Use structured bindings instead of std::tie (#10726)

Addresses part of #10350 Take advantage of C++17 feature structured bindings

There are few usages where `std::tie` is used on existing variables and for ordered operator== comparisons. These usages are not replaced.

Authors:
  - Karthikeyan (https://github.com/karthikeyann)

Approvers:
  - Nghia Truong (https://github.com/ttnghia)
  - Conor Hoekstra (https://github.com/codereport)

URL: #10726

cpp/include/cudf_test/column_wrapper.hpp

@@ -702,13 +702,11 @@ class strings_column_wrapper : public detail::column_wrapper {
   template <typename StringsIterator>
   strings_column_wrapper(StringsIterator begin, StringsIterator end) : column_wrapper{}
   {
-    std::vector<char> chars;
-    std::vector<cudf::size_type> offsets;
-    auto all_valid           = thrust::make_constant_iterator(true);
-    std::tie(chars, offsets) = detail::make_chars_and_offsets(begin, end, all_valid);
-    auto d_chars             = cudf::detail::make_device_uvector_sync(chars);
-    auto d_offsets           = cudf::detail::make_device_uvector_sync(offsets);
-    wrapped                  = cudf::make_strings_column(d_chars, d_offsets);
+    auto all_valid        = thrust::make_constant_iterator(true);
+    auto [chars, offsets] = detail::make_chars_and_offsets(begin, end, all_valid);
+    auto d_chars          = cudf::detail::make_device_uvector_sync(chars);
+    auto d_offsets        = cudf::detail::make_device_uvector_sync(offsets);
+    wrapped               = cudf::make_strings_column(d_chars, d_offsets);
   }
 
   /**
@@ -744,14 +742,12 @@ class strings_column_wrapper : public detail::column_wrapper {
     : column_wrapper{}
   {
     size_type num_strings = std::distance(begin, end);
-    std::vector<char> chars;
-    std::vector<size_type> offsets;
-    std::tie(chars, offsets) = detail::make_chars_and_offsets(begin, end, v);
-    auto null_mask           = detail::make_null_mask_vector(v, v + num_strings);
-    auto d_chars             = cudf::detail::make_device_uvector_sync(chars);
-    auto d_offsets           = cudf::detail::make_device_uvector_sync(offsets);
-    auto d_bitmask           = cudf::detail::make_device_uvector_sync(null_mask);
-    wrapped                  = cudf::make_strings_column(d_chars, d_offsets, d_bitmask);
+    auto [chars, offsets] = detail::make_chars_and_offsets(begin, end, v);
+    auto null_mask        = detail::make_null_mask_vector(v, v + num_strings);
+    auto d_chars          = cudf::detail::make_device_uvector_sync(chars);
+    auto d_offsets        = cudf::detail::make_device_uvector_sync(offsets);
+    auto d_bitmask        = cudf::detail::make_device_uvector_sync(null_mask);
+    wrapped               = cudf::make_strings_column(d_chars, d_offsets, d_bitmask);
   }
 
   /**
@@ -1479,9 +1475,7 @@ class lists_column_wrapper : public detail::column_wrapper {
     int32_t const expected_depth   = hierarchy_and_depth.second;
 
     // preprocess columns so that every column_view in 'cols' is an equivalent hierarchy
-    std::vector<std::unique_ptr<column>> stubs;
-    std::vector<column_view> cols;
-    std::tie(cols, stubs) = preprocess_columns(elements, expected_hierarchy, expected_depth);
+    auto [cols, stubs] = preprocess_columns(elements, expected_hierarchy, expected_depth);
 
     // generate offsets
     size_type count = 0;

cpp/src/io/parquet/page_enc.cu

@@ -1663,9 +1663,7 @@ dremel_data get_dremel_data(column_view h_col,
     }
   }
 
-  std::unique_ptr<rmm::device_buffer> device_view_owners;
-  column_device_view* d_nesting_levels;
-  std::tie(device_view_owners, d_nesting_levels) =
+  auto [device_view_owners, d_nesting_levels] =
     contiguous_copy_column_device_views<column_device_view>(nesting_levels, stream);
 
   thrust::exclusive_scan(
@@ -1735,10 +1733,7 @@ dremel_data get_dremel_data(column_view h_col,
     auto offset_size_at_level = column_ends[level] - column_offsets[level] + 1;
 
