Further optimisations for gpu_hist. (#4283)

- Fuse final update position functions into a single more efficient kernel

- Refactor gpu_hist with a more explicit ellpack  matrix representation

include/xgboost/tree_model.h

@@ -93,65 +93,63 @@ class RegTree {
                     "Node: 64 bit align");
     }
     /*! \brief index of left child */
-    int LeftChild() const {
+    XGBOOST_DEVICE int LeftChild() const {
       return this->cleft_;
     }
     /*! \brief index of right child */
-    int RightChild() const {
+    XGBOOST_DEVICE int RightChild() const {
       return this->cright_;
     }
     /*! \brief index of default child when feature is missing */
-    int DefaultChild() const {
+    XGBOOST_DEVICE int DefaultChild() const {
       return this->DefaultLeft() ? this->LeftChild() : this->RightChild();
     }
     /*! \brief feature index of split condition */
-    unsigned SplitIndex() const {
+    XGBOOST_DEVICE unsigned SplitIndex() const {
       return sindex_ & ((1U << 31) - 1U);
     }
     /*! \brief when feature is unknown, whether goes to left child */
-    bool DefaultLeft() const {
+    XGBOOST_DEVICE bool DefaultLeft() const {
       return (sindex_ >> 31) != 0;
     }
     /*! \brief whether current node is leaf node */
-    bool IsLeaf() const {
+    XGBOOST_DEVICE bool IsLeaf() const {
       return cleft_ == -1;
     }
     /*! \return get leaf value of leaf node */
-    bst_float LeafValue() const {
+    XGBOOST_DEVICE bst_float LeafValue() const {
       return (this->info_).leaf_value;
     }
     /*! \return get split condition of the node */
-    SplitCondT SplitCond() const {
+    XGBOOST_DEVICE SplitCondT SplitCond() const {
       return (this->info_).split_cond;
     }
     /*! \brief get parent of the node */
-    int Parent() const {
+    XGBOOST_DEVICE int Parent() const {
       return parent_ & ((1U << 31) - 1);
     }
     /*! \brief whether current node is left child */
-    bool IsLeftChild() const {
+    XGBOOST_DEVICE bool IsLeftChild() const {
       return (parent_ & (1U << 31)) != 0;
     }
     /*! \brief whether this node is deleted */
-    bool IsDeleted() const {
+    XGBOOST_DEVICE bool IsDeleted() const {
       return sindex_ == std::numeric_limits<unsigned>::max();
     }
     /*! \brief whether current node is root */
-    bool IsRoot() const {
+    XGBOOST_DEVICE bool IsRoot() const { return parent_ == -1; }
-      return parent_ == -1;
-    }
     /*!
      * \brief set the left child
      * \param nid node id to right child
      */
-    void SetLeftChild(int nid) {
+    XGBOOST_DEVICE void SetLeftChild(int nid) {
       this->cleft_ = nid;
     }
     /*!
      * \brief set the right child
      * \param nid node id to right child
      */
-    void SetRightChild(int nid) {
+    XGBOOST_DEVICE void SetRightChild(int nid) {
       this->cright_ = nid;
     }
     /*!
@@ -160,7 +158,7 @@ class RegTree {
      * \param split_cond  split condition
      * \param default_left the default direction when feature is unknown
      */
-    void SetSplit(unsigned split_index, SplitCondT split_cond,
+    XGBOOST_DEVICE void SetSplit(unsigned split_index, SplitCondT split_cond,
                           bool default_left = false) {
       if (default_left) split_index |= (1U << 31);
       this->sindex_ = split_index;
@@ -172,17 +170,17 @@ class RegTree {
      * \param right right index, could be used to store
      *        additional information
      */
-    void SetLeaf(bst_float value, int right = -1) {
+    XGBOOST_DEVICE void SetLeaf(bst_float value, int right = -1) {
       (this->info_).leaf_value = value;
       this->cleft_ = -1;
       this->cright_ = right;
     }
     /*! \brief mark that this node is deleted */
-    void MarkDelete() {
+    XGBOOST_DEVICE void MarkDelete() {
       this->sindex_ = std::numeric_limits<unsigned>::max();
     }
     // set parent
-    void SetParent(int pidx, bool is_left_child = true) {
+    XGBOOST_DEVICE void SetParent(int pidx, bool is_left_child = true) {
       if (is_left_child) pidx |= (1U << 31);
       this->parent_ = pidx;
     }

src/predictor/gpu_predictor.cu

@@ -303,6 +303,7 @@ class GPUPredictor : public xgboost::Predictor {
                              const gbm::GBTreeModel& model, size_t tree_begin,
                              size_t tree_end) {
     if (tree_end - tree_begin == 0) { return; }
+    monitor_.StartCuda("DevicePredictInternal");
 
     CHECK_EQ(model.param.size_leaf_vector, 0);
     // Copy decision trees to device
@@ -337,6 +338,7 @@ class GPUPredictor : public xgboost::Predictor {
       });
       i_batch++;
     }
+    monitor_.StopCuda("DevicePredictInternal");
   }
 
  public:
@@ -388,9 +390,11 @@ class GPUPredictor : public xgboost::Predictor {
       if (it != cache_.end()) {
         const HostDeviceVector<bst_float>& y = it->second.predictions;
         if (y.Size() != 0) {
+          monitor_.StartCuda("PredictFromCache");
           out_preds->Reshard(y.Distribution());
           out_preds->Resize(y.Size());
           out_preds->Copy(y);
+          monitor_.StopCuda("PredictFromCache");
           return true;
         }
       }
@@ -481,6 +485,7 @@ class GPUPredictor : public xgboost::Predictor {
   std::unique_ptr<Predictor> cpu_predictor_;
   std::vector<DeviceShard> shards_;
   GPUSet devices_;
+  common::Monitor monitor_;
 };
 
 XGBOOST_REGISTER_PREDICTOR(GPUPredictor, "gpu_predictor")

src/tree/updater_gpu_hist.cu

@@ -50,6 +50,133 @@ struct GPUHistMakerTrainParam
 
 DMLC_REGISTER_PARAMETER(GPUHistMakerTrainParam);
 
