From 6eaff8be2dd755ef438d57b14a23289ff24ca62c Mon Sep 17 00:00:00 2001
From: Claire Huang <cph@amazon.com>
Date: Tue, 15 Jul 2025 22:26:45 +0000
Subject: [PATCH 1/5] Implement XLAShardedTensor._spec and test

---
 test/neuron/run_tests.sh                      |  11 +-
 test/run_tests.sh                             |   1 +
 test/spmd/test_xla_dtensor_spec_conversion.py | 149 ++++++++++++++++++
 test/tpu/run_tests.sh                         |   1 +
 .../distributed/spmd/xla_sharded_tensor.py    |  87 +++++++++-
 torch_xla/distributed/spmd/xla_sharding.py    |  29 +++-
 6 files changed, 266 insertions(+), 12 deletions(-)
 create mode 100644 test/spmd/test_xla_dtensor_spec_conversion.py

diff --git a/test/neuron/run_tests.sh b/test/neuron/run_tests.sh
index 22e778945f9c..f7671cc3d827 100755
--- a/test/neuron/run_tests.sh
+++ b/test/neuron/run_tests.sh
@@ -56,6 +56,14 @@ function run_test {
   PJRT_DEVICE=NEURON NEURON_NUM_DEVICES=1 run_coverage "$@"
 }
 
