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Remove omp_get_max_threads in tree updaters. (#7590)

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Jiaming Yuan 2022-01-26 19:55:47 +08:00 committed by GitHub
parent 24789429fd
commit 5d7818e75d
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19 changed files with 142 additions and 199 deletions
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17
include/xgboost/data.h
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@ -302,22 +302,7 @@ class SparsePage {
SparsePage GetTranspose(int num_columns, int32_t n_threads) const;
void SortRows() {
auto ncol = static_cast<bst_omp_uint>(this->Size());
dmlc::OMPException exc;
#pragma omp parallel for schedule(dynamic, 1)
for (bst_omp_uint i = 0; i < ncol; ++i) {
exc.Run([&]() {
if (this->offset.HostVector()[i] < this->offset.HostVector()[i + 1]) {
std::sort(
this->data.HostVector().begin() + this->offset.HostVector()[i],
this->data.HostVector().begin() + this->offset.HostVector()[i + 1],
Entry::CmpValue);
}
});
}
exc.Rethrow();
}
void SortRows(int32_t n_threads);
/**
* \brief Pushes external data batch onto this page

4
include/xgboost/tree_updater.h
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@ -1,5 +1,5 @@
/*!
* Copyright 2014-2019 by Contributors
* Copyright 2014-2022 by XGBoost Contributors
* \file tree_updater.h
* \brief General primitive for tree learning,
* Updating a collection of trees given the information.
@ -32,7 +32,7 @@ class Json;
*/
class TreeUpdater : public Configurable {
protected:
GenericParameter const* tparam_;
GenericParameter const* ctx_;
public:
/*! \brief virtual destructor */

16
src/common/column_matrix.h
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@ -146,8 +146,7 @@ class ColumnMatrix {
}
// construct column matrix from GHistIndexMatrix
inline void Init(const GHistIndexMatrix& gmat,
double sparse_threshold) {
inline void Init(const GHistIndexMatrix& gmat, double sparse_threshold, int32_t n_threads) {
const int32_t nfeature = static_cast<int32_t>(gmat.cut.Ptrs().size() - 1);
const size_t nrow = gmat.row_ptr.size() - 1;
// identify type of each column
@ -208,12 +207,15 @@ class ColumnMatrix {
if (all_dense) {
BinTypeSize gmat_bin_size = gmat.index.GetBinTypeSize();
if (gmat_bin_size == kUint8BinsTypeSize) {
SetIndexAllDense(gmat.index.data<uint8_t>(), gmat, nrow, nfeature, noMissingValues);
SetIndexAllDense(gmat.index.data<uint8_t>(), gmat, nrow, nfeature, noMissingValues,
n_threads);
} else if (gmat_bin_size == kUint16BinsTypeSize) {
SetIndexAllDense(gmat.index.data<uint16_t>(), gmat, nrow, nfeature, noMissingValues);
SetIndexAllDense(gmat.index.data<uint16_t>(), gmat, nrow, nfeature, noMissingValues,
n_threads);
} else {
CHECK_EQ(gmat_bin_size, kUint32BinsTypeSize);
SetIndexAllDense(gmat.index.data<uint32_t>(), gmat, nrow, nfeature, noMissingValues);
SetIndexAllDense(gmat.index.data<uint32_t>(), gmat, nrow, nfeature, noMissingValues,
n_threads);
}
/* For sparse DMatrix gmat.index.getBinTypeSize() returns always kUint32BinsTypeSize
but for ColumnMatrix we still have a chance to reduce the memory consumption */
@ -266,13 +268,13 @@ class ColumnMatrix {
template <typename T>
inline void SetIndexAllDense(T *index, const GHistIndexMatrix &gmat,
const size_t nrow, const size_t nfeature,
const bool noMissingValues) {
const bool noMissingValues, int32_t n_threads) {
T* local_index = reinterpret_cast<T*>(&index_[0]);
/* missing values make sense only for column with type kDenseColumn,
and if no missing values were observed it could be handled much faster. */
if (noMissingValues) {
ParallelFor(omp_ulong(nrow), [&](omp_ulong rid) {
ParallelFor(nrow, n_threads, [&](auto rid) {
const size_t ibegin = rid*nfeature;
const size_t iend = (rid+1)*nfeature;
size_t j = 0;

10
src/data/data.cc
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@ -1035,6 +1035,16 @@ SparsePage SparsePage::GetTranspose(int num_columns, int32_t n_threads) const {
return transpose;
}
void SparsePage::SortRows(int32_t n_threads) {
auto& h_offset = this->offset.HostVector();
auto& h_data = this->data.HostVector();
common::ParallelFor(this->Size(), n_threads, [&](auto i) {
if (h_offset[i] < h_offset[i + 1]) {
std::sort(h_data.begin() + h_offset[i], h_data.begin() + h_offset[i + 1], Entry::CmpValue);
}
});
}
void SparsePage::Push(const SparsePage &batch) {
auto& data_vec = data.HostVector();
auto& offset_vec = offset.HostVector();

