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learner.cc
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/*!
* Copyright 2014-2022 by Contributors
* \file learner.cc
* \brief Implementation of learning algorithm.
* \author Tianqi Chen
*/
#include <dmlc/io.h>
#include <dmlc/parameter.h>
#include <dmlc/thread_local.h>
#include <atomic>
#include <mutex>
#include <algorithm>
#include <iomanip>
#include <limits>
#include <memory>
#include <sstream>
#include <string>
#include <stack>
#include <utility>
#include <vector>
#include "dmlc/any.h"
#include "xgboost/base.h"
#include "xgboost/c_api.h"
#include "xgboost/data.h"
#include "xgboost/model.h"
#include "xgboost/predictor.h"
#include "xgboost/feature_map.h"
#include "xgboost/gbm.h"
#include "xgboost/generic_parameters.h"
#include "xgboost/host_device_vector.h"
#include "xgboost/json.h"
#include "xgboost/learner.h"
#include "xgboost/logging.h"
#include "xgboost/metric.h"
#include "xgboost/objective.h"
#include "xgboost/parameter.h"
#include "common/common.h"
#include "common/io.h"
#include "common/observer.h"
#include "common/random.h"
#include "common/timer.h"
#include "common/charconv.h"
#include "common/version.h"
#include "common/threading_utils.h"
namespace {
const char* kMaxDeltaStepDefaultValue = "0.7";
} // anonymous namespace
namespace xgboost {
enum class DataSplitMode : int {
kAuto = 0, kCol = 1, kRow = 2
};
} // namespace xgboost
DECLARE_FIELD_ENUM_CLASS(xgboost::DataSplitMode);
namespace xgboost {
// implementation of base learner.
bool Learner::AllowLazyCheckPoint() const {
return gbm_->AllowLazyCheckPoint();
}
Learner::~Learner() = default;
/*! \brief training parameter for regression
*
* Should be deprecated, but still used for being compatible with binary IO.
* Once it's gone, `LearnerModelParam` should handle transforming `base_margin`
* with objective by itself.
*/
struct LearnerModelParamLegacy : public dmlc::Parameter<LearnerModelParamLegacy> {
/* \brief global bias */
bst_float base_score;
/* \brief number of features */
uint32_t num_feature;
/* \brief number of classes, if it is multi-class classification */
int32_t num_class;
/*! \brief Model contain additional properties */
int32_t contain_extra_attrs;
/*! \brief Model contain eval metrics */
int32_t contain_eval_metrics;
/*! \brief the version of XGBoost. */
uint32_t major_version;
uint32_t minor_version;
uint32_t num_target{1};
/*! \brief reserved field */
int reserved[26];
/*! \brief constructor */
LearnerModelParamLegacy() {
std::memset(this, 0, sizeof(LearnerModelParamLegacy));
base_score = 0.5f;
num_target = 1;
major_version = std::get<0>(Version::Self());
minor_version = std::get<1>(Version::Self());
static_assert(sizeof(LearnerModelParamLegacy) == 136,
"Do not change the size of this struct, as it will break binary IO.");
}
// Skip other legacy fields.
Json ToJson() const {
Object obj;
char floats[NumericLimits<float>::kToCharsSize];
auto ret = to_chars(floats, floats + NumericLimits<float>::kToCharsSize, base_score);
CHECK(ret.ec == std::errc());
obj["base_score"] =
std::string{floats, static_cast<size_t>(std::distance(floats, ret.ptr))};
char integers[NumericLimits<int64_t>::kToCharsSize];
ret = to_chars(integers, integers + NumericLimits<int64_t>::kToCharsSize,
static_cast<int64_t>(num_feature));
CHECK(ret.ec == std::errc());
obj["num_feature"] =
std::string{integers, static_cast<size_t>(std::distance(integers, ret.ptr))};
ret = to_chars(integers, integers + NumericLimits<int64_t>::kToCharsSize,
static_cast<int64_t>(num_class));
CHECK(ret.ec == std::errc());
obj["num_class"] =
std::string{integers, static_cast<size_t>(std::distance(integers, ret.ptr))};
ret = to_chars(integers, integers + NumericLimits<int64_t>::kToCharsSize,
static_cast<int64_t>(num_target));
obj["num_target"] =
std::string{integers, static_cast<size_t>(std::distance(integers, ret.ptr))};
return Json(std::move(obj));
}
void FromJson(Json const& obj) {
auto const& j_param = get<Object const>(obj);
