Hendrik/xgboost
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

[CORE] Refactor cache mechanism (#1540)

Browse Source
This commit is contained in:
Tianqi Chen 2016-09-02 20:39:07 -07:00 committed by GitHub
parent 6dabdd33e3
commit ecec5f7959
9 changed files with 320 additions and 421 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

4
NEWS.md
View File

@ -3,6 +3,10 @@ XGBoost Change Log
This file records the changes in xgboost library in reverse chronological order. This file records the changes in xgboost library in reverse chronological order.
## in progress version
* Refactored gbm to allow more friendly cache strategy
- Specialized some prediction routine
## v0.6 (2016.07.29) ## v0.6 (2016.07.29)
* Version 0.5 is skipped due to major improvements in the core * Version 0.5 is skipped due to major improvements in the core
* Major refactor of core library. * Major refactor of core library.

34
include/xgboost/gbm.h
View File

@ -13,8 +13,10 @@
#include <utility> #include <utility>
#include <string> #include <string>
#include <functional> #include <functional>
#include <memory>
#include "./base.h" #include "./base.h"
#include "./data.h" #include "./data.h"
#include "./objective.h"
#include "./feature_map.h" #include "./feature_map.h"
namespace xgboost { namespace xgboost {
@ -50,13 +52,6 @@ class GradientBooster {
* \param fo output stream * \param fo output stream
*/ */
virtual void Save(dmlc::Stream* fo) const = 0; virtual void Save(dmlc::Stream* fo) const = 0;
/*!
* \brief reset the predict buffer size.
* This will invalidate all the previous cached results
* and recalculate from scratch
* \param num_pbuffer The size of predict buffer.
*/
virtual void ResetPredBuffer(size_t num_pbuffer) {}
/*! /*!
* \brief whether the model allow lazy checkpoint * \brief whether the model allow lazy checkpoint
* return true if model is only updated in DoBoost * return true if model is only updated in DoBoost
@ -68,27 +63,21 @@ class GradientBooster {
/*! /*!
* \brief perform update to the model(boosting) * \brief perform update to the model(boosting)
* \param p_fmat feature matrix that provide access to features * \param p_fmat feature matrix that provide access to features
* \param buffer_offset buffer index offset of these instances, if equals -1
* this means we do not have buffer index allocated to the gbm
* \param in_gpair address of the gradient pair statistics of the data * \param in_gpair address of the gradient pair statistics of the data
* \param obj The objective function, optional, can be nullptr when use customized version
* the booster may change content of gpair * the booster may change content of gpair
*/ */
virtual void DoBoost(DMatrix* p_fmat, virtual void DoBoost(DMatrix* p_fmat,
int64_t buffer_offset, std::vector<bst_gpair>* in_gpair,
std::vector<bst_gpair>* in_gpair) = 0; ObjFunction* obj = nullptr) = 0;
/*! /*!
* \brief generate predictions for given feature matrix * \brief generate predictions for given feature matrix
* \param dmat feature matrix * \param dmat feature matrix
* \param buffer_offset buffer index offset of these instances, if equals -1
* this means we do not have buffer index allocated to the gbm
* a buffer index is assigned to each instance that requires repeative prediction
* the size of buffer is set by convention using GradientBooster.ResetPredBuffer(size);
* \param out_preds output vector to hold the predictions * \param out_preds output vector to hold the predictions
* \param ntree_limit limit the number of trees used in prediction, when it equals 0, this means * \param ntree_limit limit the number of trees used in prediction, when it equals 0, this means
* we do not limit number of trees, this parameter is only valid for gbtree, but not for gblinear * we do not limit number of trees, this parameter is only valid for gbtree, but not for gblinear
*/ */
virtual void Predict(DMatrix* dmat, virtual void Predict(DMatrix* dmat,
int64_t buffer_offset,
std::vector<float>* out_preds, std::vector<float>* out_preds,
unsigned ntree_limit = 0) = 0; unsigned ntree_limit = 0) = 0;
/*! /*!
@ -128,9 +117,14 @@ class GradientBooster {
/*! /*!
* \brief create a gradient booster from given name * \brief create a gradient booster from given name
* \param name name of gradient booster * \param name name of gradient booster
* \param cache_mats The cache data matrix of the Booster.
* \param base_margin The base margin of prediction.
* \return The created booster. * \return The created booster.
*/ */
static GradientBooster* Create(const std::string& name); static GradientBooster* Create(
const std::string& name,
const std::vector<std::shared_ptr<DMatrix> >& cache_mats,
float base_margin);
}; };
// implementing configure. // implementing configure.
@ -144,8 +138,10 @@ inline void GradientBooster::Configure(PairIter begin, PairIter end) {
* \brief Registry entry for tree updater. * \brief Registry entry for tree updater.
*/ */
struct GradientBoosterReg struct GradientBoosterReg
: public dmlc::FunctionRegEntryBase<GradientBoosterReg, : public dmlc::FunctionRegEntryBase<
std::function<GradientBooster* ()> > { GradientBoosterReg,
std::function<GradientBooster* (const std::vector<std::shared_ptr<DMatrix> > &cached_mats,
float base_margin)> > {
}; };
/*! /*!

2
include/xgboost/learner.h
View File

@ -166,7 +166,7 @@ class Learner : public rabit::Serializable {
* \param cache_data The matrix to cache the prediction. * \param cache_data The matrix to cache the prediction.
* \return Created learner. * \return Created learner.
*/ */
static Learner* Create(const std::vector<DMatrix*>& cache_data); static Learner* Create(const std::vector<std::shared_ptr<DMatrix> >& cache_data);
protected: protected:
/*! \brief internal base score of the model */ /*! \brief internal base score of the model */

