f0064c07ab
* Refactor configuration [Part II].
* General changes:
** Remove `Init` methods to avoid ambiguity.
** Remove `Configure(std::map<>)` to avoid redundant copying and prepare for
parameter validation. (`std::vector` is returned from `InitAllowUnknown`).
** Add name to tree updaters for easier debugging.
* Learner changes:
** Make `LearnerImpl` the only source of configuration.
All configurations are stored and carried out by `LearnerImpl::Configure()`.
** Remove booster in C API.
Originally kept for "compatibility reason", but did not state why. So here
we just remove it.
** Add a `metric_names_` field in `LearnerImpl`.
** Remove `LazyInit`. Configuration will always be lazy.
** Run `Configure` before every iteration.
* Predictor changes:
** Allocate both cpu and gpu predictor.
** Remove cpu_predictor from gpu_predictor.
`GBTree` is now used to dispatch the predictor.
** Remove some GPU Predictor tests.
* IO
No IO changes. The binary model format stability is tested by comparing
hashing value of save models between two commits
932 lines
36 KiB
C++
932 lines
36 KiB
C++
/*!
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* Copyright 2014 by Contributors
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* \file updater_colmaker.cc
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* \brief use columnwise update to construct a tree
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* \author Tianqi Chen
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*/
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#include <rabit/rabit.h>
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#include <xgboost/tree_updater.h>
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#include <xgboost/logging.h>
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#include <memory>
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#include <vector>
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#include <cmath>
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#include <algorithm>
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#include "param.h"
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#include "../common/random.h"
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#include "../common/bitmap.h"
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#include "split_evaluator.h"
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namespace xgboost {
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namespace tree {
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DMLC_REGISTRY_FILE_TAG(updater_colmaker);
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/*! \brief column-wise update to construct a tree */
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class ColMaker: public TreeUpdater {
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public:
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void Configure(const Args& args) override {
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param_.InitAllowUnknown(args);
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spliteval_.reset(SplitEvaluator::Create(param_.split_evaluator));
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spliteval_->Init(args);
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}
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char const* Name() const override {
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return "grow_colmaker";
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}
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void Update(HostDeviceVector<GradientPair> *gpair,
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DMatrix* dmat,
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const std::vector<RegTree*> &trees) override {
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// rescale learning rate according to size of trees
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float lr = param_.learning_rate;
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param_.learning_rate = lr / trees.size();
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// build tree
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for (auto tree : trees) {
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Builder builder(
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param_,
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std::unique_ptr<SplitEvaluator>(spliteval_->GetHostClone()));
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builder.Update(gpair->ConstHostVector(), dmat, tree);
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}
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param_.learning_rate = lr;
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}
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protected:
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// training parameter
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TrainParam param_;
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// SplitEvaluator that will be cloned for each Builder
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std::unique_ptr<SplitEvaluator> spliteval_;
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// data structure
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/*! \brief per thread x per node entry to store tmp data */
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struct ThreadEntry {
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/*! \brief statistics of data */
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GradStats stats;
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/*! \brief extra statistics of data */
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GradStats stats_extra;
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/*! \brief last feature value scanned */
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bst_float last_fvalue;
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/*! \brief first feature value scanned */
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bst_float first_fvalue;
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/*! \brief current best solution */
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SplitEntry best;
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// constructor
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ThreadEntry() : last_fvalue{0}, first_fvalue{0} {}
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};
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struct NodeEntry {
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/*! \brief statics for node entry */
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GradStats stats;
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/*! \brief loss of this node, without split */
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bst_float root_gain;
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/*! \brief weight calculated related to current data */
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bst_float weight;
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/*! \brief current best solution */
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SplitEntry best;
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// constructor
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NodeEntry() : root_gain{0.0f}, weight{0.0f} {}
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};
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// actual builder that runs the algorithm
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class Builder {
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public:
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// constructor
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explicit Builder(const TrainParam& param,
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std::unique_ptr<SplitEvaluator> spliteval)
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: param_(param), nthread_(omp_get_max_threads()),
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spliteval_(std::move(spliteval)) {}
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// update one tree, growing
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virtual void Update(const std::vector<GradientPair>& gpair,
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DMatrix* p_fmat,
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RegTree* p_tree) {
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std::vector<int> newnodes;
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this->InitData(gpair, *p_fmat, *p_tree);
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this->InitNewNode(qexpand_, gpair, *p_fmat, *p_tree);
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for (int depth = 0; depth < param_.max_depth; ++depth) {
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this->FindSplit(depth, qexpand_, gpair, p_fmat, p_tree);
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this->ResetPosition(qexpand_, p_fmat, *p_tree);
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this->UpdateQueueExpand(*p_tree, qexpand_, &newnodes);
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this->InitNewNode(newnodes, gpair, *p_fmat, *p_tree);
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for (auto nid : qexpand_) {
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if ((*p_tree)[nid].IsLeaf()) {
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continue;
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}
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int cleft = (*p_tree)[nid].LeftChild();
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int cright = (*p_tree)[nid].RightChild();
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spliteval_->AddSplit(nid,
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cleft,
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cright,
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snode_[nid].best.SplitIndex(),
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snode_[cleft].weight,
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snode_[cright].weight);
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}
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qexpand_ = newnodes;
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// if nothing left to be expand, break
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if (qexpand_.size() == 0) break;
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}
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// set all the rest expanding nodes to leaf
