117_LLM训练的高效分布式策略：从数据并行到ZeRO优化

# PyTorch DDP中的AllReduce示例
def allreduce_gradients(gradients, world_size):
    """
    使用AllReduce算法聚合梯度
    """
    # 初始化结果梯度
    result = torch.zeros_like(gradients[0])
    
    # 实现环形AllReduce
    for i in range(world_size):
        # 发送到下一个设备
        send_idx = (rank + 1) % world_size
        recv_idx = (rank - 1) % world_size
        
        # 发送本地梯度
        send_tensor = gradients[rank].clone()
        # 接收其他设备的梯度
        recv_tensor = torch.zeros_like(gradients[0])
        
        # 实际代码中使用torch.distributed.send/recv
        # 此处为简化示例
        
        # 更新本地梯度
        gradients[rank] += recv_tensor
    
    return gradients[rank]

# 梯度稀疏化示例
def sparsify_gradients(gradients, sparsity=0.9):
    """
    保留梯度中绝对值最大的10%，其余设为0
    """
    # 计算梯度绝对值
    abs_grads = torch.abs(gradients)
    
    # 计算阈值
    k = int(gradients.numel() * (1 - sparsity))
    if k <= 0:
        return torch.zeros_like(gradients)
    
    # 获取top-k阈值
    threshold = torch.topk(abs_grads.view(-1), k)[0][-1]
    
    # 稀疏化
    mask = abs_grads >= threshold
    sparse_grads = gradients * mask
    
    return sparse_grads

# 简化的1D张量并行前向计算示例
class TensorParallelLinear(nn.Module):
    def __init__(self, in_features, out_features, world_size):
        super().__init__()
        self.world_size = world_size
        # 按列分割权重矩阵
        self.local_out_features = out_features // world_size
        self.weight = nn.Parameter(
            torch.randn(self.local_out_features, in_features)
        )
        self.bias = nn.Parameter(
            torch.randn(self.local_out_features)
        )
    
    def forward(self, x):
        # 本地线性变换
        output = F.linear(x, self.weight, self.bias)
        
        # 收集所有设备的输出
        gathered_output = [torch.zeros_like(output) for _ in range(self.world_size)]
        torch.distributed.all_gather(gathered_output, output)
        
        # 拼接结果
        final_output = torch.cat(gathered_output, dim=-1)
        return final_output

# 简化的流水线并行示例
class PipelineParallelModel(nn.Module):
    def __init__(self, layers, devices):
        super().__init__()
        self.layers = layers
        self.devices = devices
        # 将层分配到不同设备
        self.layer_device_map = {i: device for i, device in enumerate(devices)}
    
    def forward(self, x, microbatches=4):
        # 将输入分割成微型批次
        x_chunks = torch.chunk(x, microbatches)
        outputs = []
        
        for x_chunk in x_chunks:
            current = x_chunk
            # 流水线前向传播
            for i, layer in enumerate(self.layers):
                # 将张量移动到层所在设备
                device = self.layer_device_map[i]
                current = current.to(device)
                current = layer(current)
            outputs.append(current.cpu())
        
        # 拼接所有微型批次的输出
        return torch.cat(outputs)

# ZeRO优化器简化实现思路
class ZeROOptimizer:
    def __init__(self, model, optimizer, stage=3):
        self.model = model
        self.optimizer = optimizer
        self.stage = stage
        self.world_size = torch.distributed.get_world_size()
        self.rank = torch.distributed.get_rank()
        
        # 根据ZeRO阶段进行初始化
        if self.stage >= 1:
            self._partition_optimizer_states()
        if self.stage >= 2:
            self._partition_gradients()
        if self.stage >= 3:
            self._partition_parameters()
    
    def _partition_optimizer_states(self):
        # 将优化器状态分区到不同设备
        pass
    
    def _partition_gradients(self):
        # 将梯度分区到不同设备
        pass
    
    def _partition_parameters(self):
        # 将模型参数分区到不同设备
        pass
    
    def step(self):
        # ZeRO优化的参数更新步骤
        pass

# 混合并行配置示例
def get_hybrid_parallel_config(model_size, hardware_config):
    """
    根据模型大小和硬件配置自动确定混合并行策略
    """
    world_size = hardware_config['num_nodes'] * hardware_config['gpus_per_node']
    
    if model_size < 10e9:  # 小于10B参数
        # 仅使用数据并行
        return {
            'data_parallel_size': world_size,
            'tensor_parallel_size': 1,
            'pipeline_parallel_size': 1
        }
    elif model_size < 100e9:  # 10B-100B参数
        # 使用数据并行和张量并行
        # 假设每个节点有8个GPU
        tensor_parallel_size = min(8, hardware_config['gpus_per_node'])
        data_parallel_size = world_size // tensor_parallel_size
        return {
            'data_parallel_size': data_parallel_size,
            'tensor_parallel_size': tensor_parallel_size,
            'pipeline_parallel_size': 1
        }
    else:  # 大于100B参数
        # 使用三级并行
        tensor_parallel_size = 8  # 每个节点内8个GPU使用张量并行
        pipeline_parallel_size = hardware_config['num_nodes']  # 跨节点使用流水线并行
        data_parallel_size = world_size // (tensor_parallel_size * pipeline_parallel_size)
        return {
            'data_parallel_size': data_parallel_size,
            'tensor_parallel_size': tensor_parallel_size,
            'pipeline_parallel_size': pipeline_parallel_size
        }

