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487
vllm/entrypoints/openai/serving_engine.py
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487
vllm/entrypoints/openai/serving_engine.py
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@@ -0,0 +1,487 @@
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import json
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import pathlib
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from dataclasses import dataclass
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from http import HTTPStatus
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from typing import Iterable, Iterator, List, Optional, Tuple, TypedDict, Union
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from pydantic import Field
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from typing_extensions import Annotated
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from vllm.config import ModelConfig
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from vllm.engine.protocol import EngineClient
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from vllm.entrypoints.logger import RequestLogger
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# yapf conflicts with isort for this block
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# yapf: disable
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from vllm.entrypoints.openai.protocol import (ChatCompletionRequest,
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CompletionRequest,
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DetokenizeRequest,
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EmbeddingRequest, ErrorResponse,
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LoadLoraAdapterRequest,
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ModelCard, ModelList,
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ModelPermission,
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TokenizeChatRequest,
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TokenizeCompletionRequest,
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TokenizeRequest,
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UnloadLoraAdapterRequest)
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# yapf: enable
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from vllm.inputs.parse import parse_and_batch_prompt
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from vllm.logger import init_logger
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from vllm.lora.request import LoRARequest
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from vllm.pooling_params import PoolingParams
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from vllm.prompt_adapter.request import PromptAdapterRequest
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from vllm.sampling_params import BeamSearchParams, SamplingParams
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from vllm.sequence import Logprob
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from vllm.transformers_utils.tokenizer import AnyTokenizer
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from vllm.utils import AtomicCounter
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logger = init_logger(__name__)
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@dataclass
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class BaseModelPath:
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name: str
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model_path: str
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@dataclass
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class PromptAdapterPath:
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name: str
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local_path: str
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@dataclass
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class LoRAModulePath:
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name: str
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path: str
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base_model_name: Optional[str] = None
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AnyRequest = Union[ChatCompletionRequest, CompletionRequest, DetokenizeRequest,
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EmbeddingRequest, TokenizeRequest]
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class TextTokensPrompt(TypedDict):
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prompt: str
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prompt_token_ids: List[int]
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class OpenAIServing:
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def __init__(
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self,
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engine_client: EngineClient,
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model_config: ModelConfig,
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base_model_paths: List[BaseModelPath],
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*,
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lora_modules: Optional[List[LoRAModulePath]],
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prompt_adapters: Optional[List[PromptAdapterPath]],
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request_logger: Optional[RequestLogger],
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return_tokens_as_token_ids: bool = False,
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):
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super().__init__()
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self.engine_client = engine_client
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self.model_config = model_config
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self.max_model_len = model_config.max_model_len
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self.base_model_paths = base_model_paths
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self.lora_id_counter = AtomicCounter(0)
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self.lora_requests = []
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if lora_modules is not None:
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self.lora_requests = [
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LoRARequest(lora_name=lora.name,
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lora_int_id=i,
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lora_path=lora.path,
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base_model_name=lora.base_model_name
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if lora.base_model_name
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and self._is_model_supported(lora.base_model_name)
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else self.base_model_paths[0].name)
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for i, lora in enumerate(lora_modules, start=1)
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]
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self.prompt_adapter_requests = []
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if prompt_adapters is not None:
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for i, prompt_adapter in enumerate(prompt_adapters, start=1):
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with pathlib.Path(prompt_adapter.local_path,
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"adapter_config.json").open() as f:
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adapter_config = json.load(f)
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num_virtual_tokens = adapter_config["num_virtual_tokens"]
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self.prompt_adapter_requests.append(
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PromptAdapterRequest(
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prompt_adapter_name=prompt_adapter.name,
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prompt_adapter_id=i,
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prompt_adapter_local_path=prompt_adapter.local_path,
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prompt_adapter_num_virtual_tokens=num_virtual_tokens))
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self.request_logger = request_logger
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self.return_tokens_as_token_ids = return_tokens_as_token_ids
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async def show_available_models(self) -> ModelList:
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"""Show available models. Right now we only have one model."""
