class DistributedParameterServer:
     def __init__(self, world_size: int, num_shards: int):
         self.world_size = world_size
         self.num_shards = num_shards
         
         # 跨節(jié)點(diǎn)存儲(chǔ)的參數(shù)分片
         self.parameter_shards = [
             torch.zeros(shard_size, requires_grad=True)
             for _ in range(num_shards)
        ]
         
         # 無鎖更新緩沖區(qū)
         self.update_buffers = {
             shard_id: AsyncUpdateBuffer(buffer_size=1024)
             for shard_id in range(num_shards)
        }
         
         # 初始化通信
         self.initialize_communication()
 
     def initialize_communication(self):
         # 設(shè)置 NCCL 用于 GPU 通信
         self.comm = ncclGetUniqueId()
         torch.distributed.init_process_group(
             backend='nccl',
             init_method='env://',
             world_size=self.world_size,
             rank=dist.get_rank()
        )
         
         # 為異步操作創(chuàng)建 CUDA 流
         self.streams = [
             torch.cuda.Stream()
             for _ in range(self.num_shards)
        ]

async def apply_updates(self, shard_id: int, updates: torch.Tensor):
         buffer = self.update_buffers[shard_id]
         
         # 在緩沖區(qū)中排隊(duì)更新
         buffer.push(updates)
         
         # 如果緩沖區(qū)已滿則處理更新
         if buffer.is_full():
             with torch.cuda.stream(self.streams[shard_id]):
                 # 聚合更新
                 aggregated = buffer.aggregate()
                 
                 # 將更新應(yīng)用到參數(shù)
                 self.parameter_shards[shard_id].add_(
                     aggregated,
                     alpha=self.learning_rate
                )
                 
                 # 清空緩沖區(qū)
                 buffer.clear()
                 
                 # 全局規(guī)約更新后的參數(shù)
                 torch.distributed.all_reduce(
                     self.parameter_shards[shard_id],
                     op=torch.distributed.ReduceOp.SUM,
                     async_op=True
                )

class DistributedTrainer:
     def __init__(
         self,
         model: nn.Module,
         optimizer: Type[torch.optim.Optimizer],
         world_size: int,
         gradient_accumulation_steps: int = 1
    ):
         self.model = model
         self.world_size = world_size
         self.grad_accum_steps = gradient_accumulation_steps
         
         # 封裝模型用于分布式訓(xùn)練
         self.model = DistributedDataParallel(
             model,
             device_ids=[local_rank],
             output_device=local_rank,
             find_unused_parameters=True
        )
         
         # 使用 ZeRO 優(yōu)化初始化優(yōu)化器
         self.optimizer = ZeROOptimizer(
             optimizer,
             model,
             overlap_comm=True,
             cpu_offload=True
        )
         
         # 用于混合精度的梯度縮放器
         self.scaler = GradScaler()
         
         # 設(shè)置梯度分桶
         self.grad_buckets = initialize_grad_buckets(
             model,
             bucket_size_mb=25
        )

@torch.cuda.amp.autocast()
     def train_step(
         self,
         batch: Dict[str, torch.Tensor]
    ) -> torch.Tensor:
         # 前向傳播
         outputs = self.model(**batch)
         loss = outputs.loss
         
         # 縮放損失用于梯度累積
         scaled_loss = loss / self.grad_accum_steps
         
         # 使用縮放后的損失進(jìn)行反向傳播
         self.scaler.scale(scaled_loss).backward()
         
         return loss.detach()
 
     def optimize_step(self):
         # 等待所有梯度計(jì)算完成
         torch.cuda.synchronize()
         
         # 反縮放梯度
         self.scaler.unscale_(self.optimizer)
         
         # 裁剪梯度
         torch.nn.utils.clip_grad_norm_(
             self.model.parameters(),
             max_norm=1.0
        )
         
