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Use std::uint64_t for row index. (#10120)

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- Use std::uint64_t instead of size_t to avoid implementation-defined type.
- Rename to bst_idx_t, to account for other types of indexing.
- Small cleanup to the base header.
This commit is contained in:
Jiaming Yuan
2024-03-15 18:43:49 +08:00
committed by GitHub
parent 56b1868278
commit 53fc17578f
57 changed files with 228 additions and 238 deletions
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8
tests/cpp/c_api/test_c_api.cc
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@@ -434,7 +434,7 @@ void MakeLabelForTest(std::shared_ptr<DMatrix> Xy, DMatrixHandle cxy) {
XGDMatrixSetInfoFromInterface(cxy, "label", s_y_int.c_str());
}
auto MakeSimpleDMatrixForTest(bst_row_t n_samples, bst_feature_t n_features, Json dconfig) {
auto MakeSimpleDMatrixForTest(bst_idx_t n_samples, bst_feature_t n_features, Json dconfig) {
HostDeviceVector<float> storage;
auto arr_int = RandomDataGenerator{n_samples, n_features, 0.5f}.GenerateArrayInterface(&storage);
@@ -451,7 +451,7 @@ auto MakeSimpleDMatrixForTest(bst_row_t n_samples, bst_feature_t n_features, Jso
return std::pair{p_fmat, Xy};
}
auto MakeQDMForTest(Context const *ctx, bst_row_t n_samples, bst_feature_t n_features,
auto MakeQDMForTest(Context const *ctx, bst_idx_t n_samples, bst_feature_t n_features,
Json dconfig) {
bst_bin_t n_bins{16};
dconfig["max_bin"] = Integer{n_bins};
@@ -483,7 +483,7 @@ auto MakeQDMForTest(Context const *ctx, bst_row_t n_samples, bst_feature_t n_fea
return std::pair{p_fmat, Xy};
}
auto MakeExtMemForTest(bst_row_t n_samples, bst_feature_t n_features, Json dconfig) {
auto MakeExtMemForTest(bst_idx_t n_samples, bst_feature_t n_features, Json dconfig) {
std::size_t n_batches{4};
NumpyArrayIterForTest iter_0{0.0f, n_samples, n_features, n_batches};
std::string s_dconfig;
@@ -525,7 +525,7 @@ void CheckResult(Context const *ctx, bst_feature_t n_features, std::shared_ptr<D
}
void TestXGDMatrixGetQuantileCut(Context const *ctx) {
bst_row_t n_samples{1024};
bst_idx_t n_samples{1024};
bst_feature_t n_features{16};
Json dconfig{Object{}};

