diff --git a/paddleseg/core/train.py b/paddleseg/core/train.py
index da14534459..b075164c40 100644
--- a/paddleseg/core/train.py
+++ b/paddleseg/core/train.py
@@ -99,8 +99,8 @@ def train(model,
         keep_checkpoint_max (int, optional): Maximum number of checkpoints to save. Default: 5.
         test_config(dict, optional): Evaluation config.
         precision (str, optional): Use AMP if precision='fp16'. If precision='fp32', the training is normal.
-        amp_level (str, optional): Auto mixed precision level. Accepted values are “O1” and “O2”: O1 represent mixed precision, 
-            the input data type of each operator will be casted by white_list and black_list; O2 represent Pure fp16, all operators 
+        amp_level (str, optional): Auto mixed precision level. Accepted values are “O1” and “O2”: O1 represent mixed precision,
+            the input data type of each operator will be casted by white_list and black_list; O2 represent Pure fp16, all operators
             parameters and input data will be casted to fp16, except operators in black_list, don’t support fp16 kernel and batchnorm. Default is O1(amp)
         profiler_options (str, optional): The option of train profiler.
         to_static_training (bool, optional): Whether to use @to_static for training.
@@ -220,9 +220,10 @@ def train(model,
                 scaled = scaler.scale(loss)  # scale the loss
                 scaled.backward()  # do backward
                 if isinstance(optimizer, paddle.distributed.fleet.Fleet):
-                    scaler.minimize(optimizer.user_defined_optimizer, scaled)
+                    scaler.step(optimizer.user_defined_optimizer)
                 else:
-                    scaler.minimize(optimizer, scaled)  # update parameters
+                    scaler.step(optimizer)  # update parameters
+                scaler.update()  # update parameters
             else:
                 logits_list = ddp_model(images) if nranks > 1 else model(images)