update trainer_result in ci

tests/test_training/test_loss.py

@@ -1,6 +1,5 @@
 import math
 import os
-import subprocess
 
 import pytest
 import torch
@@ -198,7 +197,6 @@ def train(
             )
         if gpc.is_rank_for_log():
             assert loss is not None and not math.isnan(loss.item())
-            global cur_loss_list
             cur_loss_list.append((loss.item() - moe_loss.item() if moe_loss is not None else loss.item()))
         timer("fwd-bwd").stop()
 
@@ -206,7 +204,7 @@ def train(
         trainer_result = trainer.step()
         assert trainer_result is not None
 
-        success_update, _ = trainer_result
+        success_update, _, _ = trainer_result
         assert success_update, "Error: grad norm inf or nan occurs!"
         if success_update:  # update parameters successfully
             train_state.step_count += 1