20
tests/cpp/common/test_hist_util.cu
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@@ -179,7 +179,7 @@ void TestMixedSketch() {
TEST(HistUtil, DeviceSketchMixedFeatures) { TestMixedSketch(); }
TEST(HistUtil, RemoveDuplicatedCategories) {
bst_row_t n_samples = 512;
bst_idx_t n_samples = 512;
bst_feature_t n_features = 3;
bst_cat_t n_categories = 5;
@@ -208,13 +208,13 @@ TEST(HistUtil, RemoveDuplicatedCategories) {
FeatureType::kNumerical, FeatureType::kCategorical, FeatureType::kNumerical};
ASSERT_EQ(info.feature_types.Size(), n_features);
HostDeviceVector<bst_row_t> cuts_ptr{0, n_samples, n_samples * 2, n_samples * 3};
HostDeviceVector<bst_idx_t> cuts_ptr{0, n_samples, n_samples * 2, n_samples * 3};
cuts_ptr.SetDevice(DeviceOrd::CUDA(0));
dh::device_vector<float> weight(n_samples * n_features, 0);
dh::Iota(dh::ToSpan(weight), ctx.CUDACtx()->Stream());
dh::caching_device_vector<bst_row_t> columns_ptr(4);
dh::caching_device_vector<bst_idx_t> columns_ptr(4);
for (std::size_t i = 0; i < columns_ptr.size(); ++i) {
columns_ptr[i] = i * n_samples;
}
@@ -639,7 +639,7 @@ void TestGetColumnSize(std::size_t n_samples) {
} // namespace
TEST(HistUtil, GetColumnSize) {
bst_row_t n_samples = 4096;
bst_idx_t n_samples = 4096;
TestGetColumnSize(n_samples);
}
@@ -795,11 +795,11 @@ TEST(HistUtil, AdapterSketchFromWeights) {
namespace {
class DeviceSketchWithHessianTest
: public ::testing::TestWithParam<std::tuple<bool, bst_row_t, bst_bin_t>> {
: public ::testing::TestWithParam<std::tuple<bool, bst_idx_t, bst_bin_t>> {
bst_feature_t n_features_ = 5;
bst_group_t n_groups_{3};
auto GenerateHessian(Context const* ctx, bst_row_t n_samples) const {
auto GenerateHessian(Context const* ctx, bst_idx_t n_samples) const {
HostDeviceVector<float> hessian;
auto& h_hess = hessian.HostVector();
h_hess = GenerateRandomWeights(n_samples);
@@ -844,7 +844,7 @@ class DeviceSketchWithHessianTest
protected:
Context ctx_ = MakeCUDACtx(0);
void TestLTR(Context const* ctx, bst_row_t n_samples, bst_bin_t n_bins,
void TestLTR(Context const* ctx, bst_idx_t n_samples, bst_bin_t n_bins,
std::size_t n_elements) const {
auto x = GenerateRandom(n_samples, n_features_);
@@ -897,7 +897,7 @@ class DeviceSketchWithHessianTest
}
}
void TestRegression(Context const* ctx, bst_row_t n_samples, bst_bin_t n_bins,
void TestRegression(Context const* ctx, bst_idx_t n_samples, bst_bin_t n_bins,
std::size_t n_elements) const {
auto x = GenerateRandom(n_samples, n_features_);
auto p_fmat = GetDMatrixFromData(x, n_samples, n_features_);
@@ -910,9 +910,9 @@ class DeviceSketchWithHessianTest
};
auto MakeParamsForTest() {
std::vector<bst_row_t> sizes = {1, 2, 256, 512, 1000, 1500};
std::vector<bst_idx_t> sizes = {1, 2, 256, 512, 1000, 1500};
std::vector<bst_bin_t> bin_sizes = {2, 16, 256, 512};
std::vector<std::tuple<bool, bst_row_t, bst_bin_t>> configs;
std::vector<std::tuple<bool, bst_idx_t, bst_bin_t>> configs;
for (auto n_samples : sizes) {
for (auto n_bins : bin_sizes) {
configs.emplace_back(true, n_samples, n_bins);

4
tests/cpp/common/test_quantile.cc
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@@ -50,7 +50,7 @@ void DoTestDistributedQuantile(size_t rows, size_t cols) {
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);
std::vector<bst_idx_t> column_size(cols, rows);
bst_bin_t n_bins = 64;
// Generate cuts for distributed environment.
@@ -192,7 +192,7 @@ void DoTestColSplitQuantile(size_t rows, size_t cols) {
return dmat->SliceCol(world, rank);
}()};
std::vector<bst_row_t> column_size(cols, 0);
std::vector<bst_idx_t> column_size(cols, 0);
auto const slice_size = cols / world;
auto const slice_start = slice_size * rank;
auto const slice_end = (rank == world - 1) ? cols : slice_start + slice_size;

