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Extract evaluate splits from CPU hist. (#7079)
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Other than modularizing the split evaluation function, this PR also removes some more functions including `InitNewNodes` and `BuildNodeStats` among some other unused variables.  Also, scattered code like setting leaf weights is grouped into the split evaluator and `NodeEntry` is simplified and made private.  Another subtle difference with the original implementation is that the modified code doesn't call `tree[nidx].Parent()` to traversal upward.
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@trivialfis
trivialfis committed Jul 7, 2021
1 parent d22b293 commit 615ab2b
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268 changes: 268 additions & 0 deletions src/tree/hist/evaluate_splits.h
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/*!
* Copyright 2021 by XGBoost Contributors
*/
#ifndef XGBOOST_TREE_HIST_EVALUATE_SPLITS_H_
#define XGBOOST_TREE_HIST_EVALUATE_SPLITS_H_

#include <algorithm>
#include <memory>
#include <limits>
#include <utility>
#include <vector>

#include "../param.h"
#include "../constraints.h"
#include "../split_evaluator.h"
#include "../../common/random.h"
#include "../../common/hist_util.h"
#include "../../data/gradient_index.h"

namespace xgboost {
namespace tree {

template <typename GradientSumT, typename ExpandEntry> class HistEvaluator {
private:
struct NodeEntry {
/*! \brief statics for node entry */
GradStats stats;
/*! \brief loss of this node, without split */
bst_float root_gain{0.0f};
};

private:
TrainParam param_;
std::shared_ptr<common::ColumnSampler> column_sampler_;
TreeEvaluator tree_evaluator_;
int32_t n_threads_ {0};
FeatureInteractionConstraintHost interaction_constraints_;
std::vector<NodeEntry> snode_;

// if sum of statistics for non-missing values in the node
// is equal to sum of statistics for all values:
// then - there are no missing values
// else - there are missing values
bool static SplitContainsMissingValues(const GradStats e,
const NodeEntry &snode) {
if (e.GetGrad() == snode.stats.GetGrad() &&
e.GetHess() == snode.stats.GetHess()) {
return false;
} else {
return true;
}
}

// Enumerate/Scan the split values of specific feature
// Returns the sum of gradients corresponding to the data points that contains
// a non-missing value for the particular feature fid.
template <int d_step>
GradStats EnumerateSplit(
const GHistIndexMatrix &gmat, const common::GHistRow<GradientSumT> &hist,
const NodeEntry &snode, SplitEntry *p_best, bst_feature_t fidx,
bst_node_t nidx,
TreeEvaluator::SplitEvaluator<TrainParam> const &evaluator) const {
static_assert(d_step == +1 || d_step == -1, "Invalid step.");

// aliases
const std::vector<uint32_t> &cut_ptr = gmat.cut.Ptrs();
const std::vector<bst_float> &cut_val = gmat.cut.Values();

// statistics on both sides of split
GradStats c;
GradStats e;
// best split so far
SplitEntry best;

// bin boundaries
CHECK_LE(cut_ptr[fidx],
static_cast<uint32_t>(std::numeric_limits<int32_t>::max()));
CHECK_LE(cut_ptr[fidx + 1],
static_cast<uint32_t>(std::numeric_limits<int32_t>::max()));
// imin: index (offset) of the minimum value for feature fid
// need this for backward enumeration
const auto imin = static_cast<int32_t>(cut_ptr[fidx]);
// ibegin, iend: smallest/largest cut points for feature fid
// use int to allow for value -1
int32_t ibegin, iend;
if (d_step > 0) {
ibegin = static_cast<int32_t>(cut_ptr[fidx]);
iend = static_cast<int32_t>(cut_ptr.at(fidx + 1));
} else {
ibegin = static_cast<int32_t>(cut_ptr[fidx + 1]) - 1;
iend = static_cast<int32_t>(cut_ptr[fidx]) - 1;
}

for (int32_t i = ibegin; i != iend; i += d_step) {
// start working
// try to find a split
e.Add(hist[i].GetGrad(), hist[i].GetHess());
if (e.GetHess() >= param_.min_child_weight) {
c.SetSubstract(snode.stats, e);
if (c.GetHess() >= param_.min_child_weight) {
bst_float loss_chg;
bst_float split_pt;
if (d_step > 0) {
// forward enumeration: split at right bound of each bin
loss_chg = static_cast<bst_float>(
evaluator.CalcSplitGain(param_, nidx, fidx, GradStats{e},
GradStats{c}) -
snode.root_gain);
split_pt = cut_val[i];
best.Update(loss_chg, fidx, split_pt, d_step == -1, e, c);
} else {
// backward enumeration: split at left bound of each bin
loss_chg = static_cast<bst_float>(
evaluator.CalcSplitGain(param_, nidx, fidx, GradStats{c},
GradStats{e}) -
snode.root_gain);
if (i == imin) {
// for leftmost bin, left bound is the smallest feature value
split_pt = gmat.cut.MinValues()[fidx];
} else {
split_pt = cut_val[i - 1];
}
best.Update(loss_chg, fidx, split_pt, d_step == -1, c, e);
}
}
}
}
p_best->Update(best);

return e;
}

public:
void EvaluateSplits(const common::HistCollection<GradientSumT> &hist,
GHistIndexMatrix const &gidx, const RegTree &tree,
std::vector<ExpandEntry>* p_entries) {
auto& entries = *p_entries;
// All nodes are on the same level, so we can store the shared ptr.
std::vector<std::shared_ptr<HostDeviceVector<bst_feature_t>>> features(
entries.size());
for (size_t nidx_in_set = 0; nidx_in_set < entries.size(); ++nidx_in_set) {
auto nidx = entries[nidx_in_set].nid;
features[nidx_in_set] =
column_sampler_->GetFeatureSet(tree.GetDepth(nidx));
}
CHECK(!features.empty());
const size_t grain_size =
std::max<size_t>(1, features.front()->Size() / n_threads_);
common::BlockedSpace2d space(entries.size(), [&](size_t nidx_in_set) {
return features[nidx_in_set]->Size();
}, grain_size);

