Optimization of EvaluateSplit function

src/common/threading_utils.h

@@ -0,0 +1,123 @@
+/*!
+ * Copyright 2015-2019 by Contributors
+ * \file common.h
+ * \brief Threading utilities
+ */
+#ifndef XGBOOST_COMMON_THREADING_UTILS_H_
+#define XGBOOST_COMMON_THREADING_UTILS_H_
+
+#include <vector>
+#include <algorithm>
+
+namespace xgboost {
+namespace common {
+
+// Represent simple range of indexes [begin, end)
+// Inspired by tbb::blocked_range
+class Range1d {
+ public:
+  Range1d(size_t begin, size_t end): begin_(begin), end_(end) {
+    CHECK_LT(begin, end);
+  }
+
+  size_t begin() {
+    return begin_;
+  }
+
+  size_t end() {
+    return end_;
+  }
+
+ private:
+  size_t begin_;
+  size_t end_;
+};
+
+
+// Split 2d space to balanced blocks
+// Implementation of the class is inspired by tbb::blocked_range2d
+// However, TBB provides only (n x m) 2d range (matrix) separated by blocks. Example:
+// [ 1,2,3 ]
+// [ 4,5,6 ]
+// [ 7,8,9 ]
+// But the class is able to work with different sizes in each 'row'. Example:
+// [ 1,2 ]
+// [ 3,4,5,6 ]
+// [ 7,8,9]
+// If grain_size is 2: It produces following blocks:
+// [1,2], [3,4], [5,6], [7,8], [9]
+// The class helps to process data in several tree nodes (non-balanced usually) in parallel
+// Using nested parallelism (by nodes and by data in each node)
+// it helps  to improve CPU resources utilization
+class BlockedSpace2d {
+ public:
+  // Example of space:
+  // [ 1,2 ]
+  // [ 3,4,5,6 ]
+  // [ 7,8,9]
+  // BlockedSpace2d will create following blocks (tasks) if grain_size=2:
+  // 1-block: first_dimension = 0, range of indexes in a 'row' = [0,2) (includes [1,2] values)
+  // 2-block: first_dimension = 1, range of indexes in a 'row' = [0,2) (includes [3,4] values)
+  // 3-block: first_dimension = 1, range of indexes in a 'row' = [2,4) (includes [5,6] values)
+  // 4-block: first_dimension = 2, range of indexes in a 'row' = [0,2) (includes [7,8] values)
+  // 5-block: first_dimension = 2, range of indexes in a 'row' = [2,3) (includes [9] values)
+  // Arguments:
+  // dim1 - size of the first dimension in the space
+  // getter_size_dim2 - functor to get the second dimensions for each 'row' by row-index
+  // grain_size - max size of produced blocks
+  template<typename Func>
+  BlockedSpace2d(size_t dim1, Func getter_size_dim2, size_t grain_size) {
+    for (size_t i = 0; i < dim1; ++i) {
+      const size_t size = getter_size_dim2(i);
+      const size_t n_blocks = size/grain_size + !!(size % grain_size);
+      for (size_t iblock = 0; iblock < n_blocks; ++iblock) {
+        const size_t begin = iblock * grain_size;
+        const size_t end   = std::min(begin + grain_size, size);
+        AddBlock(i, begin, end);
+      }
+    }
+  }
+
+  // Amount of blocks(tasks) in a space
+  size_t Size() const {
+    return ranges_.size();
+  }
+
+  // get index of the first dimension of i-th block(task)
+  size_t GetFirstDimension(size_t i) const {
+    CHECK_LT(i, first_dimension_.size());
+    return first_dimension_[i];
+  }
+
+  // get a range of indexes for the second dimension of i-th block(task)
+  Range1d GetRange(size_t i) const {
+    CHECK_LT(i, ranges_.size());
+    return ranges_[i];
+  }
+
+ private:
+  void AddBlock(size_t first_dimension, size_t begin, size_t end) {
+    first_dimension_.push_back(first_dimension);
+    ranges_.emplace_back(begin, end);
+  }
+
+  std::vector<Range1d> ranges_;
+  std::vector<size_t> first_dimension_;
+};
+
+
+// Wrapper to implement nested parallelism with simple omp parallel for
+template<typename Func>
+void ParallelFor2d(const BlockedSpace2d& space, Func func) {
+  const int num_blocks_in_space = static_cast<int>(space.Size());
+
+  #pragma omp parallel for
+  for (auto i = 0; i < num_blocks_in_space; i++) {
+    func(space.GetFirstDimension(i), space.GetRange(i));
+  }
+}
+
+}  // namespace common
+}  // namespace xgboost
+
+#endif  // XGBOOST_COMMON_THREADING_UTILS_H_

