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Remove update prediction cache from predictors. (#5312)

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Move this function into gbtree, and uses only updater for doing so. As now the predictor knows exactly how many trees to predict, there's no need for it to update the prediction cache.
This commit is contained in:
Jiaming Yuan
2020-02-17 11:35:47 +08:00
committed by GitHub
parent e433a379e4
commit 0110754a76
16 changed files with 118 additions and 164 deletions
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30
src/predictor/cpu_predictor.cc
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@@ -146,8 +146,12 @@ class CPUPredictor : public Predictor {
CHECK_EQ(tree_begin, 0);
auto* out_preds = &predts->predictions;
CHECK_GE(predts->version, tree_begin);
if (out_preds->Size() == 0 && dmat->Info().num_row_ != 0) {
CHECK_EQ(predts->version, 0);
}
if (predts->version == 0) {
CHECK_EQ(out_preds->Size(), 0);
// out_preds->Size() can be non-zero as it's initialized here before any tree is
// built at the 0^th iterator.
this->InitOutPredictions(dmat->Info(), out_preds, model);
}
@@ -185,30 +189,6 @@ class CPUPredictor : public Predictor {
out_preds->Size() == dmat->Info().num_row_);
}
void UpdatePredictionCache(
const gbm::GBTreeModel& model,
std::vector<std::unique_ptr<TreeUpdater>>* updaters,
int num_new_trees,
DMatrix* m,
PredictionCacheEntry* predts) override {
int old_ntree = model.trees.size() - num_new_trees;
// update cache entry
auto* out = &predts->predictions;
if (predts->predictions.Size() == 0) {
this->InitOutPredictions(m->Info(), out, model);
this->PredInternal(m, &out->HostVector(), model, 0, model.trees.size());
} else if (model.learner_model_param_->num_output_group == 1 &&
updaters->size() > 0 &&
num_new_trees == 1 &&
updaters->back()->UpdatePredictionCache(m, out)) {
{}
} else {
PredInternal(m, &out->HostVector(), model, old_ntree, model.trees.size());
}
auto delta = num_new_trees / model.learner_model_param_->num_output_group;
predts->Update(delta);
}
void PredictInstance(const SparsePage::Inst& inst,
std::vector<bst_float>* out_preds,
const gbm::GBTreeModel& model, unsigned ntree_limit) override {