diff --git a/configs/7B_sft.py b/configs/7B_sft.py
index 0058e04..09af7f4 100644
--- a/configs/7B_sft.py
+++ b/configs/7B_sft.py
@@ -152,19 +152,19 @@ zero1 parallel (dict):
     2. fsdp: bool, enable/disable torch's fully sharded data parallel, defaults to False.
 tensor parallel (dict):
     1. size: int, the size of tensor parallel.
-    2. mode: str, the mode should be 'origin_tp' or 'fstp', defaults to 'origin_tp'. If the mode is 'fstp',
-        the sequence_parallel should be True.
+    2. sp: str, the sequence parallel mode, should be in ['none', 'megatron', 'flash-attn', 'intern'],
+        defaults to 'none', means the sequence parallel will be disabled.
+    3. intern_overlap: bool, enable/disable all_gather/reduce_scatter communication overlap when using 'intern' mode sp,
+        defaults to False.
 pipeline parallel (dict):
     1. size: int, the size of pipeline parallel.
     2. interleaved_overlap: bool, enable/disable communication overlap when using interleaved pipeline scheduler,
         defaults to False.
-sequence parallel (bool): enable/disable sequence parallel, defaults to False.
 """
 parallel = dict(
     zero1=dict(size=-1, fsdp=False),
-    tensor=dict(size=8, sp="intern", intern_overlap=True),
+    tensor=dict(size=8, sp="megatron", intern_overlap=True),
     pipeline=dict(size=1, interleaved_overlap=True),
-    sequence_parallel=True,
 )
 
 cudnn_deterministic = False
diff --git a/internlm/core/scheduler/no_pipeline_scheduler.py b/internlm/core/scheduler/no_pipeline_scheduler.py
index f0caf05..56661d8 100644
--- a/internlm/core/scheduler/no_pipeline_scheduler.py
+++ b/internlm/core/scheduler/no_pipeline_scheduler.py
@@ -194,7 +194,6 @@ class NonPipelineScheduler(BaseScheduler):
             _output, _loss, _moe_loss = self._train_one_batch(
                 _data, _label, engine, forward_only, return_loss, self._grad_accum_size
             )
-            engine.optimizer.reset_reduce_bucket()
 
             if return_loss:
                 loss += _loss
diff --git a/internlm/initialize/launch.py b/internlm/initialize/launch.py
index 80611fe..0e74f76 100644
--- a/internlm/initialize/launch.py
+++ b/internlm/initialize/launch.py
@@ -306,15 +306,20 @@ def args_sanity_check():
         ), "sequence parallel does not support use_flash_attn=False"
 
     if isinstance(gpc.config.parallel["tensor"], int):
-        gpc.config.parallel["tensor"] = dict(size=gpc.config.parallel["tensor"], mode="origin_tp")
-
-    if gpc.config.parallel["tensor"].get("mode", None) is None:
-        gpc.config.parallel["tensor"]["mode"] = "origin_tp"
-
-    if gpc.config.parallel["tensor"].get("mode", None) == "fstp":
-        assert (
-            gpc.config.parallel.sequence_parallel is True
-        ), "when the tp_mode is fstp, the sequence_parallel should be True."
+        gpc.config.parallel["tensor"] = dict(size=gpc.config.parallel["tensor"], sp="none", intern_overlap=False)
+    if gpc.config.parallel["tensor"].get("sp", None) is None:
+        gpc.config.parallel["tensor"]["sp"] = "none"
+    if gpc.config.parallel["tensor"].get("intern_overlap", None) is None:
+        gpc.config.parallel["tensor"]["intern_overlap"] = False
+    assert gpc.config.parallel["tensor"].get("sp", None) in [
+        "none",
+        "megatron",
+        "flash-attn",
+        "intern",
+    ], "invalid sp mode, only ['none', 'megatron', 'flash-attn', 'intern'] is supported"
+    # adapt to old version's sequence parallel config
+    if gpc.config.parallel["tensor"].get("sp", None) in ["megatron", "flash-attn", "intern"]:
+        gpc.config.parallel.sequence_parallel = True
 
     # currently only interleaved pipeline scheduler with overlap can guarantee loss accuracy
     if hasattr(gpc.config.model, "num_chunks") and gpc.config.model.num_chunks > 1:
diff --git a/internlm/model/linear.py b/internlm/model/linear.py
index 8f57a02..2bbb941 100644
--- a/internlm/model/linear.py
+++ b/internlm/model/linear.py
@@ -451,49 +451,33 @@ class CoarseGrainedFSTPAllGatherSyncHandler:
                                         self.index_to_fsdp_modules[idx].append(child)
                                         self.module_name_index[child] = index
                                         index = index + 1
-                                        
+
                                         _full_name = f"{_chunk_name}.{idx}.{_sub_name}.{name}"
                                         setattr(child.weight, "_fstp_reduce_scatter_str", f"{_full_name}.weight")
                                         if child.bias is not None:
                                             setattr(child.bias, "_fstp_reduce_scatter_str", f"{_full_name}.bias")
-                                        # _full_name = f"{_chunk_name}.{idx}.{_sub_name}.{name}"
-                                        # setattr(child.weight, "_fstp_all_reduce_str", f"{_full_name}.weight")
-                                        # if child.bias is not None:
-                                        #     setattr(child.bias, "_fstp_all_reduce_str", f"{_full_name}.bias")
                             else:
                                 continue
                 elif isinstance(children, ScaleColumnParallelLinear):
                     self.head.append(children)
                 elif isinstance(children, Embedding1D):
                     self.embedding.append(children)
-                    
-    def get_zero_by_shape(self, size:tuple, dtype, device) -> torch.Tensor:
-        if size not in self.zero_const_pool:        
+
+    def get_zero_by_shape(self, size: tuple, dtype, device) -> torch.Tensor:
+        if size not in self.zero_const_pool:
             self.zero_const_pool[size] = torch.zeros(*size, dtype=dtype, device=device).contiguous()
-        
+
         return self.zero_const_pool[size]
 
