392 lines
14 KiB
C++
392 lines
14 KiB
C++
/*!
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* Copyright 2021-2022 by Contributors
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* \file row_set.h
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* \brief Quick Utility to compute subset of rows
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* \author Philip Cho, Tianqi Chen
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*/
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#ifndef XGBOOST_COMMON_PARTITION_BUILDER_H_
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#define XGBOOST_COMMON_PARTITION_BUILDER_H_
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#include <xgboost/data.h>
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#include <algorithm>
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#include <limits>
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#include <memory>
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#include <utility>
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#include <vector>
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#include "../tree/hist/expand_entry.h"
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#include "categorical.h"
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#include "column_matrix.h"
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#include "xgboost/context.h"
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#include "xgboost/tree_model.h"
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namespace xgboost {
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namespace common {
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// The builder is required for samples partition to left and rights children for set of nodes
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// Responsible for:
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// 1) Effective memory allocation for intermediate results for multi-thread work
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// 2) Merging partial results produced by threads into original row set (row_set_collection_)
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// BlockSize is template to enable memory alignment easily with C++11 'alignas()' feature
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template<size_t BlockSize>
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class PartitionBuilder {
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using BitVector = RBitField8;
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public:
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template<typename Func>
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void Init(const size_t n_tasks, size_t n_nodes, Func funcNTask) {
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left_right_nodes_sizes_.resize(n_nodes);
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blocks_offsets_.resize(n_nodes+1);
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blocks_offsets_[0] = 0;
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for (size_t i = 1; i < n_nodes+1; ++i) {
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blocks_offsets_[i] = blocks_offsets_[i-1] + funcNTask(i-1);
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}
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if (n_tasks > max_n_tasks_) {
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mem_blocks_.resize(n_tasks);
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max_n_tasks_ = n_tasks;
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}
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}
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// split row indexes (rid_span) to 2 parts (left_part, right_part) depending
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// on comparison of indexes values (idx_span) and split point (split_cond)
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// Handle dense columns
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// Analog of std::stable_partition, but in no-inplace manner
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template <bool default_left, bool any_missing, typename ColumnType, typename Predicate>
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inline std::pair<size_t, size_t> PartitionKernel(ColumnType* p_column,
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common::Span<const size_t> row_indices,
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common::Span<size_t> left_part,
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common::Span<size_t> right_part,
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size_t base_rowid, Predicate&& pred) {
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auto& column = *p_column;
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size_t* p_left_part = left_part.data();
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size_t* p_right_part = right_part.data();
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size_t nleft_elems = 0;
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size_t nright_elems = 0;
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auto p_row_indices = row_indices.data();
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auto n_samples = row_indices.size();
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for (size_t i = 0; i < n_samples; ++i) {
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auto rid = p_row_indices[i];
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const int32_t bin_id = column[rid - base_rowid];
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if (any_missing && bin_id == ColumnType::kMissingId) {
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if (default_left) {
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p_left_part[nleft_elems++] = rid;
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} else {
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p_right_part[nright_elems++] = rid;
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}
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} else {
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if (pred(rid, bin_id)) {
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p_left_part[nleft_elems++] = rid;
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} else {
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p_right_part[nright_elems++] = rid;
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}
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}
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}
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return {nleft_elems, nright_elems};
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}
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template <typename Pred>
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inline std::pair<size_t, size_t> PartitionRangeKernel(common::Span<const size_t> ridx,
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common::Span<size_t> left_part,
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common::Span<size_t> right_part,
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Pred pred) {
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size_t* p_left_part = left_part.data();
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size_t* p_right_part = right_part.data();
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size_t nleft_elems = 0;