+struct ExpandEntry {
+  int nid;
+  int depth;
+  DeviceSplitCandidate split;
+  uint64_t timestamp;
+  ExpandEntry() = default;
+  ExpandEntry(int nid, int depth, DeviceSplitCandidate split,
+              uint64_t timestamp)
+      : nid(nid), depth(depth), split(std::move(split)), timestamp(timestamp) {}
+  bool IsValid(const TrainParam& param, int num_leaves) const {
+    if (split.loss_chg <= kRtEps) return false;
+    if (split.left_sum.GetHess() == 0 || split.right_sum.GetHess() == 0) {
+      return false;
+    }
+    if (param.max_depth > 0 && depth == param.max_depth) return false;
+    if (param.max_leaves > 0 && num_leaves == param.max_leaves) return false;
+    return true;
+  }
+
+  static bool ChildIsValid(const TrainParam& param, int depth, int num_leaves) {
+    if (param.max_depth > 0 && depth >= param.max_depth) return false;
+    if (param.max_leaves > 0 && num_leaves >= param.max_leaves) return false;
+    return true;
+  }
+
+  friend std::ostream& operator<<(std::ostream& os, const ExpandEntry& e) {
+    os << "ExpandEntry: \n";
+    os << "nidx: " << e.nid << "\n";
+    os << "depth: " << e.depth << "\n";
+    os << "loss: " << e.split.loss_chg << "\n";
+    os << "left_sum: " << e.split.left_sum << "\n";
+    os << "right_sum: " << e.split.right_sum << "\n";
+    return os;
+  }
+};
+
+inline static bool DepthWise(ExpandEntry lhs, ExpandEntry rhs) {
+  if (lhs.depth == rhs.depth) {
+    return lhs.timestamp > rhs.timestamp;  // favor small timestamp
+  } else {
+    return lhs.depth > rhs.depth;  // favor small depth
+  }
+}
+inline static bool LossGuide(ExpandEntry lhs, ExpandEntry rhs) {
+  if (lhs.split.loss_chg == rhs.split.loss_chg) {
+    return lhs.timestamp > rhs.timestamp;  // favor small timestamp
+  } else {
+    return lhs.split.loss_chg < rhs.split.loss_chg;  // favor large loss_chg
+  }
+}
+
+// Find a gidx value for a given feature otherwise return -1 if not found
+__device__ int BinarySearchRow(bst_uint begin, bst_uint end,
+                               common::CompressedIterator<uint32_t> data,
+                               int const fidx_begin, int const fidx_end) {
+  bst_uint previous_middle = UINT32_MAX;
+  while (end != begin) {
+    auto middle = begin + (end - begin) / 2;
+    if (middle == previous_middle) {
+      break;
+    }
+    previous_middle = middle;
+
+    auto gidx = data[middle];
+
+    if (gidx >= fidx_begin && gidx < fidx_end) {
+      return gidx;
+    } else if (gidx < fidx_begin) {
+      begin = middle;
+    } else {
+      end = middle;
+    }
+  }
+  // Value is missing
+  return -1;
+}
+
+/** \brief Struct for accessing and manipulating an ellpack matrix on the
+ * device. Does not own underlying memory and may be trivially copied into
+ * kernels.*/
+struct ELLPackMatrix {
+  common::Span<uint32_t> feature_segments;
+  /*! \brief minimum value for each feature. */
+  common::Span<bst_float> min_fvalue;
+  /*! \brief Cut. */
+  common::Span<bst_float> gidx_fvalue_map;
+  /*! \brief row length for ELLPack. */
+  size_t row_stride{0};
+  common::CompressedIterator<uint32_t> gidx_iter;
+  bool is_dense;
+  int null_gidx_value;
+
+  XGBOOST_DEVICE size_t BinCount() const { return gidx_fvalue_map.size(); }
+
+  // Get a matrix element, uses binary search for look up
+  // Return NaN if missing
+  __device__ bst_float GetElement(size_t ridx, size_t fidx) const {
+    auto row_begin = row_stride * ridx;
+    auto row_end = row_begin + row_stride;
+    auto gidx = -1;
+    if (is_dense) {
+      gidx = gidx_iter[row_begin + fidx];
+    } else {
+      gidx =
+          BinarySearchRow(row_begin, row_end, gidx_iter, feature_segments[fidx],
+                          feature_segments[fidx + 1]);
+    }
+    if (gidx == -1) {
+      return nan("");
+    }
+    return gidx_fvalue_map[gidx];
+  }
+  void Init(common::Span<uint32_t> feature_segments,
+    common::Span<bst_float> min_fvalue,
+    common::Span<bst_float> gidx_fvalue_map, size_t row_stride,
+    common::CompressedIterator<uint32_t> gidx_iter, bool is_dense,
+    int null_gidx_value) {
+    this->feature_segments = feature_segments;
+    this->min_fvalue = min_fvalue;
+    this->gidx_fvalue_map = gidx_fvalue_map;
+    this->row_stride = row_stride;
+    this->gidx_iter = gidx_iter;
+    this->is_dense = is_dense;
+    this->null_gidx_value = null_gidx_value;
+  }
+};
+
 // With constraints
 template <typename GradientPairT>
 XGBOOST_DEVICE float inline LossChangeMissing(
@@ -111,19 +238,17 @@ __device__ GradientSumT ReduceFeature(common::Span<const GradientSumT> feature_h
 template <int BLOCK_THREADS, typename ReduceT, typename ScanT,
           typename MaxReduceT, typename TempStorageT, typename GradientSumT>
 __device__ void EvaluateFeature(
-    int fidx,
+    int fidx, common::Span<const GradientSumT> node_histogram,
-    common::Span<const GradientSumT> node_histogram,
+    const ELLPackMatrix& matrix,
-    common::Span<const uint32_t> feature_segments,  // cut.row_ptr
-    float min_fvalue,                               // cut.min_value
-    common::Span<const float> gidx_fvalue_map,                   // cut.cut
     DeviceSplitCandidate* best_split,  // shared memory storing best split
     const DeviceNodeStats& node, const GPUTrainingParam& param,
     TempStorageT* temp_storage,  // temp memory for cub operations
     int constraint,              // monotonic_constraints
     const ValueConstraint& value_constraint) {
   // Use pointer from cut to indicate begin and end of bins for each feature.
-  uint32_t gidx_begin = feature_segments[fidx];    // begining bin
+  uint32_t gidx_begin = matrix.feature_segments[fidx];  // begining bin
-  uint32_t gidx_end = feature_segments[fidx + 1];  // end bin for i^th feature
+  uint32_t gidx_end =
+      matrix.feature_segments[fidx + 1];  // end bin for i^th feature
 
   // Sum histogram bins for current feature
   GradientSumT const feature_sum = ReduceFeature<BLOCK_THREADS, ReduceT>(
@@ -168,16 +293,17 @@ __device__ void EvaluateFeature(
 
