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Use view for SparsePage exclusively. (#6590)

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Jiaming Yuan 2021-01-11 18:04:55 +08:00 committed by GitHub
parent 78f2cd83d7
commit f2f7dd87b8
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23 changed files with 151 additions and 113 deletions
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9
include/xgboost/data.h
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@ -252,15 +252,6 @@ class SparsePage {
/*! \brief an instance of sparse vector in the batch */
using Inst = common::Span<Entry const>;
/*! \brief get i-th row from the batch */
inline Inst operator[](size_t i) const {
const auto& data_vec = data.HostVector();
const auto& offset_vec = offset.HostVector();
size_t size = offset_vec[i + 1] - offset_vec[i];
return {data_vec.data() + offset_vec[i],
static_cast<Inst::index_type>(size)};
}
HostSparsePageView GetView() const {
return {offset.ConstHostSpan(), data.ConstHostSpan()};
}

3
src/common/hist_util.cc
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@ -78,6 +78,7 @@ void GHistIndexMatrix::Init(DMatrix* p_fmat, int max_bins) {
const size_t batch_threads = std::max(
size_t(1),
std::min(batch.Size(), static_cast<size_t>(omp_get_max_threads())));
auto page = batch.GetView();
MemStackAllocator<size_t, 128> partial_sums(batch_threads);
size_t* p_part = partial_sums.Get();
@ -92,7 +93,7 @@ void GHistIndexMatrix::Init(DMatrix* p_fmat, int max_bins) {
size_t sum = 0;
for (size_t i = ibegin; i < iend; ++i) {
sum += batch[i].size();
sum += page[i].size();
row_ptr[rbegin + 1 + i] = sum;
}
}

9
src/data/data.cc
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@ -825,19 +825,20 @@ SparsePage SparsePage::GetTranspose(int num_columns) const {
const int nthread = omp_get_max_threads();
builder.InitBudget(num_columns, nthread);
long batch_size = static_cast<long>(this->Size()); // NOLINT(*)
#pragma omp parallel for default(none) shared(batch_size, builder) schedule(static)
auto page = this->GetView();
#pragma omp parallel for default(none) shared(batch_size, builder, page) schedule(static)
for (long i = 0; i < batch_size; ++i) { // NOLINT(*)
int tid = omp_get_thread_num();
auto inst = (*this)[i];
auto inst = page[i];
for (const auto& entry : inst) {
builder.AddBudget(entry.index, tid);
}
}
builder.InitStorage();
#pragma omp parallel for default(none) shared(batch_size, builder) schedule(static)
#pragma omp parallel for default(none) shared(batch_size, builder, page) schedule(static)
for (long i = 0; i < batch_size; ++i) { // NOLINT(*)
int tid = omp_get_thread_num();
auto inst = (*this)[i];
auto inst = page[i];
for (const auto& entry : inst) {
builder.Push(
entry.index,

5
src/data/simple_dmatrix.cc
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@ -28,13 +28,12 @@ DMatrix* SimpleDMatrix::Slice(common::Span<int32_t const> ridxs) {
auto out = new SimpleDMatrix;
SparsePage& out_page = out->sparse_page_;
for (auto const &page : this->GetBatches<SparsePage>()) {
page.data.HostVector();
page.offset.HostVector();
auto batch = page.GetView();
auto& h_data = out_page.data.HostVector();
auto& h_offset = out_page.offset.HostVector();
size_t rptr{0};
for (auto ridx : ridxs) {
auto inst = page[ridx];
auto inst = batch[ridx];
rptr += inst.size();
std::copy(inst.begin(), inst.end(), std::back_inserter(h_data));
h_offset.emplace_back(rptr);

3
src/gbm/gblinear.cc
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@ -173,9 +173,10 @@ class GBLinear : public GradientBooster {
for (const auto &batch : p_fmat->GetBatches<SparsePage>()) {
// parallel over local batch
const auto nsize = static_cast<bst_omp_uint>(batch.Size());
auto page = batch.GetView();
#pragma omp parallel for schedule(static)
for (bst_omp_uint i = 0; i < nsize; ++i) {
auto inst = batch[i];
auto inst = page[i];
auto row_idx = static_cast<size_t>(batch.base_rowid + i);
// loop over output groups
for (int gid = 0; gid < ngroup; ++gid) {

