1 任务介绍

通过实现一个线性变换模型

的单机2卡分布式训练任务，来初步体验pytorch中DistributeDataparallel的使用。本文主要参考pytorch tutorial中的介绍。

2 整体流程

代码编写流程如下：

• 初始化pytorch分布式训练通信模块；
• 创建模型（这里包括本地模型和分布式模型）
• 创建损失函数和优化器
• 计算（forward 和backward）和梯度更新
• 多任务启动

3 初始化通信模块

pytorch中分布式通信模块为torch.distributed

本例中初始化代码为：

• 通过环境变量MASTER_ADDR和MASTER_PORT设置rank0的IP和PORT信息，rank0的作用相当于是协调节点，需要其他所有节点知道其访问地址;
• 本例中后端选择的是nccl，通过设置NCCL_DEBUG环境变量为INFO，输出NCCL的调试信息；
• init_process_group：执行网络通信模块的初始化工作
• backend：设置后端网络通信的实现库，可选的为gloo、nccl和mpi；
• rank：为当前rank的index，用于标记当前是第几个rank，取值为0到work_size - 1之间的值；
• world_size: 有多少个进程参与到分布式训练中;

    # set env信息
    os.environ['MASTER_ADDR'] = '127.0.0.1'
    os.environ['MASTER_PORT'] = '29500'
    os.environ['NCCL_DEBUG'] = "INFO"
    
    # create default process group
    dist.init_process_group(backend="nccl", rank=rank, world_size=world_size)

4 创建模型

通过下面的代码分别创建本地模型和分布式模型：

• nn.Linear(10, 10).to(rank): 创建线性变换模型，input size和out put size都是10，并且将模型copy到gpu上（通过rank来标识gpu 的id)
• DDP(model, device_ids=[rank]): 创建分布式模型；该模型会将local model 复制到所有副本上，并对数据进行切分，然后使得每个local model都按照mini batch进行训练。

    # create local model
    model = nn.Linear(10, 10).to(rank)

    # construct DDP model
    ddp_model = DDP(model, device_ids=[rank])

5 创建Loss和optimizer

    # define loss function and optimizer
    loss_fn = nn.MSELoss()
    optimizer = optim.SGD(ddp_model.parameters(), lr=0.001)

6 计算和梯度更新

通过ddp_model执行forward和backward计算，这样才能够达到分布式计算的效果；

    # forward pass
    outputs = ddp_model(torch.randn(20, 10).to(rank))
    labels = torch.randn(20, 10).to(rank)

    # backward pass
    loss_fn(outputs, labels).backward()
    
    # update parameters
    optimizer.step()

7 任务启动

启动一个有两个process组成的分布式任务：

• run_worker：子进程执行的function，会以fn(i, *args)的形式被调用，i为process的id（0，1，2...），*args为spawn的args参数
• args：执行进程的参数
• nprocs：进程的个数
• join：是否等待子进程执行完成

def main():
    worker_size = 2
    mp.spawn(run_worker,
        args=(worker_size,),
        nprocs=worker_size,
        join=True)

8 完整代码

import os
import torch
import torch.distributed as dist
import torch.multiprocessing as mp
import torch.nn as nn
import torch.optim as optim
from torch.nn.parallel import DistributedDataParallel as DDP


def run_worker(rank, world_size):
    os.environ['MASTER_ADDR'] = '127.0.0.1'
    os.environ['MASTER_PORT'] = '29500'
    os.environ['NCCL_DEBUG'] = "INFO"
    # create default process group
    dist.init_process_group(backend="nccl", rank=rank, world_size=world_size)

    # create local model
    model = nn.Linear(10, 10).to(rank)

    # construct DDP model
    ddp_model = DDP(model, device_ids=[rank])

    # define loss function and optimizer
    loss_fn = nn.MSELoss()
    optimizer = optim.SGD(ddp_model.parameters(), lr=0.001)

    # forward pass
    outputs = ddp_model(torch.randn(20, 10).to(rank))
    labels = torch.randn(20, 10).to(rank)

    # backward pass
    loss_fn(outputs, labels).backward()

    # update parameters
    optimizer.step()

def main():
    worker_size = 2
    mp.spawn(run_worker,
        args=(worker_size,),
        nprocs=worker_size,
        join=True)

if __name__=="__main__":
    main()

代码执行如下：

root@g48r13:/workspace/DDP# python linear-ddp.py
g48r13:350:350 [0] NCCL INFO Bootstrap : Using [0]bond0:11.139.84.88<0>
g48r13:350:350 [0] NCCL INFO NET/Plugin : No plugin found (libnccl-net.so), using internal implementation

g48r13:350:350 [0] misc/ibvwrap.cc:63 NCCL WARN Failed to open libibverbs.so[.1]
g48r13:350:350 [0] NCCL INFO NET/Socket : Using [0]bond0:11.139.84.88<0>
g48r13:350:350 [0] NCCL INFO Using network Socket
NCCL version 2.7.8+cuda10.2
g48r13:351:351 [1] NCCL INFO Bootstrap : Using [0]bond0:11.139.84.88<0>
g48r13:351:351 [1] NCCL INFO NET/Plugin : No plugin found (libnccl-net.so), using internal implementation

g48r13:351:351 [1] misc/ibvwrap.cc:63 NCCL WARN Failed to open libibverbs.so[.1]
g48r13:351:351 [1] NCCL INFO NET/Socket : Using [0]bond0:11.139.84.88<0>
g48r13:351:351 [1] NCCL INFO Using network Socket
g48r13:350:366 [0] NCCL INFO Channel 00/02 :    0   1
g48r13:351:367 [1] NCCL INFO threadThresholds 8/8/64 | 16/8/64 | 8/8/64
g48r13:350:366 [0] NCCL INFO Channel 01/02 :    0   1
g48r13:351:367 [1] NCCL INFO Trees [0] -1/-1/-1->1->0|0->1->-1/-1/-1 [1] -1/-1/-1->1->0|0->1->-1/-1/-1
g48r13:351:367 [1] NCCL INFO Setting affinity for GPU 1 to ffffffff,ffffffff
g48r13:350:366 [0] NCCL INFO threadThresholds 8/8/64 | 16/8/64 | 8/8/64
g48r13:350:366 [0] NCCL INFO Trees [0] 1/-1/-1->0->-1|-1->0->1/-1/-1 [1] 1/-1/-1->0->-1|-1->0->1/-1/-1
g48r13:350:366 [0] NCCL INFO Setting affinity for GPU 0 to ffffffff,ffffffff
g48r13:351:367 [1] NCCL INFO Channel 00 : 1[5000] -> 0[4000] via P2P/IPC
g48r13:350:366 [0] NCCL INFO Channel 00 : 0[4000] -> 1[5000] via P2P/IPC
g48r13:351:367 [1] NCCL INFO Channel 01 : 1[5000] -> 0[4000] via P2P/IPC
g48r13:350:366 [0] NCCL INFO Channel 01 : 0[4000] -> 1[5000] via P2P/IPC
g48r13:351:367 [1] NCCL INFO 2 coll channels, 2 p2p channels, 2 p2p channels per peer
g48r13:351:367 [1] NCCL INFO comm 0x7fb0b4001060 rank 1 nranks 2 cudaDev 1 busId 5000 - Init COMPLETE
g48r13:350:366 [0] NCCL INFO 2 coll channels, 2 p2p channels, 2 p2p channels per peer
g48r13:350:366 [0] NCCL INFO comm 0x7fc7a8001060 rank 0 nranks 2 cudaDev 0 busId 4000 - Init COMPLETE
g48r13:350:350 [0] NCCL INFO Launch mode Parallel