     // Best thread updates split
     if (threadIdx.x == block_max.key) {
-      int gidx = scan_begin + threadIdx.x;
+      int split_gidx = (scan_begin + threadIdx.x) - 1;
-      float fvalue =
+      float fvalue;
-          gidx == gidx_begin ? min_fvalue : gidx_fvalue_map[gidx - 1];
+      if (split_gidx < static_cast<int>(gidx_begin)) {
+        fvalue =  matrix.min_fvalue[fidx];
+      } else {
+        fvalue = matrix.gidx_fvalue_map[split_gidx];
+      }
       GradientSumT left = missing_left ? bin + missing : bin;
       GradientSumT right = parent_sum - left;
-      best_split->Update(gain, missing_left ? kLeftDir : kRightDir,
+      best_split->Update(gain, missing_left ? kLeftDir : kRightDir, fvalue,
-                         fvalue, fidx,
+                         fidx, GradientPair(left), GradientPair(right), param);
-                         GradientPair(left),
-                         GradientPair(right),
-                         param);
     }
     __syncthreads();
   }
@@ -189,10 +315,7 @@ __global__ void EvaluateSplitKernel(
         node_histogram,               // histogram for gradients
     common::Span<const int> feature_set,  // Selected features
     DeviceNodeStats node,
-    common::Span<const uint32_t>
+  ELLPackMatrix matrix,
-        d_feature_segments,                       // row_ptr form HistCutMatrix
-    common::Span<const float> d_fidx_min_map,     // min_value
-    common::Span<const float> d_gidx_fvalue_map,  // cut
     GPUTrainingParam gpu_param,
     common::Span<DeviceSplitCandidate> split_candidates,  // resulting split
     ValueConstraint value_constraint,
@@ -226,10 +349,8 @@ __global__ void EvaluateSplitKernel(
   int fidx = feature_set[blockIdx.x];
   int constraint = d_monotonic_constraints[fidx];
   EvaluateFeature<BLOCK_THREADS, SumReduceT, BlockScanT, MaxReduceT>(
-      fidx, node_histogram,
+      fidx, node_histogram, matrix, &best_split, node, gpu_param, &temp_storage,
-      d_feature_segments, d_fidx_min_map[fidx], d_gidx_fvalue_map,
+      constraint, value_constraint);
-      &best_split, node, gpu_param, &temp_storage, constraint,
-      value_constraint);
 
   __syncthreads();
 
@@ -239,32 +360,6 @@ __global__ void EvaluateSplitKernel(
   }
 }
 
-// Find a gidx value for a given feature otherwise return -1 if not found
-template <typename GidxIterT>
-__device__ int BinarySearchRow(bst_uint begin, bst_uint end, GidxIterT data,
-                               int const fidx_begin, int const fidx_end) {
-  bst_uint previous_middle = UINT32_MAX;
-  while (end != begin) {
-    auto middle = begin + (end - begin) / 2;
-    if (middle == previous_middle) {
-      break;
-    }
-    previous_middle = middle;
-
-    auto gidx = data[middle];
-
-    if (gidx >= fidx_begin && gidx < fidx_end) {
-      return gidx;
-    } else if (gidx < fidx_begin) {
-      begin = middle;
-    } else {
-      end = middle;
-    }
-  }
-  // Value is missing
-  return -1;
-}
-
 /**
  * \struct  DeviceHistogram
  *
@@ -290,7 +385,6 @@ class DeviceHistogram {
   }
 
   void Reset() {
-    dh::safe_cuda(cudaSetDevice(device_id_));
     dh::safe_cuda(cudaMemsetAsync(
         data_.data().get(), 0,
         data_.size() * sizeof(typename decltype(data_)::value_type)));
@@ -397,27 +491,27 @@ __global__ void CompressBinEllpackKernel(
 }
 
 template <typename GradientSumT>
-__global__ void SharedMemHistKernel(size_t row_stride, const bst_uint* d_ridx,
+__global__ void SharedMemHistKernel(ELLPackMatrix matrix, const bst_uint* d_ridx,
-                                    common::CompressedIterator<uint32_t> d_gidx,
-                                    int null_gidx_value,
                                     GradientSumT* d_node_hist,
                                     const GradientPair* d_gpair,
                                     size_t segment_begin, size_t n_elements) {
   extern __shared__ char smem[];
   GradientSumT* smem_arr = reinterpret_cast<GradientSumT*>(smem); // NOLINT
-  for (auto i : dh::BlockStrideRange(0, null_gidx_value)) {
+  for (auto i :
+       dh::BlockStrideRange(static_cast<size_t>(0), matrix.BinCount())) {
     smem_arr[i] = GradientSumT();
   }
   __syncthreads();
   for (auto idx : dh::GridStrideRange(static_cast<size_t>(0), n_elements)) {
-    int ridx = d_ridx[idx / row_stride + segment_begin];
+    int ridx = d_ridx[idx / matrix.row_stride + segment_begin];
-    int gidx = d_gidx[ridx * row_stride + idx % row_stride];
+    int gidx = matrix.gidx_iter[ridx * matrix.row_stride + idx % matrix.row_stride];
-    if (gidx != null_gidx_value) {
+    if (gidx != matrix.null_gidx_value) {
       AtomicAddGpair(smem_arr + gidx, d_gpair[ridx]);
     }
   }
   __syncthreads();
-  for (auto i : dh::BlockStrideRange(0, null_gidx_value)) {
+  for (auto i :
+       dh::BlockStrideRange(static_cast<size_t>(0), matrix.BinCount())) {
     AtomicAddGpair(d_node_hist + i, smem_arr[i]);
   }
 }
@@ -509,32 +603,26 @@ struct DeviceShard {
 
   dh::BulkAllocator<dh::MemoryType::kDevice> ba;
 
-  /*! \brief HistCutMatrix stored in device. */
+  ELLPackMatrix ellpack_matrix;
-  struct DeviceHistCutMatrix {
-    /*! \brief row_ptr form HistCutMatrix. */
-    dh::DVec<uint32_t> feature_segments;
-    /*! \brief minimum value for each feature. */
-    dh::DVec<bst_float> min_fvalue;
-    /*! \brief Cut. */
-    dh::DVec<bst_float> gidx_fvalue_map;
-  } d_cut;
 
   /*! \brief Range of rows for each node. */
   std::vector<Segment> ridx_segments;
   DeviceHistogram<GradientSumT> hist;
 
-  /*! \brief row length for ELLPack. */
+  /*! \brief row_ptr form HistCutMatrix. */
-  size_t row_stride;
+  dh::DVec<uint32_t> feature_segments;
-  common::CompressedIterator<uint32_t> gidx;
+  /*! \brief minimum value for each feature. */
+  dh::DVec<bst_float> min_fvalue;
+  /*! \brief Cut. */
+  dh::DVec<bst_float> gidx_fvalue_map;
+  /*! \brief global index of histogram, which is stored in ELLPack format. */
+  dh::DVec<common::CompressedByteT> gidx_buffer;
 
   /*! \brief  Row indices relative to this shard, necessary for sorting rows. */
   dh::DVec2<bst_uint> ridx;
   /*! \brief Gradient pair for each row. */
   dh::DVec<GradientPair> gpair;
 