src/tree/updater_quantile_hist.cc

@@ -28,6 +28,8 @@
 #include "../common/hist_util.h"
 #include "../common/row_set.h"
 #include "../common/column_matrix.h"
+#include "../common/threading_utils.h"
+
 
 namespace xgboost {
 namespace tree {
@@ -188,9 +190,11 @@ void QuantileHistMaker::Builder::EvaluateSplits(
     int depth,
     unsigned *timestamp,
     std::vector<ExpandEntry> *temp_qexpand_depth) {
+  this->EvaluateSplit(qexpand_depth_wise_, gmat, hist_, *p_fmat, *p_tree);
+
   for (auto const& entry : qexpand_depth_wise_) {
     int nid = entry.nid;
-    this->EvaluateSplit(nid, gmat, hist_, *p_fmat, *p_tree);
+
     if (snode_[nid].best.loss_chg < kRtEps ||
         (param_.max_depth > 0 && depth == param_.max_depth) ||
         (param_.max_leaves > 0 && (*num_leaves) == param_.max_leaves)) {
@@ -220,7 +224,8 @@ void QuantileHistMaker::Builder::ExpandWithDepthWise(
   int num_leaves = 0;
 
   // in depth_wise growing, we feed loss_chg with 0.0 since it is not used anyway
-  qexpand_depth_wise_.emplace_back(ExpandEntry(0, p_tree->GetDepth(0), 0.0, timestamp++));
+  qexpand_depth_wise_.emplace_back(ExpandEntry(ExpandEntry::kRootNid,
+      p_tree->GetDepth(ExpandEntry::kRootNid), 0.0, timestamp++));
   ++num_leaves;
   for (int depth = 0; depth < param_.max_depth + 1; depth++) {
     int starting_index = std::numeric_limits<int>::max();
@@ -254,14 +259,18 @@ void QuantileHistMaker::Builder::ExpandWithLossGuide(
   unsigned timestamp = 0;
   int num_leaves = 0;
 
-  hist_.AddHistRow(0);
-  BuildHist(gpair_h, row_set_collection_[0], gmat, gmatb, hist_[0], true);
+  hist_.AddHistRow(ExpandEntry::kRootNid);
+  BuildHist(gpair_h, row_set_collection_[ExpandEntry::kRootNid], gmat, gmatb,
+      hist_[ExpandEntry::kRootNid], true);
 
-  this->InitNewNode(0, gmat, gpair_h, *p_fmat, *p_tree);
+  this->InitNewNode(ExpandEntry::kRootNid, gmat, gpair_h, *p_fmat, *p_tree);
 
-  this->EvaluateSplit(0, gmat, hist_, *p_fmat, *p_tree);
-  qexpand_loss_guided_->push(ExpandEntry(0, p_tree->GetDepth(0),
-                                         snode_[0].best.loss_chg, timestamp++));
+  ExpandEntry node(ExpandEntry::kRootNid, p_tree->GetDepth(ExpandEntry::kRootNid),
+      snode_[ExpandEntry::kRootNid].best.loss_chg, timestamp++);
+  this->EvaluateSplit({node}, gmat, hist_, *p_fmat, *p_tree);
+  node.loss_chg = snode_[ExpandEntry::kRootNid].best.loss_chg;
+
+  qexpand_loss_guided_->push(node);
   ++num_leaves;
 
   while (!qexpand_loss_guided_->empty()) {
@@ -301,15 +310,17 @@ void QuantileHistMaker::Builder::ExpandWithLossGuide(
                            snode_[cleft].weight, snode_[cright].weight);
       interaction_constraints_.Split(nid, featureid, cleft, cright);
 
-      this->EvaluateSplit(cleft, gmat, hist_, *p_fmat, *p_tree);
-      this->EvaluateSplit(cright, gmat, hist_, *p_fmat, *p_tree);
+      ExpandEntry left_node(cleft, p_tree->GetDepth(cleft),
+                            snode_[cleft].best.loss_chg, timestamp++);
+      ExpandEntry right_node(cright, p_tree->GetDepth(cright),
+                            snode_[cright].best.loss_chg, timestamp++);
+
+      this->EvaluateSplit({left_node, right_node}, gmat, hist_, *p_fmat, *p_tree);
+      left_node.loss_chg = snode_[cleft].best.loss_chg;
+      right_node.loss_chg = snode_[cright].best.loss_chg;
 
