diff --git a/colossalai/utils/checkpoint/module_checkpoint.py b/colossalai/utils/checkpoint/module_checkpoint.py
index dc01b3b54..78dcfb681 100644
--- a/colossalai/utils/checkpoint/module_checkpoint.py
+++ b/colossalai/utils/checkpoint/module_checkpoint.py
@@ -3,7 +3,7 @@ import torch.distributed as dist
 from colossalai.tensor import ColoTensor
 from colossalai.nn.optimizer import ColossalaiOptimizer
 from colossalai.utils.checkpoint.utils import gather_tensor, scatter_tensor
-from typing import Optional
+from typing import Optional, Dict
 
 
 def save_checkpoint(path: str,
@@ -71,22 +71,23 @@ def save_checkpoint(path: str,
         dist.barrier()
 
 
-def load_checkpoint(path,
+def load_checkpoint(path: str,
                     epoch: int,
                     model: torch.nn.Module,
                     optimizer: Optional[ColossalaiOptimizer] = None,
                     lr_scheduler: torch.optim.lr_scheduler._LRScheduler = None,
-                    *args,
-                    **kwargs):
+                    torch_load_kwargs: Optional[Dict] = None,
+                    load_state_dict_kwargs: Optional[Dict] = None):
     """load_checkpoint 
     load a model, whose parameters are `ColoTensor`s.
     Args:
-        path (_type_): _description_
-        epoch (int): _description_
-        rank (int): _description_
-        model (torch.nn.Module): _description_
-        optimizer (ColossalaiOptimizer, optional): _description_. Defaults to None.
-        lr_scheduler (torch.optim.lr_scheduler._LRScheduler, optional): _description_. Defaults to None.
+        path (str): directory to save the checkpoint files.
+        epoch (int): the number of epoch
+        model (torch.nn.Module): a torch module initialized by ColoInitContext
+        optimizer (ColossalaiOptimizer, optional): optimizers. Defaults to None.
+        lr_scheduler (torch.optim.lr_scheduler._LRScheduler, optional): lr schedule. Defaults to None.
+        torch_load_kwargs: (dict, optional): The kwargs of torch.load inside the function
+        load_state_dict_kwargs (dict, optional): The kwargs of load_state_dict inside the function
     """
     rank = dist.get_rank()
     mapping = dict()
@@ -96,8 +97,8 @@ def load_checkpoint(path,
             gather_tensor(p)
 
     if rank == 0:
-        load_state = torch.load(path + '/epoch_{}_model.pth'.format(epoch), *args, **kwargs)
-        model.load_state_dict(load_state['model'])
+        load_state = torch.load(path + '/epoch_{}_model.pth'.format(epoch), **torch_load_kwargs)
+        model.load_state_dict(load_state['model'], **load_state_dict_kwargs)
     dist.barrier()
 
     # scatter loaded parameters
@@ -118,8 +119,8 @@ def load_checkpoint(path,
                     gather_tensor(t)
 
         if rank == 0:
-            colo_checkpoint = torch.load(path + '/epoch_{}_optim.pth'.format(epoch), *args, **kwargs)
-            optimizer.load_state_dict(colo_checkpoint['optim'])
+            colo_checkpoint = torch.load(path + '/epoch_{}_optim.pth'.format(epoch), **torch_load_kwargs)
+            optimizer.load_state_dict(colo_checkpoint['optim'], **load_state_dict_kwargs)
         dist.barrier()
 
         for k, v in optimizer.state_dict()['state'].items():