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sort_helper.cu
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sort_helper.cu
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/*
* Copyright (c) 2019-2022, 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 "common_utils.cuh"
#include <cudf/column/column_factories.hpp>
#include <cudf/copying.hpp>
#include <cudf/detail/copy.hpp>
#include <cudf/detail/gather.cuh>
#include <cudf/detail/gather.hpp>
#include <cudf/detail/groupby/sort_helper.hpp>
#include <cudf/detail/iterator.cuh>
#include <cudf/detail/labeling/label_segments.cuh>
#include <cudf/detail/scatter.hpp>
#include <cudf/detail/sorting.hpp>
#include <cudf/detail/structs/utilities.hpp>
#include <cudf/strings/string_view.hpp>
#include <cudf/table/experimental/row_operators.cuh>
#include <cudf/table/table_device_view.cuh>
#include <cudf/utilities/traits.hpp>
#include <rmm/cuda_stream_view.hpp>
#include <rmm/exec_policy.hpp>
#include <thrust/distance.h>
#include <thrust/fill.h>
#include <thrust/iterator/counting_iterator.h>
#include <thrust/iterator/transform_iterator.h>
#include <thrust/sequence.h>
#include <thrust/unique.h>
#include <algorithm>
#include <numeric>
#include <tuple>
namespace cudf {
namespace groupby {
namespace detail {
namespace sort {
sort_groupby_helper::sort_groupby_helper(table_view const& keys,
null_policy include_null_keys,
sorted keys_pre_sorted)
: _keys(keys),
_num_keys(-1),
_keys_pre_sorted(keys_pre_sorted),
_include_null_keys(include_null_keys)
{
using namespace cudf::structs::detail;
// Cannot depend on caller's sorting if the column contains nulls,
// and null values are to be excluded.
// Re-sort the data, to filter out nulls more easily.
if (keys_pre_sorted == sorted::YES and include_null_keys == null_policy::EXCLUDE and
has_nulls(keys)) {
_keys_pre_sorted = sorted::NO;
}
};
size_type sort_groupby_helper::num_keys(rmm::cuda_stream_view stream)
{
if (_num_keys > -1) return _num_keys;
if (_include_null_keys == null_policy::EXCLUDE and has_nulls(_keys)) {
// The number of rows w/o null values `n` is indicated by number of valid bits
// in the row bitmask. When `_include_null_keys == NO`, then only rows `[0, n)`
// in the sorted keys are considered for grouping.
_num_keys = keys_bitmask_column(stream).size() - keys_bitmask_column(stream).null_count();
} else {
_num_keys = _keys.num_rows();
}
return _num_keys;
}
column_view sort_groupby_helper::key_sort_order(rmm::cuda_stream_view stream)
{
auto sliced_key_sorted_order = [stream, this]() {
return cudf::detail::slice(this->_key_sorted_order->view(), 0, this->num_keys(stream));
};
if (_key_sorted_order) { return sliced_key_sorted_order(); }
// TODO (dm): optimization. When keys are pre sorted but ignore nulls is true,
// we still want all rows with nulls in the end. Sort is costly, so
// do a copy_if(counting, sorted_order, {bitmask.is_valid(i)})
if (_keys_pre_sorted == sorted::YES) {
_key_sorted_order = make_numeric_column(
data_type(type_to_id<size_type>()), _keys.num_rows(), mask_state::UNALLOCATED, stream);
auto d_key_sorted_order = _key_sorted_order->mutable_view().data<size_type>();
thrust::sequence(rmm::exec_policy(stream),
d_key_sorted_order,
d_key_sorted_order + _key_sorted_order->size(),
0);
return sliced_key_sorted_order();
}
if (_include_null_keys == null_policy::INCLUDE || !cudf::has_nulls(_keys)) { // SQL style
_key_sorted_order = cudf::detail::stable_sorted_order(
_keys,
{},
std::vector<null_order>(_keys.num_columns(), null_order::AFTER),
stream,
rmm::mr::get_current_device_resource());
} else { // Pandas style
// Temporarily prepend the keys table with a column that indicates the
// presence of a null value within a row. This allows moving all rows that
// contain a null value to the end of the sorted order.
auto augmented_keys = table_view({table_view({keys_bitmask_column(stream)}), _keys});
_key_sorted_order = cudf::detail::stable_sorted_order(
augmented_keys,
{},
std::vector<null_order>(_keys.num_columns() + 1, null_order::AFTER),
stream,
rmm::mr::get_current_device_resource());
// All rows with one or more null values are at the end of the resulting sorted order.
