diff --git a/fastNLP/core/callback.py b/fastNLP/core/callback.py
index 09ff860b..85903315 100644
--- a/fastNLP/core/callback.py
+++ b/fastNLP/core/callback.py
@@ -324,14 +324,14 @@ class CallbackManager(Callback):
         self._env = env
         self.callbacks = []
         if callbacks:
-            self.prepare_callbacks(callbacks)
+            self.callbacks = self.prepare_callbacks(callbacks)
 
     def prepare_callbacks(self, callbacks):
         if not callbacks:
             return []
         if isinstance(callbacks, list):
             if all([isinstance(cb, Callback) for cb in callbacks]) is True:
-                self.callbacks.extend(callbacks)
+                pass
             else:
                 obj = [not isinstance(cb, Callback) for cb in callbacks][0]
                 raise TypeError(f"Expect sub-classes of Callback. Got {type(obj)}")
diff --git a/fastNLP/core/dist_trainer.py b/fastNLP/core/dist_trainer.py
index 260b93b0..57c5f56b 100644
--- a/fastNLP/core/dist_trainer.py
+++ b/fastNLP/core/dist_trainer.py
@@ -18,6 +18,7 @@ from .optimizer import Optimizer
 from .utils import _build_args
 from .utils import _move_dict_value_to_device
 from .utils import _get_func_signature
+from pkg_resources import parse_version
 
 __all__ = [
     'get_local_rank',
@@ -103,8 +104,13 @@ class DistTrainer():
             model, optimizer = amp.initialize(model, optimizer, opt_level=self.fp16)
 
         # init DataParallel
-        self.model = DDP(model, device_ids=[self.local_rank],
-                         output_device=self.local_rank)
+        if parse_version(torch.__version__)>=parse_version('1.1'):
+            self.model = DDP(model, device_ids=[self.local_rank],
+                             output_device=self.local_rank, find_unused_parameters=True)
+        else:
+            self.model = DDP(model, device_ids=[self.local_rank],
+                             output_device=self.local_rank)
+
         self.optimizer = optimizer
         self.sampler = DistributedSampler(self.train_data)
         self.data_iterator = self._get_data_iter(self.train_data)