learner.cc

/*!
 * Copyright 2014-2019 by Contributors
 * \file learner.cc
 * \brief Implementation of learning algorithm.
 * \author Tianqi Chen
 */
#include <dmlc/io.h>
#include <dmlc/parameter.h>

#include <algorithm>
#include <iomanip>
#include <limits>
#include <sstream>
#include <string>
#include <stack>
#include <utility>
#include <vector>

#include "xgboost/base.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/version.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  */
  unsigned num_feature;
  /* \brief number of classes, if it is multi-class classification  */
  int num_class;
  /*! \brief Model contain additional properties */
  int contain_extra_attrs;
  /*! \brief Model contain eval metrics */
  int contain_eval_metrics;
  /*! \brief reserved field */
  int reserved[29];
  /*! \brief constructor */
  LearnerModelParamLegacy() {
    std::memset(this, 0, sizeof(LearnerModelParamLegacy));
    base_score = 0.5f;
  }
  // Skip other legacy fields.
  Json ToJson() const {
    Object obj;
    obj["base_score"] = std::to_string(base_score);
    obj["num_feature"] = std::to_string(num_feature);
    obj["num_class"] = std::to_string(num_class);
    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["base_score"] = get<String const>(j_param.at("base_score"));
    m["num_feature"] = get<String const>(j_param.at("num_feature"));
    m["num_class"] = get<String const>(j_param.at("num_class"));
    this->Init(m);
  }
  // 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.");
  }
};

LearnerModelParam::LearnerModelParam(
    LearnerModelParamLegacy const &user_param, float base_margin)
    : base_score{base_margin}, num_feature{user_param.num_feature},
      num_output_group{user_param.num_class == 0
                           ? 1
                           : static_cast<uint32_t>(user_param.num_class)} {}

struct LearnerTrainParam : public XGBoostParameter<LearnerTrainParam> {
  // data split mode, can be row, col, or none.
  DataSplitMode dsplit;
  // flag to disable default metric
  int disable_default_eval_metric;
  // 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(0)
        .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;

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) {
    this->UpdateAllowUnknown(Args{{"gpu_id", std::to_string(kCpuId)}});
  } else if (gpu_id != kCpuId && 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)
}

/*!
 * \brief learner that performs gradient boosting for a specific objective
 * function. It does training and prediction.
 */
class LearnerImpl : public Learner {
 public:
  explicit LearnerImpl(std::vector<std::shared_ptr<DMatrix> >  cache)
      : need_configuration_{true}, cache_(std::move(cache)) {
    monitor_.Init("Learner");
  }
  // Configuration before data is known.
  void Configure() override {
    if (!this->need_configuration_) { return; }

    monitor_.Start("Configure");
    auto old_tparam = tparam_;
    Args args = {cfg_.cbegin(), cfg_.cend()};

    tparam_.UpdateAllowUnknown(args);
    mparam_.UpdateAllowUnknown(args);
    generic_parameters_.UpdateAllowUnknown(args);
    generic_parameters_.CheckDeprecated();

    ConsoleLogger::Configure(args);
    if (generic_parameters_.nthread != 0) {
      omp_set_num_threads(generic_parameters_.nthread);
    }

    // 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
    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);
    this->ConfigureGBM(old_tparam, args);
    this->ConfigureMetrics(args);

    generic_parameters_.ConfigureGpuId(this->gbm_->UseGPU());

    learner_model_param_ = LearnerModelParam(mparam_,
                                             obj_->ProbToMargin(mparam_.base_score));

    this->need_configuration_ = false;
    if (generic_parameters_.validate_parameters) {
      this->ValidateParameters();
    }

    // FIXME(trivialfis): Clear the cache once binary IO is gone.
    monitor_.Stop("Configure");
  }

  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;
    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);
        }
      }
    }
    auto learner_model_param = mparam_.ToJson();
    for (auto const& kv : get<Object>(learner_model_param)) {
      keys.emplace_back(kv.first);
    }
    keys.emplace_back(kEvalMetric);
    keys.emplace_back("verbosity");
    keys.emplace_back("num_output_group");

    std::sort(keys.begin(), keys.end());

    std::vector<std::string> provided;
    for (auto const &kv : cfg_) {
      // FIXME(trivialfis): Make eval_metric a training parameter.
      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 may not be accurate due to some parameters are only used in language bindings but
  passed down to XGBoost core.  Or some parameters are not used but slip through this
  verification. Please open an issue if you find above cases.

