graph TD
A[数据并行] --> B[模型并行]
B --> C[流水线并行]
C --> D[优化器分片]
D --> E[混合精度]subgraph 数据并行
A1[梯度聚合] --> A2[参数同步]
endsubgraph 模型并行
B1[层间切分] --> B2[设备间通信]
endsubgraph 流水线并行
C1[微批次切分] --> C2[气泡优化]
endsubgraph ZeRO
D1[参数分区] --> D2[梯度分区]
D2 --> D3[优化器状态分区]
end

import torch
from torch.utils.checkpoint import checkpointclass CheckpointedModel(torch.nn.Module):def __init__(self, model):super().__init__()self.layers = model.layersdef forward(self, x):# 每2层设置一个检查点for i in range(0, len(self.layers), 2):x = checkpoint(self._segment, x, i, min(i+2, len(self.layers)))return xdef _segment(self, x, start, end):for i in range(start, end):x = self.layers[i](x)return x

from deepspeed.runtime.zero.stage3 import FP16_DeepSpeedZeroOptimizerclass ZeROOptimizer:def __init__(self, model, optimizer_config):# 初始化ZeRO阶段3self.optimizer = FP16_DeepSpeedZeroOptimizer(model_parameters=model.parameters(),optimizer=optimizer_config["type"],param_persistence_threshold=optimizer_config["persistence_threshold"],partition_gradients=True,contiguous_gradients=True,reduce_scatter_bucket_size=optimizer_config["bucket_size"])def step(self):# 自动处理分区优化器状态self.optimizer.step()def state_dict(self):# 全局状态收集return self.optimizer.state_dict()

class DynamicLossScaler:def __init__(self, init_scale=2**16):self.scale = init_scaleself.steps_without_overflow = 0self.min_scale = 1self.max_scale = 2**24self.overflow_threshold = 1000def scale_loss(self, loss):return loss * self.scaledef update(self, has_overflow):if has_overflow:self.scale = max(self.min_scale, self.scale / 2)self.steps_without_overflow = 0else:self.steps_without_overflow += 1if self.steps_without_overflow >= self.overflow_threshold:self.scale = min(self.max_scale, self.scale * 2)self.steps_without_overflow = 0

from torch.cuda.amp import autocastdef bf16_train_step(model, batch):# 启用BF16混合精度with autocast(dtype=torch.bfloat16):outputs = model(batch["input"])loss = compute_loss(outputs, batch["target"])# 梯度缩放和更新scaler.scale(loss).backward()scaler.step(optimizer)scaler.update()

from torch.distributed import all_reducedef bucket_all_reduce(gradients, bucket_size=25*1024**2):# 按桶大小分组梯度buckets = []current_bucket = []current_size = 0for param in gradients:grad_size = param.grad.numel() * param.grad.element_size()if current_size + grad_size > bucket_size and current_bucket:buckets.append(current_bucket)current_bucket = []current_size = 0current_bucket.append(param)current_size += grad_size# 分桶通信for bucket in buckets:grads = [p.grad for p in bucket]flat_grads = torch.cat([g.flatten() for g in grads])all_reduce(flat_grads, op=torch.distributed.ReduceOp.AVG)# 还原梯度offset = 0for param in bucket:numel = param.grad.numel()param.grad = flat_grads[offset:offset+numel].view_as(param.grad)offset += numel

import threadingclass CommunicationOverlap:def __init__(self, model):self.comm_thread = Noneself.model = modeldef backward_hook(self, param):# 梯度就绪时启动异步通信def hook(grad):if self.comm_thread and self.comm_thread.is_alive():self.comm_thread.join()self.comm_thread = threading.Thread(target=all_reduce, args=(grad,))self.comm_thread.start()return gradreturn hookdef register_hooks(self):for param in self.model.parameters():if param.requires_grad:param.register_hook(self.backward_hook(param))

class MemoryDefragmenter:def __init__(self, interval=100):self.interval = intervalself.step_count = 0def before_forward(self):if self.step_count % self.interval == 0:torch.cuda.empty_cache()self._compact_memory()def _compact_memory(self):# 创建连续缓冲区total_size = sum(p.numel() for p in model.parameters())buffer = torch.empty(total_size, dtype=torch.uint8, device='cuda')# 重定位参数offset = 0for param in model.parameters():numel = param.numel()param.data = buffer[offset:offset+numel].view_as(param.data)offset += numel

def gradient_accumulation(model, batches, accumulation_steps):model.zero_grad()for i, batch in enumerate(batches):loss = model(batch)loss = loss / accumulation_stepsloss.backward()if (i+1) % accumulation_steps == 0:optimizer.step()model.zero_grad()

graph TB
A[训练任务] --> B[资源分析器]
B --> C[策略选择器]
C --> D[执行引擎]subgraph 资源分析器
B1[GPU显存] --> B2[网络带宽]
B3[计算能力] --> B4[拓扑结构]
endsubgraph 策略选择器
C1[ZeRO阶段] --> C2[并行策略]
C3[精度策略] --> C4[通信优化]
endsubgraph 执行引擎
D1[数据并行] --> D2[流水线并行]
D3[张量并行] --> D4[混合执行]
end

def select_strategy(cluster_info):# 基于集群配置选择最优策略if cluster_info.gpu_memory < 32:  # GBreturn {"zero_stage": 3,"precision": "bf16","pipeline_parallel": 4,"tensor_parallel": 2}elif cluster_info.bandwidth > 100:  # Gbpsreturn {"zero_stage": 2,"precision": "fp16","data_parallel": 8}else:return {"zero_stage": 1,"precision": "fp32","grad_accumulation": 4}