groupby.cu

/*
 * 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.
 * You may obtain a copy of the License at
 *
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

#include <groupby/common/utils.hpp>
#include <groupby/hash/groupby_kernels.cuh>

#include <cudf/aggregation.hpp>
#include <cudf/column/column.hpp>
#include <cudf/column/column_factories.hpp>
#include <cudf/column/column_view.hpp>
#include <cudf/copying.hpp>
#include <cudf/detail/aggregation/aggregation.cuh>
#include <cudf/detail/aggregation/aggregation.hpp>
#include <cudf/detail/aggregation/result_cache.hpp>
#include <cudf/detail/binaryop.hpp>
#include <cudf/detail/gather.hpp>
#include <cudf/detail/groupby.hpp>
#include <cudf/detail/null_mask.hpp>
#include <cudf/detail/replace.hpp>
#include <cudf/detail/unary.hpp>
#include <cudf/detail/utilities/algorithm.cuh>
#include <cudf/detail/utilities/cuda.cuh>
#include <cudf/detail/utilities/hash_functions.cuh>
#include <cudf/detail/utilities/vector_factories.hpp>
#include <cudf/dictionary/dictionary_column_view.hpp>
#include <cudf/groupby.hpp>
#include <cudf/scalar/scalar.hpp>
#include <cudf/table/experimental/row_operators.cuh>
#include <cudf/table/table.hpp>
#include <cudf/table/table_device_view.cuh>
#include <cudf/table/table_view.hpp>
#include <cudf/types.hpp>
#include <cudf/utilities/traits.cuh>
#include <cudf/utilities/traits.hpp>
#include <hash/concurrent_unordered_map.cuh>

#include <rmm/cuda_stream_view.hpp>

#include <thrust/copy.h>
#include <thrust/for_each.h>
#include <thrust/iterator/counting_iterator.h>
#include <thrust/iterator/transform_iterator.h>

#include <memory>
#include <unordered_set>
#include <utility>

#include <cuda/std/atomic>

namespace cudf {
namespace groupby {
namespace detail {
namespace hash {
namespace {

// TODO: replace it with `cuco::static_map`
// https://github.com/rapidsai/cudf/issues/10401
template <typename ComparatorType>
using map_type =
  concurrent_unordered_map<cudf::size_type,
                           cudf::size_type,
                           cudf::experimental::row::hash::
                             device_row_hasher<cudf::detail::default_hash, cudf::nullate::DYNAMIC>,
                           ComparatorType>;

/**
 * @brief List of aggregation operations that can be computed with a hash-based
 * implementation.
 */
constexpr std::array<aggregation::Kind, 12> hash_aggregations{aggregation::SUM,
                                                              aggregation::PRODUCT,
                                                              aggregation::MIN,
                                                              aggregation::MAX,
                                                              aggregation::COUNT_VALID,
                                                              aggregation::COUNT_ALL,
                                                              aggregation::ARGMIN,
                                                              aggregation::ARGMAX,
                                                              aggregation::SUM_OF_SQUARES,
                                                              aggregation::MEAN,
                                                              aggregation::STD,
                                                              aggregation::VARIANCE};

// Could be hash: SUM, PRODUCT, MIN, MAX, COUNT_VALID, COUNT_ALL, ANY, ALL,
// Compound: MEAN(SUM, COUNT_VALID), VARIANCE, STD(MEAN (SUM, COUNT_VALID), COUNT_VALID),
// ARGMAX, ARGMIN

// TODO replace with std::find in C++20 onwards.
template <class T, size_t N>
constexpr bool array_contains(std::array<T, N> const& haystack, T needle)
{
  for (auto const& val : haystack) {
    if (val == needle) return true;
  }
  return false;
}

/**
 * @brief Indicates whether the specified aggregation operation can be computed
 * with a hash-based implementation.
 *
 * @param t The aggregation operation to verify
 * @return true `t` is valid for a hash based groupby
 * @return false `t` is invalid for a hash based groupby
 */
bool constexpr is_hash_aggregation(aggregation::Kind t)
{
  return array_contains(hash_aggregations, t);
}

class groupby_simple_aggregations_collector final
  : public cudf::detail::simple_aggregations_collector {
 public:
  using cudf::detail::simple_aggregations_collector::visit;

