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Pass pointer to model parameters. (#5101)

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* Pass pointer to model parameters.

This PR de-duplicates most of the model parameters except the one in
`tree_model.h`.  One difficulty is `base_score` is a model property but can be
changed at runtime by objective function.  Hence when performing model IO, we
need to save the one provided by users, instead of the one transformed by
objective.  Here we created an immutable version of `LearnerModelParam` that
represents the value of model parameter after configuration.
This commit is contained in:
Jiaming Yuan
2019-12-10 12:11:22 +08:00
committed by GitHub
parent 979f74d51a
commit e089e16e3d
33 changed files with 623 additions and 404 deletions
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16
src/predictor/gpu_predictor.cu
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@@ -217,7 +217,7 @@ class GPUPredictor : public xgboost::Predictor {
cudaMemcpyHostToDevice));
this->tree_begin_ = tree_begin;
this->tree_end_ = tree_end;
this->num_group_ = model.param.num_output_group;
this->num_group_ = model.learner_model_param_->num_output_group;
}
void PredictInternal(const SparsePage& batch,
@@ -286,9 +286,9 @@ class GPUPredictor : public xgboost::Predictor {
for (auto &batch : dmat->GetBatches<SparsePage>()) {
batch.offset.SetDevice(generic_param_->gpu_id);
batch.data.SetDevice(generic_param_->gpu_id);
PredictInternal(batch, model.param.num_feature,
PredictInternal(batch, model.learner_model_param_->num_feature,
out_preds, batch_offset);
batch_offset += batch.Size() * model.param.num_output_group;
batch_offset += batch.Size() * model.learner_model_param_->num_output_group;
}
monitor_.StopCuda("DevicePredictInternal");
@@ -317,7 +317,7 @@ class GPUPredictor : public xgboost::Predictor {
}
this->InitOutPredictions(dmat->Info(), out_preds, model);
int32_t tree_end = ntree_limit * model.param.num_output_group;
int32_t tree_end = ntree_limit * model.learner_model_param_->num_output_group;
if (ntree_limit == 0 || ntree_limit > model.trees.size()) {
tree_end = static_cast<unsigned>(model.trees.size());
@@ -347,7 +347,7 @@ class GPUPredictor : public xgboost::Predictor {
void InitOutPredictions(const MetaInfo& info,
HostDeviceVector<bst_float>* out_preds,
const gbm::GBTreeModel& model) const {
size_t n_classes = model.param.num_output_group;
size_t n_classes = model.learner_model_param_->num_output_group;
size_t n = n_classes * info.num_row_;
const HostDeviceVector<bst_float>& base_margin = info.base_margin_;
out_preds->SetDevice(generic_param_->gpu_id);
@@ -356,14 +356,14 @@ class GPUPredictor : public xgboost::Predictor {
CHECK_EQ(base_margin.Size(), n);
out_preds->Copy(base_margin);
} else {
out_preds->Fill(model.base_margin);
out_preds->Fill(model.learner_model_param_->base_score);
}
}
bool PredictFromCache(DMatrix* dmat, HostDeviceVector<bst_float>* out_preds,
const gbm::GBTreeModel& model, unsigned ntree_limit) {
if (ntree_limit == 0 ||
ntree_limit * model.param.num_output_group >= model.trees.size()) {
ntree_limit * model.learner_model_param_->num_output_group >= model.trees.size()) {
auto it = (*cache_).find(dmat);
if (it != cache_->cend()) {
const HostDeviceVector<bst_float>& y = it->second.predictions;
@@ -395,7 +395,7 @@ class GPUPredictor : public xgboost::Predictor {
this->InitOutPredictions(dmat->Info(), &predictions, model);
}
if (model.param.num_output_group == 1 && updaters->size() > 0 &&
if (model.learner_model_param_->num_output_group == 1 && updaters->size() > 0 &&
num_new_trees == 1 &&
updaters->back()->UpdatePredictionCache(e.data.get(), &predictions)) {
// do nothing