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Optional: 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
:
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.
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)
Let's use 4 GPUs!
DataParallel(
(module): Model(
(fc): Linear(in_features=5, out_features=2, bias=True)
)
)
Run the Model¶
Now we can see the sizes of input and output tensors.
In Model: input size torch.Size([6, 5]) output size torch.Size([6, 2])
In Model: input size torch.Size([8, 5]) output size torch.Size([8, 2])
/usr/local/lib/python3.10/dist-packages/torch/nn/modules/linear.py:125: UserWarning:
Attempting to run cuBLAS, but there was no current CUDA context! Attempting to set the primary context... (Triggered internally at ../aten/src/ATen/cuda/CublasHandlePool.cpp:135.)
In Model: input size torch.Size([8, 5]) output size torch.Size([8, 2])
In Model: input size torch.Size([8, 5]) output size torch.Size([8, 2])
Outside: input size torch.Size([30, 5]) output_size torch.Size([30, 2])
In Model: input size torch.Size([8, 5]) output size torch.Size([8, 2])
In Model: input size torch.Size([8, 5]) output size torch.Size([8, 2])
In Model: input size torch.Size([8, 5]) output size torch.Size([8, 2])
In Model: input size torch.Size([6, 5]) output size torch.Size([6, 2])
Outside: input size torch.Size([30, 5]) output_size torch.Size([30, 2])
In Model: input size torch.Size([8, 5]) output size torch.Size([8, 2])
In Model: input size torch.Size([8, 5]) output size torch.Size([8, 2])
In Model: input size torch.Size([8, 5]) output size torch.Size([8, 2])
In Model: input size torch.Size([6, 5]) output size torch.Size([6, 2])
Outside: input size torch.Size([30, 5]) output_size torch.Size([30, 2])
In Model: input size torch.Size([3, 5]) output size torch.Size([3, 2])
In Model: input size torch.Size([3, 5]) output size torch.Size([3, 2])
In Model: input size torch.Size([3, 5]) output size torch.Size([3, 2])
In Model: input size torch.Size([1, 5]) output size torch.Size([1, 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.
For more information, please check out https://pytorch.org/tutorials/beginner/former_torchies/parallelism_tutorial.html.
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