diff --git a/python/paddle/distributed/fleet/meta_parallel/sharding/group_sharded_optimizer_stage2.py b/python/paddle/distributed/fleet/meta_parallel/sharding/group_sharded_optimizer_stage2.py
index 2668b5808996d3598cc1e8f6abb2b27867145bc7..0414798e685141d336f05e59d09a906b565c83b0 100644
--- a/python/paddle/distributed/fleet/meta_parallel/sharding/group_sharded_optimizer_stage2.py
+++ b/python/paddle/distributed/fleet/meta_parallel/sharding/group_sharded_optimizer_stage2.py
@@ -31,6 +31,11 @@ import paddle
 from paddle.fluid import core
 from paddle.optimizer import Optimizer
 from paddle.fluid.clip import ClipGradByGlobalNorm
+from paddle.distributed import fleet, ParallelMode
+
+HybridParallelClipGrad = (
+    fleet.meta_optimizers.dygraph_optimizer.hybrid_parallel_optimizer.HybridParallelClipGrad
+)
 from paddle.distributed.collective import (
     _get_global_group,
     broadcast,
@@ -157,9 +162,18 @@ class GroupShardedOptimizerStage2(Optimizer):
                 "While using ClipGradByGlobalNorm in GroupShardedOptimizerStage2, the grad clip of original optimizer will be changed."
             )
 
-            self._optim._grad_clip = GroupShardedClipGrad(
-                self._optim._grad_clip, paddle.get_device(), self._group
-            )
+            hcg = fleet.fleet._hcg if hasattr(fleet.fleet, "_hcg") else None
+            if (
+                hcg
+                and hcg.get_parallel_mode() is not ParallelMode.DATA_PARALLEL
+            ):
+                self._optim._grad_clip = HybridParallelClipGrad(
+                    self._optim._grad_clip, hcg
+                )
+            else:
+                self._optim._grad_clip = GroupShardedClipGrad(
+                    self._optim._grad_clip, paddle.get_device(), self._group
+                )
             if self._optim._parameter_list and isinstance(
                 self._optim._parameter_list[0], dict
             ):
diff --git a/python/paddle/distributed/fleet/meta_parallel/sharding/group_sharded_utils.py b/python/paddle/distributed/fleet/meta_parallel/sharding/group_sharded_utils.py
index 4107bd83db9363704232b9d9c169dd1a63ad6af4..6832e9a7caa2125852cbdf0a56a6d64e5e294982 100644
--- a/python/paddle/distributed/fleet/meta_parallel/sharding/group_sharded_utils.py
+++ b/python/paddle/distributed/fleet/meta_parallel/sharding/group_sharded_utils.py
@@ -23,6 +23,7 @@ from paddle.fluid import core
 from paddle.fluid import layers
 from paddle.fluid.dygraph import to_variable
 from paddle.fluid.framework import dygraph_only
+from paddle.distributed import fleet, ParallelMode
 
 
 class Taskflow:
@@ -244,10 +245,18 @@ def GroupShardedScaler(scaler):
         self._found_inf = 1 if temp_found_inf_fp16 or temp_found_inf_fp32 else 0
         is_found_inf = paddle.to_tensor([self._found_inf], dtype="int32")
 
+        hcg = fleet.fleet._hcg if hasattr(fleet.fleet, "_hcg") else None
+        hybrid_parallel = (
+            hcg is not None
+            and hcg.get_parallel_mode() is not ParallelMode.DATA_PARALLEL
+        )
+
         paddle.distributed.all_reduce(
             is_found_inf,
             op=paddle.distributed.ReduceOp.MAX,
-            group=optimizer._group,
+            group=hcg.get_check_parallel_group()
+            if hybrid_parallel
+            else optimizer._group,
         )
         self._found_inf = is_found_inf.numpy()[0]