diff --git a/experimental/torch_xla2/test/test_ops.py b/experimental/torch_xla2/test/test_ops.py
index aa0d84e71dc..f93ae21c997 100644
--- a/experimental/torch_xla2/test/test_ops.py
+++ b/experimental/torch_xla2/test/test_ops.py
@@ -28,12 +28,8 @@
     "linalg.cholesky",
     "linalg.cholesky_ex",
     "linalg.det",
-    "linalg.ldl_factor",
-    "linalg.ldl_factor_ex",
     "linalg.ldl_solve",
     "linalg.lstsq",
-    "linalg.lu_factor",
-    "linalg.lu_factor_ex",
     "linalg.lu_solve",
     "linalg.matrix_norm",
     "linalg.matrix_power",
diff --git a/experimental/torch_xla2/torch_xla2/ops/jaten.py b/experimental/torch_xla2/torch_xla2/ops/jaten.py
index 5ef39d40cc8..948b86813c0 100644
--- a/experimental/torch_xla2/torch_xla2/ops/jaten.py
+++ b/experimental/torch_xla2/torch_xla2/ops/jaten.py
@@ -2412,6 +2412,30 @@ def _aten_linalg_eigh(A, UPLO='L'):
   return jnp.linalg.eigh(A, UPLO)
 
 
+@op(torch.ops.aten.linalg_ldl_factor_ex)
+def _aten_linalg_ldl_factor_ex(A, hermitian=False, check_errors=False):
+  # TODO: Replace with native LDL when available:
+  # https://github.com/jax-ml/jax/issues/12779
+  # TODO: Not tested for complex inputs. Does not support hermitian=True
+  pivots = jnp.broadcast_to(
+      jnp.arange(1, A.shape[-1]+1, dtype=jnp.int32), A.shape[:-1]
+  )
+  info = jnp.zeros(A.shape[:-2], jnp.int32)
+  C = jnp.linalg.cholesky(A)
+  if C.size == 0:
+    return C, pivots, info
+
+  # Fill diagonals of stacked matrices
+  @functools.partial(jnp.vectorize, signature='(k,k),(k,k)->(k,k)')
+  def fill_diagonal_batch(x, y):
+    return jnp.fill_diagonal(x, jnp.diag(y), inplace=False)
+
+  D = C * jnp.eye(C.shape[-1], dtype=A.dtype)
+  LD = C @ jnp.linalg.inv(D)
+  LD = fill_diagonal_batch(LD, D*D)
+  return LD, pivots, info
+
+
 @op(torch.ops.aten.linalg_lu)
 def _aten_linalg_lu(A, pivot=True, out=None):
   dtype = A.dtype
@@ -2442,6 +2466,15 @@ def perm_to_P(perm):
   return P,L,U
 
 
+@op(torch.ops.aten.linalg_lu_factor_ex)
+def _aten_linalg_lu_factor_ex(A, pivot=True, check_errors=False):
+  lu, pivots, _ = jax.lax.linalg.lu(A)
+  # PT pivots vector is 1-indexed
+  pivots = pivots + 1
+  info = jnp.zeros(A.shape[:-2], jnp.int32)
+  return lu, pivots, info
+
+
 @op(torch.ops.aten.gcd)
 def _aten_gcd(input, other):
   return jnp.gcd(input, other)