Fix integration of LearningRateFinder with EarlyStopping

src/lightning/pytorch/CHANGELOG.md

@@ -41,6 +41,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Fix `save_last` behavior in the absence of validation ([#20960](https://github.com/Lightning-AI/pytorch-lightning/pull/20960))
 
 
+- Fixed integration between `LearningRateFinder` and `EarlyStopping` ([#21056](https://github.com/Lightning-AI/pytorch-lightning/pull/21056))
+
+
 - Fix gradient calculation in `lr_finder` for `mode="exponential"`  ([#21055](https://github.com/Lightning-AI/pytorch-lightning/pull/21055))
 
 

src/lightning/pytorch/tuner/lr_finder.py

@@ -292,7 +292,8 @@ def _lr_find(
     trainer.fit_loop.restarting = False  # reset restarting flag as checkpoint restoring sets it to True
     trainer.fit_loop.epoch_loop.restarting = False  # reset restarting flag as checkpoint restoring sets it to True
     trainer.fit_loop.epoch_loop.val_loop._combined_loader = None
-
+    trainer.fit_loop._combined_loader = None  # reset data fetcher to avoid issues with the next fit
+    trainer.fit_loop.setup_data()
     return lr_finder
 
 

tests/tests_pytorch/tuner/test_lr_finder.py

@@ -23,6 +23,7 @@
 from lightning_utilities.test.warning import no_warning_call
 
 from lightning.pytorch import Trainer, seed_everything
+from lightning.pytorch.callbacks import EarlyStopping
 from lightning.pytorch.callbacks.lr_finder import LearningRateFinder
 from lightning.pytorch.demos.boring_classes import BoringModel
 from lightning.pytorch.tuner.lr_finder import _LRFinder
@@ -540,6 +541,67 @@ def training_step(self, batch: Any, batch_idx: int) -> STEP_OUTPUT:
     assert math.isclose(model.lr, suggested_lr)
 
 
+def test_lr_finder_with_early_stopping(tmp_path):
+    class ModelWithValidation(BoringModel):
+        def __init__(self):
+            super().__init__()
+            self.learning_rate = 0.1
+
+        def validation_step(self, batch, batch_idx):
+            output = self.step(batch)
+            # Log validation loss that EarlyStopping will monitor
+            self.log("val_loss", output, on_epoch=True)
+            return output
+
+        def configure_optimizers(self):
+            optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)
+
+            # Add ReduceLROnPlateau scheduler that monitors val_loss (issue #20355)
+            plateau_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
+                optimizer, mode="min", factor=0.5, patience=2
+            )
+            scheduler_config = {"scheduler": plateau_scheduler, "interval": "epoch", "monitor": "val_loss"}
+
+            return {"optimizer": optimizer, "lr_scheduler": scheduler_config}
+
+    model = ModelWithValidation()
+
+    # Both callbacks that previously caused issues
+    callbacks = [
+        LearningRateFinder(num_training_steps=100, update_attr=False),
+        EarlyStopping(monitor="val_loss", patience=3),
+    ]
+
+    trainer = Trainer(
+        default_root_dir=tmp_path,
+        max_epochs=10,
+        callbacks=callbacks,
+        limit_train_batches=5,
+        limit_val_batches=3,
+        enable_model_summary=False,
+        enable_progress_bar=False,
+    )
+
+    trainer.fit(model)
+    assert trainer.state.finished
+
+    # Verify that both callbacks were active
+    lr_finder_callback = None
+    early_stopping_callback = None
+    for callback in trainer.callbacks:
+        if isinstance(callback, LearningRateFinder):
+            lr_finder_callback = callback
+        elif isinstance(callback, EarlyStopping):
+            early_stopping_callback = callback
+
+    assert lr_finder_callback is not None, "LearningRateFinder callback should be present"
+    assert early_stopping_callback is not None, "EarlyStopping callback should be present"
+
+    # Verify learning rate finder ran and has results
+    assert lr_finder_callback.optimal_lr is not None, "Learning rate finder should have results"
+    assert lr_finder_callback.optimal_lr.suggestion() > 0, "Learning rate suggestion should be positive"
+
+
 def test_gradient_correctness():
     """Test that torch.gradient uses correct spacing parameter."""
     lr_finder = _LRFinder(mode="exponential", lr_min=1e-6, lr_max=1e-1, num_training=20)