[PyTorch] Avoid extra Tensor refcounting in _cat_out_cpu

We had a local `Tensor` when we only needed a `const Tensor&`.

Differential Revision: [D25544731](https://our.internmc.facebook.com/intern/diff/D25544731/)

**NOTE FOR REVIEWERS**: This PR has internal Facebook specific changes or comments, please review them on [Phabricator](https://our.internmc.facebook.com/intern/diff/D25544731/)!

ghstack-source-id: 118559137
Pull Request resolved: #49364

aten/src/ATen/native/TensorShape.cpp

@@ -101,6 +101,10 @@ static inline void check_cat_shape_except_dim(const Tensor & first, const Tensor
   }
 }
 
+static bool should_skip(const Tensor& t) {
+  return t.numel() == 0 && t.dim() == 1;
+}
+
 Tensor & _cat_out_cpu(Tensor& result, TensorList tensors, int64_t dim) {
   // previously, size [0] tensors were the only possible empty tensors; thus, it wasn't possible
   // to cat empty tensors unless all the other tensors were 1-dimensional, so we allowed these tensors
@@ -109,7 +113,6 @@ Tensor & _cat_out_cpu(Tensor& result, TensorList tensors, int64_t dim) {
   // FIXME: warn if this is the case
   bool allSkipped = true;
   bool allContiguous = true;
-  Tensor notSkippedTensor;
 
   // Inputs cannot alias the output tensor
   for (int64_t i = 0; i < tensors.size(); i++) {
@@ -121,19 +124,21 @@ Tensor & _cat_out_cpu(Tensor& result, TensorList tensors, int64_t dim) {
   }
   at::assert_no_internal_overlap(result);
 
-  auto should_skip = [](const Tensor& t) { return t.numel() == 0 && t.dim() == 1; };
-  for (auto const &tensor : tensors) {
-    if (should_skip(tensor)) {
-      continue;
+  const Tensor* pnotSkippedTensor = [](TensorList tensors) -> const Tensor* {
+    for (auto const &tensor : tensors) {
+      if (should_skip(tensor)) {
+        continue;
+      }
+      // we've found a non-empty tensor
+      return &tensor;
     }
-    // we've found a non-empty tensor
-    allSkipped = false;
-    notSkippedTensor = tensor;
-    break;
-  }
-  if (allSkipped) {
+    return nullptr;
+  }(tensors);
+
+  if (!pnotSkippedTensor) {
     return result;
   }
+  const Tensor& notSkippedTensor = *pnotSkippedTensor;
 
   TORCH_CHECK(tensors.size() > 0, "expected a non-empty list of Tensors");
   TORCH_CHECK(dim <= notSkippedTensor.dim(), "dimension ", dim, "out of range");
@@ -191,7 +196,7 @@ Tensor & _cat_out_cpu(Tensor& result, TensorList tensors, int64_t dim) {
   if (reuse_iterator &&
       result.is_contiguous(first_tensor_mem_format) &&
       no_type_promotion) {
-    auto source_slice = notSkippedTensor;
+    const auto& source_slice = notSkippedTensor;
     auto slice_dim_size = source_slice.size(dim);
     auto result_slice = result.narrow(dim, 0, slice_dim_size);
     auto result_slice_data = result_slice.data_ptr();