Add information about on-going DTensor API in spmd.md (#5735)

docs/spmd.md

@@ -202,6 +202,20 @@ The main use case for `XLAShardedTensor` [[RFC](https://github.com/pytorch/xla/i
 
 There is also an ongoing effort to integrate <code>XLAShardedTensor</code> into <code>DistributedTensor</code> API to support XLA backend [[RFC](https://github.com/pytorch/pytorch/issues/92909)].
 
+### DTensor Integration
+PyTorch has prototype-released [DTensor](https://github.com/pytorch/pytorch/blob/main/torch/distributed/_tensor/README.md) in 2.1.
+We are integrating PyTorch/XLA SPMD into DTensor API [RFC](https://github.com/pytorch/pytorch/issues/92909). We have a proof-of-concept integration for `distribute_tensor`, which calls `mark_sharding` annotation API to shard a tensor and its computation using XLA:
+```python
+import torch
+from torch.distributed import DeviceMesh, Shard, distribute_tensor
+
+# distribute_tensor now works with `xla` backend using PyTorch/XLA SPMD.
+mesh = DeviceMesh("xla", list(range(world_size)))
+big_tensor = torch.randn(100000, 88)
+my_dtensor = distribute_tensor(big_tensor, mesh, [Shard(0)])
+```
+
+This feature is experimental and stay tuned for more updates, examples and tutorials in the upcoming releases.
 
 ### Sharding-Aware Host-to-Device Data Loading