46. vLLM API Server 架构：高性能推理服务的设计与实现

class APIServer:
    def __init__(self, engine_args, server_args):
        # 初始化执行引擎
        self.engine = LLMEngine.from_engine_args(engine_args)
        
        # 初始化服务抽象层
        self.model_executor = ModelExecutor(self.engine)
        
        # 初始化认证授权层
        self.auth_manager = AuthManager(server_args.auth_config)
        
        # 初始化API层
        self.rest_api = FastAPI()
        self.grpc_server = grpc.server(futures.ThreadPoolExecutor())
        
        # 初始化监控日志层
        self.monitor = Monitor(server_args.monitor_config)
    
    def start(self):
        # 启动各个组件
        self._setup_routes()
        self._setup_grpc_services()
        self._start_servers()
    
    def _setup_routes(self):
        # 设置REST API路由
        pass
    
    def _setup_grpc_services(self):
        # 设置gRPC服务
        pass
    
    def _start_servers(self):
        # 启动REST和gRPC服务器
        pass

@app.post("/v1/chat/completions")
async def create_chat_completion(
    request: ChatCompletionRequest,
    auth: Annotated[AuthInfo, Depends(auth_manager.verify)],
):
    # 验证请求
    await validate_chat_completion_request(request)
    
    # 转换为vLLM内部请求格式
    vllm_request = convert_to_vllm_request(request)
    
    # 提交请求到执行引擎
    result = await model_executor.execute(vllm_request)
    
    # 转换为OpenAI API响应格式
    openai_response = convert_to_openai_response(result)
    
    # 记录监控数据
    monitor.record_request(request, result)
    
    return openai_response

syntax = "proto3";

package vllm.api.v1;

service LLMService {
  rpc ChatCompletion(ChatCompletionRequest) returns (stream ChatCompletionResponse);
  rpc Completion(CompletionRequest) returns (stream CompletionResponse);
  rpc Embedding(EmbeddingRequest) returns (EmbeddingResponse);
}

message ChatCompletionRequest {
  string model = 1;
  repeated ChatMessage messages = 2;
  float temperature = 3;
  int32 top_k = 4;
  float top_p = 5;
  int32 max_tokens = 6;
  bool stream = 7;
}

message ChatCompletionResponse {
  string id = 1;
  string object = 2;
  int64 created = 3;
  string model = 4;
  ChatCompletionChoice choice = 5;
  Usage usage = 6;
}

class AuthManager:
    def __init__(self, auth_config):
        self.auth_providers = {
            "api_key": APIKeyAuthProvider(auth_config.api_key_config),
            "oauth2": OAuth2AuthProvider(auth_config.oauth2_config),
            "jwt": JWT AuthProvider(auth_config.jwt_config),
        }
    
    def verify(self, request: Request):
        # 从请求中获取认证信息
        auth_header = request.headers.get("Authorization")
        
        # 确定认证方式
        auth_type = self._get_auth_type(auth_header)
        
        # 调用相应的认证提供者
        if auth_type not in self.auth_providers:
            raise HTTPException(status_code=401, detail="Invalid authentication scheme")
        
        return self.auth_providers[auth_type].verify(request)
    
    def _get_auth_type(self, auth_header):
        # 解析认证头部，确定认证方式
        pass

class LoadBalancer:
    def __init__(self, nodes, strategy="round_robin"):
        self.nodes = nodes
        self.strategy = strategy
        self.current_index = 0
        self.connections = {node: 0 for node in nodes}
    
    def get_next_node(self):
        if self.strategy == "round_robin":
            return self._round_robin()
        elif self.strategy == "least_connections":
            return self._least_connections()
        elif self.strategy == "weighted_round_robin":
            return self._weighted_round_robin()
        elif self.strategy == "performance_based":
            return self._performance_based()
        else:
            raise ValueError(f"Invalid load balancing strategy: {self.strategy}")
    
    def _round_robin(self):
        node = self.nodes[self.current_index]
        self.current_index = (self.current_index + 1) % len(self.nodes)
        return node
    
    def _least_connections(self):
        return min(self.connections, key=self.connections.get)
    
    def _weighted_round_robin(self):
        # 实现加权轮询策略
        pass
    
    def _performance_based(self):
        # 基于节点性能的负载均衡
        pass

@app.post("/v1/chat/completions")
async def create_chat_completion(
    request: ChatCompletionRequest,
    auth: Annotated[AuthInfo, Depends(auth_manager.verify)],
):
    # 验证请求
    await validate_chat_completion_request(request)
    
