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Multiclass prediction caching for CPU Hist (#6550)

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Co-authored-by: Kirill Shvets <kirill.shvets@intel.com>
This commit is contained in:
ShvetsKS
2021-01-12 23:42:07 +03:00
committed by GitHub
parent 03cd087da1
commit 7f4d3a91b9
5 changed files with 94 additions and 21 deletions
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27
src/gbm/gbtree.cc
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@@ -196,11 +196,17 @@ void GBTree::DoBoost(DMatrix* p_fmat,
const int ngroup = model_.learner_model_param->num_output_group;
ConfigureWithKnownData(this->cfg_, p_fmat);
monitor_.Start("BoostNewTrees");
auto* out = &predt->predictions;
CHECK_NE(ngroup, 0);
if (ngroup == 1) {
std::vector<std::unique_ptr<RegTree> > ret;
BoostNewTrees(in_gpair, p_fmat, 0, &ret);
const size_t num_new_trees = ret.size();
new_trees.push_back(std::move(ret));
if (updaters_.size() > 0 && num_new_trees == 1 && out->Size() > 0 &&
updaters_.back()->UpdatePredictionCache(p_fmat, out)) {
predt->Update(1);
}
} else {
CHECK_EQ(in_gpair->Size() % ngroup, 0U)
<< "must have exactly ngroup * nrow gpairs";
@@ -210,6 +216,7 @@ void GBTree::DoBoost(DMatrix* p_fmat,
in_gpair->DeviceIdx());
const auto& gpair_h = in_gpair->ConstHostVector();
auto nsize = static_cast<bst_omp_uint>(tmp.Size());
bool update_predict = true;
for (int gid = 0; gid < ngroup; ++gid) {
std::vector<GradientPair>& tmp_h = tmp.HostVector();
#pragma omp parallel for schedule(static)
@@ -218,7 +225,16 @@ void GBTree::DoBoost(DMatrix* p_fmat,
}
std::vector<std::unique_ptr<RegTree> > ret;
BoostNewTrees(&tmp, p_fmat, gid, &ret);
const size_t num_new_trees = ret.size();
new_trees.push_back(std::move(ret));
auto* out = &predt->predictions;
if (!(updaters_.size() > 0 && out->Size() > 0 && num_new_trees == 1 &&
updaters_.back()->UpdatePredictionCacheMulticlass(p_fmat, out, gid, ngroup))) {
update_predict = false;
}
}
if (update_predict) {
predt->Update(1);
}
}
monitor_.Stop("BoostNewTrees");
@@ -314,20 +330,9 @@ void GBTree::CommitModel(std::vector<std::vector<std::unique_ptr<RegTree>>>&& ne
DMatrix* m,
PredictionCacheEntry* predts) {
monitor_.Start("CommitModel");
int num_new_trees = 0;
for (uint32_t gid = 0; gid < model_.learner_model_param->num_output_group; ++gid) {
num_new_trees += new_trees[gid].size();
model_.CommitModel(std::move(new_trees[gid]), gid);
}
auto* out = &predts->predictions;
if (model_.learner_model_param->num_output_group == 1 &&
updaters_.size() > 0 &&
num_new_trees == 1 &&
out->Size() > 0 &&
updaters_.back()->UpdatePredictionCache(m, out)) {
auto delta = num_new_trees / model_.learner_model_param->num_output_group;
predts->Update(delta);
}
monitor_.Stop("CommitModel");
}