Avoid warning from NVCC. (#10757)
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Jiaming Yuan
GitHub
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98ac153265
@ -1,5 +1,5 @@
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/**
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* Copyright 2021-2023 by XGBoost Contributors
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* Copyright 2021-2024, XGBoost Contributors
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*/
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#include "../test_evaluate_splits.h"
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@ -11,12 +11,14 @@
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#include <xgboost/tree_model.h> // for RegTree, RTreeNodeStat
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#include <memory> // for make_shared, shared_ptr, addressof
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#include <numeric> // for iota
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#include <tuple> // for make_tuple
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#include "../../../../src/common/hist_util.h" // for HistCollection, HistogramCuts
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#include "../../../../src/common/random.h" // for ColumnSampler
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#include "../../../../src/common/row_set.h" // for RowSetCollection
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#include "../../../../src/data/gradient_index.h" // for GHistIndexMatrix
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#include "../../../../src/tree/hist/evaluate_splits.h" // for HistEvaluator
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#include "../../../../src/tree/hist/evaluate_splits.h" // for HistEvaluator, TreeEvaluator
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#include "../../../../src/tree/hist/expand_entry.h" // for CPUExpandEntry
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#include "../../../../src/tree/hist/hist_cache.h" // for BoundedHistCollection
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#include "../../../../src/tree/hist/param.h" // for HistMakerTrainParam
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@ -24,6 +26,74 @@
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#include "../../helpers.h" // for RandomDataGenerator, AllThreadsFo...
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namespace xgboost::tree {
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void TestPartitionBasedSplit::SetUp() {
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param_.UpdateAllowUnknown(Args{{"min_child_weight", "0"}, {"reg_lambda", "0"}});
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sorted_idx_.resize(n_bins_);
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std::iota(sorted_idx_.begin(), sorted_idx_.end(), 0);
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info_.num_col_ = 1;
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cuts_.cut_ptrs_.Resize(2);
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cuts_.SetCategorical(true, n_bins_);
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auto &h_cuts = cuts_.cut_ptrs_.HostVector();
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h_cuts[0] = 0;
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h_cuts[1] = n_bins_;
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auto &h_vals = cuts_.cut_values_.HostVector();
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h_vals.resize(n_bins_);
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std::iota(h_vals.begin(), h_vals.end(), 0.0);
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cuts_.min_vals_.Resize(1);
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Context ctx;
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HistMakerTrainParam hist_param;
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hist_.Reset(cuts_.TotalBins(), hist_param.MaxCachedHistNodes(ctx.Device()));
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hist_.AllocateHistograms({0});
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auto node_hist = hist_[0];
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SimpleLCG lcg;
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SimpleRealUniformDistribution<double> grad_dist{-4.0, 4.0};
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SimpleRealUniformDistribution<double> hess_dist{0.0, 4.0};
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for (auto &e : node_hist) {
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e = GradientPairPrecise{grad_dist(&lcg), hess_dist(&lcg)};
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total_gpair_ += e;
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}
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auto enumerate = [this, n_feat = info_.num_col_](common::GHistRow hist,
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GradientPairPrecise parent_sum) {
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int32_t best_thresh = -1;
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float best_score{-std::numeric_limits<float>::infinity()};
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TreeEvaluator evaluator{param_, static_cast<bst_feature_t>(n_feat), DeviceOrd::CPU()};
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auto tree_evaluator = evaluator.GetEvaluator<TrainParam>();
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GradientPairPrecise left_sum;
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auto parent_gain = tree_evaluator.CalcGain(0, param_, GradStats{total_gpair_});
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for (size_t i = 0; i < hist.size() - 1; ++i) {
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left_sum += hist[i];
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auto right_sum = parent_sum - left_sum;
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auto gain =
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tree_evaluator.CalcSplitGain(param_, 0, 0, GradStats{left_sum}, GradStats{right_sum}) -
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parent_gain;
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if (gain > best_score) {
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best_score = gain;
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best_thresh = i;
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}
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}
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return std::make_tuple(best_thresh, best_score);
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};
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// enumerate all possible partitions to find the optimal split
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do {
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std::vector<GradientPairPrecise> sorted_hist(node_hist.size());
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for (size_t i = 0; i < sorted_hist.size(); ++i) {
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sorted_hist[i] = node_hist[sorted_idx_[i]];
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}
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auto [thresh, score] = enumerate({sorted_hist}, total_gpair_);
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if (score > best_score_) {
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best_score_ = score;
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}
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} while (std::next_permutation(sorted_idx_.begin(), sorted_idx_.end()));
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}
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void TestEvaluateSplits(bool force_read_by_column) {
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Context ctx;
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ctx.nthread = 4;
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@ -12,20 +12,15 @@
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#include <cstddef> // for size_t
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#include <cstdint> // for int32_t, uint64_t, uint32_t
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#include <limits> // for numeric_limits
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#include <numeric> // for iota
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#include <tuple> // for make_tuple, tie, tuple
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#include <vector> // for vector
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#include "../../../src/common/hist_util.h" // for HistogramCuts, HistCollection, GHistRow
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#include "../../../src/tree/hist/hist_cache.h" // for HistogramCollection
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#include "../../../src/tree/hist/param.h" // for HistMakerTrainParam
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#include "../../../src/tree/param.h" // for TrainParam, GradStats
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#include "../../../src/tree/split_evaluator.h" // for TreeEvaluator
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#include "../helpers.h" // for SimpleLCG, SimpleRealUniformDistribution
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namespace xgboost::tree {
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/**
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* \brief Enumerate all possible partitions for categorical split.
