120_检查点管理：故障恢复 - 实现分布式保存机制

# 检查点频率配置示例
checkpoint_config = {
    "every_n_steps": 1000,           # 每N步保存一次
    "every_n_epochs": None,          # 每N个epoch保存一次
    "save_weights_only": False,      # 是否只保存权重
    "max_checkpoints": 10,           # 最大保存检查点数量
    "score_function": "loss",        # 评分函数，用于选择最佳检查点
    "save_best": True,               # 是否只保存最佳检查点
    "persistent_workers": True,      # 是否使用持久化工作线程
    "async_save": True,              # 是否异步保存
    "backup_dir": "/path/to/backup"  # 备份目录
}

class AdaptiveCheckpointScheduler:
    def __init__(self, base_frequency, min_frequency, max_frequency):
        self.base_frequency = base_frequency
        self.min_frequency = min_frequency
        self.max_frequency = max_frequency
        self.current_frequency = base_frequency
        self.system_monitor = SystemMonitor()  # 系统监控器
    
    def update_frequency(self, step, loss, hardware_utilization):
        # 根据损失变化率调整
        loss_change = self._calculate_loss_change(step, loss)
        
        # 根据硬件利用率调整
        if hardware_utilization["gpu"] > 95:
            self.current_frequency = min(self.current_frequency * 1.5, self.max_frequency)
        elif hardware_utilization["disk_io"] > 80:
            self.current_frequency = min(self.current_frequency * 1.2, self.max_frequency)
        elif loss_change > 0.1:  # 损失变化剧烈
            self.current_frequency = max(self.current_frequency / 1.2, self.min_frequency)
        
        return self.current_frequency
    
    def should_save(self, step):
        return step % self.current_frequency == 0

# 简化的集中式检查点保存
import torch.distributed as dist
import os

def save_checkpoint_centralized(rank, world_size, model, optimizer, epoch, output_dir):
    # 主节点收集所有参数
    if rank == 0:
        # 创建输出目录
        os.makedirs(output_dir, exist_ok=True)
        
        # 保存主节点的模型状态
        torch.save({
            'epoch': epoch,
            'model_state_dict': model.state_dict(),
            'optimizer_state_dict': optimizer.state_dict(),
        }, f'{output_dir}/checkpoint_{epoch}.pth')
        
        # 等待其他节点完成
        for r in range(1, world_size):
            # 接收其他节点的数据（实际实现需要更复杂的通信）
            pass
    else:
        # 非主节点发送数据到主节点
        pass

# 简化的分布式检查点保存
def save_checkpoint_distributed(rank, world_size, model, optimizer, epoch, output_dir):
    # 为每个节点创建独立的子目录
    node_dir = os.path.join(output_dir, f'node_{rank}')
    os.makedirs(node_dir, exist_ok=True)
    
    # 保存当前节点的状态
    torch.save({
        'epoch': epoch,
        'rank': rank,
        'world_size': world_size,
        'model_state_dict': model.state_dict(),
        'optimizer_state_dict': optimizer.state_dict(),
    }, f'{node_dir}/checkpoint_{epoch}.pth')
    
    # 同步所有节点
    dist.barrier()
    
    # 主节点创建索引文件
    if rank == 0:
        with open(f'{output_dir}/checkpoint_index.json', 'w') as f:
            json.dump({
                'epoch': epoch,
                'world_size': world_size,
                'timestamp': time.time(),
                'nodes': [f'node_{r}' for r in range(world_size)]
            }, f)

# 异步检查点保存示例
import threading
from queue import Queue

class AsyncCheckpointSaver:
    def __init__(self, max_workers=4):
        self.queue = Queue()
        self.workers = []
        self.running = True
        
        # 启动工作线程
        for _ in range(max_workers):
            worker = threading.Thread(target=self._worker)
            worker.daemon = True
            worker.start()
            self.workers.append(worker)
    
    def _worker(self):
        while self.running:
            try:
                task = self.queue.get(timeout=1)
                if task is None:  # 终止信号
                    break
                
                # 执行保存任务
                filepath, state_dict = task
                torch.save(state_dict, filepath)
                
                self.queue.task_done()
            except Exception as e:
                print(f"Checkpoint save error: {e}")
    
    def save(self, filepath, state_dict):
        # 将保存任务放入队列
        self.queue.put((filepath, state_dict))
    
    def shutdown(self):
        # 关闭所有工作线程
        self.running = False
        for _ in self.workers:
            self.queue.put(None)
        for worker in self.workers:
            worker.join()

# 分层检查点实现示例
class TieredCheckpointManager:
    def __init__(self, tiers):
        """
        tiers: 存储层次列表，按速度从快到慢排列
        每个层次包含: {"path": 路径, "capacity": 容量(GB), "priority": 优先级}
        """
        self.tiers = tiers
        self.checkpoint_metadata = {}
        
        # 初始化各层存储
        for tier in tiers:
            os.makedirs(tier["path"], exist_ok=True)
    
    def save(self, checkpoint_id, state_dict, priority=5):
        # 选择合适的存储层次
        target_tier = self._select_tier(priority)
        
        # 保存到目标层次
        filepath = os.path.join(target_tier["path"], f"checkpoint_{checkpoint_id}.pth")
        torch.save(state_dict, filepath)
        
        # 更新元数据
        self.checkpoint_metadata[checkpoint_id] = {
            "tier": target_tier["path"],
            "size": os.path.getsize(filepath),
            "timestamp": time.time(),
            "priority": priority
        }
        
        # 执行存储平衡
        self._balance_storage()
    
    def _select_tier(self, priority):
        # 根据优先级选择存储层次
        for tier in self.tiers:
            if priority >= tier.get("min_priority", 0):
                return tier
        return self.tiers[-1]  # 默认为最慢的层
    
    def _balance_storage(self):
        # 确保每层不超过容量限制，必要时迁移到更慢的层
        # 实现细节...
        pass

# 增量检查点实现示例
class IncrementalCheckpointManager:
    def __init__(self, base_dir):
        self.base_dir = base_dir
        self.current_checkpoint = None
        self.metadata_path = os.path.join(base_dir, "metadata.json")
        
        # 加载现有元数据
        if os.path.exists(self.metadata_path):
            with open(self.metadata_path, 'r') as f:
                self.metadata = json.load(f)
        else:
            self.metadata = {"checkpoints": {}, "base_checkpoint": None}
            os.makedirs(base_dir, exist_ok=True)
    
    def save(self, checkpoint_id, state_dict):
        if self.current_checkpoint is None:
            # 保存完整检查点作为基础
            base_path = os.path.join(self.base_dir, f"base_{checkpoint_id}.pth")
            torch.save(state_dict, base_path)
            
            self.current_checkpoint = checkpoint_id
            self.metadata["base_checkpoint"] = checkpoint_id
            self.metadata["checkpoints"][checkpoint_id] = {
                "type": "full",
                "path": base_path,
                "timestamp": time.time()
            }
        else:
            # 计算差异
            diff = self._calculate_diff(self.metadata["checkpoints"][self.current_checkpoint]["path"], state_dict)
            
            # 保存差异
            diff_path = os.path.join(self.base_dir, f"diff_{self.current_checkpoint}_to_{checkpoint_id}.pth")
            torch.save(diff, diff_path)
            
            self.current_checkpoint = checkpoint_id
            self.metadata["checkpoints"][checkpoint_id] = {
                "type": "incremental",
                "from": self.current_checkpoint,
                "path": diff_path,
                "timestamp": time.time()
            }
        
        # 保存元数据
        self._save_metadata()
    
    def _calculate_diff(self, prev_checkpoint_path, current_state_dict):
        # 加载前一个检查点
        prev_state_dict = torch.load(prev_checkpoint_path)
        
        # 计算差异
        diff = {}
        for key, value in current_state_dict.items():
            if key in prev_state_dict and not torch.allclose(value, prev_state_dict[key]):
                diff[key] = value - prev_state_dict[key]
        
        return diff
    
    def load(self, checkpoint_id):
        # 加载指定检查点，可能需要应用多个差异
        # 实现细节...
        pass

# 检查点压缩示例
import zstandard as zstd
import pickle

class CompressedCheckpointManager:
    def __init__(self, base_dir, compression_level=3):
        self.base_dir = base_dir
        self.compression_level = compression_level
        self.cctx = zstd.ZstdCompressor(level=compression_level)
        self.dctx = zstd.ZstdDecompressor()
        
        os.makedirs(base_dir, exist_ok=True)
    
    def save(self, checkpoint_id, state_dict):
        # 序列化状态字典
        serialized = pickle.dumps(state_dict)
        
        # 压缩数据
        compressed = self.cctx.compress(serialized)
        
        # 保存压缩数据
        filepath = os.path.join(self.base_dir, f"checkpoint_{checkpoint_id}.zst")
        with open(filepath, 'wb') as f:
            f.write(compressed)
        
        # 保存元数据
        with open(os.path.join(self.base_dir, f"metadata_{checkpoint_id}.json"), 'w') as f:
            json.dump({
                "uncompressed_size": len(serialized),
                "compressed_size": len(compressed),
                "compression_ratio": len(serialized) / len(compressed),
                "timestamp": time.time()
            }, f)
    
    def load(self, checkpoint_id):
        # 读取压缩数据
        filepath = os.path.join(self.base_dir, f"checkpoint_{checkpoint_id}.zst")
        with open(filepath, 'rb') as f:
            compressed = f.read()
        
        # 解压数据
        decompressed = self.dctx.decompress(compressed)
        
        # 反序列化
        state_dict = pickle.loads(decompressed)
        
        return state_dict

# 故障检测示例
class FaultDetector:
    def __init__(self, rank, world_size, monitor_interval=60):
        self.rank = rank
        self.world_size = world_size
        self.monitor_interval = monitor_interval
        self.last_heartbeat = time.time()
        self.heartbeat_queue = multiprocessing.Queue()
        
        # 启动监控线程
        self.monitor_thread = threading.Thread(target=self._monitor_loop)
        self.monitor_thread.daemon = True
        self.monitor_thread.start()
        
        # 启动心跳线程
        if rank == 0:
            self.heartbeat_thread = threading.Thread(target=self._heartbeat_monitor)
            self.heartbeat_thread.daemon = True
            self.heartbeat_thread.start()
    
    def _monitor_loop(self):
        while True:
            # 检查本地资源
            self._check_local_resources()
            
