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xgboost/src/tree/updater_quantile_hist.cc
Jiaming Yuan acc110c251
[MT-TREE] Support prediction cache and model slicing. (#8968) 
- Fix prediction range.
- Support prediction cache in mt-hist.
- Support model slicing.
- Make the booster a Python iterable by defining `__iter__`.
- Cleanup removed/deprecated parameters.
- A new field in the output model `iteration_indptr` for pointing to the ranges of trees for each iteration.
2023-03-27 23:10:54 +08:00

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/**
* Copyright 2017-2023 by XGBoost Contributors
* \file updater_quantile_hist.cc
* \brief use quantized feature values to construct a tree
* \author Philip Cho, Tianqi Checn, Egor Smirnov
*/
#include <algorithm> // for max, copy, transform
#include <cstddef> // for size_t
#include <cstdint> // for uint32_t, int32_t
#include <memory> // for unique_ptr, allocator, make_unique, shared_ptr
#include <numeric> // for accumulate
#include <ostream> // for basic_ostream, char_traits, operator<<
#include <utility> // for move, swap
#include <vector> // for vector
#include "../collective/communicator-inl.h" // for Allreduce, IsDistributed
#include "../collective/communicator.h" // for Operation
#include "../common/hist_util.h" // for HistogramCuts, HistCollection
#include "../common/linalg_op.h" // for begin, cbegin, cend
#include "../common/random.h" // for ColumnSampler
#include "../common/threading_utils.h" // for ParallelFor
#include "../common/timer.h" // for Monitor
#include "../common/transform_iterator.h" // for IndexTransformIter, MakeIndexTransformIter
#include "../data/gradient_index.h" // for GHistIndexMatrix
#include "common_row_partitioner.h" // for CommonRowPartitioner
#include "dmlc/omp.h" // for omp_get_thread_num
#include "dmlc/registry.h" // for DMLC_REGISTRY_FILE_TAG
#include "driver.h" // for Driver
#include "hist/evaluate_splits.h" // for HistEvaluator, HistMultiEvaluator, UpdatePre...
#include "hist/expand_entry.h" // for MultiExpandEntry, CPUExpandEntry
#include "hist/histogram.h" // for HistogramBuilder, ConstructHistSpace
#include "hist/sampler.h" // for SampleGradient
#include "param.h" // for TrainParam, SplitEntryContainer, GradStats
#include "xgboost/base.h" // for GradientPairInternal, GradientPair, bst_targ...
#include "xgboost/context.h" // for Context
#include "xgboost/data.h" // for BatchIterator, BatchSet, DMatrix, MetaInfo
#include "xgboost/host_device_vector.h" // for HostDeviceVector
#include "xgboost/linalg.h" // for All, MatrixView, TensorView, Matrix, Empty
#include "xgboost/logging.h" // for LogCheck_EQ, CHECK_EQ, CHECK, LogCheck_GE
#include "xgboost/span.h" // for Span, operator!=, SpanIterator
#include "xgboost/string_view.h" // for operator<<
#include "xgboost/task.h" // for ObjInfo
#include "xgboost/tree_model.h" // for RegTree, MTNotImplemented, RTreeNodeStat
#include "xgboost/tree_updater.h" // for TreeUpdater, TreeUpdaterReg, XGBOOST_REGISTE...
namespace xgboost::tree {
DMLC_REGISTRY_FILE_TAG(updater_quantile_hist);
BatchParam HistBatch(TrainParam const *param) { return {param->max_bin, param->sparse_threshold}; }
template <typename ExpandEntry, typename Updater>
void UpdateTree(common::Monitor *monitor_, linalg::MatrixView<GradientPair const> gpair,
Updater *updater, DMatrix *p_fmat, TrainParam const *param,
HostDeviceVector<bst_node_t> *p_out_position, RegTree *p_tree) {
monitor_->Start(__func__);
updater->InitData(p_fmat, p_tree);
Driver<ExpandEntry> driver{*param};
auto const &tree = *p_tree;
driver.Push(updater->InitRoot(p_fmat, gpair, p_tree));
auto expand_set = driver.Pop();
/**
* Note for update position
* Root:
* Not applied: No need to update position as initialization has got all the rows ordered.
