drop opt_idx from to_lightning_optimizer

update docs


update

src/pytorch_lightning/core/module.py

@@ -144,7 +144,7 @@ def optimizers(self, use_pl_optimizer: bool = True) -> MODULE_OPTIMIZERS:
         if self._fabric:
             opts: MODULE_OPTIMIZERS = self._fabric_optimizers
         elif use_pl_optimizer:
-            opts = list(self.trainer.strategy._lightning_optimizers.values())
+            opts = self.trainer.strategy._lightning_optimizers
         else:
             opts = self.trainer.optimizers
 

src/pytorch_lightning/core/optimizer.py

@@ -44,7 +44,6 @@ def __init__(self, optimizer: Optimizer):
 
         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
@@ -55,7 +54,7 @@ def optimizer(self) -> Optimizer:
 
     @classmethod
     def _to_lightning_optimizer(
-        cls, optimizer: Union[Optimizer, "LightningOptimizer"], strategy: "pl.strategies.Strategy", opt_idx: int
+            cls, optimizer: Union[Optimizer, "LightningOptimizer"], strategy: "pl.strategies.Strategy"
     ) -> "LightningOptimizer":
         if isinstance(optimizer, LightningOptimizer):
             # the user could return a `LightningOptimizer` from `configure_optimizers`, see test:
@@ -64,7 +63,6 @@ def _to_lightning_optimizer(
         else:
             lightning_optimizer = cls(optimizer)
         lightning_optimizer._strategy = proxy(strategy)
-        lightning_optimizer._optimizer_idx = opt_idx
         return lightning_optimizer
 
     @contextmanager
@@ -102,7 +100,7 @@ def step(self, closure: Optional[Callable[[], Any]] = None, **kwargs: Any) -> An
         Example::
 
             # Scenario for a GAN using manual optimization
-            def training_step(...):
+            def training_step(self, batch, batch_idx):
                 opt_gen, opt_dis = self.optimizers()
 
                 ...
@@ -124,7 +122,7 @@ def training_step(...):
 
 
             # A more advanced example
-            def training_step(self, batch, batch_idx, ...):
+            def training_step(self, batch, batch_idx):
                 opt_gen, opt_dis = self.optimizers()
 
                 ...
@@ -219,15 +217,10 @@ def _configure_optimizers(
             _validate_optim_conf(opt_dict)
         optimizers = [opt_dict["optimizer"] for opt_dict in optim_conf]
         scheduler_dict = (
-            lambda scheduler, opt_idx: dict(scheduler, opt_idx=opt_idx)
-            if isinstance(scheduler, dict)
-            else {"scheduler": scheduler, "opt_idx": opt_idx}
+            lambda scheduler: dict(scheduler) if isinstance(scheduler, dict) else {"scheduler": scheduler}
         )
-
         lr_schedulers = [
-            scheduler_dict(opt_dict["lr_scheduler"], opt_idx)
-            for opt_idx, opt_dict in enumerate(optim_conf)
-            if "lr_scheduler" in opt_dict
+            scheduler_dict(opt_dict["lr_scheduler"]) for opt_dict in optim_conf if "lr_scheduler" in opt_dict
         ]
     # single list or tuple, multiple optimizer
     elif isinstance(optim_conf, (list, tuple)) and all(isinstance(opt, Optimizable) for opt in optim_conf):

src/pytorch_lightning/loops/optimization/manual_loop.py

@@ -92,7 +92,7 @@ def run(self, kwargs: OrderedDict) -> _OUTPUTS_TYPE:
 
     def on_run_start(self) -> None:
         # inject logic around the optimizer step
-        for i, lightning_optimizer in self.trainer.strategy._lightning_optimizers.items():
+        for lightning_optimizer in self.trainer.strategy._lightning_optimizers:
             lightning_optimizer._on_before_step = self._on_before_step
             lightning_optimizer._on_after_step = self._on_after_step
 
@@ -119,7 +119,7 @@ def on_run_end(self) -> _OUTPUTS_TYPE:
         """Returns the result of this loop, i.e., the post-processed outputs from the training step."""
         output, self._output = self._output, {}  # free memory
         # reset logic around the optimizer step
-        for i, lightning_optimizer in self.trainer.strategy._lightning_optimizers.items():
+        for lightning_optimizer in self.trainer.strategy._lightning_optimizers:
             lightning_optimizer._on_before_step = do_nothing_closure
             lightning_optimizer._on_after_step = do_nothing_closure
         return output

src/pytorch_lightning/strategies/strategy.py

@@ -65,7 +65,7 @@ def __init__(
         self._model: Optional[Module] = None
         self._launcher: Optional[_Launcher] = None
         self._optimizers: List[Optimizer] = []
-        self._lightning_optimizers: Dict[int, LightningOptimizer] = {}
+        self._lightning_optimizers: List[LightningOptimizer] = []
         self.lr_scheduler_configs: List[LRSchedulerConfig] = []
 
     @property
@@ -108,9 +108,7 @@ def optimizers(self) -> List[Optimizer]:
     @optimizers.setter
     def optimizers(self, optimizers: List[Optimizer]) -> None:
         self._optimizers = optimizers
-        self._lightning_optimizers = {
-            idx: LightningOptimizer._to_lightning_optimizer(opt, self, idx) for idx, opt in enumerate(self.optimizers)
-        }
+        self._lightning_optimizers = [LightningOptimizer._to_lightning_optimizer(opt, self) for opt in optimizers]
 
     def connect(self, model: "pl.LightningModule") -> None:
         """Called by the accelerator to connect the accelerator and the model with this plugin."""
@@ -537,7 +535,7 @@ def dispatch(self, trainer: "pl.Trainer") -> None:
     def __getstate__(self) -> Dict:
         # `LightningOptimizer` overrides `self.__class__` so they cannot be pickled
         state = dict(vars(self))  # copy
-        state["_lightning_optimizers"] = {}
+        state["_lightning_optimizers"] = []
         return state
 
     def __setstate__(self, state: Dict) -> None:

tests/tests_pytorch/core/test_lightning_optimizer.py

@@ -154,7 +154,7 @@ def test_state():
         k: v
         for k, v in lightning_optimizer.__dict__.items()
         if k
-        not in {"_optimizer", "_optimizer_idx", "_strategy", "_lightning_module", "_on_before_step", "_on_after_step"}
+        not in {"_optimizer", "_strategy", "_lightning_module", "_on_before_step", "_on_after_step"}
     }
 
     assert lightning_dict == optimizer.__dict__
@@ -192,9 +192,6 @@ def test_lightning_optimizer_automatic_optimization_optimizer_step(tmpdir):
     """Test overriding step works in automatic_optimization."""
 
     class TestModel(BoringModel):
-        def training_step(self, batch, batch_idx, optimizer_idx=None):
-            return super().training_step(batch, batch_idx)
-
         def training_epoch_end(self, outputs):
             ...