针对场景4进行修改

2026-01-16 16:05:52 +08:00
parent fb473dcf1a
commit 5d1c02fb5b
602 changed files with 15130 additions and 3169 deletions
--- a/start_all_vllm.sh
+++ b/start_all_vllm.sh
@@ -0,0 +1,477 @@
+#!/bin/bash
+
+# ==============================================================================
+# 无人机自然语言控制项目 - vLLM 一键启动脚本（新增，不修改 start_all.sh）
+# ==============================================================================
+# 功能：启动所有必需的服务（vLLM推理模型、llama-server embedding模型、FastAPI后端）
+# 用法：./start_all_vllm.sh [选项]
+# ==============================================================================
+
+set -e  # 遇到错误立即退出
+
+# 颜色定义
+RED='\033[0;31m'
+GREEN='\033[0;32m'
+YELLOW='\033[1;33m'
+BLUE='\033[0;34m'
+NC='\033[0m' # No Color
+
+# 默认配置（可通过环境变量覆盖）
+PROJECT_ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+LLAMA_SERVER_DIR="${LLAMA_SERVER_DIR:-~/llama.cpp/build/bin}"
+INFERENCE_MODEL_DIR="${INFERENCE_MODEL_DIR:-/home/a/models/AWQ/Qwen3-4B-Instruct-2507}"
+VLLM_CONDA_ENV="${VLLM_CONDA_ENV:-VLLM}"
+VLLM_SPECULATIVE_CONFIG="${VLLM_SPECULATIVE_CONFIG:-}"
+VLLM_GPU_MEMORY_UTILIZATION="${VLLM_GPU_MEMORY_UTILIZATION:-0.6}"
+VLLM_SERVED_MODEL_NAME="${VLLM_SERVED_MODEL_NAME:-local-model}"
+EMBEDDING_MODEL="${EMBEDDING_MODEL:-~/models/gguf/Qwen3/Qwen3-Embedding-4B/Qwen3-Embedding-4B-Q5_K_M.gguf}"
+VENV_PATH="${VENV_PATH:-${PROJECT_ROOT}/backend_service/venv}"
+LOG_DIR="${LOG_DIR:-${PROJECT_ROOT}/logs}"
+PID_FILE="${LOG_DIR}/services_vllm.pid"
+
+# 端口配置
+INFERENCE_PORT="${INFERENCE_PORT:-8081}"
+EMBEDDING_PORT="${EMBEDDING_PORT:-8090}"
+API_PORT="${API_PORT:-8000}"
+
+# 创建日志目录
+mkdir -p "${LOG_DIR}"
+
+# ==============================================================================
+# 辅助函数
+# ==============================================================================
+
+print_info() {
+    echo -e "${BLUE}[INFO]${NC} $1"
+}
+
+print_success() {
+    echo -e "${GREEN}[SUCCESS]${NC} $1"
+}
+
+print_warning() {
+    echo -e "${YELLOW}[WARNING]${NC} $1"
+}
+
+print_error() {
+    echo -e "${RED}[ERROR]${NC} $1"
+}
+
+# 检查命令是否存在
+check_command() {
+    if ! command -v "$1" &> /dev/null; then
+        print_error "$1 命令未找到，请先安装"
+        return 1
+    fi
+    return 0
+}
+
+# 检查端口是否被占用
+check_port() {
+    local port=$1
+    if lsof -Pi :${port} -sTCP:LISTEN -t >/dev/null 2>&1 ; then
+        return 0  # 端口被占用
+    else
+        return 1  # 端口空闲
+    fi
+}
+
+# 等待服务就绪
+wait_for_service() {
+    local url=$1
+    local service_name=$2
+    local max_attempts=30
+    local attempt=0
+    
+    print_info "等待 ${service_name} 启动..."
+    while [ $attempt -lt $max_attempts ]; do
+        if curl -s "${url}" > /dev/null 2>&1; then
+            print_success "${service_name} 已就绪"
+            return 0
+        fi
+        attempt=$((attempt + 1))
+        sleep 1
+    done
+    
+    print_error "${service_name} 启动超时"
+    return 1
+}
+
+# 停止所有服务
+stop_services() {
+    print_info "正在停止所有服务..."