5
src/gbm/gbtree.cc
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@ -678,6 +678,7 @@ class Dart : public GBTree {
CHECK_EQ(preds.size(), p_fmat->Info().num_row_ * num_group);
// start collecting the prediction
for (const auto &batch : p_fmat->GetBatches<SparsePage>()) {
auto page = batch.GetView();
constexpr int kUnroll = 8;
const auto nsize = static_cast<bst_omp_uint>(batch.Size());
const bst_omp_uint rest = nsize % kUnroll;
@ -692,7 +693,7 @@ class Dart : public GBTree {
ridx[k] = static_cast<int64_t>(batch.base_rowid + i + k);
}
for (int k = 0; k < kUnroll; ++k) {
inst[k] = batch[i + k];
inst[k] = page[i + k];
}
for (int k = 0; k < kUnroll; ++k) {
for (int gid = 0; gid < num_group; ++gid) {
@ -707,7 +708,7 @@ class Dart : public GBTree {
for (bst_omp_uint i = nsize - rest; i < nsize; ++i) {
RegTree::FVec& feats = thread_temp_[0];
const auto ridx = static_cast<int64_t>(batch.base_rowid + i);
const SparsePage::Inst inst = batch[i];
const SparsePage::Inst inst = page[i];
for (int gid = 0; gid < num_group; ++gid) {
const size_t offset = ridx * num_group + gid;
preds[offset] +=

19
src/linear/coordinate_common.h
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@ -82,7 +82,8 @@ inline std::pair<double, double> GetGradient(int group_idx, int num_group, int f
DMatrix *p_fmat) {
double sum_grad = 0.0, sum_hess = 0.0;
for (const auto &batch : p_fmat->GetBatches<CSCPage>()) {
auto col = batch[fidx];
auto page = batch.GetView();
auto col = page[fidx];
const auto ndata = static_cast<bst_omp_uint>(col.size());
for (bst_omp_uint j = 0; j < ndata; ++j) {
const bst_float v = col[j].fvalue;
@ -111,7 +112,8 @@ inline std::pair<double, double> GetGradientParallel(int group_idx, int num_grou
DMatrix *p_fmat) {
double sum_grad = 0.0, sum_hess = 0.0;
for (const auto &batch : p_fmat->GetBatches<CSCPage>()) {
auto col = batch[fidx];
auto page = batch.GetView();
auto col = page[fidx];
const auto ndata = static_cast<bst_omp_uint>(col.size());
#pragma omp parallel for schedule(static) reduction(+ : sum_grad, sum_hess)
for (bst_omp_uint j = 0; j < ndata; ++j) {
@ -166,7 +168,8 @@ inline void UpdateResidualParallel(int fidx, int group_idx, int num_group,
DMatrix *p_fmat) {
if (dw == 0.0f) return;
for (const auto &batch : p_fmat->GetBatches<CSCPage>()) {
auto col = batch[fidx];
auto page = batch.GetView();
auto col = page[fidx];
// update grad value
const auto num_row = static_cast<bst_omp_uint>(col.size());
#pragma omp parallel for schedule(static)
@ -334,9 +337,10 @@ class GreedyFeatureSelector : public FeatureSelector {
// Calculate univariate gradient sums
std::fill(gpair_sums_.begin(), gpair_sums_.end(), std::make_pair(0., 0.));
for (const auto &batch : p_fmat->GetBatches<CSCPage>()) {
#pragma omp parallel for schedule(static)
auto page = batch.GetView();
#pragma omp parallel for schedule(static)
for (bst_omp_uint i = 0; i < nfeat; ++i) {
const auto col = batch[i];
const auto col = page[i];
const bst_uint ndata = col.size();
auto &sums = gpair_sums_[group_idx * nfeat + i];
for (bst_uint j = 0u; j < ndata; ++j) {
@ -399,10 +403,11 @@ class ThriftyFeatureSelector : public FeatureSelector {
// Calculate univariate gradient sums
std::fill(gpair_sums_.begin(), gpair_sums_.end(), std::make_pair(0., 0.));
for (const auto &batch : p_fmat->GetBatches<CSCPage>()) {
// column-parallel is usually faster than row-parallel
auto page = batch.GetView();
// column-parallel is usually fastaer than row-parallel
#pragma omp parallel for schedule(static)
for (bst_omp_uint i = 0; i < nfeat; ++i) {
const auto col = batch[i];
const auto col = page[i];
const bst_uint ndata = col.size();
for (bst_uint gid = 0u; gid < ngroup; ++gid) {
auto &sums = gpair_sums_[gid * nfeat + i];

