Implement drop_list_duplicates

conda/recipes/libcudf/meta.yaml

@@ -132,6 +132,7 @@ test:
     - test -f $PREFIX/include/cudf/lists/detail/concatenate.hpp
     - test -f $PREFIX/include/cudf/lists/detail/copying.hpp
     - test -f $PREFIX/include/cudf/lists/count_elements.hpp
+    - test -f $PREFIX/include/cudf/lists/drop_list_duplicates.hpp
     - test -f $PREFIX/include/cudf/lists/extract.hpp
     - test -f $PREFIX/include/cudf/lists/contains.hpp
     - test -f $PREFIX/include/cudf/lists/gather.hpp

cpp/CMakeLists.txt

@@ -253,6 +253,7 @@ add_library(cudf
     src/lists/copying/segmented_gather.cu
     src/lists/count_elements.cu
     src/lists/extract.cu
+    src/lists/drop_list_duplicates.cu
     src/lists/lists_column_factories.cu
     src/lists/lists_column_view.cu
     src/lists/segmented_sort.cu

cpp/include/cudf/lists/drop_list_duplicates.hpp

@@ -0,0 +1,63 @@
+/*
+ * Copyright (c) 2021, 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.
+ */
+#pragma once
+
+#include <cudf/column/column.hpp>
+#include <cudf/lists/lists_column_view.hpp>
+#include <cudf/stream_compaction.hpp>
+
+namespace cudf {
+namespace lists {
+/**
+ * @addtogroup lists_drop_duplicates
+ * @{
+ * @file
+ */
+
+/**
+ * @brief Create a new lists column by removing duplicated entries from each list element in the
+ * given lists column
+ *
+ * @throw cudf::logic_error if any row (list element) in the input column is a nested type.
+ *
+ * Given an `input` lists_column_view, the list elements in the column are copied to an output lists
+ * column such that their duplicated entries are dropped out to keep only the unique ones. The
+ * order of those entries within each list are not guaranteed to be preserved as in the input. In
+ * the current implementation, entries in the output lists are sorted by ascending order (nulls
+ * last), but this is not guaranteed in future implementation.
+ *
+ * @param lists_column The input lists_column_view
+ * @param nulls_equal  Flag to specify whether null entries should be considered equal
+ * @param mr           Device resource used to allocate memory
+ *
+ * @code{.pseudo}
+ * lists_column = { {1, 1, 2, 1, 3}, {4}, NULL, {}, {NULL, NULL, NULL, 5, 6, 6, 6, 5} }
+ * output = { {1, 2, 3}, {4}, NULL, {}, {5, 6, NULL} }
+ *
+ * Note that permuting the entries of each list in this output also produces another valid
+ * output.
+ * @endcode
+ *
+ * @return A list column with list elements having unique entries
+ */
+std::unique_ptr<column> drop_list_duplicates(
+  lists_column_view const& lists_column,
+  null_equality nulls_equal           = null_equality::EQUAL,
+  rmm::mr::device_memory_resource* mr = rmm::mr::get_current_device_resource());
+
+/** @} */  // end of group
+}  // namespace lists
+}  // namespace cudf

cpp/include/doxygen_groups.h

@@ -146,6 +146,7 @@
  *   @defgroup lists_contains Searching
  *   @defgroup lists_gather Gathering
  *   @defgroup lists_elements Counting
+ *   @defgroup lists_drop_duplicates Filtering
  * @}
  * @defgroup nvtext_apis NVText
  * @{