  std::vector<std::unique_ptr<aggregation>> visit(data_type col_type,
                                                  cudf::detail::min_aggregation const&) override
  {
    std::vector<std::unique_ptr<aggregation>> aggs;
    aggs.push_back(col_type.id() == type_id::STRING ? make_argmin_aggregation()
                                                    : make_min_aggregation());
    return aggs;
  }

  std::vector<std::unique_ptr<aggregation>> visit(data_type col_type,
                                                  cudf::detail::max_aggregation const&) override
  {
    std::vector<std::unique_ptr<aggregation>> aggs;
    aggs.push_back(col_type.id() == type_id::STRING ? make_argmax_aggregation()
                                                    : make_max_aggregation());
    return aggs;
  }

  std::vector<std::unique_ptr<aggregation>> visit(data_type col_type,
                                                  cudf::detail::mean_aggregation const&) override
  {
    (void)col_type;
    CUDF_EXPECTS(is_fixed_width(col_type), "MEAN aggregation expects fixed width type");
    std::vector<std::unique_ptr<aggregation>> aggs;
    aggs.push_back(make_sum_aggregation());
    // COUNT_VALID
    aggs.push_back(make_count_aggregation());

    return aggs;
  }

  std::vector<std::unique_ptr<aggregation>> visit(data_type,
                                                  cudf::detail::var_aggregation const&) override
  {
    std::vector<std::unique_ptr<aggregation>> aggs;
    aggs.push_back(make_sum_aggregation());
    // COUNT_VALID
    aggs.push_back(make_count_aggregation());

    return aggs;
  }

  std::vector<std::unique_ptr<aggregation>> visit(data_type,
                                                  cudf::detail::std_aggregation const&) override
  {
    std::vector<std::unique_ptr<aggregation>> aggs;
    aggs.push_back(make_sum_aggregation());
    // COUNT_VALID
    aggs.push_back(make_count_aggregation());

    return aggs;
  }

  std::vector<std::unique_ptr<aggregation>> visit(
    data_type, cudf::detail::correlation_aggregation const&) override
  {
    std::vector<std::unique_ptr<aggregation>> aggs;
    aggs.push_back(make_sum_aggregation());
    // COUNT_VALID
    aggs.push_back(make_count_aggregation());

    return aggs;
  }
};

template <typename ComparatorType>
class hash_compound_agg_finalizer final : public cudf::detail::aggregation_finalizer {
  column_view col;
  data_type result_type;
  cudf::detail::result_cache* sparse_results;
  cudf::detail::result_cache* dense_results;
  device_span<size_type const> gather_map;
  map_type<ComparatorType> const& map;
  bitmask_type const* __restrict__ row_bitmask;
  rmm::cuda_stream_view stream;
  rmm::mr::device_memory_resource* mr;

 public:
  using cudf::detail::aggregation_finalizer::visit;

  hash_compound_agg_finalizer(column_view col,
                              cudf::detail::result_cache* sparse_results,
                              cudf::detail::result_cache* dense_results,
                              device_span<size_type const> gather_map,
                              map_type<ComparatorType> const& map,
                              bitmask_type const* row_bitmask,
                              rmm::cuda_stream_view stream,
                              rmm::mr::device_memory_resource* mr)
    : col(col),
      sparse_results(sparse_results),
      dense_results(dense_results),
      gather_map(gather_map),
      map(map),
      row_bitmask(row_bitmask),
      stream(stream),
      mr(mr)
  {
    result_type = cudf::is_dictionary(col.type()) ? cudf::dictionary_column_view(col).keys().type()
                                                  : col.type();
  }

  auto to_dense_agg_result(cudf::aggregation const& agg)
  {
    auto s                  = sparse_results->get_result(col, agg);
    auto dense_result_table = cudf::detail::gather(table_view({std::move(s)}),
                                                   gather_map,
                                                   out_of_bounds_policy::DONT_CHECK,
                                                   cudf::detail::negative_index_policy::NOT_ALLOWED,
                                                   stream,
                                                   mr);
    return std::move(dense_result_table->release()[0]);
  }