2
src/data/simple_dmatrix.cc
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@ -67,7 +67,7 @@ BatchSet<SortedCSCPage> SimpleDMatrix::GetSortedColumnBatches() {
if (!sorted_column_page_) {
sorted_column_page_.reset(
new SortedCSCPage(sparse_page_->GetTranspose(info_.num_col_, ctx_.Threads())));
sorted_column_page_->SortRows();
sorted_column_page_->SortRows(ctx_.Threads());
}
auto begin_iter = BatchIterator<SortedCSCPage>(
new SimpleBatchIteratorImpl<SortedCSCPage>(sorted_column_page_));

2
src/data/sparse_page_source.h
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@ -339,7 +339,7 @@ class SortedCSCPageSource : public PageSourceIncMixIn<SortedCSCPage> {
this->page_->PushCSC(csr->GetTranspose(n_features_, nthreads_));
CHECK_EQ(this->page_->Size(), n_features_);
CHECK_EQ(this->page_->data.Size(), csr->data.Size());
this->page_->SortRows();
this->page_->SortRows(this->nthreads_);
page_->SetBaseRowId(csr->base_rowid);
this->WriteCache();
}

4
src/tree/tree_updater.cc
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@ -1,5 +1,5 @@
/*!
* Copyright 2015 by Contributors
* Copyright 2015-2022 by XGBoost Contributors
* \file tree_updater.cc
* \brief Registry of tree updaters.
*/
@ -21,7 +21,7 @@ TreeUpdater* TreeUpdater::Create(const std::string& name, GenericParameter const
LOG(FATAL) << "Unknown tree updater " << name;
}
auto p_updater = (e->body)(task);
p_updater->tparam_ = tparam;
p_updater->ctx_ = tparam;
return p_updater;
}

6
src/tree/updater_approx.cc
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@ -1,5 +1,5 @@
/*!
* Copyright 2021 XGBoost contributors
* Copyright 2021-2022 XGBoost contributors
*
* \brief Implementation for the approx tree method.
*/
@ -318,10 +318,10 @@ class GlobalApproxUpdater : public TreeUpdater {
param_.learning_rate = lr / trees.size();
if (hist_param_.single_precision_histogram) {
f32_impl_ = std::make_unique<GloablApproxBuilder<float>>(param_, m->Info(), tparam_,
f32_impl_ = std::make_unique<GloablApproxBuilder<float>>(param_, m->Info(), ctx_,
column_sampler_, task_, &monitor_);
} else {
f64_impl_ = std::make_unique<GloablApproxBuilder<double>>(param_, m->Info(), tparam_,
f64_impl_ = std::make_unique<GloablApproxBuilder<double>>(param_, m->Info(), ctx_,
column_sampler_, task_, &monitor_);
}

19
src/tree/updater_basemaker-inl.h
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@ -1,5 +1,5 @@
/*!
* Copyright 2014 by Contributors
* Copyright 2014-2022 by XGBoost Contributors
* \file updater_basemaker-inl.h
* \brief implement a common tree constructor
* \author Tianqi Chen
@ -220,9 +220,7 @@ class BaseMaker: public TreeUpdater {
// set default direct nodes to default
// for leaf nodes that are not fresh, mark then to ~nid,
// so that they are ignored in future statistics collection
const auto ndata = static_cast<bst_omp_uint>(p_fmat->Info().num_row_);
common::ParallelFor(ndata, [&](bst_omp_uint ridx) {
common::ParallelFor(p_fmat->Info().num_row_, ctx_->Threads(), [&](auto ridx) {
const int nid = this->DecodePosition(ridx);
if (tree[nid].IsLeaf()) {
// mark finish when it is not a fresh leaf
@ -256,8 +254,7 @@ class BaseMaker: public TreeUpdater {
auto it = std::lower_bound(sorted_split_set.begin(), sorted_split_set.end(), fid);
if (it != sorted_split_set.end() && *it == fid) {
const auto ndata = static_cast<bst_omp_uint>(col.size());
common::ParallelFor(ndata, [&](bst_omp_uint j) {
common::ParallelFor(col.size(), ctx_->Threads(), [&](auto j) {
const bst_uint ridx = col[j].index;
const bst_float fvalue = col[j].fvalue;
const int nid = this->DecodePosition(ridx);
@ -312,8 +309,7 @@ class BaseMaker: public TreeUpdater {
auto page = batch.GetView();
for (auto fid : fsplits) {
auto col = page[fid];
const auto ndata = static_cast<bst_omp_uint>(col.size());
common::ParallelFor(ndata, [&](bst_omp_uint j) {
common::ParallelFor(col.size(), ctx_->Threads(), [&](auto j) {
const bst_uint ridx = col[j].index;
const bst_float fvalue = col[j].fvalue;
const int nid = this->DecodePosition(ridx);
@ -337,10 +333,10 @@ class BaseMaker: public TreeUpdater {
std::vector< std::vector<TStats> > *p_thread_temp,
std::vector<TStats> *p_node_stats) {
std::vector< std::vector<TStats> > &thread_temp = *p_thread_temp;
thread_temp.resize(omp_get_max_threads());
thread_temp.resize(ctx_->Threads());
p_node_stats->resize(tree.param.num_nodes);
dmlc::OMPException exc;
#pragma omp parallel
#pragma omp parallel num_threads(ctx_->Threads())
{
exc.Run([&]() {
const int tid = omp_get_thread_num();
@ -352,8 +348,7 @@ class BaseMaker: public TreeUpdater {
}
exc.Rethrow();
// setup position
const auto ndata = static_cast<bst_omp_uint>(fmat.Info().num_row_);
common::ParallelFor(ndata, [&](bst_omp_uint ridx) {
common::ParallelFor(fmat.Info().num_row_, ctx_->Threads(), [&](auto ridx) {
const int nid = position_[ridx];
const int tid = omp_get_thread_num();
if (nid >= 0) {

