Hendrik/xgboost
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity
xgboost/tests/cpp/tree/hist/test_histogram.cc

488 lines
18 KiB
C++
Raw Blame History

/**
* Copyright 2018-2023 by Contributors
*/
#include <gtest/gtest.h>
#include <xgboost/context.h> // Context
#include <limits>
#include "../../../../src/common/categorical.h"
#include "../../../../src/common/row_set.h"
#include "../../../../src/tree/hist/expand_entry.h"
#include "../../../../src/tree/hist/histogram.h"
#include "../../categorical_helpers.h"
#include "../../helpers.h"
namespace xgboost {
namespace tree {
namespace {
void InitRowPartitionForTest(common::RowSetCollection *row_set, size_t n_samples, size_t base_rowid = 0) {
auto &row_indices = *row_set->Data();
row_indices.resize(n_samples);
std::iota(row_indices.begin(), row_indices.end(), base_rowid);
row_set->Init();
}
} // anonymous namespace
void TestAddHistRows(bool is_distributed) {
std::vector<CPUExpandEntry> nodes_for_explicit_hist_build_;
std::vector<CPUExpandEntry> nodes_for_subtraction_trick_;
int starting_index = std::numeric_limits<int>::max();
int sync_count = 0;
size_t constexpr kNRows = 8, kNCols = 16;
int32_t constexpr kMaxBins = 4;
auto p_fmat =
RandomDataGenerator(kNRows, kNCols, 0.8).Seed(3).GenerateDMatrix();
auto const &gmat = *(p_fmat->GetBatches<GHistIndexMatrix>(BatchParam{kMaxBins, 0.5}).begin());
RegTree tree;
tree.ExpandNode(0, 0, 0, false, 0, 0, 0, 0, 0, 0, 0);
tree.ExpandNode(tree[0].LeftChild(), 0, 0, false, 0, 0, 0, 0, 0, 0, 0);
tree.ExpandNode(tree[0].RightChild(), 0, 0, false, 0, 0, 0, 0, 0, 0, 0);
nodes_for_explicit_hist_build_.emplace_back(3, tree.GetDepth(3), 0.0f);
nodes_for_explicit_hist_build_.emplace_back(4, tree.GetDepth(4), 0.0f);
nodes_for_subtraction_trick_.emplace_back(5, tree.GetDepth(5), 0.0f);
nodes_for_subtraction_trick_.emplace_back(6, tree.GetDepth(6), 0.0f);
HistogramBuilder<CPUExpandEntry> histogram_builder;
histogram_builder.Reset(gmat.cut.TotalBins(), {kMaxBins, 0.5}, omp_get_max_threads(), 1,
is_distributed, false);
histogram_builder.AddHistRows(&starting_index, &sync_count,
nodes_for_explicit_hist_build_,
nodes_for_subtraction_trick_, &tree);
ASSERT_EQ(sync_count, 2);
ASSERT_EQ(starting_index, 3);
for (const CPUExpandEntry &node : nodes_for_explicit_hist_build_) {
ASSERT_EQ(histogram_builder.Histogram().RowExists(node.nid), true);
}
for (const CPUExpandEntry &node : nodes_for_subtraction_trick_) {
ASSERT_EQ(histogram_builder.Histogram().RowExists(node.nid), true);
}
}
TEST(CPUHistogram, AddRows) {
TestAddHistRows(true);
TestAddHistRows(false);
}
void TestSyncHist(bool is_distributed) {
size_t constexpr kNRows = 8, kNCols = 16;
int32_t constexpr kMaxBins = 4;
std::vector<CPUExpandEntry> nodes_for_explicit_hist_build_;
std::vector<CPUExpandEntry> nodes_for_subtraction_trick_;
int starting_index = std::numeric_limits<int>::max();
int sync_count = 0;
RegTree tree;
auto p_fmat =
RandomDataGenerator(kNRows, kNCols, 0.8).Seed(3).GenerateDMatrix();
auto const &gmat = *(p_fmat->GetBatches<GHistIndexMatrix>(BatchParam{kMaxBins, 0.5}).begin());
HistogramBuilder<CPUExpandEntry> histogram;
uint32_t total_bins = gmat.cut.Ptrs().back();
histogram.Reset(total_bins, {kMaxBins, 0.5}, omp_get_max_threads(), 1, is_distributed, false);
common::RowSetCollection row_set_collection_;
{
row_set_collection_.Clear();
std::vector<size_t> &row_indices = *row_set_collection_.Data();
row_indices.resize(kNRows);
std::iota(row_indices.begin(), row_indices.end(), 0);
row_set_collection_.Init();
}
// level 0
nodes_for_explicit_hist_build_.emplace_back(0, tree.GetDepth(0), 0.0f);
histogram.AddHistRows(&starting_index, &sync_count,
nodes_for_explicit_hist_build_,
nodes_for_subtraction_trick_, &tree);
tree.ExpandNode(0, 0, 0, false, 0, 0, 0, 0, 0, 0, 0);
nodes_for_explicit_hist_build_.clear();
nodes_for_subtraction_trick_.clear();
// level 1
nodes_for_explicit_hist_build_.emplace_back(tree[0].LeftChild(),
tree.GetDepth(1), 0.0f);
nodes_for_subtraction_trick_.emplace_back(tree[0].RightChild(),
tree.GetDepth(2), 0.0f);
histogram.AddHistRows(&starting_index, &sync_count,
nodes_for_explicit_hist_build_,
nodes_for_subtraction_trick_, &tree);
tree.ExpandNode(tree[0].LeftChild(), 0, 0, false, 0, 0, 0, 0, 0, 0, 0);
tree.ExpandNode(tree[0].RightChild(), 0, 0, false, 0, 0, 0, 0, 0, 0, 0);
nodes_for_explicit_hist_build_.clear();
nodes_for_subtraction_trick_.clear();
// level 2
nodes_for_explicit_hist_build_.emplace_back(3, tree.GetDepth(3), 0.0f);
nodes_for_subtraction_trick_.emplace_back(4, tree.GetDepth(4), 0.0f);
nodes_for_explicit_hist_build_.emplace_back(5, tree.GetDepth(5), 0.0f);
nodes_for_subtraction_trick_.emplace_back(6, tree.GetDepth(6), 0.0f);
histogram.AddHistRows(&starting_index, &sync_count,
nodes_for_explicit_hist_build_,
nodes_for_subtraction_trick_, &tree);
const size_t n_nodes = nodes_for_explicit_hist_build_.size();
ASSERT_EQ(n_nodes, 2ul);
row_set_collection_.AddSplit(0, tree[0].LeftChild(), tree[0].RightChild(), 4,
4);
row_set_collection_.AddSplit(1, tree[1].LeftChild(), tree[1].RightChild(), 2,
2);
row_set_collection_.AddSplit(2, tree[2].LeftChild(), tree[2].RightChild(), 2,
2);
common::BlockedSpace2d space(
n_nodes,
[&](size_t node) {
const int32_t nid = nodes_for_explicit_hist_build_[node].nid;
return row_set_collection_[nid].Size();
},
256);
std::vector<common::GHistRow> target_hists(n_nodes);
for (size_t i = 0; i < nodes_for_explicit_hist_build_.size(); ++i) {
const int32_t nid = nodes_for_explicit_hist_build_[i].nid;
target_hists[i] = histogram.Histogram()[nid];
}
// set values to specific nodes hist
std::vector<size_t> n_ids = {1, 2};
for (size_t i : n_ids) {
auto this_hist = histogram.Histogram()[i];
double *p_hist = reinterpret_cast<double *>(this_hist.data());
for (size_t bin_id = 0; bin_id < 2 * total_bins; ++bin_id) {
p_hist[bin_id] = 2 * bin_id;
}
}
n_ids[0] = 3;
n_ids[1] = 5;
for (size_t i : n_ids) {
auto this_hist = histogram.Histogram()[i];
double *p_hist = reinterpret_cast<double *>(this_hist.data());
for (size_t bin_id = 0; bin_id < 2 * total_bins; ++bin_id) {
p_hist[bin_id] = bin_id;
}
}
histogram.Buffer().Reset(1, n_nodes, space, target_hists);
// sync hist
if (is_distributed) {
histogram.SyncHistogramDistributed(&tree, nodes_for_explicit_hist_build_,
nodes_for_subtraction_trick_,
starting_index, sync_count);
} else {
histogram.SyncHistogramLocal(&tree, nodes_for_explicit_hist_build_,
nodes_for_subtraction_trick_);
}
using GHistRowT = common::GHistRow;
auto check_hist = [](const GHistRowT parent, const GHistRowT left, const GHistRowT right,
size_t begin, size_t end) {
const double *p_parent = reinterpret_cast<const double *>(parent.data());
const double *p_left = reinterpret_cast<const double *>(left.data());
const double *p_right = reinterpret_cast<const double *>(right.data());
for (size_t i = 2 * begin; i < 2 * end; ++i) {
ASSERT_EQ(p_parent[i], p_left[i] + p_right[i]);
}
};
size_t node_id = 0;
for (const CPUExpandEntry &node : nodes_for_explicit_hist_build_) {
auto this_hist = histogram.Histogram()[node.nid];
const size_t parent_id = tree[node.nid].Parent();
const size_t subtraction_node_id =
nodes_for_subtraction_trick_[node_id].nid;
auto parent_hist = histogram.Histogram()[parent_id];
auto sibling_hist = histogram.Histogram()[subtraction_node_id];
check_hist(parent_hist, this_hist, sibling_hist, 0, total_bins);
++node_id;
}
node_id = 0;
for (const CPUExpandEntry &node : nodes_for_subtraction_trick_) {
auto this_hist = histogram.Histogram()[node.nid];
const size_t parent_id = tree[node.nid].Parent();
const size_t subtraction_node_id =
nodes_for_explicit_hist_build_[node_id].nid;
auto parent_hist = histogram.Histogram()[parent_id];
auto sibling_hist = histogram.Histogram()[subtraction_node_id];
check_hist(parent_hist, this_hist, sibling_hist, 0, total_bins);
++node_id;
}
}
TEST(CPUHistogram, SyncHist) {
TestSyncHist(true);
TestSyncHist(false);
}
void TestBuildHistogram(bool is_distributed, bool force_read_by_column, bool is_col_split) {
size_t constexpr kNRows = 8, kNCols = 16;
int32_t constexpr kMaxBins = 4;
auto p_fmat = RandomDataGenerator(kNRows, kNCols, 0.8).Seed(3).GenerateDMatrix();
if (is_col_split) {
p_fmat = std::shared_ptr<DMatrix>{
p_fmat->SliceCol(collective::GetWorldSize(), collective::GetRank())};
}
auto const &gmat = *(p_fmat->GetBatches<GHistIndexMatrix>(BatchParam{kMaxBins, 0.5}).begin());
uint32_t total_bins = gmat.cut.Ptrs().back();
static double constexpr kEps = 1e-6;
std::vector<GradientPair> gpair = {
{0.23f, 0.24f}, {0.24f, 0.25f}, {0.26f, 0.27f}, {0.27f, 0.28f},
{0.27f, 0.29f}, {0.37f, 0.39f}, {0.47f, 0.49f}, {0.57f, 0.59f}};
bst_node_t nid = 0;
HistogramBuilder<CPUExpandEntry> histogram;
histogram.Reset(total_bins, {kMaxBins, 0.5}, omp_get_max_threads(), 1, is_distributed,
is_col_split);
RegTree tree;
common::RowSetCollection row_set_collection;
row_set_collection.Clear();
std::vector<size_t> &row_indices = *row_set_collection.Data();
row_indices.resize(kNRows);
std::iota(row_indices.begin(), row_indices.end(), 0);
row_set_collection.Init();
CPUExpandEntry node(RegTree::kRoot, tree.GetDepth(0), 0.0f);
std::vector<CPUExpandEntry> nodes_for_explicit_hist_build;
nodes_for_explicit_hist_build.push_back(node);
for (auto const &gidx : p_fmat->GetBatches<GHistIndexMatrix>({kMaxBins, 0.5})) {
histogram.BuildHist(0, gidx, &tree, row_set_collection,
nodes_for_explicit_hist_build, {}, gpair, force_read_by_column);
}
// Check if number of histogram bins is correct
ASSERT_EQ(histogram.Histogram()[nid].size(), gmat.cut.Ptrs().back());
std::vector<GradientPairPrecise> histogram_expected(histogram.Histogram()[nid].size());
// Compute the correct histogram (histogram_expected)
CHECK_EQ(gpair.size(), kNRows);
for (size_t rid = 0; rid < kNRows; ++rid) {
const size_t ibegin = gmat.row_ptr[rid];
const size_t iend = gmat.row_ptr[rid + 1];
for (size_t i = ibegin; i < iend; ++i) {
const size_t bin_id = gmat.index[i];
histogram_expected[bin_id] += GradientPairPrecise(gpair[rid]);
}
}
// Now validate the computed histogram returned by BuildHist
for (size_t i = 0; i < histogram.Histogram()[nid].size(); ++i) {
GradientPairPrecise sol = histogram_expected[i];
ASSERT_NEAR(sol.GetGrad(), histogram.Histogram()[nid][i].GetGrad(), kEps);
ASSERT_NEAR(sol.GetHess(), histogram.Histogram()[nid][i].GetHess(), kEps);
}
}
TEST(CPUHistogram, BuildHist) {
TestBuildHistogram(true, false, false);
TestBuildHistogram(false, false, false);
TestBuildHistogram(true, true, false);
TestBuildHistogram(false, true, false);
}
TEST(CPUHistogram, BuildHistColSplit) {
auto constexpr kWorkers = 4;
RunWithInMemoryCommunicator(kWorkers, TestBuildHistogram, true, true, true);
RunWithInMemoryCommunicator(kWorkers, TestBuildHistogram, true, false, true);
}
namespace {
template <typename GradientSumT>
void ValidateCategoricalHistogram(size_t n_categories,
common::Span<GradientSumT> onehot,
common::Span<GradientSumT> cat) {
auto cat_sum = std::accumulate(cat.cbegin(), cat.cend(), GradientPairPrecise{});
for (size_t c = 0; c < n_categories; ++c) {
auto zero = onehot[c * 2];
auto one = onehot[c * 2 + 1];
auto chosen = cat[c];
auto not_chosen = cat_sum - chosen;
ASSERT_LE(RelError(zero.GetGrad(), not_chosen.GetGrad()), kRtEps);
ASSERT_LE(RelError(zero.GetHess(), not_chosen.GetHess()), kRtEps);
ASSERT_LE(RelError(one.GetGrad(), chosen.GetGrad()), kRtEps);
ASSERT_LE(RelError(one.GetHess(), chosen.GetHess()), kRtEps);
}
}
void TestHistogramCategorical(size_t n_categories, bool force_read_by_column) {
size_t constexpr kRows = 340;
int32_t constexpr kBins = 256;
auto x = GenerateRandomCategoricalSingleColumn(kRows, n_categories);
auto cat_m = GetDMatrixFromData(x, kRows, 1);
cat_m->Info().feature_types.HostVector().push_back(FeatureType::kCategorical);
BatchParam batch_param{0, static_cast<int32_t>(kBins)};
RegTree tree;
CPUExpandEntry node(RegTree::kRoot, tree.GetDepth(0), 0.0f);
std::vector<CPUExpandEntry> nodes_for_explicit_hist_build;
nodes_for_explicit_hist_build.push_back(node);
auto gpair = GenerateRandomGradients(kRows, 0, 2);
common::RowSetCollection row_set_collection;
row_set_collection.Clear();
std::vector<size_t> &row_indices = *row_set_collection.Data();
row_indices.resize(kRows);
std::iota(row_indices.begin(), row_indices.end(), 0);
row_set_collection.Init();
/**
* Generate hist with cat data.
*/
HistogramBuilder<CPUExpandEntry> cat_hist;
for (auto const &gidx : cat_m->GetBatches<GHistIndexMatrix>({kBins, 0.5})) {
auto total_bins = gidx.cut.TotalBins();
cat_hist.Reset(total_bins, {kBins, 0.5}, omp_get_max_threads(), 1, false, false);
cat_hist.BuildHist(0, gidx, &tree, row_set_collection,
nodes_for_explicit_hist_build, {}, gpair.HostVector(),
force_read_by_column);
}
/**
* Generate hist with one hot encoded data.
*/
auto x_encoded = OneHotEncodeFeature(x, n_categories);
auto encode_m = GetDMatrixFromData(x_encoded, kRows, n_categories);
HistogramBuilder<CPUExpandEntry> onehot_hist;
for (auto const &gidx : encode_m->GetBatches<GHistIndexMatrix>({kBins, 0.5})) {
auto total_bins = gidx.cut.TotalBins();
onehot_hist.Reset(total_bins, {kBins, 0.5}, omp_get_max_threads(), 1, false, false);
onehot_hist.BuildHist(0, gidx, &tree, row_set_collection, nodes_for_explicit_hist_build, {},
gpair.HostVector(),
force_read_by_column);
}
auto cat = cat_hist.Histogram()[0];
auto onehot = onehot_hist.Histogram()[0];
ValidateCategoricalHistogram(n_categories, onehot, cat);
}
} // anonymous namespace
TEST(CPUHistogram, Categorical) {
for (size_t n_categories = 2; n_categories < 8; ++n_categories) {
TestHistogramCategorical(n_categories, false);
}
for (size_t n_categories = 2; n_categories < 8; ++n_categories) {
TestHistogramCategorical(n_categories, true);
}
}
namespace {
void TestHistogramExternalMemory(BatchParam batch_param, bool is_approx, bool force_read_by_column) {
Context ctx;
size_t constexpr kEntries = 1 << 16;
auto m = CreateSparsePageDMatrix(kEntries, "cache");
std::vector<float> hess(m->Info().num_row_, 1.0);
if (is_approx) {
batch_param.hess = hess;
}
std::vector<size_t> partition_size(1, 0);
size_t total_bins{0};
size_t n_samples{0};
auto gpair = GenerateRandomGradients(m->Info().num_row_, 0.0, 1.0);
auto const &h_gpair = gpair.HostVector();
RegTree tree;
std::vector<CPUExpandEntry> nodes;
nodes.emplace_back(0, tree.GetDepth(0), 0.0f);
common::GHistRow multi_page;
HistogramBuilder<CPUExpandEntry> multi_build;
{
/**
* Multi page
*/
std::vector<common::RowSetCollection> rows_set;
for (auto const &page : m->GetBatches<GHistIndexMatrix>(batch_param)) {
CHECK_LT(page.base_rowid, m->Info().num_row_);
auto n_rows_in_node = page.Size();
partition_size[0] = std::max(partition_size[0], n_rows_in_node);
total_bins = page.cut.TotalBins();
n_samples += n_rows_in_node;
rows_set.emplace_back();
InitRowPartitionForTest(&rows_set.back(), n_rows_in_node, page.base_rowid);
}
ASSERT_EQ(n_samples, m->Info().num_row_);
common::BlockedSpace2d space{
1, [&](size_t nidx_in_set) { return partition_size.at(nidx_in_set); },
256};
multi_build.Reset(total_bins, batch_param, ctx.Threads(), rows_set.size(), false, false);
size_t page_idx{0};
for (auto const &page : m->GetBatches<GHistIndexMatrix>(batch_param)) {
multi_build.BuildHist(page_idx, space, page, &tree, rows_set.at(page_idx), nodes, {},
h_gpair, force_read_by_column);
++page_idx;
}
ASSERT_EQ(page_idx, 2);
multi_page = multi_build.Histogram()[0];
}
HistogramBuilder<CPUExpandEntry> single_build;
common::GHistRow single_page;
{
/**
* Single page
*/
common::RowSetCollection row_set_collection;
InitRowPartitionForTest(&row_set_collection, n_samples);
single_build.Reset(total_bins, batch_param, ctx.Threads(), 1, false, false);
SparsePage concat;
std::vector<float> hess(m->Info().num_row_, 1.0f);
for (auto const& page : m->GetBatches<SparsePage>()) {
concat.Push(page);
}
auto cut = common::SketchOnDMatrix(m.get(), batch_param.max_bin, ctx.Threads(), false, hess);
GHistIndexMatrix gmat(concat, {}, cut, batch_param.max_bin, false,
std::numeric_limits<double>::quiet_NaN(), ctx.Threads());
single_build.BuildHist(0, gmat, &tree, row_set_collection, nodes, {}, h_gpair, force_read_by_column);
single_page = single_build.Histogram()[0];
}
for (size_t i = 0; i < single_page.size(); ++i) {
ASSERT_NEAR(single_page[i].GetGrad(), multi_page[i].GetGrad(), kRtEps);
ASSERT_NEAR(single_page[i].GetHess(), multi_page[i].GetHess(), kRtEps);
}
}
} // anonymous namespace
TEST(CPUHistogram, ExternalMemory) {
int32_t constexpr kBins = 256;
TestHistogramExternalMemory(BatchParam{kBins, common::Span<float>{}, false}, true, false);
TestHistogramExternalMemory(BatchParam{kBins, common::Span<float>{}, false}, true, true);
float sparse_thresh{0.5};
TestHistogramExternalMemory({kBins, sparse_thresh}, false, false);
TestHistogramExternalMemory({kBins, sparse_thresh}, false, true);
sparse_thresh = std::numeric_limits<float>::quiet_NaN();
TestHistogramExternalMemory({kBins, sparse_thresh}, false, false);
TestHistogramExternalMemory({kBins, sparse_thresh}, false, true);
}
} // namespace tree
} // namespace xgboost
Reference in New Issue View Git Blame Copy Permalink