Small cleanup to gradient index and hist. (#7668)

* Code comments.
* Const accessor to index.
* Remove some weird variables in the `Index` class.
* Simplify the `MemStackAllocator`.

src/common/column_matrix.h

@@ -266,9 +266,9 @@ class ColumnMatrix {
   }
 
   template <typename T>
-  inline void SetIndexAllDense(T *index, const GHistIndexMatrix &gmat,
+  inline void SetIndexAllDense(T const* index, const GHistIndexMatrix& gmat, const size_t nrow,
-                               const size_t nrow, const size_t nfeature,
+                               const size_t nfeature, const bool noMissingValues,
-                               const bool noMissingValues, int32_t n_threads) {
+                               int32_t n_threads) {
     T* local_index = reinterpret_cast<T*>(&index_[0]);
 
     /* missing values make sense only for column with type kDenseColumn,
@@ -313,7 +313,7 @@ class ColumnMatrix {
   }
 
   template<typename T>
-  inline void SetIndex(uint32_t* index, const GHistIndexMatrix& gmat,
+  inline void SetIndex(uint32_t const* index, const GHistIndexMatrix& gmat,
                        const size_t nfeature) {
     std::vector<size_t> num_nonzeros;
     num_nonzeros.resize(nfeature);

src/common/hist_util.h

@@ -197,19 +197,27 @@ enum BinTypeSize : uint32_t {
   kUint32BinsTypeSize = 4
 };
 
+/**
+ * \brief Optionally compressed gradient index. The compression works only with dense
+ *        data.
+ *
+ *   The main body of construction code is in gradient_index.cc, this struct is only a
+ *   storage class.
+ */
 struct Index {
-  Index() {
+  Index() { SetBinTypeSize(binTypeSize_); }
-    SetBinTypeSize(binTypeSize_);
-  }
   Index(const Index& i) = delete;
   Index& operator=(Index i) = delete;
   Index(Index&& i) = delete;
   Index& operator=(Index&& i) = delete;
   uint32_t operator[](size_t i) const {
-    if (offset_ptr_ != nullptr) {
+    if (!bin_offset_.empty()) {
-      return func_(data_ptr_, i) + offset_ptr_[i%p_];
+      // dense, compressed
+      auto fidx = i % bin_offset_.size();
+      // restore the index by adding back its feature offset.
+      return func_(data_.data(), i) + bin_offset_[fidx];
     } else {
-      return func_(data_ptr_, i);
+      return func_(data_.data(), i);
     }
   }
   void SetBinTypeSize(BinTypeSize binTypeSize) {
@@ -225,35 +233,32 @@ struct Index {
         func_ = &GetValueFromUint32;
         break;
       default:
-        CHECK(binTypeSize == kUint8BinsTypeSize  ||
+        CHECK(binTypeSize == kUint8BinsTypeSize || binTypeSize == kUint16BinsTypeSize ||
-              binTypeSize == kUint16BinsTypeSize ||
               binTypeSize == kUint32BinsTypeSize);
     }
   }
   BinTypeSize GetBinTypeSize() const {
     return binTypeSize_;
   }
-  template<typename T>
+  template <typename T>
-  T* data() const {  // NOLINT
+  T const* data() const {  // NOLINT
-    return static_cast<T*>(data_ptr_);
+    return reinterpret_cast<T const*>(data_.data());
   }
-  uint32_t* Offset() const {
+  template <typename T>
-    return offset_ptr_;
+  T* data() {  // NOLINT
+    return reinterpret_cast<T*>(data_.data());
   }
-  size_t OffsetSize() const {
+  uint32_t const* Offset() const { return bin_offset_.data(); }
-    return offset_.size();
+  size_t OffsetSize() const { return bin_offset_.size(); }
+  size_t Size() const { return data_.size() / (binTypeSize_); }
+
+  void Resize(const size_t n_bytes) {
+    data_.resize(n_bytes);
   }
-  size_t Size() const {
+  // set the offset used in compression, cut_ptrs is the CSC indptr in HistogramCuts
-    return data_.size() / (binTypeSize_);
+  void SetBinOffset(std::vector<uint32_t> const& cut_ptrs) {
-  }
+    bin_offset_.resize(cut_ptrs.size() - 1);  // resize to number of features.
-  void Resize(const size_t nBytesData) {
+    std::copy_n(cut_ptrs.begin(), bin_offset_.size(), bin_offset_.begin());
-    data_.resize(nBytesData);
-    data_ptr_ = reinterpret_cast<void*>(data_.data());
-  }
-  void ResizeOffset(const size_t nDisps) {
-    offset_.resize(nDisps);
-    offset_ptr_ = offset_.data();
-    p_ = nDisps;
   }
   std::vector<uint8_t>::const_iterator begin() const {  // NOLINT
     return data_.begin();
@@ -270,24 +275,23 @@ struct Index {
   }
 