47
src/tree/updater_colmaker.cc
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@ -1,5 +1,5 @@
/*!
* Copyright 2014-2019 by Contributors
* Copyright 2014-2022 by XGBoost Contributors
* \file updater_colmaker.cc
* \brief use columnwise update to construct a tree
* \author Tianqi Chen
@ -114,8 +114,8 @@ class ColMaker: public TreeUpdater {
interaction_constraints_.Configure(param_, dmat->Info().num_row_);
// build tree
for (auto tree : trees) {
CHECK(tparam_);
Builder builder(param_, colmaker_param_, interaction_constraints_, tparam_,
CHECK(ctx_);
Builder builder(param_, colmaker_param_, interaction_constraints_, ctx_,
column_densities_);
builder.Update(gpair->ConstHostVector(), dmat, tree);
}
@ -270,17 +270,11 @@ class ColMaker: public TreeUpdater {
}
const MetaInfo& info = fmat.Info();
// setup position
const auto ndata = static_cast<bst_omp_uint>(info.num_row_);
dmlc::OMPException exc;
#pragma omp parallel for schedule(static)
for (bst_omp_uint ridx = 0; ridx < ndata; ++ridx) {
exc.Run([&]() {
const int tid = omp_get_thread_num();
common::ParallelFor(info.num_row_, ctx_->Threads(), [&](auto ridx) {
int32_t const tid = omp_get_thread_num();
if (position_[ridx] < 0) return;
stemp_[tid][position_[ridx]].stats.Add(gpair[ridx]);
});
}
exc.Rethrow();
// sum the per thread statistics together
for (int nid : qexpand) {
GradStats stats;
@ -449,27 +443,20 @@ class ColMaker: public TreeUpdater {
// update the solution candidate
virtual void UpdateSolution(const SortedCSCPage &batch,
const std::vector<bst_feature_t> &feat_set,
const std::vector<GradientPair> &gpair,
DMatrix*) {
const std::vector<GradientPair> &gpair, DMatrix *) {
// start enumeration
const auto num_features = static_cast<bst_omp_uint>(feat_set.size());
#if defined(_OPENMP)
const auto num_features = feat_set.size();
CHECK(this->ctx_);
const int batch_size = // NOLINT
std::max(static_cast<int>(num_features / this->ctx_->Threads() / 32), 1);
#endif // defined(_OPENMP)
{
auto page = batch.GetView();
dmlc::OMPException exc;
#pragma omp parallel for schedule(dynamic, batch_size)
for (bst_omp_uint i = 0; i < num_features; ++i) {
exc.Run([&]() {
common::ParallelFor(
num_features, ctx_->Threads(), common::Sched::Dyn(batch_size), [&](auto i) {
auto evaluator = tree_evaluator_.GetEvaluator();
bst_feature_t const fid = feat_set[i];
int32_t const tid = omp_get_thread_num();
auto c = page[fid];
const bool ind =
c.size() != 0 && c[0].fvalue == c[c.size() - 1].fvalue;
const bool ind = c.size() != 0 && c[0].fvalue == c[c.size() - 1].fvalue;
if (colmaker_train_param_.NeedForwardSearch(column_densities_[fid], ind)) {
this->EnumerateSplit(c.data(), c.data() + c.size(), +1, fid, gpair, stemp_[tid],
evaluator);
@ -480,9 +467,6 @@ class ColMaker: public TreeUpdater {
}
});
}
exc.Rethrow();
}
}
// find splits at current level, do split per level
inline void FindSplit(int depth,
const std::vector<int> &qexpand,
@ -529,11 +513,9 @@ class ColMaker: public TreeUpdater {
// set default direct nodes to default
// for leaf nodes that are not fresh, mark then to ~nid,
// so that they are ignored in future statistics collection
const auto ndata = static_cast<bst_omp_uint>(p_fmat->Info().num_row_);
common::ParallelFor(ndata, [&](bst_omp_uint ridx) {
CHECK_LT(ridx, position_.size())
<< "ridx exceed bound " << "ridx="<< ridx << " pos=" << position_.size();
common::ParallelFor(p_fmat->Info().num_row_, this->ctx_->Threads(), [&](auto ridx) {
CHECK_LT(ridx, position_.size()) << "ridx exceed bound "
<< "ridx=" << ridx << " pos=" << position_.size();
const int nid = this->DecodePosition(ridx);
if (tree[nid].IsLeaf()) {
// mark finish when it is not a fresh leaf
@ -577,8 +559,7 @@ class ColMaker: public TreeUpdater {
auto page = batch.GetView();
for (auto fid : fsplits) {
auto col = page[fid];
const auto ndata = static_cast<bst_omp_uint>(col.size());
common::ParallelFor(ndata, [&](bst_omp_uint j) {
common::ParallelFor(col.size(), this->ctx_->Threads(), [&](auto j) {
const bst_uint ridx = col[j].index;
const int nid = this->DecodePosition(ridx);
const bst_float fvalue = col[j].fvalue;