+function run_test_multi_device {
+  if ! test_is_selected "$1"; then
+    return
+  fi
+  echo "Running in PjRt runtime: $@"
+  PJRT_DEVICE=NEURON run_coverage "$@"
+}
+
 function run_test_without_functionalization {
   if ! test_is_selected "$1"; then
     return
@@ -246,7 +254,8 @@ function run_xla_op_tests3 {
   run_test "$_TEST_DIR/spmd/test_xla_spmd_python_api_interaction.py"
   #run_test "$_TEST_DIR/spmd/test_dtensor_integration.py"
   #run_test "$_TEST_DIR/spmd/test_dtensor_integration2.py"
-  run_test "$_TEST_DIR/spmd/test_dtensor_convert_mesh.py"
+  run_test_multi_device "$_TEST_DIR/spmd/test_dtensor_convert_mesh.py"
+  run_test_multi_device "$_TEST_DIR/spmd/test_xla_dtensor_spec_conv.py"
   run_test "$_TEST_DIR/spmd/test_xla_auto_sharding.py"
   #run_test "$_TEST_DIR/spmd/test_spmd_parameter_wrapping.py"
   run_test "$_TEST_DIR/spmd/test_train_spmd_linear_model.py"
diff --git a/test/run_tests.sh b/test/run_tests.sh
index 93f4cb33c061..b2cc8f751d2c 100755
--- a/test/run_tests.sh
+++ b/test/run_tests.sh
@@ -254,6 +254,7 @@ function run_xla_op_tests3 {
   run_test "$_TEST_DIR/spmd/test_dtensor_integration2.py"
   run_test_multi_devices_without_func "$_TEST_DIR/spmd/test_dtensor_integration3.py"
   run_test_multi_devices "$_TEST_DIR/spmd/test_dtensor_convert_mesh.py"
+  run_test_multi_devices "$_TEST_DIR/spmd/test_xla_dtensor_spec_conversion.py"
   run_test "$_TEST_DIR/spmd/test_xla_auto_sharding.py"
   run_test "$_TEST_DIR/spmd/test_spmd_parameter_wrapping.py"
   run_test "$_TEST_DIR/spmd/test_mp_input_sharding.py"
diff --git a/test/spmd/test_xla_dtensor_spec_conversion.py b/test/spmd/test_xla_dtensor_spec_conversion.py
new file mode 100644
index 000000000000..12102f555c20
--- /dev/null
+++ b/test/spmd/test_xla_dtensor_spec_conversion.py
@@ -0,0 +1,149 @@
+import os
+import sys
+
+import torch
+from torch.distributed.tensor import DeviceMesh, Shard, distribute_tensor
+
+import torch_xla
+import torch_xla.runtime as xr
+
+import unittest
+import test_xla_sharding_base
+
+
+class XLADTensorSpecConversionTest(test_xla_sharding_base.XlaShardingTest):
+
+  @classmethod
+  def setUpClass(cls):
+    super().setUpClass()
+
+  def test_sample_test_case(self):
+    world_size = xr.global_runtime_device_count()
+    mesh = DeviceMesh("xla", torch.arange(world_size))
+    big_tensor = torch.randn(100000, 88)
+    my_dtensor = distribute_tensor(big_tensor, mesh, [Shard(0)])
+
+    assert my_dtensor._spec.mesh.device_type == mesh.device_type
+    assert my_dtensor._spec.placements == (Shard(0),)
+
+  def test_xla_to_dtensor_spec_conversion(self):
+    device_count = xr.global_runtime_device_count()
+    mesh = DeviceMesh("xla", list(range(device_count)))
+
+    # Test different sharding patterns
+    from torch.distributed.tensor.placement_types import Replicate
+    test_cases = [
+        (torch.randn(100, 50), [Shard(0)]),
+        (torch.randn(100, 50), [Shard(1)]),
+        (torch.randn(100, 50, 25), [Shard(0)]),
+        (torch.randn(100, 50), [Replicate()]),
+    ]
+
+    for tensor, placements in test_cases:
+      xla_tensor = distribute_tensor(tensor, mesh, placements)
+      spec = xla_tensor._spec
+
+      assert spec is not None
+      assert spec.mesh.device_type == "xla"
+      assert spec.tensor_meta.shape == tensor.shape
+      assert spec.tensor_meta.dtype == tensor.dtype
+      assert len(spec.placements) >= 1
+      assert spec.placements == tuple(placements)
+
+  def test_mesh_conversion(self):
+    device_count = xr.global_runtime_device_count()
+    original_mesh = DeviceMesh("xla", list(range(device_count)))
+    tensor = torch.randn(50, 50)
+    xla_tensor = distribute_tensor(tensor, original_mesh, [Shard(0)])
+
+    converted_spec = xla_tensor._spec
+
+    assert converted_spec.mesh.device_type == "xla"
+    assert converted_spec.mesh.size() == device_count
+    # assert on mesh dimensions
+    assert converted_spec.mesh.shape == original_mesh.shape
+
+  def test_spec_caching(self):
+    """Test that _spec property caches results for better performance"""
+    import time
+    device_count = xr.global_runtime_device_count()
+    mesh = DeviceMesh("xla", list(range(device_count)))
+    tensor = torch.randn(1000,
+                         1000)  # Large tensor to make spec creation noticeable
+    xla_tensor = distribute_tensor(tensor, mesh, [Shard(0)])
+
+    # first access should create and cache the spec
+    start_time = time.time()
+    spec1 = xla_tensor._spec
+    first_access_time = time.time() - start_time
+
+    # should be much faster due to caching
+    start_time = time.time()
+    spec2 = xla_tensor._spec
+    second_access_time = time.time() - start_time
+
+    assert spec1 is spec2
+    print(
+        f"First access: {first_access_time:.6f}s, Second access: {second_access_time:.6f}s"
+    )
+    assert second_access_time * 10 < first_access_time, \
+        f"Cached access should be much faster: {first_access_time:.6f}s vs {second_access_time:.6f}s"
+
+  def _create_test_tensor_and_mesh(self, tensor_shape, mesh_shape, placements):
+    """Helper to create tensor and mesh for testing"""
+    device_count = xr.global_runtime_device_count()
+    if device_count < max(mesh_shape):
+      self.skipTest(
+          f"Need at least {max(mesh_shape)} devices, got {device_count}")
+
+    mesh = DeviceMesh("xla", torch.arange(device_count).reshape(mesh_shape))
+    tensor = torch.randn(*tensor_shape)
+    return distribute_tensor(tensor, mesh, placements), mesh
+
+  def test_multi_dim_sharding_spec(self):
+    """Test _spec for multi-dimensional sharding"""
+    device_count = xr.global_runtime_device_count()
+    if device_count < 4:
+      self.skipTest("Need at least 4 devices for 2D mesh")
+
+    mesh_shape = (2, device_count // 2)
+    xla_tensor, mesh = self._create_test_tensor_and_mesh(
+        (100, 50), mesh_shape, [Shard(0), Shard(1)])
+    spec = xla_tensor._spec
+
+    assert len(spec.placements) == 2
+    assert spec.mesh.ndim == 2
+
+  def test_tensor_operations_preserve_spec(self):
+    """Test that tensor operations preserve sharding metadata"""
+    xla_tensor, mesh = self._create_test_tensor_and_mesh((100, 50), (-1,),
+                                                         [Shard(0)])
+
+    result_add = xla_tensor + 1
+    result_mul = xla_tensor * 2
+    result_relu = torch.relu(xla_tensor)
+
+    for result in [result_add, result_mul, result_relu]:
+      assert hasattr(result, '_spec')
+      assert result._spec.mesh.device_type == "xla"
+
+  def test_mixed_placement_spec(self):
+    """Test _spec for tensors with mixed shard/replicate placements"""
+    from torch.distributed.tensor.placement_types import Replicate
+    device_count = xr.global_runtime_device_count()
+    if device_count < 4:
+      self.skipTest("Need at least 4 devices for 2D mesh")
+
+    mesh_shape = (2, device_count // 2)
+    xla_tensor, mesh = self._create_test_tensor_and_mesh(
+        (100, 50), mesh_shape, [Shard(0), Replicate()])
+    spec = xla_tensor._spec
+
+    assert len(spec.placements) == 2
+    assert isinstance(spec.placements[0], Shard)
+    assert isinstance(spec.placements[1], Replicate)
+
+
+if __name__ == '__main__':
+  test = unittest.main()
+  sys.exit(0 if test.result.wasSuccessful() else 1)
diff --git a/test/tpu/run_tests.sh b/test/tpu/run_tests.sh
index 1f6f5249b93b..24f18d3bdcda 100755
--- a/test/tpu/run_tests.sh
+++ b/test/tpu/run_tests.sh
@@ -61,6 +61,7 @@ run_test "$_TEST_DIR/spmd/test_xla_spmd_python_api_interaction.py"
 run_test "$_TEST_DIR/spmd/test_xla_auto_sharding.py"
 run_test "$_TEST_DIR/spmd/test_fsdp_v2.py"
 run_test "$_TEST_DIR/spmd/test_dtensor_convert_mesh.py"
+run_test "$_TEST_DIR/spmd/test_xla_dtensor_spec_conversion.py"
 run_test "$_TEST_DIR/test_gradient_accumulation.py"
 XLA_EXPERIMENTAL=nonzero:masked_select:nms run_test "$_TEST_DIR/ds/test_dynamic_shape_models.py" -v
 run_test "$_TEST_DIR/test_autocast.py"
diff --git a/torch_xla/distributed/spmd/xla_sharded_tensor.py b/torch_xla/distributed/spmd/xla_sharded_tensor.py
index aedfd6a801e3..2f0e432a5b2e 100644
--- a/torch_xla/distributed/spmd/xla_sharded_tensor.py
+++ b/torch_xla/distributed/spmd/xla_sharded_tensor.py
@@ -6,6 +6,10 @@
 from typing import List, Tuple, Iterator, Union
 import contextlib
 import collections
+import torch_xla.runtime as xr
+from torch.distributed.tensor._dtensor_spec import DTensorSpec, TensorMeta
+from torch.distributed.device_mesh import DeviceMesh
+from torch.distributed.tensor.placement_types import Shard, Replicate
 