  private:
-  static uint32_t GetValueFromUint8(void *t, size_t i) {
+  // Functions to decompress the index.
-    return reinterpret_cast<uint8_t*>(t)[i];
+  static uint32_t GetValueFromUint8(uint8_t const* t, size_t i) { return t[i]; }
+  static uint32_t GetValueFromUint16(uint8_t const* t, size_t i) {
+    return reinterpret_cast<uint16_t const*>(t)[i];
   }
-  static uint32_t GetValueFromUint16(void* t, size_t i) {
+  static uint32_t GetValueFromUint32(uint8_t const* t, size_t i) {
-    return reinterpret_cast<uint16_t*>(t)[i];
+    return reinterpret_cast<uint32_t const*>(t)[i];
-  }
-  static uint32_t GetValueFromUint32(void* t, size_t i) {
-    return reinterpret_cast<uint32_t*>(t)[i];
   }
 
-  using Func = uint32_t (*)(void*, size_t);
+  using Func = uint32_t (*)(uint8_t const*, size_t);
 
   std::vector<uint8_t> data_;
-  std::vector<uint32_t> offset_;  // size of this field is equal to number of features
+  // starting position of each feature inside the cut values (the indptr of the CSC cut matrix
-  void* data_ptr_;
+  // HistogramCuts without the last entry.) Used for bin compression.
+  std::vector<uint32_t> bin_offset_;
+
   BinTypeSize binTypeSize_ {kUint8BinsTypeSize};
-  size_t p_ {1};
-  uint32_t* offset_ptr_ {nullptr};
   Func func_;
 };
 
@@ -304,9 +308,11 @@ int32_t XGBOOST_HOST_DEV_INLINE BinarySearchBin(size_t begin, size_t end,
     }
     previous_middle = middle;
 
+    // index into all the bins
     auto gidx = data[middle];
 
     if (gidx >= fidx_begin && gidx < fidx_end) {
+      // Found the intersection.
       return static_cast<int32_t>(gidx);
     } else if (gidx < fidx_begin) {
       begin = middle;
@@ -636,42 +642,6 @@ class GHistBuilder {
   /*! \brief number of all bins over all features */
   uint32_t nbins_ { 0 };
 };
-
-/*!
- * \brief A C-style array with in-stack allocation. As long as the array is smaller than
- * MaxStackSize, it will be allocated inside the stack. Otherwise, it will be
- * heap-allocated.
- */
-template<typename T, size_t MaxStackSize>
-class MemStackAllocator {
- public:
-  explicit MemStackAllocator(size_t required_size): required_size_(required_size) {
-  }
-
-  T* Get() {
-    if (!ptr_) {
-      if (MaxStackSize >= required_size_) {
-        ptr_ = stack_mem_;
-      } else {
-        ptr_ =  reinterpret_cast<T*>(malloc(required_size_ * sizeof(T)));
-        do_free_ = true;
-      }
-    }
-
-    return ptr_;
-  }
-
-  ~MemStackAllocator() {
-    if (do_free_) free(ptr_);
-  }
-
-
- private:
-  T* ptr_ = nullptr;
-  bool do_free_ = false;
-  size_t required_size_;
-  T stack_mem_[MaxStackSize];
-};
 }  // namespace common
 }  // namespace xgboost
 #endif  // XGBOOST_COMMON_HIST_UTIL_H_