4
src/tree/updater_gpu_hist.cu
View File

@ -875,11 +875,11 @@ class GPUHistMaker : public TreeUpdater {
if (hist_maker_param_.single_precision_histogram) {
float_maker_.reset(new GPUHistMakerSpecialised<GradientPair>(task_));
float_maker_->param_ = param;
float_maker_->Configure(args, tparam_);
float_maker_->Configure(args, ctx_);
} else {
double_maker_.reset(new GPUHistMakerSpecialised<GradientPairPrecise>(task_));
double_maker_->param_ = param;
double_maker_->Configure(args, tparam_);
double_maker_->Configure(args, ctx_);
}
}

42
src/tree/updater_histmaker.cc
View File

@ -1,5 +1,5 @@
/*!
* Copyright 2014-2019 by Contributors
* Copyright 2014-2022 by XGBoost Contributors
* \file updater_histmaker.cc
* \brief use histogram counting to construct a tree
* \author Tianqi Chen
@ -203,11 +203,8 @@ class HistMaker: public BaseMaker {
// get the best split condition for each node
std::vector<SplitEntry> sol(qexpand_.size());
std::vector<GradStats> left_sum(qexpand_.size());
auto nexpand = static_cast<bst_omp_uint>(qexpand_.size());
dmlc::OMPException exc;
#pragma omp parallel for schedule(dynamic, 1)
for (bst_omp_uint wid = 0; wid < nexpand; ++wid) {
exc.Run([&]() {
auto nexpand = qexpand_.size();
common::ParallelFor(nexpand, ctx_->Threads(), common::Sched::Dyn(1), [&](auto wid) {
const int nid = qexpand_[wid];
CHECK_EQ(node2workindex_[nid], static_cast<int>(wid));
SplitEntry &best = sol[wid];
@ -218,12 +215,10 @@ class HistMaker: public BaseMaker {
continue;
}
EnumerateSplit(this->wspace_.hset[0][i + wid * (num_feature+1)],
node_sum, feature_set[i], &best, &left_sum[wid]);
EnumerateSplit(this->wspace_.hset[0][i + wid * (num_feature + 1)], node_sum, feature_set[i],
&best, &left_sum[wid]);
}
});
}
exc.Rethrow();
// get the best result, we can synchronize the solution
for (bst_omp_uint wid = 0; wid < nexpand; ++wid) {
const bst_node_t nid = qexpand_[wid];
@ -341,27 +336,20 @@ class CQHistMaker: public HistMaker {
// if it is C++11, use lazy evaluation for Allreduce,
// to gain speedup in recovery
auto lazy_get_hist = [&]() {
thread_hist_.resize(omp_get_max_threads());
thread_hist_.resize(ctx_->Threads());
// start accumulating statistics
for (const auto &batch : p_fmat->GetBatches<SortedCSCPage>()) {
auto page = batch.GetView();
// start enumeration
const auto nsize = static_cast<bst_omp_uint>(fset.size());
dmlc::OMPException exc;
#pragma omp parallel for schedule(dynamic, 1)
for (bst_omp_uint i = 0; i < nsize; ++i) {
exc.Run([&]() {
common::ParallelFor(fset.size(), ctx_->Threads(), common::Sched::Dyn(1), [&](auto i) {
int fid = fset[i];
int offset = feat2workindex_[fid];
if (offset >= 0) {
this->UpdateHistCol(gpair, page[fid], info, tree,
fset, offset,
this->UpdateHistCol(gpair, page[fid], info, tree, fset, offset,
&thread_hist_[omp_get_thread_num()]);
}
});
}
exc.Rethrow();
}
// update node statistics.
this->GetNodeStats(gpair, *p_fmat, tree,
&thread_stats_, &node_stats_);
@ -412,7 +400,7 @@ class CQHistMaker: public HistMaker {
}
{
// get summary
thread_sketch_.resize(omp_get_max_threads());
thread_sketch_.resize(ctx_->Threads());
// TWOPASS: use the real set + split set in the column iteration.
this->SetDefaultPostion(p_fmat, tree);
@ -426,22 +414,16 @@ class CQHistMaker: public HistMaker {
this->CorrectNonDefaultPositionByBatch(batch, fsplit_set_, tree);
auto page = batch.GetView();
// start enumeration
const auto nsize = static_cast<bst_omp_uint>(work_set_.size());
dmlc::OMPException exc;
#pragma omp parallel for schedule(dynamic, 1)
for (bst_omp_uint i = 0; i < nsize; ++i) {
exc.Run([&]() {
common::ParallelFor(work_set_.size(), ctx_->Threads(), common::Sched::Dyn(1),
[&](auto i) {
int fid = work_set_[i];
int offset = feat2workindex_[fid];
if (offset >= 0) {
this->UpdateSketchCol(gpair, page[fid], tree,
work_set_size, offset,
this->UpdateSketchCol(gpair, page[fid], tree, work_set_size, offset,
&thread_sketch_[omp_get_thread_num()]);
}
});
}
exc.Rethrow();
}
for (size_t i = 0; i < sketchs_.size(); ++i) {
common::WXQuantileSketch<bst_float, bst_float>::SummaryContainer out;
sketchs_[i].GetSummary(&out);