            # 发送心跳
            if self.rank == 0:
                self._send_heartbeat()
            
            time.sleep(self.monitor_interval)
    
    def _check_local_resources(self):
        # 检查GPU内存使用
        try:
            gpu_memory = torch.cuda.memory_allocated() / (1024**3)
            if gpu_memory > torch.cuda.get_device_properties(0).total_memory / (1024**3) * 0.95:
                logging.warning(f"GPU memory usage critical: {gpu_memory:.2f}GB")
                # 触发内存预警
        except Exception as e:
            logging.error(f"GPU check failed: {e}")
            # 触发硬件故障
    
    def _send_heartbeat(self):
        # 发送心跳信号
        try:
            dist.broadcast(torch.tensor([time.time()], device='cuda'), src=0)
        except Exception as e:
            logging.error(f"Heartbeat failed: {e}")
    
    def _heartbeat_monitor(self):
        # 监控其他节点心跳
        while True:
            # 实现细节...
            time.sleep(self.monitor_interval / 2)

# 自动恢复管理器示例
class AutoRecoveryManager:
    def __init__(self, checkpoint_dir, max_retries=3, recovery_timeout=3600):
        self.checkpoint_dir = checkpoint_dir
        self.max_retries = max_retries
        self.recovery_timeout = recovery_timeout
        self.recovery_attempts = 0
        self.last_recovery_time = 0
    
    def attempt_recovery(self, model, optimizer, trainer=None):
        """尝试从最新检查点恢复训练"""
        if self.recovery_attempts >= self.max_retries:
            logging.error(f"Maximum recovery attempts ({self.max_retries}) reached")
            return False
        
        # 检查是否在恢复冷却期
        if time.time() - self.last_recovery_time < 300:  # 5分钟冷却期
            logging.warning("Recovery cooling period, skipping attempt")
            return False
        
        try:
            # 查找最新检查点
            latest_checkpoint = self._find_latest_checkpoint()
            if not latest_checkpoint:
                logging.error("No checkpoint found for recovery")
                return False
            
            logging.info(f"Attempting recovery from checkpoint: {latest_checkpoint}")
            
            # 加载检查点
            checkpoint = torch.load(latest_checkpoint, map_location='cpu')
            
            # 恢复模型
            model.load_state_dict(checkpoint['model_state_dict'])
            
            # 恢复优化器
            optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
            
            # 恢复训练器状态（如果提供）
            if trainer:
                if 'trainer_state' in checkpoint:
                    trainer.load_state_dict(checkpoint['trainer_state'])
                
                # 恢复训练迭代
                trainer.current_epoch = checkpoint.get('epoch', 0)
                trainer.global_step = checkpoint.get('global_step', 0)
                
                # 恢复随机状态
                if 'rng_state' in checkpoint:
                    torch.set_rng_state(checkpoint['rng_state'].cpu())
                    torch.cuda.set_rng_state(checkpoint['cuda_rng_state'].cpu())
            
            self.recovery_attempts += 1
            self.last_recovery_time = time.time()
            
            logging.info(f"Recovery successful from epoch {checkpoint.get('epoch', 0)}, step {checkpoint.get('global_step', 0)}")
            return True
            
        except Exception as e:
            logging.error(f"Recovery failed: {e}")
            return False
    
    def _find_latest_checkpoint(self):
        """查找最新的有效检查点"""
        # 实现细节...
        pass

# 预测性故障检测示例
class PredictiveFaultDetector:
    def __init__(self, history_window=100, prediction_horizon=10):
        self.history_window = history_window
        self.prediction_horizon = prediction_horizon
        self.metrics_history = {}
        self.fault_model = self._load_fault_model()
    
    def _load_fault_model(self):
        # 加载预训练的故障预测模型
        # 在实际应用中，这可能是一个LSTM或Transformer模型
        class SimpleFaultPredictor:
            def predict(self, features):
                # 简化的预测逻辑
                gpu_temp = features.get('gpu_temperature', 0)
                memory_pressure = features.get('memory_pressure', 0)
                
                # 基于规则的简单预测
                if gpu_temp > 90 or memory_pressure > 0.95:
                    return {'fault_probability': 0.85, 'fault_type': 'hardware'}
                return {'fault_probability': 0.1, 'fault_type': 'none'}
        
        return SimpleFaultPredictor()
    
    def update_metrics(self, metrics):
        # 更新指标历史
        for key, value in metrics.items():
            if key not in self.metrics_history:
                self.metrics_history[key] = []
            
            self.metrics_history[key].append(value)
            # 保持窗口大小
            if len(self.metrics_history[key]) > self.history_window:
                self.metrics_history[key] = self.metrics_history[key][-self.history_window:]
    
    def predict_faults(self):
        # 准备特征
        features = self._prepare_features()
        
        # 预测故障
        prediction = self.fault_model.predict(features)
        
        # 如果预测到高故障概率，触发预防措施
        if prediction['fault_probability'] > 0.7:
            self._trigger_prevention(prediction)
        
        return prediction
    
    def _prepare_features(self):
        # 从历史指标中提取特征
        features = {}
        
        # 计算统计特征
        for key, values in self.metrics_history.items():
            if len(values) > 0:
                features[f'{key}_mean'] = np.mean(values)
                features[f'{key}_std'] = np.std(values)
                features[f'{key}_trend'] = np.polyfit(range(len(values)), values, 1)[0]
        
        return features
    
    def _trigger_prevention(self, prediction):
        # 触发预防措施，如保存检查点、降低批处理大小等
        logging.warning(f"High fault probability detected: {prediction['fault_probability']:.2f}, type: {prediction['fault_type']}")
        # 实现预防措施...

# 部分恢复管理器示例
class PartialRecoveryManager:
    def __init__(self, checkpoint_manager):
        self.checkpoint_manager = checkpoint_manager
        self.component_status = {}
    
    def report_component_failure(self, component_type, component_id):
        # 报告组件故障
        if component_type not in self.component_status:
            self.component_status[component_type] = {}
        
        self.component_status[component_type][component_id] = 'failed'
        logging.error(f"Component failed: {component_type} {component_id}")
    
    def recover_failed_components(self, model, optimizer, latest_checkpoint):
        # 加载检查点
        checkpoint = torch.load(latest_checkpoint)
        
        # 恢复失败的模型组件
        if 'model' in self.component_status:
            for component_id in self.component_status['model']:
                if component_id in checkpoint['model_state_dict']:
                    # 只恢复失败的组件
                    state_dict_entry = {component_id: checkpoint['model_state_dict'][component_id]}
                    model.load_state_dict(state_dict_entry, strict=False)
                    logging.info(f"Recovered model component: {component_id}")
        
        # 恢复失败的优化器组件
        if 'optimizer' in self.component_status:
            # 实现部分优化器状态恢复
            pass
        
        # 清除已恢复的故障状态
        self.component_status = {}
        
        return True

# 容错训练器示例
class FaultTolerantTrainer:
    def __init__(self, model, optimizer, train_dataloader, config):
        self.model = model
        self.optimizer = optimizer
        self.train_dataloader = train_dataloader
        self.config = config
        
        # 初始化组件
        self.checkpoint_manager = self._create_checkpoint_manager()
        self.fault_detector = PredictiveFaultDetector()
        self.recovery_manager = AutoRecoveryManager(
            self.config['checkpoint_dir'],
            max_retries=self.config['max_recovery_retries']
        )
        
        # 训练状态
        self.current_epoch = 0
        self.global_step = 0
        self.best_metric = float('-inf')
        self.running = True
    
    def _create_checkpoint_manager(self):
        # 根据配置创建合适的检查点管理器
        if self.config.get('use_distributed_checkpoint', False):
            return DistributedCheckpointManager(
                self.config['checkpoint_dir'],
                world_size=dist.get_world_size(),
                rank=dist.get_rank()
            )
        else:
            return BasicCheckpointManager(self.config['checkpoint_dir'])
    
    def train(self, max_epochs):
        try:
            # 尝试恢复训练
            if self.config.get('auto_resume', True):
                self._attempt_resume()
            
            # 主训练循环
            for epoch in range(self.current_epoch, max_epochs):
                self.current_epoch = epoch
                
                # 预测潜在故障
                self._check_for_potential_faults()
                
                # 训练一个epoch
                self._train_epoch()
                
                # 保存检查点
                if epoch % self.config['checkpoint_interval'] == 0:
                    self._save_checkpoint(is_epoch_end=True)
                
        except KeyboardInterrupt:
            logging.info("Training interrupted by user")
            self._save_checkpoint(is_final=True)
        except Exception as e:
            logging.error(f"Training failed with error: {e}")
            
            # 尝试保存失败状态
            try:
                self._save_checkpoint(is_failure=True)
            except:
                logging.error("Failed to save failure checkpoint")
            
            # 尝试恢复
            if self.recovery_manager.attempt_recovery(self.model, self.optimizer, self):
                self.train(max_epochs)  # 递归继续训练
            else:
                raise
    
    def _train_epoch(self):
        # 训练一个epoch的实现
        for batch_idx, batch in enumerate(self.train_dataloader):
            # 更新全局步数
            self.global_step += 1
            
            try:
                # 前向传播
                outputs = self.model(**batch)
                loss = outputs.loss
                
                # 反向传播
                self.optimizer.zero_grad()
                loss.backward()
                
                # 优化器步骤
                self.optimizer.step()
                
                # 记录指标
                self._log_metrics(loss, batch_idx)
                
                # 检查点
                if self.global_step % self.config['checkpoint_interval'] == 0:
                    self._save_checkpoint()
                    
                # 预测潜在故障
                if self.global_step % self.config['fault_check_interval'] == 0:
                    self._check_for_potential_faults()
                    
            except RuntimeError as e:
                # 处理运行时错误（如内存不足）
                if 'out of memory' in str(e):
                    self._handle_oom()
                else:
                    raise
    
    def _save_checkpoint(self, is_epoch_end=False, is_final=False, is_failure=False):
        # 准备检查点数据
        checkpoint = {
            'model_state_dict': self.model.state_dict(),
            'optimizer_state_dict': self.optimizer.state_dict(),
            'epoch': self.current_epoch,
            'global_step': self.global_step,
            'best_metric': self.best_metric,
            'rng_state': torch.get_rng_state(),
            'cuda_rng_state': torch.cuda.get_rng_state(),
            'timestamp': time.time(),
            'metadata': {
                'is_epoch_end': is_epoch_end,
                'is_final': is_final,
                'is_failure': is_failure
            }
        }
        
        # 保存检查点
        checkpoint_id = f"{self.current_epoch}_{self.global_step}"
        self.checkpoint_manager.save(checkpoint_id, checkpoint)
        
        # 如果是最佳模型，保存副本
        current_metric = self._get_current_metric()
        if current_metric > self.best_metric:
            self.best_metric = current_metric
            self.checkpoint_manager.save("best", checkpoint)
    
    def _attempt_resume(self):
        # 尝试从最新检查点恢复
        return self.recovery_manager.attempt_recovery(self.model, self.optimizer, self)
    
    def _check_for_potential_faults(self):
        # 收集系统和训练指标
        metrics = self._collect_metrics()
        