-
-    def _all_gather_block_weight(self, block_index: int):
-        #block = self.index_to_block[block_index]
-        fsdp_modules = self.index_to_fsdp_modules[block_index]
-        # self.block_handles[block] = []
-        for module in fsdp_modules:
-            total_weight, weight_handle = all_gather_raw(module.weight, self.process_group, async_op=True)
-            self.FSTP_global_weights[module] = total_weight
-            self.FSTP_global_handle[module] = weight_handle
-            # self.block_handles[block].append(weight_handle)
-    
     def _all_gather_block_weight_memory_pool(self, block_index: int):
         fsdp_modules = self.index_to_fsdp_modules[block_index]
-        # self.block_handles[block] = []
         for module in fsdp_modules:
             module_index = self.module_name_index[module]
             name = self.module_name[module_index]
-            weight_handle = all_gather_raw_memory_pool(module.weight, self.process_group, async_op=True, block_index=block_index, module_name=name)
-            # self.FSTP_global_weights[module] = total_weight
+            weight_handle = all_gather_raw_memory_pool(
+                module.weight, self.process_group, async_op=True, block_index=block_index, module_name=name
+            )
             self.FSTP_global_handle[module] = weight_handle
-            # self.block_handles[block].append(weight_handle)
 
     def _register_sync_parameters_hook(self) -> None:
         """
@@ -510,41 +494,14 @@ class CoarseGrainedFSTPAllGatherSyncHandler:
             block_index = self.module_to_index[module]
             # start the all-gather for next block
             if block_index + 1 < gpc.config.NUM_LAYER:
-                # self._all_gather_block_weight(block_index + 1)
                 self._all_gather_block_weight_memory_pool(block_index + 1)
 
-        def _pre_forward_hook_for_block(block: nn.Module, inputs: Any):
-            block_index = self.block_to_index[block]
-            if block_index == 0:
-                # all gather weight for block 0
-                fsdp_modules = self.index_to_fsdp_modules[block_index]
-                for module in fsdp_modules:
-                    total_weight, weight_handle = all_gather_raw(module.weight, self.process_group, async_op=True)
-                    weight_handle.wait()
-                    self.FSTP_global_weights[module] = total_weight
-            else:
-                # wait handle for current block
-                handles = self.block_handles[block]
-                for handle in handles:
-                    handle.wait()
-        
-        def _pre_forward_hook_for_embedding(module: nn.Module, inputs: Any, output):
-            # self._all_gather_block_weight(0)
+        def _post_forward_hook_for_embedding(module: nn.Module, inputs: Any, output):
             self._all_gather_block_weight_memory_pool(0)
-            
 
-        def _post_forward_hook_for_block(block: nn.Module, input, output):
-            block_index = self.block_to_index[block]
-            fsdp_modules = self.index_to_fsdp_modules[block_index]
-            if block in self.block_handles:
-                del self.block_handles[block]
-            for module in fsdp_modules:
-                del self.FSTP_global_weights[module]
-
-        def _pre_forward_hook_for_module(module: nn.Module, inputs: Any,):
-            block_index = self.module_to_index[module]
-            handler = self.FSTP_global_handle[module]
-            handler.wait()
+        def _pre_forward_hook_for_module(module: nn.Module, inputs: Any):
+            handle = self.FSTP_global_handle[module]
+            handle.wait()
 
         def _post_forward_hook_for_module(module: nn.Module, input, output):
             if module in self.FSTP_global_weights:
@@ -552,67 +509,44 @@ class CoarseGrainedFSTPAllGatherSyncHandler:
             if module in self.FSTP_global_handle:
                 del self.FSTP_global_handle[module]
 
-        def _pre_backward_hook_for_block(block: nn.Module, grad_output):
-            # import pdb; pdb.set_trace()
-            block_index = self.block_to_index[block]
-            # if block_index == gpc.config.NUM_LAYER - 1:
-            #     # all gather weight for the last block
-            #     fsdp_modules = self.index_to_fsdp_modules[block_index]
-            #     for module in fsdp_modules:
-            #         total_weight, weight_handle = all_gather_raw(module.weight, self.process_group, async_op=True)
-            #         weight_handle.wait()
-            #         self.FSTP_global_weights[module] = total_weight
-            # else:
-            #     # wait handle for current block
-            #     handles = self.block_handles[block]
-            #     for handle in handles:
-            #         handle.wait()
-            # if block_index == gpc.config.NUM_LAYER - 1:
-            #     self._all_gather_block_weight(block_index)
-            # start the all-gather for next block
-            if block_index - 1 >= 0:
-                self._all_gather_block_weight(block_index - 1)
-        
         def _post_backward_hook_for_head(module: nn.Module, grad_input, grad_output):
             first_module = self.block_module[gpc.config.NUM_LAYER - 1][4]
             total_weight, weight_handler = all_gather_raw(first_module.weight, self.process_group, async_op=True)
             self.FSTP_global_handle[first_module] = weight_handler
             self.FSTP_global_weights[first_module] = total_weight
 
