diff --git a/colossalai/fx/passes/shard_1d_pass.py b/colossalai/fx/passes/shard_1d_pass.py
index 43c056b60..9cbdd3d45 100644
--- a/colossalai/fx/passes/shard_1d_pass.py
+++ b/colossalai/fx/passes/shard_1d_pass.py
@@ -1,13 +1,9 @@
 import torch
 from torch.fx.node import map_arg
-from torch.fx.node import Node
-from torch.fx.passes.split_module import split_module
-
-import colossalai
-from colossalai.tensor import ColoTensor, TensorSpec, distspec, ProcessGroup, ComputeSpec, ComputePattern
+from colossalai.tensor import ColoTensorSpec, distspec, ProcessGroup, ComputeSpec, ComputePattern
 
 
-def weight_split(weight: torch.nn.parameter.Parameter, dim: int) -> torch.nn.parameter.Parameter:
+def weight_split(weight: torch.Tensor, dim: int) -> torch.nn.parameter.Parameter:
     """weight_split 
     split a nn.Parameter
 
@@ -18,22 +14,20 @@ def weight_split(weight: torch.nn.parameter.Parameter, dim: int) -> torch.nn.par
     Returns:
         _type_: _description_
     """
-    #TODO: This func temporarily works with no materialization
     # Append a Tensor spec to target_module.weight.shard
     # Convert to ColoTensor: colo_tensor = ColoTensor.from_torch_tensor(tensor, spec)
-    # assert isinstance(weight, torch.nn.parameter.Parameter), \
-    #     f'The type of the input tensor should be torch.nn.parameter' \
-    #     f'Your Input tensor is {type(weight)}'
+    assert isinstance(weight, torch.Tensor), \
+        f'The type of the input tensor should be torch.nn.parameter' \
+        f'Your Input tensor is {type(weight)}'
 
     # FIXME() I initialized a PG for this tensor. Only has TP comm group.
     # we only consider the TP-only caes.
     world_size = torch.distributed.get_world_size()
     pg = ProcessGroup(tp_degree=world_size)
 
-    spec = TensorSpec(distspec.shard(pg, [dim], [pg.tp_world_size()]), ComputeSpec(ComputePattern.TP1D))
+    spec = ColoTensorSpec(pg, distspec.shard([dim], [pg.tp_world_size()]), ComputeSpec(ComputePattern.TP1D))
     # As you has constructed a Spec, why not directly convert the tensor to ColoTensor.
-    # setattr(weight, "fx_attr", spec)
-    weight.data = ColoTensor(data=weight.data, spec=spec)
+    setattr(weight, "fx_attr", spec)
     return weight
 
 
@@ -58,6 +52,7 @@ def row_shard_linear_pass(gm: torch.fx.GraphModule):
             target_module = node.graph.owning_module.get_submodule(node.target)
             if isinstance(target_module, torch.nn.Linear):
                 target_module.weight = weight_split(target_module.weight, dim=-1)
+
     gm.recompile()
     return gm