        # 更新预测模型
        self.fault_detector.update_metrics(metrics)
        
        # 预测潜在故障
        prediction = self.fault_detector.predict_faults()
        
        # 如果预测到高故障概率，提前保存检查点
        if prediction['fault_probability'] > 0.7:
            logging.warning("High fault risk detected, saving preemptive checkpoint")
            self._save_checkpoint()
    
    def _handle_oom(self):
        # 处理内存不足错误
        logging.warning("Out of memory detected")
        
        # 尝试降低批处理大小
        if hasattr(self.train_dataloader, 'batch_sampler'):
            if hasattr(self.train_dataloader.batch_sampler, 'batch_size'):
                new_batch_size = max(1, self.train_dataloader.batch_sampler.batch_size // 2)
                self.train_dataloader.batch_sampler.batch_size = new_batch_size
                logging.info(f"Reduced batch size to {new_batch_size}")
        
        # 清空缓存
        torch.cuda.empty_cache()
        
        # 保存紧急检查点
        self._save_checkpoint()
    
    def _collect_metrics(self):
        # 收集系统和训练指标
        metrics = {
            'gpu_temperature': self._get_gpu_temperature(),
            'memory_pressure': self._get_memory_pressure(),
            'training_speed': self._get_training_speed(),
            'loss_gradient': self._get_loss_gradient()
        }
        return metrics

# I/O优化的检查点管理器
class OptimizedIOCheckpointManager:
    def __init__(self, checkpoint_dir, buffer_size=1024*1024*64):  # 64MB缓冲区
        self.checkpoint_dir = checkpoint_dir
        self.buffer_size = buffer_size
        self.file_handlers = {}
        
        os.makedirs(checkpoint_dir, exist_ok=True)
    
    def save(self, checkpoint_id, state_dict, use_memory_map=False):
        filepath = os.path.join(self.checkpoint_dir, f"checkpoint_{checkpoint_id}.pth")
        
        # 使用大缓冲区进行写入
        with open(filepath, 'wb', buffering=self.buffer_size) as f:
            if use_memory_map:
                # 使用内存映射文件
                self._save_with_mmap(f, state_dict)
            else:
                # 标准保存
                torch.save(state_dict, f)
        
        return filepath
    
    def _save_with_mmap(self, file_handle, state_dict):
        # 为大型张量使用内存映射
        # 实现细节...
        pass
    
    def load(self, checkpoint_id, use_memory_map=False):
        filepath = os.path.join(self.checkpoint_dir, f"checkpoint_{checkpoint_id}.pth")
        
        if use_memory_map:
            # 使用内存映射加速加载
            return self._load_with_mmap(filepath)
        else:
            # 使用大缓冲区进行读取
            with open(filepath, 'rb', buffering=self.buffer_size) as f:
                return torch.load(f, map_location='cpu')

# 高性能分布式检查点管理器
class HighPerformanceDistributedCheckpoint:
    def __init__(self, checkpoint_dir, world_size, rank):
        self.checkpoint_dir = checkpoint_dir
        self.world_size = world_size
        self.rank = rank
        
        # 创建节点特定目录
        self.node_dir = os.path.join(checkpoint_dir, f"node_{rank}")
        os.makedirs(self.node_dir, exist_ok=True)
        
        # 配置并行I/O
        self.io_workers = self._configure_io_workers()
    
    def _configure_io_workers(self):
        # 确定I/O工作线程数量
        # 在现代系统上，通常是CPU核心数的一半
        num_workers = max(1, os.cpu_count() // 2)
        
        # 创建工作线程池
        return concurrent.futures.ThreadPoolExecutor(max_workers=num_workers)
    
    def save(self, checkpoint_id, state_dict, shard_size=1024*1024*1024):  # 1GB分片
        # 为大型张量创建分片
        futures = []
        shard_metadata = {}
        
        # 分片并异步保存
        for key, tensor in state_dict.items():
            if isinstance(tensor, torch.Tensor) and tensor.numel() > 1e6:  # 对大型张量分片
                # 计算分片数量
                tensor_size_bytes = tensor.nelement() * tensor.element_size()
                num_shards = max(1, (tensor_size_bytes + shard_size - 1) // shard_size)
                
                # 保存分片元数据
                shard_metadata[key] = {
                    "num_shards": num_shards,
                    "shape": tensor.shape,
                    "dtype": str(tensor.dtype)
                }
                
                # 异步保存每个分片
                for i in range(num_shards):
                    future = self.io_workers.submit(
                        self._save_tensor_shard,
                        key,
                        i,
                        tensor,
                        num_shards,
                        checkpoint_id
                    )
                    futures.append(future)
            else:
                # 小型张量直接保存
                future = self.io_workers.submit(
                    self._save_small_tensor,
                    key,
                    tensor,
                    checkpoint_id
                )
                futures.append(future)
        
        # 等待所有保存操作完成
        concurrent.futures.wait(futures)
        
        # 保存元数据
        self._save_metadata(checkpoint_id, shard_metadata)
        
        # 同步所有节点
        dist.barrier()
    
    def _save_tensor_shard(self, tensor_name, shard_idx, tensor, num_shards, checkpoint_id):
        # 计算分片范围
        shard_size = (tensor.numel() + num_shards - 1) // num_shards
        start_idx = shard_idx * shard_size
        end_idx = min((shard_idx + 1) * shard_size, tensor.numel())
        
        # 提取分片
        shard = tensor.view(-1)[start_idx:end_idx].clone()
        
        # 保存分片
        shard_path = os.path.join(
            self.node_dir,
            f"{checkpoint_id}_{tensor_name}_shard_{shard_idx}.pt"
        )
        torch.save(shard, shard_path)
    
    def _save_small_tensor(self, tensor_name, tensor, checkpoint_id):
        # 保存小型张量
        tensor_path = os.path.join(
            self.node_dir,
            f"{checkpoint_id}_{tensor_name}.pt"
        )
        torch.save(tensor, tensor_path)
    
    def _save_metadata(self, checkpoint_id, shard_metadata):
        # 保存元数据
        metadata_path = os.path.join(self.node_dir, f"{checkpoint_id}_metadata.json")
        with open(metadata_path, 'w') as f:
            json.dump({
                "checkpoint_id": checkpoint_id,
                "rank": self.rank,
                "world_size": self.world_size,
                "timestamp": time.time(),
                "shard_metadata": shard_metadata
            }, f)
    
    def load(self, checkpoint_id):
        # 加载元数据
        metadata = self._load_metadata(checkpoint_id)
        
        # 重建状态字典
        state_dict = {}
        futures = []
        
        # 异步加载
        for key, info in metadata["shard_metadata"].items():
            if "num_shards" in info:
                # 加载分片张量
                future = self.io_workers.submit(
                    self._load_sharded_tensor,
                    key,
                    info,
                    checkpoint_id
                )
                futures.append((key, future))
            else:
                # 加载小型张量
                future = self.io_workers.submit(
                    self._load_small_tensor,
                    key,
                    checkpoint_id
                )
                futures.append((key, future))
        
        # 收集结果
        for key, future in futures:
            state_dict[key] = future.result()
        
        return state_dict

# 高性能检查点压缩器
class HighPerformanceCheckpointCompressor:
    def __init__(self, compression_level=4, use_gpu_compression=False):
        self.compression_level = compression_level
        self.use_gpu_compression = use_gpu_compression
        
        # 初始化压缩器
        if use_gpu_compression and torch.cuda.is_available():
            # 使用GPU加速压缩（如果可用）
            self.compressor = self._init_gpu_compressor()
        else:
            # 使用CPU压缩
            self.compressor = lz4.frame.LZ4FrameCompressor(
                compression_level=compression_level
            )
    
    def _init_gpu_compressor(self):
        # 初始化GPU压缩器（简化示例）
        class GPUCompressor:
            def compress(self, data):
                # GPU压缩实现（示例）
                return lz4.frame.compress(data)
            
            def decompress(self, data):
                # GPU解压实现（示例）
                return lz4.frame.decompress(data)
        
        return GPUCompressor()
    
    def compress_checkpoint(self, state_dict):
        # 优化的检查点压缩
        compressed_state = {}
        
        # 分别处理不同类型的数据
        for key, value in state_dict.items():
            if isinstance(value, torch.Tensor):
                # 针对张量的特殊压缩
                compressed_state[key] = self._compress_tensor(value)
            elif isinstance(value, dict):
                # 递归压缩字典
                compressed_state[key] = self.compress_checkpoint(value)
            else:
                # 其他数据类型
                compressed_state[key] = value
        
        return compressed_state
    
    def _compress_tensor(self, tensor):
        # 张量压缩策略
        # 1. 对于稀疏张量，只存储非零元素
        if hasattr(tensor, 'is_sparse') and tensor.is_sparse:
            return {
                "type": "sparse",
                "indices": tensor.indices(),
                "values": tensor.values(),
                "size": tensor.size()
            }
        
        # 2. 对于低精度要求的张量，降低精度
        if tensor.dtype == torch.float32 and self._can_reduce_precision(tensor):
            return {
                "type": "reduced_precision",
                "data": tensor.to(torch.float16),
                "original_dtype": "float32"
            }
        
        # 3. 对于其他张量，直接返回
        return {
            "type": "original",
            "data": tensor
        }
    
    def _can_reduce_precision(self, tensor):
        # 检查张量是否适合降低精度
        # 检查值范围和精度要求
        min_val, max_val = tensor.min().item(), tensor.max().item()
        # 确保值在float16范围内
        if min_val > -65504 and max_val < 65504:
            # 计算量化误差
            quantized = tensor.to(torch.float16).to(torch.float32)
            max_error = torch.max(torch.abs(tensor - quantized)).item()
            # 如果最大误差小于阈值，允许降低精度
            return max_error < 1e-4
        return False

+----------------+       +------------------+
|                |       |                  |
|  训练节点 1     +------>+   共享存储系统   |
|                |       |                  |
+----------------+       +------------------+
        |                        ^
        |                        |
        v                        |
+----------------+       +------------------+
|                |       |                  |
|  训练节点 2     +------>+   检查点管理器   |
|                |       |                  |
+----------------+       +------------------+

+----------------+     +----------------+     +----------------+
|                |     |                |     |                |
|  训练节点组 1   +---->+  本地存储节点  +---->+  分布式文件系统 |
|                |     |                |     |                |
+----------------+     +----------------+     +----------------+
        |                      |                     |
        v                      v                     v
+----------------------------------------------------------+
|                                                          |
|                     元数据服务与协调器                     |
|                                                          |
+----------------------------------------------------------+

