diff --git a/examples/multi-task/run.py b/examples/multi-task/run.py
index c02a6cf1ce553d9eca9c32f2875c5ac3106d7f2e..aff2b4f137e258f38243aa9ff71c5cbc44162b83 100644
--- a/examples/multi-task/run.py
+++ b/examples/multi-task/run.py
@@ -78,6 +78,6 @@ if __name__ == '__main__':
     # step 8-2*: set saver to save model
     save_steps = int(n_steps-batch_size) // 2
     # save_steps = 10
-    trainer.set_saver(save_path=save_path, save_steps=save_steps, save_type=save_type, is_multi=True)
+    trainer.set_saver(save_path=save_path, save_steps=save_steps, save_type=save_type)
     # step 8-3: start training
     trainer.train(print_steps=print_steps)
\ No newline at end of file
diff --git a/paddlepalm/multihead_trainer.py b/paddlepalm/multihead_trainer.py
index 45550623fb1b51ebdae06641edaa837343f64fc4..39ec9711cc3c88286213461d72bc3b91329cd9ed 100644
--- a/paddlepalm/multihead_trainer.py
+++ b/paddlepalm/multihead_trainer.py
@@ -82,7 +82,9 @@ class MultiHeadTrainer(Trainer):
 
         def get_loss(i):
             head = head_dict[self._trainers[i].name]
+            self._trainers[i]._lock_prog = True
             loss_var = self._trainers[i].build_forward(backbone, head)
+            self._trainers[i]._lock_prog = False
             return loss_var
       
         task_fns = {i: lambda i=i: get_loss(i) for i in range(len(self._trainers))}
diff --git a/paddlepalm/trainer.py b/paddlepalm/trainer.py
index 5519ad6755839677e068e14518526c2b85cd5a9c..38af875d480e346995041512ac46c74b69339355 100644
--- a/paddlepalm/trainer.py
+++ b/paddlepalm/trainer.py
@@ -162,8 +162,10 @@ class Trainer(object):
         train_prog = fluid.Program()
         train_init_prog = fluid.Program()
 
-        self._train_prog = train_prog
-        self._train_init_prog = train_init_prog
+        if not self._lock_prog:
+            self._train_prog = train_prog
+            self._train_init_prog = train_init_prog
+
         if not self._lock_prog:
             with fluid.program_guard(train_prog, train_init_prog):
                 net_inputs = reader_helper.create_net_inputs(input_attrs, async=False)
@@ -505,7 +507,7 @@ class Trainer(object):
             convert=convert,
             main_program=self._train_init_prog)
 
-    def set_saver(self, save_path, save_steps, save_type='ckpt', is_multi=False):
+    def set_saver(self, save_path, save_steps, save_type='ckpt'):
         """
         create a build-in saver into trainer. A saver will automatically save checkpoint or predict model every `save_steps` training steps.
 
@@ -542,20 +544,11 @@ class Trainer(object):
             if (self._save_predict or self._save_ckpt) and self._cur_train_step % save_steps == 0:
 
                 if self._save_predict:
-                    if is_multi:
-                        self._save(save_path, suffix='-pred.step'+str(self._cur_train_step))
-                        print('predict model has been saved at '+os.path.join(save_path, 'pred.step'+str(self._cur_train_step)))
-                    else:
-                        self._save(save_path, suffix='pred.step'+str(self._cur_train_step))
-                        print('predict model has been saved at '+os.path.join(save_path, 'pred.step'+str(self._cur_train_step)))
+                    self._save(save_path, suffix='pred.step'+str(self._cur_train_step))
+                    print('predict model has been saved at '+os.path.join(save_path, 'pred.step'+str(self._cur_train_step)))
                 if self._save_ckpt:
-                    print(self._train_prog)
-                    if is_multi:
-                        fluid.io.save_persistables(self._exe, os.path.join(save_path, 'ckpt.step'+str(self._cur_train_step)), self._train_prog)
-                        print('checkpoint has been saved at '+os.path.join(save_path, 'ckpt.step'+str(self._cur_train_step)))
-                    else:
-                        fluid.io.save_persistables(self._exe, os.path.join(save_path, 'ckpt.step'+str(self._cur_train_step)), self._train_prog)
-                        print('checkpoint has been saved at '+os.path.join(save_path, 'ckpt.step'+str(self._cur_train_step)))
+                    fluid.io.save_persistables(self._exe, os.path.join(save_path, 'ckpt.step'+str(self._cur_train_step)), self._train_prog)
+                    print('checkpoint has been saved at '+os.path.join(save_path, 'ckpt.step'+str(self._cur_train_step)))
                 return True
             else:
                 return False