std::map<std::string, std::string> m;
m["num_feature"] = get<String const>(j_param.at("num_feature"));
m["num_class"] = get<String const>(j_param.at("num_class"));
auto n_targets_it = j_param.find("num_target");
if (n_targets_it != j_param.cend()) {
m["num_target"] = get<String const>(n_targets_it->second);
}
this->Init(m);
std::string str = get<String const>(j_param.at("base_score"));
from_chars(str.c_str(), str.c_str() + str.size(), base_score);
}
inline LearnerModelParamLegacy ByteSwap() const {
LearnerModelParamLegacy x = *this;
dmlc::ByteSwap(&x.base_score, sizeof(x.base_score), 1);
dmlc::ByteSwap(&x.num_feature, sizeof(x.num_feature), 1);
dmlc::ByteSwap(&x.num_class, sizeof(x.num_class), 1);
dmlc::ByteSwap(&x.contain_extra_attrs, sizeof(x.contain_extra_attrs), 1);
dmlc::ByteSwap(&x.contain_eval_metrics, sizeof(x.contain_eval_metrics), 1);
dmlc::ByteSwap(&x.major_version, sizeof(x.major_version), 1);
dmlc::ByteSwap(&x.minor_version, sizeof(x.minor_version), 1);
dmlc::ByteSwap(&x.num_target, sizeof(x.num_target), 1);
dmlc::ByteSwap(x.reserved, sizeof(x.reserved[0]), sizeof(x.reserved) / sizeof(x.reserved[0]));
return x;
}
// declare parameters
DMLC_DECLARE_PARAMETER(LearnerModelParamLegacy) {
DMLC_DECLARE_FIELD(base_score)
.set_default(0.5f)
.describe("Global bias of the model.");
DMLC_DECLARE_FIELD(num_feature)
.set_default(0)
.describe(
"Number of features in training data,"
" this parameter will be automatically detected by learner.");
DMLC_DECLARE_FIELD(num_class).set_default(0).set_lower_bound(0).describe(
"Number of class option for multi-class classifier. "
" By default equals 0 and corresponds to binary classifier.");
DMLC_DECLARE_FIELD(num_target)
.set_default(1)
.set_lower_bound(1)
.describe("Number of target for multi-target regression.");
}
};
LearnerModelParam::LearnerModelParam(LearnerModelParamLegacy const& user_param, float base_margin,
ObjInfo t)
: base_score{base_margin}, num_feature{user_param.num_feature}, task{t} {
auto n_classes = std::max(static_cast<uint32_t>(user_param.num_class), 1u);
auto n_targets = user_param.num_target;
num_output_group = std::max(n_classes, n_targets);
// For version < 1.6, n_targets == 0
CHECK(n_classes <= 1 || n_targets <= 1)
<< "Multi-class multi-output is not yet supported. n_classes:" << n_classes
<< ", n_targets:" << n_targets;
}
struct LearnerTrainParam : public XGBoostParameter<LearnerTrainParam> {
// data split mode, can be row, col, or none.
DataSplitMode dsplit {DataSplitMode::kAuto};
// flag to disable default metric
bool disable_default_eval_metric {false};
// FIXME(trivialfis): The following parameters belong to model itself, but can be
// specified by users. Move them to model parameter once we can get rid of binary IO.
std::string booster;
std::string objective;
// declare parameters
DMLC_DECLARE_PARAMETER(LearnerTrainParam) {
DMLC_DECLARE_FIELD(dsplit)
.set_default(DataSplitMode::kAuto)
.add_enum("auto", DataSplitMode::kAuto)
.add_enum("col", DataSplitMode::kCol)
.add_enum("row", DataSplitMode::kRow)
.describe("Data split mode for distributed training.");
DMLC_DECLARE_FIELD(disable_default_eval_metric)
.set_default(false)
.describe("Flag to disable default metric. Set to >0 to disable");
DMLC_DECLARE_FIELD(booster)
.set_default("gbtree")
.describe("Gradient booster used for training.");
DMLC_DECLARE_FIELD(objective)
.set_default("reg:squarederror")
.describe("Objective function used for obtaining gradient.");
}
};
DMLC_REGISTER_PARAMETER(LearnerModelParamLegacy);
DMLC_REGISTER_PARAMETER(LearnerTrainParam);
DMLC_REGISTER_PARAMETER(GenericParameter);
int constexpr GenericParameter::kCpuId;
int64_t constexpr GenericParameter::kDefaultSeed;
GenericParameter::GenericParameter() : cfs_cpu_count_{common::GetCfsCPUCount()} {}
void GenericParameter::ConfigureGpuId(bool require_gpu) {
#if defined(XGBOOST_USE_CUDA)
if (gpu_id == kCpuId) { // 0. User didn't specify the `gpu_id'
if (require_gpu) { // 1. `tree_method' or `predictor' or both are using
// GPU.
