359 lines
14 KiB
Bash
359 lines
14 KiB
Bash
#!/bin/bash
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# ============================================================================
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# MiniMind 实验脚本模版 - Experiment [VERSION]
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# ============================================================================
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#
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# 🎯 使用说明:
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# - 🧑🔬 [人类填写] - 实验开始前由人类研究者配置
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# - 🤖 [AI构建] - 实验构建过程中由AI自动替换占位符
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#
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# 使用方法:
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# 1. 复制此模版为 experiment_X.X.X.sh
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# 2. 替换所有 [PLACEHOLDER] 占位符
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# 3. 执行: bash run_file/experiment_X.X.X.sh
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# ============================================================================
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# ----------------------------------------------------------------------------
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# 🧑🔬 [人类填写] 实验基本信息
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# ----------------------------------------------------------------------------
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EXPERIMENT_VERSION="[VERSION]" # 实验版本号，如: 1.4.1
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EXPERIMENT_DESCRIPTION="[DESCRIPTION]" # 实验简短描述
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RESEARCHER_NAME="[RESEARCHER]" # 研究者姓名
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EXPERIMENT_DATE="$(date '+%Y-%m-%d %H:%M:%S')" # 自动记录实验开始时间
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# ----------------------------------------------------------------------------
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# 🤖 [AI构建] 环境配置
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# ----------------------------------------------------------------------------
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# Python环境设置
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# 注意: 根据实际环境选择激活方式
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# Option 1: Conda环境 (如果使用conda)
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# source $(conda info --base)/etc/profile.d/conda.sh
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# conda activate [CONDA_ENV]
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# Option 2: UV虚拟环境 (推荐)
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# export VIRTUAL_ENV="[VENV_PATH]"
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# source "$VIRTUAL_ENV/bin/activate"
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# 调试和监控环境变量
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export NCCL_DEBUG=INFO # NCCL 调试信息
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export PYTHONFAULTHANDLER=1 # Python 故障处理
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export CUDA_LAUNCH_BLOCKING=1 # CUDA 同步执行（调试用）
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# SwanLab 配置
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export SWANLAB_API_KEY="[SWANLAB_API_KEY]" # 🤖 [AI构建] SwanLab API密钥
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export SWANLAB_PROJECT="[SWANLAB_PROJECT]" # 🤖 [AI构建] SwanLab项目名
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# 日志配置
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LOG_DIR="out/experiment_${EXPERIMENT_VERSION}"
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mkdir -p "$LOG_DIR"
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LOG_FILE="$LOG_DIR/experiment.log"
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# ----------------------------------------------------------------------------
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# 🤖 [AI构建] 硬件配置
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# ----------------------------------------------------------------------------
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CUDA_VISIBLE_DEVICES="[CUDA_DEVICES]" # GPU设备，如: 0 或 0,1,2,3
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NUM_PROCESSES="[NUM_PROCESSES]" # 进程数，通常等于GPU数量
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MIXED_PRECISION="[MIXED_PRECISION]" # 混合精度: bf16, fp16, no
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MAIN_PROCESS_PORT="[MAIN_PROCESS_PORT]" # 主进程端口，默认: 29500
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# ----------------------------------------------------------------------------
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# 🤖 [AI构建] 模型架构参数
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# ----------------------------------------------------------------------------
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MODEL_TYPE="[MODEL_TYPE]" # 模型类型: model, model_original, model_no_feed
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MODEL_SIZE="[MODEL_SIZE]" # 模型大小 (MB)
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DIM="[DIM]" # 模型维度
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N_LAYERS="[N_LAYERS]" # Transformer层数
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N_HEADS="[N_HEADS]" # 注意力头数
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MAX_SEQ_LEN="[MAX_SEQ_LEN]" # 最大序列长度
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USE_MOE="[USE_MOE]" # 是否使用MOE: true/false
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# 知识库配置
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KNOWLEDGE_NUM="[KNOWLEDGE_NUM]" # 知识条目数量