     // Get empties at this level
-    rmm::device_uvector<size_type> empties(0, stream);
-    rmm::device_uvector<size_type> empties_idx(0, stream);
-    size_t empties_size;
-    std::tie(empties, empties_idx, empties_size) =
+    auto [empties, empties_idx, empties_size] =
       get_empties(nesting_levels[level], column_offsets[level], column_ends[level]);
 
     // Merge empty at deepest parent level with the rep, def level vals at leaf level
@@ -1819,10 +1814,7 @@ dremel_data get_dremel_data(column_view h_col,
     auto offset_size_at_level = column_ends[level] - column_offsets[level] + 1;
 
     // Get empties at this level
-    rmm::device_uvector<size_type> empties(0, stream);
-    rmm::device_uvector<size_type> empties_idx(0, stream);
-    size_t empties_size;
-    std::tie(empties, empties_idx, empties_size) =
+    auto [empties, empties_idx, empties_size] =
       get_empties(nesting_levels[level], column_offsets[level], column_ends[level]);
 
     auto offset_transformer = [new_child_offsets = new_offsets.data(),

cpp/src/io/parquet/reader_impl.cu

@@ -1729,11 +1729,7 @@ table_with_metadata reader::impl::read(size_type skip_rows,
           continue;
         }
 
-        int32_t type_width;
-        int32_t clock_rate;
-        int8_t converted_type;
-
-        std::tie(type_width, clock_rate, converted_type) =
+        auto [type_width, clock_rate, converted_type] =
           conversion_info(to_type_id(schema, _strings_to_categorical, _timestamp_type.id()),
                           _timestamp_type.id(),
                           schema.type,

cpp/src/quantiles/quantile.cu

@@ -113,10 +113,7 @@ struct quantile_functor {
         ordered_indices,
         [input = *d_input] __device__(size_type idx) { return input.is_valid_nocheck(idx); });
 
-      rmm::device_buffer mask;
-      size_type null_count;
-
-      std::tie(mask, null_count) = valid_if(
+      auto [mask, null_count] = valid_if(
         q_device.begin(),
         q_device.end(),
         [sorted_validity, interp = interp, size = size] __device__(double q) {

cpp/src/reshape/interleave_columns.cu

@@ -258,10 +258,7 @@ struct interleave_columns_impl<T, std::enable_if_t<cudf::is_fixed_width<T>()>> {
                          func_value,
                          func_validity);
 
-    rmm::device_buffer mask;
-    size_type null_count;
-
-    std::tie(mask, null_count) = valid_if(index_begin, index_end, func_validity, stream, mr);
+    auto [mask, null_count] = valid_if(index_begin, index_end, func_validity, stream, mr);
 
     output->set_null_mask(std::move(mask), null_count);
 

cpp/src/rolling/rolling_collect_list.cuh

@@ -207,9 +207,7 @@ std::unique_ptr<column> rolling_collect_list(column_view const& input,
                                             stream,
                                             mr);
 
-  rmm::device_buffer null_mask;
-  size_type null_count;
-  std::tie(null_mask, null_count) = valid_if(
+  auto [null_mask, null_count] = valid_if(
     thrust::make_counting_iterator<size_type>(0),
     thrust::make_counting_iterator<size_type>(input.size()),
     [preceding_begin, following_begin, min_periods] __device__(auto i) {

cpp/src/strings/copying/concatenate.cu

@@ -72,9 +72,7 @@ auto create_strings_device_views(host_span<column_view const> views, rmm::cuda_s
 {
   CUDF_FUNC_RANGE();
   // Assemble contiguous array of device views
-  std::unique_ptr<rmm::device_buffer> device_view_owners;
-  column_device_view* device_views_ptr;
-  std::tie(device_view_owners, device_views_ptr) =
+  auto [device_view_owners, device_views_ptr] =
     contiguous_copy_column_device_views<column_device_view>(views, stream);
 
   // Compute the partition offsets and size of offset column

cpp/tests/partitioning/hash_partition_test.cpp

@@ -67,9 +67,7 @@ TEST_F(HashPartition, ZeroPartitions)
   auto columns_to_hash = std::vector<cudf::size_type>({2});
 
   cudf::size_type const num_partitions = 0;
-  std::unique_ptr<cudf::table> output;
-  std::vector<cudf::size_type> offsets;
-  std::tie(output, offsets) = cudf::hash_partition(input, columns_to_hash, num_partitions);
+  auto [output, offsets] = cudf::hash_partition(input, columns_to_hash, num_partitions);
 