# 智能检查点管理系统架构
class IntelligentCheckpointSystem:
    def __init__(self, config):
        self.config = config
        
        # 初始化子系统
        self.storage_manager = self._create_storage_manager()
        self.checkpoint_optimizer = self._create_checkpoint_optimizer()
        self.fault_tolerance = self._create_fault_tolerance()
        self.monitoring = self._create_monitoring()
        self.scheduler = self._create_scheduler()
        
        # 启动协调服务
        self.coordinator = self._start_coordinator()
    
    def _create_storage_manager(self):
        # 创建分层存储管理器
        tiers = self.config.get('storage_tiers', [])
        return TieredStorageManager(tiers)
    
    def _create_checkpoint_optimizer(self):
        # 创建检查点优化器
        return CheckpointOptimizer(
            compression=self.config.get('compression', 'lz4'),
            sharding=self.config.get('sharding', True),
            incremental=self.config.get('incremental', True)
        )
    
    def _create_fault_tolerance(self):
        # 创建容错子系统
        return FaultToleranceSubsystem(
            redundancy=self.config.get('redundancy', 3),
            recovery_strategy=self.config.get('recovery_strategy', 'auto')
        )
    
    def _create_monitoring(self):
        # 创建监控子系统
        return MonitoringSubsystem(
            metrics=self.config.get('metrics', []),
            alert_thresholds=self.config.get('alert_thresholds', {})
        )
    
    def _create_scheduler(self):
        # 创建调度器
        return CheckpointScheduler(
            base_frequency=self.config.get('base_frequency', 1000),
            adaptive=self.config.get('adaptive', True)
        )
    
    def _start_coordinator(self):
        # 启动协调服务
        coordinator = CheckpointCoordinator(
            storage_manager=self.storage_manager,
            optimizer=self.checkpoint_optimizer,
            fault_tolerance=self.fault_tolerance,
            monitoring=self.monitoring,
            scheduler=self.scheduler
        )
        
        # 在独立进程中启动
        coordinator_process = multiprocessing.Process(target=coordinator.run)
        coordinator_process.daemon = True
        coordinator_process.start()
        
        return coordinator_process
    
    def save_checkpoint(self, checkpoint_id, state_dict):
        # 保存检查点的高级API
        # 实现细节...
        pass
    
    def load_checkpoint(self, checkpoint_id):
        # 加载检查点的高级API
        # 实现细节...
        pass

# 大规模分布式训练的检查点配置
def get_large_scale_checkpoint_config():
    return {
        # 检查点基础配置
        "checkpoint_dir": "/shared/fs/checkpoints/llm_training",
        "checkpoint_interval": 1000,           # 每1000步保存一次
        "max_checkpoints": 20,                # 最多保存20个检查点
        "save_best": True,                    # 保存最佳检查点
        
        # 分布式配置
        "distributed": True,
        "world_size": 128,                    # 128个节点
        "rank": 0,                            # 当前节点排名
        
        # 高级功能
        "async_save": True,                   # 异步保存
        "compression": {
            "enabled": True,
            "algorithm": "zstd",             # 使用Zstandard压缩
            "level": 3                        # 压缩级别
        },
        "sharding": {
            "enabled": True,
            "shard_size": 256 * 1024 * 1024   # 256MB分片
        },
        "incremental": {
            "enabled": True,
            "full_checkpoint_interval": 10    # 每10个检查点保存一次完整检查点
        },
        
        # 容错配置
        "fault_tolerance": {
            "redundancy": 3,                  # 3份冗余
            "checksum": True,                 # 启用校验和
            "verify_on_save": False,          # 保存时不验证（提高性能）
            "verify_on_load": True            # 加载时验证
        },
        
        # 存储优化
        "storage": {
            "tiered": True,
            "tiers": [
                {
                    "type": "local_ssd",
                    "path": "/local/ssd/checkpoints",
                    "priority": "high",
                    "retention": 5              # 保留5个最新检查点
                },
                {
                    "type": "nfs",
                    "path": "/shared/fs/checkpoints",
                    "priority": "medium",
                    "retention": 20             # 保留20个检查点
                },
                {
                    "type": "object_storage",
                    "path": "s3://llm-checkpoints/archive",
                    "priority": "low",
                    "retention": "auto"        # 自动管理
                }
            ]
        },
        
        # 监控
        "monitoring": {
            "enabled": True,
            "metrics": ["save_time", "load_time", "storage_usage"],
            "prometheus_endpoint": ":9090"
        }
    }

# 资源受限环境的检查点配置
def get_resource_constrained_checkpoint_config():
    return {
        "checkpoint_dir": "./local_checkpoints",
        "checkpoint_interval": 5000,           # 降低检查点频率
        "max_checkpoints": 3,                 # 只保留3个检查点
        "save_best": True,
        
        # 优化存储使用
        "compression": {
            "enabled": True,
            "algorithm": "lz4",              # 更快的压缩算法
            "level": 1                        # 低压缩级别，提高速度
        },
        "save_weights_only": False,           # 仍然保存优化器状态
        
        # 节省I/O
        "async_save": True,
        "save_on_cpu": True,                  # 在CPU上构建检查点
        
        # 容错
        "backup_before_overwrite": True,      # 覆盖前备份
        "checkpoint_validation": True         # 验证检查点完整性
    }

# PyTorch DDP检查点保存示例
import torch
import torch.distributed as dist
import os

def save_distributed_checkpoint(rank, world_size, model, optimizer, epoch, output_dir):
    # 创建检查点字典
    checkpoint = {
        'epoch': epoch,
        'model_state_dict': model.state_dict(),
        'optimizer_state_dict': optimizer.state_dict(),
    }
    
    # 为每个进程创建独立的文件名
    checkpoint_path = os.path.join(output_dir, f"checkpoint_epoch_{epoch}_rank_{rank}.pth")
    torch.save(checkpoint, checkpoint_path)
    
    # 同步所有进程
    dist.barrier()
    
    # 主进程创建元数据文件
    if rank == 0:
        metadata = {
            'epoch': epoch,
            'world_size': world_size,
            'checkpoint_files': [f"checkpoint_epoch_{epoch}_rank_{r}.pth" for r in range(world_size)],
            'timestamp': time.time()
        }
        with open(os.path.join(output_dir, f"metadata_epoch_{epoch}.json"), 'w') as f:
            json.dump(metadata, f)
        
        # 创建最新检查点的软链接（Linux/Mac）
        # 在Windows上需要使用不同的方法
        latest_link = os.path.join(output_dir, "latest_checkpoint.txt")
        with open(latest_link, 'w') as f:
            f.write(f"{epoch}")

def load_distributed_checkpoint(rank, world_size, model, optimizer, output_dir, epoch=None):
    # 如果没有指定epoch，加载最新的
    if epoch is None:
        latest_link = os.path.join(output_dir, "latest_checkpoint.txt")
        if os.path.exists(latest_link):
            with open(latest_link, 'r') as f:
                epoch = int(f.read().strip())
        else:
            raise FileNotFoundError("No checkpoint metadata found")
    
    # 加载元数据
    metadata_path = os.path.join(output_dir, f"metadata_epoch_{epoch}.json")
    if rank == 0 and not os.path.exists(metadata_path):
        raise FileNotFoundError(f"Metadata not found for epoch {epoch}")
    
    # 同步所有进程
    dist.barrier()
    
    # 加载当前进程的检查点
    checkpoint_path = os.path.join(output_dir, f"checkpoint_epoch_{epoch}_rank_{rank}.pth")
    checkpoint = torch.load(checkpoint_path, map_location='cpu')
    
    # 恢复模型和优化器
    model.load_state_dict(checkpoint['model_state_dict'])
    optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
    
    return checkpoint['epoch']

# DeepSpeed分布式检查点示例
import deepspeed

def setup_deepspeed_checkpoint(model_engine, config):
    # 配置DeepSpeed检查点
    checkpoint_config = {
        "enabled": True,
        "output_dir": config["checkpoint_dir"],
        "checkpoint_interval": config["checkpoint_interval"],
        "checkpoint_tag": "deepspeed_checkpoint",
        "export_fp16": True,
        "reset_tag": "reset_tag",
        "include_optimizer_states": True,
        "save_latest_only": False,
        "num_checkpoints_to_keep": config["max_checkpoints"]
    }
    
    # 设置检查点钩子
    model_engine.save_checkpoint = partial(
        model_engine.save_checkpoint,
        checkpoint_dir=checkpoint_config["output_dir"],
        tag=checkpoint_config["checkpoint_tag"],
        include_optimizer_states=checkpoint_config["include_optimizer_states"]
    )
    
    return checkpoint_config

def load_deepspeed_checkpoint(model_engine, checkpoint_dir, tag=None):
    # 加载最新或指定标签的检查点
    load_path, client_sd = deepspeed.utils.zero_to_fp32.load_state_dict_from_zero_checkpoint(
        checkpoint_dir, tag
    )
    
    # 恢复训练状态
    iteration = client_sd.get('global_steps', 0)
    epoch = client_sd.get('epoch', 0)
    
    return iteration, epoch

# Megatron-LM检查点配置示例
def get_megatron_checkpoint_config():
    return {
        "checkpoint_path": "/shared/fs/checkpoints/megatron",
        "save_interval": 1000,
        "load": True,
        "load_args": {
            "load_checkpoint_path": "/shared/fs/checkpoints/megatron/latest",
            "load_arg_overrides": {},
        },
        "save_args": {
            "save_checkpoint_args": {
                "save_interval": 1000,
                "keep_last_n": 10,
                "checkpoint_dir": "/shared/fs/checkpoints/megatron",
                "save_rng": True,
                "save_latest": True,
                "no_save_optim": False,
                "no_save_rng": False,
            },
        },
        "always_save_last": True,
        "reset_optim_params": False,
    }

# PyTorch Lightning检查点配置
from pytorch_lightning import Trainer
from pytorch_lightning.callbacks import ModelCheckpoint

def setup_lightning_checkpoint(config):
    checkpoint_callback = ModelCheckpoint(
        dirpath=config["checkpoint_dir"],
        filename="{epoch:02d}-{val_loss:.2f}",
        save_top_k=config["max_checkpoints"],
        verbose=True,
        monitor="val_loss",
        mode="min",
        save_last=True,
        every_n_train_steps=config["checkpoint_interval"],
        save_weights_only=False,
        save_on_train_epoch_end=True,
        auto_insert_metric_name=True,
    )
    