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size_t nright_elems = 0;
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for (auto row_id : ridx) {
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if (pred(row_id)) {
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p_left_part[nleft_elems++] = row_id;
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} else {
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p_right_part[nright_elems++] = row_id;
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}
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}
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return {nleft_elems, nright_elems};
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}
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template <typename BinIdxType, bool any_missing, bool any_cat>
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void Partition(const size_t node_in_set, std::vector<xgboost::tree::CPUExpandEntry> const &nodes,
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const common::Range1d range,
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const bst_bin_t split_cond, GHistIndexMatrix const& gmat,
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const common::ColumnMatrix& column_matrix,
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const RegTree& tree, const size_t* rid) {
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common::Span<const size_t> rid_span(rid + range.begin(), rid + range.end());
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common::Span<size_t> left = GetLeftBuffer(node_in_set, range.begin(), range.end());
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common::Span<size_t> right = GetRightBuffer(node_in_set, range.begin(), range.end());
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std::size_t nid = nodes[node_in_set].nid;
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bst_feature_t fid = tree[nid].SplitIndex();
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bool default_left = tree[nid].DefaultLeft();
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bool is_cat = tree.GetSplitTypes()[nid] == FeatureType::kCategorical;
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auto node_cats = tree.NodeCats(nid);
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auto const& cut_values = gmat.cut.Values();
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auto pred_hist = [&](auto ridx, auto bin_id) {
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if (any_cat && is_cat) {
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auto gidx = gmat.GetGindex(ridx, fid);
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bool go_left = default_left;
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if (gidx > -1) {
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go_left = Decision(node_cats, cut_values[gidx]);
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}
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return go_left;
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} else {
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return bin_id <= split_cond;
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}
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};
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auto pred_approx = [&](auto ridx) {
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auto gidx = gmat.GetGindex(ridx, fid);
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bool go_left = default_left;
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if (gidx > -1) {
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if (is_cat) {
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go_left = Decision(node_cats, cut_values[gidx]);
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} else {
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go_left = cut_values[gidx] <= nodes[node_in_set].split.split_value;
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}
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}
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return go_left;
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};
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std::pair<size_t, size_t> child_nodes_sizes;
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if (!column_matrix.IsInitialized()) {
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child_nodes_sizes = PartitionRangeKernel(rid_span, left, right, pred_approx);
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} else {
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if (column_matrix.GetColumnType(fid) == xgboost::common::kDenseColumn) {
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auto column = column_matrix.DenseColumn<BinIdxType, any_missing>(fid);
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if (default_left) {
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child_nodes_sizes = PartitionKernel<true, any_missing>(&column, rid_span, left, right,
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gmat.base_rowid, pred_hist);
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} else {
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child_nodes_sizes = PartitionKernel<false, any_missing>(&column, rid_span, left, right,
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gmat.base_rowid, pred_hist);
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}
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} else {
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CHECK_EQ(any_missing, true);
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auto column =
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column_matrix.SparseColumn<BinIdxType>(fid, rid_span.front() - gmat.base_rowid);
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if (default_left) {
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child_nodes_sizes = PartitionKernel<true, any_missing>(&column, rid_span, left, right,
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gmat.base_rowid, pred_hist);
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} else {
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child_nodes_sizes = PartitionKernel<false, any_missing>(&column, rid_span, left, right,
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gmat.base_rowid, pred_hist);
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}
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}
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}
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const size_t n_left = child_nodes_sizes.first;
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const size_t n_right = child_nodes_sizes.second;
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SetNLeftElems(node_in_set, range.begin(), n_left);
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SetNRightElems(node_in_set, range.begin(), n_right);
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}
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/**
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* @brief When data is split by column, we don't have all the features locally on the current
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* worker, so we go through all the rows and mark the bit vectors on whether the decision is made
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* to go right, or if the feature value used for the split is missing.