5
src/linear/updater_gpu_coordinate.cu
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@ -60,6 +60,7 @@ class GPUCoordinateUpdater : public LinearUpdater { // NOLINT
CHECK(p_fmat->SingleColBlock());
SparsePage const& batch = *(p_fmat->GetBatches<CSCPage>().begin());
auto page = batch.GetView();
if (IsEmpty()) {
return;
@ -72,7 +73,7 @@ class GPUCoordinateUpdater : public LinearUpdater { // NOLINT
row_ptr_ = {0};
// iterate through columns
for (size_t fidx = 0; fidx < batch.Size(); fidx++) {
common::Span<Entry const> col = batch[fidx];
common::Span<Entry const> col = page[fidx];
auto cmp = [](Entry e1, Entry e2) {
return e1.index < e2.index;
};
@ -89,7 +90,7 @@ class GPUCoordinateUpdater : public LinearUpdater { // NOLINT
data_.resize(row_ptr_.back());
gpair_.resize(num_row_ * model_param.num_output_group);
for (size_t fidx = 0; fidx < batch.Size(); fidx++) {
auto col = batch[fidx];
auto col = page[fidx];
auto seg = column_segments[fidx];
dh::safe_cuda(cudaMemcpy(
data_.data().get() + row_ptr_[fidx],

3
src/linear/updater_shotgun.cc
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@ -52,6 +52,7 @@ class ShotgunUpdater : public LinearUpdater {
selector_->Setup(*model, in_gpair->ConstHostVector(), p_fmat,
param_.reg_alpha_denorm, param_.reg_lambda_denorm, 0);
for (const auto &batch : p_fmat->GetBatches<CSCPage>()) {
auto page = batch.GetView();
const auto nfeat = static_cast<bst_omp_uint>(batch.Size());
#pragma omp parallel for schedule(static)
for (bst_omp_uint i = 0; i < nfeat; ++i) {
@ -60,7 +61,7 @@ class ShotgunUpdater : public LinearUpdater {
param_.reg_lambda_denorm);
if (ii < 0) continue;
const bst_uint fid = ii;
auto col = batch[ii];
auto col = page[ii];
for (int gid = 0; gid < ngroup; ++gid) {
double sum_grad = 0.0, sum_hess = 0.0;
for (auto& c : col) {

10
src/predictor/cpu_predictor.cc
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@ -360,18 +360,19 @@ class CPUPredictor : public Predictor {
// start collecting the prediction
for (const auto &batch : p_fmat->GetBatches<SparsePage>()) {
// parallel over local batch
auto page = batch.GetView();
const auto nsize = static_cast<bst_omp_uint>(batch.Size());
#pragma omp parallel for schedule(static)
for (bst_omp_uint i = 0; i < nsize; ++i) {
const int tid = omp_get_thread_num();
auto ridx = static_cast<size_t>(batch.base_rowid + i);
RegTree::FVec &feats = thread_temp_[tid];
feats.Fill(batch[i]);
feats.Fill(page[i]);
for (unsigned j = 0; j < ntree_limit; ++j) {
int tid = model.trees[j]->GetLeafIndex(feats);
preds[ridx * ntree_limit + j] = static_cast<bst_float>(tid);
}
feats.Drop(batch[i]);
feats.Drop(page[i]);
}
}
}
@ -407,6 +408,7 @@ class CPUPredictor : public Predictor {
const std::vector<bst_float>& base_margin = info.base_margin_.HostVector();
// start collecting the contributions
for (const auto &batch : p_fmat->GetBatches<SparsePage>()) {
auto page = batch.GetView();
// parallel over local batch
const auto nsize = static_cast<bst_omp_uint>(batch.Size());
#pragma omp parallel for schedule(static)
@ -417,7 +419,7 @@ class CPUPredictor : public Predictor {
// loop over all classes
for (int gid = 0; gid < ngroup; ++gid) {
bst_float* p_contribs = &contribs[(row_idx * ngroup + gid) * ncolumns];
feats.Fill(batch[i]);
feats.Fill(page[i]);
// calculate contributions
for (unsigned j = 0; j < ntree_limit; ++j) {
std::fill(this_tree_contribs.begin(), this_tree_contribs.end(), 0);
@ -435,7 +437,7 @@ class CPUPredictor : public Predictor {
(tree_weights == nullptr ? 1 : (*tree_weights)[j]);
}
}
feats.Drop(batch[i]);
feats.Drop(page[i]);
// add base margin to BIAS
if (base_margin.size() != 0) {
p_contribs[ncolumns - 1] += base_margin[row_idx * ngroup + gid];

