#!/bin/bash

# ============================================================================
# MiniMind 实验脚本 - Experiment 1.4.2
# ============================================================================
# 
# 🎯 实验目标: 验证连接方式对记忆库模型性能的影响
# 📝 实验描述: 使用串型连接(拼接+门控MLP)替代跳接连接(交叉注意力)
# 🔬 研究假设: 性能下降主要由连接方式造成，串型连接能显著改善效果
# ============================================================================

# ----------------------------------------------------------------------------
# 🧑‍🔬 实验基本信息
# ----------------------------------------------------------------------------
EXPERIMENT_VERSION="1_4_2"
EXPERIMENT_DESCRIPTION="Serial connection with gated MLP fusion replacing cross-attention"
RESEARCHER_NAME="Human-AI Collaboration"
EXPERIMENT_DATE="$(date '+%Y-%m-%d %H:%M:%S')"

# ----------------------------------------------------------------------------
# 🤖 环境配置
# ----------------------------------------------------------------------------

# UV虚拟环境激活
export PYTHONFAULTHANDLER=1
export CUDA_LAUNCH_BLOCKING=0  # 设为0以提高性能

# SwanLab 配置
export SWANLAB_PROJECT="MiniMind-Memory-Connection-Experiment"

# 日志配置
LOG_DIR="out/experiment_${EXPERIMENT_VERSION}"
mkdir -p "$LOG_DIR"
LOG_FILE="$LOG_DIR/experiment.log"

# ----------------------------------------------------------------------------
# 🤖 硬件配置
# ----------------------------------------------------------------------------
CUDA_VISIBLE_DEVICES="0"
NUM_PROCESSES="1"
MIXED_PRECISION="bf16"
MAIN_PROCESS_PORT="29500"

# ----------------------------------------------------------------------------
# 🤖 模型架构参数
# ----------------------------------------------------------------------------
MODEL_TYPE="model_memory"
MODEL_SIZE="26.0"
DIM="512"
N_LAYERS="8"
N_HEADS="32"
MAX_SEQ_LEN="512"
USE_MOE="false"

# 记忆库配置（与1.4.1保持一致）
KNOWLEDGE_NUM="65536"    # 64K条记忆（256x256，完全平方数）
KNOWLEDGE_DIM="128"      # 记忆向量维度
KNOWLEDGE_LENGTH="32"    # 单条记忆长度
NUM_SELECTED="8"         # 每次选择的记忆数（保持与1.4.1一致）

# ----------------------------------------------------------------------------
# 🤖 训练超参数（与1.4.1完全一致）
# ----------------------------------------------------------------------------
EPOCHS="3"
EMBEDDING_EPOCH="2"
BATCH_SIZE="64"  # 与1.4.1保持一致
ACCUMULATION_STEPS="8"
LEARNING_RATE="2e-4"
DTYPE="bfloat16"
GRAD_CLIP="1.0"
WARMUP_ITERS="0"

# 数据路径
DATA_PATH="/home/pci/ycz/Code/Minimind/dataset/stable/merged_pretrain.jsonl"
DATABASE_INIT_PATH="None"  # 随机初始化记忆库，保持一致性
CLUSTER_CACHE_PATH="None"

# 训练配置
NUM_WORKERS="1"
LOG_INTERVAL="1"
SAVE_INTERVAL="10000"

# 性能分析配置
USE_PROFILE="true"
PROFILE_INTERVAL="10"
MEMORY_MONITOR_INTERVAL="10"

# 高级功能
USE_FLASH_ATTN="true"
USE_SWANLAB="true"
SWANLAB_ONLINE="false"

# ----------------------------------------------------------------------------
# 🤖 预检查函数
# ----------------------------------------------------------------------------
check_environment() {
    echo "🔍 环境检查中..."
    
    # 检查GPU可用性
    if ! nvidia-smi &> /dev/null; then
        echo "❌ 错误: 未检测到GPU或nvidia-smi不可用"
        exit 1
    fi
    
    # 检查CUDA设备
    if ! nvidia-smi -i "$CUDA_VISIBLE_DEVICES" &> /dev/null; then
        echo "❌ 错误: GPU $CUDA_VISIBLE_DEVICES 不可用"
        exit 1
    fi
    
    # 检查Python环境
    if ! .venv/bin/python -c "import torch; print(f'PyTorch: {torch.__version__}')" 2>/dev/null; then
        echo "❌ 错误: PyTorch未正确安装"
        exit 1
    fi
    