src/common/threading_utils.h

@@ -246,6 +246,43 @@ inline int32_t OmpGetNumThreads(int32_t n_threads) {
   n_threads = std::max(n_threads, 1);
   return n_threads;
 }
+
+
+/*!
+ * \brief A C-style array with in-stack allocation. As long as the array is smaller than
+ * MaxStackSize, it will be allocated inside the stack. Otherwise, it will be
+ * heap-allocated.
+ */
+template <typename T, size_t MaxStackSize>
+class MemStackAllocator {
+ public:
+  explicit MemStackAllocator(size_t required_size) : required_size_(required_size) {
+    if (MaxStackSize >= required_size_) {
+      ptr_ = stack_mem_;
+    } else {
+      ptr_ = reinterpret_cast<T*>(malloc(required_size_ * sizeof(T)));
+    }
+    if (!ptr_) {
+      throw std::bad_alloc{};
+    }
+  }
+
+  ~MemStackAllocator() {
+    if (required_size_ > MaxStackSize) {
+      free(ptr_);
+    }
+  }
+  T& operator[](size_t i) { return ptr_[i]; }
+  T const& operator[](size_t i) const { return ptr_[i]; }
+
+  // FIXME(jiamingy): Remove this once we merge partitioner cleanup for hist.
+  auto Get() { return ptr_; }
+
+ private:
+  T* ptr_ = nullptr;
+  size_t required_size_;
+  T stack_mem_[MaxStackSize];
+};
 }  // namespace common
 }  // namespace xgboost
 

src/data/gradient_index.cc

@@ -10,6 +10,7 @@
 
 #include "../common/column_matrix.h"
 #include "../common/hist_util.h"
+#include "../common/threading_utils.h"
 
 namespace xgboost {
 
@@ -34,7 +35,6 @@ void GHistIndexMatrix::PushBatch(SparsePage const &batch,
       std::max(static_cast<size_t>(1), std::min(batch.Size(), static_cast<size_t>(n_threads)));
   auto page = batch.GetView();
   common::MemStackAllocator<size_t, 128> partial_sums(batch_threads);
-  size_t *p_part = partial_sums.Get();
 
   size_t block_size = batch.Size() / batch_threads;
 
@@ -48,10 +48,10 @@ void GHistIndexMatrix::PushBatch(SparsePage const &batch,
         size_t iend = (tid == (batch_threads - 1) ? batch.Size()
                                                   : (block_size * (tid + 1)));
 
-        size_t sum = 0;
+        size_t running_sum = 0;
-        for (size_t i = ibegin; i < iend; ++i) {
+        for (size_t ridx = ibegin; ridx < iend; ++ridx) {
-          sum += page[i].size();
+          running_sum += page[ridx].size();
-          row_ptr[rbegin + 1 + i] = sum;
+          row_ptr[rbegin + 1 + ridx] = running_sum;
         }
       });
     }
@@ -59,9 +59,9 @@ void GHistIndexMatrix::PushBatch(SparsePage const &batch,
 #pragma omp single
     {
       exc.Run([&]() {
-        p_part[0] = prev_sum;
+        partial_sums[0] = prev_sum;
         for (size_t i = 1; i < batch_threads; ++i) {
-          p_part[i] = p_part[i - 1] + row_ptr[rbegin + i * block_size];
+          partial_sums[i] = partial_sums[i - 1] + row_ptr[rbegin + i * block_size];
         }
       });
     }
@@ -74,55 +74,52 @@ void GHistIndexMatrix::PushBatch(SparsePage const &batch,
                                                   : (block_size * (tid + 1)));
 
         for (size_t i = ibegin; i < iend; ++i) {
-          row_ptr[rbegin + 1 + i] += p_part[tid];
+          row_ptr[rbegin + 1 + i] += partial_sums[tid];
         }
       });
     }
   }
   exc.Rethrow();
 
-  const size_t n_offsets = cut.Ptrs().size() - 1;
+  const size_t n_index = row_ptr[rbegin + batch.Size()];  // number of entries in this page
-  const size_t n_index = row_ptr[rbegin + batch.Size()];
   ResizeIndex(n_index, isDense_);
 
   CHECK_GT(cut.Values().size(), 0U);
 