// 2. Use device 0 as default.
this->UpdateAllowUnknown(Args{{"gpu_id", "0"}});
}
}
// 3. When booster is loaded from a memory image (Python pickle or R
// raw model), number of available GPUs could be different. Wrap around it.
int32_t n_gpus = common::AllVisibleGPUs();
if (n_gpus == 0) {
if (gpu_id != kCpuId) {
LOG(WARNING) << "No visible GPU is found, setting `gpu_id` to -1";
}
this->UpdateAllowUnknown(Args{{"gpu_id", std::to_string(kCpuId)}});
} else if (fail_on_invalid_gpu_id) {
CHECK(gpu_id == kCpuId || gpu_id < n_gpus)
<< "Only " << n_gpus << " GPUs are visible, gpu_id "
<< gpu_id << " is invalid.";
} else if (gpu_id != kCpuId && gpu_id >= n_gpus) {
LOG(WARNING) << "Only " << n_gpus
<< " GPUs are visible, setting `gpu_id` to " << gpu_id % n_gpus;
this->UpdateAllowUnknown(Args{{"gpu_id", std::to_string(gpu_id % n_gpus)}});
}
#else
// Just set it to CPU, don't think about it.
this->UpdateAllowUnknown(Args{{"gpu_id", std::to_string(kCpuId)}});
#endif // defined(XGBOOST_USE_CUDA)
}
int32_t GenericParameter::Threads() const {
auto n_threads = common::OmpGetNumThreads(nthread);
if (cfs_cpu_count_ > 0) {
n_threads = std::min(n_threads, cfs_cpu_count_);
}
return n_threads;
}
using LearnerAPIThreadLocalStore =
dmlc::ThreadLocalStore<std::map<Learner const *, XGBAPIThreadLocalEntry>>;
using ThreadLocalPredictionCache =
dmlc::ThreadLocalStore<std::map<Learner const *, PredictionContainer>>;
namespace {
StringView ModelMsg() {
return StringView{
R"doc(
If you are loading a serialized model (like pickle in Python, RDS in R) generated by
older XGBoost, please export the model by calling `Booster.save_model` from that version
first, then load it back in current version. See:
https://xgboost.readthedocs.io/en/latest/tutorials/saving_model.html
for more details about differences between saving model and serializing.
)doc"};
}
} // anonymous namespace
class LearnerConfiguration : public Learner {
private:
std::mutex config_lock_;
protected:
static std::string const kEvalMetric; // NOLINT
protected:
std::atomic<bool> need_configuration_;
std::map<std::string, std::string> cfg_;
// Stores information like best-iteration for early stopping.
std::map<std::string, std::string> attributes_;
// Name of each feature, usually set from DMatrix.
std::vector<std::string> feature_names_;
// Type of each feature, usually set from DMatrix.
std::vector<std::string> feature_types_;
common::Monitor monitor_;
LearnerModelParamLegacy mparam_;
LearnerModelParam learner_model_param_;
LearnerTrainParam tparam_;
std::vector<std::string> metric_names_;
public:
explicit LearnerConfiguration(std::vector<std::shared_ptr<DMatrix> > cache)
: need_configuration_{true} {
monitor_.Init("Learner");
auto& local_cache = (*ThreadLocalPredictionCache::Get())[this];
for (std::shared_ptr<DMatrix> const& d : cache) {
local_cache.Cache(d, GenericParameter::kCpuId);
}
}
~LearnerConfiguration() override {
auto local_cache = ThreadLocalPredictionCache::Get();
if (local_cache->find(this) != local_cache->cend()) {
local_cache->erase(this);
}
}
// Configuration before data is known.
void Configure() override {
// Varient of double checked lock
if (!this->need_configuration_) { return; }
std::lock_guard<std::mutex> guard(config_lock_);
if (!this->need_configuration_) { return; }
monitor_.Start("Configure");
auto old_tparam = tparam_;
Args args = {cfg_.cbegin(), cfg_.cend()};
tparam_.UpdateAllowUnknown(args);
auto mparam_backup = mparam_;
mparam_.UpdateAllowUnknown(args);
auto initialized = generic_parameters_.GetInitialised();
auto old_seed = generic_parameters_.seed;
generic_parameters_.UpdateAllowUnknown(args);
ConsoleLogger::Configure(args);
// add additional parameters
// These are cosntraints that need to be satisfied.
if (tparam_.dsplit == DataSplitMode::kAuto && rabit::IsDistributed()) {
tparam_.dsplit = DataSplitMode::kRow;
}
// set seed only before the model is initialized
if (!initialized || generic_parameters_.seed != old_seed) {
common::GlobalRandom().seed(generic_parameters_.seed);
}
// must precede configure gbm since num_features is required for gbm
this->ConfigureNumFeatures();
args = {cfg_.cbegin(), cfg_.cend()}; // renew
this->ConfigureObjective(old_tparam, &args);
auto task = this->ConfigureTargets();
// Before 1.0.0, we save `base_score` into binary as a transformed value by objective.