 
 @dataclass
@@ -91,10 +95,15 @@ class XLAShardedTensor(torch.Tensor):
   # >> assert len(input.shape) == len(partition_spec)
   partition_spec: Tuple[int, None]
 
-  __slots__ = ['global_tensor']
+  __slots__ = ['global_tensor', 'mesh_shape', 'partition_spec', '_cached_spec']
 
   @staticmethod
-  def __new__(cls, elem: torch.Tensor, *args, **kwargs):
+  def __new__(cls,
+              elem: torch.Tensor,
+              mesh_shape=None,
+              partition_spec=None,
+              *args,
+              **kwargs):
     # TODO(yeounoh) wrapper can take different arguments
     r = torch.Tensor._make_wrapper_subclass(  # type: ignore[attr-defined]
         cls,
@@ -106,6 +115,11 @@ def __new__(cls, elem: torch.Tensor, *args, **kwargs):
         device=elem.device,
         requires_grad=kwargs.get("requires_grad", False))
     r.global_tensor = elem.detach() if r.requires_grad else elem
+    # Store mesh and partition information for DTensor compatibility
+    if mesh_shape is not None:
+      r.mesh_shape = mesh_shape
+    if partition_spec is not None:
+      r.partition_spec = partition_spec
     return r
 
   # Shards on the devices are materialized/available after the lazy
@@ -159,7 +173,27 @@ def unwrap(elem):
       return elem.global_tensor if isinstance(elem, XLAShardedTensor) else elem
 
     def wrap(elem):
-      return XLAShardedTensor(elem) if isinstance(elem, torch.Tensor) else elem
+      if isinstance(elem,
+                    torch.Tensor) and not isinstance(elem, XLAShardedTensor):
+        # Try to get mesh/partition info from any XLAShardedTensor in args
+        mesh_shape = None
+        partition_spec = None
+
+        def find_sharded_info(x):
+          nonlocal mesh_shape, partition_spec
+          if isinstance(x, XLAShardedTensor):
+            if hasattr(x, 'mesh_shape') and x.mesh_shape:
+              mesh_shape = x.mesh_shape
+            if hasattr(x, 'partition_spec') and x.partition_spec:
+              partition_spec = x.partition_spec
+
+        tree_map(find_sharded_info, args)
+        if kwargs:
+          tree_map(find_sharded_info, kwargs)
+
+        return XLAShardedTensor(
+            elem, mesh_shape=mesh_shape, partition_spec=partition_spec)
+      return elem
 
     # no_dispatch is only needed if you use enable_python_mode.
     # It prevents infinite recursion.
@@ -169,6 +203,53 @@ def wrap(elem):
                     func(*tree_map(unwrap, args), **tree_map(unwrap, kwargs)))
     return rs
 
+  @property
+  def _spec(self):
+    """
+    Convert XLA sharding information to DTensorSpec for DTensor interface compatibility.
+    """
+    # Return cached spec if available
+    if hasattr(self, '_cached_spec'):
+      return self._cached_spec
+
+    # use existing mesh_shape
+    if hasattr(self, 'mesh_shape') and self.mesh_shape:
+      import torch_xla.runtime as xr
+      device_count = xr.global_runtime_device_count()
+      device_list = list(range(device_count))
+      mesh = DeviceMesh("xla",
+                        torch.tensor(device_list).reshape(self.mesh_shape))
+    else:
+      raise ValueError("mesh_shape must be specified to create DTensorSpec")
+
+    # use existing partition_spec
+    if hasattr(self, 'partition_spec') and self.partition_spec:
+      placements = []
+      for mesh_dim in range(
+          len(self.mesh_shape
+             ) if hasattr(self, 'mesh_shape') and self.mesh_shape else 1):
+        # find tensor dimension sharded on this mesh dimension
+        tensor_dim = None
+        for t_dim, m_dim in enumerate(self.partition_spec):
+          if m_dim == mesh_dim:
+            tensor_dim = t_dim
+            break
+        placements.append(
+            Shard(tensor_dim) if tensor_dim is not None else Replicate())
+    else:
+      raise ValueError("partition_spec must be specified to create DTensorSpec")
+
+    # tensor metadata
+    tensor_meta = TensorMeta(
+        shape=self.global_tensor.shape,
+        stride=self.global_tensor.stride(),
+        dtype=self.global_tensor.dtype)
+
+    # Create and cache the spec
+    self._cached_spec = DTensorSpec(
+        mesh=mesh, placements=tuple(placements), tensor_meta=tensor_meta)
+    return self._cached_spec
+
   @classmethod
   def __torch_function__(cls, func, types, args=(), kwargs=None):
     return super().__torch_function__(func, types, args, kwargs)
diff --git a/torch_xla/distributed/spmd/xla_sharding.py b/torch_xla/distributed/spmd/xla_sharding.py
index 49229b17cffe..751fe7e9a661 100644
--- a/torch_xla/distributed/spmd/xla_sharding.py
+++ b/torch_xla/distributed/spmd/xla_sharding.py
@@ -543,7 +543,8 @@ def enable_manual_sharding(t: Union[torch.Tensor, XLAShardedTensor],
   mesh = get_global_mesh() if mesh is None else mesh
   t = mark_sharding(unwrap_sharded_tensor(t), mesh, partition_spec)
   t = torch_xla._XLAC._spmd_full_to_shard_shape(unwrap_sharded_tensor(t))
-  return wrap_as_sharded_tensor(t)
+  return wrap_as_sharded_tensor(
+      t, mesh_shape=mesh.mesh_shape, partition_spec=partition_spec)
 