   // Expect empty table with same number of columns and zero partitions
   EXPECT_EQ(input.num_columns(), output->num_columns());
@@ -87,9 +85,7 @@ TEST_F(HashPartition, ZeroRows)
   auto columns_to_hash = std::vector<cudf::size_type>({2});
 
   cudf::size_type const num_partitions = 3;
-  std::unique_ptr<cudf::table> output;
-  std::vector<cudf::size_type> offsets;
-  std::tie(output, offsets) = cudf::hash_partition(input, columns_to_hash, num_partitions);
+  auto [output, offsets] = cudf::hash_partition(input, columns_to_hash, num_partitions);
 
   // Expect empty table with same number of columns and zero partitions
   EXPECT_EQ(input.num_columns(), output->num_columns());
@@ -104,9 +100,7 @@ TEST_F(HashPartition, ZeroColumns)
   auto columns_to_hash = std::vector<cudf::size_type>({});
 
   cudf::size_type const num_partitions = 3;
-  std::unique_ptr<cudf::table> output;
-  std::vector<cudf::size_type> offsets;
-  std::tie(output, offsets) = cudf::hash_partition(input, columns_to_hash, num_partitions);
+  auto [output, offsets] = cudf::hash_partition(input, columns_to_hash, num_partitions);
 
   // Expect empty table with same number of columns and zero partitions
   EXPECT_EQ(input.num_columns(), output->num_columns());
@@ -124,10 +118,8 @@ TEST_F(HashPartition, MixedColumnTypes)
   auto columns_to_hash = std::vector<cudf::size_type>({0, 2});
 
   cudf::size_type const num_partitions = 3;
-  std::unique_ptr<cudf::table> output1, output2;
-  std::vector<cudf::size_type> offsets1, offsets2;
-  std::tie(output1, offsets1) = cudf::hash_partition(input, columns_to_hash, num_partitions);
-  std::tie(output2, offsets2) = cudf::hash_partition(input, columns_to_hash, num_partitions);
+  auto [output1, offsets1] = cudf::hash_partition(input, columns_to_hash, num_partitions);
+  auto [output2, offsets2] = cudf::hash_partition(input, columns_to_hash, num_partitions);
 
   // Expect output to have size num_partitions
   EXPECT_EQ(static_cast<size_t>(num_partitions), offsets1.size());
@@ -148,9 +140,7 @@ TEST_F(HashPartition, NullableStrings)
   std::vector<cudf::size_type> const columns_to_hash({0});
   cudf::size_type const num_partitions = 3;
 
-  std::unique_ptr<cudf::table> result;
-  std::vector<cudf::size_type> offsets;
-  std::tie(result, offsets) = cudf::hash_partition(input, columns_to_hash, num_partitions);
+  auto [result, offsets] = cudf::hash_partition(input, columns_to_hash, num_partitions);
 
   auto const& col = result->get_column(0);
   EXPECT_EQ(0, col.null_count());
@@ -167,11 +157,9 @@ TEST_F(HashPartition, ColumnsToHash)
   auto columns_to_hash = std::vector<cudf::size_type>({0});
 
   cudf::size_type const num_partitions = 3;
-  std::unique_ptr<cudf::table> first_result, second_result;
-  std::vector<cudf::size_type> first_offsets, second_offsets;
-  std::tie(first_result, first_offsets) =
+  auto [first_result, first_offsets] =
     cudf::hash_partition(first_input, columns_to_hash, num_partitions);
-  std::tie(second_result, second_offsets) =
+  auto [second_result, second_offsets] =
     cudf::hash_partition(second_input, columns_to_hash, num_partitions);
 
