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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
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8 changed files with 15 additions and 108 deletions
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2
examples/pytorch/instance-segmentation/README.md
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@ -66,7 +66,7 @@ python run_instance_segmentation.py \
--dataloader_persistent_workers \
--dataloader_prefetch_factor 4 \
--do_eval \
--evaluation_strategy epoch \
--eval_strategy epoch \
--logging_strategy epoch \
--save_strategy epoch \
--save_total_limit 2 \

2
examples/pytorch/object-detection/README.md
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@ -56,7 +56,7 @@ python run_object_detection.py \
--greater_is_better true \
--load_best_model_at_end true \
--logging_strategy epoch \
--evaluation_strategy epoch \
--eval_strategy epoch \
--save_strategy epoch \
--save_total_limit 2 \
--push_to_hub true \

2
examples/pytorch/test_pytorch_examples.py
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@ -667,7 +667,7 @@ class ExamplesTests(TestCasePlus):
--per_device_train_batch_size 2
--per_device_eval_batch_size 1
--do_eval
--evaluation_strategy epoch
--eval_strategy epoch
--seed 32
""".split()

2
src/transformers/trainer_pt_utils.py
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@ -1263,7 +1263,7 @@ class AcceleratorConfig:
" in your script multiplied by the number of processes."
},
)
dispatch_batches: bool = field(
dispatch_batches: Optional[bool] = field(
default=None,
metadata={
"help": "If set to `True`, the dataloader prepared by the Accelerator is only iterated through on the main process"

47
src/transformers/training_args.py
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@ -768,14 +768,6 @@ class TrainingArguments:
Refer to the PyTorch doc for possible values and note that they may change across PyTorch versions.
This flag is experimental and subject to change in future releases.
split_batches (`bool`, *optional*):
Whether or not the accelerator should split the batches yielded by the dataloaders across the devices
during distributed training. If
set to `True`, the actual batch size used will be the same on any kind of distributed processes, but it
must be a
round multiple of the number of processes you are using (such as GPUs).
include_tokens_per_second (`bool`, *optional*):
Whether or not to compute the number of tokens per second per device for training speed metrics.
@ -1426,10 +1418,6 @@ class TrainingArguments:
"choices": ["auto", "apex", "cpu_amp"],
},
)
evaluation_strategy: Union[IntervalStrategy, str] = field(
default=None,
metadata={"help": "Deprecated. Use `eval_strategy` instead"},
)
push_to_hub_model_id: Optional[str] = field(
default=None, metadata={"help": "The name of the repository to which push the `Trainer`."}
)
@ -1504,16 +1492,6 @@ class TrainingArguments:
},
)
dispatch_batches: Optional[bool] = field(
default=None,
metadata={"help": "Deprecated. Pass {'dispatch_batches':VALUE} to `accelerator_config`."},
)
split_batches: Optional[bool] = field(
default=None,
metadata={"help": "Deprecated. Pass {'split_batches':True} to `accelerator_config`."},
)
include_tokens_per_second: Optional[bool] = field(
default=False,
metadata={"help": "If set to `True`, the speed metrics will include `tgs` (tokens per second per device)."},
@ -1606,13 +1584,6 @@ class TrainingArguments:
if self.disable_tqdm is None:
self.disable_tqdm = logger.getEffectiveLevel() > logging.WARN
if self.evaluation_strategy is not None:
warnings.warn(
"`evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead",
FutureWarning,
)
self.eval_strategy = self.evaluation_strategy
if isinstance(self.eval_strategy, EvaluationStrategy):
warnings.warn(
"using `EvaluationStrategy` for `eval_strategy` is deprecated and will be removed in version 5"
@ -1771,7 +1742,7 @@ class TrainingArguments:
# We need to setup the accelerator config here *before* the first call to `self.device`
if is_accelerate_available():
if not isinstance(self.accelerator_config, (AcceleratorConfig)):
if not isinstance(self.accelerator_config, AcceleratorConfig):
if self.accelerator_config is None:
self.accelerator_config = AcceleratorConfig()
elif isinstance(self.accelerator_config, dict):
@ -1786,22 +1757,6 @@ class TrainingArguments:
else:
self.accelerator_config = AcceleratorConfig.from_json_file(self.accelerator_config)
if self.dispatch_batches is not None:
warnings.warn(
"Using `--dispatch_batches` is deprecated and will be removed in version 4.41 of 🤗 Transformers. Use"
" `--accelerator_config {'dispatch_batches':VALUE} instead",
FutureWarning,
)
self.accelerator_config.dispatch_batches = self.dispatch_batches
if self.split_batches is not None:
warnings.warn(
"Using `--split_batches` is deprecated and will be removed in version 4.41 of 🤗 Transformers. Use"
" `--accelerator_config {'split_batches':VALUE} instead",
FutureWarning,
)
self.accelerator_config.split_batches = self.split_batches
# Initialize device before we proceed
if self.framework == "pt" and is_torch_available():
self.device

2
src/transformers/utils/quantization_config.py
View File

@ -646,7 +646,7 @@ class GPTQConfig(QuantizationConfigMixin):
sym: bool = True,
true_sequential: bool = True,
checkpoint_format: str = "gptq",
meta: Optional[Dict[str, any]] = None,
meta: Optional[Dict[str, Any]] = None,
backend: Optional[str] = None,
use_cuda_fp16: bool = False,
model_seqlen: Optional[int] = None,

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()