 
 def disable_manual_sharding(t: Union[torch.Tensor, XLAShardedTensor],
@@ -560,7 +561,8 @@ def disable_manual_sharding(t: Union[torch.Tensor, XLAShardedTensor],
   t = torch_xla._XLAC._spmd_shard_to_full_shape(
       unwrap_sharded_tensor(t), mesh.get_op_sharding(partition_spec),
       full_shape, t.dtype)
-  return wrap_as_sharded_tensor(t)
+  return wrap_as_sharded_tensor(
+      t, mesh_shape=mesh.mesh_shape, partition_spec=partition_spec)
 
 
 def annotate_custom_sharding(t: Union[torch.Tensor,
@@ -594,7 +596,8 @@ def annotate_custom_sharding(t: Union[torch.Tensor,
   op_sharding = mesh.get_op_sharding(partition_spec)
   annotate_func = torch_xla._XLAC._xla_annotate_custom_sharding
   annotate_func(unwrap_sharded_tensor(t), op_sharding)
-  return wrap_as_sharded_tensor(t)
+  return wrap_as_sharded_tensor(
+      t, mesh_shape=mesh.mesh_shape, partition_spec=partition_spec)
 
 
 def mark_sharding(t: Union[torch.Tensor, XLAShardedTensor], mesh: Mesh,
@@ -651,7 +654,9 @@ def mark_sharding(t: Union[torch.Tensor, XLAShardedTensor], mesh: Mesh,
   op_sharding = mesh.get_op_sharding(partition_spec)
   annotate_func = torch_xla._XLAC._xla_mark_sharding
   annotate_func(unwrap_sharded_tensor(t), op_sharding)
-  return wrap_as_sharded_tensor(t)
+  # Pass mesh and partition spec information for DTensor compatibility
+  return wrap_as_sharded_tensor(
+      t, mesh_shape=mesh.mesh_shape, partition_spec=partition_spec)
 
 
 def mark_sharding_with_gradients(
@@ -755,11 +760,19 @@ def clear_sharding(t: Union[torch.Tensor, XLAShardedTensor]) -> torch.Tensor:
   return t
 
 
-def wrap_as_sharded_tensor(
-    t: Union[torch.Tensor, XLAShardedTensor]) -> XLAShardedTensor:
+def wrap_as_sharded_tensor(t: Union[torch.Tensor, XLAShardedTensor],
+                           mesh_shape=None,
+                           partition_spec=None) -> XLAShardedTensor:
+  # pass along mesh and partition spec information
   if not isinstance(t, XLAShardedTensor):
-    return XLAShardedTensor(t)
-  return t
+    return XLAShardedTensor(
+        t, mesh_shape=mesh_shape, partition_spec=partition_spec)
+  else:
+    if mesh_shape is not None:
+      t.mesh_shape = mesh_shape
+    if partition_spec is not None:
+      t.partition_spec = partition_spec
+    return t
 
 
 def unwrap_sharded_tensor(

From d231adb9db7819ba519c00707a44dd3eeaf1756e Mon Sep 17 00:00:00 2001
From: Claire Huang <cph@amazon.com>
Date: Tue, 22 Jul 2025 18:35:54 +0000
Subject: [PATCH 2/5] Removed auto wrapping sharding propagation, added cached
 spec invalidation