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*/
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void MaskRows(const size_t node_in_set, std::vector<xgboost::tree::CPUExpandEntry> const &nodes,
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const common::Range1d range, GHistIndexMatrix const& gmat,
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const common::ColumnMatrix& column_matrix,
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const RegTree& tree, const size_t* rid,
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BitVector* decision_bits, BitVector* missing_bits) {
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common::Span<const size_t> rid_span(rid + range.begin(), rid + range.end());
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std::size_t nid = nodes[node_in_set].nid;
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bst_feature_t fid = tree[nid].SplitIndex();
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bool is_cat = tree.GetSplitTypes()[nid] == FeatureType::kCategorical;
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auto node_cats = tree.NodeCats(nid);
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auto const& cut_values = gmat.cut.Values();
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if (!column_matrix.IsInitialized()) {
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for (auto row_id : rid_span) {
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auto gidx = gmat.GetGindex(row_id, fid);
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if (gidx > -1) {
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bool go_left = false;
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if (is_cat) {
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go_left = Decision(node_cats, cut_values[gidx]);
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} else {
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go_left = cut_values[gidx] <= nodes[node_in_set].split.split_value;
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}
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if (go_left) {
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decision_bits->Set(row_id - gmat.base_rowid);
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}
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} else {
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missing_bits->Set(row_id - gmat.base_rowid);
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}
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}
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} else {
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LOG(FATAL) << "Column data split is only supported for the `approx` tree method";
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}
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}
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/**
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* @brief Once we've aggregated the decision and missing bits from all the workers, we can then
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* use them to partition the rows accordingly.
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*/
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void PartitionByMask(const size_t node_in_set,
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std::vector<xgboost::tree::CPUExpandEntry> const& nodes,
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const common::Range1d range, GHistIndexMatrix const& gmat,
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const common::ColumnMatrix& column_matrix, const RegTree& tree,
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const size_t* rid, BitVector const& decision_bits,
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BitVector const& missing_bits) {
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common::Span<const size_t> rid_span(rid + range.begin(), rid + range.end());
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common::Span<size_t> left = GetLeftBuffer(node_in_set, range.begin(), range.end());
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common::Span<size_t> right = GetRightBuffer(node_in_set, range.begin(), range.end());
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std::size_t nid = nodes[node_in_set].nid;
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bool default_left = tree[nid].DefaultLeft();
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auto pred_approx = [&](auto ridx) {
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bool go_left = default_left;
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bool is_missing = missing_bits.Check(ridx - gmat.base_rowid);
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if (!is_missing) {
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go_left = decision_bits.Check(ridx - gmat.base_rowid);
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}
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return go_left;
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};
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std::pair<size_t, size_t> child_nodes_sizes;
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if (!column_matrix.IsInitialized()) {
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child_nodes_sizes = PartitionRangeKernel(rid_span, left, right, pred_approx);
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} else {
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LOG(FATAL) << "Column data split is only supported for the `approx` tree method";
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}
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const size_t n_left = child_nodes_sizes.first;
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const size_t n_right = child_nodes_sizes.second;
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SetNLeftElems(node_in_set, range.begin(), n_left);
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SetNRightElems(node_in_set, range.begin(), n_right);
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}
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// allocate thread local memory, should be called for each specific task
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void AllocateForTask(size_t id) {
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if (mem_blocks_[id].get() == nullptr) {
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BlockInfo* local_block_ptr = new BlockInfo;
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CHECK_NE(local_block_ptr, (BlockInfo*)nullptr);
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mem_blocks_[id].reset(local_block_ptr);
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}
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}
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common::Span<size_t> GetLeftBuffer(int nid, size_t begin, size_t end) {
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const size_t task_idx = GetTaskIdx(nid, begin);
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return { mem_blocks_.at(task_idx)->Left(), end - begin };
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}
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common::Span<size_t> GetRightBuffer(int nid, size_t begin, size_t end) {
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const size_t task_idx = GetTaskIdx(nid, begin);
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return { mem_blocks_.at(task_idx)->Right(), end - begin };
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}
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void SetNLeftElems(int nid, size_t begin, size_t n_left) {
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size_t task_idx = GetTaskIdx(nid, begin);
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mem_blocks_.at(task_idx)->n_left = n_left;
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}
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void SetNRightElems(int nid, size_t begin, size_t n_right) {
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size_t task_idx = GetTaskIdx(nid, begin);
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mem_blocks_.at(task_idx)->n_right = n_right;
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}
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size_t GetNLeftElems(int nid) const {
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return left_right_nodes_sizes_[nid].first;
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}
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size_t GetNRightElems(int nid) const {
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return left_right_nodes_sizes_[nid].second;
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}
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// Each thread has partial results for some set of tree-nodes
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// The function decides order of merging partial results into final row set
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void CalculateRowOffsets() {
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for (size_t i = 0; i < blocks_offsets_.size()-1; ++i) {
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size_t n_left = 0;
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for (size_t j = blocks_offsets_[i]; j < blocks_offsets_[i+1]; ++j) {
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mem_blocks_[j]->n_offset_left = n_left;
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n_left += mem_blocks_[j]->n_left;
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}
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size_t n_right = 0;
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for (size_t j = blocks_offsets_[i]; j < blocks_offsets_[i + 1]; ++j) {
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mem_blocks_[j]->n_offset_right = n_left + n_right;
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n_right += mem_blocks_[j]->n_right;
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}
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left_right_nodes_sizes_[i] = {n_left, n_right};
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}
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}
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void MergeToArray(int nid, size_t begin, size_t* rows_indexes) {
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size_t task_idx = GetTaskIdx(nid, begin);
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size_t* left_result = rows_indexes + mem_blocks_[task_idx]->n_offset_left;
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size_t* right_result = rows_indexes + mem_blocks_[task_idx]->n_offset_right;
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const size_t* left = mem_blocks_[task_idx]->Left();
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const size_t* right = mem_blocks_[task_idx]->Right();
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std::copy_n(left, mem_blocks_[task_idx]->n_left, left_result);
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std::copy_n(right, mem_blocks_[task_idx]->n_right, right_result);
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}
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size_t GetTaskIdx(int nid, size_t begin) {
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return blocks_offsets_[nid] + begin / BlockSize;
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}
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// Copy row partitions into global cache for reuse in objective
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template <typename Sampledp>
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void LeafPartition(Context const* ctx, RegTree const& tree, RowSetCollection const& row_set,
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std::vector<bst_node_t>* p_position, Sampledp sampledp) const {
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auto& h_pos = *p_position;
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h_pos.resize(row_set.Data()->size(), std::numeric_limits<bst_node_t>::max());
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auto p_begin = row_set.Data()->data();
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ParallelFor(row_set.Size(), ctx->Threads(), [&](size_t i) {
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auto const& node = row_set[i];
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if (node.node_id < 0) {
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return;
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}
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CHECK(tree[node.node_id].IsLeaf());
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if (node.begin) { // guard for empty node.
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size_t ptr_offset = node.end - p_begin;
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CHECK_LE(ptr_offset, row_set.Data()->size()) << node.node_id;
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for (auto idx = node.begin; idx != node.end; ++idx) {
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h_pos[*idx] = sampledp(*idx) ? ~node.node_id : node.node_id;
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}
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}
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});
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}
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protected:
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struct BlockInfo{
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size_t n_left;
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size_t n_right;
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size_t n_offset_left;
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size_t n_offset_right;
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size_t* Left() {
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return &left_data_[0];
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}
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size_t* Right() {
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return &right_data_[0];
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}
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private:
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size_t left_data_[BlockSize];
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size_t right_data_[BlockSize];
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};
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std::vector<std::pair<size_t, size_t>> left_right_nodes_sizes_;
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std::vector<size_t> blocks_offsets_;
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std::vector<std::shared_ptr<BlockInfo>> mem_blocks_;
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size_t max_n_tasks_ = 0;
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};
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} // namespace common
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} // namespace xgboost
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#endif // XGBOOST_COMMON_PARTITION_BUILDER_H_
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