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Reset trainer variable should_stop when fit is called #19177

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Merged
merged 6 commits into from
Mar 14, 2025
Merged

Reset trainer variable should_stop when fit is called #19177

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102b580
trainer flag should_stop resets when fit is called
ryan597 Dec 11, 2023
cb2e20a
Merge branch 'Lightning-AI:master' into bug/18727_reset_trainer_shoul…
ryan597 Dec 18, 2023
82edacb
introduce early stopping condition to trigger should_stop
ryan597 Dec 18, 2023
c85b8fd
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Dec 18, 2023
6a62f66
Merge branch 'master' into bug/18727_reset_trainer_should_stop_on_fit
Borda Feb 16, 2024
005209c
Merge branch 'master' into bug/18727_reset_trainer_should_stop_on_fit
ryan597 Feb 26, 2024
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1 change: 1 addition & 0 deletions src/lightning/pytorch/trainer/trainer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -541,6 +541,7 @@ 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
)
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15 changes: 12 additions & 3 deletions tests/tests_pytorch/loops/test_training_epoch_loop.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,7 +17,7 @@
import pytest
import torch
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
from lightning_utilities.test.warning import no_warning_call
Expand Down Expand Up @@ -90,7 +90,16 @@ 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,
Expand All @@ -101,8 +110,8 @@ 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
Expand Down