---
 test/spmd/test_xla_dtensor_spec_conversion.py | 138 ++++++++++++++----
 .../distributed/spmd/xla_sharded_tensor.py    |  52 +++----
 torch_xla/distributed/spmd/xla_sharding.py    |  25 +++-
 3 files changed, 146 insertions(+), 69 deletions(-)

diff --git a/test/spmd/test_xla_dtensor_spec_conversion.py b/test/spmd/test_xla_dtensor_spec_conversion.py
index 12102f555c20..2fe53613e94a 100644
--- a/test/spmd/test_xla_dtensor_spec_conversion.py
+++ b/test/spmd/test_xla_dtensor_spec_conversion.py
@@ -3,9 +3,12 @@
 
 import torch
 from torch.distributed.tensor import DeviceMesh, Shard, distribute_tensor
+from torch.distributed.tensor.placement_types import Replicate
 
 import torch_xla
 import torch_xla.runtime as xr
+from torch_xla.distributed.spmd import XLAShardedTensor
+from torch_xla.distributed.spmd.xla_sharding import wrap_as_sharded_tensor
 
 import unittest
 import test_xla_sharding_base
@@ -31,7 +34,6 @@ def test_xla_to_dtensor_spec_conversion(self):
     mesh = DeviceMesh("xla", list(range(device_count)))
 
     # Test different sharding patterns
-    from torch.distributed.tensor.placement_types import Replicate
     test_cases = [
         (torch.randn(100, 50), [Shard(0)]),
         (torch.randn(100, 50), [Shard(1)]),
@@ -64,30 +66,27 @@ def test_mesh_conversion(self):
     assert converted_spec.mesh.shape == original_mesh.shape
 
   def test_spec_caching(self):
-    """Test that _spec property caches results for better performance"""
-    import time
+    """Test that _spec property caches results
+    
+    Addresses PR comment: "These sorts of tests that rely on the wall clock often lead to 
+    annoying flakes in my experience. I think it's sufficient to just test that 
+    self._cached_spec has a permanent value after the first call."
+    """
     device_count = xr.global_runtime_device_count()
     mesh = DeviceMesh("xla", list(range(device_count)))
-    tensor = torch.randn(1000,
-                         1000)  # Large tensor to make spec creation noticeable
+    tensor = torch.randn(100, 100)
     xla_tensor = distribute_tensor(tensor, mesh, [Shard(0)])
 
-    # first access should create and cache the spec
-    start_time = time.time()
+    # First access should create and cache the spec
     spec1 = xla_tensor._spec
-    first_access_time = time.time() - start_time
 
-    # should be much faster due to caching
-    start_time = time.time()
-    spec2 = xla_tensor._spec
-    second_access_time = time.time() - start_time
+    # Verify the spec is cached
+    assert xla_tensor._cached_spec is not None
+    assert xla_tensor._cached_spec is spec1
 
+    # Second access should return the cached spec
+    spec2 = xla_tensor._spec
     assert spec1 is spec2
-    print(
-        f"First access: {first_access_time:.6f}s, Second access: {second_access_time:.6f}s"
-    )
-    assert second_access_time * 10 < first_access_time, \
-        f"Cached access should be much faster: {first_access_time:.6f}s vs {second_access_time:.6f}s"
 
   def _create_test_tensor_and_mesh(self, tensor_shape, mesh_shape, placements):
     """Helper to create tensor and mesh for testing"""
@@ -114,22 +113,8 @@ def test_multi_dim_sharding_spec(self):
     assert len(spec.placements) == 2
     assert spec.mesh.ndim == 2
 
-  def test_tensor_operations_preserve_spec(self):
-    """Test that tensor operations preserve sharding metadata"""
-    xla_tensor, mesh = self._create_test_tensor_and_mesh((100, 50), (-1,),
-                                                         [Shard(0)])
-
-    result_add = xla_tensor + 1
-    result_mul = xla_tensor * 2
-    result_relu = torch.relu(xla_tensor)
-
-    for result in [result_add, result_mul, result_relu]:
-      assert hasattr(result, '_spec')
-      assert result._spec.mesh.device_type == "xla"
-
   def test_mixed_placement_spec(self):
     """Test _spec for tensors with mixed shard/replicate placements"""
-    from torch.distributed.tensor.placement_types import Replicate
     device_count = xr.global_runtime_device_count()
     if device_count < 4:
       self.skipTest("Need at least 4 devices for 2D mesh")
@@ -143,6 +128,97 @@ def test_mixed_placement_spec(self):
     assert isinstance(spec.placements[0], Shard)
     assert isinstance(spec.placements[1], Replicate)
 