  // Enables conversion of ARGMIN/ARGMAX into MIN/MAX
  auto gather_argminmax(aggregation const& agg)
  {
    auto arg_result = to_dense_agg_result(agg);
    // We make a view of ARG(MIN/MAX) result without a null mask and gather
    // using this map. The values in data buffer of ARG(MIN/MAX) result
    // corresponding to null values was initialized to ARG(MIN/MAX)_SENTINEL
    // which is an out of bounds index value (-1) and causes the gathered
    // value to be null.
    column_view null_removed_map(
      data_type(type_to_id<size_type>()),
      arg_result->size(),
      static_cast<void const*>(arg_result->view().template data<size_type>()),
      nullptr,
      0);
    auto gather_argminmax =
      cudf::detail::gather(table_view({col}),
                           null_removed_map,
                           arg_result->nullable() ? cudf::out_of_bounds_policy::NULLIFY
                                                  : cudf::out_of_bounds_policy::DONT_CHECK,
                           cudf::detail::negative_index_policy::NOT_ALLOWED,
                           stream,
                           mr);
    return std::move(gather_argminmax->release()[0]);
  }

  // Declare overloads for each kind of aggregation to dispatch
  void visit(cudf::aggregation const& agg) override
  {
    if (dense_results->has_result(col, agg)) return;
    dense_results->add_result(col, agg, to_dense_agg_result(agg));
  }

  void visit(cudf::detail::min_aggregation const& agg) override
  {
    if (dense_results->has_result(col, agg)) return;
    if (result_type.id() == type_id::STRING) {
      auto transformed_agg = make_argmin_aggregation();
      dense_results->add_result(col, agg, gather_argminmax(*transformed_agg));
    } else {
      dense_results->add_result(col, agg, to_dense_agg_result(agg));
    }
  }

  void visit(cudf::detail::max_aggregation const& agg) override
  {
    if (dense_results->has_result(col, agg)) return;

    if (result_type.id() == type_id::STRING) {
      auto transformed_agg = make_argmax_aggregation();
      dense_results->add_result(col, agg, gather_argminmax(*transformed_agg));
    } else {
      dense_results->add_result(col, agg, to_dense_agg_result(agg));
    }
  }

  void visit(cudf::detail::mean_aggregation const& agg) override
  {
    if (dense_results->has_result(col, agg)) return;

    auto sum_agg   = make_sum_aggregation();
    auto count_agg = make_count_aggregation();
    this->visit(*sum_agg);
    this->visit(*count_agg);
    column_view sum_result   = dense_results->get_result(col, *sum_agg);
    column_view count_result = dense_results->get_result(col, *count_agg);

    auto result =
      cudf::detail::binary_operation(sum_result,
                                     count_result,
                                     binary_operator::DIV,
                                     cudf::detail::target_type(result_type, aggregation::MEAN),
                                     stream,
                                     mr);
    dense_results->add_result(col, agg, std::move(result));
  }

  void visit(cudf::detail::var_aggregation const& agg) override
  {
    if (dense_results->has_result(col, agg)) return;

    auto sum_agg   = make_sum_aggregation();
    auto count_agg = make_count_aggregation();
    this->visit(*sum_agg);
    this->visit(*count_agg);
    column_view sum_result   = sparse_results->get_result(col, *sum_agg);
    column_view count_result = sparse_results->get_result(col, *count_agg);

    auto values_view = column_device_view::create(col, stream);
    auto sum_view    = column_device_view::create(sum_result, stream);
    auto count_view  = column_device_view::create(count_result, stream);

    auto var_result = make_fixed_width_column(
      cudf::detail::target_type(result_type, agg.kind), col.size(), mask_state::ALL_NULL, stream);
    auto var_result_view = mutable_column_device_view::create(var_result->mutable_view(), stream);
    mutable_table_view var_table_view{{var_result->mutable_view()}};
    cudf::detail::initialize_with_identity(var_table_view, {agg.kind}, stream);

    thrust::for_each_n(
      rmm::exec_policy(stream),
      thrust::make_counting_iterator(0),
      col.size(),
      ::cudf::detail::var_hash_functor<map_type<ComparatorType>>{
        map, row_bitmask, *var_result_view, *values_view, *sum_view, *count_view, agg._ddof});
    sparse_results->add_result(col, agg, std::move(var_result));
    dense_results->add_result(col, agg, to_dense_agg_result(agg));
  }

  void visit(cudf::detail::std_aggregation const& agg) override
  {
    if (dense_results->has_result(col, agg)) return;
    auto var_agg = make_variance_aggregation(agg._ddof);
    this->visit(*dynamic_cast<cudf::detail::var_aggregation*>(var_agg.get()));
    column_view variance = dense_results->get_result(col, *var_agg);

