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Avoid warning from NVCC. (#10757)
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@trivialfis
trivialfis committed Aug 30, 2024
1 parent 5cc7c73 commit 98ac153
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Showing 2 changed files with 75 additions and 76 deletions.
76 changes: 73 additions & 3 deletions tests/cpp/tree/hist/test_evaluate_splits.cc
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/**
* Copyright 2021-2023 by XGBoost Contributors
* Copyright 2021-2024, XGBoost Contributors
*/
#include "../test_evaluate_splits.h"

Expand All @@ -10,20 +10,90 @@
#include <xgboost/logging.h> // for CHECK_EQ
#include <xgboost/tree_model.h> // for RegTree, RTreeNodeStat

#include <memory> // for make_shared, shared_ptr, addressof
#include <memory> // for make_shared, shared_ptr, addressof
#include <numeric> // for iota
#include <tuple> // for make_tuple

#include "../../../../src/common/hist_util.h" // for HistCollection, HistogramCuts
#include "../../../../src/common/random.h" // for ColumnSampler
#include "../../../../src/common/row_set.h" // for RowSetCollection
#include "../../../../src/data/gradient_index.h" // for GHistIndexMatrix
#include "../../../../src/tree/hist/evaluate_splits.h" // for HistEvaluator
#include "../../../../src/tree/hist/evaluate_splits.h" // for HistEvaluator, TreeEvaluator
#include "../../../../src/tree/hist/expand_entry.h" // for CPUExpandEntry
#include "../../../../src/tree/hist/hist_cache.h" // for BoundedHistCollection
#include "../../../../src/tree/hist/param.h" // for HistMakerTrainParam
#include "../../../../src/tree/param.h" // for GradStats, TrainParam
#include "../../helpers.h" // for RandomDataGenerator, AllThreadsFo...

namespace xgboost::tree {
void TestPartitionBasedSplit::SetUp() {
param_.UpdateAllowUnknown(Args{{"min_child_weight", "0"}, {"reg_lambda", "0"}});
sorted_idx_.resize(n_bins_);
std::iota(sorted_idx_.begin(), sorted_idx_.end(), 0);

info_.num_col_ = 1;

cuts_.cut_ptrs_.Resize(2);
cuts_.SetCategorical(true, n_bins_);
auto &h_cuts = cuts_.cut_ptrs_.HostVector();
h_cuts[0] = 0;
h_cuts[1] = n_bins_;
auto &h_vals = cuts_.cut_values_.HostVector();
h_vals.resize(n_bins_);
std::iota(h_vals.begin(), h_vals.end(), 0.0);

cuts_.min_vals_.Resize(1);

Context ctx;
HistMakerTrainParam hist_param;
hist_.Reset(cuts_.TotalBins(), hist_param.MaxCachedHistNodes(ctx.Device()));
hist_.AllocateHistograms({0});
auto node_hist = hist_[0];

SimpleLCG lcg;
SimpleRealUniformDistribution<double> grad_dist{-4.0, 4.0};
SimpleRealUniformDistribution<double> hess_dist{0.0, 4.0};

for (auto &e : node_hist) {
e = GradientPairPrecise{grad_dist(&lcg), hess_dist(&lcg)};
total_gpair_ += e;
}

auto enumerate = [this, n_feat = info_.num_col_](common::GHistRow hist,
GradientPairPrecise parent_sum) {
int32_t best_thresh = -1;
float best_score{-std::numeric_limits<float>::infinity()};
TreeEvaluator evaluator{param_, static_cast<bst_feature_t>(n_feat), DeviceOrd::CPU()};
auto tree_evaluator = evaluator.GetEvaluator<TrainParam>();
GradientPairPrecise left_sum;
auto parent_gain = tree_evaluator.CalcGain(0, param_, GradStats{total_gpair_});
for (size_t i = 0; i < hist.size() - 1; ++i) {
left_sum += hist[i];
auto right_sum = parent_sum - left_sum;
auto gain =
tree_evaluator.CalcSplitGain(param_, 0, 0, GradStats{left_sum}, GradStats{right_sum}) -
parent_gain;
if (gain > best_score) {
best_score = gain;
best_thresh = i;
}
}
return std::make_tuple(best_thresh, best_score);
};