)W";
      LOG(WARNING) << ss.str();
    }
  }

  void CheckDataSplitMode() {
    if (rabit::IsDistributed()) {
      CHECK(tparam_.dsplit != DataSplitMode::kAuto)
        << "Precondition violated; dsplit cannot be 'auto' in distributed mode";
      if (tparam_.dsplit == DataSplitMode::kCol) {
        // 'distcol' updater hidden until it becomes functional again
        // See discussion at https://github.com/dmlc/xgboost/issues/1832
        LOG(FATAL) << "Column-wise data split is currently not supported.";
      }
    }
  }

  void LoadModel(Json const& in) override {
    CHECK(IsA<Object>(in));
    Version::Load(in, false);
    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_,
                                       cache_));
    gbm_->LoadModel(gradient_booster);

    learner_model_param_ = LearnerModelParam(mparam_,
                                             obj_->ProbToMargin(mparam_.base_score));

    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);
    }

    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);
    }
  }

  void LoadConfig(Json const& in) override {
    CHECK(IsA<Object>(in));
    Version::Load(in, true);

    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);

    tparam_.booster = get<String>(gradient_booster["name"]);
    if (!gbm_) {
      gbm_.reset(GradientBooster::Create(tparam_.booster,
                                         &generic_parameters_, &learner_model_param_,
                                         cache_));
    }
    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) {
      metric_names_[i]= get<String>(j_metrics[i]);
      metrics_[i] = std::unique_ptr<Metric>(
          Metric::Create(metric_names_[i], &generic_parameters_));
    }

    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["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());
    for (size_t i = 0; i < metrics_.size(); ++i) {
      metrics[i] = String(metrics_[i]->Name());
    }
    learner_parameters["metrics"] = Array(std::move(metrics));

    learner_parameters["generic_param"] = toJson(generic_parameters_);
  }

  // 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] == '{') {
      auto json_stream = common::FixedSizeStream(&fp);
      std::string buffer;
      json_stream.Take(&buffer);
      auto model = Json::Load({buffer.c_str(), buffer.size()});
      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";
    {
      // backward compatibility code for compatible with old model type
      // for new model, Read(&name_obj_) is suffice
      uint64_t len;
      CHECK_EQ(fi->Read(&len, sizeof(len)), sizeof(len));
      if (len >= std::numeric_limits<unsigned>::max()) {
        int gap;
        CHECK_EQ(fi->Read(&gap, sizeof(gap)), sizeof(gap))
            << "BoostLearner: wrong model format";
        len = len >> static_cast<uint64_t>(32UL);
      }
      if (len != 0) {
        tparam_.objective.resize(len);
        CHECK_EQ(fi->Read(&tparam_.objective[0], len), len)
            << "BoostLearner: wrong model format";
      }
    }
    CHECK(fi->Read(&tparam_.booster)) << "BoostLearner: wrong model format";
    // duplicated code with LazyInitModel
    obj_.reset(ObjFunction::Create(tparam_.objective, &generic_parameters_));
    gbm_.reset(GradientBooster::Create(tparam_.booster, &generic_parameters_,
                                       &learner_model_param_, cache_));
    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());
    }
    if (tparam_.objective == "count:poisson") {
      std::string max_delta_step;
      fi->Read(&max_delta_step);
      cfg_["max_delta_step"] = max_delta_step;
    }
    if (mparam_.contain_eval_metrics != 0) {
      std::vector<std::string> metr;
      fi->Read(&metr);
      for (auto name : metr) {
        metrics_.emplace_back(Metric::Create(name, &generic_parameters_));
      }
    }

    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());

    Args args = {cfg_.cbegin(), cfg_.cend()};
    generic_parameters_.UpdateAllowUnknown(args);
    gbm_->Configure(args);
    obj_->Configure({cfg_.begin(), cfg_.end()});

    for (auto& p_metric : metrics_) {
      p_metric->Configure({cfg_.begin(), cfg_.end()});
    }

    // copy dsplit from config since it will not run again during restore
    if (tparam_.dsplit == DataSplitMode::kAuto && rabit::IsDistributed()) {
      tparam_.dsplit = DataSplitMode::kRow;
    }

