await _async_serving_engine_init()
+    serving_models = await _async_serving_models_init()
     request = LoadLoraAdapterRequest(lora_name="", lora_path="")
-    response = await serving_engine.load_lora_adapter(request)
+    response = await serving_models.load_lora_adapter(request)
     assert isinstance(response, ErrorResponse)
     assert response.type == "InvalidUserInput"
     assert response.code == HTTPStatus.BAD_REQUEST
@@ -67,43 +65,43 @@ async def test_load_lora_adapter_missing_fields():
 
 @pytest.mark.asyncio
 async def test_load_lora_adapter_duplicate():
-    serving_engine = await _async_serving_engine_init()
+    serving_models = await _async_serving_models_init()
     request = LoadLoraAdapterRequest(lora_name="adapter1",
                                      lora_path="/path/to/adapter1")
-    response = await serving_engine.load_lora_adapter(request)
+    response = await serving_models.load_lora_adapter(request)
     assert response == LORA_LOADING_SUCCESS_MESSAGE.format(
         lora_name='adapter1')
-    assert len(serving_engine.lora_requests) == 1
+    assert len(serving_models.lora_requests) == 1
 
     request = LoadLoraAdapterRequest(lora_name="adapter1",
                                      lora_path="/path/to/adapter1")
-    response = await serving_engine.load_lora_adapter(request)
+    response = await serving_models.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
+    assert len(serving_models.lora_requests) == 1
 
 
 @pytest.mark.asyncio
 async def test_unload_lora_adapter_success():
-    serving_engine = await _async_serving_engine_init()
+    serving_models = await _async_serving_models_init()
     request = LoadLoraAdapterRequest(lora_name="adapter1",
                                      lora_path="/path/to/adapter1")
-    response = await serving_engine.load_lora_adapter(request)
+    response = await serving_models.load_lora_adapter(request)
-    assert len(serving_engine.lora_requests) == 1
+    assert len(serving_models.lora_requests) == 1
 
     request = UnloadLoraAdapterRequest(lora_name="adapter1")
-    response = await serving_engine.unload_lora_adapter(request)
+    response = await serving_models.unload_lora_adapter(request)
     assert response == LORA_UNLOADING_SUCCESS_MESSAGE.format(
         lora_name='adapter1')
-    assert len(serving_engine.lora_requests) == 0
+    assert len(serving_models.lora_requests) == 0
 
 
 @pytest.mark.asyncio
 async def test_unload_lora_adapter_missing_fields():
-    serving_engine = await _async_serving_engine_init()
+    serving_models = await _async_serving_models_init()
     request = UnloadLoraAdapterRequest(lora_name="", lora_int_id=None)
-    response = await serving_engine.unload_lora_adapter(request)
+    response = await serving_models.unload_lora_adapter(request)
     assert isinstance(response, ErrorResponse)
     assert response.type == "InvalidUserInput"
     assert response.code == HTTPStatus.BAD_REQUEST
@@ -111,9 +109,9 @@ async def test_unload_lora_adapter_missing_fields():
 
 @pytest.mark.asyncio
 async def test_unload_lora_adapter_not_found():
-    serving_engine = await _async_serving_engine_init()
+    serving_models = await _async_serving_models_init()
     request = UnloadLoraAdapterRequest(lora_name="nonexistent_adapter")
-    response = await serving_engine.unload_lora_adapter(request)
+    response = await serving_models.unload_lora_adapter(request)
     assert isinstance(response, ErrorResponse)
     assert response.type == "InvalidUserInput"
     assert response.code == HTTPStatus.BAD_REQUEST

vllm/entrypoints/openai/api_server.py

@@ -58,7 +58,9 @@ from vllm.entrypoints.openai.protocol import (ChatCompletionRequest,
 from vllm.entrypoints.openai.serving_chat import OpenAIServingChat
 from vllm.entrypoints.openai.serving_completion import OpenAIServingCompletion
 from vllm.entrypoints.openai.serving_embedding import OpenAIServingEmbedding
-from vllm.entrypoints.openai.serving_engine import BaseModelPath, OpenAIServing
+from vllm.entrypoints.openai.serving_engine import OpenAIServing
+from vllm.entrypoints.openai.serving_models import (BaseModelPath,
+                                                    OpenAIServingModels)
 from vllm.entrypoints.openai.serving_pooling import OpenAIServingPooling
 from vllm.entrypoints.openai.serving_score import OpenAIServingScores
 from vllm.entrypoints.openai.serving_tokenization import (
@@ -269,6 +271,10 @@ def base(request: Request) -> OpenAIServing:
     return tokenization(request)
 