// enumerate all possible partitions to find the optimal split
do {
std::vector<GradientPairPrecise> sorted_hist(node_hist.size());
for (size_t i = 0; i < sorted_hist.size(); ++i) {
sorted_hist[i] = node_hist[sorted_idx_[i]];
}
auto [thresh, score] = enumerate({sorted_hist}, total_gpair_);
if (score > best_score_) {
best_score_ = score;
}
} while (std::next_permutation(sorted_idx_.begin(), sorted_idx_.end()));
}

void TestEvaluateSplits(bool force_read_by_column) {
Context ctx;
ctx.nthread = 4;
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75 changes: 2 additions & 73 deletions tests/cpp/tree/test_evaluate_splits.h
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Expand Up @@ -12,20 +12,15 @@
#include <cstddef> // for size_t
#include <cstdint> // for int32_t, uint64_t, uint32_t
#include <limits> // for numeric_limits
#include <numeric> // for iota
#include <tuple> // for make_tuple, tie, tuple
#include <vector> // for vector

#include "../../../src/common/hist_util.h" // for HistogramCuts, HistCollection, GHistRow
#include "../../../src/tree/hist/hist_cache.h" // for HistogramCollection
#include "../../../src/tree/hist/param.h" // for HistMakerTrainParam
#include "../../../src/tree/param.h" // for TrainParam, GradStats
#include "../../../src/tree/split_evaluator.h" // for TreeEvaluator
#include "../helpers.h" // for SimpleLCG, SimpleRealUniformDistribution

namespace xgboost::tree {
/**
* \brief Enumerate all possible partitions for categorical split.
* @brief Enumerate all possible partitions for categorical split.
*/
class TestPartitionBasedSplit : public ::testing::Test {
protected:
Expand All @@ -38,73 +33,7 @@ class TestPartitionBasedSplit : public ::testing::Test {
BoundedHistCollection hist_;
GradientPairPrecise total_gpair_;

void SetUp() override {
param_.UpdateAllowUnknown(Args{{"min_child_weight", "0"}, {"reg_lambda", "0"}});
sorted_idx_.resize(n_bins_);
std::iota(sorted_idx_.begin(), sorted_idx_.end(), 0);

info_.num_col_ = 1;

cuts_.cut_ptrs_.Resize(2);
cuts_.SetCategorical(true, n_bins_);
auto &h_cuts = cuts_.cut_ptrs_.HostVector();
h_cuts[0] = 0;
h_cuts[1] = n_bins_;
auto &h_vals = cuts_.cut_values_.HostVector();
h_vals.resize(n_bins_);
std::iota(h_vals.begin(), h_vals.end(), 0.0);

cuts_.min_vals_.Resize(1);

Context ctx;
HistMakerTrainParam hist_param;
hist_.Reset(cuts_.TotalBins(), hist_param.MaxCachedHistNodes(ctx.Device()));
hist_.AllocateHistograms({0});
auto node_hist = hist_[0];

SimpleLCG lcg;
SimpleRealUniformDistribution<double> grad_dist{-4.0, 4.0};
SimpleRealUniformDistribution<double> hess_dist{0.0, 4.0};

for (auto &e : node_hist) {
e = GradientPairPrecise{grad_dist(&lcg), hess_dist(&lcg)};
total_gpair_ += e;
}

auto enumerate = [this, n_feat = info_.num_col_](common::GHistRow hist,
GradientPairPrecise parent_sum) {
int32_t best_thresh = -1;
float best_score{-std::numeric_limits<float>::infinity()};
TreeEvaluator evaluator{param_, static_cast<bst_feature_t>(n_feat), DeviceOrd::CPU()};
auto tree_evaluator = evaluator.GetEvaluator<TrainParam>();
GradientPairPrecise left_sum;
auto parent_gain = tree_evaluator.CalcGain(0, param_, GradStats{total_gpair_});
for (size_t i = 0; i < hist.size() - 1; ++i) {
left_sum += hist[i];
auto right_sum = parent_sum - left_sum;
auto gain =
tree_evaluator.CalcSplitGain(param_, 0, 0, GradStats{left_sum}, GradStats{right_sum}) -
parent_gain;
if (gain > best_score) {
best_score = gain;
best_thresh = i;
}
}
return std::make_tuple(best_thresh, best_score);
};

// enumerate all possible partitions to find the optimal split
do {
std::vector<GradientPairPrecise> sorted_hist(node_hist.size());
for (size_t i = 0; i < sorted_hist.size(); ++i) {
sorted_hist[i] = node_hist[sorted_idx_[i]];
}
auto [thresh, score] = enumerate({sorted_hist}, total_gpair_);
if (score > best_score_) {
best_score_ = score;
}
} while (std::next_permutation(sorted_idx_.begin(), sorted_idx_.end()));
}
void SetUp() override;
};

inline auto MakeCutsForTest(std::vector<float> values, std::vector<uint32_t> ptrs,
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