// After 1.0.0 we save the value provided by user and keep it immutable instead. To
// keep the stability, we initialize it in binary LoadModel instead of configuration.
// Under what condition should we omit the transformation:
//
// - base_score is loaded from old binary model.
//
// What are the other possible conditions:
//
// - model loaded from new binary or JSON.
// - model is created from scratch.
// - model is configured second time due to change of parameter
if (!learner_model_param_.Initialized() || mparam_.base_score != mparam_backup.base_score) {
learner_model_param_ =
LearnerModelParam(mparam_, obj_->ProbToMargin(mparam_.base_score), task);
}
this->ConfigureGBM(old_tparam, args);
generic_parameters_.ConfigureGpuId(this->gbm_->UseGPU());
this->ConfigureMetrics(args);
this->need_configuration_ = false;
if (generic_parameters_.validate_parameters) {
this->ValidateParameters();
}
// FIXME(trivialfis): Clear the cache once binary IO is gone.
monitor_.Stop("Configure");
}
virtual PredictionContainer* GetPredictionCache() const {
return &((*ThreadLocalPredictionCache::Get())[this]);
}
void LoadConfig(Json const& in) override {
CHECK(IsA<Object>(in));
Version::Load(in);
auto const& learner_parameters = get<Object>(in["learner"]);
FromJson(learner_parameters.at("learner_train_param"), &tparam_);
auto const& gradient_booster = learner_parameters.at("gradient_booster");
auto const& objective_fn = learner_parameters.at("objective");
if (!obj_) {
obj_.reset(ObjFunction::Create(tparam_.objective, &generic_parameters_));
}
obj_->LoadConfig(objective_fn);
learner_model_param_.task = obj_->Task();
tparam_.booster = get<String>(gradient_booster["name"]);
if (!gbm_) {
gbm_.reset(GradientBooster::Create(tparam_.booster,
&generic_parameters_, &learner_model_param_));
}
gbm_->LoadConfig(gradient_booster);
auto const& j_metrics = learner_parameters.at("metrics");
auto n_metrics = get<Array const>(j_metrics).size();
metric_names_.resize(n_metrics);
metrics_.resize(n_metrics);
for (size_t i = 0; i < n_metrics; ++i) {
auto old_serialization = IsA<String>(j_metrics[i]);
if (old_serialization) {
LOG(WARNING) << ModelMsg();
metric_names_[i] = get<String>(j_metrics[i]);
} else {
metric_names_[i] = get<String>(j_metrics[i]["name"]);
}
metrics_[i] = std::unique_ptr<Metric>(Metric::Create(metric_names_[i], &generic_parameters_));
if (!old_serialization) {
metrics_[i]->LoadConfig(j_metrics[i]);
}
}
FromJson(learner_parameters.at("generic_param"), &generic_parameters_);
// make sure the GPU ID is valid in new environment before start running configure.