* Applied: Update position is run on applied nodes so the rows are partitioned.
* Non-root:
* Not applied: That node is root of the subtree, same rule as root.
* Applied: Ditto
*/
while (!expand_set.empty()) {
// candidates that can be further splited.
std::vector<ExpandEntry> valid_candidates;
// candidaates that can be applied.
std::vector<ExpandEntry> applied;
for (auto const &candidate : expand_set) {
updater->ApplyTreeSplit(candidate, p_tree);
CHECK_GT(p_tree->LeftChild(candidate.nid), candidate.nid);
applied.push_back(candidate);
if (driver.IsChildValid(candidate)) {
valid_candidates.emplace_back(candidate);
}
}
updater->UpdatePosition(p_fmat, p_tree, applied);
std::vector<ExpandEntry> best_splits;
if (!valid_candidates.empty()) {
updater->BuildHistogram(p_fmat, p_tree, valid_candidates, gpair);
for (auto const &candidate : valid_candidates) {
auto left_child_nidx = tree.LeftChild(candidate.nid);
auto right_child_nidx = tree.RightChild(candidate.nid);
ExpandEntry l_best{left_child_nidx, tree.GetDepth(left_child_nidx)};
ExpandEntry r_best{right_child_nidx, tree.GetDepth(right_child_nidx)};
best_splits.push_back(l_best);
best_splits.push_back(r_best);
}
updater->EvaluateSplits(p_fmat, p_tree, &best_splits);
}
driver.Push(best_splits.begin(), best_splits.end());
expand_set = driver.Pop();
}
auto &h_out_position = p_out_position->HostVector();
updater->LeafPartition(tree, gpair, &h_out_position);
monitor_->Stop(__func__);
}
/**
* \brief Updater for building multi-target trees. The implementation simply iterates over
* each target.
*/
class MultiTargetHistBuilder {
private:
common::Monitor *monitor_{nullptr};
TrainParam const *param_{nullptr};
std::shared_ptr<common::ColumnSampler> col_sampler_;
std::unique_ptr<HistMultiEvaluator> evaluator_;
// Histogram builder for each target.
std::vector<HistogramBuilder<MultiExpandEntry>> histogram_builder_;
Context const *ctx_{nullptr};
// Partitioner for each data batch.
std::vector<CommonRowPartitioner> partitioner_;
// Pointer to last updated tree, used for update prediction cache.
RegTree const *p_last_tree_{nullptr};
DMatrix const * p_last_fmat_{nullptr};
ObjInfo const *task_{nullptr};
public:
void UpdatePosition(DMatrix *p_fmat, RegTree const *p_tree,
std::vector<MultiExpandEntry> const &applied) {
monitor_->Start(__func__);
std::size_t page_id{0};
for (auto const &page : p_fmat->GetBatches<GHistIndexMatrix>(HistBatch(this->param_))) {
this->partitioner_.at(page_id).UpdatePosition(this->ctx_, page, applied, p_tree);
page_id++;
}
monitor_->Stop(__func__);
}
void ApplyTreeSplit(MultiExpandEntry const &candidate, RegTree *p_tree) {
this->evaluator_->ApplyTreeSplit(candidate, p_tree);
}
void InitData(DMatrix *p_fmat, RegTree const *p_tree) {
monitor_->Start(__func__);
p_last_fmat_ = p_fmat;
std::size_t page_id = 0;
bst_bin_t n_total_bins = 0;
partitioner_.clear();
for (auto const &page : p_fmat->GetBatches<GHistIndexMatrix>(HistBatch(param_))) {
if (n_total_bins == 0) {
n_total_bins = page.cut.TotalBins();
} else {
CHECK_EQ(n_total_bins, page.cut.TotalBins());
}
partitioner_.emplace_back(ctx_, page.Size(), page.base_rowid, p_fmat->IsColumnSplit());
page_id++;
}
bst_target_t n_targets = p_tree->NumTargets();
histogram_builder_.clear();
for (std::size_t i = 0; i < n_targets; ++i) {
histogram_builder_.emplace_back();
histogram_builder_.back().Reset(n_total_bins, HistBatch(param_), ctx_->Threads(), page_id,
collective::IsDistributed(), p_fmat->IsColumnSplit());
}
evaluator_ = std::make_unique<HistMultiEvaluator>(ctx_, p_fmat->Info(), param_, col_sampler_);