-  /*! \brief The last histogram index. */
-  int null_gidx_value;
-
   dh::DVec2<int> position;
 
   dh::DVec<int> monotone_constraints;
@@ -543,8 +631,6 @@ struct DeviceShard {
   /*! \brief Sum gradient for each node. */
   std::vector<GradientPair> node_sum_gradients;
   dh::DVec<GradientPair> node_sum_gradients_d;
-  /*! \brief global index of histogram, which is stored in ELLPack format. */
-  dh::DVec<common::CompressedByteT> gidx_buffer;
   /*! \brief row offset in SparsePage (the input data). */
   thrust::device_vector<size_t> row_ptrs;
   /*! \brief On-device feature set, only actually used on one of the devices */
@@ -572,16 +658,13 @@ struct DeviceShard {
       : device_id(_device_id),
         row_begin_idx(row_begin),
         row_end_idx(row_end),
-        row_stride(0),
         n_rows(row_end - row_begin),
-        n_bins{0},
+        n_bins(0),
-        null_gidx_value(0),
         param(std::move(_param)),
         prediction_cache_initialised(false) {}
 
   /* Init row_ptrs and row_stride */
-  void InitRowPtrs(const SparsePage& row_batch) {
+  size_t InitRowPtrs(const SparsePage& row_batch) {
-    dh::safe_cuda(cudaSetDevice(device_id));
     const auto& offset_vec = row_batch.offset.HostVector();
     row_ptrs.resize(n_rows + 1);
     thrust::copy(offset_vec.data() + row_begin_idx,
@@ -595,22 +678,17 @@ struct DeviceShard {
 
     auto counting = thrust::make_counting_iterator(size_t(0));
     using TransformT = thrust::transform_iterator<decltype(get_size),
-      decltype(counting), size_t>;
+                                                  decltype(counting), size_t>;
     TransformT row_size_iter = TransformT(counting, get_size);
-    row_stride = thrust::reduce(row_size_iter, row_size_iter + n_rows, 0,
+    size_t row_stride = thrust::reduce(row_size_iter, row_size_iter + n_rows, 0,
-                                thrust::maximum<size_t>());
+                                       thrust::maximum<size_t>());
+    return row_stride;
   }
 
-  /*
-     Init:
-     n_bins, null_gidx_value, gidx_buffer, row_ptrs, gidx, gidx_fvalue_map,
-     min_fvalue, feature_segments, node_sum_gradients, ridx_segments,
-     hist
-  */
   void InitCompressedData(
-      const common::HistCutMatrix& hmat, const SparsePage& row_batch);
+      const common::HistCutMatrix& hmat, const SparsePage& row_batch, bool is_dense);
 
-  void CreateHistIndices(const SparsePage& row_batch);
+  void CreateHistIndices(const SparsePage& row_batch, size_t row_stride, int null_gidx_value);
 
   ~DeviceShard() {
     dh::safe_cuda(cudaSetDevice(device_id));
@@ -708,10 +786,9 @@ struct DeviceShard {
       int constexpr kBlockThreads = 256;
       EvaluateSplitKernel<kBlockThreads, GradientSumT>
           <<<uint32_t(d_feature_set.size()), kBlockThreads, 0, streams[i]>>>(
-              hist.GetNodeHistogram(nidx), d_feature_set, node,
+              hist.GetNodeHistogram(nidx), d_feature_set, node, ellpack_matrix,
-              d_cut.feature_segments.GetSpan(), d_cut.min_fvalue.GetSpan(),
+              gpu_param, d_split_candidates, value_constraints[nidx],
-              d_cut.gidx_fvalue_map.GetSpan(), gpu_param, d_split_candidates,
+              monotone_constraints.GetSpan());
-              value_constraints[nidx], monotone_constraints.GetSpan());
 
       // Reduce over features to find best feature
       auto d_result = d_result_all.subspan(i, 1);
@@ -756,47 +833,35 @@ struct DeviceShard {
            hist.HistogramExists(nidx_parent);
   }
 
-  void UpdatePosition(int nidx, int left_nidx, int right_nidx, int fidx,
+  void UpdatePosition(int nidx, RegTree::Node split_node) {
-                      int64_t split_gidx, bool default_dir_left, bool is_dense,
+    CHECK(!split_node.IsLeaf()) <<"Node must not be leaf";
-                      int fidx_begin,  // cut.row_ptr[fidx]
-                      int fidx_end) {  // cut.row_ptr[fidx + 1]
-    dh::safe_cuda(cudaSetDevice(device_id));
     Segment segment = ridx_segments[nidx];
     bst_uint* d_ridx = ridx.Current();
     int* d_position = position.Current();
-    common::CompressedIterator<uint32_t> d_gidx = gidx;
-    size_t row_stride = this->row_stride;
     if (left_counts.size() <= nidx) {
       left_counts.resize((nidx * 2) + 1);
     }
     int64_t* d_left_count = left_counts.data().get() + nidx;
+    auto d_matrix = this->ellpack_matrix;
     // Launch 1 thread for each row
     dh::LaunchN<1, 128>(
         device_id, segment.Size(), [=] __device__(bst_uint idx) {
           idx += segment.begin;
           bst_uint ridx = d_ridx[idx];
-          auto row_begin = row_stride * ridx;
+          bst_float element = d_matrix.GetElement(ridx, split_node.SplitIndex());
-          auto row_end = row_begin + row_stride;
+          // Missing value
-          auto gidx = -1;
+          int new_position = 0;
-          if (is_dense) {
+          if (isnan(element)) {
-            // FIXME: Maybe just search the cuts again.
+            new_position = split_node.DefaultChild();
-            gidx = d_gidx[row_begin + fidx];
           } else {
-            gidx = BinarySearchRow(row_begin, row_end, d_gidx, fidx_begin,
+            if (element <= split_node.SplitCond()) {
-                                   fidx_end);
+              new_position = split_node.LeftChild();
+            } else {
+              new_position = split_node.RightChild();
+            }
           }
-
+          CountLeft(d_left_count, new_position, split_node.LeftChild());
-          // belong to left node or right node.
+          d_position[idx] = new_position;
-          int position;
-          if (gidx >= 0) {
-            // Feature is found
-            position = gidx <= split_gidx ? left_nidx : right_nidx;
-          } else {
-            // Feature is missing
-            position = default_dir_left ? left_nidx : right_nidx;
-          }
-          CountLeft(d_left_count, position, left_nidx);
-          d_position[idx] = position;
         });
 
     // Overlap device to host memory copy (left_count) with sort
@@ -805,16 +870,16 @@ struct DeviceShard {
     dh::safe_cuda(cudaMemcpyAsync(tmp_pinned.data(), d_left_count, sizeof(int64_t),
                                   cudaMemcpyDeviceToHost, streams[0]));
 
-    SortPositionAndCopy(segment, left_nidx, right_nidx, d_left_count,
+    SortPositionAndCopy(segment, split_node.LeftChild(), split_node.RightChild(), d_left_count,
                         streams[1]);
 
     dh::safe_cuda(cudaStreamSynchronize(streams[0]));
     int64_t left_count = tmp_pinned[0];
     CHECK_LE(left_count, segment.Size());
     CHECK_GE(left_count, 0);
-    ridx_segments[left_nidx] =
+    ridx_segments[split_node.LeftChild()] =
         Segment(segment.begin, segment.begin + left_count);
-    ridx_segments[right_nidx] =
+    ridx_segments[split_node.RightChild()] =
         Segment(segment.begin + left_count, segment.end);
   }
 
