diff --git a/utils/tools.py b/utils/tools.py
index fe98a72..b9ee19b 100644
--- a/utils/tools.py
+++ b/utils/tools.py
@@ -133,6 +133,7 @@ def cal_accuracy(y_pred, y_true):
 def del_files(dir_path):
     shutil.rmtree(dir_path)
 
+
 def vali(args, accelerator, model, vali_data, vali_loader, criterion, mae_metric):
     total_loss = []
     total_mae_loss = []
@@ -161,7 +162,9 @@ def vali(args, accelerator, model, vali_data, vali_loader, criterion, mae_metric
                     outputs = model(batch_x, batch_x_mark, dec_inp, batch_y_mark)[0]
                 else:
                     outputs = model(batch_x, batch_x_mark, dec_inp, batch_y_mark)
-            # self.accelerator.wait_for_everyone()
+
+            outputs, batch_y = accelerator.gather_for_metrics((outputs, batch_y))
+
             f_dim = -1 if args.features == 'MS' else 0
             outputs = outputs[:, -args.pred_len:, f_dim:]
             batch_y = batch_y[:, -args.pred_len:, f_dim:].to(accelerator.device)
@@ -205,11 +208,15 @@ def test(args, accelerator, model, train_loader, vali_loader, criterion):
                 None
             )
         accelerator.wait_for_everyone()
+        outputs = accelerator.gather_for_metrics(outputs)
         f_dim = -1 if args.features == 'MS' else 0
         outputs = outputs[:, -args.pred_len:, f_dim:]
         pred = outputs
         true = torch.from_numpy(np.array(y)).to(accelerator.device)
         batch_y_mark = torch.ones(true.shape).to(accelerator.device)
+        true = accelerator.gather_for_metrics(true)
+        batch_y_mark = accelerator.gather_for_metrics(batch_y_mark)
+
         loss = criterion(x[:, :, 0], args.frequency_map, pred[:, :, 0], true, batch_y_mark)
 
     model.train()