354 lines
12 KiB
Bash
354 lines
12 KiB
Bash
#!/bin/bash
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# ============================================================================
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# MiniMind 实验脚本 - Experiment 1.4.3
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# ============================================================================
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#
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# 🎯 实验目标: 验证完整信息对记忆查询效果的影响
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# 📝 实验描述: 使用完整信息h替代注意力输出h_attn进行记忆查询和交叉注意力
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# 🔬 研究假设: 完整信息包含更丰富的上下文，能提升记忆查询精度和文本连贯性
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# ============================================================================
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# ----------------------------------------------------------------------------
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# 🧑🔬 实验基本信息
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# ----------------------------------------------------------------------------
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EXPERIMENT_VERSION="1_4_3"
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EXPERIMENT_DESCRIPTION="Complete information (h) for memory query instead of attention output (h_attn)"
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RESEARCHER_NAME="Human-AI Collaboration"
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EXPERIMENT_DATE="$(date '+%Y-%m-%d %H:%M:%S')"
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# ----------------------------------------------------------------------------
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# 🤖 环境配置
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# ----------------------------------------------------------------------------
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# UV虚拟环境激活
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export PYTHONFAULTHANDLER=1
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export CUDA_LAUNCH_BLOCKING=0 # 设为0以提高性能
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# SwanLab 配置
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export SWANLAB_PROJECT="MiniMind-Memory-Query-Enhancement"
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# 日志配置
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LOG_DIR="out/experiment_${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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# 🤖 硬件配置
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# ----------------------------------------------------------------------------
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CUDA_VISIBLE_DEVICES="0"
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NUM_PROCESSES="1"
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MIXED_PRECISION="bf16"
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MAIN_PROCESS_PORT="29500"
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# ----------------------------------------------------------------------------
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# 🤖 模型架构参数
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# ----------------------------------------------------------------------------
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MODEL_TYPE="model" # 使用标准model，已修改为完整信息查询
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MODEL_SIZE="26.0"
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DIM="512"
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N_LAYERS="8"
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N_HEADS="32"
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MAX_SEQ_LEN="512"
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USE_MOE="false"
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# 记忆库配置（与1.4.2保持一致以便对比）
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KNOWLEDGE_NUM="65536" # 64K条记忆（256x256，完全平方数）
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KNOWLEDGE_DIM="128" # 记忆向量维度
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KNOWLEDGE_LENGTH="32" # 单条记忆长度
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NUM_SELECTED="8" # 每次选择的记忆数
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# ----------------------------------------------------------------------------
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# 🤖 训练超参数（与1.4.2完全一致）
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# ----------------------------------------------------------------------------
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EPOCHS="3"
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EMBEDDING_EPOCH="2"
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BATCH_SIZE="64" # 与对照实验保持一致
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ACCUMULATION_STEPS="8"
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LEARNING_RATE="2e-4"
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DTYPE="bfloat16"
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GRAD_CLIP="1.0"
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WARMUP_ITERS="0"
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# 数据路径
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DATA_PATH="/home/pci/ycz/Code/Minimind/dataset/stable/merged_pretrain.jsonl"
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DATABASE_INIT_PATH="None" # 随机初始化记忆库，保持一致性
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CLUSTER_CACHE_PATH="None"
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# 训练配置
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NUM_WORKERS="1"
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LOG_INTERVAL="1"
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SAVE_INTERVAL="10000"
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# 性能分析配置
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USE_PROFILE="true"
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PROFILE_INTERVAL="10"
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MEMORY_MONITOR_INTERVAL="10"
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# 高级功能
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USE_FLASH_ATTN="true"
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USE_SWANLAB="true"
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SWANLAB_ONLINE="false"
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# ----------------------------------------------------------------------------
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# 🤖 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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if ! nvidia-smi -i "$CUDA_VISIBLE_DEVICES" &> /dev/null; then
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echo "❌ 错误: GPU $CUDA_VISIBLE_DEVICES 不可用"
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exit 1
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fi
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# 检查Python环境
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if ! .venv/bin/python -c "import torch; print(f'PyTorch: {torch.__version__}')" 2>/dev/null; then
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echo "❌ 错误: PyTorch未正确安装"
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exit 1
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fi
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# 检查数据文件
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if [[ ! -f "$DATA_PATH" ]]; then
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echo "❌ 错误: 训练数据文件不存在: $DATA_PATH"
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exit 1
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fi
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# 检查model.py中的修改是否正确
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if ! grep -q "h = x + h_attn # 计算完整信息" model/model.py; then
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echo "❌ 错误: model.py中未找到完整信息查询的修改"
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echo "请确认已正确修改MiniMindBlock.forward方法"
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exit 1
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fi
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echo "✅ 环境检查通过"
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}
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# ----------------------------------------------------------------------------
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# 🤖 实验信息记录
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# ----------------------------------------------------------------------------
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log_experiment_info() {
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echo "📝 记录实验信息..."