11
src/tree/updater_basemaker-inl.h
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@ -59,8 +59,9 @@ class BaseMaker: public TreeUpdater {
-std::numeric_limits<bst_float>::max());
// start accumulating statistics
for (const auto &batch : p_fmat->GetBatches<SortedCSCPage>()) {
auto page = batch.GetView();
for (bst_uint fid = 0; fid < batch.Size(); ++fid) {
auto c = batch[fid];
auto c = page[fid];
if (c.size() != 0) {
CHECK_LT(fid * 2, fminmax_.size());
fminmax_[fid * 2 + 0] =
@ -249,8 +250,9 @@ class BaseMaker: public TreeUpdater {
inline void CorrectNonDefaultPositionByBatch(
const SparsePage &batch, const std::vector<bst_uint> &sorted_split_set,
const RegTree &tree) {
auto page = batch.GetView();
for (size_t fid = 0; fid < batch.Size(); ++fid) {
auto col = batch[fid];
auto col = page[fid];
auto it = std::lower_bound(sorted_split_set.begin(), sorted_split_set.end(), fid);
if (it != sorted_split_set.end() && *it == fid) {
@ -308,10 +310,11 @@ class BaseMaker: public TreeUpdater {
std::vector<unsigned> fsplits;
this->GetSplitSet(nodes, tree, &fsplits);
for (const auto &batch : p_fmat->GetBatches<SortedCSCPage>()) {
auto page = batch.GetView();
for (auto fid : fsplits) {
auto col = batch[fid];
auto col = page[fid];
const auto ndata = static_cast<bst_omp_uint>(col.size());
#pragma omp parallel for schedule(static)
#pragma omp parallel for schedule(static)
for (bst_omp_uint j = 0; j < ndata; ++j) {
const bst_uint ridx = col[j].index;
const bst_float fvalue = col[j].fvalue;

9
src/tree/updater_colmaker.cc
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@ -77,8 +77,9 @@ class ColMaker: public TreeUpdater {
if (column_densities_.empty()) {
std::vector<size_t> column_size(dmat->Info().num_col_);
for (const auto &batch : dmat->GetBatches<SortedCSCPage>()) {
auto page = batch.GetView();
for (auto i = 0u; i < batch.Size(); i++) {
column_size[i] += batch[i].size();
column_size[i] += page[i].size();
}
}
column_densities_.resize(column_size.size());
@ -447,13 +448,14 @@ class ColMaker: public TreeUpdater {
#endif // defined(_OPENMP)
{
dmlc::OMPException omp_handler;
auto page = batch.GetView();
#pragma omp parallel for schedule(dynamic, batch_size)
for (bst_omp_uint i = 0; i < num_features; ++i) {
omp_handler.Run([&]() {
auto evaluator = tree_evaluator_.GetEvaluator();
bst_feature_t const fid = feat_set[i];
int32_t const tid = omp_get_thread_num();
auto c = batch[fid];
auto c = page[fid];
const bool ind =
c.size() != 0 && c[0].fvalue == c[c.size() - 1].fvalue;
if (colmaker_train_param_.NeedForwardSearch(
@ -562,8 +564,9 @@ class ColMaker: public TreeUpdater {
std::sort(fsplits.begin(), fsplits.end());
fsplits.resize(std::unique(fsplits.begin(), fsplits.end()) - fsplits.begin());
for (const auto &batch : p_fmat->GetBatches<SortedCSCPage>()) {
auto page = batch.GetView();
for (auto fid : fsplits) {
auto col = batch[fid];
auto col = page[fid];
const auto ndata = static_cast<bst_omp_uint>(col.size());
#pragma omp parallel for schedule(static)
for (bst_omp_uint j = 0; j < ndata; ++j) {

