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xgboost/tests/cpp/tree/hist/test_histogram.cc
Jiaming Yuan 54029a59af
Bound the size of the histogram cache. (#9440) 
- A new histogram collection with a limit in size.
- Unify histogram building logic between hist, multi-hist, and approx.
2023-08-08 03:21:26 +08:00

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/**
* Copyright 2018-2023 by Contributors
*/
#include <gtest/gtest.h>
#include <xgboost/base.h> // for bst_node_t, bst_bin_t, Gradient...
#include <xgboost/context.h> // for Context
#include <xgboost/data.h> // for BatchIterator, BatchSet, DMatrix
#include <xgboost/host_device_vector.h> // for HostDeviceVector
#include <xgboost/linalg.h> // for MakeTensorView
#include <xgboost/logging.h> // for Error, LogCheck_EQ, LogCheck_LT
#include <xgboost/span.h> // for Span, operator!=
#include <xgboost/tree_model.h> // for RegTree
#include <algorithm> // for max
#include <cstddef> // for size_t
#include <cstdint> // for int32_t, uint32_t
#include <functional> // for function
#include <iterator> // for back_inserter
#include <limits> // for numeric_limits
#include <memory> // for shared_ptr, allocator, unique_ptr
#include <numeric> // for iota, accumulate
#include <vector> // for vector
#include "../../../../src/collective/communicator-inl.h" // for GetRank, GetWorldSize
#include "../../../../src/common/hist_util.h" // for GHistRow, HistogramCuts, Sketch...
#include "../../../../src/common/ref_resource_view.h" // for RefResourceView
#include "../../../../src/common/row_set.h" // for RowSetCollection
#include "../../../../src/common/threading_utils.h" // for BlockedSpace2d
#include "../../../../src/data/gradient_index.h" // for GHistIndexMatrix
#include "../../../../src/tree/common_row_partitioner.h" // for CommonRowPartitioner
#include "../../../../src/tree/hist/expand_entry.h" // for CPUExpandEntry
#include "../../../../src/tree/hist/hist_cache.h" // for BoundedHistCollection
#include "../../../../src/tree/hist/histogram.h" // for HistogramBuilder
#include "../../../../src/tree/hist/param.h" // for HistMakerTrainParam
#include "../../categorical_helpers.h" // for OneHotEncodeFeature
#include "../../helpers.h" // for RandomDataGenerator, GenerateRa...
namespace xgboost::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) {
Context ctx;
std::vector<bst_node_t> nodes_to_build;
std::vector<bst_node_t> nodes_to_sub;
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>(&ctx, 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_to_build.emplace_back(3);
nodes_to_build.emplace_back(4);
nodes_to_sub.emplace_back(5);
nodes_to_sub.emplace_back(6);
HistMakerTrainParam hist_param;
HistogramBuilder histogram_builder;
histogram_builder.Reset(&ctx, gmat.cut.TotalBins(), {kMaxBins, 0.5}, is_distributed, false,
&hist_param);
histogram_builder.AddHistRows(&tree, &nodes_to_build, &nodes_to_sub, false);
for (bst_node_t const &nidx : nodes_to_build) {
ASSERT_TRUE(histogram_builder.Histogram().HistogramExists(nidx));
}
for (bst_node_t const &nidx : nodes_to_sub) {
ASSERT_TRUE(histogram_builder.Histogram().HistogramExists(nidx));
}
}
TEST(CPUHistogram, AddRows) {
TestAddHistRows(true);
TestAddHistRows(false);
}
void TestSyncHist(bool is_distributed) {
std::size_t constexpr kNRows = 8, kNCols = 16;
bst_bin_t constexpr kMaxBins = 4;
Context ctx;
std::vector<bst_bin_t> nodes_for_explicit_hist_build;
std::vector<bst_bin_t> nodes_for_subtraction_trick;
RegTree tree;
auto p_fmat = RandomDataGenerator(kNRows, kNCols, 0.8).Seed(3).GenerateDMatrix();
auto const &gmat =
*(p_fmat->GetBatches<GHistIndexMatrix>(&ctx, BatchParam{kMaxBins, 0.5}).begin());
HistogramBuilder histogram;
uint32_t total_bins = gmat.cut.Ptrs().back();
HistMakerTrainParam hist_param;
histogram.Reset(&ctx, total_bins, {kMaxBins, 0.5}, is_distributed, false, &hist_param);
