Refactor: ReduceOverDimListPlan

Now we can plan-then-execute for all reductions that use dim lists.

ghstack-source-id: 4e260c5
ghstack-comment-id: 2712063690
Pull Request resolved: pytorch/executorch#9109

Author: swolchok

kernels/portable/cpu/util/reduce_util.h

@@ -626,6 +626,28 @@ std::tuple<CTYPE, long> reduce_over_dim(
       [](CTYPE v) { return v; }, reduce_fun, in, dim, out_ix);
 }
 
+/**
+ * Execution plan for repeated reduce_over_dim_list with the same
+ * function, input tensor, and dim_list but varying out_ix.
+ */
+class ReduceOverDimListPlan {
+ public:
+  ReduceOverDimListPlan(
+      const executorch::aten::Tensor& in,
+      const executorch::aten::optional<executorch::aten::ArrayRef<int64_t>>&
+          dim_list)
+      : plan_(in, dim_list) {}
+
+  template <typename CTYPE, typename ReduceOp>
+  CTYPE execute(const ReduceOp& reduce_fun, const size_t out_ix) {
+    return plan_.execute<CTYPE, CTYPE>(
+        [](CTYPE v) { return v; }, reduce_fun, out_ix);
+  }
+
+ private:
+  MapReduceOverDimListPlan plan_;
+};
+
 /**
  * Useful to reduce a tensor `in` over a given list of dimensions `dim_list`
  * for the output element at index `out_ix` using the reduce function
@@ -652,8 +674,8 @@ CTYPE reduce_over_dim_list(
     const executorch::aten::optional<executorch::aten::ArrayRef<int64_t>>&
         dim_list,
     const size_t out_ix) {
-  return map_reduce_over_dim_list<CTYPE, CTYPE>(
-      [](CTYPE v) { return v; }, reduce_fun, in, dim_list, out_ix);
+  ReduceOverDimListPlan plan(in, dim_list);
+  return plan.execute<CTYPE>(reduce_fun, out_ix);
 }
 
 //