Pass shared pointer instead of raw pointer to Learner.

include/xgboost/learner.h

@@ -1,5 +1,5 @@
 /*!
- * Copyright 2015-2019 by Contributors
+ * Copyright 2015-2020 by Contributors
  * \file learner.h
  * \brief Learner interface that integrates objective, gbm and evaluation together.
  *  This is the user facing XGBoost training module.
@@ -59,7 +59,7 @@ class Learner : public Model, public Configurable, public rabit::Serializable {
    * \param iter current iteration number
    * \param train reference to the data matrix.
    */
-  virtual void UpdateOneIter(int iter, DMatrix* train) = 0;
+  virtual void UpdateOneIter(int iter, std::shared_ptr<DMatrix> train) = 0;
   /*!
    * \brief Do customized gradient boosting with in_gpair.
    *  in_gair can be mutated after this call.
@@ -68,7 +68,7 @@ class Learner : public Model, public Configurable, public rabit::Serializable {
    * \param in_gpair The input gradient statistics.
    */
   virtual void BoostOneIter(int iter,
-                            DMatrix* train,
+                            std::shared_ptr<DMatrix> train,
                             HostDeviceVector<GradientPair>* in_gpair) = 0;
   /*!
    * \brief evaluate the model for specific iteration using the configured metrics.
@@ -78,7 +78,7 @@ class Learner : public Model, public Configurable, public rabit::Serializable {
    * \return a string corresponding to the evaluation result
    */
   virtual std::string EvalOneIter(int iter,
-                                  const std::vector<DMatrix*>& data_sets,
+                                  const std::vector<std::shared_ptr<DMatrix>>& data_sets,
                                   const std::vector<std::string>& data_names) = 0;
   /*!
    * \brief get prediction given the model.
@@ -92,7 +92,7 @@ class Learner : public Model, public Configurable, public rabit::Serializable {
    * \param approx_contribs whether to approximate the feature contributions for speed
    * \param pred_interactions whether to compute the feature pair contributions
    */
-  virtual void Predict(DMatrix* data,
+  virtual void Predict(std::shared_ptr<DMatrix> data,
                        bool output_margin,
                        HostDeviceVector<bst_float> *out_preds,
                        unsigned ntree_limit = 0,

src/c_api/c_api.cc

@@ -1,4 +1,4 @@
-// Copyright (c) 2014-2019 by Contributors
+// Copyright (c) 2014-2020 by Contributors
 #include <dmlc/thread_local.h>
 #include <rabit/rabit.h>
 #include <rabit/c_api.h>
@@ -498,7 +498,7 @@ XGB_DLL int XGBoosterUpdateOneIter(BoosterHandle handle,
   auto *dtr =
       static_cast<std::shared_ptr<DMatrix>*>(dtrain);
 
-  bst->UpdateOneIter(iter, dtr->get());
+  bst->UpdateOneIter(iter, *dtr);
   API_END();
 }
 
@@ -519,7 +519,7 @@ XGB_DLL int XGBoosterBoostOneIter(BoosterHandle handle,
     tmp_gpair_h[i] = GradientPair(grad[i], hess[i]);
   }
 
-  bst->BoostOneIter(0, dtr->get(), &tmp_gpair);
+  bst->BoostOneIter(0, *dtr, &tmp_gpair);
   API_END();
 }
 
@@ -533,11 +533,11 @@ XGB_DLL int XGBoosterEvalOneIter(BoosterHandle handle,
   API_BEGIN();
   CHECK_HANDLE();
   auto* bst = static_cast<Learner*>(handle);
-  std::vector<DMatrix*> data_sets;
+  std::vector<std::shared_ptr<DMatrix>> data_sets;
   std::vector<std::string> data_names;
 
   for (xgboost::bst_ulong i = 0; i < len; ++i) {
-    data_sets.push_back(static_cast<std::shared_ptr<DMatrix>*>(dmats[i])->get());
+    data_sets.push_back(*static_cast<std::shared_ptr<DMatrix>*>(dmats[i]));
     data_names.emplace_back(evnames[i]);
   }
 
