Implement XLAShardedTensor._spec and test

Author: aws-cph

test/spmd/test_xla_dtensor_spec_conversion.py

@@ -3,12 +3,9 @@
 
 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
@@ -34,6 +31,7 @@ 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)]),
@@ -66,20 +64,30 @@ def test_mesh_conversion(self):
     assert converted_spec.mesh.shape == original_mesh.shape
 
   def test_spec_caching(self):
-    """Test that _spec property caches results
-    """
+    """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(100, 100)
+    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
 
-    assert xla_tensor._cached_spec is not None
-    assert xla_tensor._cached_spec is spec1
-
+    # 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"""
@@ -106,8 +114,22 @@ 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")
@@ -121,114 +143,6 @@ 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
-
-  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()

torch_xla/distributed/spmd/xla_sharded_tensor.py

@@ -10,7 +10,6 @@
 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
@@ -116,13 +115,11 @@ def __new__(cls,
         device=elem.device,
         requires_grad=kwargs.get("requires_grad", False))
     r.global_tensor = elem.detach() if r.requires_grad else elem
-
-    # 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
+    # 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
@@ -179,7 +176,27 @@ 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.
@@ -195,26 +212,25 @@ def _spec(self):
     Convert XLA sharding information to DTensorSpec for DTensor interface compatibility.
     """
     # Return cached spec if available
-    if self._cached_spec is not None:
+    if hasattr(self, '_cached_spec'):
       return self._cached_spec
 
     # use existing mesh_shape
-    if self.mesh_shape is not None:
+    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. "
-          "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. "
-      )
+      raise ValueError("mesh_shape must be specified to create DTensorSpec")
 
     # use existing partition_spec
-    if self.partition_spec is not None:
+    if hasattr(self, 'partition_spec') and self.partition_spec:
       placements = []
-      for mesh_dim in range(len(self.mesh_shape)):
+      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):
@@ -224,11 +240,7 @@ 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. "
-          "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. "
-      )
+      raise ValueError("partition_spec must be specified to create DTensorSpec")
 
     # tensor metadata
     tensor_meta = TensorMeta(
@@ -241,10 +253,6 @@ 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)

torch_xla/distributed/spmd/xla_sharding.py

@@ -765,27 +765,14 @@ 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)
-
-  # 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
+  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(