diff --git a/examples/auto_parallel/models/modeling.py b/examples/auto_parallel/models/modeling.py
index a4af1a5a..63fa2032 100644
--- a/examples/auto_parallel/models/modeling.py
+++ b/examples/auto_parallel/models/modeling.py
@@ -482,7 +482,6 @@ def __init__(self, config, ipp: Optional[int] = None):
             )
 
         self.config = config
-        self.reshard_row_and_col = ReshardLayer()
 
     def forward(
         self,
@@ -529,7 +528,6 @@ def forward(
         )
 
         attn_output = self.o_proj(attn_output)
-        attn_output = self.reshard_row_and_col(attn_output)
 
         if not output_attentions:
             attn_weights = None
@@ -670,7 +668,6 @@ def __init__(self, config, layer_idx=0, ipp=0):
         self.residual_add2 = FusedDropoutAdd(
             config.hidden_dropout_prob, mode="upscale_in_train"
         )
-        self.reshard_col = ReshardLayer()
         self.reshard_replicate = ReshardLayer()
 
     def create_moe_mlp_layer(self, layer_idx, ipp):
@@ -816,7 +813,6 @@ def forward(
                 hidden_states, token_type_ids
             )
         else:
-            hidden_states = self.reshard_col(hidden_states)
             hidden_states = self.mlp(hidden_states)
             gate_logits = None
 
@@ -1003,7 +999,6 @@ def __init__(self, config: ErnieMoEConfig):
         self.inbatch_pack_offset = None
         self.token_type_ids = None
         self.past_key_values = None
-        self.inbatch_pack_offset = None
         self.inputs_embeds = None
         self.all_hidden_states = None
         self.all_self_attns = None
@@ -1287,7 +1282,6 @@ def forward(
         self.past_key_values = past_key_values
         self.inbatch_pack_offset = inbatch_pack_offset
         self.token_type_ids = token_type_ids
-        self.inbatch_pack_offset = inbatch_pack_offset
         if use_cache is not None:
             self.config.use_cache = use_cache
         if return_dict is not None:
@@ -1698,12 +1692,6 @@ def auto_dist_config(self, prefix=""):
                     f"{prefix}ernie.layers.*.self_attn.k_proj": dist.ColWiseParallel(),
                     f"{prefix}ernie.layers.*.self_attn.v_proj": dist.ColWiseParallel(),
                     f"{prefix}ernie.layers.*.self_attn.o_proj": dist.RowWiseParallel(),
-                    f"{prefix}ernie.layers.*.self_attn.reshard_row_and_col": PrepareLayerInput(
-                        layer_input_reshard_row_and_col_hook
-                    ),
-                    f"{prefix}ernie.layers.*.reshard_col": PrepareLayerInput(
-                        layer_input_reshard_col_hook
-                    ),
                     f"{prefix}ernie.layers.*.reshard_replicate": PrepareLayerInput(
                         layer_input_reshard_replicate_hook
                     ),
@@ -1754,46 +1742,6 @@ def forward(self, input):
         return input
 
 
-def layer_input_reshard_row_and_col_hook(process_mesh):
-    def hook(layer, inputs, output=None):
-        res_inputs = []
-        for input in inputs:
-            if not input.is_dist():
-                x = dist.shard_tensor(
-                    input, process_mesh, [dist.Shard(0), dist.Shard(1)]
-                )
-                res_inputs.append(
-                    dist.reshard(x, process_mesh, [dist.Shard(0), dist.Shard(1)])
-                )
-            else:
-                res_inputs.append(
-                    dist.reshard(input, process_mesh, [dist.Shard(0), dist.Shard(1)])
-                )
-        return tuple(res_inputs)
-
-    return hook
-
-
-def layer_input_reshard_col_hook(process_mesh):
-    def hook(layer, inputs, output=None):
-        res_inputs = []
-        for input in inputs:
-            if not input.is_dist():
-                x = dist.shard_tensor(
-                    input, process_mesh, [dist.Shard(1), dist.Replicate()]
-                )
-                res_inputs.append(
-                    dist.reshard(x, process_mesh, [dist.Shard(1), dist.Replicate()])
-                )
-            else:
-                res_inputs.append(
-                    dist.reshard(input, process_mesh, [dist.Shard(1), dist.Replicate()])
-                )
-        return tuple(res_inputs)
-
-    return hook
-
-
 def layer_input_reshard_replicate_hook(process_mesh):
     def hook(layer, inputs, output=None):
         res_inputs = []
diff --git a/examples/auto_parallel/models/modeling_vpp.py b/examples/auto_parallel/models/modeling_vpp.py
index b4a811c6..a52d2c1a 100644
--- a/examples/auto_parallel/models/modeling_vpp.py
+++ b/examples/auto_parallel/models/modeling_vpp.py
@@ -29,7 +29,6 @@
     ErniePretrainedModel,
     ErnieModel,
     ErniePretrainingCriterion,
-    ReshardLayer,
     ErnieLMHead,
     ErnieDecoderLayer,
     ErnieAttention,
@@ -401,7 +400,6 @@ def __init__(self, config: ErnieMoEConfig, pp_layer_idx=None, ipp=0):
         self.all_self_attns = None
         self.next_decoder_cache = None
         self.inputs_embeds_cur_depth_list = None
-        self.reshard_replicate = ReshardLayer()
 
     def mtp_layer(
         self, hidden_states, inputs_embeds_cur_depth_list, attention_mask, position_ids