59
src/c_api/c_api.cc
View File

@ -22,7 +22,7 @@ namespace xgboost {
// booster wrapper for backward compatible reason. // booster wrapper for backward compatible reason.
class Booster { class Booster {
public: public:
explicit Booster(const std::vector<DMatrix*>& cache_mats) explicit Booster(const std::vector<std::shared_ptr<DMatrix> >& cache_mats)
: configured_(false), : configured_(false),
initialized_(false), initialized_(false),
learner_(Learner::Create(cache_mats)) {} learner_(Learner::Create(cache_mats)) {}
@ -207,8 +207,7 @@ int XGDMatrixCreateFromFile(const char *fname,
LOG(CONSOLE) << "XGBoost distributed mode detected, " LOG(CONSOLE) << "XGBoost distributed mode detected, "
<< "will split data among workers"; << "will split data among workers";
} }
*out = DMatrix::Load( *out = new std::shared_ptr<DMatrix>(DMatrix::Load(fname, false, true));
fname, false, true);
API_END(); API_END();
} }
@ -224,7 +223,7 @@ int XGDMatrixCreateFromDataIter(
scache = cache_info; scache = cache_info;
} }
NativeDataIter parser(data_handle, callback); NativeDataIter parser(data_handle, callback);
*out = DMatrix::Create(&parser, scache); *out = new std::shared_ptr<DMatrix>(DMatrix::Create(&parser, scache));
API_END(); API_END();
} }
@ -250,7 +249,7 @@ XGB_DLL int XGDMatrixCreateFromCSR(const xgboost::bst_ulong* indptr,
} }
mat.info.num_row = nindptr - 1; mat.info.num_row = nindptr - 1;
mat.info.num_nonzero = static_cast<uint64_t>(nelem); mat.info.num_nonzero = static_cast<uint64_t>(nelem);
*out = DMatrix::Create(std::move(source)); *out = new std::shared_ptr<DMatrix>(DMatrix::Create(std::move(source)));
API_END(); API_END();
} }
@ -292,7 +291,7 @@ XGB_DLL int XGDMatrixCreateFromCSC(const xgboost::bst_ulong* col_ptr,
mat.info.num_row = mat.row_ptr_.size() - 1; mat.info.num_row = mat.row_ptr_.size() - 1;
mat.info.num_col = static_cast<uint64_t>(ncol); mat.info.num_col = static_cast<uint64_t>(ncol);
mat.info.num_nonzero = nelem; mat.info.num_nonzero = nelem;
*out = DMatrix::Create(std::move(source)); *out = new std::shared_ptr<DMatrix>(DMatrix::Create(std::move(source)));
API_END(); API_END();
} }
@ -324,7 +323,7 @@ XGB_DLL int XGDMatrixCreateFromMat(const float* data,
mat.row_ptr_.push_back(mat.row_ptr_.back() + nelem); mat.row_ptr_.push_back(mat.row_ptr_.back() + nelem);
} }
mat.info.num_nonzero = mat.row_data_.size(); mat.info.num_nonzero = mat.row_data_.size();
*out = DMatrix::Create(std::move(source)); *out = new std::shared_ptr<DMatrix>(DMatrix::Create(std::move(source)));
API_END(); API_END();
} }
@ -336,7 +335,7 @@ XGB_DLL int XGDMatrixSliceDMatrix(DMatrixHandle handle,
API_BEGIN(); API_BEGIN();
data::SimpleCSRSource src; data::SimpleCSRSource src;
src.CopyFrom(static_cast<DMatrix*>(handle)); src.CopyFrom(static_cast<std::shared_ptr<DMatrix>*>(handle)->get());
data::SimpleCSRSource& ret = *source; data::SimpleCSRSource& ret = *source;
CHECK_EQ(src.info.group_ptr.size(), 0) CHECK_EQ(src.info.group_ptr.size(), 0)
@ -371,13 +370,13 @@ XGB_DLL int XGDMatrixSliceDMatrix(DMatrixHandle handle,
ret.info.root_index.push_back(src.info.root_index[ridx]); ret.info.root_index.push_back(src.info.root_index[ridx]);
} }
} }
*out = DMatrix::Create(std::move(source)); *out = new std::shared_ptr<DMatrix>(DMatrix::Create(std::move(source)));
API_END(); API_END();
} }
XGB_DLL int XGDMatrixFree(DMatrixHandle handle) { XGB_DLL int XGDMatrixFree(DMatrixHandle handle) {
API_BEGIN(); API_BEGIN();
delete static_cast<DMatrix*>(handle); delete static_cast<std::shared_ptr<DMatrix>*>(handle);
API_END(); API_END();
} }
@ -385,7 +384,7 @@ XGB_DLL int XGDMatrixSaveBinary(DMatrixHandle handle,
const char* fname, const char* fname,
int silent) { int silent) {
API_BEGIN(); API_BEGIN();
static_cast<DMatrix*>(handle)->SaveToLocalFile(fname); static_cast<std::shared_ptr<DMatrix>*>(handle)->get()->SaveToLocalFile(fname);
API_END(); API_END();
} }
@ -394,7 +393,8 @@ XGB_DLL int XGDMatrixSetFloatInfo(DMatrixHandle handle,
const float* info, const float* info,
xgboost::bst_ulong len) { xgboost::bst_ulong len) {
API_BEGIN(); API_BEGIN();
static_cast<DMatrix*>(handle)->info().SetInfo(field, info, kFloat32, len); static_cast<std::shared_ptr<DMatrix>*>(handle)
->get()->info().SetInfo(field, info, kFloat32, len);
API_END(); API_END();
} }
@ -403,7 +403,8 @@ XGB_DLL int XGDMatrixSetUIntInfo(DMatrixHandle handle,
const unsigned* info, const unsigned* info,
xgboost::bst_ulong len) { xgboost::bst_ulong len) {
API_BEGIN(); API_BEGIN();
static_cast<DMatrix*>(handle)->info().SetInfo(field, info, kUInt32, len); static_cast<std::shared_ptr<DMatrix>*>(handle)
->get()->info().SetInfo(field, info, kUInt32, len);
API_END(); API_END();
} }
@ -411,8 +412,8 @@ XGB_DLL int XGDMatrixSetGroup(DMatrixHandle handle,
const unsigned* group, const unsigned* group,
xgboost::bst_ulong len) { xgboost::bst_ulong len) {
API_BEGIN(); API_BEGIN();
DMatrix *pmat = static_cast<DMatrix*>(handle); std::shared_ptr<DMatrix> *pmat = static_cast<std::shared_ptr<DMatrix>*>(handle);
MetaInfo& info = pmat->info(); MetaInfo& info = pmat->get()->info();
info.group_ptr.resize(len + 1); info.group_ptr.resize(len + 1);
info.group_ptr[0] = 0; info.group_ptr[0] = 0;
for (uint64_t i = 0; i < len; ++i) { for (uint64_t i = 0; i < len; ++i) {
@ -426,7 +427,7 @@ XGB_DLL int XGDMatrixGetFloatInfo(const DMatrixHandle handle,
xgboost::bst_ulong* out_len, xgboost::bst_ulong* out_len,
const float** out_dptr) { const float** out_dptr) {
API_BEGIN(); API_BEGIN();
const MetaInfo& info = static_cast<const DMatrix*>(handle)->info(); const MetaInfo& info = static_cast<std::shared_ptr<DMatrix>*>(handle)->get()->info();
const std::vector<float>* vec = nullptr; const std::vector<float>* vec = nullptr;
if (!std::strcmp(field, "label")) { if (!std::strcmp(field, "label")) {
vec = &info.labels; vec = &info.labels;
@ -447,7 +448,7 @@ XGB_DLL int XGDMatrixGetUIntInfo(const DMatrixHandle handle,
xgboost::bst_ulong *out_len, xgboost::bst_ulong *out_len,
const unsigned **out_dptr) { const unsigned **out_dptr) {
API_BEGIN(); API_BEGIN();
const MetaInfo& info = static_cast<const DMatrix*>(handle)->info(); const MetaInfo& info = static_cast<std::shared_ptr<DMatrix>*>(handle)->get()->info();
const std::vector<unsigned>* vec = nullptr; const std::vector<unsigned>* vec = nullptr;
if (!std::strcmp(field, "root_index")) { if (!std::strcmp(field, "root_index")) {
vec = &info.root_index; vec = &info.root_index;
@ -462,14 +463,16 @@ XGB_DLL int XGDMatrixGetUIntInfo(const DMatrixHandle handle,
XGB_DLL int XGDMatrixNumRow(const DMatrixHandle handle, XGB_DLL int XGDMatrixNumRow(const DMatrixHandle handle,
xgboost::bst_ulong *out) { xgboost::bst_ulong *out) {
API_BEGIN(); API_BEGIN();
*out = static_cast<xgboost::bst_ulong>(static_cast<const DMatrix*>(handle)->info().num_row); *out = static_cast<xgboost::bst_ulong>(
static_cast<std::shared_ptr<DMatrix>*>(handle)->get()->info().num_row);
API_END(); API_END();
} }
XGB_DLL int XGDMatrixNumCol(const DMatrixHandle handle, XGB_DLL int XGDMatrixNumCol(const DMatrixHandle handle,
xgboost::bst_ulong *out) { xgboost::bst_ulong *out) {
API_BEGIN(); API_BEGIN();
*out = static_cast<size_t>(static_cast<const DMatrix*>(handle)->info().num_col); *out = static_cast<size_t>(
static_cast<std::shared_ptr<DMatrix>*>(handle)->get()->info().num_col);
API_END(); API_END();
} }
@ -478,9 +481,9 @@ XGB_DLL int XGBoosterCreate(const DMatrixHandle dmats[],
xgboost::bst_ulong len, xgboost::bst_ulong len,
BoosterHandle *out) { BoosterHandle *out) {
API_BEGIN(); API_BEGIN();
std::vector<DMatrix*> mats; std::vector<std::shared_ptr<DMatrix> > mats;
for (xgboost::bst_ulong i = 0; i < len; ++i) { for (xgboost::bst_ulong i = 0; i < len; ++i) {
mats.push_back(static_cast<DMatrix*>(dmats[i])); mats.push_back(*static_cast<std::shared_ptr<DMatrix>*>(dmats[i]));
} }
*out = new Booster(mats); *out = new Booster(mats);
API_END(); API_END();
@ -505,10 +508,11 @@ XGB_DLL int XGBoosterUpdateOneIter(BoosterHandle handle,
DMatrixHandle dtrain) { DMatrixHandle dtrain) {
API_BEGIN(); API_BEGIN();
Booster* bst = static_cast<Booster*>(handle); Booster* bst = static_cast<Booster*>(handle);
DMatrix *dtr = static_cast<DMatrix*>(dtrain); std::shared_ptr<DMatrix> *dtr =
static_cast<std::shared_ptr<DMatrix>*>(dtrain);
bst->LazyInit(); bst->LazyInit();
bst->learner()->UpdateOneIter(iter, dtr); bst->learner()->UpdateOneIter(iter, dtr->get());
API_END(); API_END();
} }
@ -520,14 +524,15 @@ XGB_DLL int XGBoosterBoostOneIter(BoosterHandle handle,
std::vector<bst_gpair>& tmp_gpair = XGBAPIThreadLocalStore::Get()->tmp_gpair; std::vector<bst_gpair>& tmp_gpair = XGBAPIThreadLocalStore::Get()->tmp_gpair;
API_BEGIN(); API_BEGIN();
Booster* bst = static_cast<Booster*>(handle); Booster* bst = static_cast<Booster*>(handle);
DMatrix* dtr = static_cast<DMatrix*>(dtrain); std::shared_ptr<DMatrix>* dtr =
static_cast<std::shared_ptr<DMatrix>*>(dtrain);
tmp_gpair.resize(len); tmp_gpair.resize(len);
for (xgboost::bst_ulong i = 0; i < len; ++i) { for (xgboost::bst_ulong i = 0; i < len; ++i) {
tmp_gpair[i] = bst_gpair(grad[i], hess[i]); tmp_gpair[i] = bst_gpair(grad[i], hess[i]);
} }
bst->LazyInit(); bst->LazyInit();
bst->learner()->BoostOneIter(0, dtr, &tmp_gpair); bst->learner()->BoostOneIter(0, dtr->get(), &tmp_gpair);
API_END(); API_END();
} }
@ -544,7 +549,7 @@ XGB_DLL int XGBoosterEvalOneIter(BoosterHandle handle,
std::vector<std::string> data_names; std::vector<std::string> data_names;
for (xgboost::bst_ulong i = 0; i < len; ++i) { for (xgboost::bst_ulong i = 0; i < len; ++i) {
data_sets.push_back(static_cast<DMatrix*>(dmats[i])); data_sets.push_back(static_cast<std::shared_ptr<DMatrix>*>(dmats[i])->get());
data_names.push_back(std::string(evnames[i])); data_names.push_back(std::string(evnames[i]));
} }
@ -565,7 +570,7 @@ XGB_DLL int XGBoosterPredict(BoosterHandle handle,
Booster *bst = static_cast<Booster*>(handle); Booster *bst = static_cast<Booster*>(handle);
bst->LazyInit(); bst->LazyInit();
bst->learner()->Predict( bst->learner()->Predict(
static_cast<DMatrix*>(dmat), static_cast<std::shared_ptr<DMatrix>*>(dmat)->get(),
(option_mask & 1) != 0, (option_mask & 1) != 0,
&preds, ntree_limit, &preds, ntree_limit,
(option_mask & 2) != 0); (option_mask & 2) != 0);