 
+def models(request: Request) -> OpenAIServingModels:
+    return request.app.state.openai_serving_models
+
+
 def chat(request: Request) -> Optional[OpenAIServingChat]:
     return request.app.state.openai_serving_chat
 
@@ -336,10 +342,10 @@ async def detokenize(request: DetokenizeRequest, raw_request: Request):
 
 @router.get("/v1/models")
 async def show_available_models(raw_request: Request):
-    handler = base(raw_request)
+    handler = models(raw_request)
 
-    models = await handler.show_available_models()
+    models_ = await handler.show_available_models()
-    return JSONResponse(content=models.model_dump())
+    return JSONResponse(content=models_.model_dump())
 
 
 @router.get("/version")
@@ -505,9 +511,7 @@ if envs.VLLM_ALLOW_RUNTIME_LORA_UPDATING:
     @router.post("/v1/load_lora_adapter")
     async def load_lora_adapter(request: LoadLoraAdapterRequest,
                                 raw_request: Request):
-        for route in [chat, completion, embedding]:
+        handler = models(raw_request)
-            handler = route(raw_request)
-            if handler is not None:
         response = await handler.load_lora_adapter(request)
         if isinstance(response, ErrorResponse):
             return JSONResponse(content=response.model_dump(),
@@ -518,9 +522,7 @@ if envs.VLLM_ALLOW_RUNTIME_LORA_UPDATING:
     @router.post("/v1/unload_lora_adapter")
     async def unload_lora_adapter(request: UnloadLoraAdapterRequest,
                                   raw_request: Request):
-        for route in [chat, completion, embedding]:
+        handler = models(raw_request)
-            handler = route(raw_request)
-            if handler is not None:
         response = await handler.unload_lora_adapter(request)
         if isinstance(response, ErrorResponse):
             return JSONResponse(content=response.model_dump(),
@@ -628,13 +630,18 @@ def init_app_state(
     resolved_chat_template = load_chat_template(args.chat_template)
     logger.info("Using supplied chat template:\n%s", resolved_chat_template)
 
+    state.openai_serving_models = OpenAIServingModels(
+        model_config=model_config,
+        base_model_paths=base_model_paths,
+        lora_modules=args.lora_modules,
+        prompt_adapters=args.prompt_adapters,
+    )
+    # TODO: The chat template is now broken for lora adapters :(
     state.openai_serving_chat = OpenAIServingChat(
         engine_client,
         model_config,
-        base_model_paths,
+        state.openai_serving_models,
         args.response_role,
-        lora_modules=args.lora_modules,
-        prompt_adapters=args.prompt_adapters,
         request_logger=request_logger,
         chat_template=resolved_chat_template,
         chat_template_content_format=args.chat_template_content_format,
@@ -646,16 +653,14 @@ def init_app_state(
     state.openai_serving_completion = OpenAIServingCompletion(
         engine_client,
         model_config,
-        base_model_paths,
+        state.openai_serving_models,
-        lora_modules=args.lora_modules,
-        prompt_adapters=args.prompt_adapters,
         request_logger=request_logger,
         return_tokens_as_token_ids=args.return_tokens_as_token_ids,
     ) if model_config.runner_type == "generate" else None
     state.openai_serving_pooling = OpenAIServingPooling(
         engine_client,
         model_config,
-        base_model_paths,
+        state.openai_serving_models,
         request_logger=request_logger,
         chat_template=resolved_chat_template,
         chat_template_content_format=args.chat_template_content_format,
@@ -663,7 +668,7 @@ def init_app_state(
     state.openai_serving_embedding = OpenAIServingEmbedding(
         engine_client,
         model_config,
-        base_model_paths,
+        state.openai_serving_models,
         request_logger=request_logger,
         chat_template=resolved_chat_template,
         chat_template_content_format=args.chat_template_content_format,
@@ -671,14 +676,13 @@ def init_app_state(
     state.openai_serving_scores = OpenAIServingScores(
         engine_client,
         model_config,
-        base_model_paths,
+        state.openai_serving_models,
         request_logger=request_logger
     ) if model_config.task == "score" else None
     state.openai_serving_tokenization = OpenAIServingTokenization(
         engine_client,
         model_config,
-        base_model_paths,
+        state.openai_serving_models,
-        lora_modules=args.lora_modules,
         request_logger=request_logger,
         chat_template=resolved_chat_template,
         chat_template_content_format=args.chat_template_content_format,