    this->Configure();
  }

  // Save model into binary format.  The code is about to be deprecated by more robust
  // JSON serialization format.  This function is uneffected by
  // `enable_experimental_json_serialization` as user might enable this flag for pickle
  // while still want a binary output.  As we are progressing at replacing the binary
  // format, there's no need to put too much effort on it.
  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;
    // extra attributed to be added just before saving
    if (tparam_.objective == "count:poisson") {
      auto it = cfg_.find("max_delta_step");
      if (it != cfg_.end()) {
        // write `max_delta_step` parameter as extra attribute of booster
        mparam.contain_extra_attrs = 1;
        extra_attr.emplace_back("count_poisson_max_delta_step", it->second);
      }
    }
    {
      std::vector<std::string> saved_params;
      // check if rabit_bootstrap_cache were set to non zero before adding to checkpoint
      if (cfg_.find("rabit_bootstrap_cache") != cfg_.end() &&
        (cfg_.find("rabit_bootstrap_cache"))->second != "0") {
        std::copy(saved_configs_.begin(), saved_configs_.end(),
                  std::back_inserter(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);
        }
      }
    }
    fo->Write(&mparam, sizeof(LearnerModelParamLegacy));
    fo->Write(tparam_.objective);
    fo->Write(tparam_.booster);
    gbm_->Save(fo);
    if (mparam.contain_extra_attrs != 0) {
      std::map<std::string, std::string> attr(attributes_);
      for (const auto& kv : extra_attr) {
        attr[kv.first] = kv.second;
      }
      fo->Write(std::vector<std::pair<std::string, std::string>>(
                  attr.begin(), attr.end()));
    }
    if (tparam_.objective == "count:poisson") {
      auto it = cfg_.find("max_delta_step");
      if (it != cfg_.end()) {
        fo->Write(it->second);
      } else {
        // recover value of max_delta_step from extra attributes
        auto it2 = attributes_.find("count_poisson_max_delta_step");
        const std::string max_delta_step
          = (it2 != attributes_.end()) ? it2->second : kMaxDeltaStepDefaultValue;
        fo->Write(max_delta_step);
      }
    }
    if (mparam.contain_eval_metrics != 0) {
      std::vector<std::string> metr;
      for (auto& ev : metrics_) {
        metr.emplace_back(ev->Name());
      }
      fo->Write(metr);
    }
  }

  void Save(dmlc::Stream* fo) const override {
    if (generic_parameters_.enable_experimental_json_serialization) {
      Json memory_snapshot{Object()};
      memory_snapshot["Model"] = Object();
      auto &model = memory_snapshot["Model"];
      this->SaveModel(&model);
      memory_snapshot["Config"] = Object();
      auto &config = memory_snapshot["Config"];
      this->SaveConfig(&config);
      std::string out_str;
      Json::Dump(memory_snapshot, &out_str);
      fo->Write(out_str.c_str(), out_str.size());
    } else {
      std::string binary_buf;
      common::MemoryBufferStream s(&binary_buf);
      this->SaveModel(&s);
      Json config{ Object() };
      // Do not use std::size_t as it's not portable.
      int64_t const json_offset = binary_buf.size();
      this->SaveConfig(&config);
      std::string config_str;
      Json::Dump(config, &config_str);
      // concatonate the model and config at final output, it's a temporary solution for
      // continuing support for binary model format
      fo->Write(&serialisation_header_[0], serialisation_header_.size());
      fo->Write(&json_offset, sizeof(json_offset));
      fo->Write(&binary_buf[0], binary_buf.size());
      fo->Write(&config_str[0], config_str.size());
    }
  }

  void Load(dmlc::Stream* fi) override {
    common::PeekableInStream fp(fi);
    char c {0};
    fp.PeekRead(&c, 1);
    if (c == '{') {
      std::string buffer;
      common::FixedSizeStream{&fp}.Take(&buffer);
      auto memory_snapshot = Json::Load({buffer.c_str(), buffer.size()});
      this->LoadModel(memory_snapshot["Model"]);
      this->LoadConfig(memory_snapshot["Config"]);
    } else {
      std::string header;
      header.resize(serialisation_header_.size());
      CHECK_EQ(fp.Read(&header[0], header.size()), serialisation_header_.size());
      CHECK_EQ(header, serialisation_header_);

      int64_t json_offset {-1};
      CHECK_EQ(fp.Read(&json_offset, sizeof(json_offset)), sizeof(json_offset));
      CHECK_GT(json_offset, 0);
      std::string buffer;
      common::FixedSizeStream{&fp}.Take(&buffer);

      common::MemoryFixSizeBuffer binary_buf(&buffer[0], json_offset);
      this->LoadModel(&binary_buf);

      common::MemoryFixSizeBuffer json_buf {&buffer[0] + json_offset,
                                            buffer.size() - json_offset};
      auto config = Json::Load({buffer.c_str() + json_offset, buffer.size() - json_offset});
      this->LoadConfig(config);
    }
  }