14
src/cli_main.cc
View File

@ -156,16 +156,18 @@ void CLITrain(const CLIParam& param) {
LOG(CONSOLE) << "start " << pname << ":" << rabit::GetRank(); LOG(CONSOLE) << "start " << pname << ":" << rabit::GetRank();
} }
// load in data. // load in data.
std::unique_ptr<DMatrix> dtrain( std::shared_ptr<DMatrix> dtrain(
DMatrix::Load(param.train_path, param.silent != 0, param.dsplit == 2)); DMatrix::Load(param.train_path, param.silent != 0, param.dsplit == 2));
std::vector<std::unique_ptr<DMatrix> > deval; std::vector<std::shared_ptr<DMatrix> > deval;
std::vector<DMatrix*> cache_mats, eval_datasets; std::vector<std::shared_ptr<DMatrix> > cache_mats;
cache_mats.push_back(dtrain.get()); std::vector<DMatrix*> eval_datasets;
cache_mats.push_back(dtrain);
for (size_t i = 0; i < param.eval_data_names.size(); ++i) { for (size_t i = 0; i < param.eval_data_names.size(); ++i) {
deval.emplace_back( deval.emplace_back(
DMatrix::Load(param.eval_data_paths[i], param.silent != 0, param.dsplit == 2)); std::shared_ptr<DMatrix>(DMatrix::Load(param.eval_data_paths[i],
param.silent != 0, param.dsplit == 2)));
eval_datasets.push_back(deval.back().get()); eval_datasets.push_back(deval.back().get());
cache_mats.push_back(deval.back().get()); cache_mats.push_back(deval.back());
} }
std::vector<std::string> eval_data_names = param.eval_data_names; std::vector<std::string> eval_data_names = param.eval_data_names;
if (param.eval_train) { if (param.eval_train) {

