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Remove.
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@trivialfis
trivialfis committed Jul 29, 2022
1 parent 10c9c8c commit 03adff6
Showing 1 changed file with 56 additions and 80 deletions.
136 changes: 56 additions & 80 deletions src/learner.cc
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Expand Up @@ -100,10 +100,10 @@ struct LearnerModelParamLegacy : public dmlc::Parameter<LearnerModelParamLegacy>
"Do not change the size of this struct, as it will break binary IO.");
}
// Skip other legacy fields.
Json ToJson(linalg::TensorView<float const, 1> base_score_new) const {
Json ToJson() const {
Object obj;
char floats[NumericLimits<float>::kToCharsSize];
auto ret = to_chars(floats, floats + NumericLimits<float>::kToCharsSize, base_score_new(0));
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))};

Expand All @@ -126,7 +126,7 @@ struct LearnerModelParamLegacy : public dmlc::Parameter<LearnerModelParamLegacy>

return Json(std::move(obj));
}
void FromJson(Json const& obj, linalg::Tensor<float, 1>* base_score_new) {
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"));
Expand All @@ -140,9 +140,8 @@ struct LearnerModelParamLegacy : public dmlc::Parameter<LearnerModelParamLegacy>

std::string str = get<String const>(j_param.at("base_score"));
from_chars(str.c_str(), str.c_str() + str.size(), base_score);
base_score_new->Reshape(1);
(*base_score_new)(0) = base_score;
}

inline LearnerModelParamLegacy ByteSwap() const {
LearnerModelParamLegacy x = *this;
dmlc::ByteSwap(&x.base_score, sizeof(x.base_score), 1);
Expand Down Expand Up @@ -355,9 +354,57 @@ class LearnerConfiguration : public Learner {
LearnerModelParam learner_model_param_;
LearnerTrainParam tparam_;
// Initial prediction.
linalg::Tensor<float, 1> base_score_;
std::vector<std::string> metric_names_;

/**
* \brief Calculate the `base_score` based on input data.
*/
void ConfigureLearnerParam(DMatrix const* p_fmat) {
linalg::Tensor<float, 1> base_score;
// 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
CHECK(obj_);
float world = rabit::GetWorldSize();
if (!std::isnan(mparam_.base_score)) {
// if base_score is set by user, use it.
base_score.Reshape(1);
base_score(0) = mparam_.base_score;
} else if (p_fmat) {
// otherwise, we estimate it from input data.
obj_->InitEstimation(p_fmat->Info(), &base_score);
} else {
// lastly, if data is not available (prediction for custom objective), use default.
base_score.Reshape(1);
base_score(0) = ObjFunction::DefaultBaseScore();
}

auto task = obj_->Task();
auto in = base_score.HostView();
rabit::Allreduce<rabit::op::Sum>(in.Values().data(), in.Values().size());
std::transform(linalg::cbegin(in), linalg::cend(in), linalg::begin(in),
[world](float v) { return v / world; });
mparam_.base_score = base_score(0);

linalg::Tensor<float, 1> copy(base_score.Shape(), ctx_.gpu_id);
auto out = copy.HostView();
std::transform(linalg::cbegin(in), linalg::cend(in), linalg::begin(out),
[&](float v) { return obj_->ProbToMargin(v); });

learner_model_param_ = LearnerModelParam(&ctx_, mparam_, std::move(copy), task);
CHECK(learner_model_param_.Initialized());
CHECK_NE(learner_model_param_.BaseScore(&ctx_).Size(), 0);
}

public:
explicit LearnerConfiguration(std::vector<std::shared_ptr<DMatrix> > cache)
: need_configuration_{true} {
Expand Down Expand Up @@ -432,54 +479,6 @@ class LearnerConfiguration : public Learner {
monitor_.Stop("Configure");
}

