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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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55
src/predictor/cpu_predictor.cc
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@@ -104,14 +104,43 @@ class CPUPredictor : public Predictor {
tree_begin, ntree_limit);
}
public:
void PredictBatch(DMatrix* dmat, HostDeviceVector<bst_float>* out_preds,
const gbm::GBTreeModel& model, int tree_begin,
unsigned ntree_limit = 0) override {
PredictBatch(dmat, &out_preds->data_h(), model, tree_begin, ntree_limit);
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()) {
auto it = cache_.find(dmat);
if (it != cache_.end()) {
HostDeviceVector<bst_float>& y = it->second.predictions;
if (y.size() != 0) {
out_preds->resize(y.size());
std::copy(y.data_h().begin(), y.data_h().end(),
out_preds->data_h().begin());
return true;
}
}
}
return false;
}
void PredictBatch(DMatrix* dmat, std::vector<bst_float>* out_preds,
void InitOutPredictions(const MetaInfo& info,
HostDeviceVector<bst_float>* out_preds,
const gbm::GBTreeModel& model) const {
size_t n = model.param.num_output_group * info.num_row;
const std::vector<bst_float>& base_margin = info.base_margin;
out_preds->resize(n);
std::vector<bst_float>& out_preds_h = out_preds->data_h();
if (base_margin.size() != 0) {
CHECK_EQ(out_preds->size(), n);
std::copy(base_margin.begin(), base_margin.end(), out_preds_h.begin());
} else {
std::fill(out_preds_h.begin(), out_preds_h.end(), model.base_margin);
}
}
public:
void PredictBatch(DMatrix* dmat, HostDeviceVector<bst_float>* out_preds,
const gbm::GBTreeModel& model, int tree_begin,
unsigned ntree_limit = 0) override {
if (this->PredictFromCache(dmat, out_preds, model, ntree_limit)) {
@@ -125,12 +154,14 @@ class CPUPredictor : public Predictor {
ntree_limit = static_cast<unsigned>(model.trees.size());
}
this->PredLoopInternal(dmat, out_preds, model, tree_begin, ntree_limit);
this->PredLoopInternal(dmat, &out_preds->data_h(), model,
tree_begin, ntree_limit);
}
void UpdatePredictionCache(const gbm::GBTreeModel& model,
std::vector<std::unique_ptr<TreeUpdater>>* updaters,
int num_new_trees) override {
void UpdatePredictionCache(
const gbm::GBTreeModel& model,
std::vector<std::unique_ptr<TreeUpdater>>* updaters,
int num_new_trees) override {
int old_ntree = model.trees.size() - num_new_trees;
// update cache entry
for (auto& kv : cache_) {
@@ -138,7 +169,7 @@ class CPUPredictor : public Predictor {
if (e.predictions.size() == 0) {
InitOutPredictions(e.data->info(), &(e.predictions), model);
PredLoopInternal(e.data.get(), &(e.predictions), model, 0,
PredLoopInternal(e.data.get(), &(e.predictions.data_h()), model, 0,
model.trees.size());
} else if (model.param.num_output_group == 1 && updaters->size() > 0 &&
num_new_trees == 1 &&
@@ -146,7 +177,7 @@ class CPUPredictor : public Predictor {
&(e.predictions))) {
{} // do nothing
} else {
PredLoopInternal(e.data.get(), &(e.predictions), model, old_ntree,
PredLoopInternal(e.data.get(), &(e.predictions.data_h()), model, old_ntree,
model.trees.size());
}
}