PyTorch使用多块GPU

为了能够清楚的看到数据是如何被并行处理的，我们先定义一些参数：

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

# Parameters and dataLoaders
input_size = 5
output_size = 2

batch_size = 30
data_size = 100

依据这些参数，我们来创建一个实验用的数据集：

class RandomDataset(Dataset):
    
    def __init__(self, size, length):
        self.len = length
        self.data = torch.randn(length, size)
        
    def __getitem__(self, index):
        return self.data[index]
        
    def __len__(self):
        return self.len

random = DataLoader(dataset=RandomDataset(input_size, data_size), 
                                       batch_size=batch_size, shuffle=True)

用一个最简单的模型来实验：

class Model(nn.Module):
    
    def __init__(self, input_size, output_size):
        super(Model, self).__init__()
        self.fc = nn.Linear(input_size, output_size)
    
    def forward(self, input):
        output = self.fc(input)
        print("\tIn Model: input size", input.size(), "output size", output.size())
        
    return output

接下来就是核心部分。我们使用PyTorch中的DataParallel模块来让模型同时在多块GPU上运行。

device = torch.device("cuda:0" if torch.cuda.is_availabel() else "cpu")
model = Model(input_size, output_size)
if torch.cuda.device_count() > 1:
    print("Let's use", torch.cuda.device_count(), "GPUs!")
    model = nn.DataParallel(model)

model.to(device)

现在，我们可以运行这个模型了。

for data in rand_loader:
    input = data.to(device)
    output = model(input)
    print("Outside: input size", input.size(), "output_size", output.size())

记住，我们输入的一个batch的大小是30，可以看一看当使用8块GPU时，每个batch是如何分配的。

Let's use 8 GPUs!
    In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2])
    In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2])
    In Model: input size torch.Size([2, 5]) output size torch.Size([2, 2])
    In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2])
    In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2])
    In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2])
    In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2])
    In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2])
Outside: input size torch.Size([30, 5]) output_size torch.Size([30, 2])
    In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2])
    In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2])
    In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2])
    In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2])
    In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2])
    In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2])
    In Model: input size torch.Size([2, 5]) output size torch.Size([2, 2])
    In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2])
Outside: input size torch.Size([30, 5]) output_size torch.Size([30, 2])
    In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2])
    In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2])
    In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2])
    In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2])
    In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2])
    In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2])
    In Model: input size torch.Size([4, 5]) output size torch.Size([4, 2])
    In Model: input size torch.Size([2, 5]) output size torch.Size([2, 2])
Outside: input size torch.Size([30, 5]) output_size torch.Size([30, 2])
    In Model: input size torch.Size([2, 5]) output size torch.Size([2, 2])
    In Model: input size torch.Size([2, 5]) output size torch.Size([2, 2])
    In Model: input size torch.Size([2, 5]) output size torch.Size([2, 2])
    In Model: input size torch.Size([2, 5]) output size torch.Size([2, 2])
    In Model: input size torch.Size([2, 5]) output size torch.Size([2, 2])
Outside: input size torch.Size([10, 5]) output_size torch.Size([10, 2])