p_last_tree_ = p_tree;
monitor_->Stop(__func__);
}
MultiExpandEntry InitRoot(DMatrix *p_fmat, linalg::MatrixView<GradientPair const> gpair,
RegTree *p_tree) {
monitor_->Start(__func__);
MultiExpandEntry best;
best.nid = RegTree::kRoot;
best.depth = 0;
auto n_targets = p_tree->NumTargets();
linalg::Matrix<GradientPairPrecise> root_sum_tloc =
linalg::Empty<GradientPairPrecise>(ctx_, ctx_->Threads(), n_targets);
CHECK_EQ(root_sum_tloc.Shape(1), gpair.Shape(1));
auto h_root_sum_tloc = root_sum_tloc.HostView();
common::ParallelFor(gpair.Shape(0), ctx_->Threads(), [&](auto i) {
for (bst_target_t t{0}; t < n_targets; ++t) {
h_root_sum_tloc(omp_get_thread_num(), t) += GradientPairPrecise{gpair(i, t)};
}
});
// Aggregate to the first row.
auto root_sum = h_root_sum_tloc.Slice(0, linalg::All());
for (std::int32_t tidx{1}; tidx < ctx_->Threads(); ++tidx) {
for (bst_target_t t{0}; t < n_targets; ++t) {
root_sum(t) += h_root_sum_tloc(tidx, t);
}
}
CHECK(root_sum.CContiguous());
collective::Allreduce<collective::Operation::kSum>(
reinterpret_cast<double *>(root_sum.Values().data()), root_sum.Size() * 2);
std::vector<MultiExpandEntry> nodes{best};
std::size_t i = 0;
auto space = ConstructHistSpace(partitioner_, nodes);
for (auto const &page : p_fmat->GetBatches<GHistIndexMatrix>(HistBatch(param_))) {
for (bst_target_t t{0}; t < n_targets; ++t) {
auto t_gpair = gpair.Slice(linalg::All(), t);
histogram_builder_[t].BuildHist(i, space, page, p_tree, partitioner_.at(i).Partitions(),
nodes, {}, t_gpair.Values());
}
i++;
}
auto weight = evaluator_->InitRoot(root_sum);
auto weight_t = weight.HostView();
std::transform(linalg::cbegin(weight_t), linalg::cend(weight_t), linalg::begin(weight_t),
[&](float w) { return w * param_->learning_rate; });
p_tree->SetLeaf(RegTree::kRoot, weight_t);
std::vector<common::HistCollection const *> hists;
for (bst_target_t t{0}; t < p_tree->NumTargets(); ++t) {
hists.push_back(&histogram_builder_[t].Histogram());
}
for (auto const &gmat : p_fmat->GetBatches<GHistIndexMatrix>(HistBatch(param_))) {
evaluator_->EvaluateSplits(*p_tree, hists, gmat.cut, &nodes);
break;
}
monitor_->Stop(__func__);
return nodes.front();
}
void BuildHistogram(DMatrix *p_fmat, RegTree const *p_tree,
std::vector<MultiExpandEntry> const &valid_candidates,
linalg::MatrixView<GradientPair const> gpair) {
monitor_->Start(__func__);
std::vector<MultiExpandEntry> nodes_to_build;
std::vector<MultiExpandEntry> nodes_to_sub;
for (auto const &c : valid_candidates) {
auto left_nidx = p_tree->LeftChild(c.nid);
auto right_nidx = p_tree->RightChild(c.nid);
auto build_nidx = left_nidx;
auto subtract_nidx = right_nidx;
auto lit =
common::MakeIndexTransformIter([&](auto i) { return c.split.left_sum[i].GetHess(); });
auto left_sum = std::accumulate(lit, lit + c.split.left_sum.size(), .0);
auto rit =
common::MakeIndexTransformIter([&](auto i) { return c.split.right_sum[i].GetHess(); });
auto right_sum = std::accumulate(rit, rit + c.split.right_sum.size(), .0);
auto fewer_right = right_sum < left_sum;
if (fewer_right) {
std::swap(build_nidx, subtract_nidx);
}
nodes_to_build.emplace_back(build_nidx, p_tree->GetDepth(build_nidx));
nodes_to_sub.emplace_back(subtract_nidx, p_tree->GetDepth(subtract_nidx));
}
std::size_t i = 0;
auto space = ConstructHistSpace(partitioner_, nodes_to_build);
for (auto const &page : p_fmat->GetBatches<GHistIndexMatrix>(HistBatch(param_))) {
for (std::size_t t = 0; t < p_tree->NumTargets(); ++t) {
auto t_gpair = gpair.Slice(linalg::All(), t);
// Make sure the gradient matrix is f-order.
