Document how to use multiple models and optimizers in Fabric (#16952)

docs/source-fabric/advanced/multiple_setup.rst

@@ -0,0 +1,116 @@
+:orphan:
+
+##############################
+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.
+
+.. code-block:: python
+
+    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 :meth:`~lightning.fabric.fabric.Fabric.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.
+
+.. code-block:: python
+
+    # 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``:
+
+.. code-block:: python
+
+    class AutoEncoder(torch.nn.Module):
+        def __init__(self):
+            super().__init__()
+
+            # 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:
+
+.. code-block:: python
+
+    # 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:
+
+.. code-block:: python
+
+    # 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 <https://github.com/Lightning-AI/lightning/blob/master/examples/fabric/dcgan>`_.

docs/source-fabric/index.rst

@@ -231,6 +231,14 @@ Advanced Topics
     :height: 160
     :tag: advanced
 
+.. displayitem::
+    :header: Multiple Models and Optimizers
+    :description: See how flexible Fabric is to work with multiple models and optimizers!
+    :button_link: advanced/multiple_setup.html
+    :col_css: col-md-4
+    :height: 160
+    :tag: advanced
+
 .. raw:: html
 
         </div>
@@ -275,6 +283,7 @@ Advanced Topics
 
     Efficient Gradient Accumulation <advanced/gradient_accumulation>
     Distributed Communication <advanced/distributed_communication>
+    Multiple Models and Optimizers <advanced/multiple_setup>
 
 .. toctree::
     :maxdepth: 1