117 lines
3.2 KiB
ReStructuredText
117 lines
3.2 KiB
ReStructuredText
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##############################
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Multiple Models and Optimizers
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##############################
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Fabric makes it very easy to work with multiple models and/or optimizers at once in your training workflow.
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Examples of where this comes in handy are Generative Adversarial Networks (GANs), Auto-encoders, meta-learning and more.
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----
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************************
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One model, one optimizer
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************************
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Fabric has a simple guideline you should follow:
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If you have an optimizer, you should set it up together with the model to make your code truly strategy-agnostic.
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.. code-block:: python
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import torch
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from lightning.fabric import Fabric
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fabric = Fabric()
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# Instantiate model and optimizer
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model = LitModel()
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optimizer = torch.optim.Adam(model.parameters())
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# Set up the model and optimizer together
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model, optimizer = fabric.setup(model, optimizer)
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Depending on the selected strategy, the :meth:`~lightning.fabric.fabric.Fabric.setup` method will wrap and link the model with the optimizer.
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----
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******************************
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One model, multiple optimizers
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******************************
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You can also have multiple optimizers over a single model.
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This is useful if you need specific optimizers or learning rates for parts of the model.
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.. code-block:: python
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# Instantiate model and optimizers
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model = LitModel()
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optimizer1 = torch.optim.SGD(model.layer1.parameters(), lr=0.003)
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optimizer2 = torch.optim.SGD(model.layer2.parameters(), lr=0.01)
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# Set up the model and optimizers together
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model, optimizer1, optimizer2 = fabric.setup(model, optimizer1, optimizer2)
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----
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******************************
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Multiple models, one optimizer
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******************************
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Using a single optimizer to update multiple models is possible too.
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The best way to do this is to group all your individual models under one top level ``nn.Module``:
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.. code-block:: python
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class AutoEncoder(torch.nn.Module):
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def __init__(self):
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super().__init__()
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# Group all models under a common nn.Module
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self.encoder = Encoder()
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self.decoder = Decoder()
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Now all of these models can be treated as a single one:
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.. code-block:: python
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# Instantiate the big model
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autoencoder = AutoEncoder()
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optimizer = ...
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# Set up the model(s) and optimizer together
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autoencoder, optimizer = fabric.setup(autoencoder, optimizer)
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----
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************************************
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Multiple models, multiple optimizers
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************************************
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You can pair up as many models and optimizers as you want. For example, two models with one optimizer each:
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.. code-block:: python
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# Two models
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generator = Generator()
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discriminator = Discriminator()
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# Two optimizers
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optimizer_gen = torch.optim.SGD(generator.parameters(), lr=0.01)
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optimizer_dis = torch.optim.SGD(discriminator.parameters(), lr=0.001)
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# Set up generator
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generator, optimizer_gen = fabric.setup(generator, optimizer_gen)
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# Set up discriminator
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discriminator, optimizer_dis = fabric.setup(discriminator, optimizer_dis)
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For a full example of this use case, see our `GAN example <https://github.com/Lightning-AI/lightning/blob/master/examples/fabric/dcgan>`_.
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