Simplify _get_rank() utility function

src/lightning/fabric/utilities/rank_zero.py

@@ -30,17 +30,12 @@
 )
 from typing_extensions import ParamSpec
 
-import lightning.fabric
 from lightning.fabric.utilities.imports import _UTILITIES_GREATER_EQUAL_0_10
 
 rank_zero_module.log = logging.getLogger(__name__)
 
 
-def _get_rank(
-    strategy: Optional["lightning.fabric.strategies.Strategy"] = None,
-) -> Optional[int]:
-    if strategy is not None:
-        return strategy.global_rank
+def _get_rank() -> Optional[int]:
     # SLURM_PROCID can be set even if SLURM is not managing the multiprocessing,
     # therefore LOCAL_RANK needs to be checked first
     rank_keys = ("RANK", "LOCAL_RANK", "SLURM_PROCID", "JSM_NAMESPACE_RANK")

src/lightning/pytorch/callbacks/early_stopping.py

@@ -26,7 +26,6 @@
 from typing_extensions import override
 
 import lightning.pytorch as pl
-from lightning.fabric.utilities.rank_zero import _get_rank
 from lightning.pytorch.callbacks.callback import Callback
 from lightning.pytorch.utilities.exceptions import MisconfigurationException
 from lightning.pytorch.utilities.rank_zero import rank_prefixed_message, rank_zero_warn
@@ -265,12 +264,8 @@ def _improvement_message(self, current: Tensor) -> str:
         return msg
 
     @staticmethod
-    def _log_info(trainer: Optional["pl.Trainer"], message: str, log_rank_zero_only: bool) -> None:
-        rank = _get_rank(
-            strategy=(trainer.strategy if trainer is not None else None),  # type: ignore[arg-type]
-        )
-        if trainer is not None and trainer.world_size <= 1:
-            rank = None
+    def _log_info(trainer: "pl.Trainer", message: str, log_rank_zero_only: bool) -> None:
+        rank = trainer.global_rank if trainer.world_size > 1 else None
         message = rank_prefixed_message(message, rank)
         if rank is None or not log_rank_zero_only or rank == 0:
             log.info(message)

tests/tests_pytorch/callbacks/test_early_stopping.py

@@ -480,7 +480,6 @@ def test_early_stopping_squeezes():
     es_mock.assert_called_once_with(torch.tensor(0))
 
 
-@pytest.mark.parametrize("trainer", [Trainer(), None])
 @pytest.mark.parametrize(
     ("log_rank_zero_only", "world_size", "global_rank", "expected_log"),
     [
@@ -492,15 +491,11 @@ def test_early_stopping_squeezes():
         (True, 2, 1, None),
     ],
 )
-def test_early_stopping_log_info(trainer, log_rank_zero_only, world_size, global_rank, expected_log):
+def test_early_stopping_log_info(log_rank_zero_only, world_size, global_rank, expected_log):
     """Checks if log.info() gets called with expected message when used within EarlyStopping."""
     # set the global_rank and world_size if trainer is not None
     # or else always expect the simple logging message
-    if trainer:
-        trainer.strategy.global_rank = global_rank
-        trainer.strategy.world_size = world_size
-    else:
-        expected_log = "bar"
+    trainer = Mock(global_rank=global_rank, world_size=world_size)
 
     with mock.patch("lightning.pytorch.callbacks.early_stopping.log.info") as log_mock:
         EarlyStopping._log_info(trainer, "bar", log_rank_zero_only)