Define git attributes for renormalization. (#8921)
This commit is contained in:
Jiaming Yuan
GitHub
@@ -1,111 +1,111 @@
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/*!
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* Copyright 2021 by XGBoost Contributors
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*/
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#ifndef XGBOOST_TREE_DRIVER_H_
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#define XGBOOST_TREE_DRIVER_H_
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#include <xgboost/span.h>
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#include <queue>
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#include <vector>
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#include "./param.h"
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namespace xgboost {
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namespace tree {
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template <typename ExpandEntryT>
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inline bool DepthWise(const ExpandEntryT& lhs, const ExpandEntryT& rhs) {
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return lhs.GetNodeId() > rhs.GetNodeId(); // favor small depth
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}
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template <typename ExpandEntryT>
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inline bool LossGuide(const ExpandEntryT& lhs, const ExpandEntryT& 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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// Drives execution of tree building on device
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template <typename ExpandEntryT>
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class Driver {
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using ExpandQueue =
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std::priority_queue<ExpandEntryT, std::vector<ExpandEntryT>,
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std::function<bool(ExpandEntryT, ExpandEntryT)>>;
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public:
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explicit Driver(TrainParam param, std::size_t max_node_batch_size = 256)
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: param_(param),
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max_node_batch_size_(max_node_batch_size),
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queue_(param.grow_policy == TrainParam::kDepthWise ? DepthWise<ExpandEntryT>
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: LossGuide<ExpandEntryT>) {}
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template <typename EntryIterT>
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void Push(EntryIterT begin, EntryIterT end) {
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for (auto it = begin; it != end; ++it) {
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const ExpandEntryT& e = *it;
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if (e.split.loss_chg > kRtEps) {
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queue_.push(e);
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}
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}
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}
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void Push(const std::vector<ExpandEntryT> &entries) {
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this->Push(entries.begin(), entries.end());
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}
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void Push(ExpandEntryT const& e) { queue_.push(e); }
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bool IsEmpty() {
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return queue_.empty();
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}
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// Can a child of this entry still be expanded?
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// can be used to avoid extra work
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bool IsChildValid(ExpandEntryT const& parent_entry) {
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if (param_.max_depth > 0 && parent_entry.depth + 1 >= param_.max_depth) return false;
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if (param_.max_leaves > 0 && num_leaves_ >= param_.max_leaves) return false;
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return true;
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}
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// Return the set of nodes to be expanded
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// This set has no dependencies between entries so they may be expanded in
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// parallel or asynchronously
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std::vector<ExpandEntryT> Pop() {
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if (queue_.empty()) return {};
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// Return a single entry for loss guided mode
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if (param_.grow_policy == TrainParam::kLossGuide) {
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ExpandEntryT e = queue_.top();
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queue_.pop();
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if (e.IsValid(param_, num_leaves_)) {
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num_leaves_++;
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return {e};
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} else {
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return {};
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}
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}
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// Return nodes on same level for depth wise
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std::vector<ExpandEntryT> result;
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ExpandEntryT e = queue_.top();
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int level = e.depth;
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while (e.depth == level && !queue_.empty() && result.size() < max_node_batch_size_) {
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queue_.pop();
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if (e.IsValid(param_, num_leaves_)) {
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num_leaves_++;
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result.emplace_back(e);
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}
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if (!queue_.empty()) {
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e = queue_.top();
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}
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}
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return result;
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}
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private:
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TrainParam param_;
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bst_node_t num_leaves_ = 1;
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std::size_t max_node_batch_size_;
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ExpandQueue queue_;
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};
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} // namespace tree
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} // namespace xgboost
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#endif // XGBOOST_TREE_DRIVER_H_
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/*!
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* Copyright 2021 by XGBoost Contributors
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*/
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#ifndef XGBOOST_TREE_DRIVER_H_
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#define XGBOOST_TREE_DRIVER_H_
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#include <xgboost/span.h>
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#include <queue>
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#include <vector>
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#include "./param.h"
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namespace xgboost {
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namespace tree {
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template <typename ExpandEntryT>
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inline bool DepthWise(const ExpandEntryT& lhs, const ExpandEntryT& rhs) {
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return lhs.GetNodeId() > rhs.GetNodeId(); // favor small depth
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}
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template <typename ExpandEntryT>
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inline bool LossGuide(const ExpandEntryT& lhs, const ExpandEntryT& 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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// Drives execution of tree building on device
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template <typename ExpandEntryT>
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class Driver {
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using ExpandQueue =
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std::priority_queue<ExpandEntryT, std::vector<ExpandEntryT>,
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std::function<bool(ExpandEntryT, ExpandEntryT)>>;
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public:
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explicit Driver(TrainParam param, std::size_t max_node_batch_size = 256)
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: param_(param),
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max_node_batch_size_(max_node_batch_size),
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queue_(param.grow_policy == TrainParam::kDepthWise ? DepthWise<ExpandEntryT>
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: LossGuide<ExpandEntryT>) {}
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template <typename EntryIterT>
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void Push(EntryIterT begin, EntryIterT end) {
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for (auto it = begin; it != end; ++it) {
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const ExpandEntryT& e = *it;
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if (e.split.loss_chg > kRtEps) {
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queue_.push(e);
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}
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}
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}
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void Push(const std::vector<ExpandEntryT> &entries) {
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this->Push(entries.begin(), entries.end());
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}
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void Push(ExpandEntryT const& e) { queue_.push(e); }
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bool IsEmpty() {
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return queue_.empty();
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}
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// Can a child of this entry still be expanded?
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// can be used to avoid extra work
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bool IsChildValid(ExpandEntryT const& parent_entry) {
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if (param_.max_depth > 0 && parent_entry.depth + 1 >= param_.max_depth) return false;
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if (param_.max_leaves > 0 && num_leaves_ >= param_.max_leaves) return false;
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return true;
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}
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// Return the set of nodes to be expanded
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// This set has no dependencies between entries so they may be expanded in
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// parallel or asynchronously
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std::vector<ExpandEntryT> Pop() {
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if (queue_.empty()) return {};
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// Return a single entry for loss guided mode
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if (param_.grow_policy == TrainParam::kLossGuide) {
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ExpandEntryT e = queue_.top();
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queue_.pop();
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if (e.IsValid(param_, num_leaves_)) {
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num_leaves_++;
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return {e};
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} else {
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return {};
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}
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}
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// Return nodes on same level for depth wise
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std::vector<ExpandEntryT> result;
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ExpandEntryT e = queue_.top();
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int level = e.depth;
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while (e.depth == level && !queue_.empty() && result.size() < max_node_batch_size_) {
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queue_.pop();
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if (e.IsValid(param_, num_leaves_)) {
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num_leaves_++;
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result.emplace_back(e);
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}
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if (!queue_.empty()) {
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e = queue_.top();
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}
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}
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return result;
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}
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private:
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TrainParam param_;
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bst_node_t num_leaves_ = 1;
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std::size_t max_node_batch_size_;
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ExpandQueue queue_;
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};
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} // namespace tree
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} // namespace xgboost
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#endif // XGBOOST_TREE_DRIVER_H_
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