         # 使用梯度分桶進(jìn)行優(yōu)化
         for bucket in self.grad_buckets:
             # 同步分桶梯度
             bucket.synchronize()
             
             # 應(yīng)用更新
             self.scaler.step(
                 self.optimizer,
                 bucket_idx=bucket.index
            )
             
             # 清空分桶梯度
             bucket.zero_grad()
         
         # 更新縮放器
         self.scaler.update()

def train_epoch(
         self,
         dataloader: DataLoader,
         epoch: int,
         eval_steps: int
    ):
         self.model.train()
         
         step = 0
         total_loss = 0
         
         # 訓(xùn)練循環(huán)
         for batch in dataloader:
             # 將批次數(shù)據(jù)移至 GPU
             batch = {
                 k: v.to(self.device)
                 for k, v in batch.items()
            }
             
             # 計(jì)算損失
             loss = self.train_step(batch)
             total_loss += loss.item()
             
             step += 1
             
             # 累積步數(shù)后優(yōu)化
             if step % self.grad_accum_steps == 0:
                 self.optimize_step()
             
             # 定期評(píng)估
             if step % eval_steps == 0:
                 self.evaluate(step, epoch)
                 self.model.train()

class DynamicLossScaler:
     def __init__(self, init_scale=2**15, scale_factor=2, scale_window=2000):
         self.cur_scale = init_scale
         self.scale_factor = scale_factor
         self.scale_window = scale_window
         self.num_overflows = 0
         self.num_steps = 0
         
     def scale(self, loss):
         return loss * self.cur_scale
         
     def update_scale(self, overflow):
         self.num_steps += 1
         if overflow:
             self.num_overflows += 1
             
         if self.num_steps % self.scale_window == 0:
             if self.num_overflows == 0:
                 self.cur_scale *= self.scale_factor
             else:
                 self.cur_scale /= self.scale_factor
             self.num_overflows = 0

class GradientAccumulator:
     def __init__(self, model, accumulation_steps):
         self.model = model
         self.accumulation_steps = accumulation_steps
         self.stored_gradients = {}
         self._initialize_gradient_storage()
         
     def _initialize_gradient_storage(self):
         for name, param in self.model.named_parameters():
             if param.requires_grad:
                 self.stored_gradients[name] = torch.zeros_like(param)
                 
     def accumulate_gradients(self):
         with torch.no_grad():
             for name, param in self.model.named_parameters():
                 if param.requires_grad and param.grad is not None:
                     self.stored_gradients[name] += param.grad / self.accumulation_steps
                     param.grad = None
                     
     def apply_accumulated_gradients(self):
         with torch.no_grad():
             for name, param in self.model.named_parameters():
                 if param.requires_grad:
                     param.grad = self.stored_gradients[name]
                     self.stored_gradients[name].zero_()

class ZeROStage1Optimizer:
     def __init__(self, optimizer, dp_process_group):
         self.optimizer = optimizer
         self.dp_process_group = dp_process_group
         self.world_size = dist.get_world_size(dp_process_group)
         self.rank = dist.get_rank(dp_process_group)
         self._partition_optimizer_state()
         
     def _partition_optimizer_state(self):
         for group in self.optimizer.param_groups:
             for p in group['params']:
                 if p.requires_grad:
                     state = self.optimizer.state[p]
                     
                     # 將優(yōu)化器狀態(tài)分片到不同節(jié)點(diǎn)
                     for k, v in state.items():
                         if torch.is_tensor(v):
                             partitioned = self._partition_tensor(v)
                             state[k] = partitioned
                             
     def _partition_tensor(self, tensor):
         # 計(jì)算每個(gè)進(jìn)程的分片大小
         partition_size = tensor.numel() // self.world_size
         start_idx = partition_size * self.rank
         end_idx = start_idx + partition_size
         return tensor.view(-1)[start_idx:end_idx]

def backward(self, loss):
         loss.backward()
         
