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add logging

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This commit is contained in:
Hendrik Groove 2024-10-20 17:30:17 +02:00
parent 7301022fed
commit 60a3bea7c6
1 changed files with 50 additions and 25 deletions
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71
src/learner.cc
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@ -61,6 +61,8 @@
#include "xgboost/predictor.h" // for PredictionContainer, PredictionCacheEntry #include "xgboost/predictor.h" // for PredictionContainer, PredictionCacheEntry
#include "xgboost/string_view.h" // for operator<<, StringView #include "xgboost/string_view.h" // for operator<<, StringView
#include "xgboost/task.h" // for ObjInfo #include "xgboost/task.h" // for ObjInfo
#include <iostream>
#include <exception>
namespace { namespace {
const char* kMaxDeltaStepDefaultValue = "0.7"; const char* kMaxDeltaStepDefaultValue = "0.7";
@ -1262,34 +1264,57 @@ class LearnerImpl : public LearnerIO {
return out_impl; return out_impl;
} }
void UpdateOneIter(int iter, std::shared_ptr<DMatrix> train) override { void UpdateOneIter(int iter, std::shared_ptr<DMatrix> train) override {
monitor_.Start("UpdateOneIter"); std::cerr << "Entering UpdateOneIter, iteration: " << iter << std::endl;
TrainingObserver::Instance().Update(iter); monitor_.Start("UpdateOneIter");
this->Configure(); TrainingObserver::Instance().Update(iter);
this->InitBaseScore(train.get());
if (ctx_.seed_per_iteration) { std::cerr << "Configuring..." << std::endl;
common::GlobalRandom().seed(ctx_.seed * kRandSeedMagic + iter); this->Configure();
}
this->ValidateDMatrix(train.get(), true); std::cerr << "Initializing base score..." << std::endl;
this->InitBaseScore(train.get());
auto& predt = prediction_container_.Cache(train, ctx_.Device()); if (ctx_.seed_per_iteration) {
std::cerr << "Setting seed for iteration..." << std::endl;
monitor_.Start("PredictRaw"); common::GlobalRandom().seed(ctx_.seed * kRandSeedMagic + iter);
this->PredictRaw(train.get(), &predt, true, 0, 0);
TrainingObserver::Instance().Observe(predt.predictions, "Predictions");
monitor_.Stop("PredictRaw");
monitor_.Start("GetGradient");
GetGradient(predt.predictions, train->Info(), iter, &gpair_);
monitor_.Stop("GetGradient");
TrainingObserver::Instance().Observe(*gpair_.Data(), "Gradients");
gbm_->DoBoost(train.get(), &gpair_, &predt, obj_.get());
monitor_.Stop("UpdateOneIter");
} }
std::cerr << "Validating DMatrix..." << std::endl;
this->ValidateDMatrix(train.get(), true);
std::cerr << "Caching predictions..." << std::endl;
auto& predt = prediction_container_.Cache(train, ctx_.Device());
monitor_.Start("PredictRaw");
std::cerr << "Predicting raw values..." << std::endl;
this->PredictRaw(train.get(), &predt, true, 0, 0);
TrainingObserver::Instance().Observe(predt.predictions, "Predictions");
monitor_.Stop("PredictRaw");
monitor_.Start("GetGradient");
std::cerr << "Getting gradients..." << std::endl;
try {
GetGradient(predt.predictions, train->Info(), iter, &gpair_);
} catch (const std::exception& e) {
std::cerr << "Exception in GetGradient: " << e.what() << std::endl;
throw;
}
monitor_.Stop("GetGradient");
TrainingObserver::Instance().Observe(*gpair_.Data(), "Gradients");
std::cerr << "Performing boosting..." << std::endl;
try {
gbm_->DoBoost(train.get(), &gpair_, &predt, obj_.get());
} catch (const std::exception& e) {
std::cerr << "Exception in DoBoost: " << e.what() << std::endl;
throw;
}
monitor_.Stop("UpdateOneIter");
std::cerr << "Exiting UpdateOneIter" << std::endl;
}
void BoostOneIter(int iter, std::shared_ptr<DMatrix> train, void BoostOneIter(int iter, std::shared_ptr<DMatrix> train,
linalg::Matrix<GradientPair>* in_gpair) override { linalg::Matrix<GradientPair>* in_gpair) override {
monitor_.Start("BoostOneIter"); monitor_.Start("BoostOneIter");