vllm/entrypoints/openai/cli_args.py

@@ -12,7 +12,7 @@ from typing import List, Optional, Sequence, Union, get_args
 from vllm.engine.arg_utils import AsyncEngineArgs, nullable_str
 from vllm.entrypoints.chat_utils import (ChatTemplateContentFormatOption,
                                          validate_chat_template)
-from vllm.entrypoints.openai.serving_engine import (LoRAModulePath,
+from vllm.entrypoints.openai.serving_models import (LoRAModulePath,
                                                     PromptAdapterPath)
 from vllm.entrypoints.openai.tool_parsers import ToolParserManager
 from vllm.utils import FlexibleArgumentParser

vllm/entrypoints/openai/run_batch.py

@@ -20,7 +20,8 @@ from vllm.entrypoints.openai.protocol import (BatchRequestInput,
 # yapf: enable
 from vllm.entrypoints.openai.serving_chat import OpenAIServingChat
 from vllm.entrypoints.openai.serving_embedding import OpenAIServingEmbedding
-from vllm.entrypoints.openai.serving_engine import BaseModelPath
+from vllm.entrypoints.openai.serving_models import (BaseModelPath,
+                                                    OpenAIServingModels)
 from vllm.usage.usage_lib import UsageContext
 from vllm.utils import FlexibleArgumentParser, random_uuid
 from vllm.version import __version__ as VLLM_VERSION
@@ -213,13 +214,17 @@ async def main(args):
         request_logger = RequestLogger(max_log_len=args.max_log_len)
 
     # Create the openai serving objects.
+    openai_serving_models = OpenAIServingModels(
+        model_config=model_config,
+        base_model_paths=base_model_paths,
+        lora_modules=None,
+        prompt_adapters=None,
+    )
     openai_serving_chat = OpenAIServingChat(
         engine,
         model_config,
-        base_model_paths,
+        openai_serving_models,
         args.response_role,
-        lora_modules=None,
-        prompt_adapters=None,
         request_logger=request_logger,
         chat_template=None,
         chat_template_content_format="auto",
@@ -228,7 +233,7 @@ async def main(args):
     openai_serving_embedding = OpenAIServingEmbedding(
         engine,
         model_config,
-        base_model_paths,
+        openai_serving_models,
         request_logger=request_logger,
         chat_template=None,
         chat_template_content_format="auto",

vllm/entrypoints/openai/serving_chat.py

@@ -21,10 +21,8 @@ from vllm.entrypoints.openai.protocol import (
     ChatCompletionStreamResponse, ChatMessage, DeltaFunctionCall, DeltaMessage,
     DeltaToolCall, ErrorResponse, FunctionCall, PromptTokenUsageInfo,
     RequestResponseMetadata, ToolCall, UsageInfo)
-from vllm.entrypoints.openai.serving_engine import (BaseModelPath,
+from vllm.entrypoints.openai.serving_engine import OpenAIServing
-                                                    LoRAModulePath,
+from vllm.entrypoints.openai.serving_models import OpenAIServingModels
-                                                    OpenAIServing,
-                                                    PromptAdapterPath)
 from vllm.entrypoints.openai.tool_parsers import ToolParser, ToolParserManager
 from vllm.logger import init_logger
 from vllm.outputs import CompletionOutput, RequestOutput
@@ -42,11 +40,9 @@ class OpenAIServingChat(OpenAIServing):
         self,
         engine_client: EngineClient,
         model_config: ModelConfig,
-        base_model_paths: List[BaseModelPath],
+        models: OpenAIServingModels,
         response_role: str,
         *,
-        lora_modules: Optional[List[LoRAModulePath]],
-        prompt_adapters: Optional[List[PromptAdapterPath]],
         request_logger: Optional[RequestLogger],
         chat_template: Optional[str],
         chat_template_content_format: ChatTemplateContentFormatOption,
@@ -57,9 +53,7 @@ class OpenAIServingChat(OpenAIServing):
     ) -> None:
         super().__init__(engine_client=engine_client,
                          model_config=model_config,
-                         base_model_paths=base_model_paths,
+                         models=models,
-                         lora_modules=lora_modules,
-                         prompt_adapters=prompt_adapters,
                          request_logger=request_logger,
                          return_tokens_as_token_ids=return_tokens_as_token_ids)
 