cpp/src/lists/drop_list_duplicates.cu

@@ -0,0 +1,294 @@
+/*
+ * Copyright (c) 2021, 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 <cudf/column/column_factories.hpp>
+#include <cudf/detail/copy.hpp>
+#include <cudf/detail/gather.hpp>
+#include <cudf/detail/iterator.cuh>
+#include <cudf/detail/null_mask.hpp>
+#include <cudf/detail/nvtx/ranges.hpp>
+#include <cudf/lists/detail/sorting.hpp>
+#include <cudf/lists/drop_list_duplicates.hpp>
+#include <cudf/table/row_operators.cuh>
+
+#include <rmm/cuda_stream_view.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/binary_search.h>
+#include <thrust/transform.h>
+
+namespace cudf {
+namespace lists {
+namespace detail {
+namespace {
+using offset_type = lists_column_view::offset_type;
+/**
+ * @brief Copy list entries and entry list offsets ignoring duplicates
+ *
+ * Given an array of all entries flattened from a list column and an array that maps each entry to
+ * the offset of the list containing that entry, those entries and list offsets are copied into
+ * new arrays such that the duplicated entries within each list will be ignored.
+ *
+ * @param all_lists_entries    The input array containing all list entries
+ * @param entries_list_offsets A map from list entries to their corresponding list offsets
+ * @param nulls_equal          Flag to specify whether null entries should be considered equal
+ * @param stream               CUDA stream used for device memory operations and kernel launches
+ * @param mr                   Device resource used to allocate memory
+ *
+ * @return A pair of columns, the first one contains unique list entries and the second one
+ * contains their corresponding list offsets
+ */
+template <bool has_nulls>
+std::vector<std::unique_ptr<column>> get_unique_entries_and_list_offsets(
+  column_view const& all_lists_entries,
+  column_view const& entries_list_offsets,
+  null_equality nulls_equal,
+  rmm::cuda_stream_view stream,
+  rmm::mr::device_memory_resource* mr)
+{
+  // Create an intermediate table, since the comparator only work on tables
+  auto const device_input_table =
+    cudf::table_device_view::create(table_view{{all_lists_entries}}, stream);
+  auto const comp = row_equality_comparator<has_nulls>(
+    *device_input_table, *device_input_table, nulls_equal == null_equality::EQUAL);
+
+  auto const num_entries = all_lists_entries.size();
+  // Allocate memory to store the indices of the unique entries
+  auto const unique_indices = cudf::make_numeric_column(
+    entries_list_offsets.type(), num_entries, mask_state::UNALLOCATED, stream);
+  auto const unique_indices_begin = unique_indices->mutable_view().begin<offset_type>();
+
+  auto const copy_end = thrust::unique_copy(
+    rmm::exec_policy(stream),
+    thrust::make_counting_iterator(0),
+    thrust::make_counting_iterator(num_entries),
+    unique_indices_begin,
+    [list_offsets = entries_list_offsets.begin<offset_type>(), comp] __device__(auto i, auto j) {
+      return list_offsets[i] == list_offsets[j] && comp(i, j);
+    });
+
+  // Collect unique entries and entry list offsets
+  auto const indices = cudf::detail::slice(
+    unique_indices->view(), 0, thrust::distance(unique_indices_begin, copy_end));
+  return cudf::detail::gather(table_view{{all_lists_entries, entries_list_offsets}},
+                              indices,
+                              cudf::out_of_bounds_policy::DONT_CHECK,
+                              cudf::detail::negative_index_policy::NOT_ALLOWED,
+                              stream,
+                              mr)
+    ->release();
+}
+
+/**
+ * @brief Generate a 0-based offset column for a lists column
+ *
+ * Given a lists_column_view, which may have a non-zero offset, generate a new column containing
+ * 0-based list offsets. This is done by subtracting each of the input list offset by the first
+ * offset.
+ *
+ * @code{.pseudo}
+ * Given a list column having offsets = { 3, 7, 9, 13 },
+ * then output_offsets = { 0, 4, 6, 10 }
+ * @endcode
+ *
+ * @param lists_column The input lists column
+ * @param stream       CUDA stream used for device memory operations and kernel launches
+ * @param mr           Device resource used to allocate memory
+ *
+ * @return A column containing 0-based list offsets
+ */
+std::unique_ptr<column> generate_clean_offsets(lists_column_view const& lists_column,
+                                               rmm::cuda_stream_view stream,
+                                               rmm::mr::device_memory_resource* mr)
+{
+  auto output_offsets = make_numeric_column(data_type{type_to_id<offset_type>()},