    # 配置训练器
    trainer = Trainer(
        callbacks=[checkpoint_callback],
        accelerator="gpu",
        devices=config["num_gpus"],
        strategy="ddp",
        enable_checkpointing=True,
        num_nodes=config["num_nodes"],
    )
    
    return trainer, checkpoint_callback

# 企业级检查点框架示例
class EnterpriseCheckpointFramework:
    def __init__(self, config):
        self.config = config
        self.backend = self._select_backend(config["backend"])
        self.storage = self._init_storage(config["storage"])
        self.compression = self._init_compression(config["compression"])
        self.monitor = self._init_monitoring(config["monitoring"])
        self.security = self._init_security(config["security"])
        
        # 初始化服务
        self._start_services()
    
    def _select_backend(self, backend_type):
        # 根据配置选择后端
        if backend_type == "deepspeed":
            return DeepSpeedBackend(self.config)
        elif backend_type == "megatron":
            return MegatronBackend(self.config)
        elif backend_type == "pytorch":
            return PyTorchBackend(self.config)
        else:
            raise ValueError(f"Unknown backend: {backend_type}")
    
    def _init_storage(self, storage_config):
        # 初始化存储系统
        return StorageManager(storage_config)
    
    def _init_compression(self, compression_config):
        # 初始化压缩系统
        return CompressionManager(compression_config)
    
    def _init_monitoring(self, monitoring_config):
        # 初始化监控系统
        return MonitoringSystem(monitoring_config)
    
    def _init_security(self, security_config):
        # 初始化安全系统
        return SecurityManager(security_config)
    
    def _start_services(self):
        # 启动后台服务
        self.health_check = HealthCheckService()
        self.health_check.start()
    
    def save(self, checkpoint_id, state_dict, metadata=None):
        # 企业级检查点保存流程
        with self.monitor.measure("checkpoint_save"):
            # 1. 记录开始事件
            self.monitor.log_event("checkpoint_save_start", {
                "checkpoint_id": checkpoint_id,
                "metadata": metadata
            })
            
            # 2. 安全检查
            if self.security.enabled:
                state_dict = self.security.encrypt(state_dict)
            
            # 3. 压缩数据
            if self.compression.enabled:
                state_dict = self.compression.compress(state_dict)
            
            # 4. 后端保存
            save_path = self.backend.save(checkpoint_id, state_dict)
            
            # 5. 存储管理
            storage_info = self.storage.register_checkpoint(
                checkpoint_id, save_path, metadata
            )
            
            # 6. 备份
            if self.config["backup_enabled"]:
                self.storage.create_backup(checkpoint_id)
            
            # 7. 记录完成事件
            self.monitor.log_event("checkpoint_save_complete", {
                "checkpoint_id": checkpoint_id,
                "storage_info": storage_info
            })
            
            return save_path
    
    def load(self, checkpoint_id, decrypt=True):
        # 企业级检查点加载流程
        with self.monitor.measure("checkpoint_load"):
            # 1. 记录开始事件
            self.monitor.log_event("checkpoint_load_start", {
                "checkpoint_id": checkpoint_id
            })
            
            # 2. 获取存储信息
            storage_info = self.storage.get_checkpoint_info(checkpoint_id)
            
            # 3. 后端加载
            state_dict = self.backend.load(checkpoint_id)
            
            # 4. 解压缩
            if self.compression.enabled:
                state_dict = self.compression.decompress(state_dict)
            
            # 5. 解密
            if self.security.enabled and decrypt:
                state_dict = self.security.decrypt(state_dict)
            
            # 6. 验证完整性
            if not self._verify_checkpoint(state_dict, storage_info):
                raise ValueError("Checkpoint verification failed")
            
            # 7. 记录完成事件
            self.monitor.log_event("checkpoint_load_complete", {
                "checkpoint_id": checkpoint_id
            })
            
            return state_dict
    
    def _verify_checkpoint(self, state_dict, storage_info):
        # 验证检查点完整性
        # 实现细节...
        return True

# 智能分片检查点管理器
class IntelligentShardedCheckpoint:
    def __init__(self, world_size, rank, config):
        self.world_size = world_size
        self.rank = rank
        self.config = config
        self.shard_size = config.get("shard_size", 512 * 1024 * 1024)  # 512MB默认分片大小
        self.metadata = {}
    
    def save(self, checkpoint_id, state_dict, output_dir):
        """智能分片保存"""
        # 创建输出目录
        os.makedirs(output_dir, exist_ok=True)
        
        # 计算每个张量的大小并排序
        tensor_sizes = {}
        for key, tensor in state_dict.items():
            if isinstance(tensor, torch.Tensor):
                tensor_sizes[key] = tensor.nelement() * tensor.element_size()
        
        # 按大小排序
        sorted_tensors = sorted(tensor_sizes.items(), key=lambda x: x[1], reverse=True)
        
        # 智能分片分配
        shards = self._allocate_shards(sorted_tensors)
        
        # 保存每个分片
        self._save_shards(checkpoint_id, state_dict, shards, output_dir)
        
        # 保存元数据
        self._save_metadata(checkpoint_id, shards, output_dir)
        
        # 同步所有进程
        dist.barrier()
    
    def _allocate_shards(self, sorted_tensors):
        """智能分配张量到分片"""
        shards = [[] for _ in range(self.world_size)]
        shard_sizes = [0] * self.world_size
        
        for key, size in sorted_tensors:
            # 找到当前最小的分片
            min_shard_idx = shard_sizes.index(min(shard_sizes))
            
            # 检查是否需要为大张量创建专用分片
            if size > self.shard_size * 0.8:
                # 为大张量分配专用分片
                shards[min_shard_idx].append(key)
                shard_sizes[min_shard_idx] += size
            else:
                # 尝试将小张量添加到现有分片中
                target_shard = min_shard_idx
                
                # 如果最小分片添加后会超过阈值，检查其他分片
                if shard_sizes[target_shard] + size > self.shard_size:
                    # 寻找其他合适的分片
                    for i in range(self.world_size):
                        if shard_sizes[i] + size <= self.shard_size:
                            target_shard = i
                            break
                
                shards[target_shard].append(key)
                shard_sizes[target_shard] += size
        
        return shards
    
    def _save_shards(self, checkpoint_id, state_dict, shards, output_dir):
        """保存当前进程负责的分片"""
        # 获取当前进程负责的分片
        if self.rank < len(shards):
            my_shard = shards[self.rank]
            
            # 创建分片数据
            shard_data = {}
            for key in my_shard:
                if key in state_dict:
                    shard_data[key] = state_dict[key]
            
            # 保存分片
            shard_path = os.path.join(
                output_dir,
                f"checkpoint_{checkpoint_id}_shard_{self.rank}.pth"
            )
            torch.save(shard_data, shard_path)
    
    def _save_metadata(self, checkpoint_id, shards, output_dir):
        """保存分片元数据"""
        if self.rank == 0:
            # 创建元数据
            metadata = {
                "checkpoint_id": checkpoint_id,
                "world_size": self.world_size,
                "timestamp": time.time(),
                "shards": {}
            }
            
            # 记录每个分片包含的张量
            for i, shard in enumerate(shards):
                metadata["shards"][f"shard_{i}"] = shard
            
            # 保存元数据
            metadata_path = os.path.join(output_dir, f"metadata_{checkpoint_id}.json")
            with open(metadata_path, 'w') as f:
                json.dump(metadata, f)
    
    def load(self, checkpoint_id, output_dir):
        """加载并聚合检查点"""
        # 加载元数据
        metadata_path = os.path.join(output_dir, f"metadata_{checkpoint_id}.json")
        if self.rank == 0:
            if not os.path.exists(metadata_path):
                raise FileNotFoundError(f"Metadata not found for checkpoint {checkpoint_id}")
            
            with open(metadata_path, 'r') as f:
                self.metadata = json.load(f)
        
        # 广播元数据
        metadata_tensor = torch.zeros(1, device='cuda') if self.rank == 0 else torch.ones(1, device='cuda')
        dist.broadcast(metadata_tensor, src=0)
        
        # 确保所有进程都加载了元数据
        if self.rank != 0:
            with open(metadata_path, 'r') as f:
                self.metadata = json.load(f)
        
        # 加载当前进程的分片
        shard_path = os.path.join(
            output_dir,
            f"checkpoint_{checkpoint_id}_shard_{self.rank}.pth"
        )
        
        if os.path.exists(shard_path):
            shard_data = torch.load(shard_path, map_location='cpu')
        else:
            shard_data = {}
        
        # 收集所有进程的分片数据
        all_shards_data = self._collect_all_shards(shard_data)
        
        return all_shards_data
    
    def _collect_all_shards(self, local_shard):
        """收集所有进程的分片数据"""
        # 为简单起见，这里只返回本地分片
        # 实际实现需要使用all_gather收集所有进程的数据
        # 由于all_gather可能导致内存问题，对于大型模型可能需要更复杂的策略
        return local_shard

# 分布式检查点一致性协议
class CheckpointConsistencyProtocol:
    def __init__(self, rank, world_size):
        self.rank = rank
        self.world_size = world_size
        self.consistency_group = self._create_consistency_group()
    
    def _create_consistency_group(self):
        # 创建一致性协议组
        return {
            "members": list(range(self.world_size)),
            "quorum_size": (self.world_size // 2) + 1,
            "leader": 0
        }
    
    def prepare_checkpoint(self, checkpoint_id):
        """准备检查点，确保所有节点都准备就绪"""
        # 发送准备消息
        ready_messages = self._exchange_ready_messages(checkpoint_id)
        
        # 检查是否达到法定数量
        if len([msg for msg in ready_messages if msg["status"] == "ready"]) >= self.consistency_group["quorum_size"]:
            # 通知所有节点开始保存
            self._broadcast_start_message(checkpoint_id)
            return True
        else:
            # 取消检查点
            self._broadcast_cancel_message(checkpoint_id)
            return False
    
    def finalize_checkpoint(self, checkpoint_id, success):
        """完成检查点，确认所有节点都已完成"""
        # 发送完成消息
        completion_messages = self._exchange_completion_messages(checkpoint_id, success)
        
        # 计算成功节点数量
        success_count = sum(1 for msg in completion_messages if msg["success"])
        
        # 检查一致性
        if success_count == self.world_size:
            # 所有节点都成功，提交检查点
            if self.rank == self.consistency_group["leader"]:
                self._commit_checkpoint(checkpoint_id)
            return True
        elif success_count >= self.consistency_group["quorum_size"]:
            # 多数节点成功，但有少数失败
            # 处理部分失败的情况
            if self.rank == self.consistency_group["leader"]:
                self._handle_partial_failure(checkpoint_id, completion_messages)
            return True
        else:
            # 多数节点失败，回滚检查点
            if self.rank == self.consistency_group["leader"]:
                self._rollback_checkpoint(checkpoint_id)
            return False
    
    def _exchange_ready_messages(self, checkpoint_id):
        # 交换准备消息的实现
        # 简化示例，实际实现需要使用dist.all_gather
        ready_messages = []
        if self.rank == 0:
            # 模拟收集所有节点的准备消息
            for i in range(self.world_size):
                ready_messages.append({"rank": i, "status": "ready"})
        