@@ -126,7 +120,7 @@ class OpenAIServingChat(OpenAIServing):
                 prompt_adapter_request,
             ) = self._maybe_get_adapters(request)
 
-            model_name = self._get_model_name(lora_request)
+            model_name = self.models.model_name(lora_request)
 
             tokenizer = await self.engine_client.get_tokenizer(lora_request)
 

vllm/entrypoints/openai/serving_completion.py

@@ -21,10 +21,8 @@ from vllm.entrypoints.openai.protocol import (CompletionLogProbs,
                                               RequestResponseMetadata,
                                               UsageInfo)
 # yapf: enable
-from vllm.entrypoints.openai.serving_engine import (BaseModelPath,
+from vllm.entrypoints.openai.serving_engine import OpenAIServing
-                                                    LoRAModulePath,
+from vllm.entrypoints.openai.serving_models import OpenAIServingModels
-                                                    OpenAIServing,
-                                                    PromptAdapterPath)
 from vllm.logger import init_logger
 from vllm.outputs import RequestOutput
 from vllm.sampling_params import BeamSearchParams, SamplingParams
@@ -41,18 +39,14 @@ class OpenAIServingCompletion(OpenAIServing):
         self,
         engine_client: EngineClient,
         model_config: ModelConfig,
-        base_model_paths: List[BaseModelPath],
+        models: OpenAIServingModels,
         *,
-        lora_modules: Optional[List[LoRAModulePath]],
-        prompt_adapters: Optional[List[PromptAdapterPath]],
         request_logger: Optional[RequestLogger],
         return_tokens_as_token_ids: bool = False,
     ):
         super().__init__(engine_client=engine_client,
                          model_config=model_config,
-                         base_model_paths=base_model_paths,
+                         models=models,
-                         lora_modules=lora_modules,
-                         prompt_adapters=prompt_adapters,
                          request_logger=request_logger,
                          return_tokens_as_token_ids=return_tokens_as_token_ids)
         diff_sampling_param = self.model_config.get_diff_sampling_param()
@@ -170,7 +164,7 @@ class OpenAIServingCompletion(OpenAIServing):
 
         result_generator = merge_async_iterators(*generators)
 
-        model_name = self._get_model_name(lora_request)
+        model_name = self.models.model_name(lora_request)
         num_prompts = len(engine_prompts)
 
         # Similar to the OpenAI API, when n != best_of, we do not stream the

vllm/entrypoints/openai/serving_embedding.py

@@ -16,7 +16,8 @@ from vllm.entrypoints.openai.protocol import (EmbeddingChatRequest,
                                               EmbeddingResponse,
                                               EmbeddingResponseData,
                                               ErrorResponse, UsageInfo)
-from vllm.entrypoints.openai.serving_engine import BaseModelPath, OpenAIServing
+from vllm.entrypoints.openai.serving_engine import OpenAIServing
+from vllm.entrypoints.openai.serving_models import OpenAIServingModels
 from vllm.logger import init_logger
 from vllm.outputs import (EmbeddingOutput, EmbeddingRequestOutput,
                           PoolingRequestOutput)
@@ -46,7 +47,7 @@ class OpenAIServingEmbedding(OpenAIServing):
         self,
         engine_client: EngineClient,
         model_config: ModelConfig,
-        base_model_paths: List[BaseModelPath],
+        models: OpenAIServingModels,
         *,
         request_logger: Optional[RequestLogger],
         chat_template: Optional[str],
@@ -54,9 +55,7 @@ class OpenAIServingEmbedding(OpenAIServing):
     ) -> None:
         super().__init__(engine_client=engine_client,
                          model_config=model_config,
-                         base_model_paths=base_model_paths,
+                         models=models,
-                         lora_modules=None,
-                         prompt_adapters=None,
                          request_logger=request_logger)
 