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for (const int nid : qexpand_) {
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(*p_tree)[nid].SetLeaf(snode_[nid].weight * param_.learning_rate);
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}
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// remember auxiliary statistics in the tree node
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for (int nid = 0; nid < p_tree->param.num_nodes; ++nid) {
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p_tree->Stat(nid).loss_chg = snode_[nid].best.loss_chg;
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p_tree->Stat(nid).base_weight = snode_[nid].weight;
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p_tree->Stat(nid).sum_hess = static_cast<float>(snode_[nid].stats.sum_hess);
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}
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}
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protected:
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// initialize temp data structure
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inline void InitData(const std::vector<GradientPair>& gpair,
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const DMatrix& fmat,
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const RegTree& tree) {
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CHECK_EQ(tree.param.num_nodes, tree.param.num_roots)
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<< "ColMaker: can only grow new tree";
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const std::vector<unsigned>& root_index = fmat.Info().root_index_;
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{
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// setup position
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position_.resize(gpair.size());
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CHECK_EQ(fmat.Info().num_row_, position_.size());
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if (root_index.size() == 0) {
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std::fill(position_.begin(), position_.end(), 0);
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} else {
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for (size_t ridx = 0; ridx < position_.size(); ++ridx) {
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position_[ridx] = root_index[ridx];
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CHECK_LT(root_index[ridx], (unsigned)tree.param.num_roots);
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}
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}
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// mark delete for the deleted datas
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for (size_t ridx = 0; ridx < position_.size(); ++ridx) {
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if (gpair[ridx].GetHess() < 0.0f) position_[ridx] = ~position_[ridx];
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}
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// mark subsample
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if (param_.subsample < 1.0f) {
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std::bernoulli_distribution coin_flip(param_.subsample);
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auto& rnd = common::GlobalRandom();
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for (size_t ridx = 0; ridx < position_.size(); ++ridx) {
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if (gpair[ridx].GetHess() < 0.0f) continue;
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if (!coin_flip(rnd)) position_[ridx] = ~position_[ridx];
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}
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}
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}
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{
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column_sampler_.Init(fmat.Info().num_col_, param_.colsample_bynode,
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param_.colsample_bylevel, param_.colsample_bytree);
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}
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{
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// setup temp space for each thread
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// reserve a small space
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stemp_.clear();
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stemp_.resize(this->nthread_, std::vector<ThreadEntry>());
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for (auto& i : stemp_) {
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i.clear(); i.reserve(256);
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}
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snode_.reserve(256);
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}
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{
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// expand query
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qexpand_.reserve(256); qexpand_.clear();
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for (int i = 0; i < tree.param.num_roots; ++i) {
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qexpand_.push_back(i);
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}
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}
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}
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/*!
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* \brief initialize the base_weight, root_gain,
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* and NodeEntry for all the new nodes in qexpand
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*/
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inline void InitNewNode(const std::vector<int>& qexpand,
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const std::vector<GradientPair>& gpair,
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const DMatrix& fmat,
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const RegTree& tree) {
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{
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// setup statistics space for each tree node
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for (auto& i : stemp_) {
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i.resize(tree.param.num_nodes, ThreadEntry());
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}
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snode_.resize(tree.param.num_nodes, NodeEntry());
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}
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const MetaInfo& info = fmat.Info();
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// setup position
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const auto ndata = static_cast<bst_omp_uint>(info.num_row_);
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#pragma omp parallel for schedule(static)
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for (bst_omp_uint ridx = 0; ridx < ndata; ++ridx) {
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const int tid = omp_get_thread_num();
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if (position_[ridx] < 0) continue;
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stemp_[tid][position_[ridx]].stats.Add(gpair[ridx]);
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}
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// sum the per thread statistics together
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for (int nid : qexpand) {
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GradStats stats;
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for (auto& s : stemp_) {
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stats.Add(s[nid].stats);
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}
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// update node statistics
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snode_[nid].stats = stats;
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}
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// calculating the weights
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for (int nid : qexpand) {
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bst_uint parentid = tree[nid].Parent();
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snode_[nid].weight = static_cast<float>(
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spliteval_->ComputeWeight(parentid, snode_[nid].stats));
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snode_[nid].root_gain = static_cast<float>(
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spliteval_->ComputeScore(parentid, snode_[nid].stats, snode_[nid].weight));
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}
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}
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/*! \brief update queue expand add in new leaves */
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inline void UpdateQueueExpand(const RegTree& tree,
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const std::vector<int> &qexpand,
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std::vector<int>* p_newnodes) {
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p_newnodes->clear();
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for (int nid : qexpand) {
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if (!tree[ nid ].IsLeaf()) {
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p_newnodes->push_back(tree[nid].LeftChild());
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p_newnodes->push_back(tree[nid].RightChild());
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}
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}
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}
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// parallel find the best split of current fid
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// this function does not support nested functions
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inline void ParallelFindSplit(const SparsePage::Inst &col,
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bst_uint fid,
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DMatrix *p_fmat,
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const std::vector<GradientPair> &gpair) {
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// TODO(tqchen): double check stats order.
