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Enhance inplace prediction. (#6653)

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* Accept array interface for csr and array.
* Accept an optional proxy dmatrix for metainfo.

This constructs an explicit `_ProxyDMatrix` type in Python.

* Remove unused doc.
* Add strict output.
This commit is contained in:
Jiaming Yuan
2021-02-02 11:41:46 +08:00
committed by GitHub
parent 87ab1ad607
commit 411592a347
22 changed files with 955 additions and 530 deletions
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49
src/predictor/cpu_predictor.cc
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@@ -1,5 +1,5 @@
/*!
* Copyright by Contributors 2017-2020
* Copyright by Contributors 2017-2021
*/
#include <dmlc/omp.h>
#include <dmlc/any.h>
@@ -287,7 +287,7 @@ class CPUPredictor : public Predictor {
}
template <typename Adapter>
void DispatchedInplacePredict(dmlc::any const &x,
void DispatchedInplacePredict(dmlc::any const &x, std::shared_ptr<DMatrix> p_m,
const gbm::GBTreeModel &model, float missing,
PredictionCacheEntry *out_preds,
uint32_t tree_begin, uint32_t tree_end) const {
@@ -295,33 +295,44 @@ class CPUPredictor : public Predictor {
auto m = dmlc::get<std::shared_ptr<Adapter>>(x);
CHECK_EQ(m->NumColumns(), model.learner_model_param->num_feature)
<< "Number of columns in data must equal to trained model.";
MetaInfo info;
info.num_col_ = m->NumColumns();
info.num_row_ = m->NumRows();
this->InitOutPredictions(info, &(out_preds->predictions), model);
std::vector<Entry> workspace(info.num_col_ * 8 * threads);
if (p_m) {
p_m->Info().num_row_ = m->NumRows();
this->InitOutPredictions(p_m->Info(), &(out_preds->predictions), model);
} else {
MetaInfo info;
info.num_row_ = m->NumRows();
this->InitOutPredictions(info, &(out_preds->predictions), model);
}
std::vector<Entry> workspace(m->NumColumns() * 8 * threads);
auto &predictions = out_preds->predictions.HostVector();
std::vector<RegTree::FVec> thread_temp;
InitThreadTemp(threads*kBlockOfRowsSize, model.learner_model_param->num_feature,
&thread_temp);
PredictBatchByBlockOfRowsKernel<AdapterView<Adapter>,
kBlockOfRowsSize>(AdapterView<Adapter>(
m.get(), missing, common::Span<Entry>{workspace}),
&predictions, model, tree_begin, tree_end, &thread_temp);
InitThreadTemp(threads * kBlockOfRowsSize,
model.learner_model_param->num_feature, &thread_temp);
PredictBatchByBlockOfRowsKernel<AdapterView<Adapter>, kBlockOfRowsSize>(
AdapterView<Adapter>(m.get(), missing, common::Span<Entry>{workspace}),
&predictions, model, tree_begin, tree_end, &thread_temp);
}
void InplacePredict(dmlc::any const &x, const gbm::GBTreeModel &model,
float missing, PredictionCacheEntry *out_preds,
uint32_t tree_begin, unsigned tree_end) const override {
bool InplacePredict(dmlc::any const &x, std::shared_ptr<DMatrix> p_m,
const gbm::GBTreeModel &model, float missing,
PredictionCacheEntry *out_preds, uint32_t tree_begin,
unsigned tree_end) const override {
if (x.type() == typeid(std::shared_ptr<data::DenseAdapter>)) {
this->DispatchedInplacePredict<data::DenseAdapter>(
x, model, missing, out_preds, tree_begin, tree_end);
x, p_m, model, missing, out_preds, tree_begin, tree_end);
} else if (x.type() == typeid(std::shared_ptr<data::CSRAdapter>)) {
this->DispatchedInplacePredict<data::CSRAdapter>(
x, model, missing, out_preds, tree_begin, tree_end);
x, p_m, model, missing, out_preds, tree_begin, tree_end);
} else if (x.type() == typeid(std::shared_ptr<data::ArrayAdapter>)) {
this->DispatchedInplacePredict<data::ArrayAdapter> (
x, p_m, model, missing, out_preds, tree_begin, tree_end);
} else if (x.type() == typeid(std::shared_ptr<data::CSRArrayAdapter>)) {
this->DispatchedInplacePredict<data::CSRArrayAdapter> (
x, p_m, model, missing, out_preds, tree_begin, tree_end);
} else {
LOG(FATAL) << "Data type is not supported by CPU Predictor.";
return false;
}
return true;
}
void PredictInstance(const SparsePage::Inst& inst,