        # 广播结果
        return ready_messages
    
    def _broadcast_start_message(self, checkpoint_id):
        # 广播开始消息
        # 实现细节...
        pass
    
    def _broadcast_cancel_message(self, checkpoint_id):
        # 广播取消消息
        # 实现细节...
        pass
    
    def _exchange_completion_messages(self, checkpoint_id, success):
        # 交换完成消息
        # 实现细节...
        completion_messages = []
        return completion_messages
    
    def _commit_checkpoint(self, checkpoint_id):
        # 提交检查点
        # 实现细节...
        pass
    
    def _handle_partial_failure(self, checkpoint_id, completion_messages):
        # 处理部分失败
        # 实现细节...
        pass
    
    def _rollback_checkpoint(self, checkpoint_id):
        # 回滚检查点
        # 实现细节...
        pass

# NVMe-oF检查点管理器
class NVMeoFCheckpointManager:
    def __init__(self, config):
        self.config = config
        self.nvme_devices = self._discover_nvme_devices()
        self.device_mapping = self._create_device_mapping()
    
    def _discover_nvme_devices(self):
        """发现可用的NVMe-oF设备"""
        # 在实际实现中，这将使用系统API发现NVMe设备
        # 这里简化为模拟
        return [
            {"name": "nvme0n1", "path": "/dev/nvme0n1", "capacity": 3840, "speed": "100Gbps"},
            {"name": "nvme1n1", "path": "/dev/nvme1n1", "capacity": 3840, "speed": "100Gbps"}
        ]
    
    def _create_device_mapping(self):
        """创建设备映射策略"""
        # 根据张量大小和访问模式创建映射策略
        return {
            "large_tensors": "striped",  # 大型张量使用条带化
            "small_tensors": "mirrored",  # 小型张量使用镜像
            "optimizer_states": "striped"  # 优化器状态使用条带化
        }
    
    def save(self, checkpoint_id, state_dict):
        """使用NVMe-oF保存检查点"""
        # 创建挂载点
        mount_point = self._get_mount_point(checkpoint_id)
        
        # 根据映射策略保存不同类型的数据
        futures = []
        
        # 处理大型张量
        large_tensors = {k: v for k, v in state_dict.items() 
                        if isinstance(v, torch.Tensor) and v.nelement() > 1e6}
        if large_tensors:
            future = self._save_with_strategy(large_tensors, mount_point, "large_tensors")
            futures.append(future)
        
        # 处理小型张量
        small_tensors = {k: v for k, v in state_dict.items() 
                        if isinstance(v, torch.Tensor) and v.nelement() <= 1e6}
        if small_tensors:
            future = self._save_with_strategy(small_tensors, mount_point, "small_tensors")
            futures.append(future)
        
        # 等待所有保存操作完成
        for future in futures:
            future.result()
        
        return mount_point
    
    def _save_with_strategy(self, data, mount_point, data_type):
        """使用指定策略保存数据"""
        strategy = self.device_mapping[data_type]
        
        if strategy == "striped":
            # 条带化保存，跨多个设备分割数据
            return self._save_striped(data, mount_point)
        elif strategy == "mirrored":
            # 镜像保存，在多个设备上保存副本
            return self._save_mirrored(data, mount_point)
        else:
            # 默认保存
            return self._save_default(data, mount_point)
    
    def _save_striped(self, data, mount_point):
        """条带化保存实现"""
        # 实现细节...
        pass
    
    def _save_mirrored(self, data, mount_point):
        """镜像保存实现"""
        # 实现细节...
        pass
    
    def _get_mount_point(self, checkpoint_id):
        """获取检查点挂载点"""
        return f"/mnt/nvme/checkpoints/{checkpoint_id}"

# RDMA加速的分布式检查点
class RDMACheckpointManager:
    def __init__(self, rank, world_size, rdma_config):
        self.rank = rank
        self.world_size = world_size
        self.rdma_config = rdma_config
        self.rdma_context = self._init_rdma()
    
    def _init_rdma(self):
        """初始化RDMA连接"""
        # 在实际实现中，这将使用RDMA库（如libibverbs）
        # 这里简化为模拟实现
        return {
            "connected": True,
            "endpoints": [f"rdma://node_{i}:12345" for i in range(self.world_size)]
        }
    
    def save(self, checkpoint_id, state_dict, output_dir):
        """使用RDMA保存分布式检查点"""
        # 创建本地检查点
        local_path = os.path.join(output_dir, f"local_{checkpoint_id}_rank_{self.rank}.pth")
        torch.save(state_dict, local_path)
        
        # 使用RDMA传输到其他节点
        if self.rdma_context["connected"]:
            # 确定需要传输的目标节点
            target_ranks = self._determine_target_ranks()
            
            # 使用RDMA并行传输
            self._rdma_transfer(local_path, target_ranks)
        
        # 同步所有节点
        dist.barrier()
        
        return local_path
    
    def _determine_target_ranks(self):
        """确定需要传输检查点的目标节点"""
        # 实现智能传输决策
        # 例如，只传输到存储节点或备份节点
        return list(range(self.world_size))[:3]  # 简化示例：只传输到前3个节点
    
    def _rdma_transfer(self, source_path, target_ranks):
        """使用RDMA进行检查点传输"""
        # 模拟RDMA传输
        for target_rank in target_ranks:
            if target_rank != self.rank:
                # 在实际实现中，这里将使用RDMA API发送数据
                print(f"Transferring {source_path} to rank {target_rank} via RDMA")
    
    def load(self, checkpoint_id, output_dir, preferred_source=None):
        """从RDMA源加载检查点"""
        # 如果指定了首选源，尝试从该源加载
        if preferred_source is not None and preferred_source != self.rank:
            try:
                # 使用RDMA直接加载
                return self._rdma_direct_load(checkpoint_id, preferred_source)
            except Exception as e:
                print(f"RDMA load from {preferred_source} failed: {e}")
        
        # 回退到本地加载
        local_path = os.path.join(output_dir, f"local_{checkpoint_id}_rank_{self.rank}.pth")
        return torch.load(local_path, map_location='cpu')
    
    def _rdma_direct_load(self, checkpoint_id, source_rank):
        """使用RDMA直接从源加载检查点"""
        # 模拟RDMA直接加载
        print(f"Loading checkpoint {checkpoint_id} directly from rank {source_rank} via RDMA")
        # 实际实现将使用RDMA API直接读取远程内存
        return {}

# 智能故障检测系统
class IntelligentFaultDetector:
    def __init__(self, config):
        self.config = config
        self.monitor_metrics = self.config.get("monitor_metrics", [
            "gpu_utilization", "memory_usage", "network_bandwidth", 
            "disk_iops", "temperature", "training_speed"
        ])
        self.anomaly_detector = self._init_anomaly_detector()
        self.alert_system = self._init_alert_system()
        self.logger = self._init_logger()
        self.history = []
    
    def _init_anomaly_detector(self):
        """初始化异常检测器"""
        if self.config.get("use_ml_based_detection", False):
            # 基于机器学习的异常检测
            return MLBasedAnomalyDetector(self.config["ml_config"])
        else:
            # 基于规则的异常检测
            return RuleBasedAnomalyDetector(self.config["rule_config"])
    
    def _init_alert_system(self):
        """初始化告警系统"""
        return AlertSystem(self.config["alert_config"])
    
    def _init_logger(self):
        """初始化日志系统"""
        return Logger(self.config["logging_config"])
    
    def monitor(self, metrics):
        """监控系统指标"""
        # 记录当前指标
        timestamp = time.time()
        self.history.append((timestamp, metrics))
        
        # 限制历史记录长度
        if len(self.history) > self.config.get("max_history_length", 1000):
            self.history.pop(0)
        
        # 检测异常
        anomalies = self.anomaly_detector.detect(metrics)
        
        # 处理异常
        if anomalies:
            self._handle_anomalies(anomalies, metrics)
        
        return anomalies
    
    def _handle_anomalies(self, anomalies, metrics):
        """处理检测到的异常"""
        # 记录异常
        self.logger.error("Detected anomalies:", {
            "timestamp": time.time(),
            "anomalies": anomalies,
            "metrics": metrics
        })
        
        # 发送告警
        severity = self._calculate_severity(anomalies)
        self.alert_system.send_alert(anomalies, severity)
        
        # 触发自动响应
        self._trigger_auto_response(anomalies, severity)
    
    def _calculate_severity(self, anomalies):
        """计算异常严重程度"""
        # 基于异常数量和类型计算严重程度
        if any(anomaly["type"] in ["gpu_failure", "network_partition"] for anomaly in anomalies):
            return "critical"
        elif any(anomaly["type"] in ["high_temperature", "high_memory_pressure"] for anomaly in anomalies):
            return "high"
        else:
            return "medium"
    
    def _trigger_auto_response(self, anomalies, severity):
        """触发自动响应机制"""
        if severity == "critical":
            # 触发紧急保存检查点
            self._trigger_emergency_checkpoint()
        elif severity == "high":
            # 触发预防性检查点
            self._trigger_preventive_checkpoint()
    
    def _trigger_emergency_checkpoint(self):
        """触发紧急检查点保存"""
        # 实现紧急检查点保存逻辑
        pass
    
    def _trigger_preventive_checkpoint(self):
        """触发预防性检查点保存"""
        # 实现预防性检查点保存逻辑
        pass

# 自动故障恢复框架
class AutoRecoveryFramework:
    def __init__(self, checkpoint_manager, fault_detector, config):
        self.checkpoint_manager = checkpoint_manager
        self.fault_detector = fault_detector
        self.config = config
        self.recovery_history = []
        self.recovery_state = "idle"
    
    def detect_and_recover(self, training_context):
        """检测故障并执行恢复流程"""
        # 监控系统状态
        metrics = self._collect_system_metrics()
        anomalies = self.fault_detector.monitor(metrics)
        
