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Remove omp_get_max_threads in CPU predictor. (#7519)

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This is part of the on going effort to remove the dependency on global omp variables.
This commit is contained in:
Jiaming Yuan
2022-01-04 22:12:15 +08:00
committed by GitHub
parent 5516281881
commit 68cdbc9c16
6 changed files with 68 additions and 68 deletions
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53
src/predictor/cpu_predictor.cc
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@@ -150,10 +150,12 @@ class AdapterView {
static size_t constexpr kUnroll = kUnrollLen;
public:
explicit AdapterView(Adapter *adapter, float missing,
common::Span<Entry> workplace)
: adapter_{adapter}, missing_{missing}, workspace_{workplace},
current_unroll_(omp_get_max_threads() > 0 ? omp_get_max_threads() : 1, 0) {}
explicit AdapterView(Adapter *adapter, float missing, common::Span<Entry> workplace,
int32_t n_threads)
: adapter_{adapter},
missing_{missing},
workspace_{workplace},
current_unroll_(n_threads > 0 ? n_threads : 1, 0) {}
SparsePage::Inst operator[](size_t i) {
bst_feature_t columns = adapter_->NumColumns();
auto const &batch = adapter_->Value();
@@ -186,7 +188,7 @@ template <typename DataView, size_t block_of_rows_size>
void PredictBatchByBlockOfRowsKernel(
DataView batch, std::vector<bst_float> *out_preds,
gbm::GBTreeModel const &model, int32_t tree_begin, int32_t tree_end,
std::vector<RegTree::FVec> *p_thread_temp) {
std::vector<RegTree::FVec> *p_thread_temp, int32_t n_threads) {
auto &thread_temp = *p_thread_temp;
int32_t const num_group = model.learner_model_param->num_output_group;
@@ -197,7 +199,7 @@ void PredictBatchByBlockOfRowsKernel(
const int num_feature = model.learner_model_param->num_feature;
omp_ulong n_blocks = common::DivRoundUp(nsize, block_of_rows_size);
common::ParallelFor(n_blocks, [&](bst_omp_uint block_id) {
common::ParallelFor(n_blocks, n_threads, [&](bst_omp_uint block_id) {
const size_t batch_offset = block_id * block_of_rows_size;
const size_t block_size =
std::min(nsize - batch_offset, block_of_rows_size);
@@ -252,7 +254,7 @@ class CPUPredictor : public Predictor {
void PredictDMatrix(DMatrix *p_fmat, std::vector<bst_float> *out_preds,
gbm::GBTreeModel const &model, int32_t tree_begin,
int32_t tree_end) const {
const int threads = omp_get_max_threads();
auto const n_threads = this->ctx_->Threads();
constexpr double kDensityThresh = .5;
size_t total = std::max(p_fmat->Info().num_row_ * p_fmat->Info().num_col_,
static_cast<uint64_t>(1));
@@ -261,7 +263,7 @@ class CPUPredictor : public Predictor {
bool blocked = density > kDensityThresh;
std::vector<RegTree::FVec> feat_vecs;
InitThreadTemp(threads * (blocked ? kBlockOfRowsSize : 1),
InitThreadTemp(n_threads * (blocked ? kBlockOfRowsSize : 1),
model.learner_model_param->num_feature, &feat_vecs);
for (auto const &batch : p_fmat->GetBatches<SparsePage>()) {
CHECK_EQ(out_preds->size(),
@@ -269,15 +271,14 @@ class CPUPredictor : public Predictor {
model.learner_model_param->num_output_group);
size_t constexpr kUnroll = 8;
if (blocked) {
PredictBatchByBlockOfRowsKernel<SparsePageView<kUnroll>,
kBlockOfRowsSize>(
SparsePageView<kUnroll>{&batch}, out_preds, model, tree_begin,
tree_end, &feat_vecs);
PredictBatchByBlockOfRowsKernel<SparsePageView<kUnroll>, kBlockOfRowsSize>(
SparsePageView<kUnroll>{&batch}, out_preds, model, tree_begin, tree_end, &feat_vecs,
n_threads);
} else {
PredictBatchByBlockOfRowsKernel<SparsePageView<kUnroll>, 1>(
SparsePageView<kUnroll>{&batch}, out_preds, model, tree_begin,
tree_end, &feat_vecs);
SparsePageView<kUnroll>{&batch}, out_preds, model, tree_begin, tree_end, &feat_vecs,
n_threads);
}
}
}
@@ -304,7 +305,7 @@ class CPUPredictor : public Predictor {
const gbm::GBTreeModel &model, float missing,