@@ -840,6 +905,41 @@ struct DeviceShard {
     });
   }
 
+  // After tree update is finished, update the position of all training
+  // instances to their final leaf This information is used later to update the
+  // prediction cache
+  void FinalisePosition(RegTree* p_tree) {
+    const auto d_nodes =
+        temp_memory.GetSpan<RegTree::Node>(p_tree->GetNodes().size());
+    dh::safe_cuda(cudaMemcpy(d_nodes.data(), p_tree->GetNodes().data(),
+                             d_nodes.size() * sizeof(RegTree::Node),
+                             cudaMemcpyHostToDevice));
+    auto d_position = position.Current();
+    const auto d_ridx = ridx.Current();
+    auto d_matrix = this->ellpack_matrix;
+    dh::LaunchN(device_id, position.Size(), [=] __device__(size_t idx) {
+      auto position = d_position[idx];
+      auto node = d_nodes[position];
+      bst_uint ridx = d_ridx[idx];
+
+      while (!node.IsLeaf()) {
+        bst_float element = d_matrix.GetElement(ridx, node.SplitIndex());
+        // Missing value
+        if (isnan(element)) {
+          position = node.DefaultChild();
+        } else {
+          if (element <= node.SplitCond()) {
+            position = node.LeftChild();
+          } else {
+            position = node.RightChild();
+          }
+        }
+        node = d_nodes[position];
+      }
+      d_position[idx] = position;
+    });
+  }
+
   void UpdatePredictionCache(bst_float* out_preds_d) {
     dh::safe_cuda(cudaSetDevice(device_id));
     if (!prediction_cache_initialised) {
@@ -880,14 +980,12 @@ struct SharedMemHistBuilder : public GPUHistBuilderBase<GradientSumT> {
     auto segment = shard->ridx_segments[nidx];
     auto segment_begin = segment.begin;
     auto d_node_hist = shard->hist.GetNodeHistogram(nidx);
-    auto d_gidx = shard->gidx;
     auto d_ridx = shard->ridx.Current();
     auto d_gpair = shard->gpair.Data();
 
-    int null_gidx_value = shard->null_gidx_value;
+    auto n_elements = segment.Size() * shard->ellpack_matrix.row_stride;
-    auto n_elements = segment.Size() * shard->row_stride;
 
-    const size_t smem_size = sizeof(GradientSumT) * shard->null_gidx_value;
+    const size_t smem_size = sizeof(GradientSumT) * shard->ellpack_matrix.BinCount();
     const int items_per_thread = 8;
     const int block_threads = 256;
     const int grid_size =
@@ -896,10 +994,9 @@ struct SharedMemHistBuilder : public GPUHistBuilderBase<GradientSumT> {
     if (grid_size <= 0) {
       return;
     }
-    dh::safe_cuda(cudaSetDevice(shard->device_id));
+    SharedMemHistKernel<<<grid_size, block_threads, smem_size>>>(
-    SharedMemHistKernel<<<grid_size, block_threads, smem_size>>>
+        shard->ellpack_matrix, d_ridx, d_node_hist.data(), d_gpair,
-        (shard->row_stride, d_ridx, d_gidx, null_gidx_value, d_node_hist.data(), d_gpair,
+        segment_begin, n_elements);
-         segment_begin, n_elements);
   }
 };
 
@@ -908,20 +1005,18 @@ struct GlobalMemHistBuilder : public GPUHistBuilderBase<GradientSumT> {
   void Build(DeviceShard<GradientSumT>* shard, int nidx) override {
     Segment segment = shard->ridx_segments[nidx];
     auto d_node_hist = shard->hist.GetNodeHistogram(nidx).data();
-    common::CompressedIterator<uint32_t> d_gidx = shard->gidx;
     bst_uint* d_ridx = shard->ridx.Current();
     GradientPair* d_gpair = shard->gpair.Data();
 
-    size_t const n_elements = segment.Size() * shard->row_stride;
+    size_t const n_elements = segment.Size() * shard->ellpack_matrix.row_stride;
-    size_t const row_stride = shard->row_stride;
+    auto d_matrix = shard->ellpack_matrix;
-    int const null_gidx_value = shard->null_gidx_value;
 
     dh::LaunchN(shard->device_id, n_elements, [=] __device__(size_t idx) {
-        int ridx = d_ridx[(idx / row_stride) + segment.begin];
+        int ridx = d_ridx[(idx / d_matrix.row_stride) + segment.begin];
         // lookup the index (bin) of histogram.
-        int gidx = d_gidx[ridx * row_stride + idx % row_stride];
+        int gidx = d_matrix.gidx_iter[ridx * d_matrix.row_stride + idx % d_matrix.row_stride];
 
-        if (gidx != null_gidx_value) {
+        if (gidx != d_matrix.null_gidx_value) {
           AtomicAddGpair(d_node_hist + gidx, d_gpair[ridx]);
         }
       });
@@ -930,10 +1025,10 @@ struct GlobalMemHistBuilder : public GPUHistBuilderBase<GradientSumT> {
 
 template <typename GradientSumT>
 inline void DeviceShard<GradientSumT>::InitCompressedData(
-    const common::HistCutMatrix& hmat, const SparsePage& row_batch) {
+    const common::HistCutMatrix& hmat, const SparsePage& row_batch, bool is_dense) {
-  dh::safe_cuda(cudaSetDevice(device_id));
+  size_t row_stride = this->InitRowPtrs(row_batch);
-  n_bins = hmat.NumBins();
+  n_bins = hmat.row_ptr.back();
-  null_gidx_value = hmat.NumBins();
+  int null_gidx_value = hmat.row_ptr.back();
 
   int max_nodes =
       param.max_leaves > 0 ? param.max_leaves * 2 : MaxNodesDepth(param.max_depth);
@@ -944,13 +1039,13 @@ inline void DeviceShard<GradientSumT>::InitCompressedData(
               &position, n_rows,
               &prediction_cache, n_rows,
               &node_sum_gradients_d, max_nodes,
-              &d_cut.feature_segments, hmat.row_ptr.size(),
+              &feature_segments, hmat.row_ptr.size(),
-              &d_cut.gidx_fvalue_map, hmat.cut.size(),
+              &gidx_fvalue_map, hmat.cut.size(),
-              &d_cut.min_fvalue, hmat.min_val.size(),
+              &min_fvalue, hmat.min_val.size(),
               &monotone_constraints, param.monotone_constraints.size());
-  d_cut.gidx_fvalue_map = hmat.cut;
+  gidx_fvalue_map = hmat.cut;
-  d_cut.min_fvalue = hmat.min_val;
+  min_fvalue = hmat.min_val;
-  d_cut.feature_segments = hmat.row_ptr;
+  feature_segments = hmat.row_ptr;
   monotone_constraints = param.monotone_constraints;
 
   node_sum_gradients.resize(max_nodes);
@@ -970,15 +1065,18 @@ inline void DeviceShard<GradientSumT>::InitCompressedData(
   ba.Allocate(device_id, &gidx_buffer, compressed_size_bytes);
   gidx_buffer.Fill(0);
 