generic_parameters_.ConfigureGpuId(false);
this->need_configuration_ = true;
}
void SaveConfig(Json* p_out) const override {
CHECK(!this->need_configuration_) << "Call Configure before saving model.";
Version::Save(p_out);
Json& out { *p_out };
// parameters
out["learner"] = Object();
auto& learner_parameters = out["learner"];
learner_parameters["learner_train_param"] = ToJson(tparam_);
learner_parameters["learner_model_param"] = mparam_.ToJson();
learner_parameters["gradient_booster"] = Object();
auto& gradient_booster = learner_parameters["gradient_booster"];
gbm_->SaveConfig(&gradient_booster);
learner_parameters["objective"] = Object();
auto& objective_fn = learner_parameters["objective"];
obj_->SaveConfig(&objective_fn);
std::vector<Json> metrics(metrics_.size(), Json{Object{}});
for (size_t i = 0; i < metrics_.size(); ++i) {
metrics_[i]->SaveConfig(&metrics[i]);
}
learner_parameters["metrics"] = Array(std::move(metrics));
learner_parameters["generic_param"] = ToJson(generic_parameters_);
}
void SetParam(const std::string& key, const std::string& value) override {
this->need_configuration_ = true;
if (key == kEvalMetric) {
if (std::find(metric_names_.cbegin(), metric_names_.cend(),
value) == metric_names_.cend()) {
metric_names_.emplace_back(value);
}
} else {
cfg_[key] = value;
}
}
// Short hand for setting multiple parameters
void SetParams(std::vector<std::pair<std::string, std::string>> const& args) override {
for (auto const& kv : args) {
this->SetParam(kv.first, kv.second);
}
}
uint32_t GetNumFeature() const override {
return learner_model_param_.num_feature;
}
void SetAttr(const std::string& key, const std::string& value) override {
attributes_[key] = value;
mparam_.contain_extra_attrs = 1;
}
bool GetAttr(const std::string& key, std::string* out) const override {
auto it = attributes_.find(key);
if (it == attributes_.end()) return false;
*out = it->second;
return true;
}
bool DelAttr(const std::string& key) override {
auto it = attributes_.find(key);
if (it == attributes_.end()) { return false; }
attributes_.erase(it);
return true;
}
void SetFeatureNames(std::vector<std::string> const& fn) override {
feature_names_ = fn;
}
void GetFeatureNames(std::vector<std::string>* fn) const override {
*fn = feature_names_;
}
void SetFeatureTypes(std::vector<std::string> const& ft) override {
this->feature_types_ = ft;
}
void GetFeatureTypes(std::vector<std::string>* p_ft) const override {
auto& ft = *p_ft;
ft = this->feature_types_;
}
std::vector<std::string> GetAttrNames() const override {
std::vector<std::string> out;
for (auto const& kv : attributes_) {
out.emplace_back(kv.first);
}
return out;
}
const std::map<std::string, std::string>& GetConfigurationArguments() const override {
return cfg_;
}
GenericParameter const* Ctx() const override { return &generic_parameters_; }
private:
void ValidateParameters() {
Json config { Object() };
this->SaveConfig(&config);
std::stack<Json> stack;
stack.push(config);
std::string const postfix{"_param"};
auto is_parameter = [&postfix](std::string const &key) {
return key.size() > postfix.size() &&
std::equal(postfix.rbegin(), postfix.rend(), key.rbegin());
};
// Extract all parameters
std::vector<std::string> keys;
// First global parameters
Json const global_config{ToJson(*GlobalConfigThreadLocalStore::Get())};
for (auto const& items : get<Object const>(global_config)) {
keys.emplace_back(items.first);
}
// Parameters in various xgboost components.
while (!stack.empty()) {
auto j_obj = stack.top();
stack.pop();
auto const &obj = get<Object const>(j_obj);
for (auto const &kv : obj) {
if (is_parameter(kv.first)) {
auto parameter = get<Object const>(kv.second);
std::transform(parameter.begin(), parameter.end(), std::back_inserter(keys),
[](std::pair<std::string const&, Json const&> const& kv) {
return kv.first;
});
} else if (IsA<Object>(kv.second)) {
stack.push(kv.second);
}
}
}
// FIXME(trivialfis): Make eval_metric a training parameter.
keys.emplace_back(kEvalMetric);
keys.emplace_back("num_output_group");
std::sort(keys.begin(), keys.end());
std::vector<std::string> provided;
for (auto const &kv : cfg_) {
if (std::any_of(kv.first.cbegin(), kv.first.cend(),
[](char ch) { return std::isspace(ch); })) {
LOG(FATAL) << "Invalid parameter \"" << kv.first << "\" contains whitespace.";
}
provided.push_back(kv.first);
}
std::sort(provided.begin(), provided.end());
std::vector<std::string> diff;
std::set_difference(provided.begin(), provided.end(), keys.begin(),
keys.end(), std::back_inserter(diff));
if (diff.size() != 0) {
std::stringstream ss;
ss << "\nParameters: { ";
for (size_t i = 0; i < diff.size() - 1; ++i) {
ss << "\"" << diff[i] << "\", ";
}
ss << "\"" << diff.back() << "\"";
ss << R"W( } might not be used.
This could be a false alarm, with some parameters getting used by language bindings but
then being mistakenly passed down to XGBoost core, or some parameter actually being used
but getting flagged wrongly here. Please open an issue if you find any such cases.
