Span class. (#3548)

* Add basic Span class based on ISO++20.

* Use Span<Entry const> instead of Inst in SparsePage.

* Add DeviceSpan in HostDeviceVector, use it in regression obj.

include/xgboost/data.h

@@ -15,6 +15,7 @@
 #include <string>
 #include <vector>
 #include "./base.h"
+#include "../../src/common/span.h"
 
 namespace xgboost {
 // forward declare learner.
@@ -133,7 +134,7 @@ struct Entry {
   /*!
    * \brief constructor with index and value
    * \param index The feature or row index.
-   * \param fvalue THe feature value.
+   * \param fvalue The feature value.
    */
   Entry(bst_uint index, bst_float fvalue) : index(index), fvalue(fvalue) {}
   /*! \brief reversely compare feature values */
@@ -155,24 +156,14 @@ class SparsePage {
   std::vector<Entry> data;
 
   size_t base_rowid;
+
   /*! \brief an instance of sparse vector in the batch */
-  struct Inst {
-    /*! \brief pointer to the elements*/
-    const Entry *data{nullptr};
-    /*! \brief length of the instance */
-    bst_uint length{0};
-    /*! \brief constructor */
-    Inst()  = default;
-    Inst(const Entry *data, bst_uint length) : data(data), length(length) {}
-    /*! \brief get i-th pair in the sparse vector*/
-    inline const Entry& operator[](size_t i) const {
-      return data[i];
-    }
-  };
+  using Inst = common::Span<Entry const>;
 
   /*! \brief get i-th row from the batch */
   inline Inst operator[](size_t i) const {
-    return {data.data() + offset[i], static_cast<bst_uint>(offset[i + 1] - offset[i])};
+    return {data.data() + offset[i],
+            static_cast<Inst::index_type>(offset[i + 1] - offset[i])};
   }
 
   /*! \brief constructor */
@@ -234,12 +225,12 @@ class SparsePage {
    *  \param inst an instance row
    */
   inline void Push(const Inst &inst) {
-    offset.push_back(offset.back() + inst.length);
+    offset.push_back(offset.back() + inst.size());
     size_t begin = data.size();
-    data.resize(begin + inst.length);
-    if (inst.length != 0) {
-      std::memcpy(dmlc::BeginPtr(data) + begin, inst.data,
-                  sizeof(Entry) * inst.length);
+    data.resize(begin + inst.size());
+    if (inst.size() != 0) {
+      std::memcpy(dmlc::BeginPtr(data) + begin, inst.data(),
+                  sizeof(Entry) * inst.size());
     }
   }
 
@@ -328,7 +319,7 @@ class DMatrix {
    * \brief check if column access is supported, if not, initialize column access.
    * \param max_row_perbatch auxiliary information, maximum row used in each column batch.
    *         this is a hint information that can be ignored by the implementation.
-   * \param sorted If column features should be in sorted order           
+   * \param sorted If column features should be in sorted order
    * \return Number of column blocks in the column access.
    */
   virtual void InitColAccess(size_t max_row_perbatch, bool sorted) = 0;

include/xgboost/tree_model.h

@@ -574,14 +574,14 @@ inline void RegTree::FVec::Init(size_t size) {
 }
 
 inline void RegTree::FVec::Fill(const SparsePage::Inst& inst) {
-  for (bst_uint i = 0; i < inst.length; ++i) {
+  for (bst_uint i = 0; i < inst.size(); ++i) {
     if (inst[i].index >= data_.size()) continue;
     data_[inst[i].index].fvalue = inst[i].fvalue;
   }
 }
 
 inline void RegTree::FVec::Drop(const SparsePage::Inst& inst) {
-  for (bst_uint i = 0; i < inst.length; ++i) {
+  for (bst_uint i = 0; i < inst.size(); ++i) {
     if (inst[i].index >= data_.size()) continue;
     data_[inst[i].index].flag = -1;
   }