4
src/tree/updater_prune.cc
View File

@ -1,5 +1,5 @@
/*!
* Copyright 2014-2020 by Contributors
* Copyright 2014-2022 by XGBoost Contributors
* \file updater_prune.cc
* \brief prune a tree given the statistics
* \author Tianqi Chen
@ -24,7 +24,7 @@ DMLC_REGISTRY_FILE_TAG(updater_prune);
class TreePruner: public TreeUpdater {
public:
explicit TreePruner(ObjInfo task) {
syncher_.reset(TreeUpdater::Create("sync", tparam_, task));
syncher_.reset(TreeUpdater::Create("sync", ctx_, task));
pruner_monitor_.Init("TreePruner");
}
char const* Name() const override {

54
src/tree/updater_quantile_hist.cc
View File

@ -1,5 +1,5 @@
/*!
* Copyright 2017-2021 by Contributors
* Copyright 2017-2022 by XGBoost Contributors
* \file updater_quantile_hist.cc
* \brief use quantized feature values to construct a tree
* \author Philip Cho, Tianqi Checn, Egor Smirnov
@ -40,7 +40,7 @@ DMLC_REGISTER_PARAMETER(CPUHistMakerTrainParam);
void QuantileHistMaker::Configure(const Args& args) {
// initialize pruner
if (!pruner_) {
pruner_.reset(TreeUpdater::Create("prune", tparam_, task_));
pruner_.reset(TreeUpdater::Create("prune", ctx_, task_));
}
pruner_->Configure(args);
param_.UpdateAllowUnknown(args);
@ -49,10 +49,9 @@ void QuantileHistMaker::Configure(const Args& args) {
template <typename GradientSumT>
void QuantileHistMaker::SetBuilder(const size_t n_trees,
std::unique_ptr<Builder<GradientSumT>>* builder,
DMatrix *dmat) {
std::unique_ptr<Builder<GradientSumT>>* builder, DMatrix* dmat) {
builder->reset(
new Builder<GradientSumT>(n_trees, param_, std::move(pruner_), dmat, task_));
new Builder<GradientSumT>(n_trees, param_, std::move(pruner_), dmat, task_, ctx_));
}
template<typename GradientSumT>
@ -75,7 +74,7 @@ void QuantileHistMaker::Update(HostDeviceVector<GradientPair> *gpair,
auto p_gmat = it.Page();
if (dmat != p_last_dmat_ || is_gmat_initialized_ == false) {
updater_monitor_.Start("GmatInitialization");
column_matrix_.Init(*p_gmat, param_.sparse_threshold);
column_matrix_.Init(*p_gmat, param_.sparse_threshold, ctx_->Threads());
updater_monitor_.Stop("GmatInitialization");
// A proper solution is puting cut matrix in DMatrix, see:
// https://github.com/dmlc/xgboost/issues/5143
@ -347,7 +346,7 @@ bool QuantileHistMaker::Builder<GradientSumT>::UpdatePredictionCache(
return row_set_collection_[node].Size();
}, 1024);
CHECK_EQ(out_preds.DeviceIdx(), GenericParameter::kCpuId);
common::ParallelFor2d(space, this->nthread_, [&](size_t node, common::Range1d r) {
common::ParallelFor2d(space, this->ctx_->Threads(), [&](size_t node, common::Range1d r) {
const RowSetCollection::Elem rowset = row_set_collection_[node];
if (rowset.begin != nullptr && rowset.end != nullptr) {
int nid = rowset.node_id;
@ -388,20 +387,19 @@ void QuantileHistMaker::Builder<GradientSumT>::InitSampling(const DMatrix& fmat,
}
}
#else
const size_t nthread = this->nthread_;
uint64_t initial_seed = rnd();
const size_t discard_size = info.num_row_ / nthread;
auto n_threads = static_cast<size_t>(ctx_->Threads());
const size_t discard_size = info.num_row_ / n_threads;
std::bernoulli_distribution coin_flip(param_.subsample);
dmlc::OMPException exc;
#pragma omp parallel num_threads(nthread)
#pragma omp parallel num_threads(n_threads)
{
exc.Run([&]() {
const size_t tid = omp_get_thread_num();
const size_t ibegin = tid * discard_size;
const size_t iend = (tid == (nthread - 1)) ?
info.num_row_ : ibegin + discard_size;
const size_t iend = (tid == (n_threads - 1)) ? info.num_row_ : ibegin + discard_size;
RandomReplace::MakeIf([&](size_t i, RandomReplace::EngineT& eng) {
return !(gpair_ref[i].GetHess() >= 0.0f && coin_flip(eng));
}, GradientPair(0), initial_seed, ibegin, iend, &gpair_ref);
@ -436,16 +434,9 @@ void QuantileHistMaker::Builder<GradientSumT>::InitData(