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model_cards = [
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ModelCard(id=base_model.name,
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max_model_len=self.max_model_len,
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root=base_model.model_path,
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permission=[ModelPermission()])
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for base_model in self.base_model_paths
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]
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lora_cards = [
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ModelCard(id=lora.lora_name,
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root=lora.local_path,
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parent=lora.base_model_name if lora.base_model_name else
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self.base_model_paths[0].name,
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permission=[ModelPermission()])
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for lora in self.lora_requests
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]
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prompt_adapter_cards = [
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ModelCard(id=prompt_adapter.prompt_adapter_name,
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root=self.base_model_paths[0].name,
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permission=[ModelPermission()])
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for prompt_adapter in self.prompt_adapter_requests
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]
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model_cards.extend(lora_cards)
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model_cards.extend(prompt_adapter_cards)
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return ModelList(data=model_cards)
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def create_error_response(
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self,
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message: str,
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err_type: str = "BadRequestError",
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status_code: HTTPStatus = HTTPStatus.BAD_REQUEST) -> ErrorResponse:
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return ErrorResponse(message=message,
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type=err_type,
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code=status_code.value)
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def create_streaming_error_response(
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self,
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message: str,
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err_type: str = "BadRequestError",
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status_code: HTTPStatus = HTTPStatus.BAD_REQUEST) -> str:
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json_str = json.dumps({
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"error":
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self.create_error_response(message=message,
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err_type=err_type,
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status_code=status_code).model_dump()
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})
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return json_str
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async def _check_model(
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self,
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request: AnyRequest,
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) -> Optional[ErrorResponse]:
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if self._is_model_supported(request.model):
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return None
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if request.model in [lora.lora_name for lora in self.lora_requests]:
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return None
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if request.model in [
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prompt_adapter.prompt_adapter_name
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for prompt_adapter in self.prompt_adapter_requests
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]:
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return None
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return self.create_error_response(
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message=f"The model `{request.model}` does not exist.",
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err_type="NotFoundError",
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status_code=HTTPStatus.NOT_FOUND)
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def _maybe_get_adapters(
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self, request: AnyRequest
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) -> Union[Tuple[None, None], Tuple[LoRARequest, None], Tuple[
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None, PromptAdapterRequest]]:
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if self._is_model_supported(request.model):
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return None, None
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for lora in self.lora_requests:
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if request.model == lora.lora_name:
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return lora, None
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for prompt_adapter in self.prompt_adapter_requests:
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if request.model == prompt_adapter.prompt_adapter_name:
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return None, prompt_adapter
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# if _check_model has been called earlier, this will be unreachable
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raise ValueError(f"The model `{request.model}` does not exist.")
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def _normalize_prompt_text_to_input(
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self,
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request: AnyRequest,
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tokenizer: AnyTokenizer,
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prompt: str,
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truncate_prompt_tokens: Optional[Annotated[int, Field(ge=1)]],
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add_special_tokens: bool,
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) -> TextTokensPrompt:
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if truncate_prompt_tokens is None:
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encoded = tokenizer(prompt, add_special_tokens=add_special_tokens)
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else:
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encoded = tokenizer(prompt,
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add_special_tokens=add_special_tokens,
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truncation=True,
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max_length=truncate_prompt_tokens)
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input_ids = encoded.input_ids
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input_text = prompt
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return self._validate_input(request, input_ids, input_text)
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def _normalize_prompt_tokens_to_input(
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self,
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request: AnyRequest,
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tokenizer: AnyTokenizer,
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prompt_ids: List[int],
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truncate_prompt_tokens: Optional[Annotated[int, Field(ge=1)]],
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) -> TextTokensPrompt:
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if truncate_prompt_tokens is None:
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input_ids = prompt_ids
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else:
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input_ids = prompt_ids[-truncate_prompt_tokens:]
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input_text = tokenizer.decode(input_ids)
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return self._validate_input(request, input_ids, input_text)
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def _validate_input(
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self,
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request: AnyRequest,
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input_ids: List[int],
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input_text: str,
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) -> TextTokensPrompt:
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token_num = len(input_ids)
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# Note: EmbeddingRequest doesn't have max_tokens
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if isinstance(request, EmbeddingRequest):
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if token_num > self.max_model_len:
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raise ValueError(
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f"This model's maximum context length is "
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f"{self.max_model_len} tokens. However, you requested "
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f"{token_num} tokens in the input for embedding "
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f"generation. Please reduce the length of the input.")