14
tests/cpp/common/test_quantile.cu
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@@ -27,7 +27,7 @@ TEST(GPUQuantile, Basic) {
HostDeviceVector<FeatureType> ft;
SketchContainer sketch(ft, kBins, kCols, kRows, FstCU());
dh::caching_device_vector<Entry> entries;
dh::device_vector<bst_row_t> cuts_ptr(kCols+1);
dh::device_vector<bst_idx_t> cuts_ptr(kCols+1);
thrust::fill(cuts_ptr.begin(), cuts_ptr.end(), 0);
// Push empty
sketch.Push(dh::ToSpan(entries), dh::ToSpan(cuts_ptr), dh::ToSpan(cuts_ptr), 0);
@@ -87,11 +87,11 @@ TEST(GPUQuantile, Unique) {
// if with_error is true, the test tolerates floating point error
void TestQuantileElemRank(DeviceOrd device, Span<SketchEntry const> in,
Span<bst_row_t const> d_columns_ptr, bool with_error = false) {
Span<bst_idx_t const> d_columns_ptr, bool with_error = false) {
dh::safe_cuda(cudaSetDevice(device.ordinal));
std::vector<SketchEntry> h_in(in.size());
dh::CopyDeviceSpanToVector(&h_in, in);
std::vector<bst_row_t> h_columns_ptr(d_columns_ptr.size());
std::vector<bst_idx_t> h_columns_ptr(d_columns_ptr.size());
dh::CopyDeviceSpanToVector(&h_columns_ptr, d_columns_ptr);
for (size_t i = 1; i < d_columns_ptr.size(); ++i) {
@@ -164,7 +164,7 @@ TEST(GPUQuantile, MergeEmpty) {
std::vector<SketchEntry> entries_before(sketch_0.Data().size());
dh::CopyDeviceSpanToVector(&entries_before, sketch_0.Data());
std::vector<bst_row_t> ptrs_before(sketch_0.ColumnsPtr().size());
std::vector<bst_idx_t> ptrs_before(sketch_0.ColumnsPtr().size());
dh::CopyDeviceSpanToVector(&ptrs_before, sketch_0.ColumnsPtr());
thrust::device_vector<size_t> columns_ptr(kCols + 1);
// Merge an empty sketch
@@ -172,7 +172,7 @@ TEST(GPUQuantile, MergeEmpty) {
std::vector<SketchEntry> entries_after(sketch_0.Data().size());
dh::CopyDeviceSpanToVector(&entries_after, sketch_0.Data());
std::vector<bst_row_t> ptrs_after(sketch_0.ColumnsPtr().size());
std::vector<bst_idx_t> ptrs_after(sketch_0.ColumnsPtr().size());
dh::CopyDeviceSpanToVector(&ptrs_after, sketch_0.ColumnsPtr());
CHECK_EQ(entries_before.size(), entries_after.size());
@@ -222,7 +222,7 @@ TEST(GPUQuantile, MergeBasic) {
}
auto columns_ptr = sketch_0.ColumnsPtr();
std::vector<bst_row_t> h_columns_ptr(columns_ptr.size());
std::vector<bst_idx_t> h_columns_ptr(columns_ptr.size());
dh::CopyDeviceSpanToVector(&h_columns_ptr, columns_ptr);
ASSERT_EQ(h_columns_ptr.back(), sketch_1.Data().size() + size_before_merge);
@@ -278,7 +278,7 @@ void TestMergeDuplicated(int32_t n_bins, size_t cols, size_t rows, float frac) {
TestQuantileElemRank(FstCU(), sketch_0.Data(), sketch_0.ColumnsPtr());
auto columns_ptr = sketch_0.ColumnsPtr();
std::vector<bst_row_t> h_columns_ptr(columns_ptr.size());
std::vector<bst_idx_t> h_columns_ptr(columns_ptr.size());
dh::CopyDeviceSpanToVector(&h_columns_ptr, columns_ptr);
ASSERT_EQ(h_columns_ptr.back(), sketch_1.Data().size() + size_before_merge);

11
tests/cpp/common/test_span.cu
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@@ -1,14 +1,15 @@
/*!
* Copyright 2018 XGBoost contributors
/**
* Copyright 2018-2024, XGBoost contributors
*/
#include <gtest/gtest.h>
#include <thrust/host_vector.h>
#include <thrust/device_vector.h>
#include <thrust/execution_policy.h>
#include <thrust/host_vector.h>
#include <xgboost/span.h>
#include <numeric> // for iota
#include "../../../src/common/device_helpers.cuh"
#include <xgboost/span.h>
#include "test_span.h"
namespace xgboost {

4
tests/cpp/data/test_adapter.cc
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@@ -36,7 +36,7 @@ TEST(Adapter, CSRAdapter) {
}
TEST(Adapter, CSRArrayAdapter) {
HostDeviceVector<bst_row_t> indptr;
HostDeviceVector<std::size_t> indptr;
HostDeviceVector<float> values;
HostDeviceVector<bst_feature_t> indices;
size_t n_features = 100, n_samples = 10;
@@ -155,7 +155,7 @@ TEST(Adapter, IteratorAdapter) {
ASSERT_EQ(data->Info().num_row_, kRows);
int num_batch = 0;
for (auto const& batch : data->GetBatches<SparsePage>()) {
ASSERT_EQ(batch.offset.HostVector(), std::vector<bst_row_t>({0, 2, 4, 5, 5, 7, 9, 10, 10}));
ASSERT_EQ(batch.offset.HostVector(), std::vector<bst_idx_t>({0, 2, 4, 5, 5, 7, 9, 10, 10}));
++num_batch;
}
ASSERT_EQ(num_batch, 1);

2
tests/cpp/data/test_data.cc
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@@ -13,7 +13,7 @@
namespace xgboost {
TEST(SparsePage, PushCSC) {
std::vector<bst_row_t> offset {0};
std::vector<bst_idx_t> offset {0};
std::vector<Entry> data;
SparsePage batch;
batch.offset.HostVector() = offset;