@@ -560,7 +560,7 @@ XGB_DLL int XGBoosterPredict(BoosterHandle handle,
   auto *bst = static_cast<Learner*>(handle);
   HostDeviceVector<bst_float> tmp_preds;
   bst->Predict(
-      static_cast<std::shared_ptr<DMatrix>*>(dmat)->get(),
+      *static_cast<std::shared_ptr<DMatrix>*>(dmat),
       (option_mask & 1) != 0,
       &tmp_preds, ntree_limit,
       static_cast<bool>(training),

src/cli_main.cc

@@ -1,5 +1,5 @@
 /*!
- * Copyright 2014-2019 by Contributors
+ * Copyright 2014-2020 by Contributors
  * \file cli_main.cc
  * \brief The command line interface program of xgboost.
  *  This file is not included in dynamic library.
@@ -165,20 +165,20 @@ void CLITrain(const CLIParam& param) {
           param.dsplit == 2));
   std::vector<std::shared_ptr<DMatrix> > deval;
   std::vector<std::shared_ptr<DMatrix> > cache_mats;
-  std::vector<DMatrix*> eval_datasets;
+  std::vector<std::shared_ptr<DMatrix>> eval_datasets;
   cache_mats.push_back(dtrain);
   for (size_t i = 0; i < param.eval_data_names.size(); ++i) {
     deval.emplace_back(
         std::shared_ptr<DMatrix>(DMatrix::Load(
             param.eval_data_paths[i],
             ConsoleLogger::GlobalVerbosity() > ConsoleLogger::DefaultVerbosity(),
             param.dsplit == 2)));
-    eval_datasets.push_back(deval.back().get());
+    eval_datasets.push_back(deval.back());
     cache_mats.push_back(deval.back());
   }
   std::vector<std::string> eval_data_names = param.eval_data_names;
   if (param.eval_train) {
-    eval_datasets.push_back(dtrain.get());
+    eval_datasets.push_back(dtrain);
     eval_data_names.emplace_back("train");
   }
   // initialize the learner.
@@ -203,7 +203,7 @@ void CLITrain(const CLIParam& param) {
     double elapsed = dmlc::GetTime() - start;
     if (version % 2 == 0) {
       LOG(INFO) << "boosting round " << i << ", " << elapsed << " sec elapsed";
-      learner->UpdateOneIter(i, dtrain.get());
+      learner->UpdateOneIter(i, dtrain);
       if (learner->AllowLazyCheckPoint()) {
         rabit::LazyCheckPoint(learner.get());
       } else {
@@ -305,7 +305,7 @@ void CLIPredict(const CLIParam& param) {
   CHECK_NE(param.test_path, "NULL")
       << "Test dataset parameter test:data must be specified.";
   // load data
-  std::unique_ptr<DMatrix> dtest(
+  std::shared_ptr<DMatrix> dtest(
       DMatrix::Load(
           param.test_path,
           ConsoleLogger::GlobalVerbosity() > ConsoleLogger::DefaultVerbosity(),
@@ -321,7 +321,7 @@ void CLIPredict(const CLIParam& param) {
 
   LOG(INFO) << "start prediction...";
   HostDeviceVector<bst_float> preds;
-  learner->Predict(dtest.get(), param.pred_margin, &preds, param.ntree_limit);
+  learner->Predict(dtest, param.pred_margin, &preds, param.ntree_limit);
   LOG(CONSOLE) << "writing prediction to " << param.name_pred;
 
   std::unique_ptr<dmlc::Stream> fo(

src/common/device_helpers.cuh

@@ -1,5 +1,5 @@
 /*!
- * Copyright 2017-2019 XGBoost contributors
+ * Copyright 2017-2020 XGBoost contributors
  */
 #pragma once
 #include <thrust/device_ptr.h>
@@ -9,7 +9,6 @@
 #include <thrust/system_error.h>
 #include <thrust/logical.h>
 
-#include <omp.h>
 #include <rabit/rabit.h>
 #include <cub/cub.cuh>
 #include <cub/util_allocator.cuh>

src/common/observer.h

@@ -1,11 +1,12 @@
 /*!
- * Copyright 2019 XGBoost contributors
+ * Copyright 2019-2020 XGBoost contributors
  * \file observer.h
  */
 #ifndef XGBOOST_COMMON_OBSERVER_H_
 #define XGBOOST_COMMON_OBSERVER_H_
 
