run yapf

Author: Hoomaaan

test/spmd/test_xla_dtensor_to_local.py

@@ -44,7 +44,8 @@ def test_to_local_requires_grad(self):
     tensor = torch.randn(100_000, 88, requires_grad=True)
 
     # Create XLAShardedTensor
-    sharded_tensor = XLAShardedTensor(tensor, mesh, [Shard(0)], requires_grad=tensor.requires_grad)
+    sharded_tensor = XLAShardedTensor(
+        tensor, mesh, [Shard(0)], requires_grad=tensor.requires_grad)
 
     # Verify requires_grad is set
     self.assertTrue(sharded_tensor.requires_grad)
@@ -70,7 +71,8 @@ def test_to_local_grad_independence(self):
     mesh = DeviceMesh("xla", list(range(world_size)))
 
     tensor = torch.randn(100_000, 88, requires_grad=True)
-    sharded_tensor = XLAShardedTensor(tensor, mesh, [Shard(0)], requires_grad=tensor.requires_grad)
+    sharded_tensor = XLAShardedTensor(
+        tensor, mesh, [Shard(0)], requires_grad=tensor.requires_grad)
 
     # Create gradients
     res = sharded_tensor.sum()
@@ -95,7 +97,8 @@ def test_to_local_grad_none_handling(self):
     mesh = DeviceMesh("xla", list(range(world_size)))
 
     tensor = torch.randn(100_000, 88, requires_grad=True)
-    sharded_tensor = XLAShardedTensor(tensor, mesh, [Shard(0)], requires_grad=tensor.requires_grad)
+    sharded_tensor = XLAShardedTensor(
+        tensor, mesh, [Shard(0)], requires_grad=tensor.requires_grad)
 
     # Don't do backward pass, so grad remains None
     self.assertIsNone(sharded_tensor.grad)

torch_xla/distributed/spmd/xla_sharded_tensor.py

@@ -165,7 +165,6 @@ def to_local(self):
         torch.Tensor: The global tensor representation with appropriate requires_grad setting.
     """
 
-
     if not self.requires_grad:
       #  When requires_grad is False, global_tensor is the original tensor
       return self.global_tensor