-  int nbits = common::detail::SymbolBits(num_symbols);
+  this->CreateHistIndices(row_batch, row_stride, null_gidx_value);
 
-  CreateHistIndices(row_batch);
+  ellpack_matrix.Init(
-
+      feature_segments.GetSpan(), min_fvalue.GetSpan(),
-  gidx = common::CompressedIterator<uint32_t>(gidx_buffer.Data(), num_symbols);
+      gidx_fvalue_map.GetSpan(), row_stride,
+      common::CompressedIterator<uint32_t>(gidx_buffer.Data(), num_symbols),
+      is_dense, null_gidx_value);
 
   // check if we can use shared memory for building histograms
-  // (assuming atleast we need 2 CTAs per SM to maintain decent latency hiding)
+  // (assuming atleast we need 2 CTAs per SM to maintain decent latency
-  auto histogram_size = sizeof(GradientSumT) * null_gidx_value;
+  // hiding)
+  auto histogram_size = sizeof(GradientSumT) * hmat.row_ptr.back();
   auto max_smem = dh::MaxSharedMemory(device_id);
   if (histogram_size <= max_smem) {
     hist_builder.reset(new SharedMemHistBuilder<GradientSumT>);
@@ -990,9 +1088,9 @@ inline void DeviceShard<GradientSumT>::InitCompressedData(
   hist.Init(device_id, hmat.NumBins());
 }
 
-
 template <typename GradientSumT>
-inline void DeviceShard<GradientSumT>::CreateHistIndices(const SparsePage& row_batch) {
+inline void DeviceShard<GradientSumT>::CreateHistIndices(
+    const SparsePage& row_batch, size_t row_stride, int null_gidx_value) {
   int num_symbols = n_bins + 1;
   // bin and compress entries in batches of rows
   size_t gpu_batch_nrows =
@@ -1026,7 +1124,7 @@ inline void DeviceShard<GradientSumT>::CreateHistIndices(const SparsePage& row_b
          gidx_buffer.Data(),
          row_ptrs.data().get() + batch_row_begin,
          entries_d.data().get(),
-         d_cut.gidx_fvalue_map.Data(), d_cut.feature_segments.Data(),
+         gidx_fvalue_map.Data(), feature_segments.Data(),
          batch_row_begin, batch_nrows,
          row_ptrs[batch_row_begin],
          row_stride, null_gidx_value);
@@ -1039,12 +1137,9 @@ inline void DeviceShard<GradientSumT>::CreateHistIndices(const SparsePage& row_b
   entries_d.shrink_to_fit();
 }
 
-
 template <typename GradientSumT>
 class GPUHistMakerSpecialised{
  public:
-  struct ExpandEntry;
-
   GPUHistMakerSpecialised() : initialised_{false}, p_last_fmat_{nullptr} {}
   void Init(
       const std::vector<std::pair<std::string, std::string>>& args) {
@@ -1111,7 +1206,6 @@ class GPUHistMakerSpecialised{
           shard = std::unique_ptr<DeviceShard<GradientSumT>>(
               new DeviceShard<GradientSumT>(dist_.Devices().DeviceId(i), start,
                                             start + size, param_));
-          shard->InitRowPtrs(batch);
         });
 
     // Find the cuts.
@@ -1119,13 +1213,14 @@ class GPUHistMakerSpecialised{
     common::DeviceSketch(batch, *info_, param_, &hmat_, hist_maker_param_.gpu_batch_nrows);
     n_bins_ = hmat_.row_ptr.back();
     monitor_.StopCuda("Quantiles");
+    auto is_dense = info_->num_nonzero_ == info_->num_row_ * info_->num_col_;
 
     monitor_.StartCuda("BinningCompression");
     dh::ExecuteIndexShards(
         &shards_,
         [&](int idx, std::unique_ptr<DeviceShard<GradientSumT>>& shard) {
           dh::safe_cuda(cudaSetDevice(shard->device_id));
-          shard->InitCompressedData(hmat_, batch);
+          shard->InitCompressedData(hmat_, batch, is_dense);
         });
     monitor_.StopCuda("BinningCompression");
     ++batch_iter;
@@ -1300,32 +1395,19 @@ class GPUHistMakerSpecialised{
   }
 
   void UpdatePosition(const ExpandEntry& candidate, RegTree* p_tree) {
-    int nidx = candidate.nid;
-    int left_nidx = (*p_tree)[nidx].LeftChild();
-    int right_nidx = (*p_tree)[nidx].RightChild();
-
-    // convert floating-point split_pt into corresponding bin_id
-    // split_cond = -1 indicates that split_pt is less than all known cut points
-    int64_t split_gidx = -1;
-    int64_t fidx = candidate.split.findex;
-    bool default_dir_left = candidate.split.dir == kLeftDir;
-    uint32_t fidx_begin = hmat_.row_ptr[fidx];
-    uint32_t fidx_end = hmat_.row_ptr[fidx + 1];
-    // split_gidx = i where i is the i^th bin containing split value.
-    for (auto i = fidx_begin; i < fidx_end; ++i) {
-      if (candidate.split.fvalue == hmat_.cut[i]) {
-        split_gidx = static_cast<int64_t>(i);
-      }
-    }
-    auto is_dense = info_->num_nonzero_ == info_->num_row_ * info_->num_col_;
-
     dh::ExecuteIndexShards(
         &shards_,
         [&](int idx, std::unique_ptr<DeviceShard<GradientSumT>>& shard) {
           dh::safe_cuda(cudaSetDevice(shard->device_id));
-          shard->UpdatePosition(nidx, left_nidx, right_nidx, fidx, split_gidx,
+          shard->UpdatePosition(candidate.nid,
-                                default_dir_left, is_dense, fidx_begin,
+                                p_tree->GetNodes()[candidate.nid]);
-                                fidx_end);
+        });
+  }
+  void FinalisePosition(RegTree* p_tree) {
+    dh::ExecuteIndexShards(
+        &shards_,
+        [&](int idx, std::unique_ptr<DeviceShard<GradientSumT>>& shard) {
+          shard->FinalisePosition(p_tree);
         });
   }
 
