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deepspeed学习-多机all_reduce

时间：2024-04-04 15:58:06浏览次数：19  
标签：INFO NCCL deepspeed 04 reduce py worker 3956 多机

deepspeed学习-多机all_reduce


本文演示了如何采用deepspeed做多机torch.distributed.all_reduce

一.安装nvidia-docker

curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | apt-key add -
distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | tee /etc/apt/sources.list.d/nvidia-docker.list
apt-get update
apt-get install -y nvidia-docker2

二.构建容器

1.创建容器

cd /mnt
docker stop pytorch_cuda
docker rm pytorch_cuda
nvidia-docker run -ti -e NVIDIA_VISIBLE_DEVICES=all --privileged --net=host -v $PWD:/home \
            -w /home --name pytorch_cuda  ubuntu:22.04 /bin/bash
docker start pytorch_cuda
docker exec -ti pytorch_cuda /bin/bash

2.更新apt源

sed -i "s@http://.*archive.ubuntu.com@http://repo.huaweicloud.com@g" /etc/apt/sources.list
sed -i "s@http://.*security.ubuntu.com@http://repo.huaweicloud.com@g" /etc/apt/sources.list
apt update

3.安装依赖

apt install gcc g++ vim git wget curl unzip make -y
apt install -y pkg-config  
apt install -y python3.10
apt install -y python3.10-dev
apt install -y python3-pip
apt install -y libsystemd*
apt install -y libabsl-dev
apt install -y libopencv-dev
apt install -y psmisc
apt install -y openssh-server
apt install -y gdb
apt install -y pciutils
apt install -y nfs-common
apt install -y openmpi-bin openmpi-doc libopenmpi-dev
apt install -y pdsh

4.安装cuda12.1(编译deepspeed需要)

wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-ubuntu2204.pin
wget https://developer.download.nvidia.com/compute/cuda/12.1.1/local_installers/cuda-repo-ubuntu2204-12-1-local_12.1.1-530.30.02-1_amd64.deb
cp cuda-ubuntu2204.pin /etc/apt/preferences.d/cuda-repository-pin-600
dpkg -i cuda-repo-ubuntu2204-12-1-local_12.1.1-530.30.02-1_amd64.deb
cp /var/cuda-repo-ubuntu2204-12-1-local/cuda-*-keyring.gpg /usr/share/keyrings/
apt-get update
apt-get -y install cuda-toolkit-12

5.设置ssh端口和密码(为避免跟host sshd冲突,修改了容器里sshd端口)

sed -i 's/^.*PermitRootLogin.*$/PermitRootLogin yes/g' /etc/ssh/sshd_config
sed -i 's/^.*Port.*$/Port 2223/g' /etc/ssh/sshd_config
export passwd=Hello123 && printf "${passwd}\n${passwd}\n"  | passwd root

6.运行sshd服务

cat >/usr/bin/run.sh <<EOF
#!/bin/bash
mkdir  -p /run/sshd
source ~/.bashrc
/usr/sbin/sshd -D
EOF
chmod  777 /usr/bin/run.sh
nohup /usr/bin/run.sh &

7.安装pytorch

pip install torch==2.2.2 -i https://pypi.tuna.tsinghua.edu.cn/simple

8.测试nccl

cd /home
cat > reduce_demo.py <<EOF
import os
import torch
import argparse
import torch.distributed as dist
from torch.distributed import ReduceOp
import time
 
dist.init_process_group(backend='nccl')

local_rank = int(os.environ["LOCAL_RANK"])
world_size = torch.distributed.get_world_size()
rank=rank = torch.distributed.get_rank()
 
torch.cuda.set_device(local_rank)
 
device = torch.device("cuda",local_rank)
 
dist.barrier()
tensor = (torch.ones(world_size, dtype=torch.int64) * rank).to(device)
print(tensor)
dist.barrier()
 
dist.all_reduce(tensor, op=ReduceOp.SUM)
dist.barrier()
time.sleep(2)
 
print("reduce result:",tensor)
 
dist.destroy_process_group()
EOF
torchrun -m --nnodes=1 --nproc_per_node=1 reduce_demo

9.安装 deepspeed

pip install deepspeed

10.退出容器

exit

11.上传镜像

docker commit pytorch_cuda harbor.hi20240217.com/public/pytorch_cuda:v1.0
docker login harbor.hi20240217.com
docker push harbor.hi20240217.com/public/pytorch_cuda:v1.0

三.多机环境部署(每台主机上执行)