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* @brief Enumerate all possible partitions for categorical split.
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*/
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class TestPartitionBasedSplit : public ::testing::Test {
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protected:
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@ -38,73 +33,7 @@ class TestPartitionBasedSplit : public ::testing::Test {
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BoundedHistCollection hist_;
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GradientPairPrecise total_gpair_;
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void SetUp() override {
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param_.UpdateAllowUnknown(Args{{"min_child_weight", "0"}, {"reg_lambda", "0"}});
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sorted_idx_.resize(n_bins_);
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std::iota(sorted_idx_.begin(), sorted_idx_.end(), 0);
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info_.num_col_ = 1;
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cuts_.cut_ptrs_.Resize(2);
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cuts_.SetCategorical(true, n_bins_);
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auto &h_cuts = cuts_.cut_ptrs_.HostVector();
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h_cuts[0] = 0;
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h_cuts[1] = n_bins_;
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auto &h_vals = cuts_.cut_values_.HostVector();
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h_vals.resize(n_bins_);
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std::iota(h_vals.begin(), h_vals.end(), 0.0);
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cuts_.min_vals_.Resize(1);
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Context ctx;
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HistMakerTrainParam hist_param;
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hist_.Reset(cuts_.TotalBins(), hist_param.MaxCachedHistNodes(ctx.Device()));
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hist_.AllocateHistograms({0});
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auto node_hist = hist_[0];
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SimpleLCG lcg;
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SimpleRealUniformDistribution<double> grad_dist{-4.0, 4.0};
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SimpleRealUniformDistribution<double> hess_dist{0.0, 4.0};
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for (auto &e : node_hist) {
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e = GradientPairPrecise{grad_dist(&lcg), hess_dist(&lcg)};
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total_gpair_ += e;
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}
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auto enumerate = [this, n_feat = info_.num_col_](common::GHistRow hist,
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GradientPairPrecise parent_sum) {
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int32_t best_thresh = -1;
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float best_score{-std::numeric_limits<float>::infinity()};
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TreeEvaluator evaluator{param_, static_cast<bst_feature_t>(n_feat), DeviceOrd::CPU()};
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auto tree_evaluator = evaluator.GetEvaluator<TrainParam>();
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GradientPairPrecise left_sum;
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auto parent_gain = tree_evaluator.CalcGain(0, param_, GradStats{total_gpair_});
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for (size_t i = 0; i < hist.size() - 1; ++i) {
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left_sum += hist[i];
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auto right_sum = parent_sum - left_sum;
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auto gain =
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tree_evaluator.CalcSplitGain(param_, 0, 0, GradStats{left_sum}, GradStats{right_sum}) -
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parent_gain;
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if (gain > best_score) {
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best_score = gain;
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best_thresh = i;
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}
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}
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return std::make_tuple(best_thresh, best_score);
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};
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// enumerate all possible partitions to find the optimal split
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do {
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std::vector<GradientPairPrecise> sorted_hist(node_hist.size());
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for (size_t i = 0; i < sorted_hist.size(); ++i) {
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sorted_hist[i] = node_hist[sorted_idx_[i]];
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}
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auto [thresh, score] = enumerate({sorted_hist}, total_gpair_);
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if (score > best_score_) {
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best_score_ = score;
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}
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} while (std::next_permutation(sorted_idx_.begin(), sorted_idx_.end()));
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}
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void SetUp() override;
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};
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inline auto MakeCutsForTest(std::vector<float> values, std::vector<uint32_t> ptrs,
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