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const bool ind = col.size() != 0 && col[0].fvalue == col[col.size() - 1].fvalue;
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bool need_forward = param_.NeedForwardSearch(p_fmat->GetColDensity(fid), ind);
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bool need_backward = param_.NeedBackwardSearch(p_fmat->GetColDensity(fid), ind);
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const std::vector<int> &qexpand = qexpand_;
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#pragma omp parallel
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{
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const int tid = omp_get_thread_num();
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std::vector<ThreadEntry> &temp = stemp_[tid];
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// cleanup temp statistics
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for (int j : qexpand) {
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temp[j].stats = GradStats();
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}
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bst_uint step = (col.size() + this->nthread_ - 1) / this->nthread_;
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bst_uint end = std::min(static_cast<bst_uint>(col.size()), step * (tid + 1));
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for (bst_uint i = tid * step; i < end; ++i) {
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const bst_uint ridx = col[i].index;
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const int nid = position_[ridx];
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if (nid < 0) continue;
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const bst_float fvalue = col[i].fvalue;
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if (temp[nid].stats.Empty()) {
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temp[nid].first_fvalue = fvalue;
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}
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temp[nid].stats.Add(gpair[ridx]);
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temp[nid].last_fvalue = fvalue;
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}
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}
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// start collecting the partial sum statistics
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auto nnode = static_cast<bst_omp_uint>(qexpand.size());
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#pragma omp parallel for schedule(static)
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for (bst_omp_uint j = 0; j < nnode; ++j) {
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const int nid = qexpand[j];
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GradStats sum, tmp, c;
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for (int tid = 0; tid < this->nthread_; ++tid) {
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tmp = stemp_[tid][nid].stats;
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stemp_[tid][nid].stats = sum;
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sum.Add(tmp);
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if (tid != 0) {
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std::swap(stemp_[tid - 1][nid].last_fvalue, stemp_[tid][nid].first_fvalue);
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}
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}
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for (int tid = 0; tid < this->nthread_; ++tid) {
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stemp_[tid][nid].stats_extra = sum;
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ThreadEntry &e = stemp_[tid][nid];
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bst_float fsplit;
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if (tid != 0) {
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if (stemp_[tid - 1][nid].last_fvalue != e.first_fvalue) {
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fsplit = (stemp_[tid - 1][nid].last_fvalue + e.first_fvalue) * 0.5f;
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} else {
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continue;
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}
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} else {
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fsplit = e.first_fvalue - kRtEps;
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}
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if (need_forward && tid != 0) {
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c.SetSubstract(snode_[nid].stats, e.stats);
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if (c.sum_hess >= param_.min_child_weight &&
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e.stats.sum_hess >= param_.min_child_weight) {
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auto loss_chg = static_cast<bst_float>(
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spliteval_->ComputeSplitScore(nid, fid, e.stats, c) -
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snode_[nid].root_gain);
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e.best.Update(loss_chg, fid, fsplit, false, e.stats, c);
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}
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}
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if (need_backward) {