-        def _post_backward_hook_for_block(block: nn.Module, grad_input, grad_output):
-            block_index = self.block_to_index[block]
-            fsdp_modules = self.index_to_fsdp_modules[block_index]
-            if block in self.block_handles:
-                del self.block_handles[block]
-            for module in fsdp_modules:
-                del self.FSTP_global_weights[module]
-
         def _pre_backward_hook_for_module_memory_pool(module: nn.Module, grad_output):
             block_index = self.module_to_index[module]
             name_index = self.module_name_index[module]
-            
+
             if name_index == 4 and block_index == gpc.config.NUM_LAYER - 1:
-                # total_weight, weight_handler = all_gather_raw(module.weight, self.process_group, async_op=True)
                 weight_handler = self.FSTP_global_handle[module]
                 weight_handler.wait()
-                # self.FSTP_global_weights[module] = total_weight
 
                 # start the all-gather for next module
                 next_module = self.block_module[block_index][name_index - 1]
                 next_name = self.module_name[name_index - 1]
                 weights_handler = all_gather_raw_memory_pool(
-                    next_module.weight, self.process_group, async_op=True, block_index=block_index, module_name=next_name
+                    next_module.weight,
+                    self.process_group,
+                    async_op=True,
+                    block_index=block_index,
+                    module_name=next_name,
                 )
                 self.FSTP_global_handle[next_module] = weights_handler
             elif name_index == 0:
                 handler = self.FSTP_global_handle[module]
                 handler.wait()
-                
+
                 if block_index - 1 >= 0:
                     next_module = self.block_module[block_index - 1][4]
                     name = self.module_name[4]
                     weights_handler = all_gather_raw_memory_pool(
-                        next_module.weight, self.process_group, async_op=True, block_index=block_index - 1, module_name=name,
+                        next_module.weight,
+                        self.process_group,
+                        async_op=True,
+                        block_index=block_index - 1,
+                        module_name=name,
                     )
                     self.FSTP_global_handle[next_module] = weights_handler
             else:
@@ -625,76 +559,24 @@ class CoarseGrainedFSTPAllGatherSyncHandler:
                         next_module.weight, self.process_group, async_op=True, block_index=block_index, module_name=name
                     )
                     self.FSTP_global_handle[next_module] = weights_handler
-                # if module in self.FSTP_global_handle:
-                #     handler = self.FSTP_global_handle[module]
-                #     handler.wait()
-
-        def _pre_backward_hook_for_module(module: nn.Module, grad_output):
-            block_index = self.module_to_index[module]
-            name_index = self.module_name_index[module]
-            
-            if name_index == 4 and block_index == gpc.config.NUM_LAYER - 1:
-                # total_weight, weight_handler = all_gather_raw(module.weight, self.process_group, async_op=True)
-                weight_handler = self.FSTP_global_handle[module]
-                weight_handler.wait()
-                # self.FSTP_global_weights[module] = total_weight
-
-                # start the all-gather for next module
-                next_module = self.block_module[block_index][name_index - 1]
-                self.FSTP_global_weights[next_module], weights_handler = all_gather_raw(
-                    next_module.weight, self.process_group, async_op=True
-                )
-                self.FSTP_global_handle[next_module] = weights_handler
-            elif name_index == 0:
-                handler = self.FSTP_global_handle[module]
-                handler.wait()
-                
-                if block_index - 1 >= 0:
-                    next_module = self.block_module[block_index - 1][4]
-                    self.FSTP_global_weights[next_module], weights_handler = all_gather_raw(
-                        next_module.weight, self.process_group, async_op=True
-                    )
-                    self.FSTP_global_handle[next_module] = weights_handler
-            else:
-                handler = self.FSTP_global_handle[module]
-                handler.wait()
-                if name_index != 0:
-                    next_module = self.block_module[block_index][name_index - 1]
-                    self.FSTP_global_weights[next_module], weights_handler = all_gather_raw(
-                        next_module.weight, self.process_group, async_op=True
-                    )
-                    self.FSTP_global_handle[next_module] = weights_handler
-                # if module in self.FSTP_global_handle:
-                #     handler = self.FSTP_global_handle[module]
-                #     handler.wait()
 
         def _post_backward_hook_for_module(module, grad_input, grad_output):
             if module in self.FSTP_global_weights:
                 del self.FSTP_global_weights[module]
             if module in self.FSTP_global_handle:
                 del self.FSTP_global_handle[module]
-        
+
         for embedding in self.embedding:
-            embedding.register_forward_hook(_pre_forward_hook_for_embedding)
-        
+            embedding.register_forward_hook(_post_forward_hook_for_embedding)
+
         for head in self.head:
             head.register_full_backward_hook(_post_backward_hook_for_head)
 