# 自适应梯度量化示例
def adaptive_gradient_quantization(gradients, sensitivity_threshold=0.01):
    """
    根据梯度敏感性自适应调整量化精度
    """
    # 计算梯度敏感性（如梯度范数）
    grad_norm = torch.norm(gradients)
    
    # 根据敏感性选择量化位宽
    if grad_norm > sensitivity_threshold:
        # 高敏感性，使用较高精度
        quantized = gradients.to(torch.float16)
    else:
        # 低敏感性，使用较低精度
        # 量化到8位
        min_val, max_val = gradients.min(), gradients.max()
        scale = (max_val - min_val) / 255.0
        zero_point = -min_val / scale
        quantized = ((gradients / scale + zero_point)).to(torch.uint8)
        # 记录缩放参数用于反量化
        metadata = {'scale': scale, 'zero_point': zero_point, 'dtype': 'uint8'}
    
    return quantized, metadata

# 简化的梯度检查点实现
class CheckpointModule(nn.Module):
    def __init__(self, layers, checkpoint_ratio=0.5):
        super().__init__()
        self.layers = nn.ModuleList(layers)
        self.checkpoint_ratio = checkpoint_ratio
        # 确定检查点位置
        self.checkpoint_positions = []
        for i in range(len(layers)):
            if i % int(1 / checkpoint_ratio) == 0:
                self.checkpoint_positions.append(i)
    
    def forward(self, x):
        activations = []
        
        # 前向传播，保存检查点
        for i, layer in enumerate(self.layers):
            x = layer(x)
            if i in self.checkpoint_positions:
                activations.append(x.detach())
        
        return x, activations
    
    def backward_pass(self, x, activations, grad_output):
        # 从最后一个检查点开始反向传播
        current_grad = grad_output
        
        # 反向传播最后一段
        start_idx = self.checkpoint_positions[-1]
        for i in range(len(self.layers)-1, start_idx-1, -1):
            with torch.enable_grad():
                if i == start_idx:
                    # 从检查点开始
                    x = activations.pop()
                    x.requires_grad_()
                out = self.layers[i](x)
                # 计算梯度
                grad = torch.autograd.grad(out, x, current_grad)
                current_grad = grad[0]
                x = self.layers[i-1](x) if i > 0 else x
        
        # 反向传播前面的段
        while activations:
            start_idx = self.checkpoint_positions.pop()
            prev_start_idx = self.checkpoint_positions[-1] if activations else -1
            
            for i in range(start_idx-1, prev_start_idx-1, -1):
                with torch.enable_grad():
                    if i == prev_start_idx + 1:
                        # 从检查点开始
                        x = activations.pop() if activations else x
                        x.requires_grad_()
                    out = self.layers[i](x)
                    # 计算梯度
                    grad = torch.autograd.grad(out, x, current_grad)
                    current_grad = grad[0]
                    x = self.layers[i-1](x) if i > 0 else x
        
        return current_grad

# 高性能训练配置示例
def configure_high_performance_training():
    # 设置最佳的CUDA设备顺序
    os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID'
    
    # 设置NCCL通信后端优化
    os.environ['NCCL_DEBUG'] = 'INFO'  # 可选：'WARN'或'ERROR'以减少日志
    os.environ['NCCL_IB_DISABLE'] = '0'  # 启用InfiniBand（如果可用）
    os.environ['NCCL_IB_GID_INDEX'] = '3'
    os.environ['NCCL_IB_HCA'] = subprocess.getoutput('ibstat -l').strip()
    os.environ['NCCL_IB_TC'] = '106'
    
    # 设置内存优化
    torch.backends.cudnn.benchmark = True  # 为固定输入大小启用cuDNN基准测试
    torch.backends.cudnn.deterministic = False  # 允许非确定性算法以获得更好性能
    
    # 配置混合精度训练
    torch.cuda.amp.autocast(enabled=True)
    
    return {
        'nccl_config': {
            'debug_level': os.environ.get('NCCL_DEBUG'),
            'ib_enabled': os.environ.get('NCCL_IB_DISABLE') == '0',
        },
        'cudnn_config': {
            'benchmark': torch.backends.cudnn.benchmark,
            'deterministic': torch.backends.cudnn.deterministic,
        }
    }