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);
histogram.AddHistRows(&tree, &nodes_for_explicit_hist_build, &nodes_for_subtraction_trick, false);
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());
nodes_for_subtraction_trick.emplace_back(tree[0].RightChild());
histogram.AddHistRows(&tree, &nodes_for_explicit_hist_build, &nodes_for_subtraction_trick, false);
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);
nodes_for_subtraction_trick.emplace_back(4);
nodes_for_explicit_hist_build.emplace_back(5);
nodes_for_subtraction_trick.emplace_back(6);
histogram.AddHistRows(&tree, &nodes_for_explicit_hist_build, &nodes_for_subtraction_trick, false);
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,
[&](std::size_t nidx_in_set) {
bst_node_t nidx = nodes_for_explicit_hist_build[nidx_in_set];
return row_set_collection[nidx].Size();
},
256);
std::vector<common::GHistRow> target_hists(n_nodes);
for (size_t i = 0; i < nodes_for_explicit_hist_build.size(); ++i) {
bst_node_t nidx = nodes_for_explicit_hist_build[i];
target_hists[i] = histogram.Histogram()[nidx];
}
// 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
histogram.SyncHistogram(&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 (auto const &nidx : nodes_for_explicit_hist_build) {
auto this_hist = histogram.Histogram()[nidx];
const size_t parent_id = tree[nidx].Parent();
const size_t subtraction_node_id = nodes_for_subtraction_trick[node_id];
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 (auto const &nidx : nodes_for_subtraction_trick) {
auto this_hist = histogram.Histogram()[nidx];
const size_t parent_id = tree[nidx].Parent();
const size_t subtraction_node_id = nodes_for_explicit_hist_build[node_id];
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;
Context ctx;
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>(&ctx, 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 histogram;
HistMakerTrainParam hist_param;
histogram.Reset(&ctx, total_bins, {kMaxBins, 0.5}, is_distributed, is_col_split, &hist_param);
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)};
std::vector<bst_node_t> nodes_to_build{node.nid};
std::vector<bst_node_t> dummy_sub;
histogram.AddHistRows(&tree, &nodes_to_build, &dummy_sub, false);
common::BlockedSpace2d space{
1, [&](std::size_t nidx_in_set) { return row_set_collection[nidx_in_set].Size(); }, 256};
for (auto const &gidx : p_fmat->GetBatches<GHistIndexMatrix>(&ctx, {kMaxBins, 0.5})) {
histogram.BuildHist(0, space, gidx, row_set_collection, nodes_to_build,
linalg::MakeTensorView(&ctx, gpair, gpair.size()), force_read_by_column);
}
histogram.SyncHistogram(&tree, nodes_to_build, {});
// 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;
bst_bin_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);
Context ctx;
BatchParam batch_param{0, kBins};
RegTree tree;
CPUExpandEntry node{RegTree::kRoot, tree.GetDepth(RegTree::kRoot)};
std::vector<bst_node_t> nodes_to_build;
nodes_to_build.push_back(node.nid);
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();
HistMakerTrainParam hist_param;
std::vector<bst_node_t> dummy_sub;
common::BlockedSpace2d space{
1, [&](std::size_t nidx_in_set) { return row_set_collection[nidx_in_set].Size(); }, 256};
/**
* Generate hist with cat data.
*/
HistogramBuilder cat_hist;
for (auto const &gidx : cat_m->GetBatches<GHistIndexMatrix>(&ctx, {kBins, 0.5})) {
auto total_bins = gidx.cut.TotalBins();
cat_hist.Reset(&ctx, total_bins, {kBins, 0.5}, false, false, &hist_param);
cat_hist.AddHistRows(&tree, &nodes_to_build, &dummy_sub, false);
cat_hist.BuildHist(0, space, gidx, row_set_collection, nodes_to_build,
linalg::MakeTensorView(&ctx, gpair.ConstHostSpan(), gpair.Size()),
force_read_by_column);
}
cat_hist.SyncHistogram(&tree, nodes_to_build, {});
/**
* Generate hist with one hot encoded data.
*/
auto x_encoded = OneHotEncodeFeature(x, n_categories);
auto encode_m = GetDMatrixFromData(x_encoded, kRows, n_categories);
HistogramBuilder onehot_hist;
for (auto const &gidx : encode_m->GetBatches<GHistIndexMatrix>(&ctx, {kBins, 0.5})) {
auto total_bins = gidx.cut.TotalBins();
onehot_hist.Reset(&ctx, total_bins, {kBins, 0.5}, false, false, &hist_param);
onehot_hist.AddHistRows(&tree, &nodes_to_build, &dummy_sub, false);
onehot_hist.BuildHist(0, space, gidx, row_set_collection, nodes_to_build,
linalg::MakeTensorView(&ctx, gpair.ConstHostSpan(), gpair.Size()),
force_read_by_column);
}
onehot_hist.SyncHistogram(&tree, nodes_to_build, {});
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(Context const *ctx, BatchParam batch_param, bool is_approx,
bool force_read_by_column) {
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<std::size_t> partition_size(1, 0);
bst_bin_t total_bins{0};
bst_row_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<bst_node_t> nodes{RegTree::kRoot};
common::BlockedSpace2d space{
1, [&](std::size_t nidx_in_set) { return partition_size.at(nidx_in_set); }, 256};
common::GHistRow multi_page;
HistogramBuilder multi_build;
HistMakerTrainParam hist_param;
std::vector<bst_node_t> dummy_sub;
{
/**
* Multi page
*/
std::vector<common::RowSetCollection> rows_set;
for (auto const &page : m->GetBatches<GHistIndexMatrix>(ctx, 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_);
multi_build.Reset(ctx, total_bins, batch_param, false, false, &hist_param);
multi_build.AddHistRows(&tree, &nodes, &dummy_sub, false);
std::size_t page_idx{0};
for (auto const &page : m->GetBatches<GHistIndexMatrix>(ctx, batch_param)) {
multi_build.BuildHist(page_idx, space, page, rows_set[page_idx], nodes,
linalg::MakeTensorView(ctx, h_gpair, h_gpair.size()),
force_read_by_column);
++page_idx;
}
multi_build.SyncHistogram(&tree, nodes, {});
multi_page = multi_build.Histogram()[RegTree::kRoot];
}
HistogramBuilder single_build;
common::GHistRow single_page;
{
/**
* Single page
*/
common::RowSetCollection row_set_collection;
InitRowPartitionForTest(&row_set_collection, n_samples);
single_build.Reset(ctx, total_bins, batch_param, false, false, &hist_param);
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(ctx, m.get(), batch_param.max_bin, false, hess);
GHistIndexMatrix gmat(concat, {}, cut, batch_param.max_bin, false,
std::numeric_limits<double>::quiet_NaN(), ctx->Threads());
single_build.AddHistRows(&tree, &nodes, &dummy_sub, false);
single_build.BuildHist(0, space, gmat, row_set_collection, nodes,
linalg::MakeTensorView(ctx, h_gpair, h_gpair.size()),
force_read_by_column);
single_build.SyncHistogram(&tree, nodes, {});
single_page = single_build.Histogram()[RegTree::kRoot];
}
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;
Context ctx;
TestHistogramExternalMemory(&ctx, BatchParam{kBins, common::Span<float>{}, false}, true, false);
TestHistogramExternalMemory(&ctx, BatchParam{kBins, common::Span<float>{}, false}, true, true);
float sparse_thresh{0.5};
TestHistogramExternalMemory(&ctx, {kBins, sparse_thresh}, false, false);
TestHistogramExternalMemory(&ctx, {kBins, sparse_thresh}, false, true);
sparse_thresh = std::numeric_limits<float>::quiet_NaN();
TestHistogramExternalMemory(&ctx, {kBins, sparse_thresh}, false, false);
TestHistogramExternalMemory(&ctx, {kBins, sparse_thresh}, false, true);
}
namespace {
class OverflowTest : public ::testing::TestWithParam<std::tuple<bool, bool>> {
public:
std::vector<GradientPairPrecise> TestOverflow(bool limit, bool is_distributed,
bool is_col_split) {
bst_bin_t constexpr kBins = 256;
Context ctx;
HistMakerTrainParam hist_param;
if (limit) {
hist_param.Init(Args{{"internal_max_cached_hist_node", "1"}});
}
std::shared_ptr<DMatrix> Xy =
is_col_split ? RandomDataGenerator{8192, 16, 0.5}.GenerateDMatrix(true)
: RandomDataGenerator{8192, 16, 0.5}.Bins(kBins).GenerateQuantileDMatrix(true);
if (is_col_split) {
Xy =
std::shared_ptr<DMatrix>{Xy->SliceCol(collective::GetWorldSize(), collective::GetRank())};
}
double sparse_thresh{TrainParam::DftSparseThreshold()};
auto batch = BatchParam{kBins, sparse_thresh};
bst_bin_t n_total_bins{0};
float split_cond{0};
for (auto const &page : Xy->GetBatches<GHistIndexMatrix>(&ctx, batch)) {
n_total_bins = page.cut.TotalBins();
// use a cut point in the second column for split
split_cond = page.cut.Values()[kBins + kBins / 2];
}
RegTree tree;
MultiHistogramBuilder hist_builder;
CHECK_EQ(Xy->Info().IsColumnSplit(), is_col_split);
hist_builder.Reset(&ctx, n_total_bins, tree.NumTargets(), batch, is_distributed,
Xy->Info().IsColumnSplit(), &hist_param);
std::vector<CommonRowPartitioner> partitioners;
partitioners.emplace_back(&ctx, Xy->Info().num_row_, /*base_rowid=*/0,
Xy->Info().IsColumnSplit());
auto gpair = GenerateRandomGradients(Xy->Info().num_row_, 0.0, 1.0);
CPUExpandEntry best;
hist_builder.BuildRootHist(Xy.get(), &tree, partitioners,
linalg::MakeTensorView(&ctx, gpair.ConstHostSpan(), gpair.Size(), 1),
best, batch);
best.split.Update(1.0f, 1, split_cond, false, false, GradStats{1.0, 1.0}, GradStats{1.0, 1.0});
tree.ExpandNode(best.nid, best.split.SplitIndex(), best.split.split_value, false,
/*base_weight=*/2.0f,
/*left_leaf_weight=*/1.0f, /*right_leaf_weight=*/1.0f, best.GetLossChange(),
/*sum_hess=*/2.0f, best.split.left_sum.GetHess(),
best.split.right_sum.GetHess());
std::vector<CPUExpandEntry> valid_candidates{best};
for (auto const &page : Xy->GetBatches<GHistIndexMatrix>(&ctx, batch)) {
partitioners.front().UpdatePosition(&ctx, page, valid_candidates, &tree);
}
CHECK_NE(partitioners.front()[tree.LeftChild(best.nid)].Size(), 0);
CHECK_NE(partitioners.front()[tree.RightChild(best.nid)].Size(), 0);
hist_builder.BuildHistLeftRight(
Xy.get(), &tree, partitioners, valid_candidates,
linalg::MakeTensorView(&ctx, gpair.ConstHostSpan(), gpair.Size(), 1), batch);
if (limit) {
CHECK(!hist_builder.Histogram(0).HistogramExists(best.nid));
} else {
CHECK(hist_builder.Histogram(0).HistogramExists(best.nid));
}
std::vector<GradientPairPrecise> result;
auto hist = hist_builder.Histogram(0)[tree.LeftChild(best.nid)];
std::copy(hist.cbegin(), hist.cend(), std::back_inserter(result));
hist = hist_builder.Histogram(0)[tree.RightChild(best.nid)];
std::copy(hist.cbegin(), hist.cend(), std::back_inserter(result));
return result;
}
void RunTest() {
auto param = GetParam();
auto res0 = this->TestOverflow(false, std::get<0>(param), std::get<1>(param));
auto res1 = this->TestOverflow(true, std::get<0>(param), std::get<1>(param));
ASSERT_EQ(res0, res1);
}
};
auto MakeParamsForTest() {
std::vector<std::tuple<bool, bool>> configs;
for (auto i : {true, false}) {
for (auto j : {true, false}) {
configs.emplace_back(i, j);
}
}
return configs;
}
} // anonymous namespace
TEST_P(OverflowTest, Overflow) { this->RunTest(); }
INSTANTIATE_TEST_SUITE_P(CPUHistogram, OverflowTest, ::testing::ValuesIn(MakeParamsForTest()));
} // namespace xgboost::tree
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