)W";
LOG(WARNING) << ss.str();
}
}
void ConfigureNumFeatures() {
// Compute number of global features if parameter not already set
if (mparam_.num_feature == 0) {
// TODO(hcho3): Change num_feature to 64-bit integer
unsigned num_feature = 0;
auto local_cache = this->GetPredictionCache();
for (auto& matrix : local_cache->Container()) {
CHECK(matrix.first);
CHECK(!matrix.second.ref.expired());
const uint64_t num_col = matrix.first->Info().num_col_;
CHECK_LE(num_col, static_cast<uint64_t>(std::numeric_limits<unsigned>::max()))
<< "Unfortunately, XGBoost does not support data matrices with "
<< std::numeric_limits<unsigned>::max() << " features or greater";
num_feature = std::max(num_feature, static_cast<uint32_t>(num_col));
}
rabit::Allreduce<rabit::op::Max>(&num_feature, 1);
if (num_feature > mparam_.num_feature) {
mparam_.num_feature = num_feature;
}
}
CHECK_NE(mparam_.num_feature, 0)
<< "0 feature is supplied. Are you using raw Booster interface?";
// Remove these once binary IO is gone.
cfg_["num_feature"] = common::ToString(mparam_.num_feature);
cfg_["num_class"] = common::ToString(mparam_.num_class);
}
void ConfigureGBM(LearnerTrainParam const& old, Args const& args) {
if (gbm_ == nullptr || old.booster != tparam_.booster) {
gbm_.reset(GradientBooster::Create(tparam_.booster, &generic_parameters_,
&learner_model_param_));
}
gbm_->Configure(args);
}
void ConfigureObjective(LearnerTrainParam const& old, Args* p_args) {
// Once binary IO is gone, NONE of these config is useful.
if (cfg_.find("num_class") != cfg_.cend() && cfg_.at("num_class") != "0" &&
tparam_.objective != "multi:softprob") {
cfg_["num_output_group"] = cfg_["num_class"];
if (atoi(cfg_["num_class"].c_str()) > 1 && cfg_.count("objective") == 0) {
tparam_.objective = "multi:softmax";
}
}
if (cfg_.find("max_delta_step") == cfg_.cend() &&
cfg_.find("objective") != cfg_.cend() &&
tparam_.objective == "count:poisson") {
// max_delta_step is a duplicated parameter in Poisson regression and tree param.
// Rename one of them once binary IO is gone.
cfg_["max_delta_step"] = kMaxDeltaStepDefaultValue;
}
if (obj_ == nullptr || tparam_.objective != old.objective) {
obj_.reset(ObjFunction::Create(tparam_.objective, &generic_parameters_));
}
auto& args = *p_args;
args = {cfg_.cbegin(), cfg_.cend()}; // renew
obj_->Configure(args);
}
void ConfigureMetrics(Args const& args) {
for (auto const& name : metric_names_) {
auto DupCheck = [&name](std::unique_ptr<Metric> const& m) {
return m->Name() != name;
};
if (std::all_of(metrics_.begin(), metrics_.end(), DupCheck)) {
metrics_.emplace_back(std::unique_ptr<Metric>(Metric::Create(name, &generic_parameters_)));
mparam_.contain_eval_metrics = 1;
}
}
for (auto& p_metric : metrics_) {
p_metric->Configure(args);
}
}
/**
* Get number of targets from objective function.
*/
ObjInfo ConfigureTargets() {
CHECK(this->obj_);
auto const& cache = this->GetPredictionCache()->Container();
size_t n_targets = 1;
for (auto const& d : cache) {
if (n_targets == 1) {
n_targets = this->obj_->Targets(d.first->Info());
} else {
auto t = this->obj_->Targets(d.first->Info());
CHECK(n_targets == t || 1 == t) << "Inconsistent labels.";
}
}
if (mparam_.num_target != 1) {
CHECK(n_targets == 1 || n_targets == mparam_.num_target)
<< "Inconsistent configuration of num_target. Configuration result from input data:"
<< n_targets << ", configuration from parameter:" << mparam_.num_target;
} else {
mparam_.num_target = n_targets;
}
return this->obj_->Task();
}
};
std::string const LearnerConfiguration::kEvalMetric {"eval_metric"}; // NOLINT
class LearnerIO : public LearnerConfiguration {
private:
std::set<std::string> saved_configs_ = {"num_round"};
// Used to identify the offset of JSON string when
// Will be removed once JSON takes over. Right now we still loads some RDS files from R.
