update

Author: deependujha

src/lightning/pytorch/core/hooks.py

@@ -295,28 +295,28 @@ def on_after_backward(self) -> None:
 
         """
 
-    # def on_before_optimizer_setup(self) -> None:
-    #     """Called after :meth:`~lightning.pytorch.core.hooks.ModelHooks.configure_model` but before
-    #     :meth:`~lightning.pytorch.core.hooks.ModelHooks.configure_optimizers`.
+    def on_before_optimizer_setup(self) -> None:
+        """Called after :meth:`~lightning.pytorch.core.hooks.ModelHooks.configure_model` but before
+        :meth:`~lightning.pytorch.core.hooks.ModelHooks.configure_optimizers`.
 
-    #     This hook provides a safe point to modify, freeze, or inspect model parameters before optimizers are created.
-    #     It’s particularly useful for callbacks such as
-    #     :class:`~lightning.pytorch.callbacks.finetuning.BaseFinetuning`, where parameters must be frozen
-    #     prior to optimizer setup.
+        This hook provides a safe point to modify, freeze, or inspect model parameters before optimizers are created.
+        It’s particularly useful for callbacks such as
+        :class:`~lightning.pytorch.callbacks.finetuning.BaseFinetuning`, where parameters must be frozen
+        prior to optimizer setup.
 
-    #     This hook runs once in fit stage, after the model
-    #     has been fully instantiated by ``configure_model``, but before optimizers are created by
-    #     ``configure_optimizers``.
+        This hook runs once in fit stage, after the model
+        has been fully instantiated by ``configure_model``, but before optimizers are created by
+        ``configure_optimizers``.
 
-    #     Example::
+        Example::
 
-    #         class MyFinetuneCallback(Callback):
-    #             def on_before_optimizer_setup(self, trainer, pl_module):
-    #                 # freeze the backbone before optimizers are created
-    #                 for param in pl_module.backbone.parameters():
-    #                     param.requires_grad = False
+            class MyFinetuneCallback(Callback):
+                def on_before_optimizer_setup(self, trainer, pl_module):
+                    # freeze the backbone before optimizers are created
+                    for param in pl_module.backbone.parameters():
+                        param.requires_grad = False
 
-    #     """
+        """
 
     def on_before_optimizer_step(self, optimizer: Optimizer) -> None:
         """Called before ``optimizer.step()``.

src/lightning/pytorch/trainer/trainer.py

@@ -992,7 +992,7 @@ def _run(
         # run hook `on_before_optimizer_setup` before optimizers are set up & after model is configured
         if self.state.fn == TrainerFn.FITTING:
             call._call_callback_hooks(self, "on_before_optimizer_setup")
-            # call._call_lightning_module_hook(self, "on_before_optimizer_setup")
+            call._call_lightning_module_hook(self, "on_before_optimizer_setup")
 
         # check if we should delay restoring checkpoint till later
         if not self.strategy.restore_checkpoint_after_setup: