diff --git a/internlm/model/utils.py b/internlm/model/utils.py
index b9c7c03a..19531e4a 100644
--- a/internlm/model/utils.py
+++ b/internlm/model/utils.py
@@ -1,12 +1,12 @@
 #!/usr/bin/env python
 # -*- encoding: utf-8 -*-
 
-from typing import Any, Optional, Union
+from typing import Optional
 
 import fused_dense_lib as fused_dense_cuda
 import torch
 import torch.nn.functional as F
-from flash_attn.utils.distributed import all_reduce_raw  # , reduce_scatter_raw
+from flash_attn.utils.distributed import all_reduce_raw
 from torch import Tensor
 from torch.cuda.amp import custom_bwd, custom_fwd
 from torch.distributed import ProcessGroup
@@ -397,7 +397,6 @@ def backward(ctx, grad_output, *args):
             grad_input = grad_input.contiguous()
         process_group = ctx.process_group
         all_gather_handler = ctx.all_gather_handler
-        module = ctx.module
         block_index = ctx.block_index
         module_name = ctx.module_name
 
diff --git a/internlm/solver/optimizer/hybrid_zero_optim.py b/internlm/solver/optimizer/hybrid_zero_optim.py
index d5fec315..cb8aa659 100644
--- a/internlm/solver/optimizer/hybrid_zero_optim.py
+++ b/internlm/solver/optimizer/hybrid_zero_optim.py
@@ -11,10 +11,7 @@
 
 from internlm.core.context import Config, ParallelMode
 from internlm.core.context import global_context as gpc
-from internlm.model.utils import (
-    release_reduce_scatter_memory_pool,
-    split_forward_gather_backward,
-)
+from internlm.model.utils import release_reduce_scatter_memory_pool
 from internlm.monitor import send_alert_message
 from internlm.solver.optimizer.store import (
     BucketStore,
diff --git a/internlm/solver/optimizer/store.py b/internlm/solver/optimizer/store.py
index 228045ed..f486ccec 100644
--- a/internlm/solver/optimizer/store.py
+++ b/internlm/solver/optimizer/store.py
@@ -45,7 +45,7 @@ def __init__(self, group_id, dp_parallel_mode):
 
     def num_elements_in_bucket(self, reduce_rank: int = None):
         return self._num_elements_in_bucket[reduce_rank]
-    
+
     def num_params_in_bucket(self, reduce_rank: int = None):
         return len(self._params[reduce_rank])