Refactor reduction logic for fixed-point types (#12652)

Reworks some internal source specific to fixed-point types using `cudf::reduce` by removing the duplicated code logic. This was found while working on #12573 and #10432. Since the fix is requires no dependencies, this separate PR is used to minimize code review churn. This should help with code consistency with the fixed-point-specific logic when added to segmented-reduction.
No function has changed so all existing gtests are adequate.

Authors:
  - David Wendt (https://github.com/davidwendt)

Approvers:
  - Yunsong Wang (https://github.com/PointKernel)
  - Bradley Dice (https://github.com/bdice)

URL: #12652

cpp/src/reductions/simple.cuh

@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019-2022, NVIDIA CORPORATION.
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -115,37 +115,12 @@ std::unique_ptr<scalar> fixed_point_reduction(
 {
   using Type = device_storage_type_t<DecimalXX>;
 
-  auto dcol      = cudf::column_device_view::create(col, stream);
-  auto simple_op = Op{};
-
-  // Cast initial value
-  std::optional<Type> const initial_value = [&] {
-    if (init.has_value() && init.value().get().is_valid()) {
-      using ScalarType = cudf::scalar_type_t<Type>;
-      return std::optional<Type>(
-        static_cast<const ScalarType*>(&init.value().get())->value(stream));
-    } else {
-      return std::optional<Type>(std::nullopt);
-    }
-  }();
-
-  auto result = [&] {
-    if (col.has_nulls()) {
-      auto f  = simple_op.template get_null_replacing_element_transformer<Type>();
-      auto it = thrust::make_transform_iterator(dcol->pair_begin<Type, true>(), f);
-      return cudf::reduction::detail::reduce(it, col.size(), simple_op, initial_value, stream, mr);
-    } else {
-      auto f  = simple_op.template get_element_transformer<Type>();
-      auto it = thrust::make_transform_iterator(dcol->begin<Type>(), f);
-      return cudf::reduction::detail::reduce(it, col.size(), simple_op, initial_value, stream, mr);
-    }
-  }();
+  auto result = simple_reduction<Type, Type, Op>(col, init, stream, mr);
 
   auto const scale = [&] {
     if (std::is_same_v<Op, cudf::reduction::op::product>) {
       auto const valid_count = static_cast<int32_t>(col.size() - col.null_count());
-      return numeric::scale_type{col.type().scale() *
-                                 (valid_count + (initial_value.has_value() ? 1 : 0))};
+      return numeric::scale_type{col.type().scale() * (valid_count + (init.has_value() ? 1 : 0))};
     } else if (std::is_same_v<Op, cudf::reduction::op::sum_of_squares>) {
       return numeric::scale_type{col.type().scale() * 2};
     }

cpp/tests/reductions/reduction_tests.cpp

@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019-2022, NVIDIA CORPORATION.
+ * Copyright (c) 2019-2023, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -294,7 +294,6 @@ TYPED_TEST(SumReductionTest, Sum)
                  .second);
 }
 
-using ReductionTypes = cudf::test::Types<int16_t, int32_t, float, double>;
 TYPED_TEST_SUITE(ReductionTest, cudf::test::NumericTypes);
 
 TYPED_TEST(ReductionTest, Product)