-        # for block in self.FSTP_blocks:
-            # block.register_forward_pre_hook(_pre_forward_hook_for_block)
-            # block.register_forward_hook(_post_forward_hook_for_block)
-            # block.register_full_backward_pre_hook(_pre_backward_hook_for_block)
-            # block.register_full_backward_hook(_post_backward_hook_for_block)
-
         for out_proj in self.FSTP_outs:
             out_proj.register_forward_pre_hook(_pre_forward_hook_for_out_proj)
-   
-        # for wqkv in self.FSTP_wqkvs:
-        #     wqkv.register_full_backward_pre_hook(_pre_backward_hook_for_wqkv)
 
         for module in self.FSTP_modules:
             module.register_forward_pre_hook(_pre_forward_hook_for_module)
             module.register_forward_hook(_post_forward_hook_for_module)
-            # module.register_full_backward_pre_hook(_pre_backward_hook_for_module)
             module.register_full_backward_pre_hook(_pre_backward_hook_for_module_memory_pool)
             module.register_full_backward_hook(_post_backward_hook_for_module)
diff --git a/internlm/model/modeling_internlm.py b/internlm/model/modeling_internlm.py
index 99d540f..3ed78d7 100644
--- a/internlm/model/modeling_internlm.py
+++ b/internlm/model/modeling_internlm.py
@@ -396,7 +396,7 @@ class PackedFlashInternLm1D(nn.Module):
             assert len(indexes) == 1
             # The indexes are used to indicate the actual position IDs of each token in the packed input.
             indexes = indexes[0]
-            # if the tensor parallel mode is 'fstp', the indexes should also be split in sequence dimension.
+            # if the sequence parallel mode is 'intern', the indexes should also be split in sequence dimension.
             if gpc.config.parallel.sequence_parallel and self.sp_mode == "intern":
                 indexes = split_forward_gather_backward(indexes, ParallelMode.TENSOR, dim=0)
 
diff --git a/internlm/model/utils.py b/internlm/model/utils.py
index 6757906..b1894e9 100644
--- a/internlm/model/utils.py
+++ b/internlm/model/utils.py
@@ -1,20 +1,20 @@
 #!/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
 
 from internlm.core.context import ParallelMode
 from internlm.core.context import global_context as gpc
-from internlm.utils.logger import get_logger
 from internlm.utils.common import get_current_device
+from internlm.utils.logger import get_logger
 
 logger = get_logger(__file__)
 
@@ -125,9 +125,20 @@ def all_gather_raw(input_: Tensor, process_group: ProcessGroup, async_op: bool =
     )
     return output, handle
 
-def all_gather_raw_memory_pool(input_: Tensor, process_group: ProcessGroup, async_op: bool = False, gather_dim: int = 0, block_index: int = None, module_name: str = None):
+
+def all_gather_raw_memory_pool(
+    input_: Tensor,
+    process_group: ProcessGroup,
+    async_op: bool = False,
+    gather_dim: int = 0,
+    block_index: int = None,
+    module_name: str = None,
+):
     handle = torch.distributed.all_gather_into_tensor(
-        gpc.config.block_memory[block_index % 2][module_name], input_.contiguous(), group=process_group, async_op=async_op
+        gpc.config.block_memory[block_index % 2][module_name],
+        input_.contiguous(),
+        group=process_group,
+        async_op=async_op,
     )
     return handle
 
@@ -142,23 +153,25 @@ def linear_bias_wgrad_torch(my_input, grad_output, has_d_bias):
 def reduce_scatter_raw(input_: Tensor, process_group: ProcessGroup, async_op: bool = False):
     world_size = torch.distributed.get_world_size(process_group)
     assert input_.shape[0] % world_size == 0
-    output = torch.empty(input_.shape[0] // world_size, *input_.shape[1:],
-                         dtype=input_.dtype, device=input_.device).contiguous()
-    handle = torch.distributed.reduce_scatter_tensor(output, input_.contiguous(),
-                                                     group=process_group,
-                                                     async_op=async_op)
+    output = torch.empty(
+        input_.shape[0] // world_size, *input_.shape[1:], dtype=input_.dtype, device=input_.device
+    ).contiguous()
+    handle = torch.distributed.reduce_scatter_tensor(
+        output, input_.contiguous(), group=process_group, async_op=async_op
+    )
     return output, handle
 
+
 def reduce_scatter_raw_memory_pool(input_: Tensor, process_group: ProcessGroup, async_op: bool = False):
     world_size = torch.distributed.get_world_size(process_group)
     assert input_.shape[0] % world_size == 0
     size = (input_.shape[0] // world_size, *input_.shape[1:])
     index = check_reduce_scatter_memory_pool(size)
-    output = gpc.config.reduce_scatter_memory[size]['data'][index]
+    output = gpc.config.reduce_scatter_memory[size]["data"][index]
     setattr(output, "index", index)
-    handle = torch.distributed.reduce_scatter_tensor(output, input_.contiguous(),
-                                                     group=process_group,
-                                                     async_op=async_op)
+    handle = torch.distributed.reduce_scatter_tensor(
+        output, input_.contiguous(), group=process_group, async_op=async_op
+    )
     return output, handle
 
 
@@ -444,7 +457,18 @@ class FSTPFusedDenseFunc(torch.autograd.Function):
 
