diff --git a/ppcls/engine/engine.py b/ppcls/engine/engine.py
index 71a7e182ae925e18173589dd80bb345bb04a1109..7a7bbde6b4bd431275f8e5def2190d3061c5ec6f 100644
--- a/ppcls/engine/engine.py
+++ b/ppcls/engine/engine.py
@@ -224,7 +224,7 @@ class Engine(object):
         # build optimizer
         if self.mode == 'train':
             self.optimizer, self.lr_sch = build_optimizer(
-                self.config, self.config["Global"]["epochs"],
+                self.config["Optimizer"], self.config["Global"]["epochs"],
                 len(self.train_dataloader),
                 [self.model, self.train_loss_func])
 
diff --git a/ppcls/optimizer/__init__.py b/ppcls/optimizer/__init__.py
index a440eac463e2a5ebd74cf676b2487973baea9546..3f63777202c1cc31292bcdfd6077b3b995302be8 100644
--- a/ppcls/optimizer/__init__.py
+++ b/ppcls/optimizer/__init__.py
@@ -44,8 +44,7 @@ def build_lr_scheduler(lr_config, epochs, step_each_epoch):
 
 # model_list is None in static graph
 def build_optimizer(config, epochs, step_each_epoch, model_list=None):
-    config = copy.deepcopy(config)
-    optim_config = config["Optimizer"]
+    optim_config = copy.deepcopy(config)
     if isinstance(optim_config, dict):
         # convert {'name': xxx, **optim_cfg} to [{name: {scope: xxx, **optim_cfg}}]
         optim_name = optim_config.pop("name")
@@ -93,6 +92,15 @@ def build_optimizer(config, epochs, step_each_epoch, model_list=None):
         else:
             grad_clip = None
         optim_model = []
+
+        # for static graph
+        if model_list is None:
+            optim = getattr(optimizer, optim_name)(
+                learning_rate=lr, grad_clip=grad_clip,
+                **optim_cfg)(model_list=optim_model)
+            return optim, lr
+
+        # for dynamic graph
         for i in range(len(model_list)):
             if len(model_list[i].parameters()) == 0:
                 continue