Remove unused post_optimizer_step

pytorch_lightning/accelerators/accelerator.py

@@ -258,8 +258,6 @@ def optimizer_step(self, optimizer: Optimizer, opt_idx: int, lambda_closure: Cal
         )
         if make_optimizer_step:
             self.run_optimizer_step(optimizer, opt_idx, lambda_closure, **kwargs)
-        self.precision_plugin.post_optimizer_step(optimizer, opt_idx)
-        self.training_type_plugin.post_optimizer_step(optimizer, opt_idx, **kwargs)
 
     def run_optimizer_step(
         self, optimizer: Optimizer, optimizer_idx: int, lambda_closure: Callable, **kwargs: Any

pytorch_lightning/plugins/precision/precision_plugin.py

@@ -102,9 +102,6 @@ def pre_optimizer_step(
         model.trainer.call_hook("on_before_optimizer_step", optimizer, optimizer_idx)
         return True
 
-    def post_optimizer_step(self, optimizer: Optimizer, optimizer_idx: int) -> None:
-        """Hook to do something after each optimizer step."""
-
     def clip_gradients(
         self,
         optimizer: Optimizer,

pytorch_lightning/plugins/training_type/training_type_plugin.py

@@ -18,7 +18,6 @@
 import torch
 from torch import Tensor
 from torch.nn import Module
-from torch.optim import Optimizer
 from torch.utils.data import DataLoader
 
 import pytorch_lightning as pl
@@ -141,9 +140,6 @@ def pre_backward(self, closure_loss: torch.Tensor) -> None:
     def post_backward(self, closure_loss: torch.Tensor) -> None:
         """Run after precision plugin executes backward."""
 
-    def post_optimizer_step(self, optimizer: Optimizer, optimizer_idx: int, **kwargs) -> None:
-        """Hook to do something after each optimizer step."""
-
     @property
     def model(self) -> Optional[Module]:
         """Returns the potentially wrapped LightningModule."""