+                                            lists_column.size() + 1,
+                                            mask_state::UNALLOCATED,
+                                            stream,
+                                            mr);
+  thrust::transform(
+    rmm::exec_policy(stream),
+    lists_column.offsets_begin(),
+    lists_column.offsets_end(),
+    output_offsets->mutable_view().begin<offset_type>(),
+    [first = lists_column.offsets_begin()] __device__(auto offset) { return offset - *first; });
+  return output_offsets;
+}
+
+/**
+ * @brief Populate list offsets for all list entries
+ *
+ * Given an `offsets` column_view containing offsets of a lists column and a number of all list
+ * entries in the column, generate an array that maps from each list entry to the offset of the list
+ * containing that entry.
+ *
+ * @code{.pseudo}
+ * num_entries = 10, offsets = { 0, 4, 6, 10 }
+ * output = { 1, 1, 1, 1, 2, 2, 3, 3, 3, 3 }
+ * @endcode
+ *
+ * @param num_entries The number of list entries
+ * @param offsets     Column view to the list offsets
+ * @param stream      CUDA stream used for device memory operations and kernel launches
+ * @param mr          Device resource used to allocate memory
+ *
+ * @return A column containing entry list offsets
+ */
+std::unique_ptr<column> generate_entry_list_offsets(size_type num_entries,
+                                                    column_view const& offsets,
+                                                    rmm::cuda_stream_view stream)
+{
+  auto entry_list_offsets = make_numeric_column(offsets.type(),
+                                                num_entries,
+                                                mask_state::UNALLOCATED,
+                                                stream,
+                                                rmm::mr::get_current_device_resource());
+  thrust::upper_bound(rmm::exec_policy(stream),
+                      offsets.begin<offset_type>(),
+                      offsets.end<offset_type>(),
+                      thrust::make_counting_iterator<offset_type>(0),
+                      thrust::make_counting_iterator<offset_type>(num_entries),
+                      entry_list_offsets->mutable_view().begin<offset_type>());
+  return entry_list_offsets;
+}
+
+/**
+ * @brief Generate list offsets from entry offsets
+ *
+ * Generate an array of list offsets for the final result lists column. The list
+ * offsets of the original lists column are also taken into account to make sure the result lists
+ * column will have the same empty list rows (if any) as in the original lists column.
+ *
+ * @param[in] num_entries          The number of unique entries after removing duplicates
+ * @param[in] entries_list_offsets The mapping from list entries to their list offsets
+ * @param[out] original_offsets    The list offsets of the original lists column, which
+ * will also be used to store the new list offsets
+ * @param[in] stream               CUDA stream used for device memory operations and kernel launches
+ * @param[in] mr                   Device resource used to allocate memory
+ */
+void generate_offsets(size_type num_entries,
+                      column_view const& entries_list_offsets,
+                      mutable_column_view const& original_offsets,
+                      rmm::cuda_stream_view stream)
+{
+  // Firstly, generate temporary list offsets for the unique entries, ignoring empty lists (if any)
+  // If entries_list_offsets = {1, 1, 1, 1, 2, 3, 3, 3, 4, 4 }, num_entries = 10,
+  // then new_offsets = { 0, 4, 5, 8, 10 }
+  auto const new_offsets = allocate_like(
+    original_offsets, mask_allocation_policy::NEVER, rmm::mr::get_current_device_resource());
+  thrust::copy_if(rmm::exec_policy(stream),
+                  thrust::make_counting_iterator<offset_type>(0),
+                  thrust::make_counting_iterator<offset_type>(num_entries + 1),
+                  new_offsets->mutable_view().begin<offset_type>(),
+                  [num_entries, offsets_ptr = entries_list_offsets.begin<offset_type>()] __device__(
+                    auto i) -> bool {
+                    return i == 0 || i == num_entries || offsets_ptr[i] != offsets_ptr[i - 1];
+                  });
+
+  // Generate a prefix sum of number of empty lists, storing inplace to the original lists
+  // offsets
+  // If the original list offsets is { 0, 0, 5, 5, 6, 6 } (there are 2 empty lists),
+  // and new_offsets = { 0, 4, 6 },
+  // then output = { 0, 1, 1, 2, 2, 3}
