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test_quantile.cc
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test_quantile.cc
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#include <gtest/gtest.h>
#include "test_quantile.h"
#include "../../../src/common/quantile.h"
#include "../../../src/common/hist_util.h"
namespace xgboost {
namespace common {
TEST(Quantile, LoadBalance) {
size_t constexpr kRows = 1000, kCols = 100;
auto m = RandomDataGenerator{kRows, kCols, 0}.GenerateDMatrix();
std::vector<bst_feature_t> cols_ptr;
for (auto const &page : m->GetBatches<SparsePage>()) {
cols_ptr = HostSketchContainer::LoadBalance(page, kCols, 13);
}
size_t n_cols = 0;
for (size_t i = 1; i < cols_ptr.size(); ++i) {
n_cols += cols_ptr[i] - cols_ptr[i - 1];
}
CHECK_EQ(n_cols, kCols);
}
void TestDistributedQuantile(size_t rows, size_t cols) {
std::string msg {"Skipping AllReduce test"};
int32_t constexpr kWorkers = 4;
InitRabitContext(msg, kWorkers);
auto world = rabit::GetWorldSize();
if (world != 1) {
ASSERT_EQ(world, kWorkers);
} else {
return;
}
std::vector<MetaInfo> infos(2);
auto& h_weights = infos.front().weights_.HostVector();
h_weights.resize(rows);
SimpleLCG lcg;
SimpleRealUniformDistribution<float> dist(3, 1000);
std::generate(h_weights.begin(), h_weights.end(), [&]() { return dist(&lcg); });
std::vector<bst_row_t> column_size(cols, rows);
size_t n_bins = 64;
// Generate cuts for distributed environment.
auto sparsity = 0.5f;
auto rank = rabit::GetRank();
auto m = RandomDataGenerator{rows, cols, sparsity}
.Seed(rank)
.Lower(.0f)
.Upper(1.0f)
.GenerateDMatrix();
HostSketchContainer sketch_distributed(
column_size, n_bins, m->Info().feature_types.ConstHostSpan(), false,
OmpGetNumThreads(0));
for (auto const &page : m->GetBatches<SparsePage>()) {
sketch_distributed.PushRowPage(page, m->Info());
}
HistogramCuts distributed_cuts;
sketch_distributed.MakeCuts(&distributed_cuts);
// Generate cuts for single node environment
rabit::Finalize();
CHECK_EQ(rabit::GetWorldSize(), 1);
std::for_each(column_size.begin(), column_size.end(), [=](auto& size) { size *= world; });
HostSketchContainer sketch_on_single_node(
column_size, n_bins, m->Info().feature_types.ConstHostSpan(), false,
OmpGetNumThreads(0));
for (auto rank = 0; rank < world; ++rank) {
auto m = RandomDataGenerator{rows, cols, sparsity}
.Seed(rank)
.Lower(.0f)
.Upper(1.0f)
.GenerateDMatrix();
for (auto const &page : m->GetBatches<SparsePage>()) {
sketch_on_single_node.PushRowPage(page, m->Info());
}
}
HistogramCuts single_node_cuts;
sketch_on_single_node.MakeCuts(&single_node_cuts);
auto const& sptrs = single_node_cuts.Ptrs();
auto const& dptrs = distributed_cuts.Ptrs();
auto const& svals = single_node_cuts.Values();
auto const& dvals = distributed_cuts.Values();
auto const& smins = single_node_cuts.MinValues();
auto const& dmins = distributed_cuts.MinValues();
ASSERT_EQ(sptrs.size(), dptrs.size());
for (size_t i = 0; i < sptrs.size(); ++i) {
