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xgboost/tests/cpp/data/test_gradient_index.cc
Jiaming Yuan bc267dd729
Use ptr from mmap for GHistIndexMatrix and ColumnMatrix. (#9315) 
* Use ptr from mmap for `GHistIndexMatrix` and `ColumnMatrix`.

- Define a resource for holding various types of memory pointers.
- Define ref vector for holding resources.
- Swap the underlying resources for GHist and ColumnM.
- Add documentation for current status.
- s390x support is removed. It should work if you can compile XGBoost, all the old workaround code does is to get GCC to compile.
2023-06-27 19:05:46 +08:00

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/**
* Copyright 2021-2023 by XGBoost contributors
*/
#include <gtest/gtest.h>
#include <xgboost/data.h> // for BatchIterator, BatchSet, DMatrix, BatchParam
#include <algorithm> // for sort, unique
#include <cmath> // for isnan
#include <cstddef> // for size_t
#include <limits> // for numeric_limits
#include <memory> // for shared_ptr, __shared_ptr_access, unique_ptr
#include <string> // for string
#include <tuple> // for make_tuple, tie, tuple
#include <utility> // for move
#include <vector> // for vector
#include "../../../src/common/categorical.h" // for AsCat
#include "../../../src/common/column_matrix.h" // for ColumnMatrix
#include "../../../src/common/hist_util.h" // for Index, HistogramCuts, SketchOnDMatrix
#include "../../../src/common/io.h" // for MemoryBufferStream
#include "../../../src/data/adapter.h" // for SparsePageAdapterBatch
#include "../../../src/data/gradient_index.h" // for GHistIndexMatrix
#include "../../../src/tree/param.h" // for TrainParam
#include "../helpers.h" // for GenerateRandomCategoricalSingleColumn...
#include "xgboost/base.h" // for bst_bin_t
#include "xgboost/context.h" // for Context
#include "xgboost/host_device_vector.h" // for HostDeviceVector
namespace xgboost::data {
TEST(GradientIndex, ExternalMemory) {
Context ctx;
std::unique_ptr<DMatrix> dmat = CreateSparsePageDMatrix(10000);
std::vector<size_t> base_rowids;
std::vector<float> hessian(dmat->Info().num_row_, 1);
for (auto const &page : dmat->GetBatches<GHistIndexMatrix>(&ctx, {64, hessian, true})) {
base_rowids.push_back(page.base_rowid);
}
size_t i = 0;
for (auto const &page : dmat->GetBatches<SparsePage>()) {
ASSERT_EQ(base_rowids[i], page.base_rowid);
++i;
}
base_rowids.clear();
for (auto const &page : dmat->GetBatches<GHistIndexMatrix>(&ctx, {64, hessian, false})) {
base_rowids.push_back(page.base_rowid);
}
i = 0;
for (auto const &page : dmat->GetBatches<SparsePage>()) {
ASSERT_EQ(base_rowids[i], page.base_rowid);
++i;
}
}
TEST(GradientIndex, FromCategoricalBasic) {
size_t constexpr kRows = 1000, kCats = 13, kCols = 1;
size_t max_bins = 8;
auto x = GenerateRandomCategoricalSingleColumn(kRows, kCats);
auto m = GetDMatrixFromData(x, kRows, 1);
Context ctx;
auto &h_ft = m->Info().feature_types.HostVector();
h_ft.resize(kCols, FeatureType::kCategorical);
BatchParam p(max_bins, 0.8);
GHistIndexMatrix gidx(&ctx, m.get(), max_bins, p.sparse_thresh, false, {});
auto x_copy = x;
std::sort(x_copy.begin(), x_copy.end());
auto n_uniques = std::unique(x_copy.begin(), x_copy.end()) - x_copy.begin();
ASSERT_EQ(n_uniques, kCats);
auto const &h_cut_ptr = gidx.cut.Ptrs();
auto const &h_cut_values = gidx.cut.Values();
ASSERT_EQ(h_cut_ptr.size(), 2);
ASSERT_EQ(h_cut_values.size(), kCats);
auto const &index = gidx.index;
for (size_t i = 0; i < x.size(); ++i) {
auto bin = index[i];
auto bin_value = h_cut_values.at(bin);
ASSERT_EQ(common::AsCat(x[i]), common::AsCat(bin_value));
}
}
TEST(GradientIndex, FromCategoricalLarge) {
size_t constexpr kRows = 1000, kCats = 512, kCols = 1;
bst_bin_t max_bins = 8;
auto x = GenerateRandomCategoricalSingleColumn(kRows, kCats);
auto m = GetDMatrixFromData(x, kRows, 1);
Context ctx;
auto &h_ft = m->Info().feature_types.HostVector();
h_ft.resize(kCols, FeatureType::kCategorical);
BatchParam p{max_bins, 0.8};
{
GHistIndexMatrix gidx{&ctx, m.get(), max_bins, p.sparse_thresh, false, {}};
ASSERT_TRUE(gidx.index.GetBinTypeSize() == common::kUint16BinsTypeSize);
}
{
for (auto const &page : m->GetBatches<GHistIndexMatrix>(&ctx, p)) {
common::HistogramCuts cut = page.cut;
