CANN模型并行技术实践：大模型分布式训练与推理策略

本文基于CANN开源社区的多个仓库进行技术解读

CANN组织地址：https://atomgit.com/cann

hccl仓库地址：https://atomgit.com/cann/hccl

runtime仓库地址：https://atomgit.com/cann/runtime

前言

随着模型规模的不断增大，单卡已无法容纳完整模型。模型并行技术通过将模型分割到多个设备上，使得训练超大规模模型成为可能。CANN提供了完整的模型并行支持。

本文将深入解读CANN模型并行的原理、实现方式、通信优化以及最佳实践，帮助你高效训练和部署大模型。

模型并行基础

1. 并行策略对比

# 不同并行策略对比
import torch
import torch_npu
import torch.distributed as dist

class ParallelismComparison:
    """
    并行策略对比
    """
    def data_parallelism(self):
        """
        数据并行
      
        每个设备持有完整模型，处理不同数据
        """
        example = """
        # 数据并行示例
      
        设备0: Model(完整) + Data[0:32]
        设备1: Model(完整) + Data[32:64]
        设备2: Model(完整) + Data[64:96]
        设备3: Model(完整) + Data[96:128]
      
        特点：
        - 模型复制：每个设备一份
        - 数据分片：不同设备处理不同数据
        - 梯度同步：AllReduce梯度
      
        适用场景：
        - 模型较小，单卡可容纳
        - 数据量大
        - 需要高吞吐量
        """
        return example
  
    def model_parallelism(self):
        """
        模型并行
      
        模型分割到多个设备，处理相同数据
        """
        example = """
        # 模型并行示例
      
        设备0: Layer[0-10] + Data(完整)
        设备1: Layer[11-20] + Data(完整)
        设备2: Layer[21-30] + Data(完整)
        设备3: Layer[31-40] + Data(完整)
      
        特点：
        - 模型分片：不同设备持有不同层
        - 数据复制：每个设备处理相同数据
        - 激活传递：层间传递激活值
      
        适用场景：
        - 模型超大，单卡无法容纳
        - 层数多
        - 内存受限
        """
        return example
  
    def pipeline_parallelism(self):
        """
        流水线并行
      
        模型分段，数据流水线处理
        """
        example = """
        # 流水线并行示例
      
        时间步1: 设备0处理Batch0
        时间步2: 设备0处理Batch1, 设备1处理Batch0
        时间步3: 设备0处理Batch2, 设备1处理Batch1, 设备2处理Batch0
        时间步4: 设备0处理Batch3, 设备1处理Batch2, 设备2处理Batch1, 设备3处理Batch0
      
        特点：
        - 模型分段：类似模型并行
        - 流水线：多个micro-batch并行
        - 气泡时间：需要优化
      
        适用场景：
        - 超大模型
        - 需要高吞吐量
        - 可容忍一定延迟
        """
        return example

# 打印对比
comparison = ParallelismComparison()
print(comparison.data_parallelism())
print(comparison.model_parallelism())
print(comparison.pipeline_parallelism())

2. 张量并行

# 张量并行实现
class TensorParallelism:
    """
    张量并行
  
    将张量切分到多个设备
    """
    def __init__(self, world_size):
        self.world_size = world_size
        self.rank = dist.get_rank()
  
    def column_parallel_linear(self, input, weight, bias=None):
        """
        列并行线性层
      
        权重按列切分
        """
        # 权重形状: (out_features, in_features)
        # 切分: 每个设备持有 (out_features/world_size, in_features)
      
        # 本地计算
        output_parallel = torch.matmul(input, weight.t())
      
        if bias is not None:
            output_parallel = output_parallel + bias
      
        # AllGather收集所有设备的输出
        output = self.all_gather(output_parallel)
      
        return output
  
    def row_parallel_linear(self, input, weight, bias=None):
        """
        行并行线性层
      