    auto result = cudf::detail::unary_operation(variance, unary_operator::SQRT, stream, mr);
    dense_results->add_result(col, agg, std::move(result));
  }
};
// flatten aggs to filter in single pass aggs
std::tuple<table_view, std::vector<aggregation::Kind>, std::vector<std::unique_ptr<aggregation>>>
flatten_single_pass_aggs(host_span<aggregation_request const> requests)
{
  std::vector<column_view> columns;
  std::vector<std::unique_ptr<aggregation>> aggs;
  std::vector<aggregation::Kind> agg_kinds;

  for (auto const& request : requests) {
    auto const& agg_v = request.aggregations;

    std::unordered_set<aggregation::Kind> agg_kinds_set;
    auto insert_agg = [&](column_view const& request_values, std::unique_ptr<aggregation>&& agg) {
      if (agg_kinds_set.insert(agg->kind).second) {
        agg_kinds.push_back(agg->kind);
        aggs.push_back(std::move(agg));
        columns.push_back(request_values);
      }
    };

    auto values_type = cudf::is_dictionary(request.values.type())
                         ? cudf::dictionary_column_view(request.values).keys().type()
                         : request.values.type();
    for (auto&& agg : agg_v) {
      groupby_simple_aggregations_collector collector;

      for (auto& agg_s : agg->get_simple_aggregations(values_type, collector)) {
        insert_agg(request.values, std::move(agg_s));
      }
    }
  }

  return std::make_tuple(table_view(columns), std::move(agg_kinds), std::move(aggs));
}

/**
 * @brief Gather sparse results into dense using `gather_map` and add to
 * `dense_cache`
 *
 * @see groupby_null_templated()
 */
template <typename ComparatorType>
void sparse_to_dense_results(table_view const& keys,
                             host_span<aggregation_request const> requests,
                             cudf::detail::result_cache* sparse_results,
                             cudf::detail::result_cache* dense_results,
                             device_span<size_type const> gather_map,
                             map_type<ComparatorType> const& map,
                             bool keys_have_nulls,
                             null_policy include_null_keys,
                             rmm::cuda_stream_view stream,
                             rmm::mr::device_memory_resource* mr)
{
  auto row_bitmask = bitmask_and(keys, stream, rmm::mr::get_current_device_resource()).first;
  bool skip_key_rows_with_nulls = keys_have_nulls and include_null_keys == null_policy::EXCLUDE;
  bitmask_type const* row_bitmask_ptr =
    skip_key_rows_with_nulls ? static_cast<bitmask_type*>(row_bitmask.data()) : nullptr;

  for (auto const& request : requests) {
    auto const& agg_v = request.aggregations;
    auto const& col   = request.values;

    // Given an aggregation, this will get the result from sparse_results and
    // convert and return dense, compacted result
    auto finalizer = hash_compound_agg_finalizer(
      col, sparse_results, dense_results, gather_map, map, row_bitmask_ptr, stream, mr);
    for (auto&& agg : agg_v) {
      agg->finalize(finalizer);
    }
  }
}

// make table that will hold sparse results
auto create_sparse_results_table(table_view const& flattened_values,
                                 std::vector<aggregation::Kind> aggs,
                                 rmm::cuda_stream_view stream)
{
  // TODO single allocation - room for performance improvement
  std::vector<std::unique_ptr<column>> sparse_columns;
  std::transform(
    flattened_values.begin(),
    flattened_values.end(),
    aggs.begin(),
    std::back_inserter(sparse_columns),
    [stream](auto const& col, auto const& agg) {
      bool nullable =
        (agg == aggregation::COUNT_VALID or agg == aggregation::COUNT_ALL)
          ? false
          : (col.has_nulls() or agg == aggregation::VARIANCE or agg == aggregation::STD);
      auto mask_flag = (nullable) ? mask_state::ALL_NULL : mask_state::UNALLOCATED;

      auto col_type = cudf::is_dictionary(col.type())
                        ? cudf::dictionary_column_view(col).keys().type()
                        : col.type();

      return make_fixed_width_column(
        cudf::detail::target_type(col_type, agg), col.size(), mask_flag, stream);
    });

  table sparse_table(std::move(sparse_columns));
  mutable_table_view table_view = sparse_table.mutable_view();
  cudf::detail::initialize_with_identity(table_view, aggs, stream);
  return sparse_table;
}