{
  "train_batch_size": 4096,
  "train_micro_batch_size_per_gpu": 8,
  "gradient_accumulation_steps": 8,
  "zero_optimization": {
    "stage": 3,
    "offload_optimizer": {
      "device": "cpu",
      "pin_memory": true
    },
    "offload_param": {
      "device": "cpu",
      "pin_memory": true
    },
    "overlap_comm": true,
    "contiguous_gradients": true,
    "sub_group_size": 1e9,
    "stage3_prefetch_bucket_size": 3e8,
    "stage3_param_persistence_threshold": 1e6,
    "stage3_max_live_parameters": 3e9,
    "stage3_max_reuse_distance": 3e9,
    "stage3_gather_fp16_weights_on_model_save": true
  },
  "fp16": {
    "enabled": true,
    "loss_scale": 0,
    "loss_scale_window": 1000,
    "initial_scale_power": 16,
    "hysteresis": 2,
    "min_loss_scale": 1
  },
  "activation_checkpointing": {
    "partition_activations": true,
    "cpu_checkpointing": true,
    "profile": true
  }
}

# Megatron-LM分布式训练配置
def get_megatron_config():
    return {
        # 并行度设置
        'tensor_model_parallel_size': 8,
        'pipeline_model_parallel_size': 4,
        'data_parallel_size': 8,
        
        # 模型参数
        'num_layers': 96,
        'hidden_size': 12288,
        'num_attention_heads': 96,
        
        # 训练参数
        'micro_batch_size': 4,
        'global_batch_size': 1024,
        
        # 优化器设置
        'optimizer': 'adam',
        'adam_beta1': 0.9,
        'adam_beta2': 0.95,
        'adam_eps': 1e-8,
        'weight_decay': 0.1,
        
        # 混合精度训练
        'fp16': True,
        'fp16_lm_cross_entropy': True,
        'loss_scale': 0,
        'loss_scale_window': 1000,
        
        # 梯度检查点
        'checkpoint_activations': True,
        'checkpoint_num_layers': 1,
        'partition_activations': True,
        'profile': True,
        
        # 通信优化
        'gradient_accumulation_fusion': True,
        'gradient_predivide_factor': 1.0,
        'overlap_grad_reduce': True,
        'overlap_param_gather': True
    }

# DeepSpeed ZeRO-3训练示例
import torch
import deepspeed
from transformers import AutoModelForCausalLM, AutoTokenizer

# 初始化分布式环境
deepspeed.init_distributed()

# 加载模型和分词器
model = AutoModelForCausalLM.from_pretrained("gpt2")
tokenizer = AutoTokenizer.from_pretrained("gpt2")

# 准备数据集（示例）
# 实际应用中应使用真实数据集

# 配置DeepSpeed
model_engine, optimizer, trainloader, _ = deepspeed.initialize(
    model=model,
    model_parameters=model.parameters(),
    training_data=train_dataset,
    config="./ds_config.json"  # 包含ZeRO-3配置的JSON文件
)

# 训练循环
for epoch in range(num_epochs):
    for batch in trainloader:
        # 准备输入
        inputs = tokenizer(batch["text"], return_tensors="pt").to(model_engine.device)
        
        # 前向传播
        outputs = model_engine(**inputs, labels=inputs["input_ids"])
        loss = outputs.loss
        
        # 反向传播
        model_engine.backward(loss)
        
        # 参数更新
        model_engine.step()
        
    # 保存检查点
    if model_engine.global_rank == 0:
        model_engine.save_checkpoint("checkpoints/")

# Megatron-LM张量并行配置示例
from megatron import get_args
from megatron.initialize import initialize_megatron
from megatron.model import GPTModel
from megatron.training import train

def setup_model_args():
    # 设置命令行参数
    parser = argparse.ArgumentParser()
    parser.add_argument('--tensor-model-parallel-size', type=int, default=8)
    parser.add_argument('--pipeline-model-parallel-size', type=int, default=1)
    parser.add_argument('--model-size', type=str, default='175B')
    # 添加其他必要参数
    return parser

def model_provider(pre_process=True, post_process=True):
    # 创建模型
    args = get_args()
    model = GPTModel(
        num_tokentypes=0,
        parallel_output=True,
        pre_process=pre_process,
        post_process=post_process
    )
    return model

def train_valid_test_datasets_provider(train_val_test_num_samples):
    # 提供数据集
    # 实际应用中应返回真实数据集
    pass

def main():
    # 初始化Megatron-LM
    initialize_megatron(extra_args_provider=setup_model_args)
    
    # 训练模型
    train(
        model_provider=model_provider,
        train_valid_test_datasets_provider=train_valid_test_datasets_provider
    )

if __name__ == "__main__":
    main()