Remove predict from cache. Move prediction functions into CC.

include/xgboost/c_api.h

@@ -531,6 +531,7 @@ XGB_DLL int XGBoosterSaveRabitCheckpoint(BoosterHandle handle);
  *        notice.
  *
  * \param handle handle to Booster object.
+ * \param out_len length of output string
  * \param out_str A valid pointer to array of characters.  The characters array is
  *                allocated and managed by XGBoost, while pointer to that array needs to
  *                be managed by caller.

include/xgboost/gbm.h

@@ -71,7 +71,7 @@ class GradientBooster : public Model, public Configurable {
    * \brief perform update to the model(boosting)
    * \param p_fmat feature matrix that provide access to features
    * \param in_gpair address of the gradient pair statistics of the data
-   * \param obj The objective function, optional, can be nullptr when use customized version
+   * \param prediction The output prediction cache entry that needs to be updated.
    * the booster may change content of gpair
    */
   virtual void DoBoost(DMatrix* p_fmat, HostDeviceVector<GradientPair>* in_gpair,
@@ -158,7 +158,6 @@ class GradientBooster : public Model, public Configurable {
    * \param name name of gradient booster
    * \param generic_param Pointer to runtime parameters
    * \param learner_model_param pointer to global model parameters
-   * \param cache_mats The cache data matrix of the Booster.
    * \return The created booster.
    */
   static GradientBooster* Create(

include/xgboost/generic_parameters.h

@@ -1,6 +1,6 @@
 /*!
  * Copyright 2014-2019 by Contributors
- * \file learner.cc
+ * \file generic_parameters.h
  */
 #ifndef XGBOOST_GENERIC_PARAMETERS_H_
 #define XGBOOST_GENERIC_PARAMETERS_H_

include/xgboost/json.h

@@ -406,7 +406,7 @@ class Json {
   /*! \brief Index Json object with int, used for Json Array. */
   Json& operator[](int ind)                 const { return (*ptr_)[ind]; }
 
-  /*! \Brief Return the reference to stored Json value. */
+  /*! \brief Return the reference to stored Json value. */
   Value const& GetValue() const & { return *ptr_; }
   Value const& GetValue() &&      { return *ptr_; }
   Value&       GetValue() &       { return *ptr_; }

include/xgboost/learner.h

@@ -87,6 +87,7 @@ class Learner : public Model, public Configurable, public rabit::Serializable {
    * \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 Whether the prediction result is used for training
    * \param pred_leaf whether to only predict the leaf index of each tree in a boosted tree predictor
    * \param pred_contribs whether to only predict the feature contributions
    * \param approx_contribs whether to approximate the feature contributions for speed

include/xgboost/metric.h

@@ -52,8 +52,9 @@ class Metric {
   /*!
    * \brief create a metric according to name.
    * \param name name of the metric.
-   *  name can be in form metric[@]param
-   *  and the name will be matched in the registry.
+   *        name can be in form metric[@]param and the name will be matched in the
+   *        registry.
+   * \param tparam A global generic parameter
    * \return the created metric.
    */
   static Metric* Create(const std::string& name, GenericParameter const* tparam);

include/xgboost/parameter.h

@@ -1,6 +1,6 @@
 /*!
  * Copyright 2018 by Contributors
- * \file enum_class_param.h
+ * \file parameter.h
  * \brief macro for using C++11 enum class as DMLC parameter
  * \author Hyunsu Philip Cho
  */

include/xgboost/predictor.h

@@ -134,31 +134,6 @@ class Predictor {
                             uint32_t const ntree_limit = 0) = 0;
 