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KNOWLEDGE_LENGTH="[KNOWLEDGE_LENGTH]" # 单条知识长度
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DISABLE_DB="[DISABLE_DB]" # 是否禁用数据库: true/false
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# ----------------------------------------------------------------------------
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# 🤖 [AI构建] 训练超参数
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# ----------------------------------------------------------------------------
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EPOCHS="[EPOCHS]" # 训练轮次
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EMBEDDING_EPOCH="[EMBEDDING_EPOCH]" # 嵌入层训练轮次
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BATCH_SIZE="[BATCH_SIZE]" # 批次大小
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ACCUMULATION_STEPS="[ACCUMULATION_STEPS]" # 梯度累积步数
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LEARNING_RATE="[LEARNING_RATE]" # 学习率
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DTYPE="[DTYPE]" # 数据类型: bfloat16, float16, float32
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GRAD_CLIP="[GRAD_CLIP]" # 梯度裁剪阈值
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WARMUP_ITERS="[WARMUP_ITERS]" # 预热迭代数
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# 数据和缓存路径
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DATA_PATH="[DATA_PATH]" # 训练数据路径
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DATABASE_INIT_PATH="[DATABASE_INIT_PATH]" # 数据库初始化路径
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CLUSTER_CACHE_PATH="[CLUSTER_CACHE_PATH]" # 聚类缓存路径
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# 训练配置
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NUM_WORKERS="[NUM_WORKERS]" # 数据加载工作进程数
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LOG_INTERVAL="[LOG_INTERVAL]" # 日志记录间隔
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SAVE_INTERVAL="[SAVE_INTERVAL]" # 模型保存间隔
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# 性能分析配置
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USE_PROFILE="[USE_PROFILE]" # 是否启用性能分析: true/false
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PROFILE_INTERVAL="[PROFILE_INTERVAL]" # 性能分析间隔
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MEMORY_MONITOR_INTERVAL="[MEMORY_MONITOR_INTERVAL]" # 内存监控间隔
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# 高级功能
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USE_FLASH_ATTN="[USE_FLASH_ATTN]" # 是否使用Flash Attention: true/false
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FAST_CLUSTERING="[FAST_CLUSTERING]" # 是否使用快速聚类: true/false
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# ----------------------------------------------------------------------------
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# 🤖 [AI构建] 预检查函数
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# ----------------------------------------------------------------------------
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check_environment() {
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echo "🔍 环境检查中..."
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# 检查GPU可用性
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if ! nvidia-smi &> /dev/null; then
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echo "❌ 错误: 未检测到GPU或nvidia-smi不可用"
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exit 1
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fi
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# 检查CUDA设备
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IFS=',' read -ra DEVICES <<< "$CUDA_VISIBLE_DEVICES"
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for device in "${DEVICES[@]}"; do
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if ! nvidia-smi -i "$device" &> /dev/null; then
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echo "❌ 错误: GPU $device 不可用"
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exit 1
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fi
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done
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# 检查Python环境
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if ! python -c "import torch; print(f'PyTorch: {torch.__version__}')" 2>/dev/null; then
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echo "❌ 错误: PyTorch未正确安装"
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exit 1
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fi
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# 检查数据文件
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if [[ ! -f "$DATA_PATH" ]]; then
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echo "❌ 错误: 训练数据文件不存在: $DATA_PATH"
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exit 1
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fi
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if [[ "$DATABASE_INIT_PATH" != "None" && ! -f "$DATABASE_INIT_PATH" ]]; then
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echo "❌ 错误: 数据库初始化文件不存在: $DATABASE_INIT_PATH"
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exit 1
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fi
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echo "✅ 环境检查通过"
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}
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# ----------------------------------------------------------------------------
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# 🤖 [AI构建] 实验信息记录
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# ----------------------------------------------------------------------------
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log_experiment_info() {
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echo "📝 记录实验信息..."
