diff --git a/python/paddle/distributed/fleet/meta_optimizers/dygraph_optimizer/hybrid_parallel_optimizer.py b/python/paddle/distributed/fleet/meta_optimizers/dygraph_optimizer/hybrid_parallel_optimizer.py
index 581fbc5153ad4..b00ef2cdcb0e1 100755
--- a/python/paddle/distributed/fleet/meta_optimizers/dygraph_optimizer/hybrid_parallel_optimizer.py
+++ b/python/paddle/distributed/fleet/meta_optimizers/dygraph_optimizer/hybrid_parallel_optimizer.py
@@ -50,7 +50,8 @@ def __init__(self, clip, hcg):
     @imperative_base.no_grad
     def _dygraph_clip(self, params_grads):
         params_and_grads = []
-        sum_square_list = []
+        sum_square_list_dist = []
+        sum_square_list_not_dist = []
         for p, g in params_grads:
             if g is None:
                 continue
@@ -62,18 +63,33 @@ def _dygraph_clip(self, params_grads):
                 merge_grad = layers.get_tensor_from_selected_rows(merge_grad)
             square = layers.square(merge_grad)
             sum_square = layers.reduce_sum(square)
-            sum_square_list.append(sum_square)
+
+            if p.is_distributed:
+                sum_square_list_dist.append(sum_square)
+            else:
+                sum_square_list_not_dist.append(sum_square)
 
         # all parameters have been filterd out
-        if len(sum_square_list) == 0:
+        if len(sum_square_list_dist) + len(sum_square_list_not_dist) == 0:
             return params_grads
 
-        global_norm_var = layers.concat(sum_square_list)
-        global_norm_var = layers.reduce_sum(global_norm_var)
-        # add all reduce to get global norm in world size
-        paddle.distributed.all_reduce(global_norm_var,
-                                      self._hcg.get_check_parallel_group())
-        global_norm_var = layers.sqrt(global_norm_var)
+        global_norm_var_dist = layers.concat(sum_square_list_dist) if len(
+            sum_square_list_dist) != 0 else layers.concat(
+                [paddle.to_tensor([0.])])
+        global_norm_var_dist = layers.reduce_sum(global_norm_var_dist)
+        global_norm_var_not_dist = layers.concat(
+            sum_square_list_not_dist) if len(
+                sum_square_list_not_dist) != 0 else layers.concat(
+                    [paddle.to_tensor([0.])])
+        global_norm_var_not_dist = layers.reduce_sum(global_norm_var_not_dist)
+
+        # add all reduce to get global norm of distributed params_and_grads in world size
+        # all reduce is not needed while getting global norm of non-distributed params_and_grads
+        paddle.distributed.all_reduce(
+            global_norm_var_dist, group=self._hcg.get_check_parallel_group())
+
+        global_norm_var = layers.sqrt(global_norm_var_dist +
+                                      global_norm_var_not_dist)
 
         max_global_norm = layers.fill_constant(
             shape=[1], dtype=global_norm_var.dtype, value=self.clip_norm)
@@ -96,7 +112,7 @@ def __getattr__(self, item):
         return getattr(self._clip, item)
 
     def __call__(self, params_grads):
-        return self._clip(params_grads)
+        return self._dygraph_clip(params_grads)
 
 
 class HybridParallelOptimizer:
@@ -112,7 +128,7 @@ def __init__(self, optimizer, hcg, strategy):
         self._need_dp = (self._hcg.get_data_parallel_world_size() > 1)
 
         # NOTE(shenliang03): Because of the pure DataParallel mode, the gradient synchronization 
-        # is achieved through reducer, so there is no need to call fuse_allreduce in oprimizer. 
+        # is achieved through reducer, so there is no need to call fuse_allreduce in optimizer. 
         self._dp_enable = not self._use_dp_mode and self._need_dp
 
         self._sharding_enable = (