  std::vector<std::string> DumpModel(const FeatureMap& fmap,
                                     bool with_stats,
                                     std::string format) const override {
    CHECK(!this->need_configuration_)
        << "The model hasn't been built yet.  Are you using raw Booster interface?";
    return gbm_->DumpModel(fmap, with_stats, format);
  }

  void UpdateOneIter(int iter, DMatrix* train) override {
    monitor_.Start("UpdateOneIter");
    TrainingObserver::Instance().Update(iter);
    this->Configure();
    if (generic_parameters_.seed_per_iteration || rabit::IsDistributed()) {
      common::GlobalRandom().seed(generic_parameters_.seed * kRandSeedMagic + iter);
    }
    this->CheckDataSplitMode();
    this->ValidateDMatrix(train);

    monitor_.Start("PredictRaw");
    this->PredictRaw(train, &preds_[train], true);
    monitor_.Stop("PredictRaw");
    TrainingObserver::Instance().Observe(preds_[train], "Predictions");

    monitor_.Start("GetGradient");
    obj_->GetGradient(preds_[train], train->Info(), iter, &gpair_);
    monitor_.Stop("GetGradient");
    TrainingObserver::Instance().Observe(gpair_, "Gradients");

    gbm_->DoBoost(train, &gpair_, obj_.get());
    monitor_.Stop("UpdateOneIter");
  }

  void BoostOneIter(int iter, DMatrix* train,
                    HostDeviceVector<GradientPair>* in_gpair) override {
    monitor_.Start("BoostOneIter");
    this->Configure();
    if (generic_parameters_.seed_per_iteration || rabit::IsDistributed()) {
      common::GlobalRandom().seed(generic_parameters_.seed * kRandSeedMagic + iter);
    }
    this->CheckDataSplitMode();
    this->ValidateDMatrix(train);

    gbm_->DoBoost(train, in_gpair);
    monitor_.Stop("BoostOneIter");
  }

  std::string EvalOneIter(int iter, const std::vector<DMatrix*>& data_sets,
                          const std::vector<std::string>& data_names) override {
    monitor_.Start("EvalOneIter");
    this->Configure();

    std::ostringstream os;
    os << '[' << iter << ']' << std::setiosflags(std::ios::fixed);
    if (metrics_.size() == 0 && tparam_.disable_default_eval_metric <= 0) {
      metrics_.emplace_back(Metric::Create(obj_->DefaultEvalMetric(), &generic_parameters_));
      metrics_.back()->Configure({cfg_.begin(), cfg_.end()});
    }
    for (size_t i = 0; i < data_sets.size(); ++i) {
      DMatrix * dmat = data_sets[i];
      this->ValidateDMatrix(dmat);
      this->PredictRaw(data_sets[i], &preds_[dmat], false);
      obj_->EvalTransform(&preds_[dmat]);
      for (auto& ev : metrics_) {
        os << '\t' << data_names[i] << '-' << ev->Name() << ':'
           << ev->Eval(preds_[dmat], data_sets[i]->Info(),
                       tparam_.dsplit == DataSplitMode::kRow);
      }
    }

    monitor_.Stop("EvalOneIter");
    return os.str();
  }

  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);
    }
  }

  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;
  }

  std::vector<std::string> GetAttrNames() const override {
    std::vector<std::string> out;
    for (auto const& kv : attributes_) {
      out.emplace_back(kv.first);
    }
    return out;
  }

  GenericParameter const& GetGenericParameter() const override {
    return generic_parameters_;
  }

  void Predict(DMatrix* data, bool output_margin,
               HostDeviceVector<bst_float>* out_preds, unsigned ntree_limit,
               bool training,
               bool pred_leaf, bool pred_contribs, bool approx_contribs,
               bool pred_interactions) override {
    int multiple_predictions = static_cast<int>(pred_leaf) +
                               static_cast<int>(pred_interactions) +
                               static_cast<int>(pred_contribs);
    this->Configure();
    CHECK_LE(multiple_predictions, 1) << "Perform one kind of prediction at a time.";
    if (pred_contribs) {
      gbm_->PredictContribution(data, &out_preds->HostVector(), ntree_limit, approx_contribs);
    } else if (pred_interactions) {
      gbm_->PredictInteractionContributions(data, &out_preds->HostVector(), ntree_limit,
                                            approx_contribs);
    } else if (pred_leaf) {
      gbm_->PredictLeaf(data, &out_preds->HostVector(), ntree_limit);
    } else {
      this->PredictRaw(data, out_preds, training, ntree_limit);
      if (!output_margin) {
        obj_->PredTransform(out_preds);
      }
    }
  }

