Revert frozen training arguments (#25903)

* Revert frozen training arguments

* TODO

examples/pytorch/image-pretraining/run_mae.py

@@ -163,15 +163,6 @@ 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])
@@ -362,6 +353,13 @@ 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

@@ -18,7 +18,6 @@ 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
@@ -675,10 +674,14 @@ def main():
         return result
 
     # Override the decoding parameters of Seq2SeqTrainer
-    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)
+    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
+    )
 
     # Initialize our Trainer
     trainer = Seq2SeqTrainer(

examples/research_projects/mlm_wwm/run_mlm_wwm.py

@@ -21,7 +21,6 @@ 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
@@ -367,7 +366,7 @@ 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 = dataclasses.replace(training_args, remove_unused_columns=False)
+        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

@@ -259,6 +259,7 @@ 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
 
@@ -266,8 +267,8 @@ 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 = Path(training_args.output_dir) / CONFIG_NAME
-        weights_path = Path(training_args.output_dir) / TF2_WEIGHTS_NAME
+        config_path = training_args.output_dir / CONFIG_NAME
+        weights_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

@@ -265,6 +265,7 @@ 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:
@@ -276,8 +277,8 @@ 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 = Path(training_args.output_dir) / CONFIG_NAME
-        weights_path = Path(training_args.output_dir) / TF2_WEIGHTS_NAME
+        config_path = training_args.output_dir / CONFIG_NAME
+        weights_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(