     @staticmethod
     @custom_fwd
-    def forward(ctx, x, weight, bias, return_residual=False, process_group=None, module=None, overlap_handler=None, block_index=None, module_name=None):
+    def forward(
+        ctx,
+        x,
+        weight,
+        bias,
+        return_residual=False,
+        process_group=None,
+        module=None,
+        overlap_handler=None,
+        block_index=None,
+        module_name=None,
+    ):
         ctx.compute_weight_gradient = weight.requires_grad
         ctx.return_residual = return_residual
         ctx.process_group = process_group
@@ -506,7 +530,7 @@ class FSTPFusedDenseFunc(torch.autograd.Function):
         overlap_handler = ctx.overlap_handler
         block_index = ctx.block_index
         module_name = ctx.module_name
-        
+
         if ctx.compute_weight_gradient:
             x, weight, bias = ctx.saved_tensors
             total_x = x
@@ -540,7 +564,9 @@ class FSTPFusedDenseFunc(torch.autograd.Function):
                     overlap_handler.reduce_scatter_handlers[weight._fstp_reduce_scatter_str] = (handle_grad_weight, grad_weight_async)
                     grad_weight = overlap_handler.get_zero_by_shape((grad_weight.shape[0]//torch.distributed.get_world_size(process_group), *grad_weight.shape[1:]), dtype=grad_weight.dtype, device=grad_weight.device)
                     if grad_bias is not None:
-                        grad_bias_async, handle_grad_bias = reduce_scatter_raw_memory_pool(grad_bias, process_group, async_op=True)
+                        grad_bias_async, handle_grad_bias = reduce_scatter_raw_memory_pool(
+                            grad_bias, process_group, async_op=True
+                        )
                         assert hasattr(bias, "_fstp_reduce_scatter_str")
                         overlap_handler.reduce_scatter_handlers[bias._fstp_reduce_scatter_str] = (handle_grad_bias, grad_bias_async)
                         grad_bias = overlap_handler.get_zero_by_shape((grad_bias.shape[0]//torch.distributed.get_world_size(process_group), *grad_bias.shape[1:]), dtype=grad_bias.dtype, device=grad_bias.device)
@@ -619,7 +645,9 @@ def fstp_fused_dense_func(
         x.dtype == torch.float32 and torch.is_autocast_enabled()
     )
     if x.is_cuda and weight.is_cuda and (bias is None or bias.is_cuda) and dtype_eligible:
-        return FSTPFusedDenseFunc.apply(x, weight, bias, return_residual, process_group, module, handler, block_index, module_name)
+        return FSTPFusedDenseFunc.apply(
+            x, weight, bias, return_residual, process_group, module, handler, block_index, module_name
+        )
     else:
         assert process_group is None
         out = F.linear(x, weight, bias)
@@ -666,36 +694,37 @@ def Silu(w1_o, w2_o):
 
 Silu = torch.jit.script(Silu)
 
+
 def check_reduce_scatter_memory_pool(key):
-    
     return_idx = 0
-    
+
     # if key not in dict
     if key not in gpc.config.reduce_scatter_memory:
-        gpc.config.reduce_scatter_memory[key] = {'data': [], 'used': []}
-    
+        gpc.config.reduce_scatter_memory[key] = {"data": [], "used": []}
+
     # if the data is empty
-    if len(gpc.config.reduce_scatter_memory[key]['data']) == 0:
-        gpc.config.reduce_scatter_memory[key]['data'].append(torch.zeros(key, 
-                                                             dtype=gpc.config.model.get("dtype", torch.half), 
-                                                             device=get_current_device()).contiguous())
-        gpc.config.reduce_scatter_memory[key]['used'].append(True)
+    if len(gpc.config.reduce_scatter_memory[key]["data"]) == 0:
+        gpc.config.reduce_scatter_memory[key]["data"].append(
+            torch.zeros(key, dtype=gpc.config.model.get("dtype", torch.half), device=get_current_device()).contiguous()
+        )
+        gpc.config.reduce_scatter_memory[key]["used"].append(True)
         return_idx = 0
         return return_idx
-    else: # if not empty
-        for index, used in enumerate(gpc.config.reduce_scatter_memory[key]['used']):
-            if used == False:
-                gpc.config.reduce_scatter_memory[key]['used'][index] = True
+    else:  # if not empty
+        for index, used in enumerate(gpc.config.reduce_scatter_memory[key]["used"]):
+            if used is False:
+                gpc.config.reduce_scatter_memory[key]["used"][index] = True
                 return_idx = index
                 return return_idx
         # if the memory pool is all used
-        length = len(gpc.config.reduce_scatter_memory[key]['data'])
-        gpc.config.reduce_scatter_memory[key]['data'].append(torch.zeros(key, 
-                                                             dtype=gpc.config.model.get("dtype", torch.half), 
-                                                             device=get_current_device()).contiguous())
-        gpc.config.reduce_scatter_memory[key]['used'].append(True)
+        length = len(gpc.config.reduce_scatter_memory[key]["data"])
+        gpc.config.reduce_scatter_memory[key]["data"].append(
+            torch.zeros(key, dtype=gpc.config.model.get("dtype", torch.half), device=get_current_device()).contiguous()
+        )
+        gpc.config.reduce_scatter_memory[key]["used"].append(True)
         return_idx = length
         return return_idx
 