CHECK(t_gpair.Contiguous());
histogram_builder_[t].BuildHist(i, space, page, p_tree, partitioner_.at(i).Partitions(),
nodes_to_build, nodes_to_sub, t_gpair.Values());
}
i++;
}
monitor_->Stop(__func__);
}
void EvaluateSplits(DMatrix *p_fmat, RegTree const *p_tree,
std::vector<MultiExpandEntry> *best_splits) {
monitor_->Start(__func__);
std::vector<common::HistCollection const *> hists;
for (bst_target_t t{0}; t < p_tree->NumTargets(); ++t) {
hists.push_back(&histogram_builder_[t].Histogram());
}
for (auto const &gmat : p_fmat->GetBatches<GHistIndexMatrix>(HistBatch(param_))) {
evaluator_->EvaluateSplits(*p_tree, hists, gmat.cut, best_splits);
break;
}
monitor_->Stop(__func__);
}
void LeafPartition(RegTree const &tree, linalg::MatrixView<GradientPair const> gpair,
std::vector<bst_node_t> *p_out_position) {
monitor_->Start(__func__);
if (!task_->UpdateTreeLeaf()) {
return;
}
for (auto const &part : partitioner_) {
part.LeafPartition(ctx_, tree, gpair, p_out_position);
}
monitor_->Stop(__func__);
}
public:
explicit MultiTargetHistBuilder(Context const *ctx, MetaInfo const &info, TrainParam const *param,
std::shared_ptr<common::ColumnSampler> column_sampler,
ObjInfo const *task, common::Monitor *monitor)
: monitor_{monitor},
param_{param},
col_sampler_{std::move(column_sampler)},
evaluator_{std::make_unique<HistMultiEvaluator>(ctx, info, param, col_sampler_)},
ctx_{ctx},
task_{task} {
monitor_->Init(__func__);
}
bool UpdatePredictionCache(DMatrix const *data, linalg::MatrixView<float> out_preds) const {
// p_last_fmat_ is a valid pointer as long as UpdatePredictionCache() is called in
// conjunction with Update().
