Reset trainer variable should_stop when fit is called

[ryan597]

What does this PR do?

Reset trainer variable should_stop when fit is called

If fit is called after early stopping has already stopped training, then the model will not continue training as the trainer flag should_stop is currently not reset when fit is called.
Change this to reset should_stop every time fit is called

Fixes #18727

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---

📚 Documentation preview 📚: https://pytorch-lightning--19177.org.readthedocs.build/en/19177/

[ryan597]

seems this is failing on a test that is designed to make sure the trainer stays as should_stop=True related to #15708

@pytest.mark.parametrize(("min_epochs", "min_steps", "val_count"), [(3, None, 3), (None, 3, 2)])
def test_should_stop_triggers_validation_once(min_epochs, min_steps, val_count, tmp_path):
    """Regression test for issue #15708.

    Test that the request for `should_stop=True` only triggers validation when Trainer is allowed to stop
    (min_epochs/steps is satisfied).

    """
    model = BoringModel()
    trainer = Trainer(
        default_root_dir=tmp_path,
        num_sanity_val_steps=0,
        limit_val_batches=2,
        limit_train_batches=2,
        max_epochs=3,
        min_epochs=min_epochs,
        min_steps=min_steps,
        enable_model_summary=False,
        enable_checkpointing=False,
    )
    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

[ryan597]

I have changed the above test to use an EarlyStopping condition instead of setting the flag through trainer.should_stop=True such that this test now passes with the following

+    class NewBoring(BoringModel):
+        def training_step(self, batch, batch_idx):
+            self.log("loss", self.step(batch))
+            return {"loss": self.step(batch)}
+
-    model = BoringModel()
+    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,
        limit_val_batches=2,
        limit_train_batches=2,
        max_epochs=3,
        min_epochs=min_epochs,
        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

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[codecov]

Codecov Report

All modified and coverable lines are covered by tests ✅

Project coverage is 48%. Comparing base (2a827f3) to head (005209c).
Report is 347 commits behind head on master.

❗ There is a different number of reports uploaded between BASE (2a827f3) and HEAD (005209c). Click for more details.

HEAD has 179 uploads less than BASE

Flag	BASE (2a827f3)	HEAD (005209c)
lightning	36	13
cpu	66	21
pytest	51	2
python3.10	17	9
gpu	4	2
lightning_fabric	10	0
python3.8	12	6
python3.11	17	6
app	9	0
examples	9	0
tpu	1	0
lightning_app	6	0

Additional details and impacted files

@@            Coverage Diff             @@
##           master   #19177      +/-   ##
==========================================
- Coverage      83%      48%     -35%     
==========================================
  Files         450      442       -8     
  Lines       38250    38098     -152     
==========================================
- Hits        31893    18438   -13455     
- Misses       6357    19660   +13303     

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[qqueing]

is this PR in progress??