414 lines
15 KiB
Bash
414 lines
15 KiB
Bash
#!/bin/bash
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# ============================================================================
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# MiniMind 实验脚本 - Experiment 1.4.10
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# ============================================================================
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#
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# 🎯 实验目标:
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# 基于实验1.4.9，实现四损失系统：CE + Balance + Similarity + Diversity
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# 核心创新：Gumbel-Softmax选择机制 + 可微分相似度损失 + 候选集多样性约束
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#
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# 使用方法:
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# bash run_file/experiment_1_4_10.sh
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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="1.4.10"
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EXPERIMENT_DESCRIPTION="四损失系统实验 - Gumbel-Softmax + 可微分相似度损失 + 多样性约束"
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RESEARCHER_NAME="AI Assistant"
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EXPERIMENT_DATE="$(date '+%Y-%m-%d %H:%M:%S')"
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# ----------------------------------------------------------------------------
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# 🤖 环境配置
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# ----------------------------------------------------------------------------
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# 调试和监控环境变量
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export NCCL_DEBUG=INFO
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export PYTHONFAULTHANDLER=1
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export CUDA_LAUNCH_BLOCKING=1
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# SwanLab 配置
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export SWANLAB_PROJECT="MiniMind-Experiment-1.4.10"
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# 日志配置
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LOG_DIR="out/experiment_${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="0,1,2,3"
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NUM_PROCESSES="4"
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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_memory" # 🔥 使用Token-based Memory模型
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MODEL_SIZE="50.0"
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DIM="512"
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N_LAYERS="8"
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N_HEADS="16"
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MAX_SEQ_LEN="512"
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USE_MOE="false"
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# 🔥 知识库配置（四损失系统优化）
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KNOWLEDGE_NUM="1048576" # 1M entries
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KNOWLEDGE_LENGTH="8" # 🔥 增加到16个token提升表达能力
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KNOWLEDGE_DIM="128" # 保留兼容性
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DISABLE_DB="false"
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# ----------------------------------------------------------------------------
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# 🤖 训练超参数
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# ----------------------------------------------------------------------------
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EPOCHS="3"
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EMBEDDING_EPOCH="2"
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BATCH_SIZE="48" # 🔥 降低批次大小以适应更复杂的计算
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ACCUMULATION_STEPS="8" # 🔥 增加累积步数保持有效批次大小
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LEARNING_RATE="2e-4" # 🔥 适度降低学习率提升稳定性
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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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# 🔥 四损失系统配置
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BALANCE_LOSS_COEF="0.01" # 平衡损失系数
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SIMILARITY_LOSS_COEF="0.8" # 🔥 相似度损失系数（核心损失）
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DIVERSITY_LOSS_COEF="0.2" # 🔥 多样性损失系数（避免候选重复）
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# 数据和缓存路径
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DATA_PATH="dataset/stable/merged_pretrain.jsonl"
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DATABASE_INIT_PATH="dataset/stable/sentence_trex_data.json"
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CLUSTER_CACHE_PATH="None" # 禁用聚类缓存
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VAL_DATA_PATH="dataset/stable/eval_data.json"
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# 训练配置
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NUM_WORKERS="8"
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LOG_INTERVAL="100" # 🔥 更频繁的日志记录观察四个损失
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VAL_INTERVAL="100"
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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="100"
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# 高级功能
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USE_FLASH_ATTN="true"
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FAST_CLUSTERING="true"
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# 冻结率
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FREEZE_RATIO="0.2"
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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: $DATA_PATH"
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exit 1
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fi
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if [[ ! -f "$DATABASE_INIT_PATH" ]]; then
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echo "❌ 错误: 数据库初始化文件不存在: $DATABASE_INIT_PATH"
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exit 1
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fi
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echo "✅ 环境检查通过"
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}
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# ----------------------------------------------------------------------------
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# 🤖 实验信息记录
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# ----------------------------------------------------------------------------
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log_experiment_info() {
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echo "📝 记录实验信息..."