uint32_t nbins = gmat.cut.Ptrs().back();
// initialize histogram builder
dmlc::OMPException exc;
#pragma omp parallel
{
exc.Run([&]() {
this->nthread_ = omp_get_num_threads();
});
}
exc.Rethrow();
this->histogram_builder_->Reset(
nbins, BatchParam{GenericParameter::kCpuId, param_.max_bin},
this->nthread_, 1, rabit::IsDistributed());
this->histogram_builder_->Reset(nbins, BatchParam{GenericParameter::kCpuId, param_.max_bin},
this->ctx_->Threads(), 1, rabit::IsDistributed());
std::vector<size_t>& row_indices = *row_set_collection_.Data();
row_indices.resize(info.num_row_);
@ -463,13 +454,14 @@ void QuantileHistMaker::Builder<GradientSumT>::InitData(
// We should check that the partitioning was done correctly
// and each row of the dataset fell into exactly one of the categories
}
common::MemStackAllocator<bool, 128> buff(this->nthread_);
auto n_threads = this->ctx_->Threads();
common::MemStackAllocator<bool, 128> buff(n_threads);
bool* p_buff = buff.Get();
std::fill(p_buff, p_buff + this->nthread_, false);
std::fill(p_buff, p_buff + this->ctx_->Threads(), false);
const size_t block_size = info.num_row_ / this->nthread_ + !!(info.num_row_ % this->nthread_);
const size_t block_size = info.num_row_ / n_threads + !!(info.num_row_ % n_threads);
#pragma omp parallel num_threads(this->nthread_)
#pragma omp parallel num_threads(n_threads)
{
exc.Run([&]() {
const size_t tid = omp_get_thread_num();
@ -488,7 +480,7 @@ void QuantileHistMaker::Builder<GradientSumT>::InitData(
exc.Rethrow();
bool has_neg_hess = false;
for (int32_t tid = 0; tid < this->nthread_; ++tid) {
for (int32_t tid = 0; tid < n_threads; ++tid) {
if (p_buff[tid]) {
has_neg_hess = true;
}
@ -503,7 +495,7 @@ void QuantileHistMaker::Builder<GradientSumT>::InitData(
}
row_indices.resize(j);
} else {
#pragma omp parallel num_threads(this->nthread_)
#pragma omp parallel num_threads(n_threads)
{
exc.Run([&]() {
const size_t tid = omp_get_thread_num();
@ -543,10 +535,10 @@ void QuantileHistMaker::Builder<GradientSumT>::InitData(
p_last_tree_ = &tree;
if (data_layout_ == DataLayout::kDenseDataOneBased) {
evaluator_.reset(new HistEvaluator<GradientSumT, CPUExpandEntry>{
param_, info, this->nthread_, column_sampler_, task_, true});
param_, info, this->ctx_->Threads(), column_sampler_, task_, true});
} else {
evaluator_.reset(new HistEvaluator<GradientSumT, CPUExpandEntry>{
param_, info, this->nthread_, column_sampler_, task_, false});
param_, info, this->ctx_->Threads(), column_sampler_, task_, false});
}
if (data_layout_ == DataLayout::kDenseDataZeroBased
@ -642,7 +634,7 @@ void QuantileHistMaker::Builder<GradientSumT>::ApplySplit(const std::vector<CPUE
});
// 2.3 Split elements of row_set_collection_ to left and right child-nodes for each node
// Store results in intermediate buffers from partition_builder_
common::ParallelFor2d(space, this->nthread_, [&](size_t node_in_set, common::Range1d r) {
common::ParallelFor2d(space, this->ctx_->Threads(), [&](size_t node_in_set, common::Range1d r) {
size_t begin = r.begin();
const int32_t nid = nodes[node_in_set].nid;
const size_t task_id = partition_builder_.GetTaskIdx(node_in_set, begin);
@ -673,7 +665,7 @@ void QuantileHistMaker::Builder<GradientSumT>::ApplySplit(const std::vector<CPUE
// 4. Copy elements from partition_builder_ to row_set_collection_ back
// with updated row-indexes for each tree-node
common::ParallelFor2d(space, this->nthread_, [&](size_t node_in_set, common::Range1d r) {
common::ParallelFor2d(space, this->ctx_->Threads(), [&](size_t node_in_set, common::Range1d r) {
const int32_t nid = nodes[node_in_set].nid;
partition_builder_.MergeToArray(node_in_set, r.begin(),
const_cast<size_t*>(row_set_collection_[nid].begin));