/**
 * @brief Computes all aggregations from `requests` that require a single pass
 * over the data and stores the results in `sparse_results`
 */
template <typename ComparatorType>
void compute_single_pass_aggs(table_view const& keys,
                              host_span<aggregation_request const> requests,
                              cudf::detail::result_cache* sparse_results,
                              map_type<ComparatorType>& map,
                              bool keys_have_nulls,
                              null_policy include_null_keys,
                              rmm::cuda_stream_view stream)
{
  // flatten the aggs to a table that can be operated on by aggregate_row
  auto const [flattened_values, agg_kinds, aggs] = flatten_single_pass_aggs(requests);

  // make table that will hold sparse results
  table sparse_table = create_sparse_results_table(flattened_values, agg_kinds, stream);
  // prepare to launch kernel to do the actual aggregation
  auto d_sparse_table = mutable_table_device_view::create(sparse_table, stream);
  auto d_values       = table_device_view::create(flattened_values, stream);
  auto const d_aggs   = cudf::detail::make_device_uvector_async(
    agg_kinds, stream, rmm::mr::get_current_device_resource());
  auto const skip_key_rows_with_nulls =
    keys_have_nulls and include_null_keys == null_policy::EXCLUDE;

  auto row_bitmask =
    skip_key_rows_with_nulls
      ? cudf::detail::bitmask_and(keys, stream, rmm::mr::get_current_device_resource()).first
      : rmm::device_buffer{};

  thrust::for_each_n(rmm::exec_policy(stream),
                     thrust::make_counting_iterator(0),
                     keys.num_rows(),
                     hash::compute_single_pass_aggs_fn<map_type<ComparatorType>>{
                       map,
                       *d_values,
                       *d_sparse_table,
                       d_aggs.data(),
                       static_cast<bitmask_type*>(row_bitmask.data()),
                       skip_key_rows_with_nulls});
  // Add results back to sparse_results cache
  auto sparse_result_cols = sparse_table.release();
  for (size_t i = 0; i < aggs.size(); i++) {
    // Note that the cache will make a copy of this temporary aggregation
    sparse_results->add_result(
      flattened_values.column(i), *aggs[i], std::move(sparse_result_cols[i]));
  }
}

/**
 * @brief Computes and returns a device vector containing all populated keys in
 * `map`.
 */
template <typename ComparatorType>
rmm::device_uvector<size_type> extract_populated_keys(map_type<ComparatorType> const& map,
                                                      size_type num_keys,
                                                      rmm::cuda_stream_view stream)
{
  rmm::device_uvector<size_type> populated_keys(num_keys, stream);

  auto const get_key = [] __device__(auto const& element) { return element.first; };  // first = key
  auto const key_used = [unused = map.get_unused_key()] __device__(auto key) {
    return key != unused;
  };
  auto const key_itr = thrust::make_transform_iterator(map.data(), get_key);
  auto const end_it  = cudf::detail::copy_if_safe(
    key_itr, key_itr + map.capacity(), populated_keys.begin(), key_used, stream);

  populated_keys.resize(std::distance(populated_keys.begin(), end_it), stream);
  return populated_keys;
}

/**
 * @brief Computes groupby using hash table.
 *
 * First, we create a hash table that stores the indices of unique rows in
 * `keys`. The upper limit on the number of values in this map is the number
 * of rows in `keys`.
 *
 * To store the results of aggregations, we create temporary sparse columns
 * which have the same size as input value columns. Using the hash map, we
 * determine the location within the sparse column to write the result of the
 * aggregation into.
 *
 * The sparse column results of all aggregations are stored into the cache
 * `sparse_results`. This enables the use of previously calculated results in
 * other aggregations.
 *
 * All the aggregations which can be computed in a single pass are computed
 * first, in a combined kernel. Then using these results, aggregations that
 * require multiple passes, will be computed.
 *
 * Finally, using the hash map, we generate a vector of indices of populated
 * values in sparse result columns. Then, for each aggregation originally
 * requested in `requests`, we gather sparse results into a column of dense
 * results using the aforementioned index vector. Dense results are stored into
 * the in/out parameter `cache`.
 */
std::unique_ptr<table> groupby(table_view const& keys,
                               host_span<aggregation_request const> requests,
                               cudf::detail::result_cache* cache,
                               bool const keys_have_nulls,
                               null_policy const include_null_keys,
                               rmm::cuda_stream_view stream,
                               rmm::mr::device_memory_resource* mr)
{
  auto const num_keys            = keys.num_rows();
  auto const null_keys_are_equal = null_equality::EQUAL;
  auto const has_null            = nullate::DYNAMIC{cudf::has_nested_nulls(keys)};