-  uint32_t *offsets = nullptr;
   if (isDense_) {
-    index.ResizeOffset(n_offsets);
+    index.SetBinOffset(cut.Ptrs());
-    offsets = index.Offset();
-    for (size_t i = 0; i < n_offsets; ++i) {
-      offsets[i] = cut.Ptrs()[i];
-    }
   }
+  uint32_t const *offsets = index.Offset();
 
   if (isDense_) {
+    // Inside the lambda functions, bin_idx is the index for cut value across all
+    // features. By subtracting it with starting pointer of each feature, we can reduce
+    // it to smaller value and compress it to smaller types.
     common::BinTypeSize curent_bin_size = index.GetBinTypeSize();
     if (curent_bin_size == common::kUint8BinsTypeSize) {
       common::Span<uint8_t> index_data_span = {index.data<uint8_t>(), n_index};
       SetIndexData(index_data_span, ft, batch_threads, batch, rbegin, nbins,
-                   [offsets](auto idx, auto j) {
+                   [offsets](auto bin_idx, auto fidx) {
-                     return static_cast<uint8_t>(idx - offsets[j]);
+                     return static_cast<uint8_t>(bin_idx - offsets[fidx]);
                    });
     } else if (curent_bin_size == common::kUint16BinsTypeSize) {
       common::Span<uint16_t> index_data_span = {index.data<uint16_t>(), n_index};
       SetIndexData(index_data_span, ft, batch_threads, batch, rbegin, nbins,
-                   [offsets](auto idx, auto j) {
+                   [offsets](auto bin_idx, auto fidx) {
-                     return static_cast<uint16_t>(idx - offsets[j]);
+                     return static_cast<uint16_t>(bin_idx - offsets[fidx]);
                    });
     } else {
       CHECK_EQ(curent_bin_size, common::kUint32BinsTypeSize);
       common::Span<uint32_t> index_data_span = {index.data<uint32_t>(), n_index};
       SetIndexData(index_data_span, ft, batch_threads, batch, rbegin, nbins,
-                   [offsets](auto idx, auto j) {
+                   [offsets](auto bin_idx, auto fidx) {
-                     return static_cast<uint32_t>(idx - offsets[j]);
+                     return static_cast<uint32_t>(bin_idx - offsets[fidx]);
                    });
     }
-
+  } else {
     /* For sparse DMatrix we have to store index of feature for each bin
        in index field to chose right offset. So offset is nullptr and index is
        not reduced */
-  } else {
     common::Span<uint32_t> index_data_span = {index.data<uint32_t>(), n_index};
     SetIndexData(index_data_span, ft, batch_threads, batch, rbegin, nbins,
                  [](auto idx, auto) { return idx; });
@@ -194,11 +191,13 @@ void GHistIndexMatrix::Init(SparsePage const &batch, common::Span<FeatureType co
 
 void GHistIndexMatrix::ResizeIndex(const size_t n_index, const bool isDense) {
   if ((max_num_bins - 1 <= static_cast<int>(std::numeric_limits<uint8_t>::max())) && isDense) {
+    // compress dense index to uint8
     index.SetBinTypeSize(common::kUint8BinsTypeSize);
     index.Resize((sizeof(uint8_t)) * n_index);
   } else if ((max_num_bins - 1 > static_cast<int>(std::numeric_limits<uint8_t>::max()) &&
               max_num_bins - 1 <= static_cast<int>(std::numeric_limits<uint16_t>::max())) &&
              isDense) {
+    // compress dense index to uint16
     index.SetBinTypeSize(common::kUint16BinsTypeSize);
     index.Resize((sizeof(uint16_t)) * n_index);
   } else {

src/data/gradient_index.h

@@ -21,6 +21,13 @@ namespace xgboost {
  *  index for CPU histogram.  On GPU ellpack page is used.
  */
 class GHistIndexMatrix {
+  /**
+   * \brief Push a page into index matrix, the function is only necessary because hist has
+   *        partial support for external memory.
+   *
+   * \param rbegin The beginning row index of current page. (total rows in previous pages)
+   * \param prev_sum Total number of entries in previous pages.
+   */
   void PushBatch(SparsePage const& batch, common::Span<FeatureType const> ft, size_t rbegin,
                  size_t prev_sum, uint32_t nbins, int32_t n_threads);
 