}
return sliced_key_sorted_order();
}
sort_groupby_helper::index_vector const& sort_groupby_helper::group_offsets(
rmm::cuda_stream_view stream)
{
if (_group_offsets) return *_group_offsets;
_group_offsets = std::make_unique<index_vector>(num_keys(stream) + 1, stream);
auto const comparator = cudf::experimental::row::equality::self_comparator{_keys, stream};
auto const d_key_equal = comparator.equal_to(
cudf::nullate::DYNAMIC{cudf::has_nested_nulls(_keys)}, null_equality::EQUAL);
auto const sorted_order = key_sort_order(stream).data<size_type>();
decltype(_group_offsets->begin()) result_end;
result_end = thrust::unique_copy(rmm::exec_policy(stream),
thrust::counting_iterator<size_type>(0),
thrust::counting_iterator<size_type>(num_keys(stream)),
_group_offsets->begin(),
permuted_row_equality_comparator(d_key_equal, sorted_order));
size_type num_groups = thrust::distance(_group_offsets->begin(), result_end);
_group_offsets->set_element(num_groups, num_keys(stream), stream);
_group_offsets->resize(num_groups + 1, stream);
return *_group_offsets;
}
sort_groupby_helper::index_vector const& sort_groupby_helper::group_labels(
rmm::cuda_stream_view stream)
{
if (_group_labels) return *_group_labels;
// Get group labels for future use in segmented sorting
_group_labels = std::make_unique<index_vector>(num_keys(stream), stream);
auto& group_labels = *_group_labels;
if (num_keys(stream) == 0) return group_labels;
cudf::detail::label_segments(group_offsets(stream).begin(),
group_offsets(stream).end(),
group_labels.begin(),
group_labels.end(),
stream);
return group_labels;
}
column_view sort_groupby_helper::unsorted_keys_labels(rmm::cuda_stream_view stream)
{
if (_unsorted_keys_labels) return _unsorted_keys_labels->view();
column_ptr temp_labels = make_numeric_column(
data_type(type_to_id<size_type>()), _keys.num_rows(), mask_state::ALL_NULL, stream);
auto group_labels_view = cudf::column_view(
data_type(type_to_id<size_type>()), group_labels(stream).size(), group_labels(stream).data());
auto scatter_map = key_sort_order(stream);
std::unique_ptr<table> t_unsorted_keys_labels =
cudf::detail::scatter(table_view({group_labels_view}),
scatter_map,
table_view({temp_labels->view()}),
stream,
rmm::mr::get_current_device_resource());
_unsorted_keys_labels = std::move(t_unsorted_keys_labels->release()[0]);
return _unsorted_keys_labels->view();
}
column_view sort_groupby_helper::keys_bitmask_column(rmm::cuda_stream_view stream)
{
if (_keys_bitmask_column) return _keys_bitmask_column->view();
auto [row_bitmask, null_count] = cudf::detail::bitmask_and(_keys, stream);
_keys_bitmask_column = make_numeric_column(
data_type(type_id::INT8), _keys.num_rows(), std::move(row_bitmask), null_count, stream);
auto keys_bitmask_view = _keys_bitmask_column->mutable_view();
using T = id_to_type<type_id::INT8>;
thrust::fill(
rmm::exec_policy(stream), keys_bitmask_view.begin<T>(), keys_bitmask_view.end<T>(), 0);
return _keys_bitmask_column->view();
}
sort_groupby_helper::column_ptr sort_groupby_helper::sorted_values(
column_view const& values, rmm::cuda_stream_view stream, rmm::mr::device_memory_resource* mr)
{
column_ptr values_sort_order =
cudf::detail::stable_sorted_order(table_view({unsorted_keys_labels(stream), values}),
{},
std::vector<null_order>(2, null_order::AFTER),
stream,
mr);
// Zero-copy slice this sort order so that its new size is num_keys()
column_view gather_map = cudf::detail::slice(values_sort_order->view(), 0, num_keys(stream));
auto sorted_values_table = cudf::detail::gather(table_view({values}),
gather_map,
cudf::out_of_bounds_policy::DONT_CHECK,
cudf::detail::negative_index_policy::NOT_ALLOWED,
stream,
mr);
return std::move(sorted_values_table->release()[0]);
}
sort_groupby_helper::column_ptr sort_groupby_helper::grouped_values(
column_view const& values, rmm::cuda_stream_view stream, rmm::mr::device_memory_resource* mr)
{
auto gather_map = key_sort_order(stream);
auto grouped_values_table = cudf::detail::gather(table_view({values}),
gather_map,
cudf::out_of_bounds_policy::DONT_CHECK,
cudf::detail::negative_index_policy::NOT_ALLOWED,
stream,
mr);
return std::move(grouped_values_table->release()[0]);
}
std::unique_ptr<table> sort_groupby_helper::unique_keys(rmm::cuda_stream_view stream,
rmm::mr::device_memory_resource* mr)
{
auto idx_data = key_sort_order(stream).data<size_type>();
auto gather_map_it = thrust::make_transform_iterator(
group_offsets(stream).begin(), [idx_data] __device__(size_type i) { return idx_data[i]; });
return cudf::detail::gather(_keys,
gather_map_it,
gather_map_it + num_groups(stream),
out_of_bounds_policy::DONT_CHECK,
stream,
mr);
}
std::unique_ptr<table> sort_groupby_helper::sorted_keys(rmm::cuda_stream_view stream,
rmm::mr::device_memory_resource* mr)
{
return cudf::detail::gather(_keys,
key_sort_order(stream),
cudf::out_of_bounds_policy::DONT_CHECK,
cudf::detail::negative_index_policy::NOT_ALLOWED,
stream,
mr);
}
} // namespace sort
} // namespace detail
} // namespace groupby
} // namespace cudf