+    
+    if [ -f "${PID_FILE}" ]; then
+        while read -r pid; do
+            if ps -p "$pid" > /dev/null 2>&1; then
+                print_info "停止进程 PID: $pid"
+                kill "$pid" 2>/dev/null || true
+            fi
+        done < "${PID_FILE}"
+        rm -f "${PID_FILE}"
+    fi
+    
+    # 尝试通过端口停止服务
+    for port in ${INFERENCE_PORT} ${EMBEDDING_PORT} ${API_PORT}; do
+        if check_port ${port}; then
+            local pid
+            pid=$(lsof -ti:${port})
+            if [ -n "$pid" ]; then
+                print_info "停止占用端口 ${port} 的进程 (PID: $pid)"
+                kill $pid 2>/dev/null || true
+            fi
+        fi
+    done
+    
+    print_success "所有服务已停止"
+}
+
+# 清理函数（脚本退出时调用）
+cleanup() {
+    if [ "$?" -ne 0 ]; then
+        print_error "启动过程中发生错误，正在清理..."
+    fi
+    # 注意：这里不自动停止服务，让用户手动控制
+}
+
+trap cleanup EXIT
+
+# ==============================================================================
+# 主函数
+# ==============================================================================
+
+start_services() {
+    print_info "=========================================="
+    print_info "  无人机自然语言控制项目 - vLLM 服务启动"
+    print_info "=========================================="
+    echo ""
+    
+    # 检查必要的命令
+    print_info "检查必要的命令..."
+    check_command "python3" || exit 1
+    check_command "curl" || exit 1
+    check_command "lsof" || print_warning "lsof 未安装，将无法检查端口占用"
+    check_command "conda" || exit 1
+    echo ""
+    
+    # 检查端口占用
+    print_info "检查端口占用..."
+    if check_port ${INFERENCE_PORT}; then
+        print_warning "端口 ${INFERENCE_PORT} 已被占用，推理模型可能已在运行"
+    fi
+    if check_port ${EMBEDDING_PORT}; then
+        print_warning "端口 ${EMBEDDING_PORT} 已被占用，Embedding模型可能已在运行"
+    fi
+    if check_port ${API_PORT}; then
+        print_error "端口 ${API_PORT} 已被占用，请先停止占用该端口的服务"
+        exit 1
+    fi
+    echo ""
+    
+    # 检查llama-server（用于 embedding，展开路径中的 ~）
+    local llama_server_dir_expanded
+    llama_server_dir_expanded=$(eval echo "${LLAMA_SERVER_DIR}")
+    local llama_server="${llama_server_dir_expanded}/llama-server"
+    if [ ! -f "${llama_server}" ]; then
+        print_error "llama-server 未找到: ${llama_server}"
+        print_info "请设置 LLAMA_SERVER_DIR 环境变量指向正确的路径"
+        print_info "当前路径: ${LLAMA_SERVER_DIR}"
+        print_info "展开后路径: ${llama_server_dir_expanded}"
+        exit 1
+    fi
+    print_success "找到 llama-server: ${llama_server}"
+    echo ""
+    
+    # 检查模型路径
+    local inference_model_dir_expanded
+    inference_model_dir_expanded=$(eval echo "${INFERENCE_MODEL_DIR}")
+    local embedding_model_expanded
+    embedding_model_expanded=$(eval echo "${EMBEDDING_MODEL}")
+    
+    if [ ! -e "${inference_model_dir_expanded}" ]; then
+        print_error "推理模型目录未找到: ${inference_model_dir_expanded}"
+        print_info "请设置 INFERENCE_MODEL_DIR 环境变量指向正确的模型目录"
+        exit 1
+    fi
+    if [ ! -d "${inference_model_dir_expanded}" ]; then
+        print_warning "推理模型路径不是目录: ${inference_model_dir_expanded}"
+        print_warning "vLLM 通常需要传入模型目录（包含 config/tokenizer/权重等）"
+    fi
+    print_success "找到推理模型目录: ${inference_model_dir_expanded}"
+    
+    if [ ! -f "${embedding_model_expanded}" ]; then
+        print_error "Embedding模型文件未找到: ${embedding_model_expanded}"
+        print_info "请设置 EMBEDDING_MODEL 环境变量指向正确的模型路径"
+        exit 1
+    fi
+    print_success "找到Embedding模型: ${embedding_model_expanded}"
+    echo ""
+    
+    # 检查ROS2环境
+    local ros2_setup="${PROJECT_ROOT}/install/setup.bash"
+    if [ ! -f "${ros2_setup}" ]; then
+        print_warning "ROS2 setup文件未找到: ${ros2_setup}"
