diff --git a/tutorials/developer_api_guide/tensor_parallel.py b/tutorials/developer_api_guide/tensor_parallel.py
index a94d84fe05..db610a71fa 100644
--- a/tutorials/developer_api_guide/tensor_parallel.py
+++ b/tutorials/developer_api_guide/tensor_parallel.py
@@ -1,8 +1,9 @@
 import os
 import torch
 import torch.distributed as dist
+from typing import Sequence
 from torch.distributed import DeviceMesh
-from torch.distributed._tensor import DTensor, Replicate, Shard
+from torch.distributed.tensor import DTensor, Replicate, Shard, Placement
 from torch.utils._python_dispatch import return_and_correct_aliasing
 from my_dtype_tensor_subclass import MyDTypeTensor, fill_defaults
 
@@ -101,18 +102,40 @@ def quantize(m: torch.nn.Module) -> torch.nn.Module:
     )
     return m
 
+def shard(
+    full_tensor: torch.Tensor,
+    device_mesh: DeviceMesh,
+    placements: Sequence[Placement],
+) -> DTensor:
+    """
+    Add a shard function to simplify both colwise_shard and rowwise_shard.  The
+    shard function accepts a full tensor, and returns a DTensor based on
+    indicated placements.  Goal is to move the shard function as a static method
+    of DTensor, e.g.
+        dtensor = DTensor.shard(full_tensor, device_mesh, placement)
+    """
+    from torch.distributed.tensor._utils import compute_local_shape_and_global_offset
+
+    shape, offset = compute_local_shape_and_global_offset(
+        full_tensor.shape, device_mesh, placements
+    )
+    slices = [
+        slice(cur_offset, cur_offset + cur_shape)
+        for cur_shape, cur_offset in zip(shape, offset)
+    ]
+    local_tensor = full_tensor[slices]
+    return DTensor.from_local(
+        local_tensor, device_mesh, placements
+    )
+
 def colwise_shard(m: torch.nn.Module, mesh: DeviceMesh) -> torch.nn.Module:
     """
     Shard linear layer of the model in column-wise fashion
     """
     # Column-wise is wrt to A^T, so for A it is row-wise.
-    # Number of rows per rank
     orig_weight = m.linear.weight
-    n_local_rows = orig_weight.size(0) // mesh.size()
-    rank = mesh.get_local_rank()
-    local_shard = orig_weight[rank * n_local_rows : (rank + 1) * n_local_rows, :]
     # Construct DTensor from local shard
-    dtensor = DTensor.from_local(local_shard, mesh, [Shard(0)])
+    dtensor = shard(orig_weight, mesh, [Shard(0)])
     # Replace parameter in module
     m.linear.weight = torch.nn.Parameter(
         dtensor, requires_grad=False
@@ -124,13 +147,9 @@ def rowwise_shard(m: torch.nn.Module, mesh: DeviceMesh) -> torch.nn.Module:
     Shard linear layer of the model in row-wise fashion
     """
     # Row-wise is wrt to A^T, so for A it is column-wise.
-    # Number of rows per rank
     orig_weight = m.linear.weight
-    n_local_cols = orig_weight.size(1) // mesh.size()
-    rank = mesh.get_local_rank()
-    local_shard = orig_weight[:, rank * n_local_cols : (rank + 1) * n_local_cols]
     # Construct DTensor from local shard
-    dtensor = DTensor.from_local(local_shard, mesh, [Shard(1)])
+    dtensor = shard(orig_weight, mesh, [Shard(1)])
     # Replace parameter in module
     m.linear.weight = torch.nn.Parameter(
         dtensor, requires_grad=False