if (!p_last_fmat_ || !p_last_tree_ || data != p_last_fmat_) {
return false;
}
monitor_->Start(__func__);
CHECK_EQ(out_preds.Size(), data->Info().num_row_ * p_last_tree_->NumTargets());
UpdatePredictionCacheImpl(ctx_, p_last_tree_, partitioner_, out_preds);
monitor_->Stop(__func__);
return true;
}
};
class HistBuilder {
private:
common::Monitor *monitor_;
TrainParam const *param_;
std::shared_ptr<common::ColumnSampler> col_sampler_;
std::unique_ptr<HistEvaluator<CPUExpandEntry>> evaluator_;
std::vector<CommonRowPartitioner> partitioner_;
// back pointers to tree and data matrix
const RegTree *p_last_tree_{nullptr};
DMatrix const *const p_last_fmat_{nullptr};
std::unique_ptr<HistogramBuilder<CPUExpandEntry>> histogram_builder_;
ObjInfo const *task_{nullptr};
// Context for number of threads
Context const *ctx_{nullptr};
public:
explicit HistBuilder(Context const *ctx, std::shared_ptr<common::ColumnSampler> column_sampler,
TrainParam const *param, DMatrix const *fmat, ObjInfo const *task,
common::Monitor *monitor)
: monitor_{monitor},
param_{param},
col_sampler_{std::move(column_sampler)},
evaluator_{std::make_unique<HistEvaluator<CPUExpandEntry>>(ctx, param, fmat->Info(),
col_sampler_)},
p_last_fmat_(fmat),
histogram_builder_{new HistogramBuilder<CPUExpandEntry>},
task_{task},
ctx_{ctx} {
monitor_->Init(__func__);
}
bool UpdatePredictionCache(DMatrix const *data, linalg::MatrixView<float> out_preds) const {
// p_last_fmat_ is a valid pointer as long as UpdatePredictionCache() is called in
// conjunction with Update().
if (!p_last_fmat_ || !p_last_tree_ || data != p_last_fmat_) {
return false;
}
monitor_->Start(__func__);
CHECK_EQ(out_preds.Size(), data->Info().num_row_);
UpdatePredictionCacheImpl(ctx_, p_last_tree_, partitioner_, out_preds);
monitor_->Stop(__func__);
return true;
}
public:
// initialize temp data structure
void InitData(DMatrix *fmat, RegTree const *p_tree) {
monitor_->Start(__func__);
std::size_t page_id{0};
bst_bin_t n_total_bins{0};
partitioner_.clear();
for (auto const &page : fmat->GetBatches<GHistIndexMatrix>(HistBatch(param_))) {
if (n_total_bins == 0) {
n_total_bins = page.cut.TotalBins();
} else {
CHECK_EQ(n_total_bins, page.cut.TotalBins());
}
partitioner_.emplace_back(this->ctx_, page.Size(), page.base_rowid, fmat->IsColumnSplit());
++page_id;
}
histogram_builder_->Reset(n_total_bins, HistBatch(param_), ctx_->Threads(), page_id,
collective::IsDistributed(), fmat->IsColumnSplit());
evaluator_ = std::make_unique<HistEvaluator<CPUExpandEntry>>(ctx_, this->param_, fmat->Info(),
col_sampler_);
p_last_tree_ = p_tree;
}
void EvaluateSplits(DMatrix *p_fmat, RegTree const *p_tree,
std::vector<CPUExpandEntry> *best_splits) {
monitor_->Start(__func__);
auto const &histograms = histogram_builder_->Histogram();
auto ft = p_fmat->Info().feature_types.ConstHostSpan();
for (auto const &gmat : p_fmat->GetBatches<GHistIndexMatrix>(HistBatch(param_))) {
evaluator_->EvaluateSplits(histograms, gmat.cut, ft, *p_tree, best_splits);
break;
}
monitor_->Stop(__func__);
}
void ApplyTreeSplit(CPUExpandEntry const &candidate, RegTree *p_tree) {
this->evaluator_->ApplyTreeSplit(candidate, p_tree);
}
CPUExpandEntry InitRoot(DMatrix *p_fmat, linalg::MatrixView<GradientPair const> gpair,
RegTree *p_tree) {
CPUExpandEntry node(RegTree::kRoot, p_tree->GetDepth(0));
std::size_t page_id = 0;
auto space = ConstructHistSpace(partitioner_, {node});