         self.chat_template = chat_template

vllm/entrypoints/openai/serving_engine.py

@@ -1,7 +1,5 @@
 import json
-import pathlib
 from concurrent.futures.thread import ThreadPoolExecutor
-from dataclasses import dataclass
 from http import HTTPStatus
 from typing import (Any, Callable, Dict, Iterable, Iterator, List, Mapping,
                     Optional, Sequence, Tuple, TypedDict, Union)
@@ -28,13 +26,10 @@ from vllm.entrypoints.openai.protocol import (ChatCompletionRequest,
                                               DetokenizeRequest,
                                               EmbeddingChatRequest,
                                               EmbeddingCompletionRequest,
-                                              ErrorResponse,
+                                              ErrorResponse, ScoreRequest,
-                                              LoadLoraAdapterRequest,
-                                              ModelCard, ModelList,
-                                              ModelPermission, ScoreRequest,
                                               TokenizeChatRequest,
-                                              TokenizeCompletionRequest,
+                                              TokenizeCompletionRequest)
-                                              UnloadLoraAdapterRequest)
+from vllm.entrypoints.openai.serving_models import OpenAIServingModels
 from vllm.entrypoints.openai.tool_parsers import ToolParser
 # yapf: enable
 from vllm.inputs import TokensPrompt
@@ -48,30 +43,10 @@ from vllm.sequence import Logprob
 from vllm.tracing import (contains_trace_headers, extract_trace_headers,
                           log_tracing_disabled_warning)
 from vllm.transformers_utils.tokenizer import AnyTokenizer, MistralTokenizer
-from vllm.utils import AtomicCounter, is_list_of, make_async, random_uuid
+from vllm.utils import is_list_of, make_async, random_uuid
 
 logger = init_logger(__name__)
 
-
-@dataclass
-class BaseModelPath:
-    name: str
-    model_path: str
-
-
-@dataclass
-class PromptAdapterPath:
-    name: str
-    local_path: str
-
-
-@dataclass
-class LoRAModulePath:
-    name: str
-    path: str
-    base_model_name: Optional[str] = None
-
-
 CompletionLikeRequest = Union[CompletionRequest, DetokenizeRequest,
                               EmbeddingCompletionRequest, ScoreRequest,
                               TokenizeCompletionRequest]
@@ -96,10 +71,8 @@ class OpenAIServing:
         self,
         engine_client: EngineClient,
         model_config: ModelConfig,
-        base_model_paths: List[BaseModelPath],
+        models: OpenAIServingModels,
         *,
-        lora_modules: Optional[List[LoRAModulePath]],
-        prompt_adapters: Optional[List[PromptAdapterPath]],
         request_logger: Optional[RequestLogger],
         return_tokens_as_token_ids: bool = False,
     ):
@@ -109,35 +82,7 @@ class OpenAIServing:
         self.model_config = model_config
         self.max_model_len = model_config.max_model_len
 
-        self.base_model_paths = base_model_paths
+        self.models = models
-
-        self.lora_id_counter = AtomicCounter(0)
-        self.lora_requests = []
-        if lora_modules is not None:
-            self.lora_requests = [
-                LoRARequest(lora_name=lora.name,
-                            lora_int_id=i,
-                            lora_path=lora.path,
-                            base_model_name=lora.base_model_name
-                            if lora.base_model_name
-                            and self._is_model_supported(lora.base_model_name)
-                            else self.base_model_paths[0].name)
-                for i, lora in enumerate(lora_modules, start=1)
-            ]
-
-        self.prompt_adapter_requests = []
-        if prompt_adapters is not None:
-            for i, prompt_adapter in enumerate(prompt_adapters, start=1):
-                with pathlib.Path(prompt_adapter.local_path,
-                                  "adapter_config.json").open() as f:
-                    adapter_config = json.load(f)
-                    num_virtual_tokens = adapter_config["num_virtual_tokens"]
-                self.prompt_adapter_requests.append(
-                    PromptAdapterRequest(
-                        prompt_adapter_name=prompt_adapter.name,
-                        prompt_adapter_id=i,
-                        prompt_adapter_local_path=prompt_adapter.local_path,
-                        prompt_adapter_num_virtual_tokens=num_virtual_tokens))
 
         self.request_logger = request_logger
         self.return_tokens_as_token_ids = return_tokens_as_token_ids
@@ -150,33 +95,6 @@ class OpenAIServing:
             self._tokenize_prompt_input_or_inputs,
             executor=self._tokenizer_executor)
 