std::vector<ExpandEntry> tloc_candidates(omp_get_max_threads() * entries.size());
for (size_t i = 0; i < entries.size(); ++i) {
for (decltype(n_threads_) j = 0; j < n_threads_; ++j) {
tloc_candidates[i * n_threads_ + j] = entries[i];
}
}
auto evaluator = tree_evaluator_.GetEvaluator();

common::ParallelFor2d(space, n_threads_, [&](size_t nidx_in_set, common::Range1d r) {
auto tidx = omp_get_thread_num();
auto entry = &tloc_candidates[n_threads_ * nidx_in_set + tidx];
auto best = &entry->split;
auto nidx = entry->nid;
auto histogram = hist[nidx];
auto features_set = features[nidx_in_set]->ConstHostSpan();
for (auto fidx_in_set = r.begin(); fidx_in_set < r.end(); fidx_in_set++) {
auto fidx = features_set[fidx_in_set];
if (interaction_constraints_.Query(nidx, fidx)) {
auto grad_stats = EnumerateSplit<+1>(gidx, histogram, snode_[nidx],
best, fidx, nidx, evaluator);
if (SplitContainsMissingValues(grad_stats, snode_[nidx])) {
EnumerateSplit<-1>(gidx, histogram, snode_[nidx], best, fidx, nidx,
evaluator);
}
}
}
});

for (unsigned nidx_in_set = 0; nidx_in_set < entries.size();
++nidx_in_set) {
for (auto tidx = 0; tidx < n_threads_; ++tidx) {
entries[nidx_in_set].split.Update(
tloc_candidates[n_threads_ * nidx_in_set + tidx].split);
}
}
}
// Add splits to tree, handles all statistic
void ApplyTreeSplit(ExpandEntry candidate, RegTree *p_tree) {
auto evaluator = tree_evaluator_.GetEvaluator();
RegTree &tree = *p_tree;

GradStats parent_sum = candidate.split.left_sum;
parent_sum.Add(candidate.split.right_sum);
auto base_weight =
evaluator.CalcWeight(candidate.nid, param_, GradStats{parent_sum});

auto left_weight = evaluator.CalcWeight(
candidate.nid, param_, GradStats{candidate.split.left_sum});
auto right_weight = evaluator.CalcWeight(
candidate.nid, param_, GradStats{candidate.split.right_sum});

tree.ExpandNode(candidate.nid, candidate.split.SplitIndex(),
candidate.split.split_value, candidate.split.DefaultLeft(),
base_weight, left_weight * param_.learning_rate,
right_weight * param_.learning_rate,
candidate.split.loss_chg, parent_sum.GetHess(),
candidate.split.left_sum.GetHess(),
candidate.split.right_sum.GetHess());

// Set up child constraints
auto left_child = tree[candidate.nid].LeftChild();
auto right_child = tree[candidate.nid].RightChild();
tree_evaluator_.AddSplit(candidate.nid, left_child, right_child,
tree[candidate.nid].SplitIndex(), left_weight,
right_weight);

auto max_node = std::max(left_child, tree[candidate.nid].RightChild());
max_node = std::max(candidate.nid, max_node);
snode_.resize(tree.GetNodes().size());
snode_.at(left_child).stats = candidate.split.left_sum;
snode_.at(left_child).root_gain = evaluator.CalcGain(
candidate.nid, param_, GradStats{candidate.split.left_sum});
snode_.at(right_child).stats = candidate.split.right_sum;
snode_.at(right_child).root_gain = evaluator.CalcGain(
candidate.nid, param_, GradStats{candidate.split.right_sum});

interaction_constraints_.Split(candidate.nid,
tree[candidate.nid].SplitIndex(), left_child,
right_child);
}

auto Evaluator() const { return tree_evaluator_.GetEvaluator(); }
auto const& Stats() const { return snode_; }

float InitRoot(GradStats const& root_sum) {
snode_.resize(1);
auto root_evaluator = tree_evaluator_.GetEvaluator();

snode_[0].stats = GradStats{root_sum.GetGrad(), root_sum.GetHess()};
snode_[0].root_gain = root_evaluator.CalcGain(RegTree::kRoot, param_,
GradStats{snode_[0].stats});
auto weight = root_evaluator.CalcWeight(RegTree::kRoot, param_,
GradStats{snode_[0].stats});
return weight;
}

public:
// The column sampler must be constructed by caller since we need to preserve the rng
// for the entire training session.
explicit HistEvaluator(TrainParam const &param, MetaInfo const &info,
int32_t n_threads,
std::shared_ptr<common::ColumnSampler> sampler,
bool skip_0_index = false)
: param_{param}, column_sampler_{std::move(sampler)},
tree_evaluator_{param, static_cast<bst_feature_t>(info.num_col_),
GenericParameter::kCpuId},
n_threads_{n_threads} {
interaction_constraints_.Configure(param, info.num_col_);
column_sampler_->Init(info.num_col_, info.feature_weigths.HostVector(),
param_.colsample_bynode, param_.colsample_bylevel,
param_.colsample_bytree, skip_0_index);
}
};
} // namespace tree
} // namespace xgboost
#endif // XGBOOST_TREE_HIST_EVALUATE_SPLITS_H_

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