   // Expect offsets to be equal and num_partitions in length
@@ -228,11 +216,9 @@ TEST_F(HashPartition, CustomSeedValue)
   auto columns_to_hash = std::vector<cudf::size_type>({0, 2});
 
   cudf::size_type const num_partitions = 3;
-  std::unique_ptr<cudf::table> output1, output2;
-  std::vector<cudf::size_type> offsets1, offsets2;
-  std::tie(output1, offsets1) = cudf::hash_partition(
+  auto [output1, offsets1]             = cudf::hash_partition(
     input, columns_to_hash, num_partitions, cudf::hash_id::HASH_MURMUR3, 12345);
-  std::tie(output2, offsets2) = cudf::hash_partition(
+  auto [output2, offsets2] = cudf::hash_partition(
     input, columns_to_hash, num_partitions, cudf::hash_id::HASH_MURMUR3, 12345);
 
   // Expect output to have size num_partitions
@@ -260,9 +246,7 @@ TYPED_TEST(HashPartitionFixedWidth, NullableFixedWidth)
   std::vector<cudf::size_type> const columns_to_hash({0});
   cudf::size_type const num_partitions = 3;
 
-  std::unique_ptr<cudf::table> result;
-  std::vector<cudf::size_type> offsets;
-  std::tie(result, offsets) = cudf::hash_partition(input, columns_to_hash, num_partitions);
+  auto [result, offsets] = cudf::hash_partition(input, columns_to_hash, num_partitions);
 
   auto const& col = result->get_column(0);
   EXPECT_EQ(0, col.null_count());
@@ -294,10 +278,8 @@ void run_fixed_width_test(size_t cols,
   auto columns_to_hash = std::vector<cudf::size_type>(cols);
   std::iota(columns_to_hash.begin(), columns_to_hash.end(), 0);
 
-  std::unique_ptr<cudf::table> output1, output2;
-  std::vector<cudf::size_type> offsets1, offsets2;
-  std::tie(output1, offsets1) = cudf::hash_partition(input, columns_to_hash, num_partitions);
-  std::tie(output2, offsets2) = cudf::hash_partition(input, columns_to_hash, num_partitions);
+  auto [output1, offsets1] = cudf::hash_partition(input, columns_to_hash, num_partitions);
+  auto [output2, offsets2] = cudf::hash_partition(input, columns_to_hash, num_partitions);
 
   // Expect output to have size num_partitions
   EXPECT_EQ(static_cast<size_t>(num_partitions), offsets1.size());
@@ -367,9 +349,7 @@ TEST_F(HashPartition, FixedPointColumnsToHash)
   auto columns_to_hash = std::vector<cudf::size_type>({0});
 
   cudf::size_type const num_partitions = 1;
-  std::unique_ptr<cudf::table> first_result;
-  std::vector<cudf::size_type> first_offsets;
-  std::tie(first_result, first_offsets) =
+  auto [first_result, first_offsets] =
     cudf::hash_partition(first_input, columns_to_hash, num_partitions);
 
   // Expect offsets to be equal and num_partitions in length

cpp/tests/rolling/grouped_rolling_test.cpp

@@ -340,10 +340,8 @@ class GroupedRollingTest : public cudf::test::BaseFixture {
     thrust::host_vector<bool> ref_valid(num_rows);
 
     // input data and mask
-    thrust::host_vector<T> in_col;
-    std::vector<bitmask_type> in_valid;
-    std::tie(in_col, in_valid) = cudf::test::to_host<T>(input);
-    bitmask_type* valid_mask   = in_valid.data();
+    auto [in_col, in_valid]  = cudf::test::to_host<T>(input);
+    bitmask_type* valid_mask = in_valid.data();
 
     agg_op op;
     for (size_type i = 0; i < num_rows; i++) {
@@ -973,10 +971,8 @@ class GroupedTimeRangeRollingTest : public cudf::test::BaseFixture {
     thrust::host_vector<bool> ref_valid(num_rows);
 
     // input data and mask
-    thrust::host_vector<T> in_col;
-    std::vector<bitmask_type> in_valid;
-    std::tie(in_col, in_valid) = cudf::test::to_host<T>(input);
-    bitmask_type* valid_mask   = in_valid.data();
+    auto [in_col, in_valid]  = cudf::test::to_host<T>(input);
+    bitmask_type* valid_mask = in_valid.data();
 
     agg_op op;
     for (size_type i = 0; i < num_rows; i++) {

cpp/tests/rolling/rolling_test.cpp

@@ -552,10 +552,8 @@ class RollingTest : public cudf::test::BaseFixture {
     thrust::host_vector<bool> ref_valid(num_rows);
 