+
 def release_reduce_scatter_memory_pool(size, index):
-    gpc.config.reduce_scatter_memory[size]['used'][index] = False
\ No newline at end of file
+    gpc.config.reduce_scatter_memory[size]["used"][index] = False
diff --git a/internlm/solver/optimizer/hybrid_zero_optim.py b/internlm/solver/optimizer/hybrid_zero_optim.py
index 4de5c7c..0f536ec 100644
--- a/internlm/solver/optimizer/hybrid_zero_optim.py
+++ b/internlm/solver/optimizer/hybrid_zero_optim.py
@@ -3,6 +3,7 @@
 
 import math
 from functools import partial
+from typing import List, Optional
 
 import torch
 import torch.distributed as dist
@@ -10,7 +11,7 @@ from torch.optim import Optimizer
 
 from internlm.core.context import Config, ParallelMode
 from internlm.core.context import global_context as gpc
-from internlm.model.utils import split_forward_gather_backward, release_reduce_scatter_memory_pool
+from internlm.model.utils import release_reduce_scatter_memory_pool
 from internlm.monitor import send_alert_message
 from internlm.solver.optimizer.store import (
     BucketStore,
@@ -65,7 +66,8 @@ class HybridZeroOptimizer(BaseOptimizer):
         hysteresis = grad_scal_cfg.hysteresis
         max_scale = grad_scal_cfg.max_scale
         
-        if gpc.config.parallel["tensor"]["sp"] == "intern" and gpc.config.parallel["tensor"]["intern_overlap"] == True:
+        self._fstp_handler = None
+        if gpc.config.parallel["tensor"]["sp"] == "intern" and gpc.config.parallel["tensor"]["intern_overlap"] is True:
             self._fstp_handler = gpc.config.fstp_handler
 
         # Zero related args
@@ -85,8 +87,8 @@ class HybridZeroOptimizer(BaseOptimizer):
         # it will not manage the tensors used by mixed precision training
         self._param_store = ParameterStore(ParallelMode.ZERO1)
         self._grad_store = GradientStore(ParallelMode.DATA)
-        self._bucket_store = []
-        self._bucket_store_2 = []
+        self._bucket_store: List[BucketStore] = []
+        self._accum_grad_buckets: List[BucketStore] = []
         self._bucket_in_progress = []
 
         # fp16 and fp32 params for mixed precision training
@@ -155,7 +157,7 @@ class HybridZeroOptimizer(BaseOptimizer):
             # TODO _broadcast_parallel_mode is not only used in broadcast, maybe can change its name
             self._broadcast_parallel_mode.append(zero_mode)
             self._bucket_store.append(BucketStore(group_id, param_group["dp_mode"]))
-            self._bucket_store_2.append(BucketStore(group_id, param_group["dp_mode"]))
+            self._accum_grad_buckets.append(BucketStore(group_id, param_group["dp_mode"]))
 
             # assign parameters to ranks the params in the list are sorted
             params_per_rank, no_params_ranks = self._partition_param_list(group_id, param_group)
@@ -301,9 +303,9 @@ class HybridZeroOptimizer(BaseOptimizer):
                             param=param,
                             reduce_rank=reduce_rank,
                         )
-                        
+
                         reduce_scatter_checker = partial(
-                            self._wait_reduce_scatter_and_accumulate_grad,
+                            self._wait_reduce_scatter_and_accumulate_grads,
                             param=param,
                             reduce_rank=reduce_rank,
                         )
@@ -312,7 +314,7 @@ class HybridZeroOptimizer(BaseOptimizer):
                         # NOT IMPORTANT BUT GOOD TO KNOW:
                         # args here is not grad, but allow_unreacable and accumulate_grad
                         def reduce_grad_hook(*args):  # pylint: disable=W0613
-                            if gpc.config.fstp_handler is not None:
+                            if self._fstp_handler is not None:
                                 reduce_scatter_checker()
 
                             if self.skip_grad_reduce is False:
@@ -336,56 +338,36 @@ class HybridZeroOptimizer(BaseOptimizer):
         group_id = getattr(param, "group_id")
         return tensor_rank == gpc.get_local_rank(self._broadcast_parallel_mode[group_id])
 
-    def reset_reduce_bucket(self) -> None:
-        for bucket in self._bucket_store_2:
-            for rank, params in bucket._params.items():
-                for _param in params:
-                    if not hasattr(_param, "_fstp_reduce_scatter_str"):
-                        continue
+    def _accum_grads_store_in_bucket(self, bucket: BucketStore, reduce_rank: Optional[int] = None) -> None:
+        for _param in bucket.get_param(reduce_rank):
+            if not hasattr(_param, "_fstp_reduce_scatter_str"):
+                continue
 
-                    key = getattr(_param, "_fstp_reduce_scatter_str")
-                    comm_handle, _grad = self._fstp_handler.reduce_scatter_handlers[key]
-                    comm_handle.wait()
-                    _param.grad.add_(_grad)
-                    # self._fstp_handler.reduce_scatter_handlers[key] = None
-                    # del _grad
-                    release_reduce_scatter_memory_pool(size=tuple(_grad.size()),index=_grad.index)
-                    del self._fstp_handler.reduce_scatter_handlers[key]
-                    self._fstp_handler.reduce_scatter_handlers[key] = None
-                    assert key in self._fstp_handler.reduce_scatter_handlers
+            # wait and accumulate gardient.
+            _key = getattr(_param, "_fstp_reduce_scatter_str")
+            _comm_handle, _grad = self._fstp_handler.reduce_scatter_handlers[_key]
+            _comm_handle.wait()
+            _param.grad.add_(_grad)
 