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cat > "$LOG_DIR/experiment_info.txt" << EOF
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========================================
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MiniMind 实验信息
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========================================
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实验版本: $EXPERIMENT_VERSION
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实验描述: $EXPERIMENT_DESCRIPTION
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研究者: $RESEARCHER_NAME
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开始时间: $EXPERIMENT_DATE
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========================================
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硬件配置:
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GPU设备: $CUDA_VISIBLE_DEVICES
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进程数: $NUM_PROCESSES
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混合精度: $MIXED_PRECISION
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========================================
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模型配置:
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模型类型: $MODEL_TYPE
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模型大小: $MODEL_SIZE MB
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维度: $DIM
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层数: $N_LAYERS
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注意力头数: $N_HEADS
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最大序列长度: $MAX_SEQ_LEN
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使用MOE: $USE_MOE
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========================================
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训练配置:
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训练轮次: $EPOCHS
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批次大小: $BATCH_SIZE
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学习率: $LEARNING_RATE
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梯度累积: $ACCUMULATION_STEPS
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数据类型: $DTYPE
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========================================
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数据路径:
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训练数据: $DATA_PATH
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数据库初始化: $DATABASE_INIT_PATH
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聚类缓存: $CLUSTER_CACHE_PATH
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========================================
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EOF
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}
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# ----------------------------------------------------------------------------
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# 🤖 [AI构建] 主执行函数
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# ----------------------------------------------------------------------------
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run_experiment() {
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echo "🚀 开始执行实验 $EXPERIMENT_VERSION"
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echo "📄 实验描述: $EXPERIMENT_DESCRIPTION"
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echo "⏰ 开始时间: $EXPERIMENT_DATE"
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# 构建accelerate命令
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local accelerate_cmd="CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES"
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# 根据是否使用uv选择执行方式
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if command -v uv &> /dev/null && [[ -f "pyproject.toml" ]]; then
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accelerate_cmd+=" uv run -p .venv python -m accelerate.commands.launch"
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else
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accelerate_cmd+=" accelerate launch"
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fi
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# 添加accelerate参数
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if [[ "$NUM_PROCESSES" -gt 1 ]]; then
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accelerate_cmd+=" --multi_gpu"
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fi
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accelerate_cmd+=" --num_processes=$NUM_PROCESSES"
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accelerate_cmd+=" --mixed_precision=$MIXED_PRECISION"
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accelerate_cmd+=" --main_process_port=$MAIN_PROCESS_PORT"
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accelerate_cmd+=" train_pretrain_accelerate.py"
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# 添加训练参数
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accelerate_cmd+=" --out_dir \"$LOG_DIR\""
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accelerate_cmd+=" --epochs $EPOCHS"
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accelerate_cmd+=" --embedding_epoch $EMBEDDING_EPOCH"
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accelerate_cmd+=" --batch_size $BATCH_SIZE"
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accelerate_cmd+=" --learning_rate $LEARNING_RATE"
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accelerate_cmd+=" --dtype $DTYPE"
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accelerate_cmd+=" --num_workers $NUM_WORKERS"
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accelerate_cmd+=" --accumulation_steps $ACCUMULATION_STEPS"
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accelerate_cmd+=" --grad_clip $GRAD_CLIP"
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accelerate_cmd+=" --warmup_iters $WARMUP_ITERS"
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accelerate_cmd+=" --log_interval $LOG_INTERVAL"
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accelerate_cmd+=" --save_interval $SAVE_INTERVAL"
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accelerate_cmd+=" --dim $DIM"
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accelerate_cmd+=" --n_layers $N_LAYERS"
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accelerate_cmd+=" --n_heads $N_HEADS"
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accelerate_cmd+=" --max_seq_len $MAX_SEQ_LEN"
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accelerate_cmd+=" --data_path \"$DATA_PATH\""
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accelerate_cmd+=" --knowledge_num $KNOWLEDGE_NUM"
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accelerate_cmd+=" --knowledge_length $KNOWLEDGE_LENGTH"
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accelerate_cmd+=" --database_init_path \"$DATABASE_INIT_PATH\""
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accelerate_cmd+=" --memory_monitor_interval $MEMORY_MONITOR_INTERVAL"
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accelerate_cmd+=" --model_type \"$MODEL_TYPE\""
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accelerate_cmd+=" --model_size $MODEL_SIZE"
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# 可选参数
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if [[ "$USE_PROFILE" == "true" ]]; then
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accelerate_cmd+=" --profile"
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accelerate_cmd+=" --profile_interval $PROFILE_INTERVAL"