for (auto const &gidx : p_fmat->GetBatches<GHistIndexMatrix>(HistBatch(param_))) {
std::vector<CPUExpandEntry> nodes_to_build{node};
std::vector<CPUExpandEntry> nodes_to_sub;
this->histogram_builder_->BuildHist(page_id, space, gidx, p_tree,
partitioner_.at(page_id).Partitions(), nodes_to_build,
nodes_to_sub, gpair.Slice(linalg::All(), 0).Values());
++page_id;
}
{
GradientPairPrecise grad_stat;
if (p_fmat->IsDense()) {
/**
* Specialized code for dense data: For dense data (with no missing value), the sum
* of gradient histogram is equal to snode[nid]
*/
auto const &gmat = *(p_fmat->GetBatches<GHistIndexMatrix>(HistBatch(param_)).begin());
std::vector<std::uint32_t> const &row_ptr = gmat.cut.Ptrs();
CHECK_GE(row_ptr.size(), 2);
std::uint32_t const ibegin = row_ptr[0];
std::uint32_t const iend = row_ptr[1];
auto hist = this->histogram_builder_->Histogram()[RegTree::kRoot];
auto begin = hist.data();
for (std::uint32_t i = ibegin; i < iend; ++i) {
GradientPairPrecise const &et = begin[i];
grad_stat.Add(et.GetGrad(), et.GetHess());
}
} else {
auto gpair_h = gpair.Slice(linalg::All(), 0).Values();
for (auto const &grad : gpair_h) {
grad_stat.Add(grad.GetGrad(), grad.GetHess());
}
collective::Allreduce<collective::Operation::kSum>(reinterpret_cast<double *>(&grad_stat),
2);
}
auto weight = evaluator_->InitRoot(GradStats{grad_stat});
p_tree->Stat(RegTree::kRoot).sum_hess = grad_stat.GetHess();
p_tree->Stat(RegTree::kRoot).base_weight = weight;
(*p_tree)[RegTree::kRoot].SetLeaf(param_->learning_rate * weight);
std::vector<CPUExpandEntry> entries{node};
monitor_->Start("EvaluateSplits");
auto ft = p_fmat->Info().feature_types.ConstHostSpan();
for (auto const &gmat : p_fmat->GetBatches<GHistIndexMatrix>(HistBatch(param_))) {
evaluator_->EvaluateSplits(histogram_builder_->Histogram(), gmat.cut, ft, *p_tree,
&entries);
break;
}
monitor_->Stop("EvaluateSplits");
node = entries.front();
}
return node;
}
void BuildHistogram(DMatrix *p_fmat, RegTree *p_tree,
std::vector<CPUExpandEntry> const &valid_candidates,
linalg::MatrixView<GradientPair const> gpair) {
std::vector<CPUExpandEntry> nodes_to_build(valid_candidates.size());
std::vector<CPUExpandEntry> nodes_to_sub(valid_candidates.size());
std::size_t n_idx = 0;
for (auto const &c : valid_candidates) {
auto left_nidx = (*p_tree)[c.nid].LeftChild();
auto right_nidx = (*p_tree)[c.nid].RightChild();
auto fewer_right = c.split.right_sum.GetHess() < c.split.left_sum.GetHess();
auto build_nidx = left_nidx;
auto subtract_nidx = right_nidx;
if (fewer_right) {
std::swap(build_nidx, subtract_nidx);
}
nodes_to_build[n_idx] = CPUExpandEntry{build_nidx, p_tree->GetDepth(build_nidx), {}};
nodes_to_sub[n_idx] = CPUExpandEntry{subtract_nidx, p_tree->GetDepth(subtract_nidx), {}};
n_idx++;
}
std::size_t page_id{0};
auto space = ConstructHistSpace(partitioner_, nodes_to_build);
for (auto const &gidx : p_fmat->GetBatches<GHistIndexMatrix>(HistBatch(param_))) {
histogram_builder_->BuildHist(page_id, space, gidx, p_tree,
partitioner_.at(page_id).Partitions(), nodes_to_build,
nodes_to_sub, gpair.Values());
++page_id;
}
}
void UpdatePosition(DMatrix *p_fmat, RegTree const *p_tree,
std::vector<CPUExpandEntry> const &applied) {
monitor_->Start(__func__);
std::size_t page_id{0};
for (auto const &page : p_fmat->GetBatches<GHistIndexMatrix>(HistBatch(this->param_))) {
this->partitioner_.at(page_id).UpdatePosition(this->ctx_, page, applied, p_tree);