        权重按行切分
        """
        # 权重形状: (out_features, in_features)
        # 切分: 每个设备持有 (out_features, in_features/world_size)
      
        # 输入也需要切分
        input_parallel = self.split_tensor(input, dim=-1)
      
        # 本地计算
        output_parallel = torch.matmul(input_parallel, weight.t())
      
        # AllReduce求和
        output = self.all_reduce(output_parallel)
      
        if bias is not None and self.rank == 0:
            output = output + bias
      
        return output
  
    def split_tensor(self, tensor, dim):
        """切分张量"""
        chunk_size = tensor.size(dim) // self.world_size
        start = self.rank * chunk_size
        end = start + chunk_size
      
        return tensor.narrow(dim, start, chunk_size)
  
    def all_gather(self, tensor):
        """AllGather操作"""
        tensor_list = [torch.zeros_like(tensor) for _ in range(self.world_size)]
        dist.all_gather(tensor_list, tensor)
        return torch.cat(tensor_list, dim=-1)
  
    def all_reduce(self, tensor):
        """AllReduce操作"""
        dist.all_reduce(tensor, op=dist.ReduceOp.SUM)
        return tensor

# 使用示例
# 初始化分布式环境
# dist.init_process_group(backend='hccl')

# tp = TensorParallelism(world_size=4)

# 列并行
# weight_col = torch.randn(256, 1024).npu()  # 每个设备持有1/4
# output = tp.column_parallel_linear(input, weight_col)

# 行并行
# weight_row = torch.randn(1024, 256).npu()  # 每个设备持有1/4
# output = tp.row_parallel_linear(input, weight_row)

print("张量并行将大张量切分到多个设备")

流水线并行

1. GPipe实现

# GPipe流水线并行
class GPipePipeline:
    """
    GPipe流水线并行
  
    将模型分成多个stage，micro-batch流水线执行
    """
    def __init__(self, model_stages, num_microbatches):
        self.stages = model_stages
        self.num_microbatches = num_microbatches
        self.num_stages = len(model_stages)
  
    def split_batch(self, batch, num_splits):
        """将batch分成micro-batches"""
        batch_size = batch.size(0)
        micro_batch_size = batch_size // num_splits
      
        micro_batches = []
        for i in range(num_splits):
            start = i * micro_batch_size
            end = start + micro_batch_size
            micro_batches.append(batch[start:end])
      
        return micro_batches
  
    def forward(self, batch):
        """前向传播"""
        # 分割batch
        micro_batches = self.split_batch(batch, self.num_microbatches)
      
        # 流水线执行
        outputs = []
        activations = [[] for _ in range(self.num_stages)]
      
        # 前向传播
        for micro_batch in micro_batches:
            x = micro_batch
          
            for stage_idx, stage in enumerate(self.stages):
                x = stage(x)
                activations[stage_idx].append(x)
          
            outputs.append(x)
      
        return outputs, activations
  
    def backward(self, outputs, activations, targets):
        """反向传播"""
        # 计算损失
        losses = []
        for output, target in zip(outputs, targets):
            loss = torch.nn.functional.cross_entropy(output, target)
            losses.append(loss)
      
        # 反向传播
        for loss in reversed(losses):
            loss.backward()

# 使用示例
# 定义模型stages
stage1 = torch.nn.Sequential(
    torch.nn.Linear(1024, 2048),
    torch.nn.ReLU()
)

stage2 = torch.nn.Sequential(
    torch.nn.Linear(2048, 2048),
    torch.nn.ReLU()
)

stage3 = torch.nn.Sequential(
    torch.nn.Linear(2048, 1024),
    torch.nn.ReLU()
)

stage4 = torch.nn.Linear(1024, 10)

# 创建流水线
pipeline = GPipePipeline(
    model_stages=[stage1, stage2, stage3, stage4],
    num_microbatches=4
)

# 训练
# batch = torch.randn(32, 1024)
# outputs, activations = pipeline.forward(batch)
# pipeline.backward(outputs, activations, targets)

print("GPipe通过micro-batch减少流水线气泡")