36
src/gbm/gblinear.cc
View File

@ -87,6 +87,9 @@ struct GBLinearTrainParam : public dmlc::Parameter<GBLinearTrainParam> {
*/ */
class GBLinear : public GradientBooster { class GBLinear : public GradientBooster {
public: public:
explicit GBLinear(float base_margin)
: base_margin_(base_margin) {
}
void Configure(const std::vector<std::pair<std::string, std::string> >& cfg) override { void Configure(const std::vector<std::pair<std::string, std::string> >& cfg) override {
if (model.weight.size() == 0) { if (model.weight.size() == 0) {
model.param.InitAllowUnknown(cfg); model.param.InitAllowUnknown(cfg);
@ -99,9 +102,9 @@ class GBLinear : public GradientBooster {
void Save(dmlc::Stream* fo) const override { void Save(dmlc::Stream* fo) const override {
model.Save(fo); model.Save(fo);
} }
virtual void DoBoost(DMatrix *p_fmat, void DoBoost(DMatrix *p_fmat,
int64_t buffer_offset, std::vector<bst_gpair> *in_gpair,
std::vector<bst_gpair> *in_gpair) { ObjFunction* obj) override {
// lazily initialize the model when not ready. // lazily initialize the model when not ready.
if (model.weight.size() == 0) { if (model.weight.size() == 0) {
model.InitModel(); model.InitModel();
@ -168,7 +171,6 @@ class GBLinear : public GradientBooster {
} }
void Predict(DMatrix *p_fmat, void Predict(DMatrix *p_fmat,
int64_t buffer_offset,
std::vector<float> *out_preds, std::vector<float> *out_preds,
unsigned ntree_limit) override { unsigned ntree_limit) override {
if (model.weight.size() == 0) { if (model.weight.size() == 0) {
@ -177,6 +179,11 @@ class GBLinear : public GradientBooster {
CHECK_EQ(ntree_limit, 0) CHECK_EQ(ntree_limit, 0)
<< "GBLinear::Predict ntrees is only valid for gbtree predictor"; << "GBLinear::Predict ntrees is only valid for gbtree predictor";
std::vector<float> &preds = *out_preds; std::vector<float> &preds = *out_preds;
const std::vector<bst_float>& base_margin = p_fmat->info().base_margin;
if (base_margin.size() != 0) {
CHECK_EQ(preds.size(), base_margin.size())
<< "base_margin.size does not match with prediction size";
}
preds.resize(0); preds.resize(0);
// start collecting the prediction // start collecting the prediction
dmlc::DataIter<RowBatch> *iter = p_fmat->RowIterator(); dmlc::DataIter<RowBatch> *iter = p_fmat->RowIterator();
@ -188,24 +195,27 @@ class GBLinear : public GradientBooster {
// k is number of group // k is number of group
preds.resize(preds.size() + batch.size * ngroup); preds.resize(preds.size() + batch.size * ngroup);
// parallel over local batch // parallel over local batch
const bst_omp_uint nsize = static_cast<bst_omp_uint>(batch.size); const omp_ulong nsize = static_cast<omp_ulong>(batch.size);
#pragma omp parallel for schedule(static) #pragma omp parallel for schedule(static)
for (bst_omp_uint i = 0; i < nsize; ++i) { for (omp_ulong i = 0; i < nsize; ++i) {
const size_t ridx = batch.base_rowid + i; const size_t ridx = batch.base_rowid + i;
// loop over output groups // loop over output groups
for (int gid = 0; gid < ngroup; ++gid) { for (int gid = 0; gid < ngroup; ++gid) {
this->Pred(batch[i], &preds[ridx * ngroup], gid); float margin = (base_margin.size() != 0) ?
base_margin[ridx * ngroup + gid] : base_margin_;
this->Pred(batch[i], &preds[ridx * ngroup], gid, margin);
} }
} }
} }
} }
// add base margin
void Predict(const SparseBatch::Inst &inst, void Predict(const SparseBatch::Inst &inst,
std::vector<float> *out_preds, std::vector<float> *out_preds,
unsigned ntree_limit, unsigned ntree_limit,
unsigned root_index) override { unsigned root_index) override {
const int ngroup = model.param.num_output_group; const int ngroup = model.param.num_output_group;
for (int gid = 0; gid < ngroup; ++gid) { for (int gid = 0; gid < ngroup; ++gid) {
this->Pred(inst, dmlc::BeginPtr(*out_preds), gid); this->Pred(inst, dmlc::BeginPtr(*out_preds), gid, base_margin_);
} }
} }
void PredictLeaf(DMatrix *p_fmat, void PredictLeaf(DMatrix *p_fmat,
@ -232,8 +242,8 @@ class GBLinear : public GradientBooster {
} }
protected: protected:
inline void Pred(const RowBatch::Inst &inst, float *preds, int gid) { inline void Pred(const RowBatch::Inst &inst, float *preds, int gid, float base) {
float psum = model.bias()[gid]; float psum = model.bias()[gid] + base;
for (bst_uint i = 0; i < inst.length; ++i) { for (bst_uint i = 0; i < inst.length; ++i) {
if (inst[i].index >= model.param.num_feature) continue; if (inst[i].index >= model.param.num_feature) continue;
psum += inst[i].fvalue * model[inst[i].index][gid]; psum += inst[i].fvalue * model[inst[i].index][gid];
@ -278,6 +288,8 @@ class GBLinear : public GradientBooster {
return &weight[i * param.num_output_group]; return &weight[i * param.num_output_group];
} }
}; };
// biase margin score
float base_margin_;
// model field // model field
Model model; Model model;
// training parameter // training parameter
@ -292,8 +304,8 @@ DMLC_REGISTER_PARAMETER(GBLinearTrainParam);
XGBOOST_REGISTER_GBM(GBLinear, "gblinear") XGBOOST_REGISTER_GBM(GBLinear, "gblinear")
.describe("Linear booster, implement generalized linear model.") .describe("Linear booster, implement generalized linear model.")
.set_body([]() { .set_body([](const std::vector<std::shared_ptr<DMatrix> >&cache, float base_margin) {
return new GBLinear(); return new GBLinear(base_margin);
}); });
} // namespace gbm } // namespace gbm
} // namespace xgboost } // namespace xgboost

7
src/gbm/gbm.cc
View File

@ -11,12 +11,15 @@ DMLC_REGISTRY_ENABLE(::xgboost::GradientBoosterReg);
} // namespace dmlc } // namespace dmlc
namespace xgboost { namespace xgboost {
GradientBooster* GradientBooster::Create(const std::string& name) { GradientBooster* GradientBooster::Create(
const std::string& name,
const std::vector<std::shared_ptr<DMatrix> >& cache_mats,
float base_margin) {
auto *e = ::dmlc::Registry< ::xgboost::GradientBoosterReg>::Get()->Find(name); auto *e = ::dmlc::Registry< ::xgboost::GradientBoosterReg>::Get()->Find(name);
if (e == nullptr) { if (e == nullptr) {
LOG(FATAL) << "Unknown gbm type " << name; LOG(FATAL) << "Unknown gbm type " << name;
} }
return (e->body)(); return (e->body)(cache_mats, base_margin);
} }
} // namespace xgboost } // namespace xgboost

