[SYCL]. Add implementation for loss-guided policy (#10681)
--------- Co-authored-by: Dmitry Razdoburdin <>
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@@ -79,6 +79,78 @@ void HistUpdater<GradientSumT>::BuildLocalHistograms(
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builder_monitor_.Stop("BuildLocalHistograms");
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}
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template<typename GradientSumT>
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void HistUpdater<GradientSumT>::ExpandWithLossGuide(
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const common::GHistIndexMatrix& gmat,
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RegTree* p_tree,
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const USMVector<GradientPair, MemoryType::on_device> &gpair) {
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builder_monitor_.Start("ExpandWithLossGuide");
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int num_leaves = 0;
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const auto lr = param_.learning_rate;
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ExpandEntry node(ExpandEntry::kRootNid, p_tree->GetDepth(ExpandEntry::kRootNid));
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BuildHistogramsLossGuide(node, gmat, p_tree, gpair);
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this->InitNewNode(ExpandEntry::kRootNid, gmat, gpair, *p_tree);
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this->EvaluateSplits({node}, gmat, *p_tree);
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node.split.loss_chg = snode_host_[ExpandEntry::kRootNid].best.loss_chg;
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qexpand_loss_guided_->push(node);
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++num_leaves;
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while (!qexpand_loss_guided_->empty()) {
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const ExpandEntry candidate = qexpand_loss_guided_->top();
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const int nid = candidate.nid;
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qexpand_loss_guided_->pop();
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if (!candidate.IsValid(param_, num_leaves)) {
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(*p_tree)[nid].SetLeaf(snode_host_[nid].weight * lr);
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} else {
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auto evaluator = tree_evaluator_.GetEvaluator();
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NodeEntry<GradientSumT>& e = snode_host_[nid];
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bst_float left_leaf_weight =
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evaluator.CalcWeight(nid, GradStats<GradientSumT>{e.best.left_sum}) * lr;
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bst_float right_leaf_weight =
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evaluator.CalcWeight(nid, GradStats<GradientSumT>{e.best.right_sum}) * lr;
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p_tree->ExpandNode(nid, e.best.SplitIndex(), e.best.split_value,
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e.best.DefaultLeft(), e.weight, left_leaf_weight,
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right_leaf_weight, e.best.loss_chg, e.stats.GetHess(),
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e.best.left_sum.GetHess(), e.best.right_sum.GetHess());
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this->ApplySplit({candidate}, gmat, p_tree);
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const int cleft = (*p_tree)[nid].LeftChild();
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const int cright = (*p_tree)[nid].RightChild();
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ExpandEntry left_node(cleft, p_tree->GetDepth(cleft));
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ExpandEntry right_node(cright, p_tree->GetDepth(cright));
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if (row_set_collection_[cleft].Size() < row_set_collection_[cright].Size()) {
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BuildHistogramsLossGuide(left_node, gmat, p_tree, gpair);
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} else {
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BuildHistogramsLossGuide(right_node, gmat, p_tree, gpair);
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}
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this->InitNewNode(cleft, gmat, gpair, *p_tree);
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this->InitNewNode(cright, gmat, gpair, *p_tree);
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bst_uint featureid = snode_host_[nid].best.SplitIndex();
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tree_evaluator_.AddSplit(nid, cleft, cright, featureid,
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snode_host_[cleft].weight, snode_host_[cright].weight);
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interaction_constraints_.Split(nid, featureid, cleft, cright);
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this->EvaluateSplits({left_node, right_node}, gmat, *p_tree);
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left_node.split.loss_chg = snode_host_[cleft].best.loss_chg;
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right_node.split.loss_chg = snode_host_[cright].best.loss_chg;
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qexpand_loss_guided_->push(left_node);
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qexpand_loss_guided_->push(right_node);
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++num_leaves; // give two and take one, as parent is no longer a leaf
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}
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}
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builder_monitor_.Stop("ExpandWithLossGuide");
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}
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template<typename GradientSumT>
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void HistUpdater<GradientSumT>::InitSampling(
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const USMVector<GradientPair, MemoryType::on_device> &gpair,
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@@ -249,6 +321,14 @@ void HistUpdater<GradientSumT>::InitData(
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}
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std::fill(snode_host_.begin(), snode_host_.end(), NodeEntry<GradientSumT>(param_));
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{
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if (param_.grow_policy == xgboost::tree::TrainParam::kLossGuide) {
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qexpand_loss_guided_.reset(new ExpandQueue(LossGuide));
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} else {
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LOG(WARNING) << "Depth-wise building is not yet implemented";
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}
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}
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builder_monitor_.Stop("InitData");
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}
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@@ -305,8 +385,7 @@ template <typename GradientSumT>
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void HistUpdater<GradientSumT>::InitNewNode(int nid,
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const common::GHistIndexMatrix& gmat,
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const USMVector<GradientPair,
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MemoryType::on_device> &gpair,
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const DMatrix& fmat,
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MemoryType::on_device> &gpair,
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const RegTree& tree) {
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builder_monitor_.Start("InitNewNode");
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@@ -14,6 +14,7 @@
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#include <utility>
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#include <vector>
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#include <memory>
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#include <queue>
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#include "../common/partition_builder.h"
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#include "split_evaluator.h"
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@@ -126,7 +127,6 @@ class HistUpdater {
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void InitNewNode(int nid,
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const common::GHistIndexMatrix& gmat,
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const USMVector<GradientPair, MemoryType::on_device> &gpair,
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const DMatrix& fmat,
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const RegTree& tree);
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void BuildLocalHistograms(const common::GHistIndexMatrix &gmat,
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@@ -139,6 +139,18 @@ class HistUpdater {
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RegTree *p_tree,
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const USMVector<GradientPair, MemoryType::on_device> &gpair);
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void ExpandWithLossGuide(const common::GHistIndexMatrix& gmat,
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RegTree* p_tree,
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const USMVector<GradientPair, MemoryType::on_device>& gpair);
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inline static bool LossGuide(ExpandEntry lhs, ExpandEntry rhs) {
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if (lhs.GetLossChange() == rhs.GetLossChange()) {
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return lhs.GetNodeId() > rhs.GetNodeId(); // favor small timestamp
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} else {
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return lhs.GetLossChange() < rhs.GetLossChange(); // favor large loss_chg
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}
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}
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// --data fields--
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const Context* ctx_;
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size_t sub_group_size_;
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@@ -163,6 +175,12 @@ class HistUpdater {
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const RegTree* p_last_tree_;
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DMatrix const* const p_last_fmat_;
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using ExpandQueue =
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std::priority_queue<ExpandEntry, std::vector<ExpandEntry>,
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std::function<bool(ExpandEntry, ExpandEntry)>>;
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std::unique_ptr<ExpandQueue> qexpand_loss_guided_;
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enum DataLayout { kDenseDataZeroBased, kDenseDataOneBased, kSparseData };
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DataLayout data_layout_;
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