+            # release cuda memory.
+            release_reduce_scatter_memory_pool(size=tuple(_grad.size()), index=_grad.index)
+            self._fstp_handler.reduce_scatter_handlers[_key] = None
 
-                bucket.reset_by_rank(rank)
-                
-    def _wait_reduce_scatter_and_accumulate_grad(self, param, reduce_rank=None):
+        bucket.reset_by_rank(reduce_rank)
+
+    def _wait_reduce_scatter_and_accumulate_grads(self, param, reduce_rank: Optional[int] = None):
         param_size = param.numel()
 
+        group_id = getattr(param, "group_id")
+        current_bucket = self._accum_grad_buckets[group_id]
+
         # check if the bucket is full
         # if full, will reduce the grads already in the bucket
         # after reduction, the bucket will be empty
-        group_id = getattr(param, "group_id")
-        current_bucket = self._bucket_store_2[group_id]
+        if current_bucket.num_elements_in_bucket(reduce_rank) >= self._reduce_bucket_size:
+            self._accum_grads_store_in_bucket(current_bucket, reduce_rank)
 
-        if current_bucket.num_elements_in_bucket(reduce_rank) >= 512 * 1024 * 1024:
-            # wait reduce scatter communication
-            params = current_bucket.get_param(reduce_rank)
-            for _param in params:
-                if not hasattr(_param, "_fstp_reduce_scatter_str"):
-                    continue
-
-                key = getattr(_param, "_fstp_reduce_scatter_str")
-                comm_handle, _grad = self._fstp_handler.reduce_scatter_handlers[key]
-                comm_handle.wait()
-                _param.grad.add_(_grad)
-                # self._fstp_handler.reduce_scatter_handlers[key] = None
-                # del _grad
-                release_reduce_scatter_memory_pool(size=tuple(_grad.size()),index=_grad.index)
-                del self._fstp_handler.reduce_scatter_handlers[key]
-                self._fstp_handler.reduce_scatter_handlers[key] = None
-                assert key in self._fstp_handler.reduce_scatter_handlers
-
-                current_bucket.reset_by_rank(reduce_rank)
-                
+        # otherwise, add the parameter into bucket.
         current_bucket.add_num_elements_in_bucket(param_size, reduce_rank)
         current_bucket.add_param(param, reduce_rank)
 
@@ -612,6 +594,10 @@ class HybridZeroOptimizer(BaseOptimizer):
         for group_id in range(self.num_param_groups):
             self._reduce_grads_stored_in_bucket(self._bucket_store[group_id], reduce_rank=None, last_bucket=True)
 
+        # we need to accumulate gradients left in the accumulate gardient bucket
+        for group_id in range(self.num_param_groups):
+            self._accum_grads_store_in_bucket(self._accum_grad_buckets[group_id], reduce_rank=None)
+
         # compute norm for gradients in the before bucket
         groups_norms = []
         for group_id in range(self.num_param_groups):
@@ -773,7 +759,7 @@ class HybridZeroOptimizer(BaseOptimizer):
         torch.cuda.synchronize()
         with torch.cuda.stream(self._comm_bcast_stream):
             self.broadcast_params()
-        
+
         timer("step").stop()
 
         # update gradients may not be needed here, because the sync_params function is used in initialization,
diff --git a/internlm/solver/optimizer/store.py b/internlm/solver/optimizer/store.py
index 228045e..f486cce 100644
--- a/internlm/solver/optimizer/store.py
+++ b/internlm/solver/optimizer/store.py
@@ -45,7 +45,7 @@ class BucketStore(BaseStore):
 
     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])
 
diff --git a/internlm/train/training_internlm.py b/internlm/train/training_internlm.py
index 20592c2..53996b3 100644
--- a/internlm/train/training_internlm.py
+++ b/internlm/train/training_internlm.py
@@ -107,48 +107,60 @@ def initialize_model():
 