393
src/gbm/gbtree.cc
View File

@ -15,6 +15,7 @@
#include <utility> #include <utility>
#include <string> #include <string>
#include <limits> #include <limits>
#include <algorithm>
#include "../common/common.h" #include "../common/common.h"
#include "../common/random.h" #include "../common/random.h"
@ -123,10 +124,24 @@ struct GBTreeModelParam : public dmlc::Parameter<GBTreeModelParam> {
} }
}; };
// cache entry
struct CacheEntry {
std::shared_ptr<DMatrix> data;
std::vector<float> predictions;
};
// gradient boosted trees // gradient boosted trees
class GBTree : public GradientBooster { class GBTree : public GradientBooster {
public: public:
GBTree() : num_pbuffer(0) {} explicit GBTree(float base_margin) : base_margin_(base_margin) {}
void InitCache(const std::vector<std::shared_ptr<DMatrix> > &cache) {
for (const std::shared_ptr<DMatrix>& d : cache) {
CacheEntry e;
e.data = d;
cache_[d.get()] = std::move(e);
}
}
void Configure(const std::vector<std::pair<std::string, std::string> >& cfg) override { void Configure(const std::vector<std::pair<std::string, std::string> >& cfg) override {
this->cfg = cfg; this->cfg = cfg;
@ -160,8 +175,6 @@ class GBTree : public GradientBooster {
this->cfg.clear(); this->cfg.clear();
this->cfg.push_back(std::make_pair(std::string("num_feature"), this->cfg.push_back(std::make_pair(std::string("num_feature"),
common::ToString(mparam.num_feature))); common::ToString(mparam.num_feature)));
// clear the predict buffer.
this->ResetPredBuffer(num_pbuffer);
} }
void Save(dmlc::Stream* fo) const override { void Save(dmlc::Stream* fo) const override {
@ -175,27 +188,19 @@ class GBTree : public GradientBooster {
} }
} }
void ResetPredBuffer(size_t num_pbuffer) override {
this->num_pbuffer = num_pbuffer;
pred_buffer.clear();
pred_counter.clear();
pred_buffer.resize(this->PredBufferSize(), 0.0f);
pred_counter.resize(this->PredBufferSize(), 0);
}
bool AllowLazyCheckPoint() const override { bool AllowLazyCheckPoint() const override {
return mparam.num_output_group == 1 || return mparam.num_output_group == 1 ||
tparam.updater_seq.find("distcol") != std::string::npos; tparam.updater_seq.find("distcol") != std::string::npos;
} }
void DoBoost(DMatrix* p_fmat, void DoBoost(DMatrix* p_fmat,
int64_t buffer_offset, std::vector<bst_gpair>* in_gpair,
std::vector<bst_gpair>* in_gpair) override { ObjFunction* obj) override {
const std::vector<bst_gpair>& gpair = *in_gpair; const std::vector<bst_gpair>& gpair = *in_gpair;
std::vector<std::vector<std::unique_ptr<RegTree> > > new_trees; std::vector<std::vector<std::unique_ptr<RegTree> > > new_trees;
if (mparam.num_output_group == 1) { if (mparam.num_output_group == 1) {
std::vector<std::unique_ptr<RegTree> > ret; std::vector<std::unique_ptr<RegTree> > ret;
BoostNewTrees(gpair, p_fmat, buffer_offset, 0, &ret); BoostNewTrees(gpair, p_fmat, 0, &ret);
new_trees.push_back(std::move(ret)); new_trees.push_back(std::move(ret));
} else { } else {
const int ngroup = mparam.num_output_group; const int ngroup = mparam.num_output_group;
@ -209,7 +214,7 @@ class GBTree : public GradientBooster {
tmp[i] = gpair[i * ngroup + gid]; tmp[i] = gpair[i * ngroup + gid];
} }
std::vector<std::unique_ptr<RegTree> > ret; std::vector<std::unique_ptr<RegTree> > ret;
BoostNewTrees(tmp, p_fmat, buffer_offset, gid, &ret); BoostNewTrees(tmp, p_fmat, gid, &ret);
new_trees.push_back(std::move(ret)); new_trees.push_back(std::move(ret));
} }
} }
@ -219,48 +224,21 @@ class GBTree : public GradientBooster {
} }
void Predict(DMatrix* p_fmat, void Predict(DMatrix* p_fmat,
int64_t buffer_offset,
std::vector<float>* out_preds, std::vector<float>* out_preds,
unsigned ntree_limit) override { unsigned ntree_limit) override {
const MetaInfo& info = p_fmat->info(); if (ntree_limit == 0 ||
int nthread; ntree_limit * mparam.num_output_group >= trees.size()) {
#pragma omp parallel auto it = cache_.find(p_fmat);
{ if (it != cache_.end()) {
nthread = omp_get_num_threads(); std::vector<float>& y = it->second.predictions;
} if (y.size() != 0) {
InitThreadTemp(nthread); out_preds->resize(y.size());
std::vector<float> &preds = *out_preds; std::copy(y.begin(), y.end(), out_preds->begin());
const size_t stride = p_fmat->info().num_row * mparam.num_output_group; return;
preds.resize(stride * (mparam.size_leaf_vector+1));
// start collecting the prediction
dmlc::DataIter<RowBatch>* iter = p_fmat->RowIterator();
iter->BeforeFirst();
while (iter->Next()) {
const RowBatch &batch = iter->Value();
// parallel over local batch
const bst_omp_uint nsize = static_cast<bst_omp_uint>(batch.size);
int ridx_error = 0;
#pragma omp parallel for schedule(static)
for (bst_omp_uint i = 0; i < nsize; ++i) {
const int tid = omp_get_thread_num();
RegTree::FVec &feats = thread_temp[tid];
int64_t ridx = static_cast<int64_t>(batch.base_rowid + i);
if (static_cast<size_t>(ridx) >= info.num_row) {
ridx_error = 1;
continue;
}
// loop over output groups
for (int gid = 0; gid < mparam.num_output_group; ++gid) {
this->Pred(batch[i],
buffer_offset < 0 ? -1 : buffer_offset + ridx,
gid, info.GetRoot(ridx), &feats,
&preds[ridx * mparam.num_output_group + gid], stride,
ntree_limit);
} }
} }
CHECK(!ridx_error) << "ridx out of bounds";
} }
PredLoopInternal<GBTree>(p_fmat, out_preds, 0, ntree_limit, true);
} }
void Predict(const SparseBatch::Inst& inst, void Predict(const SparseBatch::Inst& inst,
@ -271,12 +249,16 @@ class GBTree : public GradientBooster {
thread_temp.resize(1, RegTree::FVec()); thread_temp.resize(1, RegTree::FVec());
thread_temp[0].Init(mparam.num_feature); thread_temp[0].Init(mparam.num_feature);
} }
ntree_limit *= mparam.num_output_group;
if (ntree_limit == 0 || ntree_limit > trees.size()) {
ntree_limit = static_cast<unsigned>(trees.size());
}
out_preds->resize(mparam.num_output_group * (mparam.size_leaf_vector+1)); out_preds->resize(mparam.num_output_group * (mparam.size_leaf_vector+1));
// loop over output groups // loop over output groups
for (int gid = 0; gid < mparam.num_output_group; ++gid) { for (int gid = 0; gid < mparam.num_output_group; ++gid) {
this->Pred(inst, -1, gid, root_index, &thread_temp[0], (*out_preds)[gid] =
&(*out_preds)[gid], mparam.num_output_group, PredValue(inst, gid, root_index,
ntree_limit); &thread_temp[0], 0, ntree_limit) + base_margin_;
} }
} }
@ -301,6 +283,84 @@ class GBTree : public GradientBooster {
} }
protected: protected:
// internal prediction loop
// add predictions to out_preds
template<typename Derived>
inline void PredLoopInternal(
DMatrix* p_fmat,
std::vector<float>* out_preds,
unsigned tree_begin,
unsigned ntree_limit,
bool init_out_preds) {
int num_group = mparam.num_output_group;
ntree_limit *= num_group;
if (ntree_limit == 0 || ntree_limit > trees.size()) {
ntree_limit = static_cast<unsigned>(trees.size());
}
if (init_out_preds) {
size_t n = num_group * p_fmat->info().num_row;
const std::vector<float>& base_margin = p_fmat->info().base_margin;
out_preds->resize(n);
if (base_margin.size() != 0) {
CHECK_EQ(out_preds->size(), n);
std::copy(base_margin.begin(), base_margin.end(), out_preds->begin());
} else {
std::fill(out_preds->begin(), out_preds->end(), base_margin_);
}
}
if (num_group == 1) {
PredLoopSpecalize<Derived>(p_fmat, out_preds, 1,
tree_begin, ntree_limit);
} else {
PredLoopSpecalize<Derived>(p_fmat, out_preds, num_group,
tree_begin, ntree_limit);
}
}
template<typename Derived>
inline void PredLoopSpecalize(
DMatrix* p_fmat,
std::vector<float>* out_preds,
int num_group,
unsigned tree_begin,