2. 1F1B调度

# 1F1B调度策略
class OneFOneBScheduler:
    """
    1F1B (One Forward One Backward) 调度
  
    交替执行前向和反向，减少内存占用
    """
    def __init__(self, num_stages, num_microbatches):
        self.num_stages = num_stages
        self.num_microbatches = num_microbatches
  
    def schedule(self):
        """生成调度计划"""
        schedule = []
      
        # Warmup阶段：只做前向
        warmup_steps = self.num_stages - 1
        for step in range(warmup_steps):
            schedule.append(('F', step))  # Forward micro-batch step
      
        # 1F1B阶段：交替前向和反向
        for step in range(warmup_steps, self.num_microbatches):
            schedule.append(('F', step))  # Forward
            schedule.append(('B', step - warmup_steps))  # Backward
      
        # Cooldown阶段：只做反向
        for step in range(self.num_microbatches - warmup_steps, self.num_microbatches):
            schedule.append(('B', step))
      
        return schedule
  
    def execute(self, model_stage, micro_batches):
        """执行调度"""
        schedule = self.schedule()
      
        activations = {}
      
        for action, step in schedule:
            if action == 'F':
                # 前向传播
                print(f"Forward micro-batch {step}")
                output = model_stage(micro_batches[step])
                activations[step] = output
          
            elif action == 'B':
                # 反向传播
                print(f"Backward micro-batch {step}")
                activation = activations[step]
                activation.backward()
                del activations[step]  # 释放内存

# 使用示例
scheduler = OneFOneBScheduler(num_stages=4, num_microbatches=8)
schedule = scheduler.schedule()

print("1F1B调度计划:")
for action, step in schedule:
    print(f"  {action} micro-batch {step}")

# 1F1B的优势：
# - 内存效率：及时释放激活值
# - 流水线效率：减少气泡时间
# - 适合大模型：内存占用更少

通信优化

1. 通信与计算重叠

# 通信与计算重叠
class OverlapCommunication:
    """
    通信与计算重叠
  
    在计算的同时进行通信
    """
    def __init__(self, model):
        self.model = model
        self.comm_stream = torch_npu.Stream()
        self.compute_stream = torch_npu.Stream()
  
    def forward_backward_overlap(self, inputs, targets):
        """前向反向与通信重叠"""
        # 前向传播
        with torch_npu.stream(self.compute_stream):
            outputs = self.model(inputs)
            loss = torch.nn.functional.cross_entropy(outputs, targets)
      
        # 反向传播
        with torch_npu.stream(self.compute_stream):
            loss.backward()
      
        # 梯度通信（与下一批次的前向重叠）
        with torch_npu.stream(self.comm_stream):
            # 等待反向完成
            self.comm_stream.wait_stream(self.compute_stream)
          
            # AllReduce梯度
            for param in self.model.parameters():
                if param.grad is not None:
                    dist.all_reduce(param.grad, async_op=True)

# 使用示例
# model = MyModel().npu()
# overlap = OverlapCommunication(model)

# for batch in dataloader:
#     overlap.forward_backward_overlap(inputs, targets)

print("通信与计算重叠隐藏通信延迟")

2. 梯度压缩

# 梯度压缩
class GradientCompression:
    """
    梯度压缩
  
    减少通信量
    """
    def __init__(self, compression_ratio=0.01):
        self.compression_ratio = compression_ratio
  
    def compress(self, gradient):
        """压缩梯度"""
        # Top-K压缩：只保留最大的k个梯度
        k = int(gradient.numel() * self.compression_ratio)
      