@@ -64,12 +71,12 @@ class GHistIndexMatrix {
     BinIdxType* index_data = index_data_span.data();
     auto const& ptrs = cut.Ptrs();
     auto const& values = cut.Values();
-    common::ParallelFor(batch_size, batch_threads, [&](omp_ulong i) {
+    common::ParallelFor(batch_size, batch_threads, [&](omp_ulong ridx) {
       const int tid = omp_get_thread_num();
-      size_t ibegin = row_ptr[rbegin + i];
+      size_t ibegin = row_ptr[rbegin + ridx];    // index of first entry for current block
-      size_t iend = row_ptr[rbegin + i + 1];
+      size_t iend = row_ptr[rbegin + ridx + 1];  // first entry for next block
-      const size_t size = offset_vec[i + 1] - offset_vec[i];
+      const size_t size = offset_vec[ridx + 1] - offset_vec[ridx];
-      SparsePage::Inst inst = {data_ptr + offset_vec[i], size};
+      SparsePage::Inst inst = {data_ptr + offset_vec[ridx], size};
       CHECK_EQ(ibegin + inst.size(), iend);
       for (bst_uint j = 0; j < inst.size(); ++j) {
         auto e = inst[j];
@@ -103,6 +110,10 @@ class GHistIndexMatrix {
     return isDense_;
   }
   void SetDense(bool is_dense) { isDense_ = is_dense; }
+  /**
+   * \brief Get the local row index.
+   */
+  size_t RowIdx(size_t ridx) const { return row_ptr[ridx - base_rowid]; }
 
   bst_row_t Size() const {
     return row_ptr.empty() ? 0 : row_ptr.size() - 1;

src/data/gradient_index_format.cc

@@ -16,14 +16,6 @@ class GHistIndexRawFormat : public SparsePageFormat<GHistIndexMatrix> {
     }
     // indptr
     fi->Read(&page->row_ptr);
-    // offset
-    using OffsetT = std::iterator_traits<decltype(page->index.Offset())>::value_type;
-    std::vector<OffsetT> offset;
-    if (!fi->Read(&offset)) {
-      return false;
-    }
-    page->index.ResizeOffset(offset.size());
-    std::copy(offset.begin(), offset.end(), page->index.Offset());
     // data
     std::vector<uint8_t> data;
     if (!fi->Read(&data)) {
@@ -55,6 +47,9 @@ class GHistIndexRawFormat : public SparsePageFormat<GHistIndexMatrix> {
       return false;
     }
     page->SetDense(is_dense);
+    if (is_dense) {
+      page->index.SetBinOffset(page->cut.Ptrs());
+    }
     return true;
   }
 
@@ -65,13 +60,6 @@ class GHistIndexRawFormat : public SparsePageFormat<GHistIndexMatrix> {
     fo->Write(page.row_ptr);
     bytes += page.row_ptr.size() * sizeof(decltype(page.row_ptr)::value_type) +
              sizeof(uint64_t);
-    // offset
-    using OffsetT = std::iterator_traits<decltype(page.index.Offset())>::value_type;
-    std::vector<OffsetT> offset(page.index.OffsetSize());
-    std::copy(page.index.Offset(),
-              page.index.Offset() + page.index.OffsetSize(), offset.begin());
-    fo->Write(offset);
-    bytes += page.index.OffsetSize() * sizeof(OffsetT) + sizeof(uint64_t);
     // data
     std::vector<uint8_t> data(page.index.begin(), page.index.end());
     fo->Write(data);

src/tree/updater_approx.h

@@ -35,14 +35,12 @@ class ApproxRowPartitioner {
                              std::vector<uint32_t> const &cut_ptrs,
                              std::vector<float> const &cut_values) {
     int32_t gidx = -1;
-    auto const &row_ptr = index.row_ptr;
-    auto get_rid = [&](size_t ridx) { return row_ptr[ridx - index.base_rowid]; };
-
     if (index.IsDense()) {
-      gidx = index.index[get_rid(ridx) + fidx];
+      // RowIdx returns the starting pos of this row
+      gidx = index.index[index.RowIdx(ridx) + fidx];
     } else {
-      auto begin = get_rid(ridx);
+      auto begin = index.RowIdx(ridx);
-      auto end = get_rid(ridx + 1);
+      auto end = index.RowIdx(ridx + 1);
       auto f_begin = cut_ptrs[fidx];
       auto f_end = cut_ptrs[fidx + 1];
       gidx = common::BinarySearchBin(begin, end, index.index, f_begin, f_end);