     # if fsdp enabled, wrap the model
     model = wrap_FSDP_model(model)
-    
+
     gpc.config.fstp_handler = None
 
-    if gpc.config.parallel["tensor"]["sp"] == "intern" and gpc.config.parallel["tensor"]["intern_overlap"] == True:
+    if gpc.config.parallel["tensor"]["sp"] == "intern" and gpc.config.parallel["tensor"]["intern_overlap"] is True:
         handler = CoarseGrainedFSTPAllGatherSyncHandler(model, gpc.get_group(ParallelMode.TENSOR))
-        # handler = FSTPAllGatherSyncHandler(model, gpc.get_group(ParallelMode.TENSOR))
         handler._register_sync_parameters_hook()
         gpc.config.fstp_handler = handler
-        
+
         # allocate memory pool
-        block_memory = {} # containing two groups of block weight
+        block_memory = {}  # containing two groups of block weight
         hidden_size = gpc.config.HIDDEN_SIZE
         mlp_ratio = gpc.config.MLP_RATIO
         mlp_hidden_size = int(hidden_size * mlp_ratio)
         mlp_hidden_size = 256 * ((mlp_hidden_size + 256 - 1) // 256)
         world_size = gpc.get_world_size(ParallelMode.TENSOR)
-        size_key = [(3 * hidden_size // world_size, hidden_size), (mlp_hidden_size // world_size, hidden_size), (hidden_size // world_size, mlp_hidden_size), (hidden_size // world_size, hidden_size)]
-        module_name = ['Wqkv', 'out_proj', 'w1', 'w2', 'w3']
+        size_key = [
+            (3 * hidden_size // world_size, hidden_size),
+            (mlp_hidden_size // world_size, hidden_size),
+            (hidden_size // world_size, mlp_hidden_size),
+            (hidden_size // world_size, hidden_size),
+        ]
+        module_name = ["Wqkv", "out_proj", "w1", "w2", "w3"]
         for i in range(2):
             weight = {}
             for name in module_name:
-                if name == 'Wqkv':
-                    weight[name] = torch.zeros((3 * hidden_size, hidden_size), 
-                                               dtype=gpc.config.model.get("dtype", torch.half), 
-                                               device=get_current_device()).contiguous()
-                elif name == 'out_proj':
-                    weight[name] = torch.zeros((hidden_size, hidden_size), 
-                                               dtype=gpc.config.model.get("dtype", torch.half), 
-                                               device=get_current_device()).contiguous()
-                elif name == 'w1' or name == 'w2':
-                    weight[name] = torch.zeros((mlp_hidden_size, hidden_size), 
-                                               dtype=gpc.config.model.get("dtype", torch.half), 
-                                               device=get_current_device()).contiguous()
+                if name == "Wqkv":
+                    weight[name] = torch.zeros(
+                        (3 * hidden_size, hidden_size),
+                        dtype=gpc.config.model.get("dtype", torch.half),
+                        device=get_current_device(),
+                    ).contiguous()
+                elif name == "out_proj":
+                    weight[name] = torch.zeros(
+                        (hidden_size, hidden_size),
+                        dtype=gpc.config.model.get("dtype", torch.half),
+                        device=get_current_device(),
+                    ).contiguous()
+                elif name == "w1" or name == "w2":
+                    weight[name] = torch.zeros(
+                        (mlp_hidden_size, hidden_size),
+                        dtype=gpc.config.model.get("dtype", torch.half),
+                        device=get_current_device(),
+                    ).contiguous()
                 else:
-                    weight[name] = torch.zeros((hidden_size, mlp_hidden_size), 
-                                               dtype=gpc.config.model.get("dtype", torch.half), 
-                                               device=get_current_device()).contiguous()
+                    weight[name] = torch.zeros(
+                        (hidden_size, mlp_hidden_size),
+                        dtype=gpc.config.model.get("dtype", torch.half),
+                        device=get_current_device(),
+                    ).contiguous()
             block_memory[i] = weight
         reduce_scatter_memory = {}
         for key in size_key:
-            reduce_scatter_memory[key] = {'data': [], 'used': []}
-        
+            reduce_scatter_memory[key] = {"data": [], "used": []}
+
         gpc.config.block_memory = block_memory
         gpc.config.reduce_scatter_memory = reduce_scatter_memory
 
diff --git a/internlm/utils/evaluation.py b/internlm/utils/evaluation.py
index 968a1db..f708fa7 100644
--- a/internlm/utils/evaluation.py
+++ b/internlm/utils/evaluation.py
@@ -54,7 +54,7 @@ def switch_evaluation_pipeline_scheduler(trainer, num_microbatches, tensor_shape
 def switch_sequence_parallel_mode():
     prev_mode = gpc.config.parallel.sequence_parallel
     try:
-        if gpc.config.parallel["tensor"]["mode"] == "fstp":
+        if gpc.config.parallel["tensor"]["sp"] == "intern":
             gpc.config.parallel.sequence_parallel = True
         else:
             gpc.config.parallel.sequence_parallel = False
@@ -106,7 +106,7 @@ def evaluate_on_val_dls(
                         total_val_bsz = len(batch[1])
                         assert total_val_bsz % data_cfg.micro_bsz == 0
                         num_microbatches = total_val_bsz // data_cfg.micro_bsz
-                        if gpc.config.parallel["tensor"]["mode"] == "fstp":
+                        if gpc.config.parallel["tensor"]["sp"] == "intern":
                             sequence_world_size = gpc.get_world_size(ParallelMode.TENSOR)
                             tensor_shape = torch.Size(
                                 [
diff --git a/internlm/utils/gputest.py b/internlm/utils/gputest.py
index 52d9638..bf4cf1c 100644
--- a/internlm/utils/gputest.py
+++ b/internlm/utils/gputest.py
@@ -45,7 +45,7 @@ def empty_cache_and_diag(batch_count, interval=50):
             #         # import time
             #         # time.sleep(10)
             #         print(e, "rank = ", gpc.get_global_rank(), flush=True)
-                    # torch.cuda.memory._dump_snapshot(f"my_snapshot_{gpc.get_global_rank()}.pickle")
+            #         torch.cuda.memory._dump_snapshot(f"my_snapshot_{gpc.get_global_rank()}.pickle")
 
         # do empty_cache after the bench
         torch.cuda.empty_cache()
diff --git a/train.py b/train.py
index a917d12..02f2802 100644
--- a/train.py
+++ b/train.py
@@ -296,7 +296,7 @@ def main(args):
                 memory_profiler.step()
 
             prof.step()
-            
+
             if gpc.config.fstp_handler is not None:
                 gpc.config.fstp_handler.zero_const_pool = {}
                 gpc.config.fstp_handler.reduce_scatter_memory = {}