diff --git a/lib/modeling/base_model.py b/lib/modeling/base_model.py
index 27d171d..91510b9 100644
--- a/lib/modeling/base_model.py
+++ b/lib/modeling/base_model.py
@@ -83,7 +83,7 @@ class MetaModel(metaclass=ABCMeta):
         raise NotImplementedError
 
     @abstractmethod
-    def train_step(self, loss_num):
+    def train_step(self, loss_num) -> bool:
         raise NotImplementedError
 
     @abstractmethod
@@ -292,37 +292,34 @@ class BaseModel(MetaModel, nn.Module):
         del seqs
         return ipts, labs, typs, vies, seqL
 
-    def train_step(self, loss_sum):
+    def train_step(self, loss_sum) -> bool:
         '''
             Conduct loss_sum.backward(), self.optimizer.step() and self.scheduler.step().
         '''
-        
-        skip_lr_sched = False
 
         self.optimizer.zero_grad()
         if loss_sum <= 1e-9:
-            self.msg_mgr.log_warning("Find the loss sum less than 1e-9 but the training process will continue!)")
-        
+            self.msg_mgr.log_warning(
+                "Find the loss sum less than 1e-9 but the training process will continue!")
+
         if self.engine_cfg['enable_float16']:
             self.Scaler.scale(loss_sum).backward()
             self.Scaler.step(self.optimizer)
             scale = self.Scaler.get_scale()
             self.Scaler.update()
-            skip_lr_sched = (scale != self.Scaler.get_scale())
             # Warning caused by optimizer skip when NaN
             # https://discuss.pytorch.org/t/optimizer-step-before-lr-scheduler-step-error-using-gradscaler/92930/5
-            
-            #for debug
-            # for name, param in self.named_parameters():
-            #     if param.grad is None:
-            #         print(name)
+            if scale != self.Scaler.get_scale():
+                self.msg_mgr.log_debug("Training step skip. Expected the former scale equals to the present, got {} and {}".format(
+                    scale, self.Scaler.get_scale()))
+                return False
         else:
             loss_sum.backward()
             self.optimizer.step()
-            
-        if not skip_lr_sched:
-            self.iteration += 1
-            self.scheduler.step()
+
+        self.iteration += 1
+        self.scheduler.step()
+        return True
 
     def inference(self, rank):
         total_size = len(self.test_loader)
@@ -368,7 +365,9 @@ class BaseModel(MetaModel, nn.Module):
                 training_feat, visual_summary = retval['training_feat'], retval['visual_summary']
                 del retval
             loss_sum, loss_info = model.loss_aggregator(training_feat)
-            model.train_step(loss_sum)
+            ok = model.train_step(loss_sum)
+            if not ok:
+                continue
 
             visual_summary.update(loss_info)
             visual_summary['scalar/learning_rate'] = model.optimizer.param_groups[0]['lr']
@@ -403,13 +402,13 @@ class BaseModel(MetaModel, nn.Module):
             info_dict.update({
                 'labels': label_list, 'types': types_list, 'views': views_list})
 
-            if 'eval_func' in model.engine_cfg.keys():
-                eval_func = model.engine_cfg['eval_func']
+            if 'eval_func' in model.cfgs["evaluator_cfg"].keys():
+                eval_func = model.cfgs['evaluator_cfg']["eval_func"]
             else:
                 eval_func = 'identification'
             eval_func = getattr(eval_functions, eval_func)
             valid_args = get_valid_args(
-                eval_func, model.engine_cfg, ['metric'])
+                eval_func, model.cfgs["evaluator_cfg"], ['metric'])
             try:
                 dataset_name = model.cfgs['data_cfg']['test_dataset_name']
             except: