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Unify the partitioner for hist and approx.
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Co-authored-by: dmitry.razdoburdin <drazdobu@jfldaal005.jf.intel.com>
Co-authored-by: jiamingy <jm.yuan@outlook.com>
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@razdoburdin @trivialfis
3 people committed Oct 19, 2022
1 parent c69af90 commit 5bd849f
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Showing 13 changed files with 358 additions and 450 deletions.
24 changes: 21 additions & 3 deletions src/common/column_matrix.h
View file
Expand Up @@ -103,15 +103,18 @@ class SparseColumnIter : public Column<BinIdxT> {

template <typename BinIdxT, bool any_missing>
class DenseColumnIter : public Column<BinIdxT> {
public:
using ByteType = bool;

private:
using Base = Column<BinIdxT>;
/* flags for missing values in dense columns */
std::vector<bool> const& missing_flags_;
std::vector<ByteType> const& missing_flags_;
size_t feature_offset_;

public:
explicit DenseColumnIter(common::Span<const BinIdxT> index, bst_bin_t index_base,
std::vector<bool> const& missing_flags, size_t feature_offset)
std::vector<ByteType> const& missing_flags, size_t feature_offset)
: Base{index, index_base}, missing_flags_{missing_flags}, feature_offset_{feature_offset} {}
DenseColumnIter(DenseColumnIter const&) = delete;
DenseColumnIter(DenseColumnIter&&) = default;
Expand Down Expand Up @@ -153,6 +156,7 @@ class ColumnMatrix {
}

public:
using ByteType = bool;
// get number of features
bst_feature_t GetNumFeature() const { return static_cast<bst_feature_t>(type_.size()); }

Expand Down Expand Up @@ -195,6 +199,8 @@ class ColumnMatrix {
}
}

bool IsInitialized() const { return !type_.empty(); }

/**
* \brief Push batch of data for Quantile DMatrix support.
*
Expand Down Expand Up @@ -352,6 +358,13 @@ class ColumnMatrix {

fi->Read(&row_ind_);
fi->Read(&feature_offsets_);

std::vector<std::uint8_t> missing;
fi->Read(&missing);
missing_flags_.resize(missing.size());
std::transform(missing.cbegin(), missing.cend(), missing_flags_.begin(),
[](std::uint8_t flag) { return !!flag; });

index_base_ = index_base;
#if !DMLC_LITTLE_ENDIAN
std::underlying_type<BinTypeSize>::type v;
Expand Down Expand Up @@ -386,6 +399,11 @@ class ColumnMatrix {
#endif // !DMLC_LITTLE_ENDIAN
write_vec(row_ind_);
write_vec(feature_offsets_);
// dmlc can not handle bool vector
std::vector<std::uint8_t> missing(missing_flags_.size());
std::transform(missing_flags_.cbegin(), missing_flags_.cend(), missing.begin(),
[](bool flag) { return static_cast<std::uint8_t>(flag); });
write_vec(missing);

#if !DMLC_LITTLE_ENDIAN
auto v = static_cast<std::underlying_type<BinTypeSize>::type>(bins_type_size_);
Expand Down Expand Up @@ -413,7 +431,7 @@ class ColumnMatrix {

// index_base_[fid]: least bin id for feature fid
uint32_t const* index_base_;
std::vector<bool> missing_flags_;
std::vector<ByteType> missing_flags_;
BinTypeSize bins_type_size_;
bool any_missing_;
};
Expand Down
22 changes: 22 additions & 0 deletions src/common/numeric.h
View file
Expand Up @@ -4,6 +4,8 @@
#ifndef XGBOOST_COMMON_NUMERIC_H_
#define XGBOOST_COMMON_NUMERIC_H_

#include <dmlc/common.h> // OMPException

#include <algorithm> // std::max
#include <iterator> // std::iterator_traits
#include <vector>
Expand Down Expand Up @@ -106,6 +108,26 @@ inline double Reduce(Context const*, HostDeviceVector<float> const&) {
* \brief Reduction with summation.
*/
double Reduce(Context const* ctx, HostDeviceVector<float> const& values);

template <typename It>
void Iota(Context const* ctx, It first, It last,
typename std::iterator_traits<It>::value_type const& value) {
auto n = std::distance(first, last);
std::int32_t n_threads = ctx->Threads();
const size_t block_size = n / n_threads + !!(n % n_threads);
dmlc::OMPException exc;
#pragma omp parallel num_threads(n_threads)
{
exc.Run([&]() {
const size_t tid = omp_get_thread_num();
const size_t ibegin = tid * block_size;
const size_t iend = std::min(ibegin + block_size, static_cast<size_t>(n));
for (size_t i = ibegin; i < iend; ++i) {
first[i] = i + value;
}
});
}
}
} // namespace common
} // namespace xgboost

Expand Down
122 changes: 59 additions & 63 deletions src/common/partition_builder.h
View file
Expand Up @@ -17,6 +17,7 @@

#include "categorical.h"
#include "column_matrix.h"
#include "../tree/hist/expand_entry.h"
#include "xgboost/generic_parameters.h"
#include "xgboost/tree_model.h"

Expand Down Expand Up @@ -107,34 +108,42 @@ class PartitionBuilder {
}

template <typename BinIdxType, bool any_missing, bool any_cat>
void Partition(const size_t node_in_set, const size_t nid, const common::Range1d range,
void Partition(const size_t node_in_set, std::vector<xgboost::tree::CPUExpandEntry> const &nodes,
const common::Range1d range,
const bst_bin_t split_cond, GHistIndexMatrix const& gmat,
const ColumnMatrix& column_matrix, const RegTree& tree, const size_t* rid) {
const common::ColumnMatrix& column_matrix,
const RegTree& tree, const size_t* rid) {
common::Span<const size_t> rid_span(rid + range.begin(), rid + range.end());
common::Span<size_t> left = GetLeftBuffer(node_in_set, range.begin(), range.end());
common::Span<size_t> right = GetRightBuffer(node_in_set, range.begin(), range.end());
const bst_uint fid = tree[nid].SplitIndex();
const bool default_left = tree[nid].DefaultLeft();
std::size_t nid = nodes[node_in_set].nid;
bst_feature_t fid = tree[nid].SplitIndex();
bool default_left = tree[nid].DefaultLeft();
bool is_cat = tree.GetSplitTypes()[nid] == FeatureType::kCategorical;
auto node_cats = tree.NodeCats(nid);

auto const& index = gmat.index;
auto const& cut_values = gmat.cut.Values();
auto const& cut_ptrs = gmat.cut.Ptrs();

auto pred = [&](auto ridx, auto bin_id) {
auto gidx_calc = [&](auto ridx) {
auto begin = gmat.RowIdx(ridx);
if (gmat.IsDense()) {
return static_cast<bst_bin_t>(index[begin + fid]);
}
auto end = gmat.RowIdx(ridx + 1);
auto f_begin = cut_ptrs[fid];
auto f_end = cut_ptrs[fid + 1];
// bypassing the column matrix as we need the cut value instead of bin idx for categorical
// features.
return BinarySearchBin(begin, end, index, f_begin, f_end);
};

auto pred_hist = [&](auto ridx, auto bin_id) {
if (any_cat && is_cat) {
auto begin = gmat.RowIdx(ridx);
auto end = gmat.RowIdx(ridx + 1);
auto f_begin = cut_ptrs[fid];
auto f_end = cut_ptrs[fid + 1];
// bypassing the column matrix as we need the cut value instead of bin idx for categorical
// features.
auto gidx = BinarySearchBin(begin, end, index, f_begin, f_end);
bool go_left;
if (gidx == -1) {
go_left = default_left;
} else {
auto gidx = gidx_calc(ridx);
bool go_left = default_left;
if (gidx > -1) {
go_left = Decision(node_cats, cut_values[gidx], default_left);
}
return go_left;
Expand All @@ -143,25 +152,43 @@ class PartitionBuilder {
}
};

std::pair<size_t, size_t> child_nodes_sizes;
if (column_matrix.GetColumnType(fid) == xgboost::common::kDenseColumn) {
auto column = column_matrix.DenseColumn<BinIdxType, any_missing>(fid);
if (default_left) {
child_nodes_sizes = PartitionKernel<true, any_missing>(&column, rid_span, left, right,
gmat.base_rowid, pred);
} else {
child_nodes_sizes = PartitionKernel<false, any_missing>(&column, rid_span, left, right,
gmat.base_rowid, pred);
auto pred_approx = [&](auto ridx) {
auto gidx = gidx_calc(ridx);
bool go_left = default_left;
if (gidx > -1) {
if (is_cat) {
go_left = Decision(node_cats, cut_values[gidx], default_left);
} else {
go_left = cut_values[gidx] <= nodes[node_in_set].split.split_value;
}
}
return go_left;
};

std::pair<size_t, size_t> child_nodes_sizes;
if (!column_matrix.IsInitialized()) {
child_nodes_sizes = PartitionRangeKernel(rid_span, left, right, pred_approx);
} else {
CHECK_EQ(any_missing, true);
auto column = column_matrix.SparseColumn<BinIdxType>(fid, rid_span.front() - gmat.base_rowid);
if (default_left) {
child_nodes_sizes = PartitionKernel<true, any_missing>(&column, rid_span, left, right,
gmat.base_rowid, pred);
if (column_matrix.GetColumnType(fid) == xgboost::common::kDenseColumn) {
auto column = column_matrix.DenseColumn<BinIdxType, any_missing>(fid);
if (default_left) {
child_nodes_sizes = PartitionKernel<true, any_missing>(&column, rid_span, left, right,
gmat.base_rowid, pred_hist);
} else {
child_nodes_sizes = PartitionKernel<false, any_missing>(&column, rid_span, left, right,
gmat.base_rowid, pred_hist);
}
} else {
child_nodes_sizes = PartitionKernel<false, any_missing>(&column, rid_span, left, right,
gmat.base_rowid, pred);
CHECK_EQ(any_missing, true);
auto column =
column_matrix.SparseColumn<BinIdxType>(fid, rid_span.front() - gmat.base_rowid);
if (default_left) {
child_nodes_sizes = PartitionKernel<true, any_missing>(&column, rid_span, left, right,
gmat.base_rowid, pred_hist);
} else {
child_nodes_sizes = PartitionKernel<false, any_missing>(&column, rid_span, left, right,
gmat.base_rowid, pred_hist);
}
}
}

Expand All @@ -172,37 +199,6 @@ class PartitionBuilder {
SetNRightElems(node_in_set, range.begin(), n_right);
}

/**
* \brief Partition tree nodes with specific range of row indices.
*
* \tparam Pred Predicate for whether a row should be partitioned to the left node.
*
* \param node_in_set The index of node in current batch of nodes.
* \param nid The canonical node index (node index in the tree).
* \param range The range of input row index.
* \param fidx Feature index.
* \param p_row_set_collection Pointer to rows that are being partitioned.
* \param pred A callback function that returns whether current row should be
* partitioned to the left node, it should accept the row index as
* input and returns a boolean value.
*/
template <typename Pred>
void PartitionRange(const size_t node_in_set, const size_t nid, common::Range1d range,
common::RowSetCollection* p_row_set_collection, Pred pred) {
auto& row_set_collection = *p_row_set_collection;
const size_t* p_ridx = row_set_collection[nid].begin;
common::Span<const size_t> ridx(p_ridx + range.begin(), p_ridx + range.end());
common::Span<size_t> left = this->GetLeftBuffer(node_in_set, range.begin(), range.end());
common::Span<size_t> right = this->GetRightBuffer(node_in_set, range.begin(), range.end());
std::pair<size_t, size_t> child_nodes_sizes = PartitionRangeKernel(ridx, left, right, pred);

const size_t n_left = child_nodes_sizes.first;
const size_t n_right = child_nodes_sizes.second;

this->SetNLeftElems(node_in_set, range.begin(), n_left);
this->SetNRightElems(node_in_set, range.begin(), n_right);
}

// allocate thread local memory, should be called for each specific task
void AllocateForTask(size_t id) {
if (mem_blocks_[id].get() == nullptr) {
Expand Down

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