src/tree/updater_quantile_hist.cc

@@ -135,7 +135,7 @@ void QuantileHistMaker::Builder<GradientSumT>::InitRoot(
 
   {
     auto nid = RegTree::kRoot;
-    GHistRowT hist = this->histogram_builder_->Histogram()[nid];
+    auto hist = this->histogram_builder_->Histogram()[nid];
     GradientPairT grad_stat;
     if (data_layout_ == DataLayout::kDenseDataZeroBased ||
         data_layout_ == DataLayout::kDenseDataOneBased) {
@@ -149,7 +149,7 @@ void QuantileHistMaker::Builder<GradientSumT>::InitRoot(
         grad_stat.Add(et.GetGrad(), et.GetHess());
       }
     } else {
-      const RowSetCollection::Elem e = row_set_collection_[nid];
+      const common::RowSetCollection::Elem e = row_set_collection_[nid];
       for (const size_t *it = e.begin; it < e.end; ++it) {
         grad_stat.Add(gpair_h[*it].GetGrad(), gpair_h[*it].GetHess());
       }
@@ -229,7 +229,7 @@ template<typename GradientSumT>
 template <bool any_missing>
 void QuantileHistMaker::Builder<GradientSumT>::ExpandTree(
     const GHistIndexMatrix& gmat,
-    const ColumnMatrix& column_matrix,
+    const common::ColumnMatrix& column_matrix,
     DMatrix* p_fmat,
     RegTree* p_tree,
     const std::vector<GradientPair>& gpair_h) {

src/tree/updater_quantile_hist.h

@@ -147,7 +147,7 @@ class QuantileHistMaker: public TreeUpdater {
   // training parameter
   TrainParam param_;
   // column accessor
-  ColumnMatrix column_matrix_;
+  common::ColumnMatrix column_matrix_;
   DMatrix const* p_last_dmat_ {nullptr};
   bool is_gmat_initialized_ {false};
 
@@ -155,7 +155,6 @@ class QuantileHistMaker: public TreeUpdater {
   template<typename GradientSumT>
   struct Builder {
    public:
-    using GHistRowT = GHistRow<GradientSumT>;
     using GradientPairT = xgboost::detail::GradientPairInternal<GradientSumT>;
     // constructor
     explicit Builder(const size_t n_trees, const TrainParam& param,
@@ -164,7 +163,6 @@ class QuantileHistMaker: public TreeUpdater {
         : n_trees_(n_trees),
           param_(param),
           pruner_(std::move(pruner)),
-          p_last_tree_(nullptr),
           p_last_fmat_(fmat),
           histogram_builder_{new HistogramBuilder<GradientSumT, CPUExpandEntry>},
           task_{task},
@@ -172,7 +170,7 @@ class QuantileHistMaker: public TreeUpdater {
       builder_monitor_.Init("Quantile::Builder");
     }
     // update one tree, growing
-    void Update(const GHistIndexMatrix& gmat, const ColumnMatrix& column_matrix,
+    void Update(const GHistIndexMatrix& gmat, const common::ColumnMatrix& column_matrix,
                 HostDeviceVector<GradientPair>* gpair, DMatrix* p_fmat, RegTree* p_tree);
 
     bool UpdatePredictionCache(const DMatrix* data,

tests/cpp/common/test_hist_util.cc

@@ -306,8 +306,8 @@ TEST(HistUtil, IndexBinBound) {
 }
 
 template <typename T>
-void CheckIndexData(T* data_ptr, uint32_t* offsets,
+void CheckIndexData(T const* data_ptr, uint32_t const* offsets, const GHistIndexMatrix& hmat,
-                    const GHistIndexMatrix& hmat, size_t n_cols) {
+                    size_t n_cols) {
   for (size_t i = 0; i < hmat.index.Size(); ++i) {
     EXPECT_EQ(data_ptr[i] + offsets[i % n_cols], hmat.index[i]);
   }
@@ -323,7 +323,7 @@ TEST(HistUtil, IndexBinData) {
   for (auto max_bin : kBinSizes) {
     auto p_fmat = RandomDataGenerator(kRows, kCols, 0).GenerateDMatrix();
     GHistIndexMatrix hmat(p_fmat.get(), max_bin, 0.5, false, common::OmpGetNumThreads(0));
-    uint32_t* offsets = hmat.index.Offset();
+    uint32_t const* offsets = hmat.index.Offset();
     EXPECT_EQ(hmat.index.Size(), kRows*kCols);
     switch (max_bin) {
       case kBinSizes[0]:

tests/cpp/tree/hist/test_histogram.cc

@@ -6,15 +6,16 @@
 #include <limits>
 
 #include "../../../../src/common/categorical.h"
+#include "../../../../src/common/row_set.h"
+#include "../../../../src/tree/hist/expand_entry.h"
 #include "../../../../src/tree/hist/histogram.h"
-#include "../../../../src/tree/updater_quantile_hist.h"
 #include "../../categorical_helpers.h"
 #include "../../helpers.h"
 
 namespace xgboost {
 namespace tree {
 namespace {
-void InitRowPartitionForTest(RowSetCollection *row_set, size_t n_samples, size_t base_rowid = 0) {
+void InitRowPartitionForTest(common::RowSetCollection *row_set, size_t n_samples, size_t base_rowid = 0) {
   auto &row_indices = *row_set->Data();
   row_indices.resize(n_samples);
   std::iota(row_indices.begin(), row_indices.end(), base_rowid);
@@ -91,7 +92,7 @@ void TestSyncHist(bool is_distributed) {
   uint32_t total_bins = gmat.cut.Ptrs().back();
   histogram.Reset(total_bins, {kMaxBins, 0.5}, omp_get_max_threads(), 1, is_distributed);
 
-  RowSetCollection row_set_collection_;
+  common::RowSetCollection row_set_collection_;
   {
     row_set_collection_.Clear();
     std::vector<size_t> &row_indices = *row_set_collection_.Data();
@@ -256,7 +257,7 @@ void TestBuildHistogram(bool is_distributed) {
 
   RegTree tree;
 
-  RowSetCollection row_set_collection;
+  common::RowSetCollection row_set_collection;
   row_set_collection.Clear();
   std::vector<size_t> &row_indices = *row_set_collection.Data();
   row_indices.resize(kNRows);
@@ -318,7 +319,7 @@ void TestHistogramCategorical(size_t n_categories) {
 
   auto gpair = GenerateRandomGradients(kRows, 0, 2);
 
-  RowSetCollection row_set_collection;
+  common::RowSetCollection row_set_collection;
   row_set_collection.Clear();
   std::vector<size_t> &row_indices = *row_set_collection.Data();
   row_indices.resize(kRows);
@@ -381,13 +382,13 @@ void TestHistogramExternalMemory(BatchParam batch_param, bool is_approx) {
   std::vector<CPUExpandEntry> nodes;
   nodes.emplace_back(0, tree.GetDepth(0), 0.0f);
 
-  GHistRow<double> multi_page;
+  common::GHistRow<double> multi_page;
   HistogramBuilder<double, CPUExpandEntry> multi_build;
   {
     /**
      * Multi page
      */
-    std::vector<RowSetCollection> rows_set;
+    std::vector<common::RowSetCollection> rows_set;
     for (auto const &page : m->GetBatches<GHistIndexMatrix>(batch_param)) {
       CHECK_LT(page.base_rowid, m->Info().num_row_);
       auto n_rows_in_node = page.Size();
@@ -417,12 +418,12 @@ void TestHistogramExternalMemory(BatchParam batch_param, bool is_approx) {
   }
 
   HistogramBuilder<double, CPUExpandEntry> single_build;
-  GHistRow<double> single_page;
+  common::GHistRow<double> single_page;
   {
     /**
      * Single page
      */
-    RowSetCollection row_set_collection;
+    common::RowSetCollection row_set_collection;
     InitRowPartitionForTest(&row_set_collection, n_samples);
 
     single_build.Reset(total_bins, batch_param, common::OmpGetNumThreads(0), 1, false);

tests/cpp/tree/test_quantile_hist.cc

@@ -24,7 +24,6 @@ class QuantileHistMock : public QuantileHistMaker {
   template <typename GradientSumT>
   struct BuilderMock : public QuantileHistMaker::Builder<GradientSumT> {
     using RealImpl = QuantileHistMaker::Builder<GradientSumT>;
-    using GHistRowT = typename RealImpl::GHistRowT;
 
     BuilderMock(const TrainParam &param, std::unique_ptr<TreeUpdater> pruner,
                 DMatrix const *fmat, GenericParameter const* ctx)