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return TextTokensPrompt(prompt=input_text,
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prompt_token_ids=input_ids)
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# Note: TokenizeRequest and DetokenizeRequest doesn't have max_tokens
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# and does not require model context length validation
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if isinstance(request, (TokenizeCompletionRequest, TokenizeChatRequest,
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DetokenizeRequest)):
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return TextTokensPrompt(prompt=input_text,
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prompt_token_ids=input_ids)
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if request.max_tokens is None:
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if token_num >= self.max_model_len:
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raise ValueError(
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f"This model's maximum context length is "
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f"{self.max_model_len} tokens. However, you requested "
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f"{token_num} tokens in the messages, "
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f"Please reduce the length of the messages.")
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elif token_num + request.max_tokens > self.max_model_len:
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raise ValueError(
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f"This model's maximum context length is "
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f"{self.max_model_len} tokens. However, you requested "
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f"{request.max_tokens + token_num} tokens "
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f"({token_num} in the messages, "
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f"{request.max_tokens} in the completion). "
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f"Please reduce the length of the messages or completion.")
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return TextTokensPrompt(prompt=input_text, prompt_token_ids=input_ids)
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def _tokenize_prompt_input(
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self,
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request: AnyRequest,
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tokenizer: AnyTokenizer,
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prompt_input: Union[str, List[int]],
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truncate_prompt_tokens: Optional[Annotated[int, Field(ge=1)]] = None,
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add_special_tokens: bool = True,
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) -> TextTokensPrompt:
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"""
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A simpler implementation of :meth:`_tokenize_prompt_input_or_inputs`
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that assumes single input.
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"""
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return next(
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self._tokenize_prompt_inputs(
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request,
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tokenizer,
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[prompt_input],
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truncate_prompt_tokens=truncate_prompt_tokens,
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add_special_tokens=add_special_tokens,
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))
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def _tokenize_prompt_inputs(
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self,
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request: AnyRequest,
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tokenizer: AnyTokenizer,
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prompt_inputs: Iterable[Union[str, List[int]]],
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truncate_prompt_tokens: Optional[Annotated[int, Field(ge=1)]] = None,
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add_special_tokens: bool = True,
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) -> Iterator[TextTokensPrompt]:
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"""
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A simpler implementation of :meth:`_tokenize_prompt_input_or_inputs`
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that assumes multiple inputs.
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"""
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for text in prompt_inputs:
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if isinstance(text, str):
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yield self._normalize_prompt_text_to_input(
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request,
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tokenizer,
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prompt=text,
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truncate_prompt_tokens=truncate_prompt_tokens,
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add_special_tokens=add_special_tokens,
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)
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else:
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yield self._normalize_prompt_tokens_to_input(
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request,
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tokenizer,
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prompt_ids=text,
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truncate_prompt_tokens=truncate_prompt_tokens,
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)
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def _tokenize_prompt_input_or_inputs(
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self,
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request: AnyRequest,
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tokenizer: AnyTokenizer,
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input_or_inputs: Union[str, List[str], List[int], List[List[int]]],
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truncate_prompt_tokens: Optional[Annotated[int, Field(ge=1)]] = None,
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add_special_tokens: bool = True,
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) -> Iterator[TextTokensPrompt]:
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"""
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Tokenize/detokenize depending on the input format.
