Fixes

Author: carmocca

pytorch_lightning/core/optimizer.py

@@ -54,8 +54,8 @@ def __init__(self, optimizer: Optimizer):
         self._strategy: Optional[pl.strategies.Strategy] = None
         self._optimizer_idx = 0
         # to inject logic around the optimizer step, particularly useful with manual optimization
-        self.on_before_step = do_nothing_closure
-        self.on_after_step = do_nothing_closure
+        self._on_before_step = do_nothing_closure
+        self._on_after_step = do_nothing_closure
 
     @property
     def optimizer(self) -> Optimizer:
@@ -157,7 +157,7 @@ def closure_dis():
                 with opt_dis.toggle_model(sync_grad=accumulated_grad_batches):
                     opt_dis.step(closure=closure_dis)
         """
-        self.on_before_step()
+        self._on_before_step()
 
         if closure is None:
             closure = do_nothing_closure
@@ -173,7 +173,7 @@ def closure_dis():
         with self._strategy.lightning_module.trainer.profiler.profile(profiler_action):
             step_output = self._strategy.optimizer_step(self._optimizer, self._optimizer_idx, closure, **kwargs)
 
-        self.on_after_step()
+        self._on_after_step()
 
         return step_output
 

pytorch_lightning/loops/optimization/manual_loop.py

@@ -94,8 +94,8 @@ def reset(self) -> None:
     def on_run_start(self, *_: Any, **__: Any) -> None:
         # inject logic around the optimizer step
         for i, lightning_optimizer in self.trainer.strategy._lightning_optimizers.items():
-            lightning_optimizer.on_before_step = self._on_before_step
-            lightning_optimizer.on_after_step = self._on_after_step
+            lightning_optimizer._on_before_step = self._on_before_step
+            lightning_optimizer._on_after_step = self._on_after_step
 
     def advance(self, batch: Any, batch_idx: int) -> None:  # type: ignore[override]
         """Performs the training step for manual optimization.
@@ -140,8 +140,8 @@ def on_run_end(self) -> _OUTPUTS_TYPE:
         output, self._output = self._output, {}  # free memory
         # reset logic around the optimizer step
         for i, lightning_optimizer in self.trainer.strategy._lightning_optimizers.items():
-            lightning_optimizer.on_before_step = do_nothing_closure
-            lightning_optimizer.on_after_step = do_nothing_closure
+            lightning_optimizer._on_before_step = do_nothing_closure
+            lightning_optimizer._on_after_step = do_nothing_closure
         return output
 
     def _on_before_step(self) -> None:

pytorch_lightning/trainer/connectors/checkpoint_connector.py

@@ -333,7 +333,7 @@ def dump_checkpoint(self, weights_only: bool = False) -> dict:
         checkpoint = {
             # the epoch is saved for compatibility but it's not relevant for restoration
             "epoch": self.trainer.current_epoch,
-            "global_step": self.trainer.global_step + 1,
+            "global_step": self.trainer.global_step + model.automatic_optimization,
             "pytorch-lightning_version": pl.__version__,
             "state_dict": self._get_lightning_module_state_dict(),
             "loops": self._get_loops_state_dict(),

tests/core/test_lightning_optimizer.py

@@ -152,7 +152,8 @@ def test_state(tmpdir):
     lightning_dict = {
         k: v
         for k, v in lightning_optimizer.__dict__.items()
-        if k not in {"_optimizer", "_optimizer_idx", "_strategy", "_lightning_module"}
+        if k
+        not in {"_optimizer", "_optimizer_idx", "_strategy", "_lightning_module", "_on_before_step", "_on_after_step"}
     }
 
     assert lightning_dict == optimizer.__dict__