         # 對(duì)梯度進(jìn)行分片
         for name, param in self.model.named_parameters():
             if param.requires_grad:
                 # 僅保留本節(jié)點(diǎn)負(fù)責(zé)的梯度分片
                 grad_partition = self._partition_gradient(param.grad)
                 param.grad = grad_partition
                 
     def _partition_gradient(self, gradient):
         partition_size = gradient.numel() // self.world_size
         start_idx = partition_size * self.rank
         end_idx = start_idx + partition_size
         return gradient.view(-1)[start_idx:end_idx]

def forward(self, *args, **kwargs):
         # 在前向傳播前收集完整參數(shù)
         self._gather_parameters()
         
         output = self.module(*args, **kwargs)
         
         # 釋放完整參數(shù)
         self._release_parameters()
         
         return output
         
     def _gather_parameters(self):
         for name, param in self.model.named_parameters():
             if param.requires_grad:
                 # 從所有節(jié)點(diǎn)收集完整參數(shù)
                 full_param = self._all_gather_parameter(param)
                 self.temp_params[name] = param.data
                 param.data = full_param
                 
     def _release_parameters(self):
         for name, param in self.model.named_parameters():
             if param.requires_grad:
                 # 恢復(fù)到分片狀態(tài)
                 param.data = self.temp_params[name]

class GradientCheckpointing:
     def __init__(self, model, checkpoint_layers):
         self.model = model
         self.checkpoint_layers = checkpoint_layers
         self.saved_activations = {}
         
     def forward_with_checkpoint(self, x):
         activations = []
         
         for i, layer in enumerate(self.model.layers):
             if i in self.checkpoint_layers:
                 # 保存輸入，釋放中間激活值
                 activations.append(x.detach())
                 x = layer(x)
             else:
                 x = layer(x)
                 
         return x, activations

class DistributedDataLoader:
     def __init__(
         self,
         dataset: Dataset,
         batch_size: int,
         world_size: int,
         rank: int,
         num_workers: int = 4,
         prefetch_factor: int = 2
    ):
         # 跨節(jié)點(diǎn)分片數(shù)據(jù)集
         self.sampler = DistributedSampler(
             dataset,
             num_replicas=world_size,
             rank=rank,
             shuffle=True
        )
         
         # 創(chuàng)建高效的數(shù)據(jù)加載器
         self.dataloader = DataLoader(
             dataset,
             batch_size=batch_size,
             sampler=self.sampler,
             num_workers=num_workers,
             pin_memory=True,
             prefetch_factor=prefetch_factor,
             persistent_workers=True
        )
         
         # 預(yù)取緩沖區(qū)
         self.prefetch_queue = Queue(maxsize=prefetch_factor)
         self.prefetch_stream = torch.cuda.Stream()
         
         # 啟動(dòng)預(yù)取工作進(jìn)程
         self.start_prefetch_workers()

def start_prefetch_workers(self):
         def prefetch_worker():
             while True:
                 # 獲取下一個(gè)批次
                 batch = next(self.dataloader.__iter__())
                 
                 with torch.cuda.stream(self.prefetch_stream):
                     # 將批次數(shù)據(jù)移至 GPU
                     batch = {
                         k: v.pin_memory().to(
                             self.device,
                             non_blocking=True
                        )
                         for k, v in batch.items()
                    }
                     
                     # 添加到隊(duì)列
                     self.prefetch_queue.put(batch)
 
         # 啟動(dòng)預(yù)取線程
         self.prefetch_threads = [
             threading.Thread(target=prefetch_worker)
             for _ in range(2)
        ]
         
         for thread in self.prefetch_threads:
             thread.daemon = True
             thread.start()

# 設(shè)置CUDA環(huán)境
 os.environ['CUDA_VISIBLE_DEVICES'] = '0,1,2,3'
 torch.backends.cudnn.benchmark = True
 torch.backends.cudnn.deterministic = False

# NCCL配置
 os.environ['NCCL_DEBUG'] = 'INFO'
 os.environ['NCCL_SOCKET_IFNAME'] = 'eth0'
 os.environ['NCCL_IB_DISABLE'] = '0'