remove unneeded overload for nansum

Per title, fixes a few OpInfo skips

Pull Request resolved: https://github.com/pytorch/pytorch/pull/76356
Approved by: https://github.com/albanD, https://github.com/mruberry

aten/src/ATen/native/ReduceOps.cpp

@@ -1082,10 +1082,6 @@ Tensor& nansum_out(const Tensor& self, IntArrayRef dim,
   return result;
 }
 
-Tensor nansum(const Tensor &self, c10::optional<ScalarType> dtype) {
-  return at::native::nansum(self, std::vector<int64_t>{}, false, dtype);
-}
-
 Tensor nansum(const Tensor& self, IntArrayRef dim, bool keepdim, c10::optional<ScalarType> opt_dtype) {
   ScalarType dtype = get_dtype_from_self(self, opt_dtype, true);
   Tensor result = create_reduction_result(self, dim, keepdim, dtype);

aten/src/ATen/native/native_functions.yaml

@@ -4359,17 +4359,12 @@
 - func: sum.DimnameList_out(Tensor self, Dimname[1] dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)
   device_check: NoCheck   # TensorIterator
 
-- func: nansum(Tensor self, *, ScalarType? dtype=None) -> Tensor
+- func: nansum(Tensor self, int[1] dim=[], bool keepdim=False, *, ScalarType? dtype=None) -> Tensor
   variants: function, method
   dispatch:
     CPU, CUDA: nansum
 
-- func: nansum.dim_IntList(Tensor self, int[1] dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor
-  variants: function, method
-  dispatch:
-    CPU, CUDA: nansum
-
-- func: nansum.IntList_out(Tensor self, int[1] dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)
+- func: nansum.out(Tensor self, int[1] dim=[], bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)
   dispatch:
     CPU, CUDA: nansum_out
 

test/forward_backward_compatibility/check_forward_backward_compatibility.py

@@ -116,6 +116,7 @@ ALLOW_LIST = [
     ("prim::infer_squeeze_size.dim", datetime.date(9999, 1, 1)),
     ("prim::infer_squeeze_size", datetime.date(9999, 1, 1)),
     ("aten::_cat", datetime.date(2022, 5, 15)),
+    ("aten::nansum", datetime.date(2022, 5, 15)),
     ("aten::zero", datetime.date(2022, 5, 15)),
 ]
 

tools/autograd/derivatives.yaml

@@ -1495,10 +1495,7 @@
   self: sum_backward(grad, self.sizes(), dim, keepdim)
   result: auto_linear
 
-- name: nansum(Tensor self, *, ScalarType? dtype=None) -> Tensor
-  self: grad.expand(self.sizes()).to(self.scalar_type()) * self.isnan().logical_not()
-
-- name: nansum.dim_IntList(Tensor self, int[1] dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensor
+- name: nansum(Tensor self, int[1] dim=[], bool keepdim=False, *, ScalarType? dtype=None) -> Tensor
   self: nansum_backward(grad.to(self.scalar_type()), self, dim, keepdim)
 
 # We never call _linalg_svd with compute_uv=False in an autograd context, so we don't even consider it here

torch/testing/_internal/common_methods_invocations.py

@@ -16221,24 +16221,18 @@ op_db: List[OpInfo] = [
         'nansum',
         identity=0,
         nan_policy='omit',
-        supports_out=False,
+        supports_out=True,
         promotes_int_to_int64=True,
         dtypes=all_types_and(torch.bool, torch.float16, torch.bfloat16),
         ref=reference_reduction_numpy(np.nansum),
         skips=(
-            # FIXME: nansum does not support passing keepdim without passing dim
-            DecorateInfo(unittest.skip("Skipped!"), 'TestReductions', 'test_dim_default_keepdim'),
             # FIXME: nansum reduces all dimensions when dim=[]
-            DecorateInfo(unittest.skip("Skipped!"), 'TestReductions', 'test_dim_empty'),
-            DecorateInfo(unittest.skip("Skipped!"), 'TestReductions', 'test_dim_empty_keepdim'),
-            # FIXME: nansum does not support passing None to dim
-            DecorateInfo(unittest.skip("Skipped!"), 'TestReductions', 'test_dim_none'),
-            DecorateInfo(unittest.skip("Skipped!"), 'TestReductions', 'test_dim_none_keepdim'),
-            # FIXME: improve precision
+            DecorateInfo(unittest.expectedFailure, 'TestReductions', 'test_dim_empty'),
+            DecorateInfo(unittest.expectedFailure, 'TestReductions', 'test_dim_empty_keepdim'),
+            # FIXME: flaky test so skipped instead of xfailed
+            # possibly bad low precision reference in numpy
             DecorateInfo(unittest.skip("Skipped!"), 'TestReductions', 'test_ref_small_input',
                          dtypes=[torch.float16]),
-            DecorateInfo(unittest.skip("Skipped!"), 'TestReductions', 'test_ref_duplicate_values',
-                         dtypes=[torch.float16]),
         ),
     ),
     ReductionOpInfo(