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Choose predictor only when it's training. #9344

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Choose predictor only when it's training. #9344

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26 changes: 12 additions & 14 deletions src/gbm/gbtree.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -505,7 +505,7 @@ void GBTree::Slice(bst_layer_t begin, bst_layer_t end, bst_layer_t step, Gradien
out_model.param.num_parallel_tree = model_.param.num_parallel_tree;
}

void GBTree::PredictBatch(DMatrix* p_fmat, PredictionCacheEntry* out_preds, bool,
void GBTree::PredictBatch(DMatrix* p_fmat, PredictionCacheEntry* out_preds, bool is_training,
bst_layer_t layer_begin, bst_layer_t layer_end) {
CHECK(configured_);
if (layer_end == 0) {
Expand All @@ -526,7 +526,7 @@ void GBTree::PredictBatch(DMatrix* p_fmat, PredictionCacheEntry* out_preds, bool
CHECK_EQ(out_preds->version, 0);
}

auto const& predictor = GetPredictor(&out_preds->predictions, p_fmat);
auto const& predictor = GetPredictor(&out_preds->predictions, p_fmat, is_training);
if (out_preds->version == 0) {
// out_preds->Size() can be non-zero as it's initialized here before any
// tree is built at the 0^th iterator.
Expand All @@ -546,9 +546,8 @@ void GBTree::PredictBatch(DMatrix* p_fmat, PredictionCacheEntry* out_preds, bool
}
}

std::unique_ptr<Predictor> const &
GBTree::GetPredictor(HostDeviceVector<float> const *out_pred,
DMatrix *f_dmat) const {
std::unique_ptr<Predictor> const& GBTree::GetPredictor(HostDeviceVector<float> const* out_pred,
DMatrix* f_dmat, bool is_training) const {
CHECK(configured_);
if (tparam_.predictor != PredictorType::kAuto) {
if (tparam_.predictor == PredictorType::kGPUPredictor) {
Expand All @@ -574,7 +573,7 @@ GBTree::GetPredictor(HostDeviceVector<float> const *out_pred,

// Data comes from SparsePageDMatrix. Since we are loading data in pages, no need to
// prevent data copy.
if (f_dmat && !f_dmat->SingleColBlock()) {
if ((f_dmat && !f_dmat->SingleColBlock()) || !is_training) {
if (ctx_->IsCPU()) {
return cpu_predictor_;
} else {
Expand All @@ -589,12 +588,11 @@ GBTree::GetPredictor(HostDeviceVector<float> const *out_pred,
}

// Data comes from Device DMatrix.
auto is_ellpack = f_dmat && f_dmat->PageExists<EllpackPage>() &&
!f_dmat->PageExists<SparsePage>();
auto is_ellpack =
f_dmat && f_dmat->PageExists<EllpackPage>() && !f_dmat->PageExists<SparsePage>();
// Data comes from device memory, like CuDF or CuPy.
auto is_from_device =
f_dmat && f_dmat->PageExists<SparsePage>() &&
(*(f_dmat->GetBatches<SparsePage>().begin())).data.DeviceCanRead();
auto is_from_device = f_dmat && f_dmat->PageExists<SparsePage>() &&
(*(f_dmat->GetBatches<SparsePage>().begin())).data.DeviceCanRead();
auto on_device = is_ellpack || is_from_device;

