Reset trainer variable should_stop when fit is called (#19177)

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

Author: ryan597

src/lightning/pytorch/trainer/trainer.py

@@ -542,6 +542,7 @@ def fit(
         self.state.fn = TrainerFn.FITTING
         self.state.status = TrainerStatus.RUNNING
         self.training = True
+        self.should_stop = False
         call._call_and_handle_interrupt(
             self, self._fit_impl, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path
         )

tests/tests_pytorch/loops/test_training_epoch_loop.py

@@ -19,7 +19,7 @@
 from lightning_utilities.test.warning import no_warning_call
 
 from lightning.fabric.utilities.warnings import PossibleUserWarning
-from lightning.pytorch.callbacks import ModelCheckpoint
+from lightning.pytorch.callbacks import EarlyStopping, ModelCheckpoint
 from lightning.pytorch.demos.boring_classes import BoringModel
 from lightning.pytorch.trainer.trainer import Trainer
 
@@ -92,7 +92,16 @@ def test_should_stop_triggers_validation_once(min_epochs, min_steps, val_count,
     (min_epochs/steps is satisfied).
 
     """
-    model = BoringModel()
+
+    class NewBoring(BoringModel):
+        def training_step(self, batch, batch_idx):
+            self.log("loss", self.step(batch))
+            return {"loss": self.step(batch)}
+
+    model = NewBoring()
+    # create a stopping condition with a high threshold so it triggers immediately
+    # check the condition before validation so the count is unaffected
+    stopping = EarlyStopping(monitor="loss", check_on_train_epoch_end=True, stopping_threshold=100)
     trainer = Trainer(
         default_root_dir=tmp_path,
         num_sanity_val_steps=0,
@@ -103,8 +112,8 @@ def test_should_stop_triggers_validation_once(min_epochs, min_steps, val_count,
         min_steps=min_steps,
         enable_model_summary=False,
         enable_checkpointing=False,
+        callbacks=[stopping],
     )
-    trainer.should_stop = True  # Request to stop before min_epochs/min_steps are reached
     trainer.fit_loop.epoch_loop.val_loop.run = Mock()
     trainer.fit(model)
     assert trainer.fit_loop.epoch_loop.val_loop.run.call_count == val_count