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According to `OpenAI API <https://platform.openai.com/docs/api-reference/embeddings/create>`_
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, each input can be a string or array of tokens. Note that each request
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can pass one or more inputs.
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"""
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for prompt_input in parse_and_batch_prompt(input_or_inputs):
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# Although our type checking is based on mypy,
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# VSCode Pyright extension should still work properly
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# "is True" is required for Pyright to perform type narrowing
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# See: https://github.com/microsoft/pyright/issues/7672
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if prompt_input["is_tokens"] is False:
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yield self._normalize_prompt_text_to_input(
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request,
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tokenizer,
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prompt=prompt_input["content"],
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truncate_prompt_tokens=truncate_prompt_tokens,
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add_special_tokens=add_special_tokens,
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)
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else:
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yield self._normalize_prompt_tokens_to_input(
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request,
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tokenizer,
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prompt_ids=prompt_input["content"],
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truncate_prompt_tokens=truncate_prompt_tokens,
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)
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def _log_inputs(
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self,
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request_id: str,
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inputs: Union[str, List[int], TextTokensPrompt],
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params: Optional[Union[SamplingParams, PoolingParams,
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BeamSearchParams]],
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lora_request: Optional[LoRARequest],
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prompt_adapter_request: Optional[PromptAdapterRequest],
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) -> None:
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if self.request_logger is None:
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return
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if isinstance(inputs, str):
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prompt = inputs
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prompt_token_ids = None
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elif isinstance(inputs, list):
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prompt = None
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prompt_token_ids = inputs
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else:
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prompt = inputs["prompt"]
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prompt_token_ids = inputs["prompt_token_ids"]
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self.request_logger.log_inputs(
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request_id,
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prompt,
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prompt_token_ids,
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params=params,
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lora_request=lora_request,
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prompt_adapter_request=prompt_adapter_request,
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)
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@staticmethod
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def _get_decoded_token(logprob: Logprob,
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token_id: int,
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tokenizer: AnyTokenizer,
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return_as_token_id: bool = False) -> str:
|
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if return_as_token_id:
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return f"token_id:{token_id}"
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if logprob.decoded_token is not None:
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return logprob.decoded_token
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return tokenizer.decode(token_id)
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async def _check_load_lora_adapter_request(
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self, request: LoadLoraAdapterRequest) -> Optional[ErrorResponse]:
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# Check if both 'lora_name' and 'lora_path' are provided
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if not request.lora_name or not request.lora_path:
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return self.create_error_response(
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message="Both 'lora_name' and 'lora_path' must be provided.",
|
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err_type="InvalidUserInput",
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status_code=HTTPStatus.BAD_REQUEST)
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# Check if the lora adapter with the given name already exists
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if any(lora_request.lora_name == request.lora_name
|
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for lora_request in self.lora_requests):
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return self.create_error_response(
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message=
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f"The lora adapter '{request.lora_name}' has already been"
|
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"loaded.",
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err_type="InvalidUserInput",
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status_code=HTTPStatus.BAD_REQUEST)
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return None
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|
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async def _check_unload_lora_adapter_request(
|
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self,
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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:
|
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return self.create_error_response(
|
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message=
|
||||
"either 'lora_name' and 'lora_int_id' needs to be provided.",
|
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err_type="InvalidUserInput",
|
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status_code=HTTPStatus.BAD_REQUEST)
|
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|
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# 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):
|
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return self.create_error_response(
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message=
|
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f"The lora adapter '{request.lora_name}' cannot be found.",
|
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err_type="InvalidUserInput",
|
||||
status_code=HTTPStatus.BAD_REQUEST)
|
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|
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return None
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|
||||
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
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||||
unique_id = self.lora_id_counter.inc(1)
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||||
self.lora_requests.append(
|
||||
LoRARequest(lora_name=lora_name,
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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)
|
||||
Reference in New Issue
Block a user