Fix typing in pl.callbacks.lr_monitor

pyproject.toml

@@ -44,7 +44,6 @@ warn_no_return = "False"
 module = [
     "pytorch_lightning.accelerators.gpu",
     "pytorch_lightning.callbacks.finetuning",
-    "pytorch_lightning.callbacks.lr_monitor",
     "pytorch_lightning.callbacks.model_checkpoint",
     "pytorch_lightning.callbacks.progress.base",
     "pytorch_lightning.callbacks.progress.progress",

pytorch_lightning/callbacks/lr_monitor.py

@@ -86,7 +86,7 @@ def configure_optimizer(self):
 
     """
 
-    def __init__(self, logging_interval: Optional[str] = None, log_momentum: bool = False):
+    def __init__(self, logging_interval: Optional[str] = None, log_momentum: bool = False) -> None:
         if logging_interval not in (None, "step", "epoch"):
             raise MisconfigurationException("logging_interval should be `step` or `epoch` or `None`.")
 
@@ -146,6 +146,7 @@ def _check_no_key(key: str) -> bool:
         self.last_momentum_values = {name + "-momentum": None for name in names_flatten}
 
     def on_train_batch_start(self, trainer: "pl.Trainer", *args: Any, **kwargs: Any) -> None:
+        assert trainer.logger is not None
         if not trainer.logger_connector.should_update_logs:
             return
 
@@ -157,6 +158,7 @@ def on_train_batch_start(self, trainer: "pl.Trainer", *args: Any, **kwargs: Any)
                 trainer.logger.log_metrics(latest_stat, step=trainer.global_step)
 
     def on_train_epoch_start(self, trainer: "pl.Trainer", *args: Any, **kwargs: Any) -> None:
+        assert trainer.logger is not None
         if self.logging_interval != "step":
             interval = "epoch" if self.logging_interval is None else "any"
             latest_stat = self._extract_stats(trainer, interval)

pytorch_lightning/plugins/training_type/training_type_plugin.py

@@ -19,7 +19,6 @@
 from torch import Tensor
 from torch.nn import Module
 from torch.optim import Optimizer
-from torch.optim.lr_scheduler import _LRScheduler
 from torch.utils.data import DataLoader
 
 import pytorch_lightning as pl
@@ -32,7 +31,7 @@
 from pytorch_lightning.utilities.apply_func import apply_to_collection, move_data_to_device
 from pytorch_lightning.utilities.distributed import ReduceOp
 from pytorch_lightning.utilities.model_helpers import is_overridden
-from pytorch_lightning.utilities.types import _PATH, STEP_OUTPUT
+from pytorch_lightning.utilities.types import _PATH, LRSchedulerConfig, STEP_OUTPUT
 
 TBroadcast = TypeVar("TBroadcast")
 
@@ -52,7 +51,7 @@ def __init__(
         self.checkpoint_io = checkpoint_io
         self.precision_plugin = precision_plugin
         self.optimizers: List[Optimizer] = []
-        self.lr_schedulers: List[_LRScheduler] = []
+        self.lr_schedulers: List[LRSchedulerConfig] = []
         self.optimizer_frequencies: List[int] = []
         if is_overridden("post_dispatch", self, parent=Strategy):
             rank_zero_deprecation(

pytorch_lightning/trainer/optimizers.py

@@ -23,6 +23,7 @@
 from pytorch_lightning.core.optimizer import LightningOptimizer
 from pytorch_lightning.utilities import rank_zero_warn
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
+from pytorch_lightning.utilities.types import LRSchedulerConfig
 
 
 class TrainerOptimizersMixin(ABC):
@@ -122,7 +123,7 @@ def _convert_to_lightning_optimizer(trainer, optimizer):
     @staticmethod
     def _configure_schedulers(
         schedulers: list, monitor: Optional[str], is_manual_optimization: bool
-    ) -> List[Dict[str, Any]]:
+    ) -> List[LRSchedulerConfig]:
         """Convert each scheduler into dict structure with relevant information."""
         lr_schedulers = []
         default_config = _get_default_scheduler_config()

pytorch_lightning/trainer/trainer.py

@@ -107,7 +107,7 @@
     _PATH,
     _PREDICT_OUTPUT,
     EVAL_DATALOADERS,
-    LRSchedulerTypeUnion,
+    LRSchedulerConfig,
     STEP_OUTPUT,
     TRAIN_DATALOADERS,
 )
@@ -1839,11 +1839,11 @@ def optimizers(self, new_optims: Optional[List[Optimizer]]) -> None:
         self.strategy.optimizers = new_optims
 