+  def test_sharding_info_acquisition(self):
+    """Test that non-XLAShardedTensor can acquire sharding information
+
+    Tests case of 'elem is not an XLAShardedTensor but there exists 
+    sharding information we want to acquire'
+    """
+
+    device_count = xr.global_runtime_device_count()
+    mesh_shape = (device_count,)
+    partition_spec = (0, None)
+
+    regular_tensor = torch.randn(100, 50).to('xla')
+
+    sharded_tensor = wrap_as_sharded_tensor(
+        regular_tensor, mesh_shape=mesh_shape, partition_spec=partition_spec)
+
+    # Verify the tensor acquired the sharding information
+    assert isinstance(sharded_tensor, XLAShardedTensor)
+    assert sharded_tensor.mesh_shape == mesh_shape
+    assert sharded_tensor.partition_spec == partition_spec
+
+  def test_resharding_logic(self):
+    """
+    Tests wrap_as_sharded_tensor resharding before returning XLAShardedTensor t.
+    """
+
+    device_count = xr.global_runtime_device_count()
+    if device_count < 4:
+      self.skipTest("Need at least 4 devices for resharding test")
+
+    # Initial sharding
+    initial_mesh_shape = (device_count,)
+    initial_partition_spec = (0, None)
+    new_mesh_shape = (2, device_count // 2)
+    new_partition_spec = (0, 1)
+
+    # Create tensor and verify resharding
+    tensor = torch.randn(100, 50).to('xla')
+    sharded_tensor = wrap_as_sharded_tensor(
+        tensor,
+        mesh_shape=initial_mesh_shape,
+        partition_spec=initial_partition_spec)
+    initial_spec = sharded_tensor._spec
+
+    resharded_tensor = wrap_as_sharded_tensor(
+        sharded_tensor,
+        mesh_shape=new_mesh_shape,
+        partition_spec=new_partition_spec)
+
+    # Verify resharding worked and cache was invalidated
+    assert resharded_tensor.mesh_shape == new_mesh_shape
+    assert resharded_tensor.partition_spec == new_partition_spec
+    assert resharded_tensor._spec is not initial_spec
+
+  def test_spec_invalidation_on_resharding(self):
+    """Tests cases where the cached spec may become outdated.
+    """
+
+    device_count = xr.global_runtime_device_count()
+    if device_count < 4:
+      self.skipTest("Need at least 4 devices for resharding test")
+
+    tensor = torch.randn(100, 50).to('xla')
+    initial_mesh_shape = (device_count,)
+    initial_partition_spec = (0, None)
+    new_mesh_shape = (2, device_count // 2)
+    new_partition_spec = (0, 1)
+
+    sharded_tensor = wrap_as_sharded_tensor(
+        tensor,
+        mesh_shape=initial_mesh_shape,
+        partition_spec=initial_partition_spec)
+    initial_spec = sharded_tensor._spec
+    assert sharded_tensor._cached_spec is not None
+
+    # Changing mesh_shape / partition_spec through wrap_as_sharded_tensor invalidates cache
+    resharded_tensor = wrap_as_sharded_tensor(
+        sharded_tensor,
+        mesh_shape=new_mesh_shape,
+        partition_spec=initial_partition_spec)
+    assert resharded_tensor._spec is not initial_spec
+    assert resharded_tensor._spec.mesh.shape == new_mesh_shape
+
+    initial_spec = resharded_tensor._spec
+    resharded_tensor = wrap_as_sharded_tensor(
+        resharded_tensor,
+        mesh_shape=new_mesh_shape,
+        partition_spec=new_partition_spec)
+    assert resharded_tensor._spec is not initial_spec
+    assert resharded_tensor._spec.placements[1].dim == 1
+
 
 if __name__ == '__main__':
   test = unittest.main()
diff --git a/torch_xla/distributed/spmd/xla_sharded_tensor.py b/torch_xla/distributed/spmd/xla_sharded_tensor.py
index 2f0e432a5b2e..3e90c4467afe 100644
--- a/torch_xla/distributed/spmd/xla_sharded_tensor.py
+++ b/torch_xla/distributed/spmd/xla_sharded_tensor.py
@@ -10,6 +10,7 @@
 from torch.distributed.tensor._dtensor_spec import DTensorSpec, TensorMeta
 from torch.distributed.device_mesh import DeviceMesh
 from torch.distributed.tensor.placement_types import Shard, Replicate
+from torch.utils._pytree import tree_map_only
 
 
 @dataclass
@@ -115,11 +116,13 @@ def __new__(cls,
         device=elem.device,
         requires_grad=kwargs.get("requires_grad", False))
     r.global_tensor = elem.detach() if r.requires_grad else elem
-    # Store mesh and partition information for DTensor compatibility
-    if mesh_shape is not None:
-      r.mesh_shape = mesh_shape
-    if partition_spec is not None:
-      r.partition_spec = partition_spec
+
+    # Initialize mesh, partition, and spec information
+    r.mesh_shape = mesh_shape or (elem.mesh_shape if isinstance(
+        elem, XLAShardedTensor) else None)
+    r.partition_spec = partition_spec or (elem.partition_spec if isinstance(
+        elem, XLAShardedTensor) else None)
+    r._cached_spec = None
     return r
 
   # Shards on the devices are materialized/available after the lazy
@@ -144,6 +147,9 @@ def load_local_shards_(self, shards: List[XLAShard]):
     devices = [s.shard_device for s in shards]
     torch_xla._XLAC._load_local_shards(self.global_tensor, data, devices)
 
+    # Invalidate cached spec since the global_tensor data has changed
+    self.invalidate_spec_cache()
+
   @property
   def sharding_spec(self):
     return torch_xla._XLAC._get_xla_sharding_spec(self.global_tensor)
@@ -173,27 +179,7 @@ def unwrap(elem):
       return elem.global_tensor if isinstance(elem, XLAShardedTensor) else elem
 
     def wrap(elem):
-      if isinstance(elem,
-                    torch.Tensor) and not isinstance(elem, XLAShardedTensor):
-        # Try to get mesh/partition info from any XLAShardedTensor in args
-        mesh_shape = None
-        partition_spec = None
-
-        def find_sharded_info(x):
-          nonlocal mesh_shape, partition_spec
-          if isinstance(x, XLAShardedTensor):
-            if hasattr(x, 'mesh_shape') and x.mesh_shape:
-              mesh_shape = x.mesh_shape
-            if hasattr(x, 'partition_spec') and x.partition_spec:
-              partition_spec = x.partition_spec
-
-        tree_map(find_sharded_info, args)
-        if kwargs:
-          tree_map(find_sharded_info, kwargs)
-
-        return XLAShardedTensor(
-            elem, mesh_shape=mesh_shape, partition_spec=partition_spec)
-      return elem
+      return XLAShardedTensor(elem) if isinstance(elem, torch.Tensor) else elem
 
