Document gradient clipping in Fabric (#16943)

docs/source-fabric/api/fabric_methods.rst

@@ -61,6 +61,41 @@ This replaces any occurrences of ``loss.backward()`` and makes your code acceler
     fabric.backward(loss)
 
 
+clip_gradients
+==============
+
+Clip the gradients of the model to a given max value or max norm.
+This is useful if your model experiences *exploding gradients* during training.
+
+.. code-block:: python
+
+    # Clip gradients to a max value of +/- 0.5
+    fabric.clip_gradients(model, clip_val=0.5)
+
+    # Clip gradients such that their total norm is no bigger than 2.0
+    fabric.clip_gradients(model, clip_norm=2.0)
+
+    # By default, clipping by norm uses the 2-norm
+    fabric.clip_gradients(model, clip_norm=2.0, norm_type=2)
+
+    # You can also choose the infinity-norm, which clips the largest
+    # element among all
+    fabric.clip_gradients(model, clip_norm=2.0, norm_type="inf")
+
+You can also reduce the gradient clipping to just one layer or to the parameters a particular optimizer is referencing (if using multiple optimizers):
+
+.. code-block:: python
+
+    # Clip gradients on a specific layer of your model
+    fabric.clip_gradients(model.fc3, clip_val=1.0)
+
+    # Clip gradients for a specific optimizer if using multiple optimizers
+    fabric.clip_gradients(model, optimizer1, clip_val=1.0)
+
+The :meth:`~lightning.fabric.fabric.Fabric.clip_gradients` method is agnostic to the precision and strategy being used.
+Note: Gradient clipping with FSDP is not yet fully supported.
+
+
 to_device
 =========
 

src/lightning/fabric/fabric.py

@@ -372,6 +372,19 @@ class Fabric:
         norm_type: Union[float, int] = 2.0,
         error_if_nonfinite: bool = True,
     ) -> Optional[torch.Tensor]:
+        """Clip the gradients of the model to a given max value or max norm.
+
+        Args:
+            module: The module whose parameters should be clipped. This can also be just one submodule of your model.
+            optimizer: Optional optimizer. If passed, clipping will be applied to only the parameters that the
+                optimizer is referencing.
+            clip_val: If passed, gradients will be clipped to this value.
+            max_norm: If passed, clips the gradients in such a way that the p-norm of the resulting parameters is
+                no larger than the given value.
+            norm_type: The type of norm if `max_norm` was passed. Can be ``'inf'`` for infinity norm.
+                Default is the 2-norm.
+            error_if_nonfinite: An error is raised if the total norm of the gradients is NaN or infinite.
+        """
         if clip_val is not None and max_norm is not None:
             raise ValueError(
                 "Only one of `clip_val` or `max_norm` can be set as this specifies the underlying clipping algorithm!"