diff --git a/ppdet/engine/trainer.py b/ppdet/engine/trainer.py
index d3c4d7d9c51070e03304169418647ca7b9d68cdf..1829bf019a8ca804d337f25817fcdf3b34b03325 100644
--- a/ppdet/engine/trainer.py
+++ b/ppdet/engine/trainer.py
@@ -32,7 +32,6 @@ import paddle
 import paddle.nn as nn
 import paddle.distributed as dist
 from paddle.distributed import fleet
-from paddle import amp
 from paddle.static import InputSpec
 from ppdet.optimizer import ModelEMA
 
@@ -380,13 +379,21 @@ class Trainer(object):
             self.cfg['EvalDataset'] = self.cfg.EvalDataset = create(
                 "EvalDataset")()
 
+        model = self.model
         sync_bn = (getattr(self.cfg, 'norm_type', None) == 'sync_bn' and
                    self.cfg.use_gpu and self._nranks > 1)
         if sync_bn:
-            self.model = paddle.nn.SyncBatchNorm.convert_sync_batchnorm(
-                self.model)
+            model = paddle.nn.SyncBatchNorm.convert_sync_batchnorm(model)
 
-        model = self.model
+        # enabel auto mixed precision mode
+        use_amp = self.cfg.get('amp', False)
+        amp_level = self.cfg.get('amp_level', 'O1')
+        if use_amp:
+            scaler = paddle.amp.GradScaler(
+                enable=self.cfg.use_gpu or self.cfg.use_npu,
+                init_loss_scaling=self.cfg.get('init_loss_scaling', 1024))
+            model = paddle.amp.decorate(models=model, level=amp_level)
+        # get distributed model
         if self.cfg.get('fleet', False):
             model = fleet.distributed_model(model)
             self.optimizer = fleet.distributed_optimizer(self.optimizer)
@@ -394,13 +401,7 @@ class Trainer(object):
             find_unused_parameters = self.cfg[
                 'find_unused_parameters'] if 'find_unused_parameters' in self.cfg else False
             model = paddle.DataParallel(
-                self.model, find_unused_parameters=find_unused_parameters)
-
-        # enabel auto mixed precision mode
-        if self.cfg.get('amp', False):
-            scaler = amp.GradScaler(
-                enable=self.cfg.use_gpu or self.cfg.use_npu,
-                init_loss_scaling=1024)
+                model, find_unused_parameters=find_unused_parameters)
 
         self.status.update({
             'epoch_id': self.start_epoch,
@@ -436,12 +437,12 @@ class Trainer(object):
                 self._compose_callback.on_step_begin(self.status)
                 data['epoch_id'] = epoch_id
 
-                if self.cfg.get('amp', False):
-                    with amp.auto_cast(enable=self.cfg.use_gpu):
+                if use_amp:
+                    with paddle.amp.auto_cast(
+                            enable=self.cfg.use_gpu, level=amp_level):
                         # model forward
                         outputs = model(data)
                         loss = outputs['loss']
-
                     # model backward
                     scaled_loss = scaler.scale(loss)
                     scaled_loss.backward()