OpInfo test for take -- trace

Explicitly mark sparse as unsupported

Fixes: #7966

experimental/torch_xla2/test/test_ops.py

@@ -223,11 +223,7 @@
     "svd",
     "svd_lowrank",
     "take_along_dim",
-    "take",
-    "tensor_split",
-    "to_sparse",
-    "topk",
-    "trace",
+    "to_sparse", # We are not supporting sparse tensors yet.
     "triu",
     "unbind",
     "unfold_copy",
@@ -362,6 +358,13 @@ def run_export_and_compare(testcase,
         test.variant_test_name not in variant_test_name_to_skip)
 ]
 
+# Sort related ops should ignore index;
+# For example: sort( [1, 0, 0]) -> [0, 0, 1]
+# the correct index can be [1, 2, 0] or [2, 1, 0]
+should_ignore_indexes = {
+  "topk"
+}
+
 
 class TestOpInfo(TestCase):
 
@@ -392,8 +395,11 @@ def test_reference_eager(self, device, dtype, op):
         # To avoid errors during testing, replace values below 1 with 1.
         sample_input.input = self.replace_values_below_threshold(
             sample_input.input, 1)
+
+      ignore_index = op.name in should_ignore_indexes
 
-      run_export_and_compare(self, op, sample_input, check_output)
+      run_export_and_compare(self, op, sample_input, check_output, 
+                             ignore_indices=ignore_index)
 
 
 instantiate_device_type_tests(TestOpInfo, globals())

experimental/torch_xla2/torch_xla2/ops/jaten.py

@@ -2265,6 +2265,9 @@ def _aten_topk(input, k, dim=None, largest=True, sorted=True, *, out=None):
   if not largest:
     input = -input  # Find top-k of negated input if we want the smallest
 
+  if input.ndim == 0:
+    return input, jnp.array(0, dtype=jnp.int64.dtype)
+
   transpose_shape = None
   if dim != -1 and dim != len(input.shape) - 1:
     transpose_shape = list(range(len(input.shape)))

experimental/torch_xla2/torch_xla2/ops/jtorch.py

@@ -275,6 +275,11 @@ def logdet(input):
 
 
 @register_function(torch.linalg.slogdet)
-def slogdet(input):
+def linalg_slogdet(input):
   sign, logabsdet = jaten._aten__linalg_slogdet(input)
   return torch.return_types.slogdet((sign, logabsdet))
+
+
+@register_function(torch.tensor_split)
+def tensor_split(input, indices_or_sections, dim=0):
+  return jnp.array_split(input, indices_or_sections, axis=dim)