53fc17578f
- Use std::uint64_t instead of size_t to avoid implementation-defined type. - Rename to bst_idx_t, to account for other types of indexing. - Small cleanup to the base header.
76 lines
2.9 KiB
C++
76 lines
2.9 KiB
C++
/**
|
|
* Copyright 2017-2024 by Contributors
|
|
*/
|
|
#include "xgboost/predictor.h"
|
|
|
|
#include <dmlc/registry.h> // for DMLC_REGISTRY_LINK_TAG
|
|
|
|
#include <cstdint> // for int32_t
|
|
#include <string> // for string, to_string
|
|
|
|
#include "../gbm/gbtree_model.h" // for GBTreeModel
|
|
#include "xgboost/base.h" // for bst_float, Args, bst_group_t, bst_idx_t
|
|
#include "xgboost/context.h" // for Context
|
|
#include "xgboost/data.h" // for MetaInfo
|
|
#include "xgboost/host_device_vector.h" // for HostDeviceVector
|
|
#include "xgboost/learner.h" // for LearnerModelParam
|
|
#include "xgboost/linalg.h" // for Tensor, TensorView
|
|
#include "xgboost/logging.h" // for CHECK_EQ, CHECK_NE, LOG
|
|
|
|
namespace dmlc {
|
|
DMLC_REGISTRY_ENABLE(::xgboost::PredictorReg);
|
|
} // namespace dmlc
|
|
|
|
namespace xgboost {
|
|
void Predictor::Configure(Args const&) {}
|
|
|
|
Predictor* Predictor::Create(std::string const& name, Context const* ctx) {
|
|
auto* e = ::dmlc::Registry<PredictorReg>::Get()->Find(name);
|
|
if (e == nullptr) {
|
|
LOG(FATAL) << "Unknown predictor type " << name;
|
|
}
|
|
auto p_predictor = (e->body)(ctx);
|
|
return p_predictor;
|
|
}
|
|
|
|
template <int32_t D>
|
|
void ValidateBaseMarginShape(linalg::Tensor<float, D> const& margin, bst_idx_t n_samples,
|
|
bst_group_t n_groups) {
|
|
// FIXME: Bindings other than Python doesn't have shape.
|
|
std::string expected{"Invalid shape of base_margin. Expected: (" + std::to_string(n_samples) +
|
|
", " + std::to_string(n_groups) + ")"};
|
|
CHECK_EQ(margin.Shape(0), n_samples) << expected;
|
|
CHECK_EQ(margin.Shape(1), n_groups) << expected;
|
|
}
|
|
|
|
void Predictor::InitOutPredictions(const MetaInfo& info, HostDeviceVector<bst_float>* out_preds,
|
|
const gbm::GBTreeModel& model) const {
|
|
CHECK_NE(model.learner_model_param->num_output_group, 0);
|
|
auto n = static_cast<size_t>(model.learner_model_param->OutputLength() * info.num_row_);
|
|
|
|
const HostDeviceVector<bst_float>* base_margin = info.base_margin_.Data();
|
|
if (ctx_->Device().IsCUDA()) {
|
|
out_preds->SetDevice(ctx_->Device());
|
|
}
|
|
if (!base_margin->Empty()) {
|
|
out_preds->Resize(n);
|
|
ValidateBaseMarginShape(info.base_margin_, info.num_row_,
|
|
model.learner_model_param->OutputLength());
|
|
out_preds->Copy(*base_margin);
|
|
} else {
|
|
// cannot rely on the Resize to fill as it might skip if the size is already correct.
|
|
out_preds->Resize(n);
|
|
auto base_score = model.learner_model_param->BaseScore(DeviceOrd::CPU())(0);
|
|
out_preds->Fill(base_score);
|
|
}
|
|
}
|
|
} // namespace xgboost
|
|
|
|
namespace xgboost::predictor {
|
|
// List of files that will be force linked in static links.
|
|
#ifdef XGBOOST_USE_CUDA
|
|
DMLC_REGISTRY_LINK_TAG(gpu_predictor);
|
|
#endif // XGBOOST_USE_CUDA
|
|
DMLC_REGISTRY_LINK_TAG(cpu_predictor);
|
|
} // namespace xgboost::predictor
|