diff --git a/experimental/torch_xla2/test/test_core_aten_ops.py b/experimental/torch_xla2/test/test_core_aten_ops.py
index 357e41c91018..591355088984 100644
--- a/experimental/torch_xla2/test/test_core_aten_ops.py
+++ b/experimental/torch_xla2/test/test_core_aten_ops.py
@@ -8,24 +8,6 @@
 from torch.utils import _pytree as pytree
 
 
-def diff_output(testcase, output1, output2, rtol, atol, equal_nan=True):
-  if isinstance(output1, torch.Tensor):
-    testcase.assertIsInstance(output2, torch.Tensor)
-    output2_cpu = output2.detach().cpu()
-    if output2_cpu.dtype != output1.dtype:
-      output2_cpu = output2_cpu.to(output1.dtype)
-    testcase.assertTrue(
-        torch.allclose(
-            output1, output2_cpu, atol=atol, rtol=rtol, equal_nan=equal_nan))
-  elif isinstance(output1, (tuple, list)):
-    testcase.assertIsInstance(output2, (tuple, list))
-    testcase.assertEqual(len(output1), len(output2))
-    for o1, o2 in zip(output1, output2):
-      diff_output(testcase, o1, o2, rtol, atol)
-  else:
-    testcase.assertEqual(output1, output2)
-
-
 def run_export_and_compare(testcase,
                            func,
                            args,
diff --git a/experimental/torch_xla2/torch_xla2/_ops.py b/experimental/torch_xla2/torch_xla2/_ops.py
index 334c63bb4239..0bb5656de5a1 100644
--- a/experimental/torch_xla2/torch_xla2/_ops.py
+++ b/experimental/torch_xla2/torch_xla2/_ops.py
@@ -57,12 +57,7 @@ def _aten_add(x, y):
 
   assert x.dtype == y.dtype, (x.dtype, y.dtype)
   """
-  try:
-    return x + y
-  except Exception as e:
-    import pdb
-
-    pdb.set_trace()
+  return x + y
 
 
 @op(torch.ops.aten.add_, is_jax_func=False)
@@ -239,6 +234,7 @@ def _aten_div(x, y, rounding_mode=""):
     res = jnp.trunc(res)
   return res
 
+
 @op(torch.ops.aten.div_, is_jax_func=False)
 def _aten_div_(x, y, rounding_mode=""):
   x._elem = _aten_div(x._elem, y._elem, rounding_mode)
@@ -376,14 +372,9 @@ def _aten_ne(x, y):
 
 @op(torch.ops.aten.cumsum)
 def _aten_cumsum(x, y, dtype=None):
-  try:
-    dtype = tensor.t2j_dtype(dtype)
-    res = jnp.cumsum(x, y, dtype)
-    return res
-  except Exception as e:
-    import pdb
-
-    pdb.set_trace()
+  dtype = tensor.t2j_dtype(dtype)
+  res = jnp.cumsum(x, y, dtype)
+  return res
 
 
 @op(torch.ops.aten.native_layer_norm)