Add tests for no_trainer and fix existing examples (#16656)

* Fixed some bugs involving saving during epochs
* Added tests mimicking the existing examples tests
* Added in json exporting to all `no_trainer` examples for consistency

examples/pytorch/token-classification/run_ner_no_trainer.py

@@ -19,6 +19,7 @@ without using a Trainer.
 """
 
 import argparse
+import json
 import logging
 import math
 import os
@@ -639,7 +640,10 @@ def main():
 
             if isinstance(checkpointing_steps, int):
                 if completed_steps % checkpointing_steps == 0:
-                    accelerator.save_state(f"step_{completed_steps}")
+                    output_dir = f"step_{completed_steps}"
+                    if args.output_dir is not None:
+                        output_dir = os.path.join(args.output_dir, output_dir)
+                    accelerator.save_state(output_dir)
 
             if completed_steps >= args.max_train_steps:
                 break
@@ -662,7 +666,6 @@ def main():
                 references=refs,
             )  # predictions and preferences are expected to be a nested list of labels, not label_ids
 
-        # eval_metric = metric.compute()
         eval_metric = compute_metrics()
         accelerator.print(f"epoch {epoch}:", eval_metric)
         if args.with_tracking:
@@ -686,7 +689,10 @@ def main():
                 )
 
         if args.checkpointing_steps == "epoch":
-            accelerator.save_state(f"epoch_{epoch}")
+            output_dir = f"epoch_{epoch}"
+            if args.output_dir is not None:
+                output_dir = os.path.join(args.output_dir, output_dir)
+            accelerator.save_state(output_dir)
 
     if args.output_dir is not None:
         accelerator.wait_for_everyone()
@@ -697,6 +703,9 @@ def main():
             if args.push_to_hub:
                 repo.push_to_hub(commit_message="End of training", auto_lfs_prune=True)
 
+        with open(os.path.join(args.output_dir, "all_results.json"), "w") as f:
+            json.dump({"eval_accuracy": eval_metric["accuracy"], "train_loss": float(loss.cpu().detach().numpy())}, f)
+
 
 if __name__ == "__main__":
     main()