        # 展平
        flat_grad = gradient.flatten()
      
        # 找到top-k
        _, indices = torch.topk(torch.abs(flat_grad), k)
      
        # 创建稀疏梯度
        values = flat_grad[indices]
      
        return indices, values
  
    def decompress(self, indices, values, shape):
        """解压缩梯度"""
        # 创建零张量
        gradient = torch.zeros(shape).flatten()
      
        # 填充值
        gradient[indices] = values
      
        # 恢复形状
        return gradient.view(shape)
  
    def communicate_compressed(self, gradient):
        """通信压缩梯度"""
        # 压缩
        indices, values = self.compress(gradient)
      
        # 通信（只传输indices和values）
        # dist.all_reduce(indices)
        # dist.all_reduce(values)
      
        # 解压缩
        decompressed = self.decompress(indices, values, gradient.shape)
      
        return decompressed

# 使用示例
compressor = GradientCompression(compression_ratio=0.01)

# gradient = torch.randn(1000, 1000)
# compressed_grad = compressor.communicate_compressed(gradient)

print("梯度压缩减少通信量，加速训练")

混合并行

1. 3D并行

# 3D并行（数据+张量+流水线）
class ThreeDParallelism:
    """
    3D并行
  
    结合数据并行、张量并行和流水线并行
    """
    def __init__(self, dp_size, tp_size, pp_size):
        self.dp_size = dp_size  # 数据并行度
        self.tp_size = tp_size  # 张量并行度
        self.pp_size = pp_size  # 流水线并行度
      
        # 总设备数
        self.world_size = dp_size * tp_size * pp_size
  
    def get_parallel_groups(self):
        """获取并行组"""
        groups = {
            'data_parallel': [],
            'tensor_parallel': [],
            'pipeline_parallel': []
        }
      
        # 数据并行组：相同模型分片的设备
        for pp in range(self.pp_size):
            for tp in range(self.tp_size):
                group = []
                for dp in range(self.dp_size):
                    rank = dp * (self.tp_size * self.pp_size) + pp * self.tp_size + tp
                    group.append(rank)
                groups['data_parallel'].append(group)
      
        # 张量并行组：同一流水线stage的设备
        for dp in range(self.dp_size):
            for pp in range(self.pp_size):
                group = []
                for tp in range(self.tp_size):
                    rank = dp * (self.tp_size * self.pp_size) + pp * self.tp_size + tp
                    group.append(rank)
                groups['tensor_parallel'].append(group)
      
        # 流水线并行组：同一张量分片的设备
        for dp in range(self.dp_size):
            for tp in range(self.tp_size):
                group = []
                for pp in range(self.pp_size):
                    rank = dp * (self.tp_size * self.pp_size) + pp * self.tp_size + tp
                    group.append(rank)
                groups['pipeline_parallel'].append(group)
      
        return groups

# 使用示例
# 配置：8个数据并行，4个张量并行，2个流水线并行
parallel_3d = ThreeDParallelism(dp_size=8, tp_size=4, pp_size=2)
groups = parallel_3d.get_parallel_groups()

print(f"总设备数: {parallel_3d.world_size}")
print(f"数据并行组数: {len(groups['data_parallel'])}")
print(f"张量并行组数: {len(groups['tensor_parallel'])}")
print(f"流水线并行组数: {len(groups['pipeline_parallel'])}")

# 3D并行的优势：
# - 灵活性：可根据模型和硬件调整
# - 扩展性：支持超大规模训练
# - 效率：充分利用所有设备

总结

CANN模型并行技术要点：

• 并行策略：数据并行、模型并行、流水线并行、张量并行
• 流水线优化：GPipe、1F1B调度
• 通信优化：计算通信重叠、梯度压缩
• 混合并行：3D并行、灵活组合
• 最佳实践：根据模型和硬件选择策略

通过合理使用模型并行技术，可以训练和部署超大规模模型。

相关链接

hccl仓库地址：https://atomgit.com/cann/hccl

runtime仓库地址：https://atomgit.com/cann/runtime

CANN组织地址：https://atomgit.com/cann