-    async def show_available_models(self) -> ModelList:
-        """Show available models. Right now we only have one model."""
-        model_cards = [
-            ModelCard(id=base_model.name,
-                      max_model_len=self.max_model_len,
-                      root=base_model.model_path,
-                      permission=[ModelPermission()])
-            for base_model in self.base_model_paths
-        ]
-        lora_cards = [
-            ModelCard(id=lora.lora_name,
-                      root=lora.local_path,
-                      parent=lora.base_model_name if lora.base_model_name else
-                      self.base_model_paths[0].name,
-                      permission=[ModelPermission()])
-            for lora in self.lora_requests
-        ]
-        prompt_adapter_cards = [
-            ModelCard(id=prompt_adapter.prompt_adapter_name,
-                      root=self.base_model_paths[0].name,
-                      permission=[ModelPermission()])
-            for prompt_adapter in self.prompt_adapter_requests
-        ]
-        model_cards.extend(lora_cards)
-        model_cards.extend(prompt_adapter_cards)
-        return ModelList(data=model_cards)
-
     def create_error_response(
             self,
             message: str,
@@ -205,11 +123,13 @@ class OpenAIServing:
     ) -> Optional[ErrorResponse]:
         if self._is_model_supported(request.model):
             return None
-        if request.model in [lora.lora_name for lora in self.lora_requests]:
+        if request.model in [
+                lora.lora_name for lora in self.models.lora_requests
+        ]:
             return None
         if request.model in [
                 prompt_adapter.prompt_adapter_name
-                for prompt_adapter in self.prompt_adapter_requests
+                for prompt_adapter in self.models.prompt_adapter_requests
         ]:
             return None
         return self.create_error_response(
@@ -223,10 +143,10 @@ class OpenAIServing:
             None, PromptAdapterRequest]]:
         if self._is_model_supported(request.model):
             return None, None
-        for lora in self.lora_requests:
+        for lora in self.models.lora_requests:
             if request.model == lora.lora_name:
                 return lora, None
-        for prompt_adapter in self.prompt_adapter_requests:
+        for prompt_adapter in self.models.prompt_adapter_requests:
             if request.model == prompt_adapter.prompt_adapter_name:
                 return None, prompt_adapter
         # if _check_model has been called earlier, this will be unreachable
@@ -588,91 +508,5 @@ class OpenAIServing:
             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."
-
     def _is_model_supported(self, model_name):
-        return any(model.name == model_name for model in self.base_model_paths)
+        return self.models.is_base_model(model_name)
-
-    def _get_model_name(self, lora: Optional[LoRARequest]):
-        """
-        Returns the appropriate model name depending on the availability
-        and support of the LoRA or base model.
-        Parameters:
-        - lora: LoRARequest that contain a base_model_name.
-        Returns:
-        - str: The name of the base model or the first available model path.
-        """
-        if lora is not None:
-            return lora.lora_name
-        return self.base_model_paths[0].name

