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Rename and extract Context. (#8528)

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* Rename `GenericParameter` to `Context`.
* Rename header file to reflect the change.
* Rename all references.
This commit is contained in:
Jiaming Yuan
2022-12-07 04:58:54 +08:00
committed by GitHub
parent 05fc6f3ca9
commit 3e26107a9c
105 changed files with 548 additions and 574 deletions
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8
src/gbm/gblinear.cc
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@@ -71,7 +71,7 @@ void LinearCheckLayer(unsigned layer_begin) {
*/
class GBLinear : public GradientBooster {
public:
explicit GBLinear(LearnerModelParam const* learner_model_param, GenericParameter const* ctx)
explicit GBLinear(LearnerModelParam const* learner_model_param, Context const* ctx)
: GradientBooster{ctx},
learner_model_param_{learner_model_param},
model_{learner_model_param},
@@ -179,7 +179,7 @@ class GBLinear : public GradientBooster {
unsigned) override {
model_.LazyInitModel();
LinearCheckLayer(layer_begin);
auto base_margin = p_fmat->Info().base_margin_.View(GenericParameter::kCpuId);
auto base_margin = p_fmat->Info().base_margin_.View(Context::kCpuId);
const int ngroup = model_.learner_model_param->num_output_group;
const size_t ncolumns = model_.learner_model_param->num_feature + 1;
// allocate space for (#features + bias) times #groups times #rows
@@ -250,7 +250,7 @@ class GBLinear : public GradientBooster {
linalg::TensorView<float, 2> scores{
*out_scores,
{learner_model_param_->num_feature, n_groups},
GenericParameter::kCpuId};
Context::kCpuId};
for (size_t i = 0; i < learner_model_param_->num_feature; ++i) {
for (bst_group_t g = 0; g < n_groups; ++g) {
scores(i, g) = model_[i][g];
@@ -355,7 +355,7 @@ DMLC_REGISTER_PARAMETER(GBLinearTrainParam);
XGBOOST_REGISTER_GBM(GBLinear, "gblinear")
.describe("Linear booster, implement generalized linear model.")
.set_body([](LearnerModelParam const* booster_config, GenericParameter const* ctx) {
.set_body([](LearnerModelParam const* booster_config, Context const* ctx) {
return new GBLinear(booster_config, ctx);
});
} // namespace gbm

10
src/gbm/gbm.cc
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@@ -3,21 +3,23 @@
* \file gbm.cc
* \brief Registry of gradient boosters.
*/
#include "xgboost/gbm.h"
#include <dmlc/registry.h>
#include <memory>
#include <string>
#include <vector>
#include <memory>
#include "xgboost/gbm.h"
#include "xgboost/context.h"
#include "xgboost/learner.h"
#include "xgboost/generic_parameters.h"
namespace dmlc {
DMLC_REGISTRY_ENABLE(::xgboost::GradientBoosterReg);
} // namespace dmlc
namespace xgboost {
GradientBooster* GradientBooster::Create(const std::string& name, GenericParameter const* ctx,
GradientBooster* GradientBooster::Create(const std::string& name, Context const* ctx,
LearnerModelParam const* learner_model_param) {
auto *e = ::dmlc::Registry< ::xgboost::GradientBoosterReg>::Get()->Find(name);
if (e == nullptr) {