 #include <iostream>
+#include <limits>
 #include <string>
 #include <vector>
 
@@ -63,7 +64,8 @@ class TrainingObserver {
   }
   /*\brief Observe data hosted by `std::vector'. */
   template <typename T>
-  void Observe(std::vector<T> const& h_vec, std::string name) const {
+  void Observe(std::vector<T> const& h_vec, std::string name,
+               size_t n = std::numeric_limits<std::size_t>::max()) const {
     if (XGBOOST_EXPECT(!observe_, true)) { return; }
     OBSERVER_PRINT << "Procedure: " << name << OBSERVER_ENDL;
 
@@ -72,20 +74,25 @@ class TrainingObserver {
       if (i % 8 == 0) {
         OBSERVER_PRINT << OBSERVER_NEWLINE;
       }
+      if ((i + 1) == n) {
+        break;
+      }
     }
     OBSERVER_PRINT << OBSERVER_ENDL;
   }
   /*\brief Observe data hosted by `HostDeviceVector'. */
   template <typename T>
-  void Observe(HostDeviceVector<T> const& vec, std::string name) const {
+  void Observe(HostDeviceVector<T> const& vec, std::string name,
+               size_t n = std::numeric_limits<std::size_t>::max()) const {
     if (XGBOOST_EXPECT(!observe_, true)) { return; }
     auto const& h_vec = vec.HostVector();
-    this->Observe(h_vec, name);
+    this->Observe(h_vec, name, n);
   }
   template <typename T>
-  void Observe(HostDeviceVector<T>* vec, std::string name) const {
+  void Observe(HostDeviceVector<T>* vec, std::string name,
+               size_t n = std::numeric_limits<std::size_t>::max()) const {
     if (XGBOOST_EXPECT(!observe_, true)) { return; }
-    this->Observe(*vec, name);
+    this->Observe(*vec, name, n);
   }
 
   /*\brief Observe objects with `XGBoostParamer' type. */

src/gbm/gblinear.cc

@@ -1,5 +1,5 @@
 /*!
- * Copyright 2014 by Contributors
+ * Copyright 2014-2020 by Contributors
  * \file gblinear.cc
  * \brief Implementation of Linear booster, with L1/L2 regularization: Elastic Net
  *        the update rule is parallel coordinate descent (shotgun)
@@ -239,7 +239,7 @@ class GBLinear : public GradientBooster {
   void PredictBatchInternal(DMatrix *p_fmat,
                             std::vector<bst_float> *out_preds) {
     monitor_.Start("PredictBatchInternal");
-      model_.LazyInitModel();
+    model_.LazyInitModel();
     std::vector<bst_float> &preds = *out_preds;
     const auto& base_margin = p_fmat->Info().base_margin_.ConstHostVector();
     // start collecting the prediction
@@ -250,6 +250,9 @@ class GBLinear : public GradientBooster {
       // k is number of group
       // parallel over local batch
       const auto nsize = static_cast<omp_ulong>(batch.Size());
+      if (base_margin.size() != 0) {
+        CHECK_EQ(base_margin.size(), nsize * ngroup);
+      }
 #pragma omp parallel for schedule(static)
       for (omp_ulong i = 0; i < nsize; ++i) {
         const size_t ridx = batch.base_rowid + i;

src/learner.cc

@@ -1,5 +1,5 @@
 /*!
- * Copyright 2014-2019 by Contributors
+ * Copyright 2014-2020 by Contributors
  * \file learner.cc
  * \brief Implementation of learning algorithm.
  * \author Tianqi Chen
@@ -691,45 +691,45 @@ class LearnerImpl : public Learner {
     return gbm_->DumpModel(fmap, with_stats, format);
   }
 
-  void UpdateOneIter(int iter, DMatrix* train) override {
+  void UpdateOneIter(int iter, std::shared_ptr<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);
+    this->ValidateDMatrix(train.get());
 
     monitor_.Start("PredictRaw");
-    this->PredictRaw(train, &preds_[train], true);
+    this->PredictRaw(train.get(), &preds_[train.get()], true);
     monitor_.Stop("PredictRaw");
-    TrainingObserver::Instance().Observe(preds_[train], "Predictions");
+    TrainingObserver::Instance().Observe(preds_[train.get()], "Predictions");
 
     monitor_.Start("GetGradient");
-    obj_->GetGradient(preds_[train], train->Info(), iter, &gpair_);
+    obj_->GetGradient(preds_[train.get()], train->Info(), iter, &gpair_);
     monitor_.Stop("GetGradient");
     TrainingObserver::Instance().Observe(gpair_, "Gradients");
 