1.创建容器

cd /mnt			
docker pull harbor.hi20240217.com/public/pytorch_cuda:v1.0
docker run -ti --privileged --net=host -v $PWD:/home \
            -w /home --rm harbor.hi20240217.com/public/pytorch_cuda:v1.0 /bin/bash

2.生成密钥

rm -rf ~/.ssh/*
ssh-keygen

3.创建ssh主机列表

tee ~/.ssh/config <<-'EOF'
Host worker_1
        User  root
        Hostname 192.168.1.100
        port 2223
        IdentityFile ~/.ssh/id_rsa
Host worker_2
        User  root
        Hostname 192.168.1.101
        port 2223
        IdentityFile ~/.ssh/id_rsa        
EOF

4.启动sshd

nohup /usr/bin/run.sh &

5.拷贝密钥

ssh-copy-id worker_1
ssh-copy-id worker_2

6.准备测试代码

cd /home
cat > reduce_demo.py <<EOF
import os
import torch
import argparse
import torch.distributed as dist
from torch.distributed import ReduceOp
import time
 
dist.init_process_group(backend='nccl')

local_rank = int(os.environ["LOCAL_RANK"])
world_size = torch.distributed.get_world_size()
rank=rank = torch.distributed.get_rank()
 
torch.cuda.set_device(local_rank)
 
device = torch.device("cuda",local_rank)
 
dist.barrier()
tensor = (torch.ones(world_size, dtype=torch.int64) * rank).to(device)
print(tensor)
dist.barrier()
 
dist.all_reduce(tensor, op=ReduceOp.SUM)
dist.barrier()
time.sleep(2)
 
print("reduce result:",tensor)
 
dist.destroy_process_group()
EOF

四.运行测试程序(随便找一台)

1.创建主机列表

cd /home
tee hostfile <<-'EOF'
worker_1 slots=1
worker_2 slots=1
EOF

2.运行程序

export NCCL_DEBUG=info 
export NCCL_SOCKET_IFNAME=enp5s0 
export NCCL_IB_DISABLE=1	
deepspeed --hostfile ./hostfile reduce_demo.py

