add torch.no_grad when in eval mode (#17020)

* add torch.no_grad when in eval mode

* make style quality

examples/pytorch/image-classification/run_image_classification_no_trainer.py

@@ -469,6 +469,7 @@ def main():
         model.eval()
         samples_seen = 0
         for step, batch in enumerate(eval_dataloader):
+            with torch.no_grad():
                 outputs = model(**batch)
             predictions = outputs.logits.argmax(dim=-1)
             predictions, references = accelerator.gather((predictions, batch["labels"]))

examples/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py

@@ -579,6 +579,7 @@ def main():
         model.eval()
         samples_seen = 0
         for step, batch in enumerate(tqdm(eval_dataloader, disable=not accelerator.is_local_main_process)):
+            with torch.no_grad():
                 outputs = model(**batch)
 
             upsampled_logits = torch.nn.functional.interpolate(

examples/pytorch/text-classification/run_glue_no_trainer.py

@@ -22,6 +22,7 @@ import random
 from pathlib import Path
 
 import datasets
+import torch
 from datasets import load_dataset, load_metric
 from torch.utils.data import DataLoader
 from tqdm.auto import tqdm
@@ -514,6 +515,7 @@ def main():
         model.eval()
         samples_seen = 0
         for step, batch in enumerate(eval_dataloader):
+            with torch.no_grad():
                 outputs = model(**batch)
             predictions = outputs.logits.argmax(dim=-1) if not is_regression else outputs.logits.squeeze()
             predictions, references = accelerator.gather((predictions, batch["labels"]))

templates/adding_a_new_example_script/{{cookiecutter.directory_name}}/run_{{cookiecutter.example_shortcut}}.py

@@ -28,6 +28,7 @@ from dataclasses import dataclass, field
 from typing import Optional, List
 
 import datasets
+import torch
 from datasets import load_dataset
 
 import transformers
@@ -871,6 +872,7 @@ def main():
 
         model.eval()
         for step, batch in enumerate(eval_dataloader):
+            with torch.no_grad(): 
                 outputs = model(**batch)
             predictions = outputs.logits.argmax(dim=-1)
             metric.add_batch(