2
tests/cpp/data/test_device_adapter.cu
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@@ -62,7 +62,7 @@ TEST(DeviceAdapter, GetRowCounts) {
.Device(ctx.Device())
.GenerateArrayInterface(&storage);
auto adapter = CupyAdapter{str_arr};
HostDeviceVector<bst_row_t> offset(adapter.NumRows() + 1, 0);
HostDeviceVector<bst_idx_t> offset(adapter.NumRows() + 1, 0);
offset.SetDevice(ctx.Device());
auto rstride = GetRowCounts(adapter.Value(), offset.DeviceSpan(), ctx.Device(),
std::numeric_limits<float>::quiet_NaN());

2
tests/cpp/data/test_metainfo.cc
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@@ -231,7 +231,7 @@ TEST(MetaInfo, LoadQid) {
const std::vector<xgboost::bst_uint> expected_group_ptr{0, 4, 8, 12};
CHECK(info.group_ptr_ == expected_group_ptr);
const std::vector<xgboost::bst_row_t> expected_offset{
const std::vector<xgboost::bst_idx_t> expected_offset{
0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60
};
const std::vector<xgboost::Entry> expected_data{

2
tests/cpp/data/test_simple_dmatrix.cc
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@@ -223,7 +223,7 @@ TEST(SimpleDMatrix, FromFile) {
auto batch = page.GetView();
EXPECT_EQ(batch.Size(), kExpectedNumRow);
EXPECT_EQ(page.offset.HostVector(),
std::vector<bst_row_t>({0, 3, 6, 9, 12, 15, 15}));
std::vector<bst_idx_t>({0, 3, 6, 9, 12, 15, 15}));
EXPECT_EQ(page.base_rowid, 0);
for (auto i = 0ull; i < batch.Size() - 1; i++) {

2
tests/cpp/gbm/test_gbtree.cc
View File

@@ -171,7 +171,7 @@ TEST(GBTree, ChoosePredictor) {
}
TEST(GBTree, ChooseTreeMethod) {
bst_row_t n_samples{128};
bst_idx_t n_samples{128};
bst_feature_t n_features{64};
auto Xy = RandomDataGenerator{n_samples, n_features, 0.5f}.GenerateDMatrix(true);

2
tests/cpp/gbm/test_gbtree.cu
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@@ -18,7 +18,7 @@
namespace xgboost {
void TestInplaceFallback(Context const* ctx) {
// prepare data
bst_row_t n_samples{1024};
bst_idx_t n_samples{1024};
bst_feature_t n_features{32};
HostDeviceVector<float> X_storage;
// use a different device than the learner