+        print_warning "如果项目已与ROS2解耦，可以忽略此警告"
+    else
+        print_success "找到ROS2 setup文件: ${ros2_setup}"
+    fi
+    echo ""
+    
+    # 检查venv虚拟环境
+    local venv_path_expanded
+    venv_path_expanded=$(eval echo "${VENV_PATH}")
+    print_info "检查venv虚拟环境: ${venv_path_expanded}"
+    if [ ! -d "${venv_path_expanded}" ]; then
+        print_error "venv虚拟环境目录不存在: ${venv_path_expanded}"
+        print_info "请先创建venv环境: python3 -m venv ${venv_path_expanded}"
+        print_info "然后安装依赖: ${venv_path_expanded}/bin/pip install -r backend_service/requirements.txt"
+        exit 1
+    fi
+    if [ ! -f "${venv_path_expanded}/bin/activate" ]; then
+        print_error "venv激活脚本不存在: ${venv_path_expanded}/bin/activate"
+        print_error "这看起来不是一个有效的venv环境"
+        exit 1
+    fi
+    print_success "venv虚拟环境存在: ${venv_path_expanded}"
+    echo ""
+    
+    # 初始化PID文件
+    > "${PID_FILE}"
+    
+    # ==========================================================================
+    # 启动推理模型服务（vLLM）
+    # ==========================================================================
+    print_info "启动推理模型服务(vLLM) (端口 ${INFERENCE_PORT})..."
+    bash -lc "
+        set -e
+        CONDA_BASE=\$(conda info --base)
+        if [ -z \"\${CONDA_BASE}\" ] || [ ! -f \"\${CONDA_BASE}/etc/profile.d/conda.sh\" ]; then
+            echo \"[ERROR] conda.sh 未找到：\${CONDA_BASE}/etc/profile.d/conda.sh\" 1>&2
+            exit 1
+        fi
+        source \"\${CONDA_BASE}/etc/profile.d/conda.sh\"
+        conda activate \"${VLLM_CONDA_ENV}\"
+        export VLLM_USE_V1=1
+        echo \"[DEBUG] conda_env=${VLLM_CONDA_ENV}\"
+        echo \"[DEBUG] CONDA_PREFIX=\${CONDA_PREFIX}\"
+        echo \"[DEBUG] which python: \$(which python)\"
+        echo \"[DEBUG] which vllm: \$(which vllm)\"
+        vllm --version || true
+        python -c \"import sys; print('[DEBUG] python:', sys.version)\" || true
+        python -c \"import torch; print('[DEBUG] torch:', torch.__version__, 'cuda:', torch.version.cuda, 'available:', torch.cuda.is_available())\" || true
+
+        SPEC_ARGS=()
+        if [ -n \"${VLLM_SPECULATIVE_CONFIG}\" ]; then
+            SPEC_ARGS+=(--speculative-config \"${VLLM_SPECULATIVE_CONFIG}\")
+        fi
+        exec vllm serve \"${inference_model_dir_expanded}\" \
+          --host=0.0.0.0 \
+          --port=${INFERENCE_PORT} \
+          --dtype=auto \
+          --max-num-seqs=1 \
+          --max-model-len=16384 \
+          \"\${SPEC_ARGS[@]}\" \
+          --served-model-name \"${VLLM_SERVED_MODEL_NAME}\" \
+          --trust-remote-code \
+          --gpu-memory-utilization=${VLLM_GPU_MEMORY_UTILIZATION} \
+          --uvicorn-log-level=debug \
+          --max-parallel-loading-workers 4
+    " > "${LOG_DIR}/vllm_inference_model.log" 2>&1 &
+    local inference_pid=$!
+    echo $inference_pid >> "${PID_FILE}"
+    print_success "推理模型服务(vLLM)已启动 (PID: $inference_pid)"
+    print_info "日志文件: ${LOG_DIR}/vllm_inference_model.log"
+    echo ""
+    
+    # ==========================================================================
+    # 启动Embedding模型服务（llama-server）
+    # ==========================================================================
+    print_info "启动Embedding模型服务 (端口 ${EMBEDDING_PORT})..."
+    cd "${llama_server_dir_expanded}"
+    nohup ./llama-server \
+        -m "${embedding_model_expanded}" \
+        --gpu-layers 36 \
+        --port ${EMBEDDING_PORT} \
+        --embeddings \
+        --pooling last \
+        --host 0.0.0.0 \
+        > "${LOG_DIR}/embedding_model.log" 2>&1 &
+    local embedding_pid=$!
