updating gather function with gather_for_metrics in run_wav2vec2_pretraining (#18877)

Co-authored-by: Arun Rajaram <arunrajaram@Aruns-MacBook-Pro.local>

examples/pytorch/speech-pretraining/run_wav2vec2_pretraining_no_trainer.py

@@ -596,7 +596,7 @@ def main():
             # make sure that `num_losses` is summed for distributed training
             # and average gradients over losses of all devices
             if accelerator.state.num_processes > 1:
-                num_losses = accelerator.gather(num_losses).sum()
+                num_losses = accelerator.gather_for_metrics(num_losses).sum()
                 gradient_multiplier = accelerator.state.num_processes / num_losses
                 multiply_grads(model.module.parameters(), gradient_multiplier)
             else:
@@ -647,10 +647,10 @@ def main():
                 outputs.diversity_loss.detach()
 
                 if accelerator.state.num_processes > 1:
-                    loss = accelerator.gather(loss).sum()
-                    outputs.contrastive_loss = accelerator.gather(outputs.contrastive_loss).sum()
-                    outputs.diversity_loss = accelerator.gather(outputs.diversity_loss).sum()
-                    percent_masked = accelerator.gather(percent_masked).sum()
+                    loss = accelerator.gather_for_metrics(loss).sum()
+                    outputs.contrastive_loss = accelerator.gather_for_metrics(outputs.contrastive_loss).sum()
+                    outputs.diversity_loss = accelerator.gather_for_metrics(outputs.diversity_loss).sum()
+                    percent_masked = accelerator.gather_for_metrics(percent_masked).sum()
 
                 train_logs = {
                     "loss": (loss * args.gradient_accumulation_steps) / num_losses,
@@ -713,7 +713,7 @@ def main():
 
         # sum over devices in multi-processing
         if accelerator.num_processes > 1:
-            val_logs = {k: accelerator.gather(v).sum() for k, v in val_logs.items()}
+            val_logs = {k: accelerator.gather_for_metrics(v).sum() for k, v in val_logs.items()}
 
         val_logs = {k: v / val_logs["val_num_losses"] for k, v in val_logs.items()}