Make training args fully immutable (#25435)

* Make training args fully immutable

* Working tests, PyTorch

* In test_trainer

* during testing

* Use proper dataclass way

* Fix test

* Another one

* Fix tf

* Lingering slow

* Exception

* Clean

examples/pytorch/image-pretraining/run_mae.py

@@ -163,6 +163,15 @@ class CustomTrainingArguments(TrainingArguments):
         default=1e-3, metadata={"help": "Base learning rate: absolute_lr = base_lr * total_batch_size / 256."}
     )
 
+    def __post_init__(self):
+        # Compute absolute learning rate while args are mutable
+        super().__post_init__()
+        if self.base_learning_rate is not None:
+            total_train_batch_size = self.train_batch_size * self.gradient_accumulation_steps * self.world_size
+            delattr(self, "_frozen")
+            self.learning_rate = self.base_learning_rate * total_train_batch_size / 256
+            setattr(self, "_frozen", True)
+
 
 def collate_fn(examples):
     pixel_values = torch.stack([example["pixel_values"] for example in examples])
@@ -353,13 +362,6 @@ def main():
         # Set the validation transforms
         ds["validation"].set_transform(preprocess_images)
 
-    # Compute absolute learning rate
-    total_train_batch_size = (
-        training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
-    )
-    if training_args.base_learning_rate is not None:
-        training_args.learning_rate = training_args.base_learning_rate * total_train_batch_size / 256
-
     # Initialize our trainer
     trainer = Trainer(
         model=model,

examples/pytorch/summarization/run_summarization.py

@@ -18,6 +18,7 @@ Fine-tuning the library models for sequence to sequence.
 """
 # You can also adapt this script on your own sequence to sequence task. Pointers for this are left as comments.
 
+import dataclasses
 import logging
 import os
 import sys
@@ -674,14 +675,10 @@ def main():
         return result
 
     # Override the decoding parameters of Seq2SeqTrainer
-    training_args.generation_max_length = (
-        training_args.generation_max_length
-        if training_args.generation_max_length is not None
-        else data_args.val_max_target_length
-    )
-    training_args.generation_num_beams = (
-        data_args.num_beams if data_args.num_beams is not None else training_args.generation_num_beams
-    )
+    if training_args.generation_max_length is None:
+        training_args = dataclasses.replace(training_args, generation_max_length=data_args.val_max_target_length)
+    if training_args.generation_num_beams is None:
+        training_args = dataclasses.replace(training_args, generation_num_beams=data_args.num_beams)
 
     # Initialize our Trainer
     trainer = Seq2SeqTrainer(

examples/research_projects/mlm_wwm/run_mlm_wwm.py

@@ -21,6 +21,7 @@ https://huggingface.co/models?filter=fill-mask
 """
 # You can also adapt this script on your own masked language modeling task. Pointers for this are left as comments.
 
+import dataclasses
 import json
 import logging
 import math
@@ -366,7 +367,7 @@ def main():
     # If we have ref files, need to avoid it removed by trainer
     has_ref = data_args.train_ref_file or data_args.validation_ref_file
     if has_ref:
-        training_args.remove_unused_columns = False
+        training_args = dataclasses.replace(training_args, remove_unused_columns=False)
 
     # Data collator
     # This one will take care of randomly masking the tokens.

examples/tensorflow/language-modeling/run_clm.py

@@ -259,7 +259,6 @@ def main():
             assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, json or txt file."
 
     if training_args.output_dir is not None:
-        training_args.output_dir = Path(training_args.output_dir)
         os.makedirs(training_args.output_dir, exist_ok=True)
     # endregion
 
@@ -267,8 +266,8 @@ def main():
     # Detecting last checkpoint.
     checkpoint = None
     if len(os.listdir(training_args.output_dir)) > 0 and not training_args.overwrite_output_dir:
-        config_path = training_args.output_dir / CONFIG_NAME
-        weights_path = training_args.output_dir / TF2_WEIGHTS_NAME
+        config_path = Path(training_args.output_dir) / CONFIG_NAME
+        weights_path = Path(training_args.output_dir) / TF2_WEIGHTS_NAME
         if config_path.is_file() and weights_path.is_file():
             checkpoint = training_args.output_dir
             logger.info(

examples/tensorflow/language-modeling/run_mlm.py

@@ -265,7 +265,6 @@ def main():
             assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, json or txt file."
 
     if training_args.output_dir is not None:
-        training_args.output_dir = Path(training_args.output_dir)
         os.makedirs(training_args.output_dir, exist_ok=True)
 
     if isinstance(training_args.strategy, tf.distribute.TPUStrategy) and not data_args.pad_to_max_length:
@@ -277,8 +276,8 @@ def main():
     # Detecting last checkpoint.
     checkpoint = None
     if len(os.listdir(training_args.output_dir)) > 0 and not training_args.overwrite_output_dir:
-        config_path = training_args.output_dir / CONFIG_NAME
-        weights_path = training_args.output_dir / TF2_WEIGHTS_NAME
+        config_path = Path(training_args.output_dir) / CONFIG_NAME
+        weights_path = Path(training_args.output_dir) / TF2_WEIGHTS_NAME
         if config_path.is_file() and weights_path.is_file():
             checkpoint = training_args.output_dir
             logger.warning(

src/transformers/training_args.py

@@ -18,7 +18,7 @@ import json
 import math
 import os
 import warnings
-from dataclasses import asdict, dataclass, field, fields
+from dataclasses import FrozenInstanceError, asdict, dataclass, field, fields
 from datetime import timedelta
 from enum import Enum
 from pathlib import Path
@@ -1687,6 +1687,16 @@ class TrainingArguments:
                 mixed_precision_dtype = "bf16"
             os.environ["ACCELERATE_MIXED_PRECISION"] = mixed_precision_dtype
 
+        # Finally set the `TrainingArguments` to be immutable
+        self._frozen = True
+
+    def __setattr__(self, name, value):
+        # Once fully through the `__post_init__`, `TrainingArguments` are immutable
+        if not name.startswith("_") and getattr(self, "_frozen", False):
+            raise FrozenInstanceError(f"cannot assign to field {name}")
+        else:
+            super().__setattr__(name, value)
+
     def __str__(self):
         self_as_dict = asdict(self)
 

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:
@@ -529,7 +529,8 @@ 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.
-        trainer.args.seed = 314
+        args = TrainingArguments("./regression", learning_rate=0.1, seed=314)
+        trainer = Trainer(args=args, train_dataset=train_dataset, model_init=lambda: RegressionModel())
         trainer.train()
         self.check_trained_model(trainer.model, alternate_seed=True)
 

tests/trainer/test_trainer_distributed.py

@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import dataclasses
 from typing import Dict
 
 import numpy as np
@@ -205,7 +206,14 @@ if __name__ == "__main__":
             logger.error(p.metrics)
             exit(1)
 
-        trainer.args.eval_accumulation_steps = 2
+        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,
+        )
 
         metrics = trainer.evaluate()
         logger.info(metrics)
@@ -219,15 +227,22 @@ if __name__ == "__main__":
             logger.error(p.metrics)
             exit(1)
 
-        trainer.args.eval_accumulation_steps = None
+        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,
+        )
 
     # 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.per_device_train_batch_size = 1
-    training_args.max_steps = 1
-    training_args.dispatch_batches = False
+    training_args = dataclasses.replace(
+        training_args, per_device_train_batch_size=1, max_steps=1, dispatch_batches=False
+    )
     trainer = Trainer(model, training_args, train_dataset=train_dataset)
     trainer.train()