diff --git a/colossalai/zero/sharded_optim/sharded_optim_v2.py b/colossalai/zero/sharded_optim/sharded_optim_v2.py
index 680f86962..c4fbf1b7c 100644
--- a/colossalai/zero/sharded_optim/sharded_optim_v2.py
+++ b/colossalai/zero/sharded_optim/sharded_optim_v2.py
@@ -50,7 +50,7 @@ class ShardedOptimizerV2(ColossalaiOptimizer):
         You must use ``ShardedOptimizerV2`` with ``ShardedModelV2``.
 
     Note:
-        Make sure you enable ``use_memory_tracer`` in ``ShardedModelV2``,
+        Make sure you set ``tensor_placement_policy`` in ``ShardedModelV2`` to `"auto"`,
         if you set ``gpu_margin_mem_ratio > 0``.
 
     Args:
@@ -59,7 +59,6 @@ class ShardedOptimizerV2(ColossalaiOptimizer):
         optimizer (Optimizer): An Optimizer instance.
         gpu_margin_mem_ratio (float, optional): The ratio of GPU remaining memory (after the first forward-backward) 
             which will be used when using hybrid CPU optimizer. 
-            Make sure `reuse_fp16_shard` is enabled in `ShardedModelV2`, if `gpu_margin_mem_ratio` > `0.0`.
             This argument is meaningless when `tensor_placement_policy` of `ShardedModelV2` is not "auto".
             Defaults to 0.0.
         initial_scale (float, optional): Initial scale used by DynamicGradScaler. Defaults to 2**32.
@@ -119,8 +118,8 @@ class ShardedOptimizerV2(ColossalaiOptimizer):
 
         # Store fp32 param shards
         self._register_master_weight()
-        if self.gpu_margin_mem_ratio != 0.0 and isinstance(sharded_model._tensor_placement_policy,
-                                                           AutoTensorPlacementPolicy):
+        if self.gpu_margin_mem_ratio != 0.0 and not isinstance(sharded_model._tensor_placement_policy,
+                                                               AutoTensorPlacementPolicy):
             self._logger.warning(f'gpu_margin_mem_ratio is meaningless when tensor_placement_policy is not "auto"')
         self._logger.debug(f"After init ShardedOptimizerV2 consumes {self.get_memory_usage()[0] / 1e6} MB CUDA Memory!",
                            ranks=[0])