vllm/entrypoints/openai/serving_models.py

@@ -0,0 +1,210 @@
+import json
+import pathlib
+from dataclasses import dataclass
+from http import HTTPStatus
+from typing import List, Optional, Union
+
+from vllm.config import ModelConfig
+from vllm.entrypoints.openai.protocol import (ErrorResponse,
+                                              LoadLoraAdapterRequest,
+                                              ModelCard, ModelList,
+                                              ModelPermission,
+                                              UnloadLoraAdapterRequest)
+from vllm.lora.request import LoRARequest
+from vllm.prompt_adapter.request import PromptAdapterRequest
+from vllm.utils import AtomicCounter
+
+
+@dataclass
+class BaseModelPath:
+    name: str
+    model_path: str
+
+
+@dataclass
+class PromptAdapterPath:
+    name: str
+    local_path: str
+
+
+@dataclass
+class LoRAModulePath:
+    name: str
+    path: str
+    base_model_name: Optional[str] = None
+
+
+class OpenAIServingModels:
+    """Shared instance to hold data about the loaded base model(s) and adapters.
+
+    Handles the routes:
+    - /v1/models
+    - /v1/load_lora_adapter
+    - /v1/unload_lora_adapter
+    """
+
+    def __init__(
+        self,
+        model_config: ModelConfig,
+        base_model_paths: List[BaseModelPath],
+        *,
+        lora_modules: Optional[List[LoRAModulePath]] = None,
+        prompt_adapters: Optional[List[PromptAdapterPath]] = None,
+    ):
+        super().__init__()
+
+        self.base_model_paths = base_model_paths
+        self.max_model_len = model_config.max_model_len
+
+        self.lora_id_counter = AtomicCounter(0)
+        self.lora_requests = []
+        if lora_modules is not None:
+            self.lora_requests = [
+                LoRARequest(lora_name=lora.name,
+                            lora_int_id=i,
+                            lora_path=lora.path,
+                            base_model_name=lora.base_model_name
+                            if lora.base_model_name
+                            and self.is_base_model(lora.base_model_name) else
+                            self.base_model_paths[0].name)
+                for i, lora in enumerate(lora_modules, start=1)
+            ]
+
+        self.prompt_adapter_requests = []
+        if prompt_adapters is not None:
+            for i, prompt_adapter in enumerate(prompt_adapters, start=1):
+                with pathlib.Path(prompt_adapter.local_path,
+                                  "adapter_config.json").open() as f:
+                    adapter_config = json.load(f)
+                    num_virtual_tokens = adapter_config["num_virtual_tokens"]
+                self.prompt_adapter_requests.append(
+                    PromptAdapterRequest(
+                        prompt_adapter_name=prompt_adapter.name,
+                        prompt_adapter_id=i,
+                        prompt_adapter_local_path=prompt_adapter.local_path,
+                        prompt_adapter_num_virtual_tokens=num_virtual_tokens))
+
+    def is_base_model(self, model_name):
+        return any(model.name == model_name for model in self.base_model_paths)
+
+    def model_name(self, lora_request: Optional[LoRARequest] = None) -> str:
+        """Returns the appropriate model name depending on the availability
+        and support of the LoRA or base model.
+        Parameters:
+        - lora: LoRARequest that contain a base_model_name.
+        Returns:
+        - str: The name of the base model or the first available model path.
+        """
+        if lora_request is not None:
+            return lora_request.lora_name
+        return self.base_model_paths[0].name
+
+    async def show_available_models(self) -> ModelList:
+        """Show available models. This includes the base model and all 
+        adapters"""
+        model_cards = [
+            ModelCard(id=base_model.name,
+                      max_model_len=self.max_model_len,
+                      root=base_model.model_path,
+                      permission=[ModelPermission()])
+            for base_model in self.base_model_paths
+        ]
+        lora_cards = [
+            ModelCard(id=lora.lora_name,
+                      root=lora.local_path,
+                      parent=lora.base_model_name if lora.base_model_name else
+                      self.base_model_paths[0].name,
+                      permission=[ModelPermission()])
+            for lora in self.lora_requests
+        ]
+        prompt_adapter_cards = [
+            ModelCard(id=prompt_adapter.prompt_adapter_name,
+                      root=self.base_model_paths[0].name,
+                      permission=[ModelPermission()])
+            for prompt_adapter in self.prompt_adapter_requests
+        ]
+        model_cards.extend(lora_cards)
+        model_cards.extend(prompt_adapter_cards)
+        return ModelList(data=model_cards)
+
+    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."
+
+    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 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 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 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 create_error_response(
+                message=
+                f"The lora adapter '{request.lora_name}' cannot be found.",
+                err_type="InvalidUserInput",
+                status_code=HTTPStatus.BAD_REQUEST)
+
+        return None
+
+
+def create_error_response(
+        message: str,
+        err_type: str = "BadRequestError",
+        status_code: HTTPStatus = HTTPStatus.BAD_REQUEST) -> ErrorResponse:
+    return ErrorResponse(message=message,
+                         type=err_type,
+                         code=status_code.value)

