DATA PARALLELISM
Authors: Sung Kim and Jenny Kang
In this tutorial, we will learn how to use multiple GPUs using DataParallel.
It’s very easy to use GPUs with PyTorch. You can put the model on a GPU:
device = torch.device("cuda:0")
model.to(device)
Then, you can copy all your tensors to the GPU:
mytensor = my_tensor.to(device)
Please note that just calling my_tensor.to(device) returns a new copy of my_tensor on GPU instead of rewriting my_tensor. You need to assign it to a new tensor and use that tensor on the GPU.
It’s natural to execute your forward, backward propagations on multiple GPUs. However, Pytorch will only use one GPU by default. You can easily run your operations on multiple GPUs by making your model run parallelly using DataParallel:
model = nn.DataParallel(model)
That’s the core behind this tutorial. We will explore it in more detail below.
Imports and parameters
Import PyTorch modules and define parameters.
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
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
Dummy DataSet
Make a dummy (random) dataset. You just need to implement the getitem
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)
Simple Model
For the demo, our model just gets an input, performs a linear operation, and gives an output. However, you can use DataParallel on any model (CNN, RNN, Capsule Net etc.)
We’ve placed a print statement inside the model to monitor the size of input and output tensors. Please pay attention to what is printed at batch rank 0.
class Model(nn.Module):
# Our model
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
Create Model and DataParallel
This is the core part of the tutorial. First, we need to make a model instance and check if we have multiple GPUs. If we have multiple GPUs, we can wrap our model using nn.DataParallel. Then we can put our model on GPUs by model.to(device)
model = Model(input_size, output_size)
if torch.cuda.device_count() > 1:
print("Let's use", torch.cuda.device_count(), "GPUs!")
# dim = 0 [30, xxx] -> [10, ...], [10, ...], [10, ...] on 3 GPUs
model = nn.DataParallel(model)
model.to(device)
Out:
Let's use 2 GPUs!
Run the Model
Now we can see the sizes of input and output tensors.
for data in rand_loader:
input = data.to(device)
output = model(input)
print("Outside: input size", input.size(),
"output_size", output.size())
Out:
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])
Results
If you have no GPU or one GPU, when we batch 30 inputs and 30 outputs, the model gets 30 and outputs 30 as expected. But if you have multiple GPUs, then you can get results like this.
2 GPUs
If you have 2, you will see:
# on 2 GPUs
Let's 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])
3 GPUs
If you have 3 GPUs, you will see:
Let's 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])
8 GPUs
If you have 8, you will see:
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])
Summary
DataParallel splits your data automatically and sends job orders to multiple models on several GPUs. After each model finishes their job, DataParallel collects and merges the results before returning it to you.
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