AI开发平台MODELARTS-基于训练作业启动PyTorch DDP训练示例:代码示例

时间：2024-01-04 11:05:15

AI开发平台MODELARTS

代码示例

文件目录结构如下所示，将以下文件上传至OBS桶中：

code                             # 代码根目录
 └─torch_ddp.py                # PyTorch DDP训练代码文件
 └─main.py                     # 使用PyTorch预置框架功能，通过mp.spawn命令启动训练的启动文件
 └─torchlaunch.sh              # 使用自定义镜像功能，通过torch.distributed.launch命令启动训练的启动文件
 └─torchrun.sh                 # 使用自定义镜像功能，通过torch.distributed.run命令启动训练的启动文件

torch_ddp.py内容如下：

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

# 用于通过 mp.spawn 启动
def init_from_arg(local_rank, base_rank, world_size, init_method):
    rank = base_rank + local_rank
    dist.init_process_group("nccl", rank=rank, init_method=init_method, world_size=world_size)
    ddp_train(local_rank)

# 用于通过 torch.distributed.launch 或 torch.distributed.run 启动
def init_from_env():
    dist.init_process_group(backend='nccl', init_method='env://')
    local_rank=int(os.environ["LOCAL_RANK"])
    ddp_train(local_rank)

def cleanup():
    dist.destroy_process_group()

class ToyModel(nn.Module):
    def __init__(self):
        super(ToyModel, self).__init__()
        self.net1 = nn.Linear(10, 10)
        self.relu = nn.ReLU()
        self.net2 = nn.Linear(10, 5)
    def forward(self, x):
        return self.net2(self.relu(self.net1(x)))

def ddp_train(device_id):
    # create model and move it to GPU with id rank
    model = ToyModel().to(device_id)
    ddp_model = DDP(model, device_ids=[device_id])
    loss_fn = nn.MSELoss()
    optimizer = optim.SGD(ddp_model.parameters(), lr=0.001)
    optimizer.zero_grad()
    outputs = ddp_model(torch.randn(20, 10))
    labels = torch.randn(20, 5).to(device_id)
    loss_fn(outputs, labels).backward()
    optimizer.step()
    cleanup()

if __name__ == "__main__":
    init_from_env()

main.py内容如下：

import argparse
import torch
import torch.multiprocessing as mp

parser = argparse.ArgumentParser(description='ddp demo args')
parser.add_argument('--world_size', type=int, required=True)
parser.add_argument('--rank', type=int, required=True)
parser.add_argument('--init_method', type=str, required=True)
args, unknown = parser.parse_known_args()

if __name__ == "__main__":
    n_gpus = torch.cuda.device_count()
    world_size = n_gpus * args.world_size
    base_rank = n_gpus * args.rank
    # 调用 DDP 示例代码中的启动函数
    from torch_ddp import init_from_arg
    mp.spawn(init_from_arg,
        args=(base_rank, world_size, args.init_method),
        nprocs=n_gpus,
        join=True)

torchlaunch.sh内容如下：

#!/bin/bash
# 系统默认环境变量，不建议修改
MASTER_HOST="$VC_WORKER_HOSTS"
MASTER_ADDR="${VC_WORKER_HOSTS%%,*}"
MASTER_PORT="6060"
JOB_ID="1234"
NNODES="$MA_NUM_HOSTS"
NODE_RANK="$VC_TASK_INDEX"
NGPUS_PER_NODE="$MA_NUM_GPUS"

# 自定义环境变量，指定python脚本和参数
PYTHON_SCRIPT=${MA_JOB_DIR}/code/torch_ddp.py
PYTHON_ARGS=""

CMD="python -m torch.distributed.launch \
    --nnodes=$NNODES \
    --node_rank=$NODE_RANK \
    --nproc_per_node=$NGPUS_PER_NODE \
    --master_addr $MASTER_ADDR \
    --master_port=$MASTER_PORT \
    --use_env \
    $PYTHON_SCRIPT \
    $PYTHON_ARGS
"
echo $CMD
$CMD

torchrun.sh内容如下：

PyTorch 2.1版本需要将“rdzv_backend”参数设置为“static：--rdzv_backend=static”。

#!/bin/bash
# 系统默认环境变量，不建议修改
MASTER_HOST="$VC_WORKER_HOSTS"
MASTER_ADDR="${VC_WORKER_HOSTS%%,*}"
MASTER_PORT="6060"
JOB_ID="1234"
NNODES="$MA_NUM_HOSTS"
NODE_RANK="$VC_TASK_INDEX"
NGPUS_PER_NODE="$MA_NUM_GPUS"

# 自定义环境变量，指定python脚本和参数
PYTHON_SCRIPT=${MA_JOB_DIR}/code/torch_ddp.py
PYTHON_ARGS=""

if [[ $NODE_RANK == 0 ]]; then
    EXT_ARGS="--rdzv_conf=is_host=1"
else
    EXT_ARGS=""
fi

CMD="python -m torch.distributed.run \
    --nnodes=$NNODES \
    --node_rank=$NODE_RANK \
    $EXT_ARGS \
    --nproc_per_node=$NGPUS_PER_NODE \
    --rdzv_id=$JOB_ID \
    --rdzv_backend=c10d \
    --rdzv_endpoint=$MASTER_ADDR:$MASTER_PORT \
    $PYTHON_SCRIPT \
    $PYTHON_ARGS
    "
echo $CMD
$CMD

上一篇：AI开发平台MODELARTS-基于训练作业启动PyTorch DDP训练示例:创建训练作业

下一篇：AI开发平台MODELARTS-基于训练作业启动PyTorch DDP训练示例:创建训练作业