15
src/tree/updater_quantile_hist.h
View File

@ -1,5 +1,5 @@
/*!
* Copyright 2017-2021 by Contributors
* Copyright 2017-2022 by XGBoost Contributors
* \file updater_quantile_hist.h
* \brief use quantized feature values to construct a tree
* \author Philip Cho, Tianqi Chen, Egor Smirnov
@ -155,14 +155,16 @@ class QuantileHistMaker: public TreeUpdater {
using GradientPairT = xgboost::detail::GradientPairInternal<GradientSumT>;
// constructor
explicit Builder(const size_t n_trees, const TrainParam& param,
std::unique_ptr<TreeUpdater> pruner, DMatrix const* fmat, ObjInfo task)
std::unique_ptr<TreeUpdater> pruner, DMatrix const* fmat, ObjInfo task,
GenericParameter const* ctx)
: n_trees_(n_trees),
param_(param),
pruner_(std::move(pruner)),
p_last_tree_(nullptr),
p_last_fmat_(fmat),
histogram_builder_{new HistogramBuilder<GradientSumT, CPUExpandEntry>},
task_{task} {
task_{task},
ctx_{ctx} {
builder_monitor_.Init("Quantile::Builder");
}
// update one tree, growing
@ -225,8 +227,6 @@ class QuantileHistMaker: public TreeUpdater {
// --data fields--
const size_t n_trees_;
const TrainParam& param_;
// number of omp thread used during training
int nthread_;
std::shared_ptr<common::ColumnSampler> column_sampler_{
std::make_shared<common::ColumnSampler>()};
@ -258,9 +258,10 @@ class QuantileHistMaker: public TreeUpdater {
enum class DataLayout { kDenseDataZeroBased, kDenseDataOneBased, kSparseData };
DataLayout data_layout_;
std::unique_ptr<HistogramBuilder<GradientSumT, CPUExpandEntry>>
histogram_builder_;
std::unique_ptr<HistogramBuilder<GradientSumT, CPUExpandEntry>> histogram_builder_;
ObjInfo task_;
// Context for number of threads
GenericParameter const* ctx_;
common::Monitor builder_monitor_;
};

10
src/tree/updater_refresh.cc
View File

@ -1,5 +1,5 @@
/*!
* Copyright 2014 by Contributors
* Copyright 2014-2022 by XGBoost Contributors
* \file updater_refresh.cc
* \brief refresh the statistics and leaf value on the tree on the dataset
* \author Tianqi Chen
@ -51,11 +51,11 @@ class TreeRefresher: public TreeUpdater {
std::vector<std::vector<GradStats> > stemp;
std::vector<RegTree::FVec> fvec_temp;
// setup temp space for each thread
const int nthread = omp_get_max_threads();
const int nthread = ctx_->Threads();
fvec_temp.resize(nthread, RegTree::FVec());
stemp.resize(nthread, std::vector<GradStats>());
dmlc::OMPException exc;
#pragma omp parallel
#pragma omp parallel num_threads(nthread)
{
exc.Run([&]() {
int tid = omp_get_thread_num();
@ -78,7 +78,7 @@ class TreeRefresher: public TreeUpdater {
auto page = batch.GetView();
CHECK_LT(batch.Size(), std::numeric_limits<unsigned>::max());
const auto nbatch = static_cast<bst_omp_uint>(batch.Size());
common::ParallelFor(nbatch, [&](bst_omp_uint i) {
common::ParallelFor(nbatch, ctx_->Threads(), [&](bst_omp_uint i) {
SparsePage::Inst inst = page[i];
const int tid = omp_get_thread_num();
const auto ridx = static_cast<bst_uint>(batch.base_rowid + i);
@ -95,7 +95,7 @@ class TreeRefresher: public TreeUpdater {
}
// aggregate the statistics
auto num_nodes = static_cast<int>(stemp[0].size());
common::ParallelFor(num_nodes, [&](int nid) {
common::ParallelFor(num_nodes, ctx_->Threads(), [&](int nid) {
for (int tid = 1; tid < nthread; ++tid) {
stemp[0][nid].Add(stemp[tid][nid]);
}