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tmp.SetSubstract(sum, e.stats);
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c.SetSubstract(snode_[nid].stats, tmp);
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if (c.sum_hess >= param_.min_child_weight &&
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tmp.sum_hess >= param_.min_child_weight) {
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auto loss_chg = static_cast<bst_float>(
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spliteval_->ComputeSplitScore(nid, fid, tmp, c) -
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snode_[nid].root_gain);
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e.best.Update(loss_chg, fid, fsplit, true, tmp, c);
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}
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}
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}
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if (need_backward) {
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tmp = sum;
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ThreadEntry &e = stemp_[this->nthread_-1][nid];
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c.SetSubstract(snode_[nid].stats, tmp);
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if (c.sum_hess >= param_.min_child_weight &&
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tmp.sum_hess >= param_.min_child_weight) {
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auto loss_chg = static_cast<bst_float>(
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spliteval_->ComputeSplitScore(nid, fid, tmp, c) -
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snode_[nid].root_gain);
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e.best.Update(loss_chg, fid, e.last_fvalue + kRtEps, true, tmp, c);
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}
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}
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}
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// rescan, generate candidate split
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#pragma omp parallel
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{
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GradStats c, cright;
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const int tid = omp_get_thread_num();
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std::vector<ThreadEntry> &temp = stemp_[tid];
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bst_uint step = (col.size() + this->nthread_ - 1) / this->nthread_;
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bst_uint end = std::min(static_cast<bst_uint>(col.size()), step * (tid + 1));
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for (bst_uint i = tid * step; i < end; ++i) {
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const bst_uint ridx = col[i].index;
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const int nid = position_[ridx];
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if (nid < 0) continue;
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const bst_float fvalue = col[i].fvalue;
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// get the statistics of nid
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ThreadEntry &e = temp[nid];
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if (e.stats.Empty()) {
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e.stats.Add(gpair[ridx]);
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e.first_fvalue = fvalue;
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} else {
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// forward default right
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if (fvalue != e.first_fvalue) {
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if (need_forward) {
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c.SetSubstract(snode_[nid].stats, e.stats);
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if (c.sum_hess >= param_.min_child_weight &&
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e.stats.sum_hess >= param_.min_child_weight) {
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auto loss_chg = static_cast<bst_float>(
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spliteval_->ComputeSplitScore(nid, fid, e.stats, c) -
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snode_[nid].root_gain);
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e.best.Update(loss_chg, fid, (fvalue + e.first_fvalue) * 0.5f,
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false, e.stats, c);
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}
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}
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if (need_backward) {
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cright.SetSubstract(e.stats_extra, e.stats);
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c.SetSubstract(snode_[nid].stats, cright);
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if (c.sum_hess >= param_.min_child_weight &&
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cright.sum_hess >= param_.min_child_weight) {
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auto loss_chg = static_cast<bst_float>(
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spliteval_->ComputeSplitScore(nid, fid, c, cright) -