   /**
-   * \fn  virtual void Predictor::UpdatePredictionCache( const gbm::GBTreeModel
-   * &model, std::vector<std::unique_ptr<TreeUpdater> >* updaters, int
-   * num_new_trees) = 0;
-   *
-   * \brief Update the internal prediction cache using newly added trees. Will
-   * use the tree updater to do this if possible. Should be called as a part of
-   * the tree boosting process to facilitate the look up of predictions
-   * at a later time.
-   *
-   * \param           model         The model.
-   * \param [in,out]  updaters      The updater sequence for gradient boosting.
-   * \param           num_new_trees Number of new trees.
-   */
-
-  virtual void UpdatePredictionCache(
-      const gbm::GBTreeModel& model,
-      std::vector<std::unique_ptr<TreeUpdater>>* updaters,
-      int num_new_trees,
-      DMatrix* m,
-      PredictionCacheEntry* predts) = 0;
-
-  /**
-   * \fn  virtual void Predictor::PredictInstance( const SparsePage::Inst&
-   * inst, std::vector<bst_float>* out_preds, const gbm::GBTreeModel& model,
-   *
    * \brief online prediction function, predict score for one instance at a time
    * NOTE: use the batch prediction interface if possible, batch prediction is
    * usually more efficient than online prediction This function is NOT
@@ -234,7 +209,6 @@ class Predictor {
    *
    * \param name           Name of the predictor.
    * \param generic_param  Pointer to runtime parameters.
-   * \param cache          Pointer to prediction cache.
    */
   static Predictor* Create(
       std::string const& name, GenericParameter const* generic_param);

include/xgboost/tree_updater.h

@@ -73,6 +73,7 @@ class TreeUpdater : public Configurable {
   /*!
    * \brief Create a tree updater given name
    * \param name Name of the tree updater.
+   * \param tparam A global runtime parameter
    */
   static TreeUpdater* Create(const std::string& name, GenericParameter const* tparam);
 };

src/common/hist_util.h

@@ -199,7 +199,7 @@ class CutsBuilder {
   }
 
   void AddCutPoint(WQSketch::SummaryContainer const& summary, int max_bin) {
-    int required_cuts = std::min(static_cast<int>(summary.size), max_bin);
+    size_t required_cuts = std::min(summary.size, static_cast<size_t>(max_bin));
     for (size_t i = 1; i < required_cuts; ++i) {
       bst_float cpt = summary.data[i].value;
       if (i == 1 || cpt > p_cuts_->cut_values_.back()) {

src/common/transform.h

@@ -181,8 +181,7 @@ class Transform {
    * \param func    A callable object, accepting a size_t thread index,
    *                  followed by a set of Span classes.
    * \param range   Range object specifying parallel threads index range.
-   * \param devices GPUSet specifying GPUs to use, when compiling for CPU,
-   *                  this should be GPUSet::Empty().
+   * \param device  Specify GPU to use.
    * \param shard Whether Shard for HostDeviceVector is needed.
    */
   template <typename Functor>

src/gbm/gbtree.cc

@@ -296,8 +296,15 @@ void GBTree::CommitModel(std::vector<std::vector<std::unique_ptr<RegTree>>>&& ne
     num_new_trees += new_trees[gid].size();
     model_.CommitModel(std::move(new_trees[gid]), gid);
   }
-  CHECK(configured_);
-  GetPredictor()->UpdatePredictionCache(model_, &updaters_, num_new_trees, m, predts);
+  auto* out = &predts->predictions;
+  if (model_.learner_model_param_->num_output_group == 1 &&
+      updaters_.size() > 0 &&
+      num_new_trees == 1 &&
+      out->Size() > 0 &&
+      updaters_.back()->UpdatePredictionCache(m, out)) {
+    auto delta = num_new_trees / model_.learner_model_param_->num_output_group;
+    predts->Update(delta);
+  }
   monitor_.Stop("CommitModel");
 }
 
@@ -357,6 +364,76 @@ void GBTree::SaveModel(Json* p_out) const {
   model_.SaveModel(&model);
 }
 