// Use GPU Predictor if data is already on device and gpu_id is set.
Expand Down Expand Up @@ -750,7 +748,7 @@ class Dart : public GBTree {
bool training, unsigned layer_begin,
unsigned layer_end) const {
CHECK(!this->model_.learner_model_param->IsVectorLeaf()) << "dart" << MTNotImplemented();
auto &predictor = this->GetPredictor(&p_out_preds->predictions, p_fmat);
auto &predictor = this->GetPredictor(&p_out_preds->predictions, p_fmat, training);
CHECK(predictor);
predictor->InitOutPredictions(p_fmat->Info(), &p_out_preds->predictions,
model_);
Expand Down Expand Up @@ -843,7 +841,7 @@ class Dart : public GBTree {
}
CHECK(success) << msg;
} else {
predictor = this->GetPredictor().get();
predictor = this->GetPredictor(nullptr, nullptr, false).get();
bool success = predictor->InplacePredict(p_fmat, model_, missing, &predts, i, i + 1);
CHECK(success) << msg << std::endl
<< "Current Predictor: "
Expand Down Expand Up @@ -886,7 +884,7 @@ class Dart : public GBTree {
std::vector<bst_float> *out_preds,
unsigned layer_begin, unsigned layer_end) override {
DropTrees(false);
auto &predictor = this->GetPredictor();
auto& predictor = this->GetPredictor(nullptr, nullptr, false);
uint32_t _, tree_end;
std::tie(_, tree_end) = detail::LayerToTree(model_, layer_begin, layer_end);
predictor->PredictInstance(inst, out_preds, model_, tree_end);
Expand Down
17 changes: 9 additions & 8 deletions src/gbm/gbtree.h
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Original file line number Diff line number Diff line change
Expand Up @@ -260,8 +260,8 @@ class GBTree : public GradientBooster {
}
LOG(FATAL) << msg;
} else {
bool success = this->GetPredictor()->InplacePredict(p_m, model_, missing, out_preds,
tree_begin, tree_end);
bool success = this->GetPredictor(nullptr, nullptr, false)
->InplacePredict(p_m, model_, missing, out_preds, tree_begin, tree_end);
CHECK(success) << msg << std::endl
<< "Current Predictor: "
<< (tparam_.predictor == PredictorType::kCPUPredictor
Expand Down Expand Up @@ -349,7 +349,7 @@ class GBTree : public GradientBooster {
auto [tree_begin, tree_end] = detail::LayerToTree(model_, layer_begin, layer_end);
CHECK_EQ(tree_begin, 0) << "Predict leaf supports only iteration end: (0, "
"n_iteration), use model slicing instead.";
this->GetPredictor()->PredictLeaf(p_fmat, out_preds, model_, tree_end);
this->GetPredictor(nullptr, nullptr, false)->PredictLeaf(p_fmat, out_preds, model_, tree_end);
}

void PredictContribution(DMatrix* p_fmat,
Expand All @@ -361,7 +361,7 @@ class GBTree : public GradientBooster {
CHECK_EQ(tree_begin, 0)
<< "Predict contribution supports only iteration end: (0, "
"n_iteration), using model slicing instead.";
this->GetPredictor()->PredictContribution(
this->GetPredictor(nullptr, nullptr, false)->PredictContribution(
p_fmat, out_contribs, model_, tree_end, nullptr, approximate);
}

Expand All @@ -373,8 +373,9 @@ class GBTree : public GradientBooster {
CHECK_EQ(tree_begin, 0)
<< "Predict interaction contribution supports only iteration end: (0, "
"n_iteration), using model slicing instead.";
this->GetPredictor()->PredictInteractionContributions(
p_fmat, out_contribs, model_, tree_end, nullptr, approximate);
this->GetPredictor(nullptr, nullptr, false)
->PredictInteractionContributions(p_fmat, out_contribs, model_, tree_end, nullptr,
approximate);
}

[[nodiscard]] std::vector<std::string> DumpModel(const FeatureMap& fmap, bool with_stats,
Expand All @@ -390,8 +391,8 @@ class GBTree : public GradientBooster {
std::vector<HostDeviceVector<bst_node_t>>* out_position,
std::vector<std::unique_ptr<RegTree>>* ret);

std::unique_ptr<Predictor> const& GetPredictor(HostDeviceVector<float> const* out_pred = nullptr,
DMatrix* f_dmat = nullptr) const;
std::unique_ptr<Predictor> const& GetPredictor(HostDeviceVector<float> const* out_pred,
DMatrix* f_dmat, bool is_training) const;

// commit new trees all at once
virtual void CommitModel(TreesOneIter&& new_trees);
Expand Down