unsigned tree_end) {
const MetaInfo& info = p_fmat->info();
int nthread;
#pragma omp parallel
{
nthread = omp_get_num_threads();
}
CHECK_EQ(num_group, mparam.num_output_group);
InitThreadTemp(nthread);
std::vector<float> &preds = *out_preds;
CHECK_EQ(mparam.size_leaf_vector, 0)
<< "size_leaf_vector is enforced to 0 so far";
CHECK_EQ(preds.size(), p_fmat->info().num_row * num_group);
// start collecting the prediction
dmlc::DataIter<RowBatch>* iter = p_fmat->RowIterator();
Derived* self = static_cast<Derived*>(this);
iter->BeforeFirst();
while (iter->Next()) {
const RowBatch &batch = iter->Value();
// parallel over local batch
const bst_omp_uint nsize = static_cast<bst_omp_uint>(batch.size);
#pragma omp parallel for schedule(static)
for (bst_omp_uint i = 0; i < nsize; ++i) {
const int tid = omp_get_thread_num();
RegTree::FVec &feats = thread_temp[tid];
int64_t ridx = static_cast<int64_t>(batch.base_rowid + i);
CHECK_LT(static_cast<size_t>(ridx), info.num_row);
for (int gid = 0; gid < num_group; ++gid) {
size_t offset = ridx * num_group + gid;
preds[offset] +=
self->PredValue(batch[i], gid, info.GetRoot(ridx),
&feats, tree_begin, tree_end);
}
}
}
}
// initialize updater before using them // initialize updater before using them
inline void InitUpdater() { inline void InitUpdater() {
if (updaters.size() != 0) return; if (updaters.size() != 0) return;
@ -316,7 +376,6 @@ class GBTree : public GradientBooster {
inline void inline void
BoostNewTrees(const std::vector<bst_gpair> &gpair, BoostNewTrees(const std::vector<bst_gpair> &gpair,
DMatrix *p_fmat, DMatrix *p_fmat,
int64_t buffer_offset,
int bst_group, int bst_group,
std::vector<std::unique_ptr<RegTree> >* ret) { std::vector<std::unique_ptr<RegTree> >* ret) {
this->InitUpdater(); this->InitUpdater();
@ -334,111 +393,50 @@ class GBTree : public GradientBooster {
for (auto& up : updaters) { for (auto& up : updaters) {
up->Update(gpair, p_fmat, new_trees); up->Update(gpair, p_fmat, new_trees);
} }
// optimization, update buffer, if possible
// this is only under distributed column mode
// for safety check of lazy checkpoint
if (buffer_offset >= 0 &&
new_trees.size() == 1 && updaters.size() > 0 &&
updaters.back()->GetLeafPosition() != nullptr) {
CHECK_EQ(p_fmat->info().num_row, p_fmat->buffered_rowset().size());
this->UpdateBufferByPosition(p_fmat,
buffer_offset,
bst_group,
*new_trees[0],
updaters.back()->GetLeafPosition());
}
} }
// commit new trees all at once // commit new trees all at once
virtual void virtual void
CommitModel(std::vector<std::unique_ptr<RegTree> >&& new_trees, CommitModel(std::vector<std::unique_ptr<RegTree> >&& new_trees,
int bst_group) { int bst_group) {
size_t old_ntree = trees.size();
for (size_t i = 0; i < new_trees.size(); ++i) { for (size_t i = 0; i < new_trees.size(); ++i) {
trees.push_back(std::move(new_trees[i])); trees.push_back(std::move(new_trees[i]));
tree_info.push_back(bst_group); tree_info.push_back(bst_group);
} }
mparam.num_trees += static_cast<int>(new_trees.size()); mparam.num_trees += static_cast<int>(new_trees.size());
// update cache entry
for (auto &kv : cache_) {
CacheEntry& e = kv.second;
if (e.predictions.size() == 0) {
PredLoopInternal<GBTree>(
e.data.get(), &(e.predictions),
0, trees.size(), true);
} else {
PredLoopInternal<GBTree>(
e.data.get(), &(e.predictions),
old_ntree, trees.size(), false);
} }
// update buffer by pre-cached position
inline void UpdateBufferByPosition(DMatrix *p_fmat,
int64_t buffer_offset,
int bst_group,
const RegTree &new_tree,
const int* leaf_position) {
const RowSet& rowset = p_fmat->buffered_rowset();
const bst_omp_uint ndata = static_cast<bst_omp_uint>(rowset.size());
int pred_counter_error = 0, tid_error = 0;
#pragma omp parallel for schedule(static)
for (bst_omp_uint i = 0; i < ndata; ++i) {
const bst_uint ridx = rowset[i];
const int64_t bid = this->BufferOffset(buffer_offset + ridx, bst_group);
const int tid = leaf_position[ridx];
if (pred_counter[bid] != trees.size()) {
pred_counter_error = 1;
continue;
} }
if (tid < 0) {
tid_error = 1;
continue;
}
pred_buffer[bid] += new_tree[tid].leaf_value();
for (int i = 0; i < mparam.size_leaf_vector; ++i) {
pred_buffer[bid + i + 1] += new_tree.leafvec(tid)[i];
}
pred_counter[bid] += tparam.num_parallel_tree;
}
CHECK(!pred_counter_error) << "incorrect pred_counter[bid]";
CHECK(!tid_error) << "tid cannot be negative";
} }
// make a prediction for a single instance // make a prediction for a single instance
inline void Pred(const RowBatch::Inst &inst, inline float PredValue(const RowBatch::Inst &inst,
int64_t buffer_index,
int bst_group, int bst_group,
unsigned root_index, unsigned root_index,
RegTree::FVec *p_feats, RegTree::FVec *p_feats,
float *out_pred, unsigned tree_begin,
size_t stride, unsigned tree_end) {
unsigned ntree_limit) {
size_t itop = 0;
float psum = 0.0f; float psum = 0.0f;
// sum of leaf vector
std::vector<float> vec_psum(mparam.size_leaf_vector, 0.0f);
const int64_t bid = this->BufferOffset(buffer_index, bst_group);
// number of valid trees
unsigned treeleft = ntree_limit == 0 ? std::numeric_limits<unsigned>::max() : ntree_limit;
// load buffered results if any
if (bid >= 0 && ntree_limit == 0) {
itop = pred_counter[bid];
psum = pred_buffer[bid];
for (int i = 0; i < mparam.size_leaf_vector; ++i) {
vec_psum[i] = pred_buffer[bid + i + 1];
}
}
if (itop != trees.size()) {
p_feats->Fill(inst); p_feats->Fill(inst);
for (size_t i = itop; i < trees.size(); ++i) { for (size_t i = tree_begin; i < tree_end; ++i) {
if (tree_info[i] == bst_group) { if (tree_info[i] == bst_group) {
int tid = trees[i]->GetLeafIndex(*p_feats, root_index); int tid = trees[i]->GetLeafIndex(*p_feats, root_index);
psum += (*trees[i])[tid].leaf_value(); psum += (*trees[i])[tid].leaf_value();
for (int j = 0; j < mparam.size_leaf_vector; ++j) {
vec_psum[j] += trees[i]->leafvec(tid)[j];
}
if (--treeleft == 0) break;
} }
} }
p_feats->Drop(inst); p_feats->Drop(inst);
} return psum;
// updated the buffered results
if (bid >= 0 && ntree_limit == 0) {
pred_counter[bid] = static_cast<unsigned>(trees.size());
pred_buffer[bid] = psum;
for (int i = 0; i < mparam.size_leaf_vector; ++i) {
pred_buffer[bid + i + 1] = vec_psum[i];
}
}
out_pred[0] = psum;
for (int i = 0; i < mparam.size_leaf_vector; ++i) {
out_pred[stride * (i + 1)] = vec_psum[i];
}
} }
// predict independent leaf index // predict independent leaf index
inline void PredPath(DMatrix *p_fmat, inline void PredPath(DMatrix *p_fmat,
@ -446,6 +444,7 @@ class GBTree : public GradientBooster {
unsigned ntree_limit) { unsigned ntree_limit) {
const MetaInfo& info = p_fmat->info(); const MetaInfo& info = p_fmat->info();
// number of valid trees // number of valid trees
ntree_limit *= mparam.num_output_group;
if (ntree_limit == 0 || ntree_limit > trees.size()) { if (ntree_limit == 0 || ntree_limit > trees.size()) {
ntree_limit = static_cast<unsigned>(trees.size()); ntree_limit = static_cast<unsigned>(trees.size());
} }
@ -482,22 +481,9 @@ class GBTree : public GradientBooster {
} }
} }
} }
/*! \return size of prediction buffer actually needed */
inline size_t PredBufferSize() const {
return mparam.num_output_group * num_pbuffer * (mparam.size_leaf_vector + 1);
}
/*!
* \brief get the buffer offset given a buffer index and group id
* \return calculated buffer offset
*/
inline int64_t BufferOffset(int64_t buffer_index, int bst_group) const {
if (buffer_index < 0) return -1;
size_t bidx = static_cast<size_t>(buffer_index);
CHECK_LT(bidx, num_pbuffer);