    # 转换为vLLM内部请求格式
    vllm_request = convert_to_vllm_request(request)
    
    if request.stream:
        # 流式响应处理
        async def stream_generator():
            async for result in model_executor.execute_stream(vllm_request):
                openai_response = convert_to_openai_response(result)
                yield f"data: {json.dumps(openai_response)}\n\n"
        
        # 记录监控数据
        monitor.record_request(request, None, is_stream=True)
        
        return StreamingResponse(stream_generator(), media_type="text/event-stream")
    else:
        # 非流式响应处理
        result = await model_executor.execute(vllm_request)
        openai_response = convert_to_openai_response(result)
        monitor.record_request(request, result)
        return openai_response

class LLMServiceServicer(vllm_api_pb2_grpc.LLMServiceServicer):
    def __init__(self, model_executor, monitor):
        self.model_executor = model_executor
        self.monitor = monitor
    
    async def ChatCompletion(self, request, context):
        # 转换为vLLM内部请求格式
        vllm_request = convert_to_vllm_request(request)
        
        # 执行推理并流式返回结果
        async for result in self.model_executor.execute_stream(vllm_request):
            # 转换为gRPC响应格式
            grpc_response = convert_to_grpc_response(result)
            
            # 发送响应
            yield grpc_response
        
        # 记录监控数据
        self.monitor.record_request(request, result)

FROM nvidia/cuda:12.1.1-cudnn8-devel-ubuntu22.04

# 安装依赖
RUN apt-get update && apt-get install -y \n    python3-pip \n    python3-dev \n    git \n    && rm -rf /var/lib/apt/lists/*

# 安装vLLM
RUN pip3 install vllm

# 暴露端口
EXPOSE 8000 8080

# 启动命令
CMD ["python3", "-m", "vllm.entrypoints.api_server", "--model", "meta-llama/Llama-2-7b-chat-hf"]

version: '3.8'

services:
  vllm-api-server:
    build: .
    image: vllm-api-server:latest
    ports:
      - "8000:8000"  # REST API端口
      - "8080:8080"  # gRPC API端口
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 1
              capabilities: [gpu]
    environment:
      - VLLM_MODEL=meta-llama/Llama-2-7b-chat-hf
      - VLLM_PORT=8000
      - VLLM_GRPC_PORT=8080
      - VLLM_NUM_GPUS=1
    volumes:
      - ./models:/app/models
  
  prometheus:
    image: prom/prometheus:latest
    ports:
      - "9090:9090"
    volumes:
      - ./prometheus.yml:/etc/prometheus/prometheus.yml
  
  grafana:
    image: grafana/grafana:latest
    ports:
      - "3000:3000"
    volumes:
      - ./grafana:/var/lib/grafana

# 安装vLLM
pip install vllm

# 安装其他依赖
pip install fastapi uvicorn grpcio protobuf pydantic

# 启动vLLM API Server（REST + gRPC）
python -m vllm.entrypoints.api_server \
    --model meta-llama/Llama-2-7b-chat-hf \
    --port 8000 \
    --grpc-port 8080 \
    --num-gpus 1

# 使用curl测试Chat Completion API
curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model": "meta-llama/Llama-2-7b-chat-hf", "messages": [{"role": "user", "content": "Hello, how are you?"}], "temperature": 0.7, "max_tokens": 100}'

# 使用Python测试gRPC API
import grpc
import vllm.api.v1.llm_service_pb2 as llm_service_pb2
import vllm.api.v1.llm_service_pb2_grpc as llm_service_pb2_grpc

# 建立gRPC连接
channel = grpc.insecure_channel("localhost:8080")
stub = llm_service_pb2_grpc.LLMServiceStub(channel)

# 构造请求
request = llm_service_pb2.ChatCompletionRequest(
    model="meta-llama/Llama-2-7b-chat-hf",
    messages=[
        llm_service_pb2.ChatMessage(
            role="user",
            content="Hello, how are you?"
        )
    ],
    temperature=0.7,
    max_tokens=100
)

# 发送请求并接收响应
response = stub.ChatCompletion(request)

# 处理响应
for resp in response:
    print(resp.choice.message.content)

global:
  scrape_interval: 15s
  
scrape_configs:
  - job_name: 'vllm-api-server'
    static_configs:
      - targets: ['vllm-api-server:8000']
    metrics_path: '/metrics'
  
  - job_name: 'prometheus'
    static_configs:
      - targets: ['localhost:9090']