3.运行日志

[2024-04-04 15:32:08,327] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2024-04-04 15:32:08,838] [INFO] [runner.py:463:main] Using IP address of 192.168.1.100 for node worker_1
[2024-04-04 15:32:08,838] [INFO] [multinode_runner.py:80:get_cmd] Running on the following workers: worker_1,worker_2
[2024-04-04 15:32:08,839] [INFO] [runner.py:568:main] cmd = pdsh -S -f 1024 -w worker_1,worker_2 export NCCL_SOCKET_IFNAME=enp5s0; export NCCL_DEBUG=info; export NCCL_IB_DISABLE=1; export PYTHONPATH=/home;  cd /home; /usr/bin/python3 -u -m deepspeed.launcher.launch --world_info=eyJ3b3JrZXJfMSI6IFswXSwgIndvcmtlcl8yIjogWzBdfQ== --node_rank=%n --master_addr=192.168.1.100 --master_port=29500 reduce_demo.py
worker_2: [2024-04-04 15:32:09,956] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect)
worker_2: [2024-04-04 15:32:10,111] [INFO] [launch.py:138:main] 1 NCCL_SOCKET_IFNAME=enp5s0
worker_2: [2024-04-04 15:32:10,111] [INFO] [launch.py:138:main] 1 NCCL_DEBUG=info
worker_2: [2024-04-04 15:32:10,111] [INFO] [launch.py:138:main] 1 NCCL_IB_DISABLE=1
worker_2: [2024-04-04 15:32:10,111] [INFO] [launch.py:145:main] WORLD INFO DICT: {'worker_1': [0], 'worker_2': [0]}
worker_2: [2024-04-04 15:32:10,111] [INFO] [launch.py:151:main] nnodes=2, num_local_procs=1, node_rank=1
worker_2: [2024-04-04 15:32:10,111] [INFO] [launch.py:162:main] global_rank_mapping=defaultdict(<class 'list'>, {'worker_1': [0], 'worker_2': [1]})
worker_2: [2024-04-04 15:32:10,111] [INFO] [launch.py:163:main] dist_world_size=2
worker_2: [2024-04-04 15:32:10,111] [INFO] [launch.py:165:main] Setting CUDA_VISIBLE_DEVICES=0
worker_2: [2024-04-04 15:32:10,112] [INFO] [launch.py:253:main] process 4296 spawned with command: ['/usr/bin/python3', '-u', 'reduce_demo.py', '--local_rank=0']
worker_1: [2024-04-04 15:32:11,037] [INFO] [real_accelerator.py:191:get_accelerator] Setting ds_accelerator to cuda (auto detect)
worker_1: [2024-04-04 15:32:11,314] [INFO] [launch.py:138:main] 0 NCCL_SOCKET_IFNAME=enp5s0
worker_1: [2024-04-04 15:32:11,314] [INFO] [launch.py:138:main] 0 NCCL_DEBUG=info
worker_1: [2024-04-04 15:32:11,314] [INFO] [launch.py:138:main] 0 NCCL_IB_DISABLE=1
worker_1: [2024-04-04 15:32:11,314] [INFO] [launch.py:145:main] WORLD INFO DICT: {'worker_1': [0], 'worker_2': [0]}
worker_1: [2024-04-04 15:32:11,314] [INFO] [launch.py:151:main] nnodes=2, num_local_procs=1, node_rank=0
worker_1: [2024-04-04 15:32:11,314] [INFO] [launch.py:162:main] global_rank_mapping=defaultdict(<class 'list'>, {'worker_1': [0], 'worker_2': [1]})
worker_1: [2024-04-04 15:32:11,314] [INFO] [launch.py:163:main] dist_world_size=2
worker_1: [2024-04-04 15:32:11,314] [INFO] [launch.py:165:main] Setting CUDA_VISIBLE_DEVICES=0
worker_1: [2024-04-04 15:32:11,315] [INFO] [launch.py:253:main] process 3956 spawned with command: ['/usr/bin/python3', '-u', 'reduce_demo.py', '--local_rank=0']
worker_1: test-X99:3956:3956 [0] NCCL INFO NCCL_SOCKET_IFNAME set by environment to enp5s0
worker_1: test-X99:3956:3956 [0] NCCL INFO Bootstrap : Using enp5s0:192.168.1.100<0>
worker_1: test-X99:3956:3956 [0] NCCL INFO NET/Plugin : dlerror=libnccl-net.so: cannot open shared object file: No such file or directory No plugin found (libnccl-net.so), using internal implementation
worker_1: test-X99:3956:3956 [0] NCCL INFO cudaDriverVersion 12020
worker_1: NCCL version 2.19.3+cuda12.3
worker_2: work:4296:4296 [0] NCCL INFO cudaDriverVersion 12020
worker_2: work:4296:4296 [0] NCCL INFO NCCL_SOCKET_IFNAME set by environment to enp5s0
worker_2: work:4296:4296 [0] NCCL INFO Bootstrap : Using enp5s0:192.168.1.101<0>
worker_2: work:4296:4296 [0] NCCL INFO NET/Plugin : dlerror=libnccl-net.so: cannot open shared object file: No such file or directory No plugin found (libnccl-net.so), using internal implementation
worker_2: work:4296:4316 [0] NCCL INFO NCCL_IB_DISABLE set by environment to 1.
worker_2: work:4296:4316 [0] NCCL INFO NCCL_SOCKET_IFNAME set by environment to enp5s0
worker_2: work:4296:4316 [0] NCCL INFO NET/Socket : Using [0]enp5s0:192.168.1.101<0>