  const std::map<std::string, std::string>& GetConfigurationArguments() const override {
    return cfg_;
  }

 protected:
  /*!
   * \brief get un-transformed prediction
   * \param data training data matrix
   * \param out_preds output vector that stores the prediction
   * \param ntree_limit limit number of trees used for boosted tree
   *   predictor, when it equals 0, this means we are using all the trees
   * \param training allow dropout when the DART booster is being used
   */
  void PredictRaw(DMatrix* data, HostDeviceVector<bst_float>* out_preds,
                  bool training,
                  unsigned ntree_limit = 0) const {
    CHECK(gbm_ != nullptr)
        << "Predict must happen after Load or configuration";
    this->ValidateDMatrix(data);
    gbm_->PredictBatch(data, out_preds, training, ntree_limit);
  }

  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") {
      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);
    }
  }

  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_, cache_));
    }
    gbm_->Configure(args);
  }

  // set number of features correctly.
  void ConfigureNumFeatures() {
    // estimate feature bound
    // TODO(hcho3): Change num_feature to 64-bit integer
    unsigned num_feature = 0;
    for (auto & matrix : cache_) {
      CHECK(matrix != nullptr);
      const uint64_t num_col = matrix->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));
    }
    // run allreduce on num_feature to find the maximum value
    rabit::Allreduce<rabit::op::Max>(&num_feature, 1, nullptr, nullptr, "num_feature");
    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?";
    learner_model_param_.num_feature = mparam_.num_feature;
    // Remove these once binary IO is gone.
    cfg_["num_feature"] = common::ToString(mparam_.num_feature);
    cfg_["num_class"] = common::ToString(mparam_.num_class);
  }

  void ValidateDMatrix(DMatrix* p_fmat) const {
    MetaInfo const& info = p_fmat->Info();
    auto const& weights = info.weights_;
    if (info.group_ptr_.size() != 0 && weights.Size() != 0) {
      CHECK(weights.Size() == info.group_ptr_.size() - 1)
          << "\n"
          << "weights size: " << weights.Size()            << ", "
          << "groups size: "  << info.group_ptr_.size() -1 << ", "
          << "num rows: "     << p_fmat->Info().num_row_   << "\n"
          << "Number of weights should be equal to number of groups in ranking task.";
    }

    auto const row_based_split = [this]() {
      return tparam_.dsplit == DataSplitMode::kRow ||
             tparam_.dsplit == DataSplitMode::kAuto;
    };
    bool const valid_features =
        !row_based_split() ||
        (learner_model_param_.num_feature == p_fmat->Info().num_col_);
    std::string const msg {
      "Number of columns does not match number of features in booster."
    };
    if (generic_parameters_.validate_features) {
      CHECK_EQ(learner_model_param_.num_feature, p_fmat->Info().num_col_) << msg;
    } else if (!valid_features) {
      // Remove this and make the equality check fatal once spark can fix all failing tests.
      LOG(WARNING) << msg << " "
                   << "Columns: " << p_fmat->Info().num_col_ << " "
                   << "Features: " << learner_model_param_.num_feature;
    }
  }

  // model parameter
  LearnerModelParamLegacy mparam_;
  LearnerModelParam learner_model_param_;
  LearnerTrainParam tparam_;
  // Used to identify the offset of JSON string when
  // `enable_experimental_json_serialization' is set to false.  Will be removed once JSON
  // takes over.
  std::string const serialisation_header_ { u8"CONFIG-offset:" };
  // configurations
  std::map<std::string, std::string> cfg_;
  std::map<std::string, std::string> attributes_;
  std::vector<std::string> metric_names_;
  static std::string const kEvalMetric;  // NOLINT
  // temporal storages for prediction
  std::map<DMatrix*, HostDeviceVector<bst_float>> preds_;
  // gradient pairs
  HostDeviceVector<GradientPair> gpair_;
  bool need_configuration_;

 private:
  /*! \brief random number transformation seed. */
  static int32_t constexpr kRandSeedMagic = 127;
  // internal cached dmatrix
  std::vector<std::shared_ptr<DMatrix> > cache_;

  common::Monitor monitor_;

  /*! \brief (Deprecated) saved config keys used to restore failed worker */
  std::set<std::string> saved_configs_ = {"num_round"};
};

std::string const LearnerImpl::kEvalMetric {"eval_metric"};  // NOLINT

constexpr int32_t LearnerImpl::kRandSeedMagic;

Learner* Learner::Create(
    const std::vector<std::shared_ptr<DMatrix> >& cache_data) {
  return new LearnerImpl(cache_data);
}
}  // namespace xgboost