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snode_[nid].root_gain);
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e.best.Update(loss_chg, fid, (fvalue + e.first_fvalue) * 0.5f, true, c, cright);
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}
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}
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}
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e.stats.Add(gpair[ridx]);
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e.first_fvalue = fvalue;
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}
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}
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}
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}
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// update enumeration solution
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inline void UpdateEnumeration(int nid, GradientPair gstats,
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bst_float fvalue, int d_step, bst_uint fid,
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GradStats &c, std::vector<ThreadEntry> &temp) { // NOLINT(*)
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// get the statistics of nid
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ThreadEntry &e = temp[nid];
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// test if first hit, this is fine, because we set 0 during init
|
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if (e.stats.Empty()) {
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e.stats.Add(gstats);
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e.last_fvalue = fvalue;
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} else {
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// try to find a split
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if (fvalue != e.last_fvalue &&
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e.stats.sum_hess >= param_.min_child_weight) {
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c.SetSubstract(snode_[nid].stats, e.stats);
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if (c.sum_hess >= param_.min_child_weight) {
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bst_float loss_chg;
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if (d_step == -1) {
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loss_chg = static_cast<bst_float>(
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spliteval_->ComputeSplitScore(nid, fid, c, e.stats) -
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snode_[nid].root_gain);
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e.best.Update(loss_chg, fid, (fvalue + e.last_fvalue) * 0.5f,
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d_step == -1, c, e.stats);
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} else {
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loss_chg = static_cast<bst_float>(
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spliteval_->ComputeSplitScore(nid, fid, e.stats, c) -
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snode_[nid].root_gain);
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e.best.Update(loss_chg, fid, (fvalue + e.last_fvalue) * 0.5f,
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d_step == -1, e.stats, c);
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}
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}
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}
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// update the statistics
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e.stats.Add(gstats);
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e.last_fvalue = fvalue;
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}
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}
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// same as EnumerateSplit, with cacheline prefetch optimization
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inline void EnumerateSplitCacheOpt(const Entry *begin,
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const Entry *end,
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int d_step,
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bst_uint fid,
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const std::vector<GradientPair> &gpair,
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std::vector<ThreadEntry> &temp) { // NOLINT(*)
|
|
const std::vector<int> &qexpand = qexpand_;
|
|
// clear all the temp statistics
|
|
for (auto nid : qexpand) {
|
|
temp[nid].stats = GradStats();
|
|
}
|
|
// left statistics
|
|
GradStats c;
|
|
// local cache buffer for position and gradient pair
|
|
constexpr int kBuffer = 32;
|
|
int buf_position[kBuffer] = {};
|
|
GradientPair buf_gpair[kBuffer] = {};
|
|
// aligned ending position
|
|
const Entry *align_end;
|
|
if (d_step > 0) {
|
|
align_end = begin + (end - begin) / kBuffer * kBuffer;
|
|
} else {
|
|
align_end = begin - (begin - end) / kBuffer * kBuffer;
|
|
}
|
|
int i;
|
|
const Entry *it;
|
|
const int align_step = d_step * kBuffer;
|
|
// internal cached loop
|
|
for (it = begin; it != align_end; it += align_step) {
|
|
const Entry *p;
|
|
for (i = 0, p = it; i < kBuffer; ++i, p += d_step) {
|
|
buf_position[i] = position_[p->index];
|
|
buf_gpair[i] = gpair[p->index];
|
|
}
|
|
for (i = 0, p = it; i < kBuffer; ++i, p += d_step) {
|
|
const int nid = buf_position[i];
|
|
if (nid < 0) continue;
|
|