16
tests/cpp/helpers.cc
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@@ -216,7 +216,7 @@ SimpleLCG::StateType SimpleLCG::Max() const { return max(); }
static_assert(SimpleLCG::max() - SimpleLCG::min());
void RandomDataGenerator::GenerateLabels(std::shared_ptr<DMatrix> p_fmat) const {
RandomDataGenerator{static_cast<bst_row_t>(p_fmat->Info().num_row_), this->n_targets_, 0.0f}.GenerateDense(
RandomDataGenerator{static_cast<bst_idx_t>(p_fmat->Info().num_row_), this->n_targets_, 0.0f}.GenerateDense(
p_fmat->Info().labels.Data());
CHECK_EQ(p_fmat->Info().labels.Size(), this->rows_ * this->n_targets_);
p_fmat->Info().labels.Reshape(this->rows_, this->n_targets_);
@@ -334,7 +334,7 @@ std::string RandomDataGenerator::GenerateColumnarArrayInterface(
}
void RandomDataGenerator::GenerateCSR(
HostDeviceVector<float>* value, HostDeviceVector<bst_row_t>* row_ptr,
HostDeviceVector<float>* value, HostDeviceVector<std::size_t>* row_ptr,
HostDeviceVector<bst_feature_t>* columns) const {
auto& h_value = value->HostVector();
auto& h_rptr = row_ptr->HostVector();
@@ -381,7 +381,7 @@ void RandomDataGenerator::GenerateCSR(
[[nodiscard]] std::shared_ptr<DMatrix> RandomDataGenerator::GenerateDMatrix(
bool with_label, bool float_label, size_t classes, DataSplitMode data_split_mode) const {
HostDeviceVector<float> data;
HostDeviceVector<bst_row_t> rptrs;
HostDeviceVector<std::size_t> rptrs;
HostDeviceVector<bst_feature_t> columns;
this->GenerateCSR(&data, &rptrs, &columns);
data::CSRAdapter adapter(rptrs.HostPointer(), columns.HostPointer(), data.HostPointer(), rows_,
@@ -447,7 +447,7 @@ void RandomDataGenerator::GenerateCSR(
// Loop over the batches and count the number of pages
std::size_t batch_count = 0;
bst_row_t row_count = 0;
bst_idx_t row_count = 0;
for (const auto& batch : dmat->GetBatches<xgboost::SparsePage>()) {
batch_count++;
row_count += batch.Size();
@@ -458,7 +458,7 @@ void RandomDataGenerator::GenerateCSR(
EXPECT_EQ(row_count, dmat->Info().num_row_);
if (with_label) {
RandomDataGenerator{static_cast<bst_row_t>(dmat->Info().num_row_), this->n_targets_, 0.0f}.GenerateDense(
RandomDataGenerator{static_cast<bst_idx_t>(dmat->Info().num_row_), this->n_targets_, 0.0f}.GenerateDense(
dmat->Info().labels.Data());
CHECK_EQ(dmat->Info().labels.Size(), this->rows_ * this->n_targets_);
dmat->Info().labels.Reshape(this->rows_, this->n_targets_);
@@ -488,7 +488,7 @@ int CudaArrayIterForTest::Next() {
}
#endif // !defined(XGBOOST_USE_CUDA)
NumpyArrayIterForTest::NumpyArrayIterForTest(float sparsity, size_t rows, size_t cols,
NumpyArrayIterForTest::NumpyArrayIterForTest(float sparsity, bst_idx_t rows, size_t cols,
size_t batches)
: ArrayIterForTest{sparsity, rows, cols, batches} {
rng_->Device(DeviceOrd::CPU());
@@ -515,7 +515,7 @@ std::shared_ptr<DMatrix> GetDMatrixFromData(const std::vector<float>& x, std::si
return p_fmat;
}
std::unique_ptr<DMatrix> CreateSparsePageDMatrix(bst_row_t n_samples, bst_feature_t n_features,
std::unique_ptr<DMatrix> CreateSparsePageDMatrix(bst_idx_t n_samples, bst_feature_t n_features,
size_t n_batches, std::string prefix) {
CHECK_GE(n_samples, n_batches);
NumpyArrayIterForTest iter(0, n_samples, n_features, n_batches);
@@ -662,7 +662,7 @@ std::unique_ptr<GradientBooster> CreateTrainedGBM(std::string name, Args kwargs,
return gbm;
}
ArrayIterForTest::ArrayIterForTest(float sparsity, size_t rows, size_t cols, size_t batches)
ArrayIterForTest::ArrayIterForTest(float sparsity, bst_idx_t rows, size_t cols, size_t batches)
: rows_{rows}, cols_{cols}, n_batches_{batches} {
XGProxyDMatrixCreate(&proxy_);
rng_ = std::make_unique<RandomDataGenerator>(rows_, cols_, sparsity);