GHistIndexMatrix gidx{m->Info(), std::move(cut), max_bins};
ASSERT_EQ(gidx.MaxNumBinPerFeat(), kCats);
}
}
}
TEST(GradientIndex, PushBatch) {
size_t constexpr kRows = 64, kCols = 4;
bst_bin_t max_bins = 64;
float st = 0.5;
Context ctx;
auto test = [&](float sparisty) {
auto m = RandomDataGenerator{kRows, kCols, sparisty}.GenerateDMatrix(true);
auto cuts = common::SketchOnDMatrix(&ctx, m.get(), max_bins, false, {});
common::HistogramCuts copy_cuts = cuts;
ASSERT_EQ(m->Info().num_row_, kRows);
ASSERT_EQ(m->Info().num_col_, kCols);
GHistIndexMatrix gmat{m->Info(), std::move(copy_cuts), max_bins};
for (auto const &page : m->GetBatches<SparsePage>()) {
SparsePageAdapterBatch batch{page.GetView()};
gmat.PushAdapterBatch(m->Ctx(), 0, 0, batch, std::numeric_limits<float>::quiet_NaN(), {}, st,
m->Info().num_row_);
gmat.PushAdapterBatchColumns(m->Ctx(), batch, std::numeric_limits<float>::quiet_NaN(), 0);
}
for (auto const &page : m->GetBatches<GHistIndexMatrix>(&ctx, BatchParam{max_bins, st})) {
for (size_t i = 0; i < kRows; ++i) {
for (size_t j = 0; j < kCols; ++j) {
auto v0 = gmat.GetFvalue(i, j, false);
auto v1 = page.GetFvalue(i, j, false);
if (sparisty == 0.0) {
ASSERT_FALSE(std::isnan(v0));
}
if (!std::isnan(v0)) {
ASSERT_EQ(v0, v1);
}
}
}
}
};
test(0.0f);
test(0.5f);
test(0.9f);
}
#if defined(XGBOOST_USE_CUDA)
namespace {
class GHistIndexMatrixTest : public testing::TestWithParam<std::tuple<float, float>> {
protected:
void Run(float density, double threshold) {
// Only testing with small sample size as the cuts might be different between host and
// device.
size_t n_samples{128}, n_features{13};
Context ctx;
auto Xy = RandomDataGenerator{n_samples, n_features, 1 - density}.GenerateDMatrix(true);
std::unique_ptr<GHistIndexMatrix> from_ellpack;
ASSERT_TRUE(Xy->SingleColBlock());
bst_bin_t constexpr kBins{17};
auto p = BatchParam{kBins, threshold};
Context gpu_ctx;
gpu_ctx.gpu_id = 0;
for (auto const &page : Xy->GetBatches<EllpackPage>(
&gpu_ctx, BatchParam{kBins, tree::TrainParam::DftSparseThreshold()})) {
from_ellpack = std::make_unique<GHistIndexMatrix>(&ctx, Xy->Info(), page, p);
}
for (auto const &from_sparse_page : Xy->GetBatches<GHistIndexMatrix>(&ctx, p)) {
ASSERT_EQ(from_sparse_page.IsDense(), from_ellpack->IsDense());
ASSERT_EQ(from_sparse_page.base_rowid, 0);
ASSERT_EQ(from_sparse_page.base_rowid, from_ellpack->base_rowid);
ASSERT_EQ(from_sparse_page.Size(), from_ellpack->Size());
ASSERT_EQ(from_sparse_page.index.Size(), from_ellpack->index.Size());
auto const &gidx_from_sparse = from_sparse_page.index;
auto const &gidx_from_ellpack = from_ellpack->index;
for (size_t i = 0; i < gidx_from_sparse.Size(); ++i) {
ASSERT_EQ(gidx_from_sparse[i], gidx_from_ellpack[i]);
}
auto const &columns_from_sparse = from_sparse_page.Transpose();
auto const &columns_from_ellpack = from_ellpack->Transpose();
ASSERT_EQ(columns_from_sparse.AnyMissing(), columns_from_ellpack.AnyMissing());
ASSERT_EQ(columns_from_sparse.GetTypeSize(), columns_from_ellpack.GetTypeSize());
ASSERT_EQ(columns_from_sparse.GetNumFeature(), columns_from_ellpack.GetNumFeature());
for (size_t i = 0; i < n_features; ++i) {
ASSERT_EQ(columns_from_sparse.GetColumnType(i), columns_from_ellpack.GetColumnType(i));
}
std::string from_sparse_buf;
{
common::AlignedMemWriteStream fo{&from_sparse_buf};
auto n_bytes = columns_from_sparse.Write(&fo);
ASSERT_EQ(fo.Tell(), n_bytes);
}
std::string from_ellpack_buf;
{
common::AlignedMemWriteStream fo{&from_ellpack_buf};
auto n_bytes = columns_from_sparse.Write(&fo);
ASSERT_EQ(fo.Tell(), n_bytes);
}
ASSERT_EQ(from_sparse_buf, from_ellpack_buf);
}
}
};
} // anonymous namespace
TEST_P(GHistIndexMatrixTest, FromEllpack) {
float sparsity;
double thresh;
std::tie(sparsity, thresh) = GetParam();
this->Run(sparsity, thresh);
}
INSTANTIATE_TEST_SUITE_P(GHistIndexMatrix, GHistIndexMatrixTest,
testing::Values(std::make_tuple(1.f, .0), // no missing
std::make_tuple(.2f, .8), // sparse columns
std::make_tuple(.8f, .2), // dense columns
std::make_tuple(1.f, .2), // no missing
std::make_tuple(.5f, .6), // sparse columns
std::make_tuple(.6f, .4))); // dense columns
#endif // defined(XGBOOST_USE_CUDA)
} // namespace xgboost::data
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