ASSERT_EQ(sptrs[i], dptrs[i]);
}
ASSERT_EQ(svals.size(), dvals.size());
for (size_t i = 0; i < svals.size(); ++i) {
ASSERT_NEAR(svals[i], dvals[i], 2e-2f);
}
ASSERT_EQ(smins.size(), dmins.size());
for (size_t i = 0; i < smins.size(); ++i) {
ASSERT_FLOAT_EQ(smins[i], dmins[i]);
}
}
TEST(Quantile, DistributedBasic) {
#if defined(__unix__)
constexpr size_t kRows = 10, kCols = 10;
TestDistributedQuantile(kRows, kCols);
#endif
}
TEST(Quantile, Distributed) {
#if defined(__unix__)
constexpr size_t kRows = 1000, kCols = 200;
TestDistributedQuantile(kRows, kCols);
#endif
}
TEST(Quantile, SameOnAllWorkers) {
#if defined(__unix__)
std::string msg{"Skipping Quantile AllreduceBasic test"};
int32_t constexpr kWorkers = 4;
InitRabitContext(msg, kWorkers);
auto world = rabit::GetWorldSize();
if (world != 1) {
CHECK_EQ(world, kWorkers);
} else {
LOG(WARNING) << msg;
return;
}
constexpr size_t kRows = 1000, kCols = 100;
RunWithSeedsAndBins(
kRows, [=](int32_t seed, size_t n_bins, MetaInfo const &info) {
auto rank = rabit::GetRank();
HostDeviceVector<float> storage;
auto m = RandomDataGenerator{kRows, kCols, 0}
.Device(0)
.Seed(rank + seed)
.GenerateDMatrix();
auto cuts = SketchOnDMatrix(m.get(), n_bins);
std::vector<float> cut_values(cuts.Values().size() * world, 0);
std::vector<
typename std::remove_reference_t<decltype(cuts.Ptrs())>::value_type>
cut_ptrs(cuts.Ptrs().size() * world, 0);
std::vector<float> cut_min_values(cuts.MinValues().size() * world, 0);
size_t value_size = cuts.Values().size();
rabit::Allreduce<rabit::op::Max>(&value_size, 1);
size_t ptr_size = cuts.Ptrs().size();
rabit::Allreduce<rabit::op::Max>(&ptr_size, 1);
CHECK_EQ(ptr_size, kCols + 1);
size_t min_value_size = cuts.MinValues().size();
rabit::Allreduce<rabit::op::Max>(&min_value_size, 1);
CHECK_EQ(min_value_size, kCols);
size_t value_offset = value_size * rank;
std::copy(cuts.Values().begin(), cuts.Values().end(),
cut_values.begin() + value_offset);
size_t ptr_offset = ptr_size * rank;
std::copy(cuts.Ptrs().cbegin(), cuts.Ptrs().cend(),
cut_ptrs.begin() + ptr_offset);
size_t min_values_offset = min_value_size * rank;
std::copy(cuts.MinValues().cbegin(), cuts.MinValues().cend(),
cut_min_values.begin() + min_values_offset);
rabit::Allreduce<rabit::op::Sum>(cut_values.data(), cut_values.size());
rabit::Allreduce<rabit::op::Sum>(cut_ptrs.data(), cut_ptrs.size());
rabit::Allreduce<rabit::op::Sum>(cut_min_values.data(), cut_min_values.size());
for (int32_t i = 0; i < world; i++) {
for (size_t j = 0; j < value_size; ++j) {
size_t idx = i * value_size + j;
ASSERT_NEAR(cuts.Values().at(j), cut_values.at(idx), kRtEps);
}
for (size_t j = 0; j < ptr_size; ++j) {
size_t idx = i * ptr_size + j;
ASSERT_EQ(cuts.Ptrs().at(j), cut_ptrs.at(idx));
}
for (size_t j = 0; j < min_value_size; ++j) {
size_t idx = i * min_value_size + j;
ASSERT_EQ(cuts.MinValues().at(j), cut_min_values.at(idx));
}
}
});
rabit::Finalize();
#endif // defined(__unix__)
}
} // namespace common
} // namespace xgboost