20
tests/cpp/helpers.h
View File

@@ -223,7 +223,7 @@ Json GetArrayInterface(HostDeviceVector<T> const* storage, size_t rows, size_t c
// Generate in-memory random data without using DMatrix.
class RandomDataGenerator {
bst_row_t rows_;
bst_idx_t rows_;
size_t cols_;
float sparsity_;
@@ -246,7 +246,7 @@ class RandomDataGenerator {
void GenerateLabels(std::shared_ptr<DMatrix> p_fmat) const;
public:
RandomDataGenerator(bst_row_t rows, size_t cols, float sparsity)
RandomDataGenerator(bst_idx_t rows, size_t cols, float sparsity)
: rows_{rows}, cols_{cols}, sparsity_{sparsity}, lcg_{seed_} {}
RandomDataGenerator& Lower(float v) {
@@ -308,7 +308,7 @@ class RandomDataGenerator {
std::string GenerateColumnarArrayInterface(std::vector<HostDeviceVector<float>>* data) const;
void GenerateCSR(HostDeviceVector<float>* value, HostDeviceVector<bst_row_t>* row_ptr,
void GenerateCSR(HostDeviceVector<float>* value, HostDeviceVector<std::size_t>* row_ptr,
HostDeviceVector<bst_feature_t>* columns) const;
[[nodiscard]] std::shared_ptr<DMatrix> GenerateDMatrix(
@@ -354,7 +354,7 @@ std::shared_ptr<DMatrix> GetDMatrixFromData(const std::vector<float>& x, std::si
*
* \return A Sparse DMatrix with n_batches.
*/
std::unique_ptr<DMatrix> CreateSparsePageDMatrix(bst_row_t n_samples, bst_feature_t n_features,
std::unique_ptr<DMatrix> CreateSparsePageDMatrix(bst_idx_t n_samples, bst_feature_t n_features,
size_t n_batches, std::string prefix = "cache");
/**
@@ -413,12 +413,12 @@ inline HostDeviceVector<GradientPair> GenerateRandomGradients(const size_t n_row
return gpair;
}
inline linalg::Matrix<GradientPair> GenerateRandomGradients(Context const* ctx, bst_row_t n_rows,
inline linalg::Matrix<GradientPair> GenerateRandomGradients(Context const* ctx, bst_idx_t n_rows,
bst_target_t n_targets,
float lower = 0.0f,
float upper = 1.0f) {
auto g = GenerateRandomGradients(n_rows * n_targets, lower, upper);
linalg::Matrix<GradientPair> gpair({n_rows, static_cast<bst_row_t>(n_targets)}, ctx->Device());
linalg::Matrix<GradientPair> gpair({n_rows, static_cast<bst_idx_t>(n_targets)}, ctx->Device());
gpair.Data()->Copy(g);
return gpair;
}
@@ -434,12 +434,12 @@ class ArrayIterForTest {
std::vector<std::string> batches_;
std::string interface_;
size_t rows_;
bst_idx_t rows_;
size_t cols_;
size_t n_batches_;
public:
size_t static constexpr Rows() { return 1024; }
bst_idx_t static constexpr Rows() { return 1024; }
size_t static constexpr Batches() { return 100; }
size_t static constexpr Cols() { return 13; }
@@ -451,7 +451,7 @@ class ArrayIterForTest {
[[nodiscard]] std::size_t Iter() const { return iter_; }
auto Proxy() -> decltype(proxy_) { return proxy_; }
explicit ArrayIterForTest(float sparsity, size_t rows, size_t cols, size_t batches);
explicit ArrayIterForTest(float sparsity, bst_idx_t rows, size_t cols, size_t batches);
/**
* \brief Create iterator with user provided data.
*/
@@ -470,7 +470,7 @@ class CudaArrayIterForTest : public ArrayIterForTest {
class NumpyArrayIterForTest : public ArrayIterForTest {
public:
explicit NumpyArrayIterForTest(float sparsity, size_t rows = Rows(), size_t cols = Cols(),
explicit NumpyArrayIterForTest(float sparsity, bst_idx_t rows = Rows(), size_t cols = Cols(),
size_t batches = Batches());
explicit NumpyArrayIterForTest(Context const* ctx, HostDeviceVector<float> const& data,
std::size_t n_samples, bst_feature_t n_features,

2
tests/cpp/histogram_helpers.h
View File

@@ -47,7 +47,7 @@ inline std::unique_ptr<EllpackPageImpl> BuildEllpackPage(int n_rows, int n_cols,
0.26f, 0.71f, 1.83f});
cmat.SetMins({0.1f, 0.2f, 0.3f, 0.1f, 0.2f, 0.3f, 0.2f, 0.2f});
bst_row_t row_stride = 0;
bst_idx_t row_stride = 0;
const auto &offset_vec = batch.offset.ConstHostVector();
for (size_t i = 1; i < offset_vec.size(); ++i) {
row_stride = std::max(row_stride, offset_vec[i] - offset_vec[i-1]);

4
tests/cpp/plugin/test_sycl_predictor.cc
View File

@@ -43,7 +43,7 @@ TEST(SyclPredictor, ExternalMemory) {
}
TEST(SyclPredictor, InplacePredict) {
bst_row_t constexpr kRows{128};
bst_idx_t constexpr kRows{128};
bst_feature_t constexpr kCols{64};
Context ctx;
auto gen = RandomDataGenerator{kRows, kCols, 0.5}.Device(ctx.Device());
@@ -106,4 +106,4 @@ TEST(SyclPredictor, Multi) {
TestVectorLeafPrediction(&ctx);
}
} // namespace xgboost
} // namespace xgboost

4
tests/cpp/predictor/test_cpu_predictor.cc
View File

@@ -65,7 +65,7 @@ TEST(CpuPredictor, ExternalMemory) {
}
TEST(CpuPredictor, InplacePredict) {
bst_row_t constexpr kRows{128};
bst_idx_t constexpr kRows{128};
bst_feature_t constexpr kCols{64};
Context ctx;
auto gen = RandomDataGenerator{kRows, kCols, 0.5}.Device(ctx.Device());
@@ -83,7 +83,7 @@ TEST(CpuPredictor, InplacePredict) {
{
HostDeviceVector<float> data;
HostDeviceVector<bst_row_t> rptrs;
HostDeviceVector<std::size_t> rptrs;
HostDeviceVector<bst_feature_t> columns;
gen.GenerateCSR(&data, &rptrs, &columns);
auto data_interface = GetArrayInterface(&data, kRows * kCols, 1);