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fi
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if [[ "$USE_FLASH_ATTN" == "true" ]]; then
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accelerate_cmd+=" --use_flash_attn"
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fi
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if [[ "$FAST_CLUSTERING" == "true" ]]; then
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accelerate_cmd+=" --fast_clustering"
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fi
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if [[ "$DISABLE_DB" == "true" ]]; then
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accelerate_cmd+=" --disable_db"
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fi
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if [[ "$CLUSTER_CACHE_PATH" != "None" ]]; then
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accelerate_cmd+=" --cluster_cache_path \"$CLUSTER_CACHE_PATH\""
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fi
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# SwanLab配置
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accelerate_cmd+=" --use_swanlab"
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accelerate_cmd+=" --swanlab_project \"$SWANLAB_PROJECT\""
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echo "📋 执行命令:"
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echo "$accelerate_cmd"
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echo
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# 记录命令到日志文件
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echo "执行命令: $accelerate_cmd" >> "$LOG_FILE"
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echo "开始时间: $(date)" >> "$LOG_FILE"
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# 使用nohup执行训练（后台运行，输出写入日志文件）
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echo "🔄 使用nohup后台运行训练，输出将写入日志文件: $LOG_FILE"
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echo "开始时间: $(date)" >> "$LOG_FILE"
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# 创建训练脚本
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train_script="/tmp/train_${EXPERIMENT_VERSION}.sh"
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cat > "$train_script" << EOF
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#!/bin/bash
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cd /home/pci/ycz/Code/pretrain-worktree
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source /home/pci/ycz/Code/pretrain-worktree/.venv/bin/activate
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$accelerate_cmd
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echo "结束时间: \$(date)"
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echo "退出代码: \$?"
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EOF
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chmod +x "$train_script"
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# 使用nohup后台运行
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nohup bash "$train_script" >> "$LOG_FILE" 2>&1 &
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local train_pid=$!
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echo "🔥 训练进程已启动，PID: $train_pid"
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echo "训练PID: $train_pid" >> "$LOG_FILE"
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echo "训练脚本: $train_script" >> "$LOG_FILE"
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# 等待几秒确保进程启动
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sleep 5
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# 检查进程是否还在运行
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if kill -0 $train_pid 2>/dev/null; then
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echo "✅ 训练进程正在后台运行"
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echo "📋 实时查看日志: tail -f $LOG_FILE"
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echo "📋 检查进程状态: ps -p $train_pid"
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echo "🛑 停止训练: kill $train_pid"
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echo "⏰ 预计训练时间: 根据配置而定"
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echo "📈 SwanLab: https://swanlab.cn/project/$SWANLAB_PROJECT"
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echo ""
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echo "训练正在后台运行，可以安全关闭终端。"
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else
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echo "❌ 训练进程启动失败"
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echo "📋 查看日志: $LOG_FILE"
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exit 1
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fi
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}
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# ----------------------------------------------------------------------------
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# 🤖 [AI构建] 清理函数
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# ----------------------------------------------------------------------------
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cleanup() {
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echo "🧹 清理临时文件..."
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# 在这里添加清理逻辑
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}
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# ----------------------------------------------------------------------------
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# 🤖 [AI构建] 信号处理
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# ----------------------------------------------------------------------------
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trap cleanup EXIT
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trap 'echo "❌ 实验被中断"; cleanup; exit 130' INT TERM
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# ----------------------------------------------------------------------------
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# 🤖 [AI构建] 主程序入口
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# ----------------------------------------------------------------------------
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main() {
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echo "============================================================================"
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echo "🧠 MiniMind 预训练实验"
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echo "============================================================================"
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# 执行检查和初始化
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check_environment
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log_experiment_info
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# 运行实验
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run_experiment
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echo "============================================================================"
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echo "✅ 实验 $EXPERIMENT_VERSION 完成"
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echo "📅 完成时间: $(date)"
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echo "============================================================================"
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}
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# 执行主程序
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main "$@" |