+    echo $embedding_pid >> "${PID_FILE}"
+    print_success "Embedding模型服务已启动 (PID: $embedding_pid)"
+    print_info "日志文件: ${LOG_DIR}/embedding_model.log"
+    echo ""
+    
+    # ==========================================================================
+    # 等待模型服务就绪
+    # ==========================================================================
+    print_info "等待模型服务就绪..."
+    sleep 3  # 给服务一些启动时间
+    
+    # 等待推理模型服务（vLLM）
+    if ! wait_for_service "http://localhost:${INFERENCE_PORT}/health" "推理模型服务(vLLM)"; then
+        if ! wait_for_service "http://localhost:${INFERENCE_PORT}/v1/models" "推理模型服务(vLLM)"; then
+            print_warning "推理模型服务(vLLM)可能未完全就绪，但将继续启动"
+        fi
+    fi
+    
+    # 等待Embedding模型服务
+    if ! wait_for_service "http://localhost:${EMBEDDING_PORT}/health" "Embedding模型服务"; then
+        if ! wait_for_service "http://localhost:${EMBEDDING_PORT}/v1/models" "Embedding模型服务"; then
+            print_warning "Embedding模型服务可能未完全就绪，但将继续启动"
+        fi
+    fi
+    echo ""
+    
+    # ==========================================================================
+    # 启动FastAPI后端服务
+    # ==========================================================================
+    print_info "启动FastAPI后端服务 (端口 ${API_PORT})..."
+    cd "${PROJECT_ROOT}"
+    
+    # 尝试找到 dot 命令 (Graphviz) 路径，以便后端生成图片
+    DOT_PATH=$(which dot || true)
+    DOT_DIR=""
+    if [ -n "$DOT_PATH" ]; then
+        DOT_DIR=$(dirname "$DOT_PATH")
+        print_info "找到 Graphviz dot 命令: $DOT_PATH"
+    else
+        print_warning "未找到 dot 命令，可视化图片生成可能会失败"
+        # 尝试默认的 miniconda 路径作为备选
+        if [ -f "/home/a/miniconda3/bin/dot" ]; then
+             DOT_DIR="/home/a/miniconda3/bin"
+             print_info "使用备选路径: $DOT_DIR"
+        fi
+    fi
+
+    bash -c "
+        # 激活ROS2环境（如果存在）
+        if [ -f '${ros2_setup}' ]; then
+            source '${ros2_setup}'
+        fi
+        
+        # 确保 Graphviz 在 PATH 中
+        if [ -n '${DOT_DIR}' ]; then
+            export PATH=\"${DOT_DIR}:\$PATH\"
+        fi
+
+        # 让后端默认使用 vLLM 暴露的模型名，避免请求 local-model 导致 404
+        OPENAI_MODEL=\"\${OPENAI_MODEL:-${VLLM_SERVED_MODEL_NAME}}\" \
+        CLASSIFIER_MODEL=\"\${CLASSIFIER_MODEL:-${VLLM_SERVED_MODEL_NAME}}\" \
+        SIMPLE_MODEL=\"\${SIMPLE_MODEL:-${VLLM_SERVED_MODEL_NAME}}\" \
+        COMPLEX_MODEL=\"\${COMPLEX_MODEL:-${VLLM_SERVED_MODEL_NAME}}\" \
+        cd '${PROJECT_ROOT}/backend_service' && \
+        '${venv_path_expanded}/bin/python' -m uvicorn src.main:app --host 0.0.0.0 --port ${API_PORT}
+    " > "${LOG_DIR}/fastapi.log" 2>&1 &
+    local api_pid=$!
