Remove deprecated training arguments (#36946)

* Remove deprecated training arguments

* More fixes

* More fixes

* More fixes

Author: cyyever

examples/pytorch/instance-segmentation/README.md

@@ -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 \

examples/pytorch/object-detection/README.md

@@ -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 \

examples/pytorch/test_pytorch_examples.py

@@ -667,7 +667,7 @@ def test_run_instance_segmentation(self):
             --per_device_train_batch_size 2
             --per_device_eval_batch_size 1
             --do_eval
-            --evaluation_strategy epoch
+            --eval_strategy epoch
             --seed 32
         """.split()
 

src/transformers/trainer_pt_utils.py

@@ -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"

src/transformers/training_args.py

@@ -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 @@ def __post_init__(self):
         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 @@ def __post_init__(self):
 
         # 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 @@ def __post_init__(self):
                 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

src/transformers/utils/quantization_config.py

@@ -646,7 +646,7 @@ def __init__(
         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,

tests/trainer/test_trainer.py

@@ -28,7 +28,7 @@
 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 @@ def __init__(self):
                 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 @@ def test_load_best_model_with_save(self):
         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 @@ def test_load_best_model_with_save(self):
         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 @@ def test_accelerator_config_from_dict(self):
             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 @@ def test_accelerator_config_from_partial(self):
             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 @@ def model_init(trial):
         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:

tests/trainer/test_trainer_distributed.py

@@ -200,6 +200,8 @@ def compute_metrics(p: EvalPrediction) -> Dict:
     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()