     @property
-    def lr_schedulers(self) -> List[LRSchedulerTypeUnion]:
+    def lr_schedulers(self) -> List[LRSchedulerConfig]:
         return self.strategy.lr_schedulers
 
     @lr_schedulers.setter
-    def lr_schedulers(self, new_schedulers: List[LRSchedulerTypeUnion]) -> None:
+    def lr_schedulers(self, new_schedulers: List[LRSchedulerConfig]) -> None:
         self.strategy.lr_schedulers = new_schedulers
 
     @property

pytorch_lightning/utilities/types.py

@@ -14,15 +14,16 @@
 """
 Convention:
  - Do not include any `_TYPE` suffix
- - Types used in public hooks (as those in the `LightningModule` and `Callback`) should be public (no trailing `_`)
+ - Types used in public hooks (as those in the `LightningModule` and `Callback`) should be public (no leading `_`)
 """
 from pathlib import Path
-from typing import Any, Dict, Iterator, List, Mapping, Sequence, Type, Union
+from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Type, Union
 
 import torch
-from torch.optim.lr_scheduler import _LRScheduler, ReduceLROnPlateau
+from torch.optim import Optimizer
 from torch.utils.data import DataLoader
 from torchmetrics import Metric
+from typing_extensions import TypedDict
 
 _NUMBER = Union[int, float]
 _METRIC = Union[Metric, torch.Tensor, _NUMBER]
@@ -43,7 +44,75 @@
     Dict[str, Sequence[DataLoader]],
 ]
 EVAL_DATALOADERS = Union[DataLoader, Sequence[DataLoader]]
+
+
+# Copied from `torch.optim.lr_scheduler.pyi`
+# Missing attributes were added to improve typing
+class _LRScheduler:
+    optimizer: Optimizer
+
+    def __init__(self, optimizer: Optimizer, last_epoch: int = ...) -> None:
+        ...
+
+    def state_dict(self) -> dict:
+        ...
+
+    def load_state_dict(self, state_dict: dict) -> None:
+        ...
+
+    def get_last_lr(self) -> List[float]:
+        ...
+
+    def get_lr(self) -> float:
+        ...
+
+    def step(self, epoch: Optional[int] = ...) -> None:
+        ...
+
+
+# Copied from `torch.optim.lr_scheduler.pyi`
+# Missing attributes were added to improve typing
+class ReduceLROnPlateau:
+    in_cooldown: bool
+    optimizer: Optimizer
+
+    def __init__(
+        self,
+        optimizer: Optimizer,
+        mode: str = ...,
+        factor: float = ...,
+        patience: int = ...,
+        verbose: bool = ...,
+        threshold: float = ...,
+        threshold_mode: str = ...,
+        cooldown: int = ...,
+        min_lr: float = ...,
+        eps: float = ...,
+    ) -> None:
+        ...
+
+    def step(self, metrics: Any, epoch: Optional[int] = ...) -> None:
+        ...
+
+    def state_dict(self) -> dict:
+        ...
+
+    def load_state_dict(self, state_dict: dict) -> None:
+        ...
+
+
 # todo: improve LRSchedulerType naming/typing
-LRSchedulerTypeTuple = (_LRScheduler, ReduceLROnPlateau)
-LRSchedulerTypeUnion = Union[_LRScheduler, ReduceLROnPlateau]
-LRSchedulerType = Union[Type[_LRScheduler], Type[ReduceLROnPlateau]]
+LRSchedulerTypeTuple = (torch.optim.lr_scheduler._LRScheduler, torch.optim.lr_scheduler.ReduceLROnPlateau)
+LRSchedulerTypeUnion = Union[torch.optim.lr_scheduler._LRScheduler, torch.optim.lr_scheduler.ReduceLROnPlateau]
+LRSchedulerType = Union[Type[torch.optim.lr_scheduler._LRScheduler], Type[torch.optim.lr_scheduler.ReduceLROnPlateau]]
+
+
+class LRSchedulerConfig(TypedDict):
+    scheduler: Union[_LRScheduler, ReduceLROnPlateau]
+    name: Optional[str]
+    interval: str
+    frequency: int
+    reduce_on_plateau: bool
+    monitor: Optional[str]
+    strict: bool
+    opt_idx: Optional[int]