[CORE] Refactor cache mechanism (#1540)

include/xgboost/gbm.h

@@ -13,8 +13,10 @@
 #include <utility>
 #include <string>
 #include <functional>
+#include <memory>
 #include "./base.h"
 #include "./data.h"
+#include "./objective.h"
 #include "./feature_map.h"
 
 namespace xgboost {
@@ -50,13 +52,6 @@ class GradientBooster {
    * \param fo output stream
    */
   virtual void Save(dmlc::Stream* fo) const = 0;
-  /*!
-   * \brief reset the predict buffer size.
-   *  This will invalidate all the previous cached results
-   *  and recalculate from scratch
-   * \param num_pbuffer The size of predict buffer.
-   */
-  virtual void ResetPredBuffer(size_t num_pbuffer) {}
   /*!
    * \brief whether the model allow lazy checkpoint
    * return true if model is only updated in DoBoost
@@ -68,27 +63,21 @@ class GradientBooster {
   /*!
    * \brief perform update to the model(boosting)
    * \param p_fmat feature matrix that provide access to features
-   * \param buffer_offset buffer index offset of these instances, if equals -1
-   *        this means we do not have buffer index allocated to the gbm
    * \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
    * the booster may change content of gpair
    */
   virtual void DoBoost(DMatrix* p_fmat,
-                       int64_t buffer_offset,
-                       std::vector<bst_gpair>* in_gpair) = 0;
+                       std::vector<bst_gpair>* in_gpair,
+                       ObjFunction* obj = nullptr) = 0;
   /*!
    * \brief generate predictions for given feature matrix
    * \param dmat feature matrix
-   * \param buffer_offset buffer index offset of these instances, if equals -1
-   *        this means we do not have buffer index allocated to the gbm
-   *  a buffer index is assigned to each instance that requires repeative prediction
-   *  the size of buffer is set by convention using GradientBooster.ResetPredBuffer(size);
    * \param out_preds output vector to hold the predictions
    * \param ntree_limit limit the number of trees used in prediction, when it equals 0, this means
    *    we do not limit number of trees, this parameter is only valid for gbtree, but not for gblinear
    */
   virtual void Predict(DMatrix* dmat,
-                       int64_t buffer_offset,
                        std::vector<float>* out_preds,
                        unsigned ntree_limit = 0) = 0;
   /*!
@@ -128,9 +117,14 @@ class GradientBooster {
   /*!
    * \brief create a gradient booster from given name
    * \param name name of gradient booster
+   * \param cache_mats The cache data matrix of the Booster.
+   * \param base_margin The base margin of prediction.
    * \return The created booster.
    */
-  static GradientBooster* Create(const std::string& name);
+  static GradientBooster* Create(
+      const std::string& name,
+      const std::vector<std::shared_ptr<DMatrix> >& cache_mats,
+      float base_margin);
 };
 
 // implementing configure.
@@ -144,8 +138,10 @@ inline void GradientBooster::Configure(PairIter begin, PairIter end) {
  * \brief Registry entry for tree updater.
  */
 struct GradientBoosterReg
-    : public dmlc::FunctionRegEntryBase<GradientBoosterReg,
-                                        std::function<GradientBooster* ()> > {
+    : public dmlc::FunctionRegEntryBase<
+  GradientBoosterReg,
+  std::function<GradientBooster* (const std::vector<std::shared_ptr<DMatrix> > &cached_mats,
+                                  float base_margin)> > {
 };
 
 /*!

include/xgboost/learner.h

@@ -166,7 +166,7 @@ class Learner : public rabit::Serializable {
    * \param cache_data The matrix to cache the prediction.
    * \return Created learner.
    */
-  static Learner* Create(const std::vector<DMatrix*>& cache_data);
+  static Learner* Create(const std::vector<std::shared_ptr<DMatrix> >& cache_data);
 
  protected:
   /*! \brief internal base score of the model */