worker_2: work:4296:4316 [0] NCCL INFO Using non-device net plugin version 0
worker_2: work:4296:4316 [0] NCCL INFO Using network Socket
worker_1: test-X99:3956:4018 [0] NCCL INFO NCCL_IB_DISABLE set by environment to 1.
worker_1: test-X99:3956:4018 [0] NCCL INFO NCCL_SOCKET_IFNAME set by environment to enp5s0
worker_1: test-X99:3956:4018 [0] NCCL INFO NET/Socket : Using [0]enp5s0:192.168.1.100<0>
worker_1: test-X99:3956:4018 [0] NCCL INFO Using non-device net plugin version 0
worker_1: test-X99:3956:4018 [0] NCCL INFO Using network Socket
worker_2: work:4296:4316 [0] NCCL INFO comm 0x559a38e9b2a0 rank 1 nranks 2 cudaDev 0 nvmlDev 0 busId 9000 commId 0x7b537e17c819b798 - Init START
worker_1: test-X99:3956:4018 [0] NCCL INFO comm 0x557ee0bc6880 rank 0 nranks 2 cudaDev 0 nvmlDev 0 busId 3000 commId 0x7b537e17c819b798 - Init START
worker_2: work:4296:4316 [0] NCCL INFO Trees [0] -1/-1/-1->1->0 [1] 0/-1/-1->1->-1
worker_2: work:4296:4316 [0] NCCL INFO P2P Chunksize set to 131072
worker_1: test-X99:3956:4018 [0] NCCL INFO Channel 00/02 :    0   1
worker_1: test-X99:3956:4018 [0] NCCL INFO Channel 01/02 :    0   1
worker_1: test-X99:3956:4018 [0] NCCL INFO Trees [0] 1/-1/-1->0->-1 [1] -1/-1/-1->0->1
worker_1: test-X99:3956:4018 [0] NCCL INFO P2P Chunksize set to 131072
worker_2: work:4296:4316 [0] NCCL INFO Channel 00/0 : 0[0] -> 1[0] [receive] via NET/Socket/0
worker_2: work:4296:4316 [0] NCCL INFO Channel 01/0 : 0[0] -> 1[0] [receive] via NET/Socket/0
worker_2: work:4296:4316 [0] NCCL INFO Channel 00/0 : 1[0] -> 0[0] [send] via NET/Socket/0
worker_2: work:4296:4316 [0] NCCL INFO Channel 01/0 : 1[0] -> 0[0] [send] via NET/Socket/0
worker_1: test-X99:3956:4018 [0] NCCL INFO Channel 00/0 : 1[0] -> 0[0] [receive] via NET/Socket/0
worker_1: test-X99:3956:4018 [0] NCCL INFO Channel 01/0 : 1[0] -> 0[0] [receive] via NET/Socket/0
worker_1: test-X99:3956:4018 [0] NCCL INFO Channel 00/0 : 0[0] -> 1[0] [send] via NET/Socket/0
worker_1: test-X99:3956:4018 [0] NCCL INFO Channel 01/0 : 0[0] -> 1[0] [send] via NET/Socket/0
worker_2: work:4296:4316 [0] NCCL INFO Connected all rings
worker_2: work:4296:4316 [0] NCCL INFO Connected all trees
worker_2: work:4296:4316 [0] NCCL INFO threadThresholds 8/8/64 | 16/8/64 | 512 | 512
worker_2: work:4296:4316 [0] NCCL INFO 2 coll channels, 0 nvls channels, 2 p2p channels, 2 p2p channels per peer
worker_1: test-X99:3956:4018 [0] NCCL INFO Connected all rings
worker_1: test-X99:3956:4018 [0] NCCL INFO Connected all trees
worker_1: test-X99:3956:4018 [0] NCCL INFO threadThresholds 8/8/64 | 16/8/64 | 512 | 512
worker_1: test-X99:3956:4018 [0] NCCL INFO 2 coll channels, 0 nvls channels, 2 p2p channels, 2 p2p channels per peer
worker_2: work:4296:4316 [0] NCCL INFO comm 0x559a38e9b2a0 rank 1 nranks 2 cudaDev 0 nvmlDev 0 busId 9000 commId 0x7b537e17c819b798 - Init COMPLETE
worker_1: test-X99:3956:4018 [0] NCCL INFO comm 0x557ee0bc6880 rank 0 nranks 2 cudaDev 0 nvmlDev 0 busId 3000 commId 0x7b537e17c819b798 - Init COMPLETE
worker_2: tensor([1, 1], device='cuda:0')
worker_1: tensor([0, 0], device='cuda:0')
worker_1: reduce result: tensor([1, 1], device='cuda:0')
worker_2: reduce result: tensor([1, 1], device='cuda:0')
worker_1: test-X99:3956:4019 [0] NCCL INFO [Service thread] Connection closed by localRank 0
worker_2: work:4296:4317 [0] NCCL INFO [Service thread] Connection closed by localRank 0
worker_2: work:4296:4296 [0] NCCL INFO comm 0x559a38e9b2a0 rank 1 nranks 2 cudaDev 0 busId 9000 - Abort COMPLETE
worker_1: test-X99:3956:3956 [0] NCCL INFO comm 0x557ee0bc6880 rank 0 nranks 2 cudaDev 0 busId 3000 - Abort COMPLETE
worker_2: [2024-04-04 15:32:16,118] [INFO] [launch.py:348:main] Process 4296 exits successfully.
worker_1: [2024-04-04 15:32:16,320] [INFO] [launch.py:348:main] Process 3956 exits successfully.

标签：INFO,NCCL,deepspeed,04,reduce,py,worker,3956,多机
From： https://blog.csdn.net/m0_61864577/article/details/137376285

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