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Specify the number of threads for parallel sort. (#8735)

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* Specify the number of threads for parallel sort.

- Pass context object into argsort.
- Replace macros with inline functions.
This commit is contained in:
Jiaming Yuan
2023-02-16 00:20:19 +08:00
committed by GitHub
parent c7c485d052
commit 282b1729da
24 changed files with 254 additions and 143 deletions
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31
src/tree/hist/evaluate_splits.h
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@@ -34,10 +34,10 @@ class HistEvaluator {
};
private:
Context const* ctx_;
TrainParam param_;
std::shared_ptr<common::ColumnSampler> column_sampler_;
TreeEvaluator tree_evaluator_;
int32_t n_threads_ {0};
FeatureInteractionConstraintHost interaction_constraints_;
std::vector<NodeEntry> snode_;
@@ -283,6 +283,7 @@ class HistEvaluator {
void EvaluateSplits(const common::HistCollection &hist, common::HistogramCuts const &cut,
common::Span<FeatureType const> feature_types, const RegTree &tree,
std::vector<ExpandEntry> *p_entries) {
auto n_threads = ctx_->Threads();
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(
@@ -294,23 +295,23 @@ class HistEvaluator {
}
CHECK(!features.empty());
const size_t grain_size =
std::max<size_t>(1, features.front()->Size() / n_threads_);
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(n_threads_ * entries.size());
std::vector<ExpandEntry> tloc_candidates(n_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];
for (decltype(n_threads) j = 0; j < n_threads; ++j) {
tloc_candidates[i * n_threads + j] = entries[i];
}
}
auto evaluator = tree_evaluator_.GetEvaluator();
auto const& cut_ptrs = cut.Ptrs();
common::ParallelFor2d(space, n_threads_, [&](size_t nidx_in_set, common::Range1d r) {
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 entry = &tloc_candidates[n_threads * nidx_in_set + tidx];
auto best = &entry->split;
auto nidx = entry->nid;
auto histogram = hist[nidx];
@@ -349,9 +350,9 @@ class HistEvaluator {
for (unsigned nidx_in_set = 0; nidx_in_set < entries.size();
++nidx_in_set) {
for (auto tidx = 0; tidx < n_threads_; ++tidx) {
for (auto tidx = 0; tidx < n_threads; ++tidx) {
entries[nidx_in_set].split.Update(
tloc_candidates[n_threads_ * nidx_in_set + tidx].split);
tloc_candidates[n_threads * nidx_in_set + tidx].split);
}
}
}
@@ -424,15 +425,15 @@ class HistEvaluator {
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,
explicit HistEvaluator(Context const* ctx, TrainParam const &param, MetaInfo const &info,
std::shared_ptr<common::ColumnSampler> sampler)
: param_{param},
: ctx_{ctx}, param_{param},
column_sampler_{std::move(sampler)},
tree_evaluator_{param, static_cast<bst_feature_t>(info.num_col_), Context::kCpuId},
n_threads_{n_threads} {
tree_evaluator_{param, static_cast<bst_feature_t>(info.num_col_), Context::kCpuId} {
interaction_constraints_.Configure(param, info.num_col_);
column_sampler_->Init(info.num_col_, info.feature_weights.HostVector(), param_.colsample_bynode,
param_.colsample_bylevel, param_.colsample_bytree);
column_sampler_->Init(ctx, info.num_col_, info.feature_weights.HostVector(),
param_.colsample_bynode, param_.colsample_bylevel,
param_.colsample_bytree);
}
};