pytorch利用多个GPU并行计算

参考:

https://pytorch.org/docs/stable/nn.html

https://github.com/apachecn/pytorch-doczh/blob/master/docs/1.0/blitz_data_parallel_tutorial.md

https://blog.csdn.net/Answer3664/article/details/98992409

1. torch.nn.DataParallel

torch.nn.DataParallel(module, device_ids=None, output_device=None, dim=0)

在正向传递中,模块在每个设备上复制,每个副本处理一部分输入。在向后传递期间,来自每个副本的渐变被加到原始模块中

  • module:需要并行处理的模型

  • device_ids:并行处理的设备,默认使用所有的cuda

  • output_device:输出的位置,默认输出到cuda:0

例子:

net = torch.nn.DataParallel(model, device_ids=[0, 1, 2])
output = net(input_var)  # input_var can be on any device, including CPU

torch.nn.DataParallel()返回一个新的模型,能够将输入数据自动分配到所使用的GPU上。所以输入数据的数量应该大于所使用的设备的数量。

2. 一个完整例子

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
 
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
 
 
# 随机数据集
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
 
 
rand_loader = DataLoader(dataset=RandomDataset(input_size, data_size),
                         batch_size=batch_size, shuffle=True)
 
 
# 以简单模型为例,同样可以用于CNN, RNN 等复杂模型
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('In model: input size', input.size(), 'output size:', output.size())
        return output
 
 
# 实例
model = Model(input_size, output_size)
 
if torch.cuda.device_count() > 1:
    print("Use", torch.cuda.device_count(), 'gpus')
    model = nn.DataParallel(model)
 
model.to(device)
 
for data in rand_loader:
    input = data.to(device)
    output = model(input)
    print('Outside: input size ', input.size(), 'output size: ', output.size())

输出:

In model: input size torch.Size([30, 5]) output size: torch.Size([30, 2])
Outside: input size  torch.Size([30, 5]) output size:  torch.Size([30, 2])
In model: input size torch.Size([30, 5]) output size: torch.Size([30, 2])
Outside: input size  torch.Size([30, 5]) output size:  torch.Size([30, 2])
In model: input size torch.Size([30, 5]) output size: torch.Size([30, 2])
Outside: input size  torch.Size([30, 5]) output size:  torch.Size([30, 2])
In model: input size torch.Size([10, 5]) output size: torch.Size([10, 2])
Outside: input size  torch.Size([10, 5]) output size:  torch.Size([10, 2])

若有2个GPU:

Use 2 GPUs!
    In Model: input size torch.Size([15, 5]) output size torch.Size([15, 2])
    In Model: input size torch.Size([15, 5]) output size torch.Size([15, 2])
Outside: input size torch.Size([30, 5]) output_size torch.Size([30, 2])
    In Model: input size torch.Size([15, 5]) output size torch.Size([15, 2])
    In Model: input size torch.Size([15, 5]) output size torch.Size([15, 2])
Outside: input size torch.Size([30, 5]) output_size torch.Size([30, 2])
    In Model: input size torch.Size([15, 5]) output size torch.Size([15, 2])
    In Model: input size torch.Size([15, 5]) output size torch.Size([15, 2])
Outside: input size torch.Size([30, 5]) output_size torch.Size([30, 2])
    In Model: input size torch.Size([5, 5]) output size torch.Size([5, 2])
    In Model: input size torch.Size([5, 5]) output size torch.Size([5, 2])
Outside: input size torch.Size([10, 5]) output_size torch.Size([10, 2])

若有3个GPU:

Use 3 GPUs!
    In Model: input size torch.Size([10, 5]) output size torch.Size([10, 2])
    In Model: input size torch.Size([10, 5]) output size torch.Size([10, 2])
    In Model: input size torch.Size([10, 5]) output size torch.Size([10, 2])
Outside: input size torch.Size([30, 5]) output_size torch.Size([30, 2])
    In Model: input size torch.Size([10, 5]) output size torch.Size([10, 2])
    In Model: input size torch.Size([10, 5]) output size torch.Size([10, 2])
    In Model: input size torch.Size([10, 5]) output size torch.Size([10, 2])
Outside: input size torch.Size([30, 5]) output_size torch.Size([30, 2])
    In Model: input size torch.Size([10, 5]) output size torch.Size([10, 2])
    In Model: input size torch.Size([10, 5]) output size torch.Size([10, 2])
    In Model: input size torch.Size([10, 5]) output size torch.Size([10, 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([2, 5]) output size torch.Size([2, 2])
Outside: input size torch.Size([10, 5]) output_size torch.Size([10, 2])

总结:

  • DataParallel自动的划分数据,并将作业发送到多个GPU上的多个模型。

  • DataParallel会在每个模型完成作业后,收集与合并结果然后返回给你。

原文地址:https://www.cnblogs.com/douzujun/p/13426548.html