std::string const serialisation_header_ { u8"CONFIG-offset:" };
public:
explicit LearnerIO(std::vector<std::shared_ptr<DMatrix>> cache) : LearnerConfiguration{cache} {}
void LoadModel(Json const& in) override {
CHECK(IsA<Object>(in));
auto version = Version::Load(in);
if (std::get<0>(version) == 1 && std::get<1>(version) < 6) {
LOG(WARNING)
<< "Found JSON model saved before XGBoost 1.6, please save the model using current "
"version again. The support for old JSON model will be discontinued in XGBoost 2.3.";
}
auto const& learner = get<Object>(in["learner"]);
mparam_.FromJson(learner.at("learner_model_param"));
auto const& objective_fn = learner.at("objective");
std::string name = get<String>(objective_fn["name"]);
tparam_.UpdateAllowUnknown(Args{{"objective", name}});
obj_.reset(ObjFunction::Create(name, &generic_parameters_));
obj_->LoadConfig(objective_fn);
auto const& gradient_booster = learner.at("gradient_booster");
name = get<String>(gradient_booster["name"]);
tparam_.UpdateAllowUnknown(Args{{"booster", name}});
gbm_.reset(
GradientBooster::Create(tparam_.booster, &generic_parameters_, &learner_model_param_));
gbm_->LoadModel(gradient_booster);
auto const& j_attributes = get<Object const>(learner.at("attributes"));
attributes_.clear();
for (auto const& kv : j_attributes) {
attributes_[kv.first] = get<String const>(kv.second);
}
// feature names and types are saved in xgboost 1.4
auto it = learner.find("feature_names");
if (it != learner.cend()) {
auto const& feature_names = get<Array const>(it->second);
feature_names_.resize(feature_names.size());
std::transform(feature_names.cbegin(), feature_names.cend(), feature_names_.begin(),
[](Json const& fn) { return get<String const>(fn); });
}
it = learner.find("feature_types");
if (it != learner.cend()) {
auto const& feature_types = get<Array const>(it->second);
feature_types_.resize(feature_types.size());
std::transform(feature_types.cbegin(), feature_types.cend(), feature_types_.begin(),
[](Json const& fn) { return get<String const>(fn); });
}
this->need_configuration_ = true;
}
void SaveModel(Json* p_out) const override {
CHECK(!this->need_configuration_) << "Call Configure before saving model.";
Version::Save(p_out);
Json& out { *p_out };
out["learner"] = Object();
auto& learner = out["learner"];
learner["learner_model_param"] = mparam_.ToJson();
learner["gradient_booster"] = Object();
auto& gradient_booster = learner["gradient_booster"];
gbm_->SaveModel(&gradient_booster);
learner["objective"] = Object();
auto& objective_fn = learner["objective"];
obj_->SaveConfig(&objective_fn);
learner["attributes"] = Object();
for (auto const& kv : attributes_) {
learner["attributes"][kv.first] = String(kv.second);
}
learner["feature_names"] = Array();
auto& feature_names = get<Array>(learner["feature_names"]);
for (auto const& name : feature_names_) {
feature_names.emplace_back(name);
}
learner["feature_types"] = Array();
auto& feature_types = get<Array>(learner["feature_types"]);
for (auto const& type : feature_types_) {
feature_types.emplace_back(type);
}
}
// About to be deprecated by JSON format
void LoadModel(dmlc::Stream* fi) override {
generic_parameters_.UpdateAllowUnknown(Args{});
tparam_.Init(std::vector<std::pair<std::string, std::string>>{});
// TODO(tqchen) mark deprecation of old format.
common::PeekableInStream fp(fi);
// backward compatible header check.
std::string header;
header.resize(4);
if (fp.PeekRead(&header[0], 4) == 4) {
CHECK_NE(header, "bs64")
<< "Base64 format is no longer supported in brick.";
if (header == "binf") {
CHECK_EQ(fp.Read(&header[0], 4), 4U);
}
}
if (header[0] == '{') { // Dispatch to JSON
auto buffer = common::ReadAll(fi, &fp);
Json model;
if (header[1] == '"') {
model = Json::Load(StringView{buffer});
} else if (std::isalpha(header[1])) {
model = Json::Load(StringView{buffer}, std::ios::binary);
} else {
LOG(FATAL) << "Invalid model format";
}
this->LoadModel(model);
return;
}
// use the peekable reader.
