Move GHistIndex into DMatrix. (#7064)

src/data/gradient_index.h

@@ -0,0 +1,86 @@
+/*!
+ * Copyright 2017-2021 by Contributors
+ * \brief Data type for fast histogram aggregation.
+ */
+#ifndef XGBOOST_DATA_GRADIENT_INDEX_H_
+#define XGBOOST_DATA_GRADIENT_INDEX_H_
+#include <vector>
+#include "xgboost/base.h"
+#include "xgboost/data.h"
+#include "../common/hist_util.h"
+#include "../common/threading_utils.h"
+
+namespace xgboost {
+/*!
+ * \brief preprocessed global index matrix, in CSR format
+ *
+ *  Transform floating values to integer index in histogram This is a global histogram
+ *  index for CPU histogram.  On GPU ellpack page is used.
+ */
+class GHistIndexMatrix {
+ public:
+  /*! \brief row pointer to rows by element position */
+  std::vector<size_t> row_ptr;
+  /*! \brief The index data */
+  common::Index index;
+  /*! \brief hit count of each index */
+  std::vector<size_t> hit_count;
+  /*! \brief The corresponding cuts */
+  common::HistogramCuts cut;
+  DMatrix* p_fmat;
+  size_t max_num_bins;
+
+  GHistIndexMatrix(DMatrix* x, int32_t max_bin) {
+    this->Init(x, max_bin);
+  }
+  // Create a global histogram matrix, given cut
+  void Init(DMatrix* p_fmat, int max_num_bins);
+
+  // specific method for sparse data as no possibility to reduce allocated memory
+  template <typename BinIdxType, typename GetOffset>
+  void SetIndexData(common::Span<BinIdxType> index_data_span,
+                    size_t batch_threads, const SparsePage &batch,
+                    size_t rbegin, size_t nbins, GetOffset get_offset) {
+    const xgboost::Entry *data_ptr = batch.data.HostVector().data();
+    const std::vector<bst_row_t> &offset_vec = batch.offset.HostVector();
+    const size_t batch_size = batch.Size();
+    CHECK_LT(batch_size, offset_vec.size());
+    BinIdxType* index_data = index_data_span.data();
+    common::ParallelFor(omp_ulong(batch_size), batch_threads, [&](omp_ulong i) {
+      const int tid = omp_get_thread_num();
+      size_t ibegin = row_ptr[rbegin + i];
+      size_t iend = row_ptr[rbegin + i + 1];
+      const size_t size = offset_vec[i + 1] - offset_vec[i];
+      SparsePage::Inst inst = {data_ptr + offset_vec[i], size};
+      CHECK_EQ(ibegin + inst.size(), iend);
+      for (bst_uint j = 0; j < inst.size(); ++j) {
+        uint32_t idx = cut.SearchBin(inst[j]);
+        index_data[ibegin + j] = get_offset(idx, j);
+        ++hit_count_tloc_[tid * nbins + idx];
+      }
+    });
+  }
+
+  void ResizeIndex(const size_t n_index,
+                   const bool isDense);
+
+  inline void GetFeatureCounts(size_t* counts) const {
+    auto nfeature = cut.Ptrs().size() - 1;
+    for (unsigned fid = 0; fid < nfeature; ++fid) {
+      auto ibegin = cut.Ptrs()[fid];
+      auto iend = cut.Ptrs()[fid + 1];
+      for (auto i = ibegin; i < iend; ++i) {
+        counts[fid] += hit_count[i];
+      }
+    }
+  }
+  inline bool IsDense() const {
+    return isDense_;
+  }
+
+ private:
+  std::vector<size_t> hit_count_tloc_;
+  bool isDense_;
+};
+}      // namespace xgboost
+#endif  // XGBOOST_DATA_GRADIENT_INDEX_H_