Hendrik/xgboost
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Optimize CPU prediction (#6696)

Browse Source 
Co-authored-by: Shvets Kirill <kirill.shvets@intel.com>
This commit is contained in:
ShvetsKS 2021-02-16 09:41:22 +03:00 committed by GitHub
parent dc97b5f19f
commit 9f15b9e322
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
1 changed files with 18 additions and 10 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

28
src/predictor/cpu_predictor.cc
View File

@ -65,10 +65,13 @@ inline void PredictByAllTrees(gbm::GBTreeModel const &model, const size_t tree_b
}
template <typename DataView>
void FVecFill(const size_t block_size, const size_t batch_offset, DataView* batch,
const size_t fvec_offset, std::vector<RegTree::FVec>* p_feats) {
void FVecFill(const size_t block_size, const size_t batch_offset, const int num_feature,
DataView* batch, const size_t fvec_offset, std::vector<RegTree::FVec>* p_feats) {
for (size_t i = 0; i < block_size; ++i) {
RegTree::FVec &feats = (*p_feats)[fvec_offset + i];
if (feats.Size() == 0) {
feats.Init(num_feature);
}
const SparsePage::Inst inst = (*batch)[batch_offset + i];
feats.Fill(inst);
}
@ -152,7 +155,7 @@ void PredictBatchByBlockOfRowsKernel(DataView batch, std::vector<bst_float> *out
<< "size_leaf_vector is enforced to 0 so far";
// parallel over local batch
const auto nsize = static_cast<bst_omp_uint>(batch.Size());
const int num_feature = model.learner_model_param->num_feature;
const bst_omp_uint n_row_blocks = (nsize) / block_of_rows_size + !!((nsize) % block_of_rows_size);
#pragma omp parallel for schedule(static)
for (bst_omp_uint block_id = 0; block_id < n_row_blocks; ++block_id) {
@ -160,7 +163,7 @@ void PredictBatchByBlockOfRowsKernel(DataView batch, std::vector<bst_float> *out
const size_t block_size = std::min(nsize - batch_offset, block_of_rows_size);
const size_t fvec_offset = omp_get_thread_num() * block_of_rows_size;
FVecFill(block_size, batch_offset, &batch, fvec_offset, p_thread_temp);
FVecFill(block_size, batch_offset, num_feature, &batch, fvec_offset, p_thread_temp);
// process block of rows through all trees to keep cache locality
PredictByAllTrees(model, tree_begin, tree_end, out_preds, batch_offset + batch.base_rowid,
num_group, thread_temp, fvec_offset, block_size);
@ -175,9 +178,6 @@ class CPUPredictor : public Predictor {
int prev_thread_temp_size = out->size();
if (prev_thread_temp_size < nthread) {
out->resize(nthread, RegTree::FVec());
for (int i = prev_thread_temp_size; i < nthread; ++i) {
(*out)[i].Init(num_feature);
}
}
}
@ -321,7 +321,8 @@ class CPUPredictor : public Predictor {
const gbm::GBTreeModel& model, unsigned ntree_limit) const override {
const int nthread = omp_get_max_threads();
std::vector<RegTree::FVec> feat_vecs;
InitThreadTemp(nthread, model.learner_model_param->num_feature, &feat_vecs);
const int num_feature = model.learner_model_param->num_feature;
InitThreadTemp(nthread, num_feature, &feat_vecs);
const MetaInfo& info = p_fmat->Info();
// number of valid trees
if (ntree_limit == 0 || ntree_limit > model.trees.size()) {
@ -339,6 +340,9 @@ class CPUPredictor : public Predictor {
const int tid = omp_get_thread_num();
auto ridx = static_cast<size_t>(batch.base_rowid + i);
RegTree::FVec &feats = feat_vecs[tid];
if (feats.Size() == 0) {
feats.Init(num_feature);
}
feats.Fill(page[i]);
for (unsigned j = 0; j < ntree_limit; ++j) {
int tid = model.trees[j]->GetLeafIndex(feats);
@ -355,8 +359,9 @@ class CPUPredictor : public Predictor {
bool approximate, int condition,
unsigned condition_feature) const override {
const int nthread = omp_get_max_threads();
const int num_feature = model.learner_model_param->num_feature;
std::vector<RegTree::FVec> feat_vecs;
InitThreadTemp(nthread, model.learner_model_param->num_feature, &feat_vecs);
InitThreadTemp(nthread, num_feature, &feat_vecs);
const MetaInfo& info = p_fmat->Info();
// number of valid trees
if (ntree_limit == 0 || ntree_limit > model.trees.size()) {
@ -364,7 +369,7 @@ class CPUPredictor : public Predictor {
}
const int ngroup = model.learner_model_param->num_output_group;
CHECK_NE(ngroup, 0);
size_t const ncolumns = model.learner_model_param->num_feature + 1;
size_t const ncolumns = num_feature + 1;
CHECK_NE(ncolumns, 0);
// allocate space for (number of features + bias) times the number of rows
std::vector<bst_float>& contribs = out_contribs->HostVector();
@ -387,6 +392,9 @@ class CPUPredictor : public Predictor {
for (bst_omp_uint i = 0; i < nsize; ++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) {
feats.Init(num_feature);
}
std::vector<bst_float> this_tree_contribs(ncolumns);
// loop over all classes
for (int gid = 0; gid < ngroup; ++gid) {