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cat > "$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 (Token-based Memory + 四损失系统)
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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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知识库大小: $KNOWLEDGE_NUM (1M entries)
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知识长度: $KNOWLEDGE_LENGTH (增强表达能力)
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知识维度: $KNOWLEDGE_DIM (兼容性保留)
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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 (优化显存使用)
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学习率: $LEARNING_RATE (稳定性优化)
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梯度累积: $ACCUMULATION_STEPS (保持有效批次)
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数据类型: $DTYPE
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========================================
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🔥 四损失系统配置:
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平衡损失系数: $BALANCE_LOSS_COEF (记忆选择平衡)
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相似度损失系数: $SIMILARITY_LOSS_COEF (语义匹配优化)
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多样性损失系数: $DIVERSITY_LOSS_COEF (候选集多样性)
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========================================
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🔥 Gumbel-Softmax配置:
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候选项数量: 32 (Product Key生成)
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选择数量: 1 (Gumbel-Softmax选择最佳)
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温度参数: 1.0 (平衡探索与利用)
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选择机制: 硬选择 + Straight-Through Estimator
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========================================
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🔥 核心创新对比:
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传统方法: 16个记忆平均融合 (缺乏语义针对性)
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新方法: 32候选→1最佳 (语义相似度驱动)
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旧相似度损失: no_grad计算 (不可微分)
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新相似度损失: 可微分优化 (直接指导学习)
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新增多样性: 候选集内部差异性约束
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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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验证数据: $VAL_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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预期改进:
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1. 相似度损失收敛: 从震荡→稳定下降
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2. 记忆选择质量: 更精准的语义匹配
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3. 生成文本质量: 更好的连贯性和相关性
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4. 四损失平衡: CE主导，其他损失辅助
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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 accelerate launch --config_file accelerate_config.yaml 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+=" --val_interval $VAL_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+=" --val_data_path \"$VAL_DATA_PATH\""
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train_cmd+=" --knowledge_num $KNOWLEDGE_NUM"
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train_cmd+=" --knowledge_length $KNOWLEDGE_LENGTH"
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train_cmd+=" --database_init_path \"$DATABASE_INIT_PATH\""
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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+=" --freeze_ratio $FREEZE_RATIO"
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# 🔥 四损失系统参数
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train_cmd+=" --balance_loss_coef $BALANCE_LOSS_COEF"
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train_cmd+=" --similarity_loss_coef $SIMILARITY_LOSS_COEF"
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train_cmd+=" --diversity_loss_coef $DIVERSITY_LOSS_COEF"
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# 可选参数
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if [[ "$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 [[ "$FAST_CLUSTERING" == "true" ]]; then
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train_cmd+=" --fast_clustering"
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fi
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if [[ "$CLUSTER_CACHE_PATH" != "None" ]]; then
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train_cmd+=" --cluster_cache_path \"$CLUSTER_CACHE_PATH\""
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fi
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# SwanLab配置
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train_cmd+=" --use_swanlab"
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train_cmd+=" --swanlab_project \"$SWANLAB_PROJECT\""
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# train_cmd+=" --swanlab_online True"
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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，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: $LOG_FILE"
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# 创建训练脚本
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train_script="/tmp/train_${EXPERIMENT_VERSION}.sh"
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cat > "$train_script" << EOF
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#!/bin/bash
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# cd /home/pci/nas/AI_Large_Model_Team/ycz/Minimind
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# source .venv/bin/activate
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$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 -p $train_pid"
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echo "🛑 停止训练: kill $train_pid"
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echo "📈 SwanLab: https://swanlab.cn/project/$SWANLAB_PROJECT"
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echo ""
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echo "🧠 四损失系统机制正在测试中..."
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echo " 🔥 损失结构: CE + Balance + Similarity + Diversity"
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echo " 🔥 候选机制: 32个候选 → Gumbel-Softmax选择1个最佳"
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echo " 🔥 相似度损失: 可微分优化 (修复震荡问题)"
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echo " 🔥 多样性约束: 候选集内部差异性正则化"
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echo " 🔥 选择策略: 语义相似度驱动 vs 随机平均"
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echo ""
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echo "📊 与实验1.4.9对比:"
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echo " - 选择机制: 平均融合 → 最优选择"
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echo " - 相似度损失: 不可微分 → 可微分"
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echo " - 候选多样性: 无约束 → 多样性正则化"
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echo " - 损失系统: 三损失 → 四损失平衡"
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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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echo ""
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echo "🎯 预期改进:"
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echo " - 相似度损失: 稳定收敛 (不再震荡)"
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echo " - CE Loss: < 0.8 (改善语言建模)"
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echo " - 生成质量: 更连贯的文本输出"
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echo " - 记忆选择: 更精准的语义匹配"
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echo ""
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echo "⏱️ 预计训练时间: 18-22小时 (额外计算开销)"
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echo "📊 预计GPU占用: ~24GB (Gumbel-Softmax + 多样性计算)"
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echo ""
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echo "🔍 关键监控指标:"
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echo " - Similarity Loss: 期望从1.9震荡→稳定下降"
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echo " - Diversity Loss: 保持适中值避免过度惩罚"
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echo " - Selection Entropy: 监控选择多样性"
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echo " - Selected Similarity: 观察选中记忆的相似度"
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echo ""
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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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# 删除临时验证文件
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rm -f /tmp/temp_val.jsonl
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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 预训练实验 1.4.10"
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echo "🎯 四损失系统 - Gumbel-Softmax + 可微分相似度损失 + 多样性约束"
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echo "============================================================================"
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echo ""
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echo "🔥 核心创新:"
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echo " ► 四损失架构: CE + Balance + Similarity + Diversity"
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echo " ► Gumbel-Softmax: 32候选→1最佳 (可微分离散选择)"
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echo " ► 相似度损失: 可微分优化 (修复震荡)"
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echo " ► 多样性约束: 候选集内部差异性正则化"
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echo " ► 语义选择: 相似度驱动 vs 平均融合"
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echo ""
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echo "🎯 实验假设:"
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echo " ✓ 可微分相似度损失解决震荡问题"
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echo " ✓ 语义驱动选择改善记忆利用质量"
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echo " ✓ 多样性约束防止候选集退化"
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echo " ✓ 四损失平衡提升整体模型性能"
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echo ""
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echo "🔧 关键技术细节:"
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echo " ► Straight-Through Estimator确保梯度流"
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echo " ► 候选集多样性通过余弦相似度矩阵计算"
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echo " ► Gumbel噪声增强选择随机性"
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echo " ► 硬选择保持离散性，软梯度保持可微性"
|
||
echo ""
|
||
echo "============================================================================"
|
||
|
||
# 执行检查和初始化
|
||
check_environment
|
||
log_experiment_info
|
||
|
||
# 运行实验
|
||
run_experiment
|
||
|
||
echo "============================================================================"
|
||
echo "✅ 实验 $EXPERIMENT_VERSION 启动完成"
|
||
echo "📅 启动时间: $(date)"
|
||
echo "============================================================================"
|
||
}
|
||
|
||
# 执行主程序
|
||
main "$@" |