    # 检查数据文件
    if [[ ! -f "$DATA_PATH" ]]; then
        echo "❌ 错误: 训练数据文件不存在: $DATA_PATH"
        exit 1
    fi
    
    # 检查model_memory.py是否存在GatedMemoryFusion
    if ! grep -q "GatedMemoryFusion" model/model_memory.py; then
        echo "❌ 错误: model_memory.py中未找到GatedMemoryFusion类"
        echo "请确认已正确修改模型文件"
        exit 1
    fi
    
    echo "✅ 环境检查通过"
}

# ----------------------------------------------------------------------------
# 🤖 实验信息记录
# ----------------------------------------------------------------------------
log_experiment_info() {
    echo "📝 记录实验信息..."
    cat > "$LOG_DIR/experiment_info.txt" << EOF
========================================
MiniMind 连接方式实验信息
========================================
实验版本: $EXPERIMENT_VERSION
实验描述: $EXPERIMENT_DESCRIPTION
研究者: $RESEARCHER_NAME
开始时间: $EXPERIMENT_DATE
========================================
核心改进:
- 保留Product Key Memory记忆选择机制
- 使用串型连接替代跳接连接
- 门控MLP融合替代交叉注意力
- 拼接h_attn和选中记忆进行处理
========================================
对照实验:
- 基准实验: 1.4.0 (model_original)
- 对比实验: 1.4.1 (交叉注意力)
- 本实验: 1.4.2 (门控MLP融合)
========================================
硬件配置:
GPU设备: $CUDA_VISIBLE_DEVICES
进程数: $NUM_PROCESSES
混合精度: $MIXED_PRECISION
========================================
模型配置:
模型类型: $MODEL_TYPE (串型连接版本)
模型大小: $MODEL_SIZE MB
维度: $DIM
层数: $N_LAYERS
注意力头数: $N_HEADS
最大序列长度: $MAX_SEQ_LEN
记忆库条目数: $KNOWLEDGE_NUM
记忆向量维度: $KNOWLEDGE_DIM
每次选择记忆数: $NUM_SELECTED
========================================
训练配置:
训练轮次: $EPOCHS
批次大小: $BATCH_SIZE
学习率: $LEARNING_RATE
梯度累积: $ACCUMULATION_STEPS
数据类型: $DTYPE
========================================
数据路径:
训练数据: $DATA_PATH
记忆库初始化: $DATABASE_INIT_PATH
========================================
EOF
}

# ----------------------------------------------------------------------------
# 🤖 主执行函数
# ----------------------------------------------------------------------------
run_experiment() {
    echo "🚀 开始执行实验 $EXPERIMENT_VERSION"
    echo "📄 实验描述: $EXPERIMENT_DESCRIPTION"
    echo "⏰ 开始时间: $EXPERIMENT_DATE"
    
    # 构建训练命令
    local train_cmd="CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES uv run python -m accelerate.commands.launch"
    train_cmd+=" --num_processes=$NUM_PROCESSES"
    train_cmd+=" --mixed_precision=$MIXED_PRECISION"
    train_cmd+=" --main_process_port=$MAIN_PROCESS_PORT"
    train_cmd+=" train_pretrain_accelerate.py"
    
    # 添加训练参数
    train_cmd+=" --out_dir \"$LOG_DIR\""
    train_cmd+=" --epochs $EPOCHS"
    train_cmd+=" --embedding_epoch $EMBEDDING_EPOCH"
    train_cmd+=" --batch_size $BATCH_SIZE"
    train_cmd+=" --learning_rate $LEARNING_RATE"
    train_cmd+=" --dtype $DTYPE"
    train_cmd+=" --num_workers $NUM_WORKERS"
    train_cmd+=" --accumulation_steps $ACCUMULATION_STEPS"
    train_cmd+=" --grad_clip $GRAD_CLIP"
    train_cmd+=" --warmup_iters $WARMUP_ITERS"
    train_cmd+=" --log_interval $LOG_INTERVAL"
    train_cmd+=" --save_interval $SAVE_INTERVAL"
    train_cmd+=" --dim $DIM"
    train_cmd+=" --n_layers $N_LAYERS"
    train_cmd+=" --n_heads $N_HEADS"
    train_cmd+=" --max_seq_len $MAX_SEQ_LEN"
    train_cmd+=" --data_path \"$DATA_PATH\""
    train_cmd+=" --knowledge_num $KNOWLEDGE_NUM"
    train_cmd+=" --knowledge_length $KNOWLEDGE_LENGTH"
    train_cmd+=" --knowledge_dim $KNOWLEDGE_DIM"
    train_cmd+=" --memory_monitor_interval $MEMORY_MONITOR_INTERVAL"
    train_cmd+=" --model_type \"$MODEL_TYPE\""
    train_cmd+=" --model_size $MODEL_SIZE"
    train_cmd+=" --swanlab_online $SWANLAB_ONLINE"
    train_cmd+=" --database_init_path \"$DATABASE_INIT_PATH\""
    
