Fix deprecated arg issue (#29372)

* Fix deprecated arg issue

* Trainer check too

* Check for dict or dataclass

* Simplify, make config always AcceleratorConfig

* Upstream to Trainer

src/transformers/trainer.py

@@ -80,7 +80,6 @@ from .trainer_callback import (
     TrainerState,
 )
 from .trainer_pt_utils import (
-    AcceleratorConfig,
     DistributedTensorGatherer,
     IterableDatasetShard,
     LabelSmoother,
@@ -4116,21 +4115,10 @@ class Trainer:
         gradient_accumulation_plugin = GradientAccumulationPlugin(**grad_acc_kwargs)
 
         # create accelerator object
-        accelerator_kwargs = {}
-        if self.args.accelerator_config is not None:
-            accelerator_kwargs = self.args.accelerator_config
-            # dict and AcceleratorConfigs are parseable, json files are not
-            if isinstance(accelerator_kwargs, AcceleratorConfig):
-                accelerator_kwargs = accelerator_kwargs.to_dict()
-            elif isinstance(accelerator_kwargs, dict):
-                # Some values may need to go through non-accelerate aligned defaults
-                # and we need to run the `__post_init__` to set them
-                accelerator_kwargs = AcceleratorConfig(**accelerator_kwargs).to_dict()
-
         self.accelerator = Accelerator(
             deepspeed_plugin=self.args.deepspeed_plugin,
             gradient_accumulation_plugin=gradient_accumulation_plugin,
-            **accelerator_kwargs,
+            **self.args.accelerator_config.to_dict(),
         )
         # some Trainer classes need to use `gather` instead of `gather_for_metrics`, thus we store a flag
         self.gather_function = self.accelerator.gather_for_metrics

src/transformers/training_args.py

@@ -1737,9 +1737,11 @@ class TrainingArguments:
             os.environ[f"{prefix}USE_ORIG_PARAMS"] = self.fsdp_config.get("use_orig_params", "true")
 
         if is_accelerate_available():
-            if not isinstance(self.accelerator_config, (AcceleratorConfig, dict)):
+            if not isinstance(self.accelerator_config, (AcceleratorConfig)):
                 if self.accelerator_config is None:
                     self.accelerator_config = AcceleratorConfig()
+                elif isinstance(self.accelerator_config, dict):
+                    self.accelerator_config = AcceleratorConfig(**self.accelerator_config)
                 else:
                     self.accelerator_config = AcceleratorConfig.from_json_file(self.accelerator_config)
             if self.dispatch_batches is not None:
@@ -1748,7 +1750,7 @@ class TrainingArguments:
                     " `--accelerator_config {'dispatch_batches':VALUE} instead",
                     FutureWarning,
                 )
-                self.accelerator_config["dispatch_batches"] = self.dispatch_batches
+                self.accelerator_config.dispatch_batches = self.dispatch_batches
 
             if self.split_batches is not None:
                 warnings.warn(
@@ -1756,7 +1758,7 @@ class TrainingArguments:
                     " `--accelerator_config {'split_batches':VALUE} instead",
                     FutureWarning,
                 )
-                self.accelerator_config["split_batches"] = self.split_batches
+                self.accelerator_config.split_batches = self.split_batches
 
         if self.tpu_metrics_debug:
             warnings.warn(

tests/trainer/test_trainer.py

@@ -2633,6 +2633,20 @@ class TrainerIntegrationTest(TestCasePlus, TrainerIntegrationCommon):
             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)
+
 
 @require_torch
 @is_staging_test