4
tests/cpp/predictor/test_predictor.cc
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@@ -186,7 +186,7 @@ void TestTrainingPrediction(Context const *ctx, size_t rows, size_t bins,
}
}
void TestInplacePrediction(Context const *ctx, std::shared_ptr<DMatrix> x, bst_row_t rows,
void TestInplacePrediction(Context const *ctx, std::shared_ptr<DMatrix> x, bst_idx_t rows,
bst_feature_t cols) {
std::size_t constexpr kClasses { 4 };
auto gen = RandomDataGenerator{rows, cols, 0.5}.Device(ctx->Device());
@@ -255,7 +255,7 @@ std::unique_ptr<Learner> LearnerForTest(Context const *ctx, std::shared_ptr<DMat
return learner;
}
void VerifyPredictionWithLesserFeatures(Learner *learner, bst_row_t kRows,
void VerifyPredictionWithLesserFeatures(Learner *learner, bst_idx_t kRows,
std::shared_ptr<DMatrix> m_test,
std::shared_ptr<DMatrix> m_invalid) {
HostDeviceVector<float> prediction;

2
tests/cpp/predictor/test_predictor.h
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@@ -92,7 +92,7 @@ void TestTrainingPrediction(Context const* ctx, size_t rows, size_t bins,
std::shared_ptr<DMatrix> p_full, std::shared_ptr<DMatrix> p_hist,
bool check_contribs = false);
void TestInplacePrediction(Context const* ctx, std::shared_ptr<DMatrix> x, bst_row_t rows,
void TestInplacePrediction(Context const* ctx, std::shared_ptr<DMatrix> x, bst_idx_t rows,
bst_feature_t cols);
void TestPredictionWithLesserFeatures(Context const* ctx);

2
tests/cpp/test_helpers.cc
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@@ -11,7 +11,7 @@ TEST(RandomDataGenerator, DMatrix) {
auto p_dmatrix = RandomDataGenerator{kRows, kCols, kSparsity}.GenerateDMatrix();
HostDeviceVector<float> csr_value;
HostDeviceVector<bst_row_t> csr_rptr;
HostDeviceVector<std::size_t> csr_rptr;
HostDeviceVector<bst_feature_t> csr_cidx;
RandomDataGenerator{kRows, kCols, kSparsity}.GenerateCSR(&csr_value, &csr_rptr, &csr_cidx);

2
tests/cpp/test_learner.cc
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@@ -217,7 +217,7 @@ TEST(Learner, JsonModelIO) {
}
TEST(Learner, ConfigIO) {
bst_row_t n_samples = 128;
bst_idx_t n_samples = 128;
bst_feature_t n_features = 12;
std::shared_ptr<DMatrix> p_fmat{
RandomDataGenerator{n_samples, n_features, 0}.GenerateDMatrix(true, false, 2)};