  auto preprocessed_keys = cudf::experimental::row::hash::preprocessed_table::create(keys, stream);
  auto const comparator  = cudf::experimental::row::equality::self_comparator{preprocessed_keys};
  auto const row_hash    = cudf::experimental::row::hash::row_hasher{std::move(preprocessed_keys)};
  auto const d_row_hash  = row_hash.device_hasher(has_null);

  size_type constexpr unused_key{std::numeric_limits<size_type>::max()};
  size_type constexpr unused_value{std::numeric_limits<size_type>::max()};

  // Cache of sparse results where the location of aggregate value in each
  // column is indexed by the hash map
  cudf::detail::result_cache sparse_results(requests.size());

  auto const comparator_helper = [&](auto const d_key_equal) {
    using allocator_type = typename map_type<decltype(d_key_equal)>::allocator_type;

    auto const map = map_type<decltype(d_key_equal)>::create(compute_hash_table_size(num_keys),
                                                             stream,
                                                             unused_key,
                                                             unused_value,
                                                             d_row_hash,
                                                             d_key_equal,
                                                             allocator_type());
    // Compute all single pass aggs first
    compute_single_pass_aggs(
      keys, requests, &sparse_results, *map, keys_have_nulls, include_null_keys, stream);

    // Extract the populated indices from the hash map and create a gather map.
    // Gathering using this map from sparse results will give dense results.
    auto gather_map = extract_populated_keys(*map, keys.num_rows(), stream);

    // Compact all results from sparse_results and insert into cache
    sparse_to_dense_results(keys,
                            requests,
                            &sparse_results,
                            cache,
                            gather_map,
                            *map,
                            keys_have_nulls,
                            include_null_keys,
                            stream,
                            mr);

    return cudf::detail::gather(keys,
                                gather_map,
                                out_of_bounds_policy::DONT_CHECK,
                                cudf::detail::negative_index_policy::NOT_ALLOWED,
                                stream,
                                mr);
  };

  if (cudf::detail::has_nested_columns(keys)) {
    auto const d_key_equal = comparator.equal_to<true>(has_null, null_keys_are_equal);
    return comparator_helper(d_key_equal);
  } else {
    auto const d_key_equal = comparator.equal_to<false>(has_null, null_keys_are_equal);
    return comparator_helper(d_key_equal);
  }
}

}  // namespace

/**
 * @brief Indicates if a set of aggregation requests can be satisfied with a
 * hash-based groupby implementation.
 *
 * @param requests The set of columns to aggregate and the aggregations to
 * perform
 * @return true A hash-based groupby should be used
 * @return false A hash-based groupby should not be used
 */
bool can_use_hash_groupby(host_span<aggregation_request const> requests)
{
  return std::all_of(requests.begin(), requests.end(), [](aggregation_request const& r) {
    // Currently, structs are not supported in any of hash-based aggregations.
    // Therefore, if any request contains structs then we must fallback to sort-based aggregations.
    // TODO: Support structs in hash-based aggregations.
    auto const v_type = is_dictionary(r.values.type())
                          ? cudf::dictionary_column_view(r.values).keys().type()
                          : r.values.type();

    return not(r.values.type().id() == type_id::STRUCT) and
           std::all_of(r.aggregations.begin(), r.aggregations.end(), [v_type](auto const& a) {
             return cudf::has_atomic_support(cudf::detail::target_type(v_type, a->kind)) and
                    is_hash_aggregation(a->kind);
           });
  });
}

// Hash-based groupby
std::pair<std::unique_ptr<table>, std::vector<aggregation_result>> groupby(
  table_view const& keys,
  host_span<aggregation_request const> requests,
  null_policy include_null_keys,
  rmm::cuda_stream_view stream,
  rmm::mr::device_memory_resource* mr)
{
  cudf::detail::result_cache cache(requests.size());

  std::unique_ptr<table> unique_keys =
    groupby(keys, requests, &cache, cudf::has_nulls(keys), include_null_keys, stream, mr);

  return std::pair(std::move(unique_keys), extract_results(requests, cache, stream, mr));
}
}  // namespace hash
}  // namespace detail
}  // namespace groupby
}  // namespace cudf