Revert frozen training arguments (#25903)

* Revert frozen training arguments

* TODO

tests/trainer/test_trainer.py

@@ -139,9 +139,9 @@ class RegressionTrainingArguments(TrainingArguments):
     b: float = 0.0
 
     def __post_init__(self):
+        super().__post_init__()
         # save resources not dealing with reporting (also avoids the warning when it's not set)
         self.report_to = []
-        super().__post_init__()
 
 
 class RepeatDataset:
@@ -529,8 +529,7 @@ class TrainerIntegrationPrerunTest(TestCasePlus, TrainerIntegrationCommon):
         self.check_trained_model(trainer.model)
 
         # Re-training should restart from scratch, thus lead the same results and new seed should be used.
-        args = TrainingArguments("./regression", learning_rate=0.1, seed=314)
-        trainer = Trainer(args=args, train_dataset=train_dataset, model_init=lambda: RegressionModel())
+        trainer.args.seed = 314
         trainer.train()
         self.check_trained_model(trainer.model, alternate_seed=True)
 

tests/trainer/test_trainer_distributed.py

@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import dataclasses
 from typing import Dict
 
 import numpy as np
@@ -206,14 +205,7 @@ if __name__ == "__main__":
             logger.error(p.metrics)
             exit(1)
 
-        training_args = dataclasses.replace(training_args, eval_accumulation_steps=2)
-        trainer = Trainer(
-            model=DummyModel(),
-            args=training_args,
-            data_collator=DummyDataCollator(),
-            eval_dataset=dataset,
-            compute_metrics=compute_metrics,
-        )
+        trainer.args.eval_accumulation_steps = 2
 
         metrics = trainer.evaluate()
         logger.info(metrics)
@@ -227,22 +219,15 @@ if __name__ == "__main__":
             logger.error(p.metrics)
             exit(1)
 
-        training_args = dataclasses.replace(training_args, eval_accumulation_steps=None)
-        trainer = Trainer(
-            model=DummyModel(),
-            args=training_args,
-            data_collator=DummyDataCollator(),
-            eval_dataset=dataset,
-            compute_metrics=compute_metrics,
-        )
+        trainer.args.eval_accumulation_steps = None
 
     # Check that `dispatch_batches=False` will work on a finite iterable dataset
 
     train_dataset = FiniteIterableDataset(label_names=["labels", "extra"], length=1)
 
     model = RegressionModel()
-    training_args = dataclasses.replace(
-        training_args, per_device_train_batch_size=1, max_steps=1, dispatch_batches=False
-    )
+    training_args.per_device_train_batch_size = 1
+    training_args.max_steps = 1
+    training_args.dispatch_batches = False
     trainer = Trainer(model, training_args, train_dataset=train_dataset)
     trainer.train()