        # 如果检测到需要恢复的异常
        if anomalies and self.recovery_state == "idle":
            self.recovery_state = "detected"
            return self._perform_recovery(training_context, anomalies)
        
        return False
    
    def _collect_system_metrics(self):
        """收集系统指标"""
        # 在实际实现中，这将收集GPU、内存、网络等指标
        # 这里简化为模拟
        return {
            "gpu_utilization": random.uniform(0, 100),
            "memory_usage": random.uniform(0, 100),
            "network_bandwidth": random.uniform(0, 1000),
            "disk_iops": random.uniform(0, 10000),
            "temperature": random.uniform(40, 95),
            "training_speed": random.uniform(0, 100)
        }
    
    def _perform_recovery(self, training_context, anomalies):
        """执行恢复流程"""
        try:
            # 记录恢复开始
            recovery_id = f"recovery_{int(time.time())}"
            self.recovery_history.append({
                "id": recovery_id,
                "start_time": time.time(),
                "anomalies": anomalies,
                "state": "started"
            })
            
            # 根据异常类型确定恢复策略
            recovery_strategy = self._select_recovery_strategy(anomalies)
            
            # 执行恢复
            success = self._execute_recovery_strategy(
                recovery_id, 
                training_context, 
                recovery_strategy
            )
            
            # 更新恢复状态
            self._update_recovery_status(recovery_id, success)
            
            return success
        except Exception as e:
            # 记录恢复失败
            self._log_recovery_failure(anomalies, str(e))
            return False
        finally:
            # 重置恢复状态
            self.recovery_state = "idle"
    
    def _select_recovery_strategy(self, anomalies):
        """选择合适的恢复策略"""
        # 基于异常类型选择恢复策略
        if any(anomaly["type"] == "node_failure" for anomaly in anomalies):
            return "node_replacement"
        elif any(anomaly["type"] == "network_partition" for anomaly in anomalies):
            return "network_recovery"
        elif any(anomaly["type"] in ["gpu_failure", "high_temperature"] for anomaly in anomalies):
            return "gpu_fallback"
        else:
            return "checkpoint_recovery"
    
    def _execute_recovery_strategy(self, recovery_id, training_context, strategy):
        """执行选定的恢复策略"""
        if strategy == "node_replacement":
            return self._recover_node_failure(recovery_id, training_context)
        elif strategy == "network_recovery":
            return self._recover_network_partition(recovery_id, training_context)
        elif strategy == "gpu_fallback":
            return self._recover_gpu_failure(recovery_id, training_context)
        elif strategy == "checkpoint_recovery":
            return self._recover_from_checkpoint(recovery_id, training_context)
        else:
            raise ValueError(f"Unknown recovery strategy: {strategy}")
    
    def _recover_node_failure(self, recovery_id, training_context):
        """恢复节点故障"""
        # 1. 识别失败的节点
        failed_nodes = self._identify_failed_nodes()
        
        # 2. 选择最新的有效检查点
        checkpoint_id = self._select_latest_valid_checkpoint()
        
        # 3. 在剩余节点上重启训练
        # 实际实现将涉及更多细节
        return True
    
    def _recover_network_partition(self, recovery_id, training_context):
        """恢复网络分区"""
        # 实现网络分区恢复逻辑
        return True
    
    def _recover_gpu_failure(self, recovery_id, training_context):
        """恢复GPU故障"""
        # 实现GPU故障恢复逻辑
        return True
    
    def _recover_from_checkpoint(self, recovery_id, training_context):
        """从检查点恢复"""
        # 选择最合适的检查点
        checkpoint_id = self._select_latest_valid_checkpoint()
        
        # 加载检查点
        state_dict = self.checkpoint_manager.load(checkpoint_id)
        
        # 恢复训练状态
        self._restore_training_state(training_context, state_dict)
        
        return True
    
    def _identify_failed_nodes(self):
        """识别失败的节点"""
        # 实现节点故障检测逻辑
        return []
    
    def _select_latest_valid_checkpoint(self):
        """选择最新的有效检查点"""
        # 实现检查点选择逻辑
        return "latest"
    
    def _restore_training_state(self, training_context, state_dict):
        """恢复训练状态"""
        # 实现训练状态恢复逻辑
        pass
    
    def _update_recovery_status(self, recovery_id, success):
        """更新恢复状态"""
        for recovery in self.recovery_history:
            if recovery["id"] == recovery_id:
                recovery["end_time"] = time.time()
                recovery["duration"] = recovery["end_time"] - recovery["start_time"]
                recovery["success"] = success
                recovery["state"] = "completed"
    
    def _log_recovery_failure(self, anomalies, error):
        """记录恢复失败"""
        # 实现日志记录逻辑
        pass

# 预测性故障恢复系统
class PredictiveRecoverySystem:
    def __init__(self, config):
        self.config = config
        self.ml_model = self._load_prediction_model()
        self.feature_extractor = FeatureExtractor()
        self.recovery_scheduler = RecoveryScheduler()
        self.predictions = []
    
    def _load_prediction_model(self):
        """加载预测模型"""
        # 实现模型加载逻辑
        return PredictiveModel()
    
    def predict_failures(self, historical_data, current_state):
        """预测潜在故障"""
        # 提取特征
        features = self.feature_extractor.extract(
            historical_data, 
            current_state
        )
        
        # 生成预测
        predictions = self.ml_model.predict(features)
        
        # 记录预测结果
        self.predictions.append({
            "timestamp": time.time(),
            "predictions": predictions,
            "confidence": self.ml_model.confidence()
        })
        
        return predictions
    
    def schedule_preventive_actions(self, predictions, training_plan):
        """安排预防性操作"""
        # 根据预测结果安排预防性操作
        actions = self.recovery_scheduler.schedule(
            predictions,
            training_plan
        )
        
        return actions
    
    def execute_preventive_action(self, action, training_context):
        """执行预防性操作"""
        if action["type"] == "preventive_checkpoint":
            return self._create_preventive_checkpoint(action, training_context)
        elif action["type"] == "resource_reallocation":
            return self._reallocate_resources(action, training_context)
        elif action["type"] == "reduced_workload":
            return self._reduce_workload(action, training_context)
        else:
            raise ValueError(f"Unknown preventive action: {action['type']}")
    
    def _create_preventive_checkpoint(self, action, training_context):
        """创建预防性检查点"""
        # 实现预防性检查点创建逻辑
        pass
    
    def _reallocate_resources(self, action, training_context):
        """重新分配资源"""
        # 实现资源重新分配逻辑
        pass
    
    def _reduce_workload(self, action, training_context):
        """减少工作负载"""
        # 实现工作负载减少逻辑
        pass

# GPT-5检查点管理系统配置示例
def get_gpt5_checkpoint_config():
    return {
        "system": {
            "name": "GPT-5 Checkpoint Management System",
            "version": "3.0",
            "description": "Enterprise-grade checkpoint management for GPT-5 training"
        },
        "checkpoint": {
            "base_dir": "/mnt/nvmeo-gpfs/gpt5/checkpoints",
            "interval": {
                "steps": 2000,  # 每2000步保存一次常规检查点
                "time": 3600,  # 每小时保存一次时间检查点
                "loss_threshold": 0.005  # 损失下降超过阈值时保存
            },
            "retention": {
                "latest": 50,      # 保留最新的50个检查点
                "best": 20,        # 保留性能最佳的20个检查点
                "daily": 90,       # 保留90天的每日检查点
                "weekly": 52       # 保留一年的每周检查点
            },
            "compression": {
                "enabled": True,
                "algorithm": "zstd",  # 使用Zstandard压缩
                "level": 9,          # 高压缩级别
                "workers": 16        # 并行压缩工作器数量
            },
            "encryption": {
                "enabled": True,
                "algorithm": "AES-256-GCM",
                "key_rotation": {
                    "enabled": True,
                    "interval_days": 7
                }
            }
        },
        "distributed": {
            "strategy": "intelligent_sharding",  # 智能分片策略
            "world_size": 1024,                   # 分布式节点数量
            "replication_factor": 3,              # 每个检查点的副本数量
            "consistency": {
                "protocol": "quorum_based",       # 基于法定人数的一致性协议
                "quorum_size": 683               # 2/3 + 1的节点数
            }
        },
        "storage": {
            "tiered": {
                "hot": {
                    "type": "nvmeof_cluster",
                    "capacity": "10PB",
                    "nodes": 256,
                    "retention_days": 7
                },
                "warm": {
                    "type": "gpfs_cluster",
                    "capacity": "100PB",
                    "nodes": 1024,
                    "retention_days": 60
                },
                "cold": {
                    "type": "object_storage",
                    "capacity": "500PB",
                    "provider": "aws_s3",
                    "retention_days": 365
                }
            },
            "cache": {
                "enabled": True,
                "size_per_node_gb": 2048,  # 每节点2TB缓存
                "eviction_policy": "lru"
            }
        },
        "fault_tolerance": {
            "emergency_checkpoint": {
                "enabled": True,
                "triggers": [
                    "gpu_failure",
                    "network_partition",
                    "power_failure_warning",
                    "temperature_critical"
                ],
                "compression_level": 0,  # 紧急检查点不压缩以加快速度
                "timeout_seconds": 300   # 5分钟超时
            },
            "auto_recovery": {
                "enabled": True,
                "strategies": [
                    "node_replacement",
                    "network_recovery",
                    "reduced_precision_fallback",
                    "model_parallelism_adjustment"
                ],
                "max_retries": 3,
                "notification_channels": ["slack", "email", "pagerduty"]
            }
        },
        "monitoring": {
            "metrics": [
                "checkpoint_save_time",
                "checkpoint_load_time",
                "storage_usage",
                "compression_ratio",
                "recovery_success_rate",
                "failure_prediction_accuracy"
            ],
            "sampling_rate": 1.0,  # 100%采样率
            "retention_days": 30,
            "dashboards": [
                "checkpoint_overview",
                "storage_trends",
                "recovery_metrics",
                "failure_prediction"
            ]
        },
        "prediction": {
            "enabled": True,
            "model": {
                "type": "lstm_transformer",
                "retraining_frequency_days": 7
            },
            "features": [
                "historical_failures",
                "system_metrics_trends",
                "workload_patterns",
                "environmental_conditions"
            ],
            "threshold": 0.7  # 预测置信度阈值
        }
    }

# 高性能检查点优化实现
class HighPerformanceCheckpointOptimizer:
    def __init__(self, rank, world_size, config):
        self.rank = rank
        self.world_size = world_size
        self.config = config
        self.io_threads = config.get("io_threads", 32)
        self.buffer_size = config.get("buffer_size", 4 * 1024 * 1024 * 1024)  # 4GB缓冲区
        self.network_priority = config.get("network_priority", "high")
        