+void GBTree::PredictBatch(DMatrix* p_fmat,
+                          PredictionCacheEntry* out_preds,
+                          bool training,
+                          unsigned ntree_limit) {
+  CHECK(configured_);
+  GetPredictor(&out_preds->predictions, p_fmat)
+      ->PredictBatch(p_fmat, out_preds, model_, 0, ntree_limit);
+}
+
+std::unique_ptr<Predictor> const &
+GBTree::GetPredictor(HostDeviceVector<float> const *out_pred,
+                     DMatrix *f_dmat) const {
+  CHECK(configured_);
+  if (tparam_.predictor != PredictorType::kAuto) {
+    if (tparam_.predictor == PredictorType::kGPUPredictor) {
+#if defined(XGBOOST_USE_CUDA)
+      CHECK(gpu_predictor_);
+      return gpu_predictor_;
+#else
+      this->AssertGPUSupport();
+#endif  // defined(XGBOOST_USE_CUDA)
+    }
+    CHECK(cpu_predictor_);
+    return cpu_predictor_;
+  }
+
+  auto on_device =
+      f_dmat &&
+      (*(f_dmat->GetBatches<SparsePage>().begin())).data.DeviceCanRead();
+
+  // Use GPU Predictor if data is already on device.
+  if (on_device) {
+#if defined(XGBOOST_USE_CUDA)
+    CHECK(gpu_predictor_);
+    return gpu_predictor_;
+#else
+    LOG(FATAL) << "Data is on CUDA device, but XGBoost is not compiled with "
+                  "CUDA support.";
+    return cpu_predictor_;
+#endif  // defined(XGBOOST_USE_CUDA)
+  }
+
+  // GPU_Hist by default has prediction cache calculated from quantile values,
+  // so GPU Predictor is not used for training dataset.  But when XGBoost
+  // performs continue training with an existing model, the prediction cache is
+  // not availbale and number of trees doesn't equal zero, the whole training
+  // dataset got copied into GPU for precise prediction.  This condition tries
+  // to avoid such copy by calling CPU Predictor instead.
+  if ((out_pred && out_pred->Size() == 0) && (model_.param.num_trees != 0) &&
+      // FIXME(trivialfis): Implement a better method for testing whether data
+      // is on device after DMatrix refactoring is done.
+      !on_device) {
+    CHECK(cpu_predictor_);
+    return cpu_predictor_;
+  }
+
+  if (tparam_.tree_method == TreeMethod::kGPUHist) {
+#if defined(XGBOOST_USE_CUDA)
+    CHECK(gpu_predictor_);
+    return gpu_predictor_;
+#else
+    this->AssertGPUSupport();
+    return cpu_predictor_;
+#endif  // defined(XGBOOST_USE_CUDA)
+  }
+
+  CHECK(cpu_predictor_);
+  return cpu_predictor_;
+}
+
 class Dart : public GBTree {
  public:
   explicit Dart(LearnerModelParam const* booster_config) :

src/gbm/gbtree.h

@@ -201,11 +201,7 @@ class GBTree : public GradientBooster {
   void PredictBatch(DMatrix* p_fmat,
                     PredictionCacheEntry* out_preds,
                     bool training,
-                    unsigned ntree_limit) override {
-    CHECK(configured_);
-    GetPredictor(&out_preds->predictions, p_fmat)->PredictBatch(
-        p_fmat, out_preds, model_, 0, ntree_limit);
-  }
+                    unsigned ntree_limit) override;
 
   void PredictInstance(const SparsePage::Inst& inst,
                        std::vector<bst_float>* out_preds,
@@ -256,62 +252,7 @@ class GBTree : public GradientBooster {
                      std::vector<std::unique_ptr<RegTree> >* ret);
 