-      qexpand_loss_guided_->push(ExpandEntry(cleft, p_tree->GetDepth(cleft),
-                                 snode_[cleft].best.loss_chg,
-                                 timestamp++));
-      qexpand_loss_guided_->push(ExpandEntry(cright, p_tree->GetDepth(cright),
-                                 snode_[cright].best.loss_chg,
-                                 timestamp++));
+      qexpand_loss_guided_->push(left_node);
+      qexpand_loss_guided_->push(right_node);
 
       ++num_leaves;  // give two and take one, as parent is no longer a leaf
     }
@@ -554,40 +565,77 @@ void QuantileHistMaker::Builder::InitData(const GHistIndexMatrix& gmat,
   builder_monitor_.Stop("InitData");
 }
 
-void QuantileHistMaker::Builder::EvaluateSplit(const int nid,
+// if sum of statistics for non-missing values in the node
+// is equal to sum of statistics for all values:
+// then - there are no missing values
+// else - there are missing values
+bool QuantileHistMaker::Builder::SplitContainsMissingValues(const GradStats e,
+                                                            const NodeEntry& snode) {
+  if (e.GetGrad() == snode.stats.GetGrad() && e.GetHess() == snode.stats.GetHess()) {
+    return false;
+  } else {
+    return true;
+  }
+}
+
+// nodes_set - set of nodes to be processed in parallel
+void QuantileHistMaker::Builder::EvaluateSplit(const std::vector<ExpandEntry>& nodes_set,
                                                const GHistIndexMatrix& gmat,
                                                const HistCollection& hist,
                                                const DMatrix& fmat,
                                                const RegTree& tree) {
   builder_monitor_.Start("EvaluateSplit");
-  // start enumeration
-  const MetaInfo& info = fmat.Info();
-  auto p_feature_set = column_sampler_.GetFeatureSet(tree.GetDepth(nid));
-  auto const& feature_set = p_feature_set->HostVector();
-  const auto nfeature = static_cast<bst_feature_t>(feature_set.size());
+
+  const size_t n_nodes_in_set = nodes_set.size();
   const auto nthread = static_cast<bst_omp_uint>(this->nthread_);
-  best_split_tloc_.resize(nthread);
-#pragma omp parallel for schedule(static) num_threads(nthread)
-  for (bst_omp_uint tid = 0; tid < nthread; ++tid) {
-    best_split_tloc_[tid] = snode_[nid].best;
+
+  using FeatureSetType = std::shared_ptr<HostDeviceVector<bst_feature_t>>;
+  std::vector<FeatureSetType> features_sets(n_nodes_in_set);
+  best_split_tloc_.resize(nthread * n_nodes_in_set);
+
+  // Generate feature set for each tree node
+  for (size_t nid_in_set = 0; nid_in_set < n_nodes_in_set; ++nid_in_set) {
+    const int32_t nid = nodes_set[nid_in_set].nid;
+    features_sets[nid_in_set] = column_sampler_.GetFeatureSet(tree.GetDepth(nid));
+
+    for (unsigned tid = 0; tid < nthread; ++tid) {
+      best_split_tloc_[nthread*nid_in_set + tid] = snode_[nid].best;
+    }
   }
-  GHistRow node_hist = hist[nid];
 