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cat > "$LOG_DIR/experiment_info.txt" << EOF
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========================================
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MiniMind 记忆查询增强实验信息
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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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核心改进:
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- 记忆查询使用完整信息h替代注意力输出h_attn
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- 交叉注意力输入也使用完整信息h
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- 保持Product Key Memory选择机制不变
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- 保持交叉注意力架构不变
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========================================
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技术细节:
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原方案: db, db_embeddings = self.knowledge_dataset.search_index(h_attn)
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h_attn = self.cross_attention(h_attn, db_embeddings)
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新方案: h = x + h_attn # 计算完整信息
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db, db_embeddings = self.knowledge_dataset.search_index(h)
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memory_output = self.cross_attention(h, db_embeddings)
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========================================
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对照实验:
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- 基准实验: 1.4.0 (model_original, Loss: 1.9)
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- 对比实验: 1.4.1 (h_attn查询, Loss: 0.6, 但文本碎片化)
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- 本实验: 1.4.3 (h完整信息查询)
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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 (完整信息查询版本)
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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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记忆库条目数: $KNOWLEDGE_NUM
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记忆向量维度: $KNOWLEDGE_DIM
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每次选择记忆数: $NUM_SELECTED
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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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记忆库初始化: $DATABASE_INIT_PATH
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========================================
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EOF
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}
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# ----------------------------------------------------------------------------
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# 🤖 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: $EXPERIMENT_DATE"
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# 构建训练命令
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local train_cmd="CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES uv run python -m accelerate.commands.launch"
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train_cmd+=" --num_processes=$NUM_PROCESSES"
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train_cmd+=" --mixed_precision=$MIXED_PRECISION"
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train_cmd+=" --main_process_port=$MAIN_PROCESS_PORT"
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train_cmd+=" train_pretrain_accelerate.py"
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# 添加训练参数
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train_cmd+=" --out_dir \"$LOG_DIR\""
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train_cmd+=" --epochs $EPOCHS"
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train_cmd+=" --embedding_epoch $EMBEDDING_EPOCH"
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train_cmd+=" --batch_size $BATCH_SIZE"
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train_cmd+=" --learning_rate $LEARNING_RATE"
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train_cmd+=" --dtype $DTYPE"
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train_cmd+=" --num_workers $NUM_WORKERS"
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train_cmd+=" --accumulation_steps $ACCUMULATION_STEPS"
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train_cmd+=" --grad_clip $GRAD_CLIP"
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train_cmd+=" --warmup_iters $WARMUP_ITERS"
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train_cmd+=" --log_interval $LOG_INTERVAL"
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train_cmd+=" --save_interval $SAVE_INTERVAL"
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train_cmd+=" --dim $DIM"
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train_cmd+=" --n_layers $N_LAYERS"
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train_cmd+=" --n_heads $N_HEADS"
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train_cmd+=" --max_seq_len $MAX_SEQ_LEN"
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train_cmd+=" --data_path \"$DATA_PATH\""
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train_cmd+=" --knowledge_num $KNOWLEDGE_NUM"
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train_cmd+=" --knowledge_length $KNOWLEDGE_LENGTH"
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train_cmd+=" --knowledge_dim $KNOWLEDGE_DIM"
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train_cmd+=" --memory_monitor_interval $MEMORY_MONITOR_INTERVAL"
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train_cmd+=" --model_type \"$MODEL_TYPE\""
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train_cmd+=" --model_size $MODEL_SIZE"
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train_cmd+=" --swanlab_online $SWANLAB_ONLINE"
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train_cmd+=" --database_init_path \"$DATABASE_INIT_PATH\""
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# 可选参数
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if [[ "$USE_PROFILE" == "true" ]]; then
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train_cmd+=" --profile"
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train_cmd+=" --profile_interval $PROFILE_INTERVAL"
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fi
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if [[ "$USE_FLASH_ATTN" == "true" ]]; then
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train_cmd+=" --use_flash_attn"
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fi
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if [[ "$USE_SWANLAB" == "true" ]]; then
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train_cmd+=" --use_swanlab"
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train_cmd+=" --swanlab_project \"$SWANLAB_PROJECT\""
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fi
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echo "📋 执行命令:"
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echo "$train_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: $train_cmd" >> "$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）
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echo "🔄 使用nohup后台运行训练，输出将写入日志文件: $LOG_FILE"
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# 创建训练脚本
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train_script="/tmp/train_${EXPERIMENT_VERSION}.sh"
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cat > "$train_script" << EOF
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#!/bin/bash
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cd /home/pci/ycz/Code/pretrain-worktree
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export PYTHONFAULTHANDLER=1
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export SWANLAB_PROJECT="$SWANLAB_PROJECT"
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$train_cmd
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echo "结束时间: \$(date)"
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echo "退出代码: \$?"
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EOF
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chmod +x "$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 aux | grep train_pretrain_accelerate"
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echo "🛑 停止训练: kill $train_pid"
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echo "⏰ 预计训练时间: 10-15小时 (3 epochs, RTX 4090)"
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echo "📈 SwanLab: 本地模式，输出目录中查看"
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echo ""
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echo "🎯 实验重点:"
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echo " - 对比完整信息h vs 注意力输出h_attn的查询效果"
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echo " - 验证是否能改善文本连贯性问题"
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echo " - 观察Loss收敛情况和生成质量"
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echo " - 期望: Loss保持低水平，文本连贯性提升"
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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: $LOG_FILE"
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exit 1
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fi
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}
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# ----------------------------------------------------------------------------
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# 🤖 清理函数
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# ----------------------------------------------------------------------------
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cleanup() {
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echo "🧹 清理临时文件..."
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# 清理临时脚本
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if [[ -f "/tmp/train_${EXPERIMENT_VERSION}.sh" ]]; then
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rm -f "/tmp/train_${EXPERIMENT_VERSION}.sh"
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fi
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}
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# ----------------------------------------------------------------------------
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# 🤖 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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# 🤖 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 记忆查询增强实验"
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echo "============================================================================"
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echo "🎯 实验版本: $EXPERIMENT_VERSION"
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echo "📝 实验目标: 完整信息查询vs注意力输出查询"
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echo "🔬 核心假设: 完整信息能提升记忆查询精度和文本连贯性"
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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 已启动"
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echo "📅 启动时间: $(date)"
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echo "🔍 对照实验: 1.4.1 (h_attn查询) vs 1.4.3 (h完整信息查询)"
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echo "============================================================================"
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}
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# 执行主程序
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main "$@" |