/**
* \brief Calculate the `base_score` based on input data.
*/
void ConfigureLearnerParam(DMatrix const* p_fmat) {
// 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
CHECK(obj_);
float world = rabit::GetWorldSize();
if (base_score_.Size() != 0) {
// do nothing
} else if (!std::isnan(mparam_.base_score)) {
// if base_score is set by user, use it.
base_score_.Reshape(1);
base_score_(0) = mparam_.base_score;
} else if (p_fmat) {
// otherwise, we estimate it from input data.
obj_->InitEstimation(p_fmat->Info(), &base_score_);
} else {
// lastly, if data is not available (prediction for custom objective), use default.
base_score_.Reshape(1);
base_score_(0) = ObjFunction::DefaultBaseScore();
}

auto task = obj_->Task();
rabit::Allreduce<rabit::op::Sum>(base_score_.Data()->HostVector().data(),
base_score_.Data()->Size());
linalg::Tensor<float, 1> copy{base_score_.Shape(), ctx_.gpu_id};

auto in = base_score_.HostView();
auto out = copy.HostView();
std::transform(linalg::cbegin(in), linalg::cend(in), linalg::begin(out),
[&](float v) { return obj_->ProbToMargin(v / world); });

learner_model_param_ = LearnerModelParam(&ctx_, mparam_, std::move(copy), task);
CHECK(learner_model_param_.Initialized());
CHECK_NE(learner_model_param_.BaseScore(&ctx_).Size(), 0);
}

virtual PredictionContainer* GetPredictionCache() const {
return &((*ThreadLocalPredictionCache::Get())[this]);
}
Expand Down Expand Up @@ -547,7 +546,7 @@ class LearnerConfiguration : public Learner {
auto& learner_parameters = out["learner"];

learner_parameters["learner_train_param"] = ToJson(tparam_);
learner_parameters["learner_model_param"] = mparam_.ToJson(base_score_.HostView());
learner_parameters["learner_model_param"] = mparam_.ToJson();
learner_parameters["gradient_booster"] = Object();
auto& gradient_booster = learner_parameters["gradient_booster"];
gbm_->SaveConfig(&gradient_booster);
Expand Down Expand Up @@ -836,7 +835,7 @@ class LearnerIO : public LearnerConfiguration {
}

auto const& learner = get<Object>(in["learner"]);
mparam_.FromJson(learner.at("learner_model_param"), &base_score_);
mparam_.FromJson(learner.at("learner_model_param"));

auto const& objective_fn = learner.at("objective");

Expand Down Expand Up @@ -886,7 +885,7 @@ class LearnerIO : public LearnerConfiguration {
out["learner"] = Object();
auto& learner = out["learner"];

learner["learner_model_param"] = mparam_.ToJson(base_score_.HostView());
learner["learner_model_param"] = mparam_.ToJson();
learner["gradient_booster"] = Object();
auto& gradient_booster = learner["gradient_booster"];
gbm_->SaveModel(&gradient_booster);
Expand Down Expand Up @@ -1037,18 +1036,6 @@ class LearnerIO : public LearnerConfiguration {
this->SetParam(kEvalMetric, n);
}
}
auto it = attributes_.find("base_score");
if (it != attributes_.cend()) {
auto const& base_score_str = it->second;
auto loaded = Json::Load(StringView{base_score_str});
auto const& base_score = get<Array const>(loaded);
base_score_.Reshape(base_score.size());
auto& h_result = base_score_.Data()->HostVector();
h_result.clear();
for (auto const& v : base_score) {
h_result.push_back(get<Number const>(v));
}
}

if (warn_old_model) {
LOG(WARNING) << "Loading model from XGBoost < 1.0.0, consider saving it "
Expand Down Expand Up @@ -1108,17 +1095,6 @@ class LearnerIO : public LearnerConfiguration {
extra_attr.emplace_back("metrics", os.str());
}

{
// serialize base score
std::vector<Json> base_score;
for (auto v : base_score_.Data()->HostVector()) {
base_score.emplace_back(Number(v));
}
std::string base_score_str;
Json::Dump(Json(std::move(base_score)), &base_score_str);
extra_attr.emplace_back("base_score", base_score_str);
}

std::string header {"binf"};
fo->Write(header.data(), 4);
if (DMLC_IO_NO_ENDIAN_SWAP) {
Expand Down

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