     # no_dispatch is only needed if you use enable_python_mode.
     # It prevents infinite recursion.
@@ -209,11 +195,11 @@ def _spec(self):
     Convert XLA sharding information to DTensorSpec for DTensor interface compatibility.
     """
     # Return cached spec if available
-    if hasattr(self, '_cached_spec'):
+    if self._cached_spec is not None:
       return self._cached_spec
 
     # use existing mesh_shape
-    if hasattr(self, 'mesh_shape') and self.mesh_shape:
+    if self.mesh_shape is not None:
       import torch_xla.runtime as xr
       device_count = xr.global_runtime_device_count()
       device_list = list(range(device_count))
@@ -223,11 +209,9 @@ def _spec(self):
       raise ValueError("mesh_shape must be specified to create DTensorSpec")
 
     # use existing partition_spec
-    if hasattr(self, 'partition_spec') and self.partition_spec:
+    if self.partition_spec is not None:
       placements = []
-      for mesh_dim in range(
-          len(self.mesh_shape
-             ) if hasattr(self, 'mesh_shape') and self.mesh_shape else 1):
+      for mesh_dim in range(len(self.mesh_shape)):
         # find tensor dimension sharded on this mesh dimension
         tensor_dim = None
         for t_dim, m_dim in enumerate(self.partition_spec):
@@ -250,6 +234,10 @@ def _spec(self):
         mesh=mesh, placements=tuple(placements), tensor_meta=tensor_meta)
     return self._cached_spec
 
+  def invalidate_spec_cache(self):
+    """Invalidate the cached DTensorSpec."""
+    self._cached_spec = None
+
   @classmethod
   def __torch_function__(cls, func, types, args=(), kwargs=None):
     return super().__torch_function__(func, types, args, kwargs)
diff --git a/torch_xla/distributed/spmd/xla_sharding.py b/torch_xla/distributed/spmd/xla_sharding.py
index 751fe7e9a661..5f4d4378e7d2 100644
--- a/torch_xla/distributed/spmd/xla_sharding.py
+++ b/torch_xla/distributed/spmd/xla_sharding.py
@@ -765,14 +765,27 @@ def wrap_as_sharded_tensor(t: Union[torch.Tensor, XLAShardedTensor],
                            partition_spec=None) -> XLAShardedTensor:
   # pass along mesh and partition spec information
   if not isinstance(t, XLAShardedTensor):
+    # Create a new XLAShardedTensor
     return XLAShardedTensor(
         t, mesh_shape=mesh_shape, partition_spec=partition_spec)
-  else:
-    if mesh_shape is not None:
-      t.mesh_shape = mesh_shape
-    if partition_spec is not None:
-      t.partition_spec = partition_spec
-    return t
+
+  # Update existing XLAShardedTensor if needed
+  needs_invalidate = False
+
+  # Always set mesh_shape and partition_spec if provided
+  if mesh_shape is not None:
+    t.mesh_shape = mesh_shape
+    needs_invalidate = True
+
+  if partition_spec is not None:
+    t.partition_spec = partition_spec
+    needs_invalidate = True
+
+  # Invalidate cached spec if resharding occurred
+  if needs_invalidate:
+    t.invalidate_spec_cache()
+
+  return t
 
 
 def unwrap_sharded_tensor(

From 60cd54296b73f91785bebf7ead8122a822185cfd Mon Sep 17 00:00:00 2001
From: Claire Huang <cph@amazon.com>
Date: Tue, 22 Jul 2025 18:40:12 +0000
Subject: [PATCH 3/5] Removing lazy import

---
 test/spmd/test_xla_dtensor_spec_conversion.py    | 7 -------
 torch_xla/distributed/spmd/xla_sharded_tensor.py | 1 -
 2 files changed, 8 deletions(-)

diff --git a/test/spmd/test_xla_dtensor_spec_conversion.py b/test/spmd/test_xla_dtensor_spec_conversion.py
index 2fe53613e94a..e48a323bd151 100644
--- a/test/spmd/test_xla_dtensor_spec_conversion.py
+++ b/test/spmd/test_xla_dtensor_spec_conversion.py
@@ -67,24 +67,17 @@ def test_mesh_conversion(self):
 
   def test_spec_caching(self):
     """Test that _spec property caches results
-    
-    Addresses PR comment: "These sorts of tests that rely on the wall clock often lead to 
-    annoying flakes in my experience. I think it's sufficient to just test that 
-    self._cached_spec has a permanent value after the first call."
     """
     device_count = xr.global_runtime_device_count()
     mesh = DeviceMesh("xla", list(range(device_count)))
     tensor = torch.randn(100, 100)
     xla_tensor = distribute_tensor(tensor, mesh, [Shard(0)])
 
-    # First access should create and cache the spec
     spec1 = xla_tensor._spec
 
-    # Verify the spec is cached
     assert xla_tensor._cached_spec is not None
     assert xla_tensor._cached_spec is spec1
 