-    gbm_->DoBoost(train, &gpair_, obj_.get());
+    gbm_->DoBoost(train.get(), &gpair_, obj_.get());
     monitor_.Stop("UpdateOneIter");
   }
 
-  void BoostOneIter(int iter, DMatrix* train,
+  void BoostOneIter(int iter, std::shared_ptr<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);
+    this->ValidateDMatrix(train.get());
 
-    gbm_->DoBoost(train, in_gpair);
+    gbm_->DoBoost(train.get(), in_gpair);
     monitor_.Stop("BoostOneIter");
   }
 
-  std::string EvalOneIter(int iter, const std::vector<DMatrix*>& data_sets,
+  std::string EvalOneIter(int iter, const std::vector<std::shared_ptr<DMatrix>>& data_sets,
                           const std::vector<std::string>& data_names) override {
     monitor_.Start("EvalOneIter");
     this->Configure();
@@ -741,9 +741,9 @@ class LearnerImpl : public Learner {
       metrics_.back()->Configure({cfg_.begin(), cfg_.end()});
     }
     for (size_t i = 0; i < data_sets.size(); ++i) {
-      DMatrix * dmat = data_sets[i];
+      DMatrix * dmat = data_sets[i].get();
       this->ValidateDMatrix(dmat);
-      this->PredictRaw(data_sets[i], &preds_[dmat], false);
+      this->PredictRaw(dmat, &preds_[dmat], false);
       obj_->EvalTransform(&preds_[dmat]);
       for (auto& ev : metrics_) {
         os << '\t' << data_names[i] << '-' << ev->Name() << ':'
@@ -805,7 +805,7 @@ class LearnerImpl : public Learner {
     return generic_parameters_;
   }
 
-  void Predict(DMatrix* data, bool output_margin,
+  void Predict(std::shared_ptr<DMatrix> data, bool output_margin,
                HostDeviceVector<bst_float>* out_preds, unsigned ntree_limit,
                bool training,
                bool pred_leaf, bool pred_contribs, bool approx_contribs,
@@ -816,14 +816,14 @@ class LearnerImpl : public Learner {
     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);
+      gbm_->PredictContribution(data.get(), &out_preds->HostVector(), ntree_limit, approx_contribs);
     } else if (pred_interactions) {
-      gbm_->PredictInteractionContributions(data, &out_preds->HostVector(), ntree_limit,
+      gbm_->PredictInteractionContributions(data.get(), &out_preds->HostVector(), ntree_limit,
                                             approx_contribs);
     } else if (pred_leaf) {
-      gbm_->PredictLeaf(data, &out_preds->HostVector(), ntree_limit);
+      gbm_->PredictLeaf(data.get(), &out_preds->HostVector(), ntree_limit);
     } else {
-      this->PredictRaw(data, out_preds, training, ntree_limit);
+      this->PredictRaw(data.get(), out_preds, training, ntree_limit);
       if (!output_margin) {
         obj_->PredTransform(out_preds);
       }

tests/cpp/c_api/test_c_api.cc

@@ -1,4 +1,6 @@
-// Copyright (c) 2019 by Contributors
+/*!
+ * Copyright 2019-2020 XGBoost contributors
+ */
 #include <gtest/gtest.h>
 #include <xgboost/version_config.h>
 #include <xgboost/c_api.h>
@@ -92,7 +94,7 @@ TEST(c_api, ConfigIO) {
   std::shared_ptr<Learner> learner { Learner::Create(mat) };
 
   BoosterHandle handle = learner.get();
-  learner->UpdateOneIter(0, p_dmat.get());
+  learner->UpdateOneIter(0, p_dmat);
 
   char const* out[1];
   bst_ulong len {0};
@@ -127,7 +129,7 @@ TEST(c_api, JsonModelIO) {
 
   std::shared_ptr<Learner> learner { Learner::Create(mat) };
 
-  learner->UpdateOneIter(0, p_dmat.get());
+  learner->UpdateOneIter(0, p_dmat);
   BoosterHandle handle = learner.get();
 
   std::string modelfile_0 = tempdir.path + "/model_0.json";