page_id++;
}
monitor_->Stop(__func__);
}
void LeafPartition(RegTree const &tree, linalg::MatrixView<GradientPair const> gpair,
std::vector<bst_node_t> *p_out_position) {
monitor_->Start(__func__);
if (!task_->UpdateTreeLeaf()) {
return;
}
for (auto const &part : partitioner_) {
part.LeafPartition(ctx_, tree, gpair, p_out_position);
}
monitor_->Stop(__func__);
}
};
/*! \brief construct a tree using quantized feature values */
class QuantileHistMaker : public TreeUpdater {
std::unique_ptr<HistBuilder> p_impl_{nullptr};
std::unique_ptr<MultiTargetHistBuilder> p_mtimpl_{nullptr};
std::shared_ptr<common::ColumnSampler> column_sampler_ =
std::make_shared<common::ColumnSampler>();
common::Monitor monitor_;
ObjInfo const *task_{nullptr};
public:
explicit QuantileHistMaker(Context const *ctx, ObjInfo const *task)
: TreeUpdater{ctx}, task_{task} {}
void Configure(const Args &) override {}
void LoadConfig(Json const &) override {}
void SaveConfig(Json *) const override {}
[[nodiscard]] char const *Name() const override { return "grow_quantile_histmaker"; }
void Update(TrainParam const *param, HostDeviceVector<GradientPair> *gpair, DMatrix *p_fmat,
common::Span<HostDeviceVector<bst_node_t>> out_position,
const std::vector<RegTree *> &trees) override {
if (trees.front()->IsMultiTarget()) {
CHECK(param->monotone_constraints.empty()) << "monotone constraint" << MTNotImplemented();
if (!p_mtimpl_) {
this->p_mtimpl_ = std::make_unique<MultiTargetHistBuilder>(
ctx_, p_fmat->Info(), param, column_sampler_, task_, &monitor_);
}
} else {
if (!p_impl_) {
p_impl_ =
std::make_unique<HistBuilder>(ctx_, column_sampler_, param, p_fmat, task_, &monitor_);
}
}
bst_target_t n_targets = trees.front()->NumTargets();
auto h_gpair =
linalg::MakeTensorView(ctx_, gpair->HostSpan(), p_fmat->Info().num_row_, n_targets);
linalg::Matrix<GradientPair> sample_out;
auto h_sample_out = h_gpair;
auto need_copy = [&] { return trees.size() > 1 || n_targets > 1; };
if (need_copy()) {
// allocate buffer
sample_out = decltype(sample_out){h_gpair.Shape(), ctx_->gpu_id, linalg::Order::kF};
h_sample_out = sample_out.HostView();
}
for (auto tree_it = trees.begin(); tree_it != trees.end(); ++tree_it) {
if (need_copy()) {
// Copy gradient into buffer for sampling. This converts C-order to F-order.
std::copy(linalg::cbegin(h_gpair), linalg::cend(h_gpair), linalg::begin(h_sample_out));
}
SampleGradient(ctx_, *param, h_sample_out);
auto *h_out_position = &out_position[tree_it - trees.begin()];
if ((*tree_it)->IsMultiTarget()) {
UpdateTree<MultiExpandEntry>(&monitor_, h_sample_out, p_mtimpl_.get(), p_fmat, param,
h_out_position, *tree_it);
} else {
UpdateTree<CPUExpandEntry>(&monitor_, h_sample_out, p_impl_.get(), p_fmat, param,
h_out_position, *tree_it);
}
}
}
bool UpdatePredictionCache(const DMatrix *data, linalg::MatrixView<float> out_preds) override {
if (p_impl_) {
return p_impl_->UpdatePredictionCache(data, out_preds);
} else if (p_mtimpl_) {
return p_mtimpl_->UpdatePredictionCache(data, out_preds);
} else {
return false;
}
}
[[nodiscard]] bool HasNodePosition() const override { return true; }
};
XGBOOST_REGISTER_TREE_UPDATER(QuantileHistMaker, "grow_quantile_histmaker")
.describe("Grow tree using quantized histogram.")
.set_body([](Context const *ctx, ObjInfo const *task) {
return new QuantileHistMaker{ctx, task};
});
} // namespace xgboost::tree
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