-    # Second access should return the cached spec
     spec2 = xla_tensor._spec
     assert spec1 is spec2
 
diff --git a/torch_xla/distributed/spmd/xla_sharded_tensor.py b/torch_xla/distributed/spmd/xla_sharded_tensor.py
index 3e90c4467afe..ed590e22fd87 100644
--- a/torch_xla/distributed/spmd/xla_sharded_tensor.py
+++ b/torch_xla/distributed/spmd/xla_sharded_tensor.py
@@ -200,7 +200,6 @@ def _spec(self):
 
     # use existing mesh_shape
     if self.mesh_shape is not None:
-      import torch_xla.runtime as xr
       device_count = xr.global_runtime_device_count()
       device_list = list(range(device_count))
       mesh = DeviceMesh("xla",

From 91714d4809806eb7f1110da97b00c383b730a613 Mon Sep 17 00:00:00 2001
From: Claire Huang <cph@amazon.com>
Date: Tue, 22 Jul 2025 20:43:48 +0000
Subject: [PATCH 4/5] Added test for catching thrown error in spec

---
 test/spmd/test_xla_dtensor_spec_conversion.py   | 17 +++++++++++++++++
 .../distributed/spmd/xla_sharded_tensor.py      | 12 ++++++++++--
 2 files changed, 27 insertions(+), 2 deletions(-)

diff --git a/test/spmd/test_xla_dtensor_spec_conversion.py b/test/spmd/test_xla_dtensor_spec_conversion.py
index e48a323bd151..81cb8a4aa2e4 100644
--- a/test/spmd/test_xla_dtensor_spec_conversion.py
+++ b/test/spmd/test_xla_dtensor_spec_conversion.py
@@ -212,6 +212,23 @@ def test_spec_invalidation_on_resharding(self):
     assert resharded_tensor._spec is not initial_spec
     assert resharded_tensor._spec.placements[1].dim == 1
 
+  def test_auto_wrapped_tensor_spec_failure(self):
+    """Test that auto-wrapped tensors fail when accessing _spec property.
+    
+    Auto-wrapped tensors are created through operations that trigger __torch_dispatch__
+    but don't yet have access to the sharding propagation done through open xla,
+    causing ._spec to fail. 
+    """
+    device_count = xr.global_runtime_device_count()
+    mesh = DeviceMesh("xla", torch.arange(device_count))
+    tensor = torch.randn(4, 4)
+    sharded_tensor = distribute_tensor(tensor, mesh, [Shard(0)])
+
+    auto_wrapped = sharded_tensor + sharded_tensor
+
+    with self.assertRaises(ValueError):
+      _ = auto_wrapped._spec
+
 
 if __name__ == '__main__':
   test = unittest.main()
diff --git a/torch_xla/distributed/spmd/xla_sharded_tensor.py b/torch_xla/distributed/spmd/xla_sharded_tensor.py
index ed590e22fd87..a20d530f3faa 100644
--- a/torch_xla/distributed/spmd/xla_sharded_tensor.py
+++ b/torch_xla/distributed/spmd/xla_sharded_tensor.py
@@ -205,7 +205,11 @@ def _spec(self):
       mesh = DeviceMesh("xla",
                         torch.tensor(device_list).reshape(self.mesh_shape))
     else:
-      raise ValueError("mesh_shape must be specified to create DTensorSpec")
+      raise ValueError(
+          "mesh_shape must be specified to create DTensorSpec. "
+          "If this tensor was created through torch operations, it may be auto-wrapped. "
+          "Use wrap_as_sharded_tensor() to set mesh_shape before accessing _spec. "
+      )
 
     # use existing partition_spec
     if self.partition_spec is not None:
@@ -220,7 +224,11 @@ def _spec(self):
         placements.append(
             Shard(tensor_dim) if tensor_dim is not None else Replicate())
     else:
-      raise ValueError("partition_spec must be specified to create DTensorSpec")
+      raise ValueError(
+          "partition_spec must be specified to create DTensorSpec. "
+          "If this tensor was created through torch operations, it may be auto-wrapped. "
+          "Use wrap_as_sharded_tensor() to set partition_spec before accessing _spec. "
+      )
 
     # tensor metadata
     tensor_meta = TensorMeta(

From 5a61b1dd2cdeb5d78db143df9e628e9414c8be03 Mon Sep 17 00:00:00 2001
From: Claire Huang <cph@amazon.com>
Date: Wed, 23 Jul 2025 18:20:50 +0000
Subject: [PATCH 5/5] Update test_mark_shard_scalar to reflect mesh_shape
 implementation

---
 test/spmd/test_xla_sharding.py | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/test/spmd/test_xla_sharding.py b/test/spmd/test_xla_sharding.py
index 7b1be7574a1f..48b760f6e3f0 100644
--- a/test/spmd/test_xla_sharding.py
+++ b/test/spmd/test_xla_sharding.py
@@ -1162,10 +1162,6 @@ def test_mark_shard_scalar(self):
       self.assertIsInstance(shard.indices, type(Ellipsis))
       self.assertEqual(shard.replica_id, i)
 
-    # It looks like mesh_shape attribute is never implemented.
-    with self.assertRaises(AttributeError):
-      xt.mesh_shape
-
   def test_global_mesh(self):
     expected_mesh = self._get_mesh((1, self.n_devices))
     xs.set_global_mesh(expected_mesh)