return (bidx + num_pbuffer * bst_group) * (mparam.size_leaf_vector + 1);
}
// --- data structure --- // --- data structure ---
// base margin
float base_margin_;
// training parameter // training parameter
GBTreeTrainParam tparam; GBTreeTrainParam tparam;
// model parameter // model parameter
@ -506,13 +492,8 @@ class GBTree : public GradientBooster {
std::vector<std::unique_ptr<RegTree> > trees; std::vector<std::unique_ptr<RegTree> > trees;
/*! \brief some information indicator of the tree, reserved */ /*! \brief some information indicator of the tree, reserved */
std::vector<int> tree_info; std::vector<int> tree_info;
/*! \brief predict buffer size */
size_t num_pbuffer;
/*! \brief prediction buffer */
std::vector<float> pred_buffer;
/*! \brief prediction buffer counter, remember the prediction */
std::vector<unsigned> pred_counter;
// ----training fields---- // ----training fields----
std::unordered_map<DMatrix*, CacheEntry> cache_;
// configurations for tree // configurations for tree
std::vector<std::pair<std::string, std::string> > cfg; std::vector<std::pair<std::string, std::string> > cfg;
// temporal storage for per thread // temporal storage for per thread
@ -524,7 +505,7 @@ class GBTree : public GradientBooster {
// dart // dart
class Dart : public GBTree { class Dart : public GBTree {
public: public:
Dart() {} explicit Dart(float base_margin) : GBTree(base_margin) {}
void Configure(const std::vector<std::pair<std::string, std::string> >& cfg) override { void Configure(const std::vector<std::pair<std::string, std::string> >& cfg) override {
GBTree::Configure(cfg); GBTree::Configure(cfg);
@ -550,44 +531,10 @@ class Dart : public GBTree {
// predict the leaf scores with dropout if ntree_limit = 0 // predict the leaf scores with dropout if ntree_limit = 0
void Predict(DMatrix* p_fmat, void Predict(DMatrix* p_fmat,
int64_t buffer_offset,
std::vector<float>* out_preds, std::vector<float>* out_preds,
unsigned ntree_limit) override { unsigned ntree_limit) override {
DropTrees(ntree_limit); DropTrees(ntree_limit);
const MetaInfo& info = p_fmat->info(); PredLoopInternal<Dart>(p_fmat, out_preds, 0, ntree_limit, true);
int nthread;
#pragma omp parallel
{
nthread = omp_get_num_threads();
}
InitThreadTemp(nthread);
std::vector<float> &preds = *out_preds;
const size_t stride = p_fmat->info().num_row * mparam.num_output_group;
preds.resize(stride * (mparam.size_leaf_vector+1));
// start collecting the prediction
dmlc::DataIter<RowBatch>* iter = p_fmat->RowIterator();
iter->BeforeFirst();
while (iter->Next()) {
const RowBatch &batch = iter->Value();
// parallel over local batch
const bst_omp_uint nsize = static_cast<bst_omp_uint>(batch.size);
#pragma omp parallel for schedule(static)
for (bst_omp_uint i = 0; i < nsize; ++i) {
const int tid = omp_get_thread_num();
RegTree::FVec &feats = thread_temp[tid];
int64_t ridx = static_cast<int64_t>(batch.base_rowid + i);
CHECK_LT(static_cast<size_t>(ridx), info.num_row);
// loop over output groups
for (int gid = 0; gid < mparam.num_output_group; ++gid) {
this->Pred(batch[i],
buffer_offset < 0 ? -1 : buffer_offset + ridx,
gid, info.GetRoot(ridx), &feats,
&preds[ridx * mparam.num_output_group + gid], stride,
ntree_limit);
}
}
}
} }
void Predict(const SparseBatch::Inst& inst, void Predict(const SparseBatch::Inst& inst,
@ -599,20 +546,24 @@ class Dart : public GBTree {
thread_temp.resize(1, RegTree::FVec()); thread_temp.resize(1, RegTree::FVec());
thread_temp[0].Init(mparam.num_feature); thread_temp[0].Init(mparam.num_feature);
} }
out_preds->resize(mparam.num_output_group * (mparam.size_leaf_vector+1)); out_preds->resize(mparam.num_output_group);
ntree_limit *= mparam.num_output_group;
if (ntree_limit == 0 || ntree_limit > trees.size()) {
ntree_limit = static_cast<unsigned>(trees.size());
}
// loop over output groups // loop over output groups
for (int gid = 0; gid < mparam.num_output_group; ++gid) { for (int gid = 0; gid < mparam.num_output_group; ++gid) {
this->Pred(inst, -1, gid, root_index, &thread_temp[0], (*out_preds)[gid]
&(*out_preds)[gid], mparam.num_output_group, = PredValue(inst, gid, root_index,
ntree_limit); &thread_temp[0], 0, ntree_limit) + base_margin_;
} }
} }
protected: protected:
friend class GBTree;
// commit new trees all at once // commit new trees all at once
virtual void void CommitModel(std::vector<std::unique_ptr<RegTree> >&& new_trees,
CommitModel(std::vector<std::unique_ptr<RegTree> >&& new_trees, int bst_group) override {
int bst_group) {
for (size_t i = 0; i < new_trees.size(); ++i) { for (size_t i = 0; i < new_trees.size(); ++i) {
trees.push_back(std::move(new_trees[i])); trees.push_back(std::move(new_trees[i]));
tree_info.push_back(bst_group); tree_info.push_back(bst_group);
@ -625,44 +576,25 @@ class Dart : public GBTree {
} }
} }
// predict the leaf scores without dropped trees // predict the leaf scores without dropped trees
inline void Pred(const RowBatch::Inst &inst, inline float PredValue(const RowBatch::Inst &inst,
int64_t buffer_index,
int bst_group, int bst_group,
unsigned root_index, unsigned root_index,
RegTree::FVec *p_feats, RegTree::FVec *p_feats,
float *out_pred, unsigned tree_begin,
size_t stride, unsigned tree_end) {
unsigned ntree_limit) {
float psum = 0.0f; float psum = 0.0f;
// sum of leaf vector
std::vector<float> vec_psum(mparam.size_leaf_vector, 0.0f);
const int64_t bid = this->BufferOffset(buffer_index, bst_group);
p_feats->Fill(inst); p_feats->Fill(inst);
for (size_t i = 0; i < trees.size(); ++i) { for (size_t i = tree_begin; i < tree_end; ++i) {
if (tree_info[i] == bst_group) { if (tree_info[i] == bst_group) {
bool drop = (std::find(idx_drop.begin(), idx_drop.end(), i) != idx_drop.end()); bool drop = (std::binary_search(idx_drop.begin(), idx_drop.end(), i));
if (!drop) { if (!drop) {
int tid = trees[i]->GetLeafIndex(*p_feats, root_index); int tid = trees[i]->GetLeafIndex(*p_feats, root_index);
psum += weight_drop[i] * (*trees[i])[tid].leaf_value(); psum += weight_drop[i] * (*trees[i])[tid].leaf_value();
for (int j = 0; j < mparam.size_leaf_vector; ++j) {
vec_psum[j] += weight_drop[i] * trees[i]->leafvec(tid)[j];
}
} }
} }
} }
p_feats->Drop(inst); p_feats->Drop(inst);
// updated the buffered results return psum;
if (bid >= 0 && ntree_limit == 0) {
pred_counter[bid] = static_cast<unsigned>(trees.size());
pred_buffer[bid] = psum;
for (int i = 0; i < mparam.size_leaf_vector; ++i) {
pred_buffer[bid + i + 1] = vec_psum[i];
}
}
out_pred[0] = psum;
for (int i = 0; i < mparam.size_leaf_vector; ++i) {
out_pred[stride * (i + 1)] = vec_psum[i];
}
} }
// select dropped trees // select dropped trees
@ -744,13 +676,16 @@ DMLC_REGISTER_PARAMETER(DartTrainParam);
XGBOOST_REGISTER_GBM(GBTree, "gbtree") XGBOOST_REGISTER_GBM(GBTree, "gbtree")
.describe("Tree booster, gradient boosted trees.") .describe("Tree booster, gradient boosted trees.")
.set_body([]() { .set_body([](const std::vector<std::shared_ptr<DMatrix> >& cached_mats, float base_margin) {
return new GBTree(); GBTree* p = new GBTree(base_margin);
p->InitCache(cached_mats);
return p;
}); });
XGBOOST_REGISTER_GBM(Dart, "dart") XGBOOST_REGISTER_GBM(Dart, "dart")
.describe("Tree booster, dart.") .describe("Tree booster, dart.")
.set_body([]() { .set_body([](const std::vector<std::shared_ptr<DMatrix> >& cached_mats, float base_margin) {
return new Dart(); GBTree* p = new Dart(base_margin);
return p;
}); });
} // namespace gbm } // namespace gbm
} // namespace xgboost } // namespace xgboost