    # 可选参数
    if [[ "$USE_PROFILE" == "true" ]]; then
        train_cmd+=" --profile"
        train_cmd+=" --profile_interval $PROFILE_INTERVAL"
    fi
    
    if [[ "$USE_FLASH_ATTN" == "true" ]]; then
        train_cmd+=" --use_flash_attn"
    fi
    
    if [[ "$USE_SWANLAB" == "true" ]]; then
        train_cmd+=" --use_swanlab"
        train_cmd+=" --swanlab_project \"$SWANLAB_PROJECT\""
    fi
    
    echo "📋 执行命令:"
    echo "$train_cmd"
    echo
    
    # 记录命令到日志文件
    echo "执行命令: $train_cmd" >> "$LOG_FILE"
    echo "开始时间: $(date)" >> "$LOG_FILE"
    
    # 使用nohup执行训练（后台运行）
    echo "🔄 使用nohup后台运行训练，输出将写入日志文件: $LOG_FILE"
    
    # 创建训练脚本
    train_script="/tmp/train_${EXPERIMENT_VERSION}.sh"
    cat > "$train_script" << EOF
#!/bin/bash
cd /home/pci/ycz/Code/pretrain-worktree
export PYTHONFAULTHANDLER=1
export SWANLAB_PROJECT="$SWANLAB_PROJECT"
$train_cmd
echo "结束时间: \$(date)"
echo "退出代码: \$?"
EOF
    chmod +x "$train_script"
    
    # 使用nohup后台运行
    nohup bash "$train_script" >> "$LOG_FILE" 2>&1 &
    local train_pid=$!
    
    echo "🔥 训练进程已启动，PID: $train_pid"
    echo "训练PID: $train_pid" >> "$LOG_FILE"
    echo "训练脚本: $train_script" >> "$LOG_FILE"
    
    # 等待几秒确保进程启动
    sleep 5
    
    # 检查进程是否还在运行
    if kill -0 $train_pid 2>/dev/null; then
        echo "✅ 训练进程正在后台运行"
        echo "📋 实时查看日志: tail -f $LOG_FILE"
        echo "📋 检查进程状态: ps aux | grep train_pretrain_accelerate"
        echo "🛑 停止训练: kill $train_pid"        
        echo "⏰ 预计训练时间: 10-15小时 (3 epochs, RTX 4090)"
        echo "📈 SwanLab: 本地模式，输出目录中查看"
        echo ""
        echo "🎯 实验重点:"
        echo "  - 对比串型连接vs跳接连接的效果"
        echo "  - 验证连接方式是否是性能下降的主因"
        echo "  - 观察门控MLP融合的训练稳定性"
        echo "  - 期望Loss接近baseline (2.4-2.5)"
        echo ""
        echo "训练正在后台运行，可以安全关闭终端。"
    else
        echo "❌ 训练进程启动失败"
        echo "📋 查看日志: $LOG_FILE"
        exit 1
    fi
}

# ----------------------------------------------------------------------------
# 🤖 清理函数
# ----------------------------------------------------------------------------
cleanup() {
    echo "🧹 清理临时文件..."
    # 清理临时脚本
    if [[ -f "/tmp/train_${EXPERIMENT_VERSION}.sh" ]]; then
        rm -f "/tmp/train_${EXPERIMENT_VERSION}.sh"
    fi
}

# ----------------------------------------------------------------------------
# 🤖 信号处理
# ----------------------------------------------------------------------------
trap cleanup EXIT
trap 'echo "❌ 实验被中断"; cleanup; exit 130' INT TERM

# ----------------------------------------------------------------------------
# 🤖 主程序入口
# ----------------------------------------------------------------------------
main() {
    echo "============================================================================"
    echo "🧠 MiniMind 连接方式对比实验"
    echo "============================================================================"
    echo "🎯 实验版本: $EXPERIMENT_VERSION"
    echo "📝 实验目标: 串型连接(门控MLP)vs跳接连接(交叉注意力)"
    echo "🔬 核心假设: 连接方式是性能下降的主要原因"
    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 已启动"
    echo "📅 启动时间: $(date)"
    echo "🔍 对照实验: 1.4.1 (交叉注意力) vs 1.4.2 (门控MLP)"
    echo "============================================================================"
}

# 执行主程序
main "$@"