Multiple Models and Optimizers

Fabric makes it very easy to work with multiple models and/or optimizers at once in your training workflow. Examples of where this comes in handy are Generative Adversarial Networks (GANs), Auto-encoders, meta-learning and more.

One model, one optimizer

Fabric has a simple guideline you should follow: If you have an optimizer, you should set it up together with the model to make your code truly strategy-agnostic.

import torch
from lightning.fabric import Fabric

fabric = Fabric()

# Instantiate model and optimizer
model = LitModel()
optimizer = torch.optim.Adam(model.parameters())

# Set up the model and optimizer together
model, optimizer = fabric.setup(model, optimizer)

Depending on the selected strategy, the setup() method will wrap and link the model with the optimizer.

One model, multiple optimizers

You can also have multiple optimizers over a single model. This is useful if you need specific optimizers or learning rates for parts of the model.

# Instantiate model and optimizers
model = LitModel()
optimizer1 = torch.optim.SGD(model.layer1.parameters(), lr=0.003)
optimizer2 = torch.optim.SGD(model.layer2.parameters(), lr=0.01)

# Set up the model and optimizers together
model, optimizer1, optimizer2 = fabric.setup(model, optimizer1, optimizer2)

Multiple models, one optimizer

Using a single optimizer to update multiple models is possible too. The best way to do this is to group all your individual models under one top level nn.Module:

class AutoEncoder(torch.nn.Module):
    def __init__(self):

        # Group all models under a common nn.Module
        self.encoder = Encoder()
        self.decoder = Decoder()

Now all of these models can be treated as a single one:

# Instantiate the big model
autoencoder = AutoEncoder()
optimizer = ...

# Set up the model(s) and optimizer together
autoencoder, optimizer = fabric.setup(autoencoder, optimizer)

Multiple models, multiple optimizers

You can pair up as many models and optimizers as you want. For example, two models with one optimizer each:

# Two models
generator = Generator()
discriminator = Discriminator()

# Two optimizers
optimizer_gen = torch.optim.SGD(generator.parameters(), lr=0.01)
optimizer_dis = torch.optim.SGD(discriminator.parameters(), lr=0.001)

# Set up generator
generator, optimizer_gen = fabric.setup(generator, optimizer_gen)
# Set up discriminator
discriminator, optimizer_dis = fabric.setup(discriminator, optimizer_dis)

For a full example of this use case, see our GAN example.