   std::unique_ptr<Predictor> const& GetPredictor(HostDeviceVector<float> const* out_pred = nullptr,
-                                                 DMatrix* f_dmat = nullptr) const {
-    CHECK(configured_);
-    if (tparam_.predictor != PredictorType::kAuto) {
-      if (tparam_.predictor == PredictorType::kGPUPredictor) {
-#if defined(XGBOOST_USE_CUDA)
-        CHECK(gpu_predictor_);
-        return gpu_predictor_;
-#else
-        this->AssertGPUSupport();
-#endif  // defined(XGBOOST_USE_CUDA)
-      }
-      CHECK(cpu_predictor_);
-      return cpu_predictor_;
-    }
-
-    auto on_device = f_dmat && (*(f_dmat->GetBatches<SparsePage>().begin())).data.DeviceCanRead();
-
-    // Use GPU Predictor if data is already on device.
-    if (on_device) {
-#if defined(XGBOOST_USE_CUDA)
-      CHECK(gpu_predictor_);
-      return gpu_predictor_;
-#else
-      LOG(FATAL) << "Data is on CUDA device, but XGBoost is not compiled with CUDA support.";
-      return cpu_predictor_;
-#endif  // defined(XGBOOST_USE_CUDA)
-    }
-
-    // GPU_Hist by default has prediction cache calculated from quantile values, so GPU
-    // Predictor is not used for training dataset.  But when XGBoost performs continue
-    // training with an existing model, the prediction cache is not availbale and number
-    // of trees doesn't equal zero, the whole training dataset got copied into GPU for
-    // precise prediction.  This condition tries to avoid such copy by calling CPU
-    // Predictor instead.
-    if ((out_pred && out_pred->Size() == 0) &&
-        (model_.param.num_trees != 0) &&
-        // FIXME(trivialfis): Implement a better method for testing whether data is on
-        // device after DMatrix refactoring is done.
-        !on_device) {
-      CHECK(cpu_predictor_);
-      return cpu_predictor_;
-    }
-
-    if (tparam_.tree_method == TreeMethod::kGPUHist) {
-#if defined(XGBOOST_USE_CUDA)
-      CHECK(gpu_predictor_);
-      return gpu_predictor_;
-#else
-      this->AssertGPUSupport();
-      return cpu_predictor_;
-#endif  // defined(XGBOOST_USE_CUDA)
-    }
-
-    CHECK(cpu_predictor_);
-    return cpu_predictor_;
-  }
+                                                 DMatrix* f_dmat = nullptr) const;
 
   // commit new trees all at once
   virtual void CommitModel(std::vector<std::vector<std::unique_ptr<RegTree>>>&& new_trees,

src/predictor/cpu_predictor.cc

@@ -146,8 +146,12 @@ class CPUPredictor : public Predictor {
     CHECK_EQ(tree_begin, 0);
     auto* out_preds = &predts->predictions;
     CHECK_GE(predts->version, tree_begin);
+    if (out_preds->Size() == 0 && dmat->Info().num_row_ != 0) {
+      CHECK_EQ(predts->version, 0);
+    }
     if (predts->version == 0) {
-      CHECK_EQ(out_preds->Size(), 0);
+      // out_preds->Size() can be non-zero as it's initialized here before any tree is
+      // built at the 0^th iterator.
       this->InitOutPredictions(dmat->Info(), out_preds, model);
     }
 
@@ -185,30 +189,6 @@ class CPUPredictor : public Predictor {
           out_preds->Size() == dmat->Info().num_row_);
   }
 
-  void UpdatePredictionCache(
-      const gbm::GBTreeModel& model,
-      std::vector<std::unique_ptr<TreeUpdater>>* updaters,
-      int num_new_trees,
-      DMatrix* m,
-      PredictionCacheEntry* predts) override {
-    int old_ntree = model.trees.size() - num_new_trees;
-    // update cache entry
-    auto* out = &predts->predictions;
-    if (predts->predictions.Size() == 0) {
-      this->InitOutPredictions(m->Info(), out, model);
-      this->PredInternal(m, &out->HostVector(), model, 0, model.trees.size());
-    } else if (model.learner_model_param_->num_output_group == 1 &&
-               updaters->size() > 0 &&
-               num_new_trees == 1 &&
-               updaters->back()->UpdatePredictionCache(m, out)) {
-      {}
-    } else {
-      PredInternal(m, &out->HostVector(), model, old_ntree, model.trees.size());
-    }
-    auto delta = num_new_trees / model.learner_model_param_->num_output_group;
-    predts->Update(delta);
-  }
-
   void PredictInstance(const SparsePage::Inst& inst,
                        std::vector<bst_float>* out_preds,
                        const gbm::GBTreeModel& model, unsigned ntree_limit) override {