diff --git a/colossalai/engine/gradient_handler/_moe_gradient_handler.py b/colossalai/engine/gradient_handler/_moe_gradient_handler.py
index 02cea5e67..b499345d4 100644
--- a/colossalai/engine/gradient_handler/_moe_gradient_handler.py
+++ b/colossalai/engine/gradient_handler/_moe_gradient_handler.py
@@ -1,45 +1,46 @@
-from colossalai.core import global_context as gpc
-from colossalai.registry import GRADIENT_HANDLER
-from colossalai.utils.moe import get_moe_epsize_param_dict
-from ._base_gradient_handler import BaseGradientHandler
-from ...context.parallel_mode import ParallelMode
-from .utils import bucket_allreduce
-from colossalai.context.moe_context import MOE_CONTEXT
-
-
-@GRADIENT_HANDLER.register_module
-class MoeGradientHandler(BaseGradientHandler):
-    """A helper class to handle all-reduce operations in a data parallel group and
-    moe model parallel. A all-reduce collective communication will be operated in
-    :func:`handle_gradient` among a data parallel group.
-    For better performance, it bucketizes the gradients of all parameters that are
-    the same type to improve the efficiency of communication.
-
-    Args:
-        model (Module): Model where the gradients accumulate.
-        optimizer (Optimizer): Optimizer for updating the parameters.
-    """
-
-    def __init__(self, model, optimizer=None):
-        super().__init__(model, optimizer)
-
-    def handle_gradient(self):
-        """A method running an all-reduce operation in a data parallel group.
-        Then running an all-reduce operation for all parameters in experts
-        across moe model parallel group
-        """
-        global_data = gpc.data_parallel_size
-
-        if global_data > 1:
-            epsize_param_dict = get_moe_epsize_param_dict(self._model)
-
-            # epsize is 1, indicating the params are replicated among processes in data parallelism
-            # use the ParallelMode.DATA to get data parallel group
-            # reduce gradients for all parameters in data parallelism
-            if 1 in epsize_param_dict:
-                bucket_allreduce(param_list=epsize_param_dict[1], group=gpc.get_group(ParallelMode.DATA))
-
-            for ep_size in epsize_param_dict:
-                if ep_size != 1 and ep_size != MOE_CONTEXT.world_size:
-                    bucket_allreduce(param_list=epsize_param_dict[ep_size],
-                                     group=MOE_CONTEXT.parallel_info_dict[ep_size].dp_group)
+from colossalai.context.moe_context import MOE_CONTEXT
+from colossalai.core import global_context as gpc
+from colossalai.registry import GRADIENT_HANDLER
+from colossalai.utils.moe import get_moe_epsize_param_dict
+
+from ...context.parallel_mode import ParallelMode
+from ._base_gradient_handler import BaseGradientHandler
+from .utils import bucket_allreduce
+
+
+@GRADIENT_HANDLER.register_module
+class MoeGradientHandler(BaseGradientHandler):
+    """A helper class to handle all-reduce operations in a data parallel group and
+    moe model parallel. A all-reduce collective communication will be operated in
+    :func:`handle_gradient` among a data parallel group.
+    For better performance, it bucketizes the gradients of all parameters that are
+    the same type to improve the efficiency of communication.
+
+    Args:
+        model (Module): Model where the gradients accumulate.
+        optimizer (Optimizer): Optimizer for updating the parameters.
+    """
+
+    def __init__(self, model, optimizer=None):
+        super().__init__(model, optimizer)
+
+    def handle_gradient(self):
+        """A method running an all-reduce operation in a data parallel group.
+        Then running an all-reduce operation for all parameters in experts
+        across moe model parallel group
+        """
+        global_data = gpc.data_parallel_size
+
+        if global_data > 1:
+            epsize_param_dict = get_moe_epsize_param_dict(self._model)
+
+            # epsize is 1, indicating the params are replicated among processes in data parallelism
+            # use the ParallelMode.DATA to get data parallel group
+            # reduce gradients for all parameters in data parallelism
+            if 1 in epsize_param_dict:
+                bucket_allreduce(param_list=epsize_param_dict[1], group=gpc.get_group(ParallelMode.DATA))
+
+            for ep_size in epsize_param_dict:
+                if ep_size != 1 and ep_size != MOE_CONTEXT.world_size:
+                    bucket_allreduce(param_list=epsize_param_dict[ep_size],
+                                     group=MOE_CONTEXT.parallel_info_dict[ep_size].dp_group)