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test_hist_util.cc
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test_hist_util.cc
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/*!
* Copyright 2019-2021 by XGBoost Contributors
*/
#include <gtest/gtest.h>
#include <vector>
#include <string>
#include <utility>
#include "../../../src/common/hist_util.h"
#include "../../../src/data/gradient_index.h"
#include "../helpers.h"
#include "test_hist_util.h"
namespace xgboost {
namespace common {
size_t GetNThreads() {
size_t nthreads;
#pragma omp parallel
{
#pragma omp master
nthreads = omp_get_num_threads();
}
return nthreads;
}
template <typename GradientSumT>
void ParallelGHistBuilderReset() {
constexpr size_t kBins = 10;
constexpr size_t kNodes = 5;
constexpr size_t kNodesExtended = 10;
constexpr size_t kTasksPerNode = 10;
constexpr double kValue = 1.0;
const size_t nthreads = GetNThreads();
HistCollection<GradientSumT> collection;
collection.Init(kBins);
for(size_t inode = 0; inode < kNodesExtended; inode++) {
collection.AddHistRow(inode);
}
collection.AllocateAllData();
ParallelGHistBuilder<GradientSumT> hist_builder;
hist_builder.Init(kBins);
std::vector<GHistRow<GradientSumT>> target_hist(kNodes);
for(size_t i = 0; i < target_hist.size(); ++i) {
target_hist[i] = collection[i];
}
common::BlockedSpace2d space(kNodes, [&](size_t node) { return kTasksPerNode; }, 1);
hist_builder.Reset(nthreads, kNodes, space, target_hist);
common::ParallelFor2d(space, nthreads, [&](size_t inode, common::Range1d r) {
const size_t tid = omp_get_thread_num();
GHistRow<GradientSumT> hist = hist_builder.GetInitializedHist(tid, inode);
// fill hist by some non-null values
for(size_t j = 0; j < kBins; ++j) {
hist[j].Add(kValue, kValue);
}
});
// reset and extend buffer
target_hist.resize(kNodesExtended);
for(size_t i = 0; i < target_hist.size(); ++i) {
target_hist[i] = collection[i];
}
common::BlockedSpace2d space2(kNodesExtended, [&](size_t node) { return kTasksPerNode; }, 1);
hist_builder.Reset(nthreads, kNodesExtended, space2, target_hist);
common::ParallelFor2d(space2, nthreads, [&](size_t inode, common::Range1d r) {
const size_t tid = omp_get_thread_num();
GHistRow<GradientSumT> hist = hist_builder.GetInitializedHist(tid, inode);
// fill hist by some non-null values
for(size_t j = 0; j < kBins; ++j) {
ASSERT_EQ(0.0, hist[j].GetGrad());
ASSERT_EQ(0.0, hist[j].GetHess());
}
});
}
template <typename GradientSumT>
void ParallelGHistBuilderReduceHist(){
constexpr size_t kBins = 10;
constexpr size_t kNodes = 5;
constexpr size_t kTasksPerNode = 10;
constexpr double kValue = 1.0;
const size_t nthreads = GetNThreads();
HistCollection<GradientSumT> collection;
collection.Init(kBins);
for(size_t inode = 0; inode < kNodes; inode++) {
collection.AddHistRow(inode);
}
collection.AllocateAllData();
ParallelGHistBuilder<GradientSumT> hist_builder;
hist_builder.Init(kBins);
std::vector<GHistRow<GradientSumT>> target_hist(kNodes);
for(size_t i = 0; i < target_hist.size(); ++i) {
target_hist[i] = collection[i];
}
common::BlockedSpace2d space(kNodes, [&](size_t node) { return kTasksPerNode; }, 1);
hist_builder.Reset(nthreads, kNodes, space, target_hist);
// Simple analog of BuildHist function, works in parallel for both tree-nodes and data in node
common::ParallelFor2d(space, nthreads, [&](size_t inode, common::Range1d r) {
const size_t tid = omp_get_thread_num();
GHistRow<GradientSumT> hist = hist_builder.GetInitializedHist(tid, inode);
for(size_t i = 0; i < kBins; ++i) {
hist[i].Add(kValue, kValue);
}
});
for(size_t inode = 0; inode < kNodes; inode++) {
hist_builder.ReduceHist(inode, 0, kBins);
// We had kTasksPerNode tasks to add kValue to each bin for each node
// So, after reducing we expect to have (kValue * kTasksPerNode) in each node
for(size_t i = 0; i < kBins; ++i) {
ASSERT_EQ(kValue * kTasksPerNode, collection[inode][i].GetGrad());
ASSERT_EQ(kValue * kTasksPerNode, collection[inode][i].GetHess());
}
}
}
TEST(ParallelGHistBuilder, ResetDouble) {
ParallelGHistBuilderReset<double>();
}
TEST(ParallelGHistBuilder, ResetFloat) {
ParallelGHistBuilderReset<float>();
}
TEST(ParallelGHistBuilder, ReduceHistDouble) {
ParallelGHistBuilderReduceHist<double>();
}
TEST(ParallelGHistBuilder, ReduceHistFloat) {
ParallelGHistBuilderReduceHist<float>();
}
TEST(CutsBuilder, SearchGroupInd) {
size_t constexpr kNumGroups = 4;
size_t constexpr kRows = 17;
size_t constexpr kCols = 15;
auto p_mat = RandomDataGenerator(kRows, kCols, 0).GenerateDMatrix();
std::vector<bst_int> group(kNumGroups);
group[0] = 2;
group[1] = 3;
group[2] = 7;
group[3] = 5;
p_mat->Info().SetInfo(
"group", group.data(), DataType::kUInt32, kNumGroups);
HistogramCuts hmat;
size_t group_ind = HostSketchContainer::SearchGroupIndFromRow(p_mat->Info().group_ptr_, 0);
ASSERT_EQ(group_ind, 0ul);
group_ind = HostSketchContainer::SearchGroupIndFromRow(p_mat->Info().group_ptr_, 5);
ASSERT_EQ(group_ind, 2ul);
EXPECT_ANY_THROW(HostSketchContainer::SearchGroupIndFromRow(p_mat->Info().group_ptr_, 17));
p_mat->Info().Validate(-1);
EXPECT_THROW(HostSketchContainer::SearchGroupIndFromRow(p_mat->Info().group_ptr_, 17),
dmlc::Error);
std::vector<bst_uint> group_ptr {0, 1, 2};
CHECK_EQ(HostSketchContainer::SearchGroupIndFromRow(group_ptr, 1), 1);
}
TEST(HistUtil, DenseCutsCategorical) {
int categorical_sizes[] = {2, 6, 8, 12};
int num_bins = 256;
int sizes[] = {25, 100, 1000};
for (auto n : sizes) {
for (auto num_categories : categorical_sizes) {
auto x = GenerateRandomCategoricalSingleColumn(n, num_categories);
std::vector<float> x_sorted(x);
std::sort(x_sorted.begin(), x_sorted.end());
auto dmat = GetDMatrixFromData(x, n, 1);
HistogramCuts cuts = SketchOnDMatrix(dmat.get(), num_bins);
auto cuts_from_sketch = cuts.Values();
EXPECT_LT(cuts.MinValues()[0], x_sorted.front());
EXPECT_GT(cuts_from_sketch.front(), x_sorted.front());
EXPECT_GE(cuts_from_sketch.back(), x_sorted.back());
EXPECT_EQ(cuts_from_sketch.size(), static_cast<size_t>(num_categories));
}
}
}
TEST(HistUtil, DenseCutsAccuracyTest) {
int bin_sizes[] = {2, 16, 256, 512};
int sizes[] = {100};
// omp_set_num_threads(1);
int num_columns = 5;
for (auto num_rows : sizes) {
auto x = GenerateRandom(num_rows, num_columns);
auto dmat = GetDMatrixFromData(x, num_rows, num_columns);
for (auto num_bins : bin_sizes) {
HistogramCuts cuts = SketchOnDMatrix(dmat.get(), num_bins);
ValidateCuts(cuts, dmat.get(), num_bins);
}
}
}
TEST(HistUtil, DenseCutsAccuracyTestWeights) {
int bin_sizes[] = {2, 16, 256, 512};
int sizes[] = {100, 1000, 1500};
int num_columns = 5;
for (auto num_rows : sizes) {
auto x = GenerateRandom(num_rows, num_columns);
auto dmat = GetDMatrixFromData(x, num_rows, num_columns);
auto w = GenerateRandomWeights(num_rows);
dmat->Info().weights_.HostVector() = w;
for (auto num_bins : bin_sizes) {
HistogramCuts cuts = SketchOnDMatrix(dmat.get(), num_bins);
ValidateCuts(cuts, dmat.get(), num_bins);
}
}
}
TEST(HistUtil, QuantileWithHessian) {
int bin_sizes[] = {2, 16, 256, 512};
int sizes[] = {1000, 1500};
int num_columns = 5;
for (auto num_rows : sizes) {
auto x = GenerateRandom(num_rows, num_columns);
auto dmat = GetDMatrixFromData(x, num_rows, num_columns);
auto w = GenerateRandomWeights(num_rows);
auto hessian = GenerateRandomWeights(num_rows);
std::mt19937 rng(0);
std::shuffle(hessian.begin(), hessian.end(), rng);
dmat->Info().weights_.HostVector() = w;
for (auto num_bins : bin_sizes) {
HistogramCuts cuts_hess = SketchOnDMatrix(dmat.get(), num_bins, hessian);
for (size_t i = 0; i < w.size(); ++i) {
dmat->Info().weights_.HostVector()[i] = w[i] * hessian[i];
}
ValidateCuts(cuts_hess, dmat.get(), num_bins);
HistogramCuts cuts_wh = SketchOnDMatrix(dmat.get(), num_bins);
ValidateCuts(cuts_wh, dmat.get(), num_bins);
ASSERT_EQ(cuts_hess.Values().size(), cuts_wh.Values().size());
for (size_t i = 0; i < cuts_hess.Values().size(); ++i) {
ASSERT_NEAR(cuts_wh.Values()[i], cuts_hess.Values()[i], kRtEps);
}
dmat->Info().weights_.HostVector() = w;
}
}
}
TEST(HistUtil, DenseCutsExternalMemory) {
int bin_sizes[] = {2, 16, 256, 512};
int sizes[] = {100, 1000, 1500};
int num_columns = 5;
for (auto num_rows : sizes) {
auto x = GenerateRandom(num_rows, num_columns);
dmlc::TemporaryDirectory tmpdir;
auto dmat =
GetExternalMemoryDMatrixFromData(x, num_rows, num_columns, 50, tmpdir);
for (auto num_bins : bin_sizes) {
HistogramCuts cuts = SketchOnDMatrix(dmat.get(), num_bins);
ValidateCuts(cuts, dmat.get(), num_bins);
}
}
}
TEST(HistUtil, IndexBinBound) {
uint64_t bin_sizes[] = { static_cast<uint64_t>(std::numeric_limits<uint8_t>::max()) + 1,
static_cast<uint64_t>(std::numeric_limits<uint16_t>::max()) + 1,
static_cast<uint64_t>(std::numeric_limits<uint16_t>::max()) + 2 };
BinTypeSize expected_bin_type_sizes[] = {kUint8BinsTypeSize,
kUint16BinsTypeSize,
kUint32BinsTypeSize};
size_t constexpr kRows = 100;
size_t constexpr kCols = 10;
size_t bin_id = 0;
for (auto max_bin : bin_sizes) {
auto p_fmat = RandomDataGenerator(kRows, kCols, 0).GenerateDMatrix();
GHistIndexMatrix hmat(p_fmat.get(), max_bin);
EXPECT_EQ(hmat.index.Size(), kRows*kCols);
EXPECT_EQ(expected_bin_type_sizes[bin_id++], hmat.index.GetBinTypeSize());
}
}
template <typename T>
void CheckIndexData(T* data_ptr, uint32_t* offsets,
const GHistIndexMatrix& hmat, size_t n_cols) {
for (size_t i = 0; i < hmat.index.Size(); ++i) {
EXPECT_EQ(data_ptr[i] + offsets[i % n_cols], hmat.index[i]);
}
}
TEST(HistUtil, IndexBinData) {
uint64_t constexpr kBinSizes[] = { static_cast<uint64_t>(std::numeric_limits<uint8_t>::max()) + 1,
static_cast<uint64_t>(std::numeric_limits<uint16_t>::max()) + 1,
static_cast<uint64_t>(std::numeric_limits<uint16_t>::max()) + 2 };
size_t constexpr kRows = 100;
size_t constexpr kCols = 10;
for (auto max_bin : kBinSizes) {
auto p_fmat = RandomDataGenerator(kRows, kCols, 0).GenerateDMatrix();
GHistIndexMatrix hmat(p_fmat.get(), max_bin);
uint32_t* offsets = hmat.index.Offset();
EXPECT_EQ(hmat.index.Size(), kRows*kCols);
switch (max_bin) {
case kBinSizes[0]:
CheckIndexData(hmat.index.data<uint8_t>(),
offsets, hmat, kCols);
break;
case kBinSizes[1]:
CheckIndexData(hmat.index.data<uint16_t>(),
offsets, hmat, kCols);
break;
case kBinSizes[2]:
CheckIndexData(hmat.index.data<uint32_t>(),
offsets, hmat, kCols);
break;
}
}
}
void TestSketchFromWeights(bool with_group) {
size_t constexpr kRows = 300, kCols = 20, kBins = 256;
size_t constexpr kGroups = 10;
auto m =
RandomDataGenerator{kRows, kCols, 0}.Device(0).GenerateDMatrix();
common::HistogramCuts cuts = SketchOnDMatrix(m.get(), kBins);
MetaInfo info;
auto& h_weights = info.weights_.HostVector();
if (with_group) {
h_weights.resize(kGroups);
} else {
h_weights.resize(kRows);
}
std::fill(h_weights.begin(), h_weights.end(), 1.0f);
std::vector<bst_group_t> groups(kGroups);
if (with_group) {
for (size_t i = 0; i < kGroups; ++i) {
groups[i] = kRows / kGroups;
}
info.SetInfo("group", groups.data(), DataType::kUInt32, kGroups);
}
info.num_row_ = kRows;
info.num_col_ = kCols;
// Assign weights.
if (with_group) {
m->Info().SetInfo("group", groups.data(), DataType::kUInt32, kGroups);
}
m->Info().SetInfo("weight", h_weights.data(), DataType::kFloat32, h_weights.size());
m->Info().num_col_ = kCols;
m->Info().num_row_ = kRows;
ASSERT_EQ(cuts.Ptrs().size(), kCols + 1);
ValidateCuts(cuts, m.get(), kBins);
if (with_group) {
HistogramCuts non_weighted = SketchOnDMatrix(m.get(), kBins);
for (size_t i = 0; i < cuts.Values().size(); ++i) {
EXPECT_EQ(cuts.Values()[i], non_weighted.Values()[i]);
}
for (size_t i = 0; i < cuts.MinValues().size(); ++i) {
ASSERT_EQ(cuts.MinValues()[i], non_weighted.MinValues()[i]);
}
for (size_t i = 0; i < cuts.Ptrs().size(); ++i) {
ASSERT_EQ(cuts.Ptrs().at(i), non_weighted.Ptrs().at(i));
}
}
}
TEST(HistUtil, SketchFromWeights) {
TestSketchFromWeights(true);
TestSketchFromWeights(false);
}
TEST(HistUtil, SketchCategoricalFeatures) {
TestCategoricalSketch(1000, 256, 32, false,
[](DMatrix *p_fmat, int32_t num_bins) {
return SketchOnDMatrix(p_fmat, num_bins);
});
TestCategoricalSketch(1000, 256, 32, true,
[](DMatrix *p_fmat, int32_t num_bins) {
return SketchOnDMatrix(p_fmat, num_bins);
});
}
} // namespace common
} // namespace xgboost