-#pragma omp parallel for schedule(dynamic) num_threads(nthread)
-  for (bst_omp_uint i = 0; i < nfeature; ++i) {  // NOLINT(*)
-    const auto feature_id = static_cast<bst_uint>(feature_set[i]);
+  // Create 2D space (# of nodes to process x # of features to process)
+  // to process them in parallel
+  common::BlockedSpace2d space(n_nodes_in_set, [&](size_t nid_in_set) {
+      return features_sets[nid_in_set]->Size();
+  }, 1);
+
+  // Start parallel enumeration for all tree nodes in the set and all features
+  common::ParallelFor2d(space, [&](size_t nid_in_set, common::Range1d r) {
+    const int32_t nid = nodes_set[nid_in_set].nid;
     const auto tid = static_cast<unsigned>(omp_get_thread_num());
-    const auto node_id = static_cast<bst_uint>(nid);
-    if (interaction_constraints_.Query(node_id, feature_id)) {
-      this->EnumerateSplit(-1, gmat, node_hist, snode_[nid], info,
-                           &best_split_tloc_[tid], feature_id, node_id);
-      this->EnumerateSplit(+1, gmat, node_hist, snode_[nid], info,
-                           &best_split_tloc_[tid], feature_id, node_id);
+    GHistRow node_hist = hist[nid];
+
+    for (auto idx_in_feature_set = r.begin(); idx_in_feature_set < r.end(); ++idx_in_feature_set) {
+      const auto fid = features_sets[nid_in_set]->ConstHostVector()[idx_in_feature_set];
+      if (interaction_constraints_.Query(nid, fid)) {
+        auto grad_stats = this->EnumerateSplit<+1>(gmat, node_hist, snode_[nid], fmat.Info(),
+                               &best_split_tloc_[nthread*nid_in_set + tid], fid, nid);
+        if (SplitContainsMissingValues(grad_stats, snode_[nid])) {
+          this->EnumerateSplit<-1>(gmat, node_hist, snode_[nid], fmat.Info(),
+                               &best_split_tloc_[nthread*nid_in_set + tid], fid, nid);
+        }
+      }
+    }
+  });
+
+  // Find Best Split across threads for each node in nodes set
+  for (unsigned nid_in_set = 0; nid_in_set < n_nodes_in_set; ++nid_in_set) {
+    const int32_t nid = nodes_set[nid_in_set].nid;
+    for (unsigned tid = 0; tid < nthread; ++tid) {
+      snode_[nid].best.Update(best_split_tloc_[nthread*nid_in_set + tid]);
     }
   }
-  for (unsigned tid = 0; tid < nthread; ++tid) {
-    snode_[nid].best.Update(best_split_tloc_[tid]);
-  }
+
   builder_monitor_.Stop("EvaluateSplit");
 }
 
@@ -831,8 +879,12 @@ void QuantileHistMaker::Builder::InitNewNode(int nid,
   builder_monitor_.Stop("InitNewNode");
 }
 
-// enumerate the split values of specific feature
-void QuantileHistMaker::Builder::EnumerateSplit(int d_step,
+
+// Enumerate the split values of specific feature.
+// Returns the sum of gradients corresponding to the data points that contains a non-missing value
+// for the particular feature fid.
+template<int d_step>
+GradStats QuantileHistMaker::Builder::EnumerateSplit(
                                                 const GHistIndexMatrix& gmat,
                                                 const GHistRow& hist,
                                                 const NodeEntry& snode,
@@ -904,6 +956,8 @@ void QuantileHistMaker::Builder::EnumerateSplit(int d_step,
     }
   }
   p_best->Update(best);
+
+  return e;
 }
 
 XGBOOST_REGISTER_TREE_UPDATER(FastHistMaker, "grow_fast_histmaker")

src/tree/updater_quantile_hist.h

@@ -180,6 +180,7 @@ class QuantileHistMaker: public TreeUpdater {
    protected:
     /* tree growing policies */
     struct ExpandEntry {
+      static const int kRootNid = 0;
       int nid;
       int depth;
       bst_float loss_chg;
@@ -194,7 +195,7 @@ class QuantileHistMaker: public TreeUpdater {
                   const DMatrix& fmat,
                   const RegTree& tree);
 
-    void EvaluateSplit(const int nid,
+    void EvaluateSplit(const std::vector<ExpandEntry>& nodes_set,
                        const GHistIndexMatrix& gmat,
                        const HistCollection& hist,
                        const DMatrix& fmat,
@@ -229,8 +230,11 @@ class QuantileHistMaker: public TreeUpdater {
                      const DMatrix& fmat,
                      const RegTree& tree);
 
-    // enumerate the split values of specific feature
-    void EnumerateSplit(int d_step,
+    // Enumerate the split values of specific feature
+    // Returns the sum of gradients corresponding to the data points that contains a non-missing
+    // value for the particular feature fid.
+    template<int d_step>
+    GradStats EnumerateSplit(
                         const GHistIndexMatrix& gmat,
                         const GHistRow& hist,
                         const NodeEntry& snode,
@@ -239,6 +243,12 @@ class QuantileHistMaker: public TreeUpdater {
                         bst_uint fid,
                         bst_uint nodeID);
 
+    // if sum of statistics for non-missing values in the node
+    // is equal to sum of statistics for all values:
+    // then - there are no missing values
+    // else - there are missing values
+    bool SplitContainsMissingValues(const GradStats e, const NodeEntry& snode);
+
     void ExpandWithDepthWise(const GHistIndexMatrix &gmat,
                              const GHistIndexBlockMatrix &gmatb,
                              const ColumnMatrix &column_matrix,