fi = &fp;
// read parameter
CHECK_EQ(fi->Read(&mparam_, sizeof(mparam_)), sizeof(mparam_))
<< "BoostLearner: wrong model format";
if (!DMLC_IO_NO_ENDIAN_SWAP) {
mparam_ = mparam_.ByteSwap();
}
if (mparam_.num_target == 0) {
mparam_.num_target = 1;
}
CHECK(fi->Read(&tparam_.objective)) << "BoostLearner: wrong model format";
CHECK(fi->Read(&tparam_.booster)) << "BoostLearner: wrong model format";
obj_.reset(ObjFunction::Create(tparam_.objective, &generic_parameters_));
gbm_.reset(GradientBooster::Create(tparam_.booster, &generic_parameters_,
&learner_model_param_));
gbm_->Load(fi);
if (mparam_.contain_extra_attrs != 0) {
std::vector<std::pair<std::string, std::string> > attr;
fi->Read(&attr);
for (auto& kv : attr) {
const std::string prefix = "SAVED_PARAM_";
if (kv.first.find(prefix) == 0) {
const std::string saved_param = kv.first.substr(prefix.length());
if (saved_configs_.find(saved_param) != saved_configs_.end()) {
cfg_[saved_param] = kv.second;
}
}
}
attributes_ = std::map<std::string, std::string>(attr.begin(), attr.end());
}
bool warn_old_model { false };
if (attributes_.find("count_poisson_max_delta_step") != attributes_.cend()) {
// Loading model from < 1.0.0, objective is not saved.
cfg_["max_delta_step"] = attributes_.at("count_poisson_max_delta_step");
attributes_.erase("count_poisson_max_delta_step");
warn_old_model = true;
} else {
warn_old_model = false;
}
if (mparam_.major_version < 1) {
// Before 1.0.0, base_score is saved as a transformed value, and there's no version
// attribute (saved a 0) in the saved model.
std::string multi{"multi:"};
if (!std::equal(multi.cbegin(), multi.cend(), tparam_.objective.cbegin())) {
HostDeviceVector<float> t;
t.HostVector().resize(1);
t.HostVector().at(0) = mparam_.base_score;
this->obj_->PredTransform(&t);
auto base_score = t.HostVector().at(0);
mparam_.base_score = base_score;
}
warn_old_model = true;
}
learner_model_param_ =
LearnerModelParam(mparam_, obj_->ProbToMargin(mparam_.base_score), obj_->Task());
if (attributes_.find("objective") != attributes_.cend()) {
auto obj_str = attributes_.at("objective");
auto j_obj = Json::Load({obj_str.c_str(), obj_str.size()});
obj_->LoadConfig(j_obj);
attributes_.erase("objective");
} else {
warn_old_model = true;
}
if (attributes_.find("metrics") != attributes_.cend()) {
auto metrics_str = attributes_.at("metrics");
std::vector<std::string> names { common::Split(metrics_str, ';') };
attributes_.erase("metrics");
for (auto const& n : names) {
this->SetParam(kEvalMetric, n);
}
}
if (warn_old_model) {
LOG(WARNING) << "Loading model from XGBoost < 1.0.0, consider saving it "
"again for improved compatibility";
}
// Renew the version.
mparam_.major_version = std::get<0>(Version::Self());
mparam_.minor_version = std::get<1>(Version::Self());
cfg_["num_class"] = common::ToString(mparam_.num_class);
cfg_["num_feature"] = common::ToString(mparam_.num_feature);
auto n = tparam_.__DICT__();
cfg_.insert(n.cbegin(), n.cend());
// copy dsplit from config since it will not run again during restore
if (tparam_.dsplit == DataSplitMode::kAuto && rabit::IsDistributed()) {
tparam_.dsplit = DataSplitMode::kRow;
}
this->need_configuration_ = true;
}
// Save model into binary format. The code is about to be deprecated by more robust
// JSON serialization format.
void SaveModel(dmlc::Stream* fo) const override {
LearnerModelParamLegacy mparam = mparam_; // make a copy to potentially modify
std::vector<std::pair<std::string, std::string> > extra_attr;
mparam.contain_extra_attrs = 1;
{
std::vector<std::string> saved_params;
for (const auto& key : saved_params) {
auto it = cfg_.find(key);
if (it != cfg_.end()) {
mparam.contain_extra_attrs = 1;
extra_attr.emplace_back("SAVED_PARAM_" + key, it->second);
}
}
}
{
// Similar to JSON model IO, we save the objective.
Json j_obj { Object() };
obj_->SaveConfig(&j_obj);
std::string obj_doc;
Json::Dump(j_obj, &obj_doc);
extra_attr.emplace_back("objective", obj_doc);
}
// As of 1.0.0, JVM Package and R Package uses Save/Load model for serialization.
// Remove this part once they are ported to use actual serialization methods.
if (mparam.contain_eval_metrics != 0) {
std::stringstream os;
for (auto& ev : metrics_) {
os << ev->Name() << ";";
}
extra_attr.emplace_back("metrics", os.str());
}
std::string header {"binf"};
fo->Write(header.data(), 4);
if (DMLC_IO_NO_ENDIAN_SWAP) {
fo->Write(&mparam, sizeof(LearnerModelParamLegacy));
} else {