Improve pytorch examples for fp16 (#9796)

* Pad to 8x for fp16 multiple choice example (#9752)

* Pad to 8x for fp16 squad trainer example (#9752)

* Pad to 8x for fp16 ner example (#9752)

* Pad to 8x for fp16 swag example (#9752)

* Pad to 8x for fp16 qa beam search example (#9752)

* Pad to 8x for fp16 qa example (#9752)

* Pad to 8x for fp16 seq2seq example (#9752)

* Pad to 8x for fp16 glue example (#9752)

* Pad to 8x for fp16 new ner example (#9752)

* update script template #9752

* Update examples/multiple-choice/run_swag.py

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update examples/question-answering/run_qa.py

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update examples/question-answering/run_qa_beam_search.py

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* improve code quality #9752

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

examples/legacy/multiple_choice/run_multiple_choice.py

@@ -28,6 +28,7 @@ from transformers import (
     AutoConfig,
     AutoModelForMultipleChoice,
     AutoTokenizer,
+    DataCollatorWithPadding,
     EvalPrediction,
     HfArgumentParser,
     Trainer,
@@ -188,6 +189,9 @@ def main():
         preds = np.argmax(p.predictions, axis=1)
         return {"acc": simple_accuracy(preds, p.label_ids)}
 
+    # Data collator
+    data_collator = DataCollatorWithPadding(tokenizer, pad_to_multiple_of=8) if training_args.fp16 else None
+
     # Initialize our Trainer
     trainer = Trainer(
         model=model,
@@ -195,6 +199,7 @@ def main():
         train_dataset=train_dataset,
         eval_dataset=eval_dataset,
         compute_metrics=compute_metrics,
+        data_collator=data_collator,
     )
 
     # Training

examples/legacy/question-answering/run_squad_trainer.py

@@ -23,7 +23,14 @@ from dataclasses import dataclass, field
 from typing import Optional
 
 import transformers
-from transformers import AutoConfig, AutoModelForQuestionAnswering, AutoTokenizer, HfArgumentParser, SquadDataset
+from transformers import (
+    AutoConfig,
+    AutoModelForQuestionAnswering,
+    AutoTokenizer,
+    DataCollatorWithPadding,
+    HfArgumentParser,
+    SquadDataset,
+)
 from transformers import SquadDataTrainingArguments as DataTrainingArguments
 from transformers import Trainer, TrainingArguments
 from transformers.trainer_utils import is_main_process
@@ -145,12 +152,16 @@ def main():
         else None
     )
 
+    # Data collator
+    data_collator = DataCollatorWithPadding(tokenizer, pad_to_multiple_of=8) if training_args.fp16 else None
+
     # Initialize our Trainer
     trainer = Trainer(
         model=model,
         args=training_args,
         train_dataset=train_dataset,
         eval_dataset=eval_dataset,
+        data_collator=data_collator,
     )
 
     # Training

examples/legacy/token-classification/run_ner.py

@@ -30,6 +30,7 @@ from transformers import (
     AutoConfig,
     AutoModelForTokenClassification,
     AutoTokenizer,
+    DataCollatorWithPadding,
     EvalPrediction,
     HfArgumentParser,
     Trainer,
@@ -237,6 +238,9 @@ def main():
             "f1": f1_score(out_label_list, preds_list),
         }
 
+    # Data collator
+    data_collator = DataCollatorWithPadding(tokenizer, pad_to_multiple_of=8) if training_args.fp16 else None
+
     # Initialize our Trainer
     trainer = Trainer(
         model=model,
@@ -244,6 +248,7 @@ def main():
         train_dataset=train_dataset,
         eval_dataset=eval_dataset,
         compute_metrics=compute_metrics,
+        data_collator=data_collator,
     )
 
     # Training