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Remove deprecated training arguments (#36946)

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* Remove deprecated training arguments

* More fixes

* More fixes

* More fixes
This commit is contained in:
cyyever
2025-03-27 00:44:48 +08:00
committed by GitHub
parent 44715225e3
commit 2b550c47b2
8 changed files with 15 additions and 108 deletions
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62
tests/trainer/test_trainer.py
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@@ -28,7 +28,7 @@ import unittest
from functools import partial
from itertools import product
from pathlib import Path
from typing import Any, Dict, List
from typing import Any
from unittest.mock import Mock, patch
import numpy as np
@@ -2982,7 +2982,7 @@ class TrainerIntegrationTest(TestCasePlus, TrainerIntegrationCommon):
self.tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
self.tokenizer.add_tokens(["<NEW_TOKEN1>", "<NEW_TOKEN2>"])
def __call__(self, features: List[Any], return_tensors="pt") -> Dict[str, Any]:
def __call__(self, features: list[Any], return_tensors="pt") -> dict[str, Any]:
return default_data_collator(features, return_tensors)
data_collator = FakeCollator()
@@ -2999,7 +2999,7 @@ class TrainerIntegrationTest(TestCasePlus, TrainerIntegrationCommon):
trainer = get_regression_trainer(
output_dir=tmp_dir,
save_steps=5,
evaluation_strategy="steps",
eval_strategy="steps",
eval_steps=5,
max_steps=9,
)
@@ -3020,7 +3020,7 @@ class TrainerIntegrationTest(TestCasePlus, TrainerIntegrationCommon):
trainer = get_regression_trainer(
output_dir=tmp_dir,
save_steps=5,
evaluation_strategy="steps",
eval_strategy="steps",
eval_steps=5,
load_best_model_at_end=True,
save_total_limit=2,
@@ -4260,7 +4260,7 @@ class TrainerIntegrationTest(TestCasePlus, TrainerIntegrationCommon):
model = RegressionPreTrainedModel(config)
eval_dataset = SampleIterableDataset()
accelerator_config = {
accelerator_config: dict[str, Any] = {
"split_batches": True,
"dispatch_batches": True,
"even_batches": False,
@@ -4370,56 +4370,6 @@ class TrainerIntegrationTest(TestCasePlus, TrainerIntegrationCommon):
self.assertEqual(trainer.accelerator.even_batches, True)
self.assertEqual(trainer.accelerator.use_seedable_sampler, True)
def test_accelerator_config_from_dict_with_deprecated_args(self):
# Checks that accelerator kwargs can be passed through
# and the accelerator is initialized respectively
# and maintains the deprecated args if passed in
with tempfile.TemporaryDirectory() as tmp_dir:
config = RegressionModelConfig(a=1.5, b=2.5)
model = RegressionPreTrainedModel(config)
eval_dataset = SampleIterableDataset()
# Leaves all options as something *not* basic
with self.assertWarns(FutureWarning) as cm:
args = RegressionTrainingArguments(
output_dir=tmp_dir,
accelerator_config={
"split_batches": True,
},
dispatch_batches=False,
)
self.assertIn("dispatch_batches", str(cm.warnings[0].message))
trainer = Trainer(model=model, args=args, eval_dataset=eval_dataset)
self.assertEqual(trainer.accelerator.dispatch_batches, False)
self.assertEqual(trainer.accelerator.split_batches, True)
with self.assertWarns(FutureWarning) as cm:
args = RegressionTrainingArguments(
output_dir=tmp_dir,
accelerator_config={
"even_batches": False,
},
split_batches=True,
)
self.assertIn("split_batches", str(cm.warnings[0].message))
trainer = Trainer(model=model, args=args, eval_dataset=eval_dataset)
self.assertEqual(trainer.accelerator.split_batches, True)
self.assertEqual(trainer.accelerator.even_batches, False)
self.assertEqual(trainer.accelerator.dispatch_batches, None)
def test_accelerator_config_only_deprecated_args(self):
with tempfile.TemporaryDirectory() as tmp_dir:
with self.assertWarns(FutureWarning) as cm:
args = RegressionTrainingArguments(
output_dir=tmp_dir,
split_batches=True,
)
self.assertIn("split_batches", str(cm.warnings[0].message))
config = RegressionModelConfig(a=1.5, b=2.5)
model = RegressionPreTrainedModel(config)
eval_dataset = SampleIterableDataset()
trainer = Trainer(model=model, args=args, eval_dataset=eval_dataset)
self.assertEqual(trainer.accelerator.split_batches, True)
def test_accelerator_custom_state(self):
AcceleratorState._reset_state(reset_partial_state=True)
with tempfile.TemporaryDirectory() as tmp_dir:
@@ -5191,7 +5141,7 @@ class TrainerHyperParameterMultiObjectOptunaIntegrationTest(unittest.TestCase):
def hp_name(trial):
return MyTrialShortNamer.shortname(trial.params)
def compute_objective(metrics: Dict[str, float]) -> List[float]:
def compute_objective(metrics: dict[str, float]) -> list[float]:
return metrics["eval_loss"], metrics["eval_accuracy"]
with tempfile.TemporaryDirectory() as tmp_dir:

4
tests/trainer/test_trainer_distributed.py
View File

@@ -200,6 +200,8 @@ if __name__ == "__main__":
model = RegressionModel()
training_args.per_device_train_batch_size = 1
training_args.max_steps = 1
training_args.dispatch_batches = False
training_args.accelerator_config = {
"dispatch_batches": False,
}
trainer = Trainer(model, training_args, train_dataset=train_dataset)
trainer.train()