        # 初始化优化组件
        self.io_optimizer = self._init_io_optimizer()
        self.network_optimizer = self._init_network_optimizer()
        self.scheduler = self._init_scheduler()
    
    def _init_io_optimizer(self):
        """初始化I/O优化器"""
        return IOOptimizer(
            threads=self.io_threads,
            buffer_size=self.buffer_size
        )
    
    def _init_network_optimizer(self):
        """初始化网络优化器"""
        return NetworkOptimizer(
            priority=self.network_priority
        )
    
    def _init_scheduler(self):
        """初始化调度器"""
        return CheckpointScheduler()
    
    def optimize_checkpoint_save(self, state_dict, path):
        """优化检查点保存过程"""
        # 1. 确定最优保存时间窗口
        save_window = self.scheduler.determine_optimal_save_window()
        
        # 2. 对张量进行预排序和分组
        sorted_groups = self._sort_and_group_tensors(state_dict)
        
        # 3. 应用I/O优化
        self.io_optimizer.configure_for_write()
        
        # 4. 应用网络优化
        self.network_optimizer.reserve_bandwidth()
        
        # 5. 分块并行保存
        save_results = self._parallel_save_tensors(sorted_groups, path)
        
        # 6. 恢复网络设置
        self.network_optimizer.release_bandwidth()
        
        return save_results
    
    def optimize_checkpoint_load(self, path):
        """优化检查点加载过程"""
        # 1. 预取元数据
        metadata = self._prefetch_metadata(path)
        
        # 2. 应用I/O优化
        self.io_optimizer.configure_for_read()
        
        # 3. 应用网络优化
        self.network_optimizer.reserve_bandwidth()
        
        # 4. 按需并行加载
        state_dict = self._parallel_load_tensors(metadata, path)
        
        # 5. 恢复网络设置
        self.network_optimizer.release_bandwidth()
        
        return state_dict
    
    def _sort_and_group_tensors(self, state_dict):
        """对张量进行排序和分组"""
        # 实现张量排序和分组逻辑
        # 基于大小、访问频率等进行优化排序
        return []
    
    def _parallel_save_tensors(self, tensor_groups, path):
        """并行保存张量"""
        # 实现并行保存逻辑
        return {"success": True}
    
    def _prefetch_metadata(self, path):
        """预取检查点元数据"""
        # 实现元数据预取逻辑
        return {}
    
    def _parallel_load_tensors(self, metadata, path):
        """并行加载张量"""
        # 实现并行加载逻辑
        return {}

# 企业级检查点系统迁移实现
def migrate_checkpoint_system(old_config, new_config):
    # 1. 准备迁移环境
    prepare_migration_environment(old_config, new_config)
    
    # 2. 配置新系统
    new_system = setup_new_checkpoint_system(new_config)
    
    # 3. 建立数据转换管道
    conversion_pipeline = create_conversion_pipeline(old_config, new_config)
    
    # 4. 执行迁移
    migration_results = execute_migration(conversion_pipeline)
    
    # 5. 验证迁移结果
    validation_results = validate_migration(migration_results)
    
    # 6. 切换系统
    switch_result = switch_to_new_system(new_system)
    
    return {
        "migration": migration_results,
        "validation": validation_results,
        "switch": switch_result
    }

def create_conversion_pipeline(old_config, new_config):
    """创建检查点转换管道"""
    return {
        "steps": [
            # 1. 读取旧格式检查点
            {
                "name": "read_old_checkpoint",
                "function": read_old_format_checkpoint,
                "config": {
                    "source_dir": old_config["checkpoint_dir"],
                    "parallel_readers": 8
                }
            },
            # 2. 数据格式转换
            {
                "name": "convert_format",
                "function": convert_checkpoint_format,
                "config": {
                    "target_format": "distributed_sharded",
                    "world_size": new_config["world_size"]
                }
            },
            # 3. 应用压缩
            {
                "name": "apply_compression",
                "function": compress_checkpoint,
                "config": {
                    "algorithm": new_config["compression_algorithm"],
                    "level": new_config["compression_level"]
                }
            },
            # 4. 应用加密
            {
                "name": "apply_encryption",
                "function": encrypt_checkpoint,
                "config": {
                    "algorithm": new_config["encryption_algorithm"],
                    "key_management": "hsm"
                }
            },
            # 5. 写入新系统
            {
                "name": "write_new_checkpoint",
                "function": write_new_format_checkpoint,
                "config": {
                    "target_dir": new_config["checkpoint_dir"],
                    "storage_tier": "hot"
                }
            }
        ],
        "error_handling": {
            "strategy": "rollback",
            "max_retries": 3
        },
        "validation": {
            "enabled": True,
            "sample_rate": 0.1  # 10%的检查点进行验证
        }
    }

# 分布式检查点故障排除工具
def troubleshoot_checkpoint_issue(issue_type, environment_info):
    """自动诊断和解决检查点问题"""
    # 收集诊断信息
    diagnostic_info = collect_diagnostic_info(environment_info)
    
    # 根据问题类型选择排查策略
    if issue_type == "save_failure":
        return troubleshoot_save_failure(diagnostic_info)
    elif issue_type == "load_failure":
        return troubleshoot_load_failure(diagnostic_info)
    elif issue_type == "performance_issue":
        return troubleshoot_performance_issue(diagnostic_info)
    elif issue_type == "consistency_issue":
        return troubleshoot_consistency_issue(diagnostic_info)
    else:
        return {
            "status": "error",
            "message": f"Unknown issue type: {issue_type}"
        }

def troubleshoot_save_failure(diagnostic_info):
    """排查检查点保存失败问题"""
    # 检查磁盘空间
    if check_disk_space(diagnostic_info["storage_paths"]) < 10:  # 小于10%空间
        return {
            "status": "resolved",
            "action": "cleanup_storage",
            "details": "Low disk space detected. Clean up old checkpoints or allocate more storage."
        }
    
    # 检查文件系统权限
    if not check_file_permissions(diagnostic_info["checkpoint_dir"]):
        return {
            "status": "resolved",
            "action": "fix_permissions",
            "details": "Permission issue detected. Ensure the process has write access to the checkpoint directory."
        }
    
    # 检查网络连接（分布式环境）
    if diagnostic_info["is_distributed"]:
        network_status = check_network_connections(diagnostic_info["nodes"])
        if not network_status["all_connected"]:
            return {
                "status": "resolved",
                "action": "fix_network",
                "details": f"Network connectivity issues detected. Failed connections: {network_status['failed_connections']}"
            }
    
    # 检查硬件状态
    hardware_issues = check_hardware_status()
    if hardware_issues:
        return {
            "status": "resolved",
            "action": "replace_hardware",
            "details": f"Hardware issues detected: {hardware_issues}"
        }
    
    # 无法自动解决的问题
    return {
        "status": "needs_attention",
        "recommended_action": "manual_investigation",
        "details": "Unable to automatically diagnose the issue. Please check logs and contact support."
    }

# 检查点性能优化配置生成器
def generate_performance_optimization_config(scenario):
    """根据场景生成优化配置"""
    if scenario == "high_performance_training":
        return {
            "checkpoint": {
                "interval": {
                    "steps": 5000,  # 减少保存频率
                    "max_time_hours": 4
                },
                "compression": {
                    "enabled": True,
                    "algorithm": "lz4",  # 快速压缩，牺牲部分压缩率
                    "level": 3
                },
                "async_save": {
                    "enabled": True,
                    "buffer_size_gb": 8,
                    "dedicated_workers": 4
                },
                "skip_validation": True  # 保存时跳过验证以加快速度
            }
        }
    elif scenario == "large_model_training":
        return {
            "checkpoint": {
                "sharding": {
                    "enabled": True,
                    "strategy": "model_parallelism_aware",
                    "shard_size_gb": 2
                },
                "incremental": {
                    "enabled": True,
                    "delta_compression": True
                },
                "selective": {
                    "enabled": True,
                    "components": ["model", "optimizer", "rng"]
                },
                "gradient_accumulation_aware": True
            }
        }
    elif scenario == "cost_efficient_training":
        return {
            "checkpoint": {
                "storage": {
                    "tiering": {
                        "enabled": True,
                        "tiers": ["hot", "warm", "cold"]
                    },
                    "retention": {
                        "latest": 10,
                        "best": 5,
                        "periodic": "daily"
                    }
                },
                "compression": {
                    "enabled": True,
                    "algorithm": "zstd",
                    "level": 15  # 高压缩率
                },
                "deduplication": {
                    "enabled": True,
                    "window_size_mb": 256
                }
            }
        }
    else:
        return {
            "checkpoint": {
                "default_optimizations": True
            }
        }

# 企业级检查点系统检查清单
def enterprise_checkpoint_readiness_checklist():
    return {
        "reliability": [
            {"item": "检查点自动验证", "required": True, "description": "每次保存后自动验证检查点完整性"},
            {"item": "多副本保存", "required": True, "description": "关键检查点保存至少3个副本"},
            {"item": "一致性保证", "required": True, "description": "分布式环境下实现强一致性"},
            {"item": "事务性写入", "required": False, "description": "使用事务确保检查点原子性"},
            {"item": "版本控制", "required": True, "description": "支持检查点版本回滚"}
        ],
        "security": [
            {"item": "数据加密", "required": True, "description": "静态和传输中的检查点加密"},
            {"item": "访问控制", "required": True, "description": "基于角色的访问控制"},
            {"item": "审计日志", "required": True, "description": "记录所有检查点访问操作"},
            {"item": "密钥轮换", "required": False, "description": "定期轮换加密密钥"},
            {"item": "合规认证", "required": False, "description": "满足行业合规要求（如SOC2、GDPR）"}
        ],
        "performance": [
            {"item": "异步操作", "required": True, "description": "非阻塞式检查点保存"},
            {"item": "压缩优化", "required": True, "description": "高效的检查点压缩"},
            {"item": "缓存机制", "required": True, "description": "智能缓存策略"},
            {"item": "并行处理", "required": True, "description": "多线程I/O操作"},
            {"item": "性能监控", "required": True, "description": "实时监控检查点操作性能"}
        ],
        "scalability": [
            {"item": "分布式架构", "required": True, "description": "支持大规模分布式环境"},
            {"item": "横向扩展", "required": True, "description": "能够随节点数量线性扩展"},
            {"item": "分层存储", "required": True, "description": "支持多级存储架构"},
            {"item": "动态资源分配", "required": False, "description": "根据负载动态调整资源"},
            {"item": "弹性伸缩", "required": False, "description": "支持自动扩缩容"}
        ],
        "operational": [
            {"item": "自动恢复", "required": True, "description": "故障时自动恢复训练"},
            {"item": "告警机制", "required": True, "description": "检查点问题实时告警"},
            {"item": "备份策略", "required": True, "description": "定期备份到独立位置"},
            {"item": "灾难恢复", "required": False, "description": "支持跨区域灾难恢复"},
            {"item": "文档完善", "required": True, "description": "详细的操作和恢复文档"}
        ]
    }