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Let EarlyStoppingCallback not require load_best_model_at_end (#35101)

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Zach Mueller 2025-01-10 10:25:32 -05:00 committed by GitHub
parent 0aaf124fb9
commit b02828e4af
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2 changed files with 23 additions and 2 deletions
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8
src/transformers/trainer_callback.py
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@ -707,10 +707,14 @@ class EarlyStoppingCallback(TrainerCallback, ExportableState):
self.early_stopping_patience_counter += 1
def on_train_begin(self, args, state, control, **kwargs):
assert args.load_best_model_at_end, "EarlyStoppingCallback requires load_best_model_at_end = True"
if not args.load_best_model_at_end:
logger.warning(
"Using EarlyStoppingCallback without load_best_model_at_end=True. "
"Once training is finished, the best model will not be loaded automatically."
)
assert (
args.metric_for_best_model is not None
), "EarlyStoppingCallback requires metric_for_best_model is defined"
), "EarlyStoppingCallback requires metric_for_best_model to be defined"
assert (
args.eval_strategy != IntervalStrategy.NO
), "EarlyStoppingCallback requires IntervalStrategy of steps or epoch"

17
tests/trainer/test_trainer.py
View File

@ -3484,6 +3484,23 @@ class TrainerIntegrationTest(TestCasePlus, TrainerIntegrationCommon):
except AssertionError:
self.assertEqual(trainer.state.global_step, 0)
# even if load_best_model_at_end is False, `best_model_checkpoint` should be set
with tempfile.TemporaryDirectory() as tmp_dir:
trainer = get_regression_trainer(
output_dir=tmp_dir,
num_train_epochs=20,
gradient_accumulation_steps=1,
per_device_train_batch_size=16,
load_best_model_at_end=False,
eval_strategy=IntervalStrategy.EPOCH,
save_strategy=IntervalStrategy.EPOCH,
compute_metrics=AlmostAccuracy(),
metric_for_best_model="accuracy",
)
trainer.add_callback(EarlyStoppingCallback(1, 0.0001))
train_output = trainer.train()
self.assertIsNotNone(trainer.state.best_model_checkpoint)
def test_flos_extraction(self):
with tempfile.TemporaryDirectory() as tmp_dir:
trainer = get_regression_trainer(learning_rate=0.1, output_dir=tmp_dir)