@@ -1380,20 +1462,22 @@ class GPUHistMakerSpecialised{
     while (!qexpand_->empty()) {
       ExpandEntry candidate = qexpand_->top();
       qexpand_->pop();
-      if (!candidate.IsValid(param_, num_leaves)) continue;
+      if (!candidate.IsValid(param_, num_leaves)) {
+        continue;
+      }
 
       this->ApplySplit(candidate, p_tree);
-      monitor_.StartCuda("UpdatePosition");
-      this->UpdatePosition(candidate, p_tree);
-      monitor_.StopCuda("UpdatePosition");
       num_leaves++;
 
       int left_child_nidx = tree[candidate.nid].LeftChild();
       int right_child_nidx = tree[candidate.nid].RightChild();
-
       // Only create child entries if needed
       if (ExpandEntry::ChildIsValid(param_, tree.GetDepth(left_child_nidx),
                                     num_leaves)) {
+        monitor_.StartCuda("UpdatePosition");
+        this->UpdatePosition(candidate, p_tree);
+        monitor_.StopCuda("UpdatePosition");
+
         monitor_.StartCuda("BuildHist");
         this->BuildHistLeftRight(candidate.nid, left_child_nidx,
                                  right_child_nidx);
@@ -1407,10 +1491,14 @@ class GPUHistMakerSpecialised{
                                    timestamp++));
         qexpand_->push(ExpandEntry(right_child_nidx,
                                    tree.GetDepth(right_child_nidx),
-                                    splits.at(1), timestamp++));
+                                   splits.at(1), timestamp++));
         monitor_.StopCuda("EvaluateSplits");
       }
     }
+
+    monitor_.StartCuda("FinalisePosition");
+    this->FinalisePosition(p_tree);
+    monitor_.StopCuda("FinalisePosition");
   }
 
   bool UpdatePredictionCache(
@@ -1431,64 +1519,6 @@ class GPUHistMakerSpecialised{
     return true;
   }
 
-  struct ExpandEntry {
-    int nid;
-    int depth;
-    DeviceSplitCandidate split;
-    uint64_t timestamp;
-    ExpandEntry(int _nid, int _depth, const DeviceSplitCandidate _split,
-                uint64_t _timestamp) :
-        nid{_nid}, depth{_depth}, split(std::move(_split)),
-        timestamp{_timestamp} {}
-    bool IsValid(const TrainParam& param, int num_leaves) const {
-      if (split.loss_chg <= kRtEps) {
-        return false;
-      }
-      if (split.left_sum.GetHess() == 0 || split.right_sum.GetHess() == 0) {
-        return false;
-      }
-      if (param.max_depth > 0 && depth == param.max_depth) {
-        return false;
-      }
-      if (param.max_leaves > 0 && num_leaves == param.max_leaves) {
-        return false;
-      }
-      return true;
-    }
-
-    static bool ChildIsValid(const TrainParam& param, int depth,
-                             int num_leaves) {
-      if (param.max_depth > 0 && depth >= param.max_depth) return false;
-      if (param.max_leaves > 0 && num_leaves >= param.max_leaves) return false;
-      return true;
-    }
-
-    friend std::ostream& operator<<(std::ostream& os, const ExpandEntry& e) {
-      os << "ExpandEntry: \n";
-      os << "nidx: " << e.nid << "\n";
-      os << "depth: " << e.depth << "\n";
-      os << "loss: " << e.split.loss_chg << "\n";
-      os << "left_sum: " << e.split.left_sum << "\n";
-      os << "right_sum: " << e.split.right_sum << "\n";
-      return os;
-    }
-  };
-
-  inline static bool DepthWise(ExpandEntry lhs, ExpandEntry rhs) {
-    if (lhs.depth == rhs.depth) {
-      return lhs.timestamp > rhs.timestamp;  // favor small timestamp
-    } else {
-      return lhs.depth > rhs.depth;  // favor small depth
-    }
-  }
-  inline static bool LossGuide(ExpandEntry lhs, ExpandEntry rhs) {
-    if (lhs.split.loss_chg == rhs.split.loss_chg) {
-      return lhs.timestamp > rhs.timestamp;  // favor small timestamp
-    } else {
-      return lhs.split.loss_chg < rhs.split.loss_chg;  // favor large loss_chg
-    }
-  }
-
   TrainParam param_;            // NOLINT
   common::HistCutMatrix hmat_;  // NOLINT
   MetaInfo* info_;              // NOLINT
@@ -1507,8 +1537,9 @@ class GPUHistMakerSpecialised{
   GPUHistMakerTrainParam hist_maker_param_;
   common::GHistIndexMatrix gmat_;
 
-  using ExpandQueue = std::priority_queue<ExpandEntry, std::vector<ExpandEntry>,
+  using ExpandQueue =
-    std::function<bool(ExpandEntry, ExpandEntry)>>;
+      std::priority_queue<ExpandEntry, std::vector<ExpandEntry>,
+                          std::function<bool(ExpandEntry, ExpandEntry)>>;
   std::unique_ptr<ExpandQueue> qexpand_;
   dh::AllReducer reducer_;
 

tests/cpp/helpers.h

@@ -26,7 +26,7 @@
 
 bool FileExists(const std::string& filename);
 
-long GetFileSize(const std::string& filename);
+int64_t GetFileSize(const std::string& filename);
 
 void CreateSimpleTestData(const std::string& filename);
 

tests/cpp/tree/test_gpu_hist.cu

@@ -39,8 +39,9 @@ void BuildGidx(DeviceShard<GradientSumT>* shard, int n_rows, int n_cols,
               0.26f, 0.74f, 1.98f,
               0.26f, 0.71f, 1.83f};
 
-  shard->InitRowPtrs(batch);
+  auto is_dense = (*dmat)->Info().num_nonzero_ ==
-  shard->InitCompressedData(cmat, batch);
+                  (*dmat)->Info().num_row_ * (*dmat)->Info().num_col_;
+  shard->InitCompressedData(cmat, batch, is_dense);
 
   delete dmat;
 }
@@ -59,7 +60,7 @@ TEST(GpuHist, BuildGidxDense) {
   h_gidx_buffer = shard.gidx_buffer.AsVector();
   common::CompressedIterator<uint32_t> gidx(h_gidx_buffer.data(), 25);
 
-  ASSERT_EQ(shard.row_stride, kNCols);
+  ASSERT_EQ(shard.ellpack_matrix.row_stride, kNCols);
 
   std::vector<uint32_t> solution = {
     0, 3, 8,  9, 14, 17, 20, 21,
@@ -98,7 +99,7 @@ TEST(GpuHist, BuildGidxSparse) {
   h_gidx_buffer = shard.gidx_buffer.AsVector();
   common::CompressedIterator<uint32_t> gidx(h_gidx_buffer.data(), 25);
 
-  ASSERT_LE(shard.row_stride, 3);
+  ASSERT_LE(shard.ellpack_matrix.row_stride, 3);
 
   // row_stride = 3, 16 rows, 48 entries for ELLPack
   std::vector<uint32_t> solution = {
@@ -106,7 +107,7 @@ TEST(GpuHist, BuildGidxSparse) {
     24, 24, 24, 24, 24,  5, 24, 24,  0, 16, 24, 15, 24, 24, 24, 24,
     24,  7, 14, 16,  4, 24, 24, 24, 24, 24,  9, 24, 24,  1, 24, 24
   };
-  for (size_t i = 0; i < kNRows * shard.row_stride; ++i) {
+  for (size_t i = 0; i < kNRows * shard.ellpack_matrix.row_stride; ++i) {
     ASSERT_EQ(solution[i], gidx[i]);
   }
 }
@@ -256,16 +257,19 @@ TEST(GpuHist, EvaluateSplits) {
   common::HistCutMatrix cmat = GetHostCutMatrix();
 