82
src/learner.cc
View File

@ -118,20 +118,8 @@ DMLC_REGISTER_PARAMETER(LearnerTrainParam);
*/ */
class LearnerImpl : public Learner { class LearnerImpl : public Learner {
public: public:
explicit LearnerImpl(const std::vector<DMatrix*>& cache_mats) explicit LearnerImpl(const std::vector<std::shared_ptr<DMatrix> >& cache)
noexcept(false) { : cache_(cache) {
// setup the cache setting in constructor.
CHECK_EQ(cache_.size(), 0);
size_t buffer_size = 0;
for (auto it = cache_mats.begin(); it != cache_mats.end(); ++it) {
// avoid duplication.
if (std::find(cache_mats.begin(), it, *it) != it) continue;
DMatrix* pmat = *it;
pmat->cache_learner_ptr_ = this;
cache_.push_back(CacheEntry(pmat, buffer_size, pmat->info().num_row));
buffer_size += pmat->info().num_row;
}
pred_buffer_size_ = buffer_size;
// boosted tree // boosted tree
name_obj_ = "reg:linear"; name_obj_ = "reg:linear";
name_gbm_ = "gbtree"; name_gbm_ = "gbtree";
@ -257,7 +245,7 @@ class LearnerImpl : public Learner {
<< "BoostLearner: wrong model format"; << "BoostLearner: wrong model format";
// duplicated code with LazyInitModel // duplicated code with LazyInitModel
obj_.reset(ObjFunction::Create(name_obj_)); obj_.reset(ObjFunction::Create(name_obj_));
gbm_.reset(GradientBooster::Create(name_gbm_)); gbm_.reset(GradientBooster::Create(name_gbm_, cache_, mparam.base_score));
gbm_->Load(fi); gbm_->Load(fi);
if (mparam.contain_extra_attrs != 0) { if (mparam.contain_extra_attrs != 0) {
std::vector<std::pair<std::string, std::string> > attr; std::vector<std::pair<std::string, std::string> > attr;
@ -265,8 +253,6 @@ class LearnerImpl : public Learner {
attributes_ = std::map<std::string, std::string>( attributes_ = std::map<std::string, std::string>(
attr.begin(), attr.end()); attr.begin(), attr.end());
} }
this->base_score_ = mparam.base_score;
gbm_->ResetPredBuffer(pred_buffer_size_);
cfg_["num_class"] = common::ToString(mparam.num_class); cfg_["num_class"] = common::ToString(mparam.num_class);
cfg_["num_feature"] = common::ToString(mparam.num_feature); cfg_["num_feature"] = common::ToString(mparam.num_feature);
obj_->Configure(cfg_.begin(), cfg_.end()); obj_->Configure(cfg_.begin(), cfg_.end());
@ -294,7 +280,7 @@ class LearnerImpl : public Learner {
this->LazyInitDMatrix(train); this->LazyInitDMatrix(train);
this->PredictRaw(train, &preds_); this->PredictRaw(train, &preds_);
obj_->GetGradient(preds_, train->info(), iter, &gpair_); obj_->GetGradient(preds_, train->info(), iter, &gpair_);
gbm_->DoBoost(train, this->FindBufferOffset(train), &gpair_); gbm_->DoBoost(train, &gpair_, obj_.get());
} }
void BoostOneIter(int iter, void BoostOneIter(int iter,
@ -304,7 +290,7 @@ class LearnerImpl : public Learner {
common::GlobalRandom().seed(tparam.seed * kRandSeedMagic + iter); common::GlobalRandom().seed(tparam.seed * kRandSeedMagic + iter);
} }
this->LazyInitDMatrix(train); this->LazyInitDMatrix(train);
gbm_->DoBoost(train, this->FindBufferOffset(train), in_gpair); gbm_->DoBoost(train, in_gpair);
} }
std::string EvalOneIter(int iter, std::string EvalOneIter(int iter,
@ -435,28 +421,24 @@ class LearnerImpl : public Learner {
// estimate feature bound // estimate feature bound
unsigned num_feature = 0; unsigned num_feature = 0;
for (size_t i = 0; i < cache_.size(); ++i) { for (size_t i = 0; i < cache_.size(); ++i) {
CHECK(cache_[i] != nullptr);
num_feature = std::max(num_feature, num_feature = std::max(num_feature,
static_cast<unsigned>(cache_[i].mat_->info().num_col)); static_cast<unsigned>(cache_[i]->info().num_col));
} }
// run allreduce on num_feature to find the maximum value // run allreduce on num_feature to find the maximum value
rabit::Allreduce<rabit::op::Max>(&num_feature, 1); rabit::Allreduce<rabit::op::Max>(&num_feature, 1);
if (num_feature > mparam.num_feature) { if (num_feature > mparam.num_feature) {
mparam.num_feature = num_feature; mparam.num_feature = num_feature;
} }
// setup // setup
cfg_["num_feature"] = common::ToString(mparam.num_feature); cfg_["num_feature"] = common::ToString(mparam.num_feature);
CHECK(obj_.get() == nullptr && gbm_.get() == nullptr); CHECK(obj_.get() == nullptr && gbm_.get() == nullptr);
obj_.reset(ObjFunction::Create(name_obj_)); obj_.reset(ObjFunction::Create(name_obj_));
gbm_.reset(GradientBooster::Create(name_gbm_));
gbm_->Configure(cfg_.begin(), cfg_.end());
obj_->Configure(cfg_.begin(), cfg_.end()); obj_->Configure(cfg_.begin(), cfg_.end());
// reset the base score // reset the base score
mparam.base_score = obj_->ProbToMargin(mparam.base_score); mparam.base_score = obj_->ProbToMargin(mparam.base_score);
gbm_.reset(GradientBooster::Create(name_gbm_, cache_, mparam.base_score));
this->base_score_ = mparam.base_score; gbm_->Configure(cfg_.begin(), cfg_.end());
gbm_->ResetPredBuffer(pred_buffer_size_);
} }
/*! /*!
* \brief get un-transformed prediction * \brief get un-transformed prediction
@ -471,29 +453,9 @@ class LearnerImpl : public Learner {
CHECK(gbm_.get() != nullptr) CHECK(gbm_.get() != nullptr)
<< "Predict must happen after Load or InitModel"; << "Predict must happen after Load or InitModel";
gbm_->Predict(data, gbm_->Predict(data,
this->FindBufferOffset(data),
out_preds, out_preds,
ntree_limit); ntree_limit);
// add base margin
std::vector<float>& preds = *out_preds;
const bst_omp_uint ndata = static_cast<bst_omp_uint>(preds.size());
const std::vector<bst_float>& base_margin = data->info().base_margin;
if (base_margin.size() != 0) {
CHECK_EQ(preds.size(), base_margin.size())
<< "base_margin.size does not match with prediction size";
#pragma omp parallel for schedule(static)
for (bst_omp_uint j = 0; j < ndata; ++j) {
preds[j] += base_margin[j];
} }
} else {
#pragma omp parallel for schedule(static)
for (bst_omp_uint j = 0; j < ndata; ++j) {
preds[j] += this->base_score_;
}
}
}
// cached size of predict buffer
size_t pred_buffer_size_;
// model parameter // model parameter
LearnerModelParam mparam; LearnerModelParam mparam;
// training parameter // training parameter
@ -514,31 +476,11 @@ class LearnerImpl : public Learner {
private: private:
/*! \brief random number transformation seed. */ /*! \brief random number transformation seed. */
static const int kRandSeedMagic = 127; static const int kRandSeedMagic = 127;
// cache entry object that helps handle feature caching // internal cached dmatrix
struct CacheEntry { std::vector<std::shared_ptr<DMatrix> > cache_;
const DMatrix* mat_;
size_t buffer_offset_;
size_t num_row_;
CacheEntry(const DMatrix* mat, size_t buffer_offset, size_t num_row)
:mat_(mat), buffer_offset_(buffer_offset), num_row_(num_row) {}
}; };
// find internal buffer offset for certain matrix, if not exist, return -1 Learner* Learner::Create(const std::vector<std::shared_ptr<DMatrix> >& cache_data) {
inline int64_t FindBufferOffset(const DMatrix* mat) const {
for (size_t i = 0; i < cache_.size(); ++i) {
if (cache_[i].mat_ == mat && mat->cache_learner_ptr_ == this) {
if (cache_[i].num_row_ == mat->info().num_row) {
return static_cast<int64_t>(cache_[i].buffer_offset_);
}
}
}
return -1;
}
/*! \brief the entries indicates that we have internal prediction cache */
std::vector<CacheEntry> cache_;
};
Learner* Learner::Create(const std::vector<DMatrix*>& cache_data) {
return new LearnerImpl(cache_data); return new LearnerImpl(cache_data);
} }
} // namespace xgboost } // namespace xgboost