+  auto const iter_trans_begin = cudf::detail::make_counting_transform_iterator(
+    0, [offsets = original_offsets.begin<offset_type>()] __device__(auto i) {
+      return (i > 0 && offsets[i] == offsets[i - 1]) ? 1 : 0;
+    });
+  thrust::inclusive_scan(rmm::exec_policy(stream),
+                         iter_trans_begin,
+                         iter_trans_begin + original_offsets.size(),
+                         original_offsets.begin<offset_type>());
+
+  // Generate the final list offsets
+  // If the original list offsets are { 0, 0, 5, 5, 6, 6 }, the new offsets are { 0, 4, 6 },
+  //  and the prefix sums of empty lists are { 0, 1, 1, 2, 2, 3 },
+  //  then output = { 0, 0, 4, 4, 5, 5 }
+  thrust::transform(rmm::exec_policy(stream),
+                    thrust::make_counting_iterator<offset_type>(0),
+                    thrust::make_counting_iterator<offset_type>(original_offsets.size()),
+                    original_offsets.begin<offset_type>(),
+                    [prefix_sum_empty_lists = original_offsets.begin<offset_type>(),
+                     offsets = new_offsets->view().begin<offset_type>()] __device__(auto i) {
+                      return offsets[i - prefix_sum_empty_lists[i]];
+                    });
+}
+/**
+ * @copydoc cudf::lists::drop_list_duplicates
+ *
+ * @param stream CUDA stream used for device memory operations and kernel launches
+ */
+std::unique_ptr<column> drop_list_duplicates(lists_column_view const& lists_column,
+                                             null_equality nulls_equal,
+                                             rmm::cuda_stream_view stream,
+                                             rmm::mr::device_memory_resource* mr)
+{
+  if (lists_column.is_empty()) return cudf::empty_like(lists_column.parent());
+  if (cudf::is_nested(lists_column.child().type())) {
+    CUDF_FAIL("Nested types are not supported in drop_list_duplicates.");
+  }
+
+  // Call segmented sort on the list elements and store them in a temporary column sorted_list
+  auto const sorted_lists =
+    detail::sort_lists(lists_column, order::ASCENDING, null_order::AFTER, stream);
+
+  // Flatten all entries (depth = 1) of the lists column
+  auto const all_lists_entries = lists_column_view(sorted_lists->view()).get_sliced_child(stream);
+
+  // Generate a 0-based offset column
+  auto lists_offsets = detail::generate_clean_offsets(lists_column, stream, mr);
+
+  // Generate a mapping from list entries to offsets of the lists containing those entries
+  auto const entries_list_offsets =
+    detail::generate_entry_list_offsets(all_lists_entries.size(), lists_offsets->view(), stream);
+
+  // Copy non-duplicated entries (along with their list offsets) to new arrays
+  auto unique_entries_and_list_offsets =
+    all_lists_entries.has_nulls()
+      ? detail::get_unique_entries_and_list_offsets<true>(
+          all_lists_entries, entries_list_offsets->view(), nulls_equal, stream, mr)
+      : detail::get_unique_entries_and_list_offsets<false>(
+          all_lists_entries, entries_list_offsets->view(), nulls_equal, stream, mr);
+
+  // Generate offsets for the new lists column
+  detail::generate_offsets(unique_entries_and_list_offsets.front()->size(),
+                           unique_entries_and_list_offsets.back()->view(),
+                           lists_offsets->mutable_view(),
+                           stream);
+
+  // Construct a new lists column without duplicated entries
+  return make_lists_column(lists_column.size(),
+                           std::move(lists_offsets),
+                           std::move(unique_entries_and_list_offsets.front()),
+                           lists_column.null_count(),
+                           cudf::detail::copy_bitmask(lists_column.parent(), stream, mr));
+}
+
+}  // anonymous namespace
+}  // namespace detail
+
+/**
+ * @copydoc cudf::lists::drop_list_duplicates
+ */
+std::unique_ptr<column> drop_list_duplicates(lists_column_view const& lists_column,
+                                             null_equality nulls_equal,
+                                             rmm::mr::device_memory_resource* mr)
+{
+  CUDF_FUNC_RANGE();
+  return detail::drop_list_duplicates(lists_column, nulls_equal, rmm::cuda_stream_default, mr);
+}
+
+}  // namespace lists
+}  // namespace cudf

cpp/tests/CMakeLists.txt

@@ -432,6 +432,7 @@ ConfigureTest(AST_TEST ast/transform_tests.cpp)
 ConfigureTest(LISTS_TEST
     lists/contains_tests.cpp
     lists/count_elements_tests.cpp
+    lists/drop_list_duplicates_tests.cpp
     lists/extract_tests.cpp
     lists/sort_lists_tests.cpp)