     // input data and mask
-    thrust::host_vector<T> in_col;
-    std::vector<bitmask_type> in_valid;
-    std::tie(in_col, in_valid) = cudf::test::to_host<T>(input);
-    bitmask_type* valid_mask   = in_valid.data();
+    auto [in_col, in_valid]  = cudf::test::to_host<T>(input);
+    bitmask_type* valid_mask = in_valid.data();
 
     agg_op op;
     for (size_type i = 0; i < num_rows; i++) {

cpp/tests/sort/rank_test.cpp

@@ -91,8 +91,7 @@ struct Rank : public BaseFixture {
            test_case_t{table_view{{col1, col2, col3}},
                        table_view{{col1_rank, col2_rank, col3_rank}}},
          }) {
-      table_view input, output;
-      std::tie(input, output) = test_case;
+      auto [input, output] = test_case;
 
       run_rank_test(input,
                     output,

cpp/tests/transform/row_bit_count_test.cu

@@ -123,9 +123,7 @@ TYPED_TEST(RowBitCountTyped, Lists)
 {
   using T = TypeParam;
 
-  std::unique_ptr<column> col;
-  std::unique_ptr<column> expected_sizes;
-  std::tie(col, expected_sizes) = build_list_column<T>();
+  auto [col, expected_sizes] = build_list_column<T>();
 
   table_view t({*col});
   auto result = cudf::row_bit_count(t);
@@ -326,9 +324,7 @@ TEST_F(RowBitCount, StructsNoNulls)
 
 TEST_F(RowBitCount, StructsNulls)
 {
-  std::unique_ptr<column> struct_col;
-  std::unique_ptr<column> expected_sizes;
-  std::tie(struct_col, expected_sizes) = build_struct_column();
+  auto [struct_col, expected_sizes] = build_struct_column();
   table_view t({*struct_col});
   auto result = cudf::row_bit_count(t);
 
@@ -440,9 +436,7 @@ TEST_F(RowBitCount, NestedTypes)
 {
   // List<Struct<List<int>, float, List<int>, int16>
   {
-    std::unique_ptr<column> col_no_nulls;
-    std::unique_ptr<column> expected_sizes;
-    std::tie(col_no_nulls, expected_sizes) =
+    auto [col_no_nulls, expected_sizes] =
       build_nested_and_expected_column({1, 1, 1, 1, 1, 1, 1, 1});
     table_view no_nulls_t({*col_no_nulls});
     auto no_nulls_result = cudf::row_bit_count(no_nulls_t);
@@ -600,19 +594,13 @@ struct sum_functor {
 TEST_F(RowBitCount, Table)
 {
   // complex nested column
-  std::unique_ptr<column> col0;
-  std::unique_ptr<column> col0_sizes;
-  std::tie(col0, col0_sizes) = build_nested_and_expected_column({1, 1, 1, 1, 1, 1, 1, 1});
+  auto [col0, col0_sizes] = build_nested_and_expected_column({1, 1, 1, 1, 1, 1, 1, 1});
 
   // struct column
-  std::unique_ptr<column> col1;
-  std::unique_ptr<column> col1_sizes;
-  std::tie(col1, col1_sizes) = build_struct_column();
+  auto [col1, col1_sizes] = build_struct_column();
 
   // list column
-  std::unique_ptr<column> col2;
-  std::unique_ptr<column> col2_sizes;
-  std::tie(col2, col2_sizes) = build_list_column<int16_t>();
+  auto [col2, col2_sizes] = build_list_column<int16_t>();
 
   table_view t({*col0, *col1, *col2});
   auto result = cudf::row_bit_count(t);

cpp/tests/unary/cast_tests.cpp

@@ -174,9 +174,7 @@ void validate_cast_result(cudf::column_view expected, cudf::column_view actual)
 {
   using namespace cudf::test;
   // round-trip through the host because sizeof(T) may not equal sizeof(R)
-  thrust::host_vector<T> h_data;
-  std::vector<cudf::bitmask_type> null_mask;
-  std::tie(h_data, null_mask) = to_host<T>(expected);
+  auto [h_data, null_mask] = to_host<T>(expected);
   if (null_mask.empty()) {
     CUDF_TEST_EXPECT_COLUMNS_EQUAL(make_column<R, T>(h_data), actual);
   } else {