PredictionCacheEntry *out_preds,
uint32_t tree_begin, uint32_t tree_end) const {
auto threads = omp_get_max_threads();
auto const n_threads = this->ctx_->Threads();
auto m = dmlc::get<std::shared_ptr<Adapter>>(x);
CHECK_EQ(m->NumColumns(), model.learner_model_param->num_feature)
<< "Number of columns in data must equal to trained model.";
@@ -316,14 +317,14 @@ class CPUPredictor : public Predictor {
info.num_row_ = m->NumRows();
this->InitOutPredictions(info, &(out_preds->predictions), model);
}
std::vector<Entry> workspace(m->NumColumns() * 8 * threads);
std::vector<Entry> workspace(m->NumColumns() * 8 * n_threads);
auto &predictions = out_preds->predictions.HostVector();
std::vector<RegTree::FVec> thread_temp;
InitThreadTemp(threads * kBlockSize, model.learner_model_param->num_feature,
InitThreadTemp(n_threads * kBlockSize, model.learner_model_param->num_feature,
&thread_temp);
PredictBatchByBlockOfRowsKernel<AdapterView<Adapter>, kBlockSize>(
AdapterView<Adapter>(m.get(), missing, common::Span<Entry>{workspace}),
&predictions, model, tree_begin, tree_end, &thread_temp);
AdapterView<Adapter>(m.get(), missing, common::Span<Entry>{workspace}, n_threads),
&predictions, model, tree_begin, tree_end, &thread_temp, n_threads);
}
bool InplacePredict(dmlc::any const &x, std::shared_ptr<DMatrix> p_m,
@@ -370,10 +371,10 @@ class CPUPredictor : public Predictor {
void PredictLeaf(DMatrix* p_fmat, HostDeviceVector<bst_float>* out_preds,
const gbm::GBTreeModel& model, unsigned ntree_limit) const override {
const int nthread = omp_get_max_threads();
auto const n_threads = this->ctx_->Threads();
std::vector<RegTree::FVec> feat_vecs;
const int num_feature = model.learner_model_param->num_feature;
InitThreadTemp(nthread, num_feature, &feat_vecs);
InitThreadTemp(n_threads, num_feature, &feat_vecs);
const MetaInfo& info = p_fmat->Info();
// number of valid trees
if (ntree_limit == 0 || ntree_limit > model.trees.size()) {
@@ -386,7 +387,7 @@ class CPUPredictor : public Predictor {
// parallel over local batch
auto page = batch.GetView();
const auto nsize = static_cast<bst_omp_uint>(batch.Size());
common::ParallelFor(nsize, [&](bst_omp_uint i) {
common::ParallelFor(nsize, n_threads, [&](bst_omp_uint i) {
const int tid = omp_get_thread_num();
auto ridx = static_cast<size_t>(batch.base_rowid + i);
RegTree::FVec &feats = feat_vecs[tid];
@@ -411,10 +412,10 @@ class CPUPredictor : public Predictor {
std::vector<bst_float> const *tree_weights,
bool approximate, int condition,
unsigned condition_feature) const override {
const int nthread = omp_get_max_threads();
auto const n_threads = this->ctx_->Threads();
const int num_feature = model.learner_model_param->num_feature;
std::vector<RegTree::FVec> feat_vecs;
InitThreadTemp(nthread, num_feature, &feat_vecs);
InitThreadTemp(n_threads, num_feature, &feat_vecs);
const MetaInfo& info = p_fmat->Info();
// number of valid trees
if (ntree_limit == 0 || ntree_limit > model.trees.size()) {
@@ -432,7 +433,7 @@ class CPUPredictor : public Predictor {
std::fill(contribs.begin(), contribs.end(), 0);
// initialize tree node mean values
std::vector<std::vector<float>> mean_values(ntree_limit);
common::ParallelFor(bst_omp_uint(ntree_limit), [&](bst_omp_uint i) {
common::ParallelFor(ntree_limit, n_threads, [&](bst_omp_uint i) {
FillNodeMeanValues(model.trees[i].get(), &(mean_values[i]));
});
auto base_margin = info.base_margin_.View(GenericParameter::kCpuId);
@@ -441,7 +442,7 @@ class CPUPredictor : public Predictor {
auto page = batch.GetView();
// parallel over local batch
const auto nsize = static_cast<bst_omp_uint>(batch.Size());
common::ParallelFor(nsize, [&](bst_omp_uint i) {
common::ParallelFor(nsize, n_threads, [&](bst_omp_uint i) {
auto row_idx = static_cast<size_t>(batch.base_rowid + i);
RegTree::FVec &feats = feat_vecs[omp_get_thread_num()];
if (feats.Size() == 0) {