this->UpdateEnumeration(nid, buf_gpair[i],
|
|
p->fvalue, d_step,
|
|
fid, c, temp);
|
|
}
|
|
}
|
|
// finish up the ending piece
|
|
for (it = align_end, i = 0; it != end; ++i, it += d_step) {
|
|
buf_position[i] = position_[it->index];
|
|
buf_gpair[i] = gpair[it->index];
|
|
}
|
|
for (it = align_end, i = 0; it != end; ++i, it += d_step) {
|
|
const int nid = buf_position[i];
|
|
if (nid < 0) continue;
|
|
this->UpdateEnumeration(nid, buf_gpair[i],
|
|
it->fvalue, d_step,
|
|
fid, c, temp);
|
|
}
|
|
// finish updating all statistics, check if it is possible to include all sum statistics
|
|
for (int nid : qexpand) {
|
|
ThreadEntry &e = temp[nid];
|
|
c.SetSubstract(snode_[nid].stats, e.stats);
|
|
if (e.stats.sum_hess >= param_.min_child_weight &&
|
|
c.sum_hess >= param_.min_child_weight) {
|
|
bst_float loss_chg;
|
|
const bst_float gap = std::abs(e.last_fvalue) + kRtEps;
|
|
const bst_float delta = d_step == +1 ? gap: -gap;
|
|
if (d_step == -1) {
|
|
loss_chg = static_cast<bst_float>(
|
|
spliteval_->ComputeSplitScore(nid, fid, c, e.stats) -
|
|
snode_[nid].root_gain);
|
|
e.best.Update(loss_chg, fid, e.last_fvalue + delta, d_step == -1, c,
|
|
e.stats);
|
|
} else {
|
|
loss_chg = static_cast<bst_float>(
|
|
spliteval_->ComputeSplitScore(nid, fid, e.stats, c) -
|
|
snode_[nid].root_gain);
|
|
e.best.Update(loss_chg, fid, e.last_fvalue + delta, d_step == -1,
|
|
e.stats, c);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
// enumerate the split values of specific feature
|
|
inline void EnumerateSplit(const Entry *begin,
|
|
const Entry *end,
|
|
int d_step,
|
|
bst_uint fid,
|
|
const std::vector<GradientPair> &gpair,
|
|
const MetaInfo &info,
|
|
std::vector<ThreadEntry> &temp) { // NOLINT(*)
|
|
// use cacheline aware optimization
|
|
if (param_.cache_opt != 0) {
|
|
EnumerateSplitCacheOpt(begin, end, d_step, fid, gpair, temp);
|
|
return;
|
|
}
|
|
const std::vector<int> &qexpand = qexpand_;
|
|
// clear all the temp statistics
|
|
for (auto nid : qexpand) {
|
|
temp[nid].stats = GradStats();
|
|
}
|
|
// left statistics
|
|
GradStats c;
|
|
for (const Entry *it = begin; it != end; it += d_step) {
|
|
const bst_uint ridx = it->index;
|
|
const int nid = position_[ridx];
|
|
if (nid < 0) continue;
|
|
// start working
|
|
const bst_float fvalue = it->fvalue;
|
|
// get the statistics of nid
|
|
ThreadEntry &e = temp[nid];
|
|
// test if first hit, this is fine, because we set 0 during init
|
|
if (e.stats.Empty()) {
|
|
e.stats.Add(gpair[ridx]);
|
|
e.last_fvalue = fvalue;
|
|
} else {
|
|
// try to find a split
|
|
if (fvalue != e.last_fvalue &&
|
|
e.stats.sum_hess >= param_.min_child_weight) {
|
|
c.SetSubstract(snode_[nid].stats, e.stats);
|
|
if (c.sum_hess >= param_.min_child_weight) {
|
|
bst_float loss_chg;
|
|
if (d_step == -1) {
|
|
loss_chg = static_cast<bst_float>(
|
|
spliteval_->ComputeSplitScore(nid, fid, c, e.stats) -
|
|
snode_[nid].root_gain);
|
|
e.best.Update(loss_chg, fid, (fvalue + e.last_fvalue) * 0.5f,
|
|
d_step == -1, c, e.stats);
|
|
} else {
|
|
loss_chg = static_cast<bst_float>(
|
|
spliteval_->ComputeSplitScore(nid, fid, e.stats, c) -
|
|
snode_[nid].root_gain);
|
|
e.best.Update(loss_chg, fid, (fvalue + e.last_fvalue) * 0.5f,
|
|
d_step == -1, e.stats, c);
|
|
}
|
|
}
|
|
}
|
|
// update the statistics
|
|
e.stats.Add(gpair[ridx]);
|
|
e.last_fvalue = fvalue;
|
|
}
|
|
}
|
|
// finish updating all statistics, check if it is possible to include all sum statistics
|
|
for (int nid : qexpand) {
|
|
ThreadEntry &e = temp[nid];
|
|
c.SetSubstract(snode_[nid].stats, e.stats);
|
|
if (e.stats.sum_hess >= param_.min_child_weight &&
|
|
c.sum_hess >= param_.min_child_weight) {
|
|
bst_float loss_chg;
|
|
GradStats left_sum;
|
|
GradStats right_sum;
|
|
if (d_step == -1) {
|
|
left_sum = c;
|
|
right_sum = e.stats;
|
|
} else {
|
|
left_sum = e.stats;
|
|
right_sum = c;
|
|
}
|
|
loss_chg = static_cast<bst_float>(
|
|
spliteval_->ComputeSplitScore(nid, fid, left_sum, right_sum) -
|
|
snode_[nid].root_gain);
|
|
const bst_float gap = std::abs(e.last_fvalue) + kRtEps;
|
|
const bst_float delta = d_step == +1 ? gap: -gap;
|
|
e.best.Update(loss_chg, fid, e.last_fvalue + delta, d_step == -1, left_sum, right_sum);
|
|
}
|
|
}
|
|
}
|
|
|
|
// update the solution candidate
|
|
virtual void UpdateSolution(const SparsePage &batch,
|
|
const std::vector<int> &feat_set,
|
|
const std::vector<GradientPair> &gpair,
|
|
DMatrix*p_fmat) {
|
|
const MetaInfo& info = p_fmat->Info();
|
|
// start enumeration
|
|
const auto num_features = static_cast<bst_omp_uint>(feat_set.size());
|
|
#if defined(_OPENMP)
|
|
const int batch_size = // NOLINT
|
|
std::max(static_cast<int>(num_features / this->nthread_ / 32), 1);
|
|
#endif // defined(_OPENMP)
|
|
int poption = param_.parallel_option;
|
|
if (poption == 2) {
|
|
poption = static_cast<int>(num_features) * 2 < this->nthread_ ? 1 : 0;
|
|
}
|
|
if (poption == 0) {
|
|
#pragma omp parallel for schedule(dynamic, batch_size)
|
|
for (bst_omp_uint i = 0; i < num_features; ++i) {
|
|
int fid = feat_set[i];
|
|
const int tid = omp_get_thread_num();
|
|
auto c = batch[fid];
|
|
const bool ind = c.size() != 0 && c[0].fvalue == c[c.size() - 1].fvalue;
|
|
if (param_.NeedForwardSearch(p_fmat->GetColDensity(fid), ind)) {
|
|
this->EnumerateSplit(c.data(), c.data() + c.size(), +1,
|
|
fid, gpair, info, stemp_[tid]);
|
|
}
|
|
if (param_.NeedBackwardSearch(p_fmat->GetColDensity(fid), ind)) {
|
|
