diff --git a/colossalai/zero/sharded_optim/sharded_optim_v2.py b/colossalai/zero/sharded_optim/sharded_optim_v2.py
index c67ef514f..3ee8b09c2 100644
--- a/colossalai/zero/sharded_optim/sharded_optim_v2.py
+++ b/colossalai/zero/sharded_optim/sharded_optim_v2.py
@@ -135,18 +135,18 @@ class ShardedOptimizerV2(ColossalaiOptimizer):
 
         # assign master param pointers to p.data.
         # We will not trigger data copy here.
-        for group in self.optimizer.param_groups:
+        for group in self.optim.param_groups:
             for p in group['params']:
                 p.data = self.master_params[p]
                 # Now p.data is sharded
                 # So optimizer states are sharded naturally
 
-        ret = self.optimizer.step(*args, **kwargs)
+        ret = self.optim.step(*args, **kwargs)
 
         # Copy master param data (fp32) to payload of col_attr (fp16)
         # TODO() improve efficiency by gathering tensors into a chunk and transfering
         # a chunk.
-        for group in self.optimizer.param_groups:
+        for group in self.optim.param_groups:
             for p in group['params']:
                 is_param_sharded = p.col_attr.data.is_sharded
                 if not is_param_sharded:
@@ -190,7 +190,7 @@ class ShardedOptimizerV2(ColossalaiOptimizer):
         self._found_overflow.fill_(0.0)
 
         # check for overflow
-        for group in self.optimizer.param_groups:
+        for group in self.optim.param_groups:
             for p in group['params']:
                 if has_inf_or_nan(p.grad):
                     self._found_overflow.fill_(1.0)
@@ -206,7 +206,7 @@ class ShardedOptimizerV2(ColossalaiOptimizer):
 
     def _unscale_grads(self):
         assert self.optim_state == OptimState.SCALED
-        for group in self.optimizer.param_groups:
+        for group in self.optim.param_groups:
             for p in group['params']:
                 if p.grad is not None:
                     p.grad.data.div_(self.loss_scale)
@@ -216,7 +216,7 @@ class ShardedOptimizerV2(ColossalaiOptimizer):
         # We must set grad to None
         # Because we will judge whether local grad accumulation
         # is enabled by wheter grad is None
-        self.optimizer.zero_grad(set_to_none=True)
+        self.optim.zero_grad(set_to_none=True)
 
     def sync_grad(self):
         pass