   // Copy cut matrix to device.
-  DeviceShard<GradientPairPrecise>::DeviceHistCutMatrix cut;
   shard->ba.Allocate(0,
-                     &(shard->d_cut.feature_segments), cmat.row_ptr.size(),
+                     &(shard->feature_segments), cmat.row_ptr.size(),
-                     &(shard->d_cut.min_fvalue), cmat.min_val.size(),
+                     &(shard->min_fvalue), cmat.min_val.size(),
-                     &(shard->d_cut.gidx_fvalue_map), 24,
+                     &(shard->gidx_fvalue_map), 24,
                      &(shard->monotone_constraints), kNCols);
-  shard->d_cut.feature_segments.copy(cmat.row_ptr.begin(), cmat.row_ptr.end());
+  shard->feature_segments.copy(cmat.row_ptr.begin(), cmat.row_ptr.end());
-  shard->d_cut.gidx_fvalue_map.copy(cmat.cut.begin(), cmat.cut.end());
+  shard->gidx_fvalue_map.copy(cmat.cut.begin(), cmat.cut.end());
   shard->monotone_constraints.copy(param.monotone_constraints.begin(),
                                    param.monotone_constraints.end());
+  shard->ellpack_matrix.feature_segments = shard->feature_segments.GetSpan();
+  shard->ellpack_matrix.gidx_fvalue_map = shard->gidx_fvalue_map.GetSpan();
+  shard->min_fvalue.copy(cmat.min_val.begin(), cmat.min_val.end());
+  shard->ellpack_matrix.min_fvalue = shard->min_fvalue.GetSpan();
 
   // Initialize DeviceShard::hist
   shard->hist.Init(0, (max_bins - 1) * kNCols);
@@ -339,7 +343,7 @@ TEST(GpuHist, ApplySplit) {
   shard->ridx_segments[0] = Segment(0, kNRows);
   shard->ba.Allocate(0, &(shard->ridx), kNRows,
                      &(shard->position), kNRows);
-  shard->row_stride = kNCols;
+  shard->ellpack_matrix.row_stride = kNCols;
   thrust::sequence(shard->ridx.CurrentDVec().tbegin(),
                    shard->ridx.CurrentDVec().tend());
   // Initialize GPUHistMaker
@@ -351,11 +355,9 @@ TEST(GpuHist, ApplySplit) {
                    0.59, 4,  // fvalue has to be equal to one of the cut field
                    GradientPair(8.2, 2.8), GradientPair(6.3, 3.6),
                    GPUTrainingParam(param));
-  GPUHistMakerSpecialised<GradientPairPrecise>::ExpandEntry candidate_entry {0, 0, candidate, 0};
+  ExpandEntry candidate_entry {0, 0, candidate, 0};
   candidate_entry.nid = kNId;
 
-  auto const& nodes = tree.GetNodes();
-
   // Used to get bin_id in update position.
   common::HistCutMatrix cmat = GetHostCutMatrix();
   hist_maker.hmat_ = cmat;
@@ -370,19 +372,31 @@ TEST(GpuHist, ApplySplit) {
   int row_stride = kNCols;
   int num_symbols = n_bins + 1;
   size_t compressed_size_bytes =
-      common::CompressedBufferWriter::CalculateBufferSize(
+      common::CompressedBufferWriter::CalculateBufferSize(row_stride * kNRows,
-          row_stride * kNRows, num_symbols);
+                                                          num_symbols);
-  shard->ba.Allocate(0, &(shard->gidx_buffer), compressed_size_bytes);
+  shard->ba.Allocate(0, &(shard->gidx_buffer), compressed_size_bytes,
+                     &(shard->feature_segments), cmat.row_ptr.size(),
+                     &(shard->min_fvalue), cmat.min_val.size(),
+                     &(shard->gidx_fvalue_map), 24);
+  shard->feature_segments.copy(cmat.row_ptr.begin(), cmat.row_ptr.end());
+  shard->gidx_fvalue_map.copy(cmat.cut.begin(), cmat.cut.end());
+  shard->ellpack_matrix.feature_segments = shard->feature_segments.GetSpan();
+  shard->ellpack_matrix.gidx_fvalue_map = shard->gidx_fvalue_map.GetSpan();
+  shard->min_fvalue.copy(cmat.min_val.begin(), cmat.min_val.end());
+  shard->ellpack_matrix.min_fvalue = shard->min_fvalue.GetSpan();
+  shard->ellpack_matrix.is_dense = true;
 
   common::CompressedBufferWriter wr(num_symbols);
-  std::vector<int> h_gidx (kNRows * row_stride);
+  // gidx 14 should go right, 12 goes left
-  std::iota(h_gidx.begin(), h_gidx.end(), 0);
+  std::vector<int> h_gidx (kNRows * row_stride, 14);
+  h_gidx[4] = 12;
+  h_gidx[12] = 12;
   std::vector<common::CompressedByteT> h_gidx_compressed (compressed_size_bytes);
 
   wr.Write(h_gidx_compressed.data(), h_gidx.begin(), h_gidx.end());
   shard->gidx_buffer.copy(h_gidx_compressed.begin(), h_gidx_compressed.end());
 
-  shard->gidx = common::CompressedIterator<uint32_t>(
+  shard->ellpack_matrix.gidx_iter = common::CompressedIterator<uint32_t>(
       shard->gidx_buffer.Data(), num_symbols);
 
   hist_maker.info_ = &info;
@@ -395,8 +409,8 @@ TEST(GpuHist, ApplySplit) {
   int right_nidx = tree[kNId].RightChild();
 
   ASSERT_EQ(shard->ridx_segments[left_nidx].begin, 0);
-  ASSERT_EQ(shard->ridx_segments[left_nidx].end, 6);
+  ASSERT_EQ(shard->ridx_segments[left_nidx].end, 2);
-  ASSERT_EQ(shard->ridx_segments[right_nidx].begin, 6);
+  ASSERT_EQ(shard->ridx_segments[right_nidx].begin, 2);
   ASSERT_EQ(shard->ridx_segments[right_nidx].end, 16);
 }
 
@@ -417,7 +431,7 @@ void TestSortPosition(const std::vector<int>& position_in, int left_idx,
       common::Span<int>(position_out.data().get(), position_out.size()),
       common::Span<bst_uint>(ridx.data().get(), ridx.size()),
       common::Span<bst_uint>(ridx_out.data().get(), ridx_out.size()), left_idx,
-      right_idx, d_left_count.data().get());
+      right_idx, d_left_count.data().get(), nullptr);
   thrust::host_vector<int> position_result = position_out;
   thrust::host_vector<int> ridx_result = ridx_out;