PiPPy: Enhancing PyTorch with Automated Pipeline Parallelism

PiPPy is a tool that allows users to automate the process of creating pipeline parallelism in their PyTorch code. This can help speed up the training process for deep learning models by distributing the work across multiple devices or nodes.

In this tutorial, we will walk you through the steps to set up PiPPy and use it to parallelize a simple PyTorch model. By the end of this tutorial, you should have a good understanding of how to leverage PiPPy to increase the performance of your PyTorch-based deep learning models.

1. Install PiPPy
   First, you’ll need to download and install PiPPy. You can install it using pip by running the following command in your terminal:

pip install pippy

1. Import necessary packages
   Next, open up a new Python script and import the necessary packages. Here’s an example of how to do this:

import torch
from pippy import Pipe

1. Define your model
   For this tutorial, let’s create a simple PyTorch model to train. In this example, we’ll create a simple neural network with two hidden layers. Here’s an example of how to define the model:

class SimpleNN(torch.nn.Module):
    def __init__(self):
        super(SimpleNN, self).__init__()
        self.fc1 = torch.nn.Linear(784, 256)
        self.fc2 = torch.nn.Linear(256, 128)
        self.fc3 = torch.nn.Linear(128, 10)

    def forward(self, x):
        x = torch.relu(self.fc1(x))
        x = torch.relu(self.fc2(x))
        x = self.fc3(x)
        return x

1. Split the model into pipes
   Now, we’ll create multiple instances of the model and split them into pipes using the Pipe class provided by PiPPy. Here’s an example of how to do this:

model_pipe1 = SimpleNN()
model_pipe2 = SimpleNN()
model_pipe3 = SimpleNN()

piped_model = Pipe(model_pipe1, model_pipe2, model_pipe3)

1. Define your training loop
   Next, define your training loop as you would normally for a PyTorch model. Here’s an example of how to do this:

criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(piped_model.parameters(), lr=0.001)

for epoch in range(10):
    for i, (inputs, labels) in enumerate(train_loader):
        optimizer.zero_grad()
        outputs = piped_model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        if i % 100 == 0:
            print(f'Epoch {epoch}, Iteration {i}, Loss: {loss.item()}')

1. Run your code with PiPPy
   Finally, you can run your code with PiPPy to take advantage of automated pipeline parallelism. PiPPy will handle the distribution of the work across multiple devices or nodes for you. Here’s an example of how to do this:

torch.set_num_threads(1)  # Set the number of threads to 1
piped_model = piped_model.cuda()  # Move the model to the GPU

piped_model.train()  # Set the model in training mode

# Now you can run your training loop as usual

And that’s it! You have successfully set up PiPPy and used it to parallelize a simple PyTorch model. By following this tutorial, you should now have a good understanding of how to leverage PiPPy to increase the performance of your PyTorch-based deep learning models.