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Replaced std::vector-based interfaces with HostDeviceVector-based interfaces. (#3116)

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* Replaced std::vector-based interfaces with HostDeviceVector-based interfaces.

- replacement was performed in the learner, boosters, predictors,
  updaters, and objective functions
- only interfaces used in training were replaced;
  interfaces like PredictInstance() still use std::vector
- refactoring necessary for replacement of interfaces was also performed,
  such as using HostDeviceVector in prediction cache

* HostDeviceVector-based interfaces for custom objective function example plugin.
This commit is contained in:
Andrew V. Adinetz
2018-02-28 01:00:04 +01:00
committed by Rory Mitchell
parent 11bfa8584d
commit d5992dd881
38 changed files with 371 additions and 519 deletions
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28
src/learner.cc
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@@ -362,17 +362,17 @@ class LearnerImpl : public Learner {
}
this->LazyInitDMatrix(train);
monitor.Start("PredictRaw");
this->PredictRaw(train, &preds2_);
this->PredictRaw(train, &preds_);
monitor.Stop("PredictRaw");
monitor.Start("GetGradient");
obj_->GetGradient(&preds2_, train->info(), iter, &gpair_);
obj_->GetGradient(&preds_, train->info(), iter, &gpair_);
monitor.Stop("GetGradient");
gbm_->DoBoost(train, &gpair_, obj_.get());
monitor.Stop("UpdateOneIter");
}
void BoostOneIter(int iter, DMatrix* train,
std::vector<bst_gpair>* in_gpair) override {
HostDeviceVector<bst_gpair>* in_gpair) override {
monitor.Start("BoostOneIter");
if (tparam.seed_per_iteration || rabit::IsDistributed()) {
common::GlobalRandom().seed(tparam.seed * kRandSeedMagic + iter);
@@ -395,7 +395,7 @@ class LearnerImpl : public Learner {
obj_->EvalTransform(&preds_);
for (auto& ev : metrics_) {
os << '\t' << data_names[i] << '-' << ev->Name() << ':'
<< ev->Eval(preds_, data_sets[i]->info(), tparam.dsplit == 2);
<< ev->Eval(preds_.data_h(), data_sets[i]->info(), tparam.dsplit == 2);
}
}
@@ -438,19 +438,20 @@ class LearnerImpl : public Learner {
this->PredictRaw(data, &preds_);
obj_->EvalTransform(&preds_);
return std::make_pair(metric,
ev->Eval(preds_, data->info(), tparam.dsplit == 2));
ev->Eval(preds_.data_h(), data->info(), tparam.dsplit == 2));
}
void Predict(DMatrix* data, bool output_margin,
std::vector<bst_float>* out_preds, unsigned ntree_limit,
HostDeviceVector<bst_float>* out_preds, unsigned ntree_limit,
bool pred_leaf, bool pred_contribs, bool approx_contribs,
bool pred_interactions) const override {
if (pred_contribs) {
gbm_->PredictContribution(data, out_preds, ntree_limit, approx_contribs);
gbm_->PredictContribution(data, &out_preds->data_h(), ntree_limit, approx_contribs);
} else if (pred_interactions) {
gbm_->PredictInteractionContributions(data, out_preds, ntree_limit, approx_contribs);
gbm_->PredictInteractionContributions(data, &out_preds->data_h(), ntree_limit,
approx_contribs);
} else if (pred_leaf) {
gbm_->PredictLeaf(data, out_preds, ntree_limit);
gbm_->PredictLeaf(data, &out_preds->data_h(), ntree_limit);
} else {
this->PredictRaw(data, out_preds, ntree_limit);
if (!output_margin) {
@@ -546,12 +547,6 @@ class LearnerImpl : public Learner {
* \param ntree_limit limit number of trees used for boosted tree
* predictor, when it equals 0, this means we are using all the trees
*/
inline void PredictRaw(DMatrix* data, std::vector<bst_float>* out_preds,
unsigned ntree_limit = 0) const {
CHECK(gbm_.get() != nullptr)
<< "Predict must happen after Load or InitModel";
gbm_->PredictBatch(data, out_preds, ntree_limit);
}
inline void PredictRaw(DMatrix* data, HostDeviceVector<bst_float>* out_preds,
unsigned ntree_limit = 0) const {
CHECK(gbm_.get() != nullptr)
@@ -572,8 +567,7 @@ class LearnerImpl : public Learner {
// name of objective function
std::string name_obj_;
// temporal storages for prediction
std::vector<bst_float> preds_;
HostDeviceVector<bst_float> preds2_;
HostDeviceVector<bst_float> preds_;
// gradient pairs
HostDeviceVector<bst_gpair> gpair_;