this->EnumerateSplit(c.data() + c.size() - 1, c.data() - 1, -1,
|
|
fid, gpair, info, stemp_[tid]);
|
|
}
|
|
}
|
|
} else {
|
|
for (bst_omp_uint fid = 0; fid < num_features; ++fid) {
|
|
this->ParallelFindSplit(batch[fid], fid,
|
|
p_fmat, gpair);
|
|
}
|
|
}
|
|
}
|
|
// find splits at current level, do split per level
|
|
inline void FindSplit(int depth,
|
|
const std::vector<int> &qexpand,
|
|
const std::vector<GradientPair> &gpair,
|
|
DMatrix *p_fmat,
|
|
RegTree *p_tree) {
|
|
auto feat_set = column_sampler_.GetFeatureSet(depth);
|
|
for (const auto &batch : p_fmat->GetSortedColumnBatches()) {
|
|
this->UpdateSolution(batch, feat_set->HostVector(), gpair, p_fmat);
|
|
}
|
|
// after this each thread's stemp will get the best candidates, aggregate results
|
|
this->SyncBestSolution(qexpand);
|
|
// get the best result, we can synchronize the solution
|
|
for (int nid : qexpand) {
|
|
NodeEntry &e = snode_[nid];
|
|
// now we know the solution in snode[nid], set split
|
|
if (e.best.loss_chg > kRtEps) {
|
|
bst_float left_leaf_weight =
|
|
spliteval_->ComputeWeight(nid, e.best.left_sum) *
|
|
param_.learning_rate;
|
|
bst_float right_leaf_weight =
|
|
spliteval_->ComputeWeight(nid, e.best.right_sum) *
|
|
param_.learning_rate;
|
|
p_tree->ExpandNode(nid, e.best.SplitIndex(), e.best.split_value,
|
|
e.best.DefaultLeft(), e.weight, left_leaf_weight,
|
|
right_leaf_weight, e.best.loss_chg,
|
|
e.stats.sum_hess);
|
|
} else {
|
|
(*p_tree)[nid].SetLeaf(e.weight * param_.learning_rate);
|
|
}
|
|
}
|
|
}
|
|
// reset position of each data points after split is created in the tree
|
|
inline void ResetPosition(const std::vector<int> &qexpand,
|
|
DMatrix* p_fmat,
|
|
const RegTree& tree) {
|
|
// set the positions in the nondefault
|
|
this->SetNonDefaultPosition(qexpand, p_fmat, tree);
|
|
// set rest of instances to default position
|
|
// set default direct nodes to default
|
|
// for leaf nodes that are not fresh, mark then to ~nid,
|
|
// so that they are ignored in future statistics collection
|
|
const auto ndata = static_cast<bst_omp_uint>(p_fmat->Info().num_row_);
|
|
|
|
#pragma omp parallel for schedule(static)
|
|
for (bst_omp_uint ridx = 0; ridx < ndata; ++ridx) {
|
|
CHECK_LT(ridx, position_.size())
|
|
<< "ridx exceed bound " << "ridx="<< ridx << " pos=" << position_.size();
|
|
const int nid = this->DecodePosition(ridx);
|
|
if (tree[nid].IsLeaf()) {
|
|
// mark finish when it is not a fresh leaf
|
|
if (tree[nid].RightChild() == -1) {
|
|
position_[ridx] = ~nid;
|
|
}
|
|
} else {
|
|
// push to default branch
|
|
if (tree[nid].DefaultLeft()) {
|
|
this->SetEncodePosition(ridx, tree[nid].LeftChild());
|
|
} else {
|
|
this->SetEncodePosition(ridx, tree[nid].RightChild());
|
|
}
|
|
}
|
|
}
|
|
}
|
|
// customization part
|
|
// synchronize the best solution of each node
|
|
virtual void SyncBestSolution(const std::vector<int> &qexpand) {
|
|
for (int nid : qexpand) {
|
|
NodeEntry &e = snode_[nid];
|
|
for (int tid = 0; tid < this->nthread_; ++tid) {
|
|
e.best.Update(stemp_[tid][nid].best);
|
|
}
|
|
}
|
|
}
|
|
virtual void SetNonDefaultPosition(const std::vector<int> &qexpand,
|
|
DMatrix *p_fmat,
|
|
const RegTree &tree) {
|
|
// step 1, classify the non-default data into right places
|
|
std::vector<unsigned> fsplits;
|
|
for (int nid : qexpand) {
|
|
if (!tree[nid].IsLeaf()) {
|
|
fsplits.push_back(tree[nid].SplitIndex());
|
|
}
|
|
}
|
|
std::sort(fsplits.begin(), fsplits.end());
|
|
fsplits.resize(std::unique(fsplits.begin(), fsplits.end()) - fsplits.begin());
|
|
for (const auto &batch : p_fmat->GetSortedColumnBatches()) {
|
|
for (auto fid : fsplits) {
|
|
auto col = batch[fid];
|
|
const auto ndata = static_cast<bst_omp_uint>(col.size());
|
|
#pragma omp parallel for schedule(static)
|
|
for (bst_omp_uint j = 0; j < ndata; ++j) {
|
|
const bst_uint ridx = col[j].index;
|
|
const int nid = this->DecodePosition(ridx);
|
|
const bst_float fvalue = col[j].fvalue;
|
|
// go back to parent, correct those who are not default
|
|
if (!tree[nid].IsLeaf() && tree[nid].SplitIndex() == fid) {
|
|
if (fvalue < tree[nid].SplitCond()) {
|
|
this->SetEncodePosition(ridx, tree[nid].LeftChild());
|
|
} else {
|
|
this->SetEncodePosition(ridx, tree[nid].RightChild());
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
// utils to get/set position, with encoded format
|
|
// return decoded position
|
|
inline int DecodePosition(bst_uint ridx) const {
|
|
const int pid = position_[ridx];
|
|
return pid < 0 ? ~pid : pid;
|
|
}
|
|
// encode the encoded position value for ridx
|
|
inline void SetEncodePosition(bst_uint ridx, int nid) {
|
|
if (position_[ridx] < 0) {
|
|
position_[ridx] = ~nid;
|
|
} else {
|
|
position_[ridx] = nid;
|
|
}
|
|
}
|
|
// --data fields--
|
|
const TrainParam& param_;
|
|
// number of omp thread used during training
|
|
const int nthread_;
|
|
common::ColumnSampler column_sampler_;
|
|
// Instance Data: current node position in the tree of each instance
|
|
std::vector<int> position_;
|
|
// PerThread x PerTreeNode: statistics for per thread construction
|
|
std::vector< std::vector<ThreadEntry> > stemp_;
|
|
/*! \brief TreeNode Data: statistics for each constructed node */
|
|
std::vector<NodeEntry> snode_;
|
|
/*! \brief queue of nodes to be expanded */
|
|
std::vector<int> qexpand_;
|
|
// Evaluates splits and computes optimal weights for a given split
|
|
std::unique_ptr<SplitEvaluator> spliteval_;
|
|
};
|
|
};
|
|
|
|
// distributed column maker
|
|