vllm/entrypoints/openai/serving_pooling.py

@@ -15,7 +15,8 @@ from vllm.entrypoints.openai.protocol import (ErrorResponse,
                                               PoolingChatRequest,
                                               PoolingRequest, PoolingResponse,
                                               PoolingResponseData, UsageInfo)
-from vllm.entrypoints.openai.serving_engine import BaseModelPath, OpenAIServing
+from vllm.entrypoints.openai.serving_engine import OpenAIServing
+from vllm.entrypoints.openai.serving_models import OpenAIServingModels
 from vllm.logger import init_logger
 from vllm.outputs import PoolingOutput, PoolingRequestOutput
 from vllm.utils import merge_async_iterators
@@ -44,7 +45,7 @@ class OpenAIServingPooling(OpenAIServing):
         self,
         engine_client: EngineClient,
         model_config: ModelConfig,
-        base_model_paths: List[BaseModelPath],
+        models: OpenAIServingModels,
         *,
         request_logger: Optional[RequestLogger],
         chat_template: Optional[str],
@@ -52,9 +53,7 @@ class OpenAIServingPooling(OpenAIServing):
     ) -> None:
         super().__init__(engine_client=engine_client,
                          model_config=model_config,
-                         base_model_paths=base_model_paths,
+                         models=models,
-                         lora_modules=None,
-                         prompt_adapters=None,
                          request_logger=request_logger)
 
         self.chat_template = chat_template

vllm/entrypoints/openai/serving_score.py

@@ -10,7 +10,8 @@ from vllm.entrypoints.logger import RequestLogger
 from vllm.entrypoints.openai.protocol import (ErrorResponse, ScoreRequest,
                                               ScoreResponse, ScoreResponseData,
                                               UsageInfo)
-from vllm.entrypoints.openai.serving_engine import BaseModelPath, OpenAIServing
+from vllm.entrypoints.openai.serving_engine import OpenAIServing
+from vllm.entrypoints.openai.serving_models import OpenAIServingModels
 from vllm.inputs.data import TokensPrompt
 from vllm.logger import init_logger
 from vllm.outputs import PoolingRequestOutput, ScoringRequestOutput
@@ -50,15 +51,13 @@ class OpenAIServingScores(OpenAIServing):
         self,
         engine_client: EngineClient,
         model_config: ModelConfig,
-        base_model_paths: List[BaseModelPath],
+        models: OpenAIServingModels,
         *,
         request_logger: Optional[RequestLogger],
     ) -> None:
         super().__init__(engine_client=engine_client,
                          model_config=model_config,
-                         base_model_paths=base_model_paths,
+                         models=models,
-                         lora_modules=None,
-                         prompt_adapters=None,
                          request_logger=request_logger)
 
     async def create_score(

vllm/entrypoints/openai/serving_tokenization.py

@@ -15,9 +15,8 @@ from vllm.entrypoints.openai.protocol import (DetokenizeRequest,
                                               TokenizeRequest,
                                               TokenizeResponse)
 # yapf: enable
-from vllm.entrypoints.openai.serving_engine import (BaseModelPath,
+from vllm.entrypoints.openai.serving_engine import OpenAIServing
-                                                    LoRAModulePath,
+from vllm.entrypoints.openai.serving_models import OpenAIServingModels
-                                                    OpenAIServing)
 from vllm.logger import init_logger
 
 logger = init_logger(__name__)
@@ -29,18 +28,15 @@ class OpenAIServingTokenization(OpenAIServing):
         self,
         engine_client: EngineClient,
         model_config: ModelConfig,
-        base_model_paths: List[BaseModelPath],
+        models: OpenAIServingModels,
         *,
-        lora_modules: Optional[List[LoRAModulePath]],
         request_logger: Optional[RequestLogger],
         chat_template: Optional[str],
         chat_template_content_format: ChatTemplateContentFormatOption,
     ) -> None:
         super().__init__(engine_client=engine_client,
                          model_config=model_config,
-                         base_model_paths=base_model_paths,
+                         models=models,
-                         lora_modules=lora_modules,
-                         prompt_adapters=None,
                          request_logger=request_logger)
 
         self.chat_template = chat_template