+    echo $api_pid >> "${PID_FILE}"
+    print_success "FastAPI服务已启动 (PID: $api_pid)"
+    print_info "日志文件: ${LOG_DIR}/fastapi.log"
+    echo ""
+    
+    # 等待FastAPI服务就绪
+    sleep 3
+    if wait_for_service "http://localhost:${API_PORT}/docs" "FastAPI服务"; then
+        print_success "所有服务已成功启动！"
+    else
+        print_warning "FastAPI服务可能未完全就绪，请检查日志: ${LOG_DIR}/fastapi.log"
+    fi
+    echo ""
+    
+    # 显示服务访问信息
+    print_info "=========================================="
+    print_info "  服务启动完成！"
+    print_info "=========================================="
+    print_info "推理模型API(vLLM): http://localhost:${INFERENCE_PORT}/v1"
+    print_info "Embedding模型API: http://localhost:${EMBEDDING_PORT}/v1"
+    print_info "FastAPI后端: http://localhost:${API_PORT}"
+    print_info "API文档: http://localhost:${API_PORT}/docs"
+    print_info ""
+    print_info "日志文件位置:"
+    print_info "  - 推理模型(vLLM): ${LOG_DIR}/vllm_inference_model.log"
+    print_info "  - Embedding模型: ${LOG_DIR}/embedding_model.log"
+    print_info "  - FastAPI服务: ${LOG_DIR}/fastapi.log"
+    print_info ""
+    print_info "按 Ctrl+C 停止所有服务"
+    print_info "=========================================="
+    echo ""
+    
+    # 设置信号处理，确保Ctrl+C时能清理
+    trap 'print_info "\n正在停止服务..."; stop_services; exit 0' INT TERM
+    
+    # 保持脚本运行
+    print_info "所有服务正在运行中，查看日志请使用:"
+    print_info "  tail -f ${LOG_DIR}/*.log"
+    echo ""
+    
+    wait
+}
+
+# ==============================================================================
+# 脚本入口
+# ==============================================================================
+
+case "${1:-start}" in
+    start)
+        start_services
+        ;;
+    stop)
+        stop_services
+        ;;
+    restart)
+        stop_services
+        sleep 2
+        start_services
+        ;;
+    status)
+        print_info "检查服务状态..."
+        if [ -f "${PID_FILE}" ]; then
+            print_info "已记录的服务进程:"
+            while read -r pid; do
+                if ps -p "$pid" > /dev/null 2>&1; then
+                    print_success "PID $pid: 运行中"
+                else
+                    print_warning "PID $pid: 已停止"
+                fi
+            done < "${PID_FILE}"
+        else
+            print_info "未找到PID文件，服务可能未启动"
+        fi
+        echo ""
+        print_info "端口占用情况:"
+        for port in ${INFERENCE_PORT} ${EMBEDDING_PORT} ${API_PORT}; do
+            if check_port ${port}; then
+                local pid
+                pid=$(lsof -ti:${port})
+                print_success "端口 ${port}: 被占用 (PID: $pid)"
+            else
+                print_warning "端口 ${port}: 空闲"
+            fi
+        done
+        ;;
+    *)
+        echo "用法: $0 {start|stop|restart|status}"
+        echo ""
+        echo "命令说明:"
+        echo "  start   - 启动所有服务（默认）"
+        echo "  stop    - 停止所有服务"
+        echo "  restart - 重启所有服务"
+        echo "  status  - 查看服务状态"
+        echo ""
+        echo "环境变量配置:"
+        echo "  LLAMA_SERVER_DIR     - llama-server所在目录 (默认: ~/llama.cpp/build/bin)"
+        echo "  INFERENCE_MODEL_DIR  - 推理模型目录(vLLM) (默认: /home/a/models/safetensors/Qwen3-4B)"
+        echo "  VLLM_CONDA_ENV       - conda环境名 (默认: vllm；如果你的是 VLLM，请设置为 VLLM)"
+        echo "  VLLM_SPECULATIVE_CONFIG - vLLM speculative JSON（可选；例如: {\"model\":\"/home/a/qwen3-4b-eagle3\",\"num_speculative_tokens\":5,\"method\":\"eagle3\"}）"
+        echo "  VLLM_GPU_MEMORY_UTILIZATION - vLLM 显存占用比例 (默认: 0.85)"
+        echo "  VLLM_SERVED_MODEL_NAME - vLLM 对外模型名 (默认: local-model；后端也会用它作为默认模型名)"
+        echo "  EMBEDDING_MODEL      - Embedding模型路径 (默认: ~/models/gguf/Qwen3/Qwen3-Embedding-4B/Qwen3-Embedding-4B-Q5_K_M.gguf)"
+        echo "  VENV_PATH            - venv虚拟环境路径 (默认: \${PROJECT_ROOT}/backend_service/venv)"
+        echo "  LOG_DIR              - 日志目录 (默认: \${PROJECT_ROOT}/logs)"
+        echo "  INFERENCE_PORT       - 推理端口 (默认: 8081)"
+        echo "  EMBEDDING_PORT       - embedding端口 (默认: 8090)"
+        echo "  API_PORT             - 后端端口 (默认: 8000)"
+        exit 1
+        ;;
+esac
+