深度学习中的并行策略概述：2 Data Parallelism

数据并行（Data Parallelism）的核心在于将模型的数据处理过程并行化。具体来说，面对大规模数据批次时，将其拆分为较小的子批次，并在多个计算设备上同时进行处理。每个设备负责处理一个子批次，实现并行计算。处理完成后，将各个设备上的计算结果汇总，以便对模型进行统一更新。由于其在深度学习中的普遍应用，数据并行成为了一种广泛支持的并行计算策略，并在主流框架中得到了良好的实现。

以下代码展示了如何在PyTorch中使用nn.DataParallel和DistributedDataParallel实现数据并行，以加速模型的训练过程。

使用nn.DataParallel实现数据并行

import torch
import torch.nn as nn
from torch.utils.data import Dataset, DataLoader

# 假设我们有一个简单的数据集类
class SimpleDataset(Dataset):
    def __init__(self, data, target):
        self.data = data
        self.target = target

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

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

# 假设我们有一个简单的神经网络模型
class SimpleModel(nn.Module):
    def __init__(self, input_dim):
        super(SimpleModel, self).__init__()
        self.fc = nn.Linear(input_dim, 1)

    def forward(self, x):
        return torch.sigmoid(self.fc(x))

# 假设我们有一些数据
n_sample = 100
n_dim = 10
batch_size = 10
X = torch.randn(n_sample, n_dim)
Y = torch.randint(0, 2, (n_sample,)).float()
dataset = SimpleDataset(X, Y)
data_loader = DataLoader(dataset, batch_size=batch_size, shuffle=True)

# 初始化模型
device_ids = [0, 1, 2]  # 指定使用的GPU编号
model = SimpleModel(n_dim).to(device_ids[0])
model = nn.DataParallel(model, device_ids=device_ids)

# 定义优化器和损失函数
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
criterion = nn.BCELoss()

# 训练模型
for epoch in range(10):
    for batch_idx, (inputs, targets) in enumerate(data_loader):
        inputs, targets = inputs.to('cuda'), targets.to('cuda')
        outputs = model(inputs)
        loss = criterion(outputs, targets.unsqueeze(1))
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        print(f'Epoch {epoch}, Batch {batch_idx}, Loss: {loss.item()}')

使用DistributedDataParallel实现数据并行

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, Dataset
import torch.distributed as dist
from torch.nn.parallel import DistributedDataParallel as DDP

# 假设我们有一个简单的数据集类
class SimpleDataset(Dataset):
    def __init__(self, data, target):
        self.data = data
        self.target = target

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

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

# 假设我们有一个简单的神经网络模型
class SimpleModel(nn.Module):
    def __init__(self, input_dim):
        super(SimpleModel, self).__init__()
        self.fc = nn.Linear(input_dim, 1)

    def forward(self, x):
        return torch.sigmoid(self.fc(x))

# 初始化进程组
def init_process(rank, world_size, backend='nccl'):
    dist.init_process_group(backend, rank=rank, world_size=world_size)

# 训练函数
def train(rank, world_size):
    init_process(rank, world_size)
    torch.cuda.set_device(rank)
    model = SimpleModel(10).to(rank)
    model = DDP(model, device_ids=[rank])

    dataset = SimpleDataset(torch.randn(100, 10), torch.randint(0, 2, (100,)).float())
    sampler = torch.utils.data.distributed.DistributedSampler(dataset, num_replicas=world_size, rank=rank)
    data_loader = DataLoader(dataset, batch_size=10, sampler=sampler)

    optimizer = optim.SGD(model.parameters(), lr=0.01)
    criterion = nn.BCELoss()

    for epoch in range(10):
        for inputs, targets in data_loader:
            inputs, targets = inputs.to(rank), targets.to(rank)
            optimizer.zero_grad()
            outputs = model(inputs)
            loss = criterion(outputs, targets.unsqueeze(1))
            loss.backward()
            optimizer.step()

if __name__ == "__main__":
    world_size = 4
    torch.multiprocessing.spawn(train, args=(world_size,), nprocs=world_size, join=True)