class DistColMaker : public ColMaker {
|
|
public:
|
|
void Configure(const Args& args) override {
|
|
param_.InitAllowUnknown(args);
|
|
pruner_.reset(TreeUpdater::Create("prune", tparam_));
|
|
pruner_->Configure(args);
|
|
spliteval_.reset(SplitEvaluator::Create(param_.split_evaluator));
|
|
spliteval_->Init(args);
|
|
}
|
|
|
|
char const* Name() const override {
|
|
return "distcol";
|
|
}
|
|
|
|
void Update(HostDeviceVector<GradientPair> *gpair,
|
|
DMatrix* dmat,
|
|
const std::vector<RegTree*> &trees) override {
|
|
CHECK_EQ(trees.size(), 1U) << "DistColMaker: only support one tree at a time";
|
|
Builder builder(
|
|
param_,
|
|
std::unique_ptr<SplitEvaluator>(spliteval_->GetHostClone()));
|
|
// build the tree
|
|
builder.Update(gpair->ConstHostVector(), dmat, trees[0]);
|
|
//// prune the tree, note that pruner will sync the tree
|
|
pruner_->Update(gpair, dmat, trees);
|
|
// update position after the tree is pruned
|
|
builder.UpdatePosition(dmat, *trees[0]);
|
|
}
|
|
|
|
private:
|
|
class Builder : public ColMaker::Builder {
|
|
public:
|
|
explicit Builder(const TrainParam ¶m,
|
|
std::unique_ptr<SplitEvaluator> spliteval)
|
|
: ColMaker::Builder(param, std::move(spliteval)) {}
|
|
inline void UpdatePosition(DMatrix* p_fmat, const RegTree &tree) {
|
|
const auto ndata = static_cast<bst_omp_uint>(p_fmat->Info().num_row_);
|
|
#pragma omp parallel for schedule(static)
|
|
for (bst_omp_uint ridx = 0; ridx < ndata; ++ridx) {
|
|
int nid = this->DecodePosition(ridx);
|
|
while (tree[nid].IsDeleted()) {
|
|
nid = tree[nid].Parent();
|
|
CHECK_GE(nid, 0);
|
|
}
|
|
this->position_[ridx] = nid;
|
|
}
|
|
}
|
|
inline const int* GetLeafPosition() const {
|
|
return dmlc::BeginPtr(this->position_);
|
|
}
|
|
|
|
protected:
|
|
void SetNonDefaultPosition(const std::vector<int> &qexpand, DMatrix *p_fmat,
|
|
const RegTree &tree) override {
|
|
// step 2, classify the non-default data into right places
|
|
std::vector<unsigned> fsplits;
|
|
for (int nid : qexpand) {
|
|
if (!tree[nid].IsLeaf()) {
|
|
fsplits.push_back(tree[nid].SplitIndex());
|
|
}
|
|
}
|
|
// get the candidate split index
|
|
std::sort(fsplits.begin(), fsplits.end());
|
|
fsplits.resize(std::unique(fsplits.begin(), fsplits.end()) - fsplits.begin());
|
|
while (fsplits.size() != 0 && fsplits.back() >= p_fmat->Info().num_col_) {
|
|
fsplits.pop_back();
|
|
}
|
|
// bitmap is only word concurrent, set to bool first
|
|
{
|
|
auto ndata = static_cast<bst_omp_uint>(this->position_.size());
|
|
boolmap_.resize(ndata);
|
|
#pragma omp parallel for schedule(static)
|
|
for (bst_omp_uint j = 0; j < ndata; ++j) {
|
|
boolmap_[j] = 0;
|
|
}
|
|
}
|
|
for (const auto &batch : p_fmat->GetSortedColumnBatches()) {
|
|
for (auto fid : fsplits) {
|
|
auto col = batch[fid];
|
|
const auto ndata = static_cast<bst_omp_uint>(col.size());
|
|
#pragma omp parallel for schedule(static)
|
|
for (bst_omp_uint j = 0; j < ndata; ++j) {
|
|
const bst_uint ridx = col[j].index;
|
|
const bst_float fvalue = col[j].fvalue;
|
|
const int nid = this->DecodePosition(ridx);
|
|
if (!tree[nid].IsLeaf() && tree[nid].SplitIndex() == fid) {
|
|
if (fvalue < tree[nid].SplitCond()) {
|
|
if (!tree[nid].DefaultLeft()) boolmap_[ridx] = 1;
|
|
} else {
|
|
if (tree[nid].DefaultLeft()) boolmap_[ridx] = 1;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
bitmap_.InitFromBool(boolmap_);
|
|
// communicate bitmap
|
|
rabit::Allreduce<rabit::op::BitOR>(dmlc::BeginPtr(bitmap_.data), bitmap_.data.size());
|
|
// get the new position
|
|
const auto ndata = static_cast<bst_omp_uint>(p_fmat->Info().num_row_);
|
|
#pragma omp parallel for schedule(static)
|
|
for (bst_omp_uint ridx = 0; ridx < ndata; ++ridx) {
|
|
const int nid = this->DecodePosition(ridx);
|
|
if (bitmap_.Get(ridx)) {
|
|
CHECK(!tree[nid].IsLeaf()) << "inconsistent reduce information";
|
|
if (tree[nid].DefaultLeft()) {
|
|
this->SetEncodePosition(ridx, tree[nid].RightChild());
|
|
} else {
|
|
this->SetEncodePosition(ridx, tree[nid].LeftChild());
|
|
}
|
|
}
|
|
}
|
|
}
|
|
// synchronize the best solution of each node
|
|
void SyncBestSolution(const std::vector<int> &qexpand) override {
|
|
std::vector<SplitEntry> vec;
|
|
for (int nid : qexpand) {
|
|
for (int tid = 0; tid < this->nthread_; ++tid) {
|
|
this->snode_[nid].best.Update(this->stemp_[tid][nid].best);
|
|
}
|
|
vec.push_back(this->snode_[nid].best);
|
|
}
|
|
// TODO(tqchen) lazy version
|
|
// communicate best solution
|
|
reducer_.Allreduce(dmlc::BeginPtr(vec), vec.size());
|
|
// assign solution back
|
|
for (size_t i = 0; i < qexpand.size(); ++i) {
|
|
const int nid = qexpand[i];
|
|
this->snode_[nid].best = vec[i];
|
|
}
|
|
}
|
|
|
|
private:
|
|
common::BitMap bitmap_;
|
|
std::vector<int> boolmap_;
|
|
rabit::Reducer<SplitEntry, SplitEntry::Reduce> reducer_;
|
|
};
|
|
// we directly introduce pruner here
|
|
std::unique_ptr<TreeUpdater> pruner_;
|
|
// training parameter
|
|
TrainParam param_;
|
|
// Cloned for each builder instantiation
|
|
std::unique_ptr<SplitEvaluator> spliteval_;
|
|
};
|
|
|
|
XGBOOST_REGISTER_TREE_UPDATER(ColMaker, "grow_colmaker")
|
|
.describe("Grow tree with parallelization over columns.")
|
|
.set_body([]() {
|
|
return new ColMaker();
|
|
});
|
|
|
|
XGBOOST_REGISTER_TREE_UPDATER(DistColMaker, "distcol")
|
|
.describe("Distributed column split version of tree maker.")
|
|
.set_body([]() {
|
|
return new DistColMaker();
|
|
});
|
|
} // namespace tree
|
|
} // namespace xgboost
|