8
tests/cpp/tree/gpu_hist/test_evaluate_splits.cu
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@@ -363,7 +363,7 @@ TEST(GpuHist, EvaluateSingleSplitMissing) {
GPUTrainingParam param{tparam};
thrust::device_vector<bst_feature_t> feature_set = std::vector<bst_feature_t>{0};
thrust::device_vector<uint32_t> feature_segments = std::vector<bst_row_t>{0, 2};
thrust::device_vector<uint32_t> feature_segments = std::vector<bst_idx_t>{0, 2};
thrust::device_vector<float> feature_values = std::vector<float>{1.0, 2.0};
thrust::device_vector<float> feature_min_values = std::vector<float>{0.0};
auto feature_histogram = ConvertToInteger(&ctx, {{-0.5, 0.5}, {0.5, 0.5}});
@@ -412,7 +412,7 @@ TEST(GpuHist, EvaluateSingleSplitFeatureSampling) {
GPUTrainingParam param{tparam};
thrust::device_vector<bst_feature_t> feature_set = std::vector<bst_feature_t>{1};
thrust::device_vector<uint32_t> feature_segments = std::vector<bst_row_t>{0, 2, 4};
thrust::device_vector<uint32_t> feature_segments = std::vector<bst_idx_t>{0, 2, 4};
thrust::device_vector<float> feature_values = std::vector<float>{1.0, 2.0, 11.0, 12.0};
thrust::device_vector<float> feature_min_values = std::vector<float>{0.0, 10.0};
auto feature_histogram =
@@ -446,7 +446,7 @@ TEST(GpuHist, EvaluateSingleSplitBreakTies) {
GPUTrainingParam param{tparam};
thrust::device_vector<bst_feature_t> feature_set = std::vector<bst_feature_t>{0, 1};
thrust::device_vector<uint32_t> feature_segments = std::vector<bst_row_t>{0, 2, 4};
thrust::device_vector<uint32_t> feature_segments = std::vector<bst_idx_t>{0, 2, 4};
thrust::device_vector<float> feature_values = std::vector<float>{1.0, 2.0, 11.0, 12.0};
thrust::device_vector<float> feature_min_values = std::vector<float>{0.0, 10.0};
auto feature_histogram =
@@ -478,7 +478,7 @@ TEST(GpuHist, EvaluateSplits) {
GPUTrainingParam param{tparam};
thrust::device_vector<bst_feature_t> feature_set = std::vector<bst_feature_t>{0, 1};
thrust::device_vector<uint32_t> feature_segments = std::vector<bst_row_t>{0, 2, 4};
thrust::device_vector<uint32_t> feature_segments = std::vector<bst_idx_t>{0, 2, 4};
thrust::device_vector<float> feature_values = std::vector<float>{1.0, 2.0, 11.0, 12.0};
thrust::device_vector<float> feature_min_values = std::vector<float>{0.0, 0.0};
auto feature_histogram_left =

4
tests/cpp/tree/hist/test_histogram.cc
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@@ -409,9 +409,9 @@ void TestHistogramExternalMemory(Context const *ctx, BatchParam batch_param, boo
batch_param.hess = hess;
}
std::vector<std::size_t> partition_size(1, 0);
std::vector<bst_idx_t> partition_size(1, 0);
bst_bin_t total_bins{0};
bst_row_t n_samples{0};
bst_idx_t n_samples{0};
auto gpair = GenerateRandomGradients(m->Info().num_row_, 0.0, 1.0);
auto const &h_gpair = gpair.HostVector();

4
tests/cpp/tree/test_gpu_hist.cu
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@@ -440,7 +440,7 @@ RegTree GetHistTree(Context const* ctx, DMatrix* dmat) {
return tree;
}
void VerifyHistColumnSplit(bst_row_t rows, bst_feature_t cols, RegTree const& expected_tree) {
void VerifyHistColumnSplit(bst_idx_t rows, bst_feature_t cols, RegTree const& expected_tree) {
Context ctx(MakeCUDACtx(GPUIDX));
auto Xy = RandomDataGenerator{rows, cols, 0}.GenerateDMatrix(true);
@@ -490,7 +490,7 @@ RegTree GetApproxTree(Context const* ctx, DMatrix* dmat) {
return tree;
}
void VerifyApproxColumnSplit(bst_row_t rows, bst_feature_t cols, RegTree const& expected_tree) {
void VerifyApproxColumnSplit(bst_idx_t rows, bst_feature_t cols, RegTree const& expected_tree) {
Context ctx(MakeCUDACtx(GPUIDX));
auto Xy = RandomDataGenerator{rows, cols, 0}.GenerateDMatrix(true);

2
tests/cpp/tree/test_quantile_hist.cc
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@@ -201,7 +201,7 @@ TEST(QuantileHist, PartitionerColSplit) { TestColumnSplitPartitioner<CPUExpandEn
TEST(QuantileHist, MultiPartitionerColSplit) { TestColumnSplitPartitioner<MultiExpandEntry>(3); }
namespace {
void VerifyColumnSplit(Context const* ctx, bst_row_t rows, bst_feature_t cols, bst_target_t n_targets,
void VerifyColumnSplit(Context const* ctx, bst_idx_t rows, bst_feature_t cols, bst_target_t n_targets,
RegTree const& expected_tree) {
auto Xy = RandomDataGenerator{rows, cols, 0}.GenerateDMatrix(true);
linalg::Matrix<GradientPair> gpair = GenerateRandomGradients(ctx, rows, n_targets);

2
tests/cpp/tree/test_refresh.cc
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@@ -15,7 +15,7 @@
namespace xgboost::tree {
TEST(Updater, Refresh) {
bst_row_t constexpr kRows = 8;
bst_idx_t constexpr kRows = 8;
bst_feature_t constexpr kCols = 16;
Context ctx;