14
src/gbm/gbtree.cc
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@@ -67,7 +67,7 @@ void GBTree::Configure(const Args& cfg) {
#if defined(XGBOOST_USE_ONEAPI)
if (!oneapi_predictor_) {
oneapi_predictor_ = std::unique_ptr<Predictor>(
Predictor::Create("oneapi_predictor", this->generic_param_));
Predictor::Create("oneapi_predictor", this->ctx_));
}
oneapi_predictor_->Configure(cfg);
#endif // defined(XGBOOST_USE_ONEAPI)
@@ -204,7 +204,7 @@ void GPUCopyGradient(HostDeviceVector<GradientPair> const*, bst_group_t, bst_gro
void CopyGradient(HostDeviceVector<GradientPair> const* in_gpair, int32_t n_threads,
bst_group_t n_groups, bst_group_t group_id,
HostDeviceVector<GradientPair>* out_gpair) {
if (in_gpair->DeviceIdx() != GenericParameter::kCpuId) {
if (in_gpair->DeviceIdx() != Context::kCpuId) {
GPUCopyGradient(in_gpair, n_groups, group_id, out_gpair);
} else {
std::vector<GradientPair> &tmp_h = out_gpair->HostVector();
@@ -651,7 +651,7 @@ void GPUDartInplacePredictInc(common::Span<float> /*out_predts*/, common::Span<f
class Dart : public GBTree {
public:
explicit Dart(LearnerModelParam const* booster_config, GenericParameter const* ctx)
explicit Dart(LearnerModelParam const* booster_config, Context const* ctx)
: GBTree(booster_config, ctx) {}
void Configure(const Args& cfg) override {
@@ -741,7 +741,7 @@ class Dart : public GBTree {
auto n_groups = model_.learner_model_param->num_output_group;
PredictionCacheEntry predts; // temporary storage for prediction
if (ctx_->gpu_id != GenericParameter::kCpuId) {
if (ctx_->gpu_id != Context::kCpuId) {
predts.predictions.SetDevice(ctx_->gpu_id);
}
predts.predictions.Resize(p_fmat->Info().num_row_ * n_groups, 0);
@@ -763,7 +763,7 @@ class Dart : public GBTree {
CHECK_EQ(p_out_preds->predictions.Size(), predts.predictions.Size());
size_t n_rows = p_fmat->Info().num_row_;
if (predts.predictions.DeviceIdx() != GenericParameter::kCpuId) {
if (predts.predictions.DeviceIdx() != Context::kCpuId) {
p_out_preds->predictions.SetDevice(predts.predictions.DeviceIdx());
GPUDartPredictInc(p_out_preds->predictions.DeviceSpan(),
predts.predictions.DeviceSpan(), w, n_rows, n_groups,
@@ -1019,13 +1019,13 @@ DMLC_REGISTER_PARAMETER(DartTrainParam);
XGBOOST_REGISTER_GBM(GBTree, "gbtree")
.describe("Tree booster, gradient boosted trees.")
.set_body([](LearnerModelParam const* booster_config, GenericParameter const* ctx) {
.set_body([](LearnerModelParam const* booster_config, Context const* ctx) {
auto* p = new GBTree(booster_config, ctx);
return p;
});
XGBOOST_REGISTER_GBM(Dart, "dart")
.describe("Tree booster, dart.")
.set_body([](LearnerModelParam const* booster_config, GenericParameter const* ctx) {
.set_body([](LearnerModelParam const* booster_config, Context const* ctx) {
GBTree* p = new Dart(booster_config, ctx);
return p;
});

6
src/gbm/gbtree.cu
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@@ -1,10 +1,10 @@
/*!
* Copyright 2021 by Contributors
*/
#include "xgboost/span.h"
#include "xgboost/generic_parameters.h"
#include "xgboost/linalg.h"
#include "../common/device_helpers.cuh"
#include "xgboost/context.h"
#include "xgboost/linalg.h"
#include "xgboost/span.h"
namespace xgboost {
namespace gbm {

2
src/gbm/gbtree.h
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@@ -190,7 +190,7 @@ bool SliceTrees(int32_t layer_begin, int32_t layer_end, int32_t step, GBTreeMode
// gradient boosted trees
class GBTree : public GradientBooster {
public:
explicit GBTree(LearnerModelParam const* booster_config, GenericParameter const* ctx)
explicit GBTree(LearnerModelParam const* booster_config, Context const* ctx)
: GradientBooster{ctx}, model_(booster_config, ctx_) {}
void Configure(const Args& cfg) override;

15
src/gbm/gbtree_model.h
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@@ -5,16 +5,17 @@
#ifndef XGBOOST_GBM_GBTREE_MODEL_H_
#define XGBOOST_GBM_GBTREE_MODEL_H_
#include <dmlc/parameter.h>
#include <dmlc/io.h>
#include <xgboost/model.h>
#include <xgboost/tree_model.h>
#include <xgboost/parameter.h>
#include <dmlc/parameter.h>
#include <xgboost/context.h>
#include <xgboost/learner.h>
#include <xgboost/model.h>
#include <xgboost/parameter.h>
#include <xgboost/tree_model.h>
#include <memory>
#include <utility>
#include <string>
#include <utility>
#include <vector>
#include "../common/threading_utils.h"
@@ -89,7 +90,7 @@ struct GBTreeModelParam : public dmlc::Parameter<GBTreeModelParam> {
struct GBTreeModel : public Model {
public:
explicit GBTreeModel(LearnerModelParam const* learner_model, GenericParameter const* ctx)
explicit GBTreeModel(LearnerModelParam const* learner_model, Context const* ctx)
: learner_model_param{learner_model}, ctx_{ctx} {}
void Configure(const Args& cfg) {
// initialize model parameters if not yet been initialized.
@@ -143,7 +144,7 @@ struct GBTreeModel : public Model {
std::vector<int> tree_info;
private:
GenericParameter const* ctx_;
Context const* ctx_;
};
} // namespace gbm
} // namespace xgboost