6
tests/cpp/common/test_column_matrix.cc
View File

@ -19,7 +19,7 @@ TEST(DenseColumn, Test) {
auto dmat = RandomDataGenerator(100, 10, 0.0).GenerateDMatrix();
GHistIndexMatrix gmat(dmat.get(), max_num_bin, false, common::OmpGetNumThreads(0));
ColumnMatrix column_matrix;
column_matrix.Init(gmat, 0.2);
column_matrix.Init(gmat, 0.2, common::OmpGetNumThreads(0));
for (auto i = 0ull; i < dmat->Info().num_row_; i++) {
for (auto j = 0ull; j < dmat->Info().num_col_; j++) {
@ -66,7 +66,7 @@ TEST(SparseColumn, Test) {
auto dmat = RandomDataGenerator(100, 1, 0.85).GenerateDMatrix();
GHistIndexMatrix gmat(dmat.get(), max_num_bin, false, common::OmpGetNumThreads(0));
ColumnMatrix column_matrix;
column_matrix.Init(gmat, 0.5);
column_matrix.Init(gmat, 0.5, common::OmpGetNumThreads(0));
switch (column_matrix.GetTypeSize()) {
case kUint8BinsTypeSize: {
auto col = column_matrix.GetColumn<uint8_t, true>(0);
@ -106,7 +106,7 @@ TEST(DenseColumnWithMissing, Test) {
auto dmat = RandomDataGenerator(100, 1, 0.5).GenerateDMatrix();
GHistIndexMatrix gmat(dmat.get(), max_num_bin, false, common::OmpGetNumThreads(0));
ColumnMatrix column_matrix;
column_matrix.Init(gmat, 0.2);
column_matrix.Init(gmat, 0.2, common::OmpGetNumThreads(0));
switch (column_matrix.GetTypeSize()) {
case kUint8BinsTypeSize: {
auto col = column_matrix.GetColumn<uint8_t, true>(0);

2
tests/cpp/data/test_data.cc
View File

@ -76,7 +76,7 @@ TEST(SparsePage, PushCSCAfterTranspose) {
// Make sure that the final sparse page has the right number of entries
ASSERT_EQ(kEntries, page.data.Size());
page.SortRows();
page.SortRows(common::OmpGetNumThreads(0));
auto v = page.GetView();
for (size_t i = 0; i < v.Size(); ++i) {
auto column = v[i];

19
tests/cpp/tree/test_quantile_hist.cc
View File

@ -27,8 +27,8 @@ class QuantileHistMock : public QuantileHistMaker {
using GHistRowT = typename RealImpl::GHistRowT;
BuilderMock(const TrainParam &param, std::unique_ptr<TreeUpdater> pruner,
DMatrix const *fmat)
: RealImpl(1, param, std::move(pruner), fmat, ObjInfo{ObjInfo::kRegression}) {}
DMatrix const *fmat, GenericParameter const* ctx)
: RealImpl(1, param, std::move(pruner), fmat, ObjInfo{ObjInfo::kRegression}, ctx) {}
public:
void TestInitData(const GHistIndexMatrix& gmat,
@ -166,7 +166,7 @@ class QuantileHistMock : public QuantileHistMaker {
ColumnMatrix cm;
// treat everything as dense, as this is what we intend to test here
cm.Init(gmat, 0.0);
cm.Init(gmat, 0.0, common::OmpGetNumThreads(0));
RealImpl::InitData(gmat, *dmat, tree, &row_gpairs);
const size_t num_row = dmat->Info().num_row_;
// split by feature 0
@ -222,6 +222,7 @@ class QuantileHistMock : public QuantileHistMaker {
int static constexpr kNRows = 8, kNCols = 16;
std::shared_ptr<xgboost::DMatrix> dmat_;
GenericParameter ctx_;
const std::vector<std::pair<std::string, std::string> > cfg_;
std::shared_ptr<BuilderMock<float> > float_builder_;
std::shared_ptr<BuilderMock<double> > double_builder_;
@ -233,18 +234,12 @@ class QuantileHistMock : public QuantileHistMaker {
QuantileHistMaker{ObjInfo{ObjInfo::kRegression}}, cfg_{args} {
QuantileHistMaker::Configure(args);
dmat_ = RandomDataGenerator(kNRows, kNCols, 0.8).Seed(3).GenerateDMatrix();
ctx_.UpdateAllowUnknown(Args{});
if (single_precision_histogram) {
float_builder_.reset(
new BuilderMock<float>(
param_,
std::move(pruner_),
dmat_.get()));
float_builder_.reset(new BuilderMock<float>(param_, std::move(pruner_), dmat_.get(), &ctx_));
} else {
double_builder_.reset(
new BuilderMock<double>(
param_,
std::move(pruner_),
dmat_.get()));
new BuilderMock<double>(param_, std::move(pruner_), dmat_.get(), &ctx_));
}
}
~QuantileHistMock() override = default;