pytoch单卡改多卡ddp训练

参考：https://blog.csdn.net/weixin_44966641/article/details/121872773
单卡代码，启动代码 python train.py：

import torch
import torch.nn as nn
from torch.optim import SGD
from torch.autograd import Variable
from torch.utils.data import Dataset, DataLoader
import os
import argparse

parser = argparse.ArgumentParser()
parser.add_argument('--gpu_id', type=str, default='0,2')
parser.add_argument('--batchSize', type=int, default=32)
parser.add_argument('--epochs', type=int, default=500)
parser.add_argument('--dataset-size', type=int, default=128)
parser.add_argument('--num-classes', type=int, default=10)
config = parser.parse_args()

os.environ['CUDA_VISIBLE_DEVICES'] = config.gpu_id

# 定义一个随机数据集，随机生成样本
class RandomDataset(Dataset):
    def __init__(self, dataset_size, image_size=32):
        images = torch.randn(dataset_size, 3, image_size, image_size)
        labels = torch.zeros(dataset_size, dtype=int)
        self.data = list(zip(images, labels))

    def __getitem__(self, index):
        return self.data[index]

    def __len__(self):
        return len(self.data)

# 定义模型，简单的一层卷积加一层全连接softmax
class Model(nn.Module):
    def __init__(self, num_classes):
        super(Model, self).__init__()
        self.conv2d = nn.Conv2d(3, 16, 3)
        self.fc = nn.Linear(30*30*16, num_classes)
        self.softmax = nn.Softmax(dim=1)

    def forward(self, x):
        batch_size = x.shape[0]
        x = self.conv2d(x)
        x = x.reshape(batch_size, -1)
        x = self.fc(x)
        out = self.softmax(x)
        return out

# 实例化模型、数据集、加载器和优化器
model = Model(config.num_classes)
dataset = RandomDataset(config.dataset_size)
loader = DataLoader(dataset, batch_size=config.batchSize, shuffle=True)
loss_func = nn.CrossEntropyLoss()

if torch.cuda.is_available():
    model.cuda()
optimizer = SGD(model.parameters(), lr=0.1, momentum=0.9)

# 若使用DP，仅需一行
# if torch.cuda.device_count > 1: model = nn.DataParallel(model)
# 我们不用DP，而将用DDP

# 开始训练
for epoch in range(config.epochs):
    for step, (images, labels) in enumerate(loader):
        if torch.cuda.is_available(): 
            images = images.cuda()
            labels = labels.cuda()
        preds = model(images)
        loss = loss_func(preds, labels)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        
        print(f'Step: {step}, Loss: {loss.item()}')

    print(f'Epoch {epoch} Finished !')

多卡ddp训练代代码，启动代码：

import torch
import torch.nn as nn
from torch.optim import SGD
from torch.autograd import Variable
from torch.utils.data import Dataset, DataLoader
from torch.utils.data.distributed import DistributedSampler
import os
import argparse

# 定义一个随机数据集
class RandomDataset(Dataset):
    def __init__(self, dataset_size, image_size=32):
        images = torch.randn(dataset_size, 3, image_size, image_size)
        labels = torch.zeros(dataset_size, dtype=int)
        self.data = list(zip(images, labels))

    def __getitem__(self, index):
        return self.data[index]

    def __len__(self):
        return len(self.data)

# 定义模型
class Model(nn.Module):
    def __init__(self, num_classes):
        super(Model, self).__init__()
        self.conv2d = nn.Conv2d(3, 16, 3)
        self.fc = nn.Linear(30*30*16, num_classes)
        self.softmax = nn.Softmax(dim=1)

    def forward(self, x):
        batch_size = x.shape[0]
        x = self.conv2d(x)
        x = x.reshape(batch_size, -1)
        x = self.fc(x)
        out = self.softmax(x)
        return out


parser = argparse.ArgumentParser()
parser.add_argument('--gpu_id', type=str, default='0,1,2,3')
parser.add_argument('--batchSize', type=int, default=64)
parser.add_argument('--epochs', type=int, default=500)
parser.add_argument('--dataset-size', type=int, default=1024)
parser.add_argument('--num-classes', type=int, default=10)
config = parser.parse_args()

os.environ['CUDA_VISIBLE_DEVICES'] = config.gpu_id
torch.distributed.init_process_group(backend='nccl', init_method='env://')

local_rank = torch.distributed.get_rank()
torch.cuda.set_device(local_rank)
device = torch.device("cuda", local_rank)

# 实例化模型、数据集和加载器loader
model = Model(config.num_classes)

dataset = RandomDataset(config.dataset_size)
sampler = DistributedSampler(dataset) # 这个sampler会自动分配数据到各个gpu上
loader = DataLoader(dataset, batch_size=config.batchSize, sampler=sampler)

# loader = DataLoader(dataset, batch_size=config.batchSize, shuffle=True)
loss_func = nn.CrossEntropyLoss()

if torch.cuda.is_available():
    model.cuda()
model = torch.nn.parallel.DistributedDataParallel(model)
optimizer = SGD(model.parameters(), lr=0.1, momentum=0.9)


# 开始训练
for epoch in range(config.epochs):
    for step, (images, labels) in enumerate(loader):
        if torch.cuda.is_available(): 
            images = images.cuda()
            labels = labels.cuda()
        preds = model(images)
        # print(f"data: {images.device}, model: {next(model.parameters()).device}")
        loss = loss_func(preds, labels)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        
        print(f'Step: {step}, Loss: {loss.item()}')

    print(f'Epoch {epoch} Finished !')

启动代码

CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.run --nproc_per_node=4 --master_port 12355 train-tmp.py --batchSize 256 --epochs 5000

或者

 torchrun  --nproc_per_node=2 train-tmp.py --batchSize 64 --epochs 500

这里的卡设置要小于等于代码里可见的卡设置。