12
src/tree/updater_histmaker.cc
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@ -338,6 +338,7 @@ class CQHistMaker: public HistMaker {
thread_hist_.resize(omp_get_max_threads());
// start accumulating statistics
for (const auto &batch : p_fmat->GetBatches<SortedCSCPage>()) {
auto page = batch.GetView();
// start enumeration
const auto nsize = static_cast<bst_omp_uint>(fset.size());
#pragma omp parallel for schedule(dynamic, 1)
@ -345,7 +346,7 @@ class CQHistMaker: public HistMaker {
int fid = fset[i];
int offset = feat2workindex_[fid];
if (offset >= 0) {
this->UpdateHistCol(gpair, batch[fid], info, tree,
this->UpdateHistCol(gpair, page[fid], info, tree,
fset, offset,
&thread_hist_[omp_get_thread_num()]);
}
@ -413,15 +414,15 @@ class CQHistMaker: public HistMaker {
for (const auto &batch : p_fmat->GetBatches<SortedCSCPage>()) {
// TWOPASS: use the real set + split set in the column iteration.
this->CorrectNonDefaultPositionByBatch(batch, fsplit_set_, tree);
auto page = batch.GetView();
// start enumeration
const auto nsize = static_cast<bst_omp_uint>(work_set_.size());
#pragma omp parallel for schedule(dynamic, 1)
#pragma omp parallel for schedule(dynamic, 1)
for (bst_omp_uint i = 0; i < nsize; ++i) {
int fid = work_set_[i];
int offset = feat2workindex_[fid];
if (offset >= 0) {
this->UpdateSketchCol(gpair, batch[fid], tree,
this->UpdateSketchCol(gpair, page[fid], tree,
work_set_size, offset,
&thread_sketch_[omp_get_thread_num()]);
}
@ -696,6 +697,7 @@ class GlobalProposalHistMaker: public CQHistMaker {
for (const auto &batch : p_fmat->GetBatches<SortedCSCPage>()) {
// TWOPASS: use the real set + split set in the column iteration.
this->CorrectNonDefaultPositionByBatch(batch, this->fsplit_set_, tree);
auto page = batch.GetView();
// start enumeration
const auto nsize = static_cast<bst_omp_uint>(this->work_set_.size());
@ -704,7 +706,7 @@ class GlobalProposalHistMaker: public CQHistMaker {
int fid = this->work_set_[i];
int offset = this->feat2workindex_[fid];
if (offset >= 0) {
this->UpdateHistCol(gpair, batch[fid], info, tree,
this->UpdateHistCol(gpair, page[fid], info, tree,
fset, offset,
&this->thread_hist_[omp_get_thread_num()]);
}

5
src/tree/updater_refresh.cc
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@ -69,11 +69,12 @@ class TreeRefresher: public TreeUpdater {
const MetaInfo &info = p_fmat->Info();
// start accumulating statistics
for (const auto &batch : p_fmat->GetBatches<SparsePage>()) {
auto page = batch.GetView();
CHECK_LT(batch.Size(), std::numeric_limits<unsigned>::max());
const auto nbatch = static_cast<bst_omp_uint>(batch.Size());
#pragma omp parallel for schedule(static)
#pragma omp parallel for schedule(static)
for (bst_omp_uint i = 0; i < nbatch; ++i) {
SparsePage::Inst inst = batch[i];
SparsePage::Inst inst = page[i];
const int tid = omp_get_thread_num();
const auto ridx = static_cast<bst_uint>(batch.base_rowid + i);
RegTree::FVec &feats = fvec_temp[tid];

12
tests/cpp/c_api/test_c_api.cc
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@ -30,10 +30,11 @@ TEST(CAPI, XGDMatrixCreateFromMatDT) {
ASSERT_EQ(info.num_nonzero_, 6ul);
for (const auto &batch : (*dmat