diff --git a/colossalai/zero/sharded_model/sharded_model_v2.py b/colossalai/zero/sharded_model/sharded_model_v2.py
index 5e06eb646..9940ea5e5 100644
--- a/colossalai/zero/sharded_model/sharded_model_v2.py
+++ b/colossalai/zero/sharded_model/sharded_model_v2.py
@@ -76,7 +76,9 @@ class ShardedModelV2(nn.Module):
                  fp32_reduce_scatter: bool = False,
                  tensor_placement_policy: str = 'cuda',
                  gradient_predivide_factor: Optional[float] = 1.0,
-                 reuse_fp16_shard: bool = False):
+                 reuse_fp16_shard: bool = False,
+                 *args,
+                 **kwargs):
         assert not isinstance(module, ShardedModelV2), 'Nested ShardedModelV2 is not supported.'
         super().__init__()
         self.logger = get_dist_logger()
@@ -119,6 +121,14 @@ class ShardedModelV2(nn.Module):
         self._tensor_placement_policy: TensorPlacementPolicy = TensorPlacementPolicyFactory.create(
             tensor_placement_policy)(mem_stats_collector=self._memstats_collector)
 
+        if 'warmup_non_model_data_ratio' in kwargs:
+            if tensor_placement_policy != 'auto':
+                self.logger.warning('setting warmup_non_model_data_ratio is useless if not use auto placement')
+            else:
+                ratio = kwargs['warmup_non_model_data_ratio']
+                self._tensor_placement_policy._warmup_non_model_data_ratio = ratio
+                self.logger.info(f'setting warmup_non_model_data_ratio as {ratio} for auto placement')
+
         self._stateful_tensor_mgr = StatefulTensorMgr(self._tensor_placement_policy)
         param_tensor_list = [p.colo_attr.sharded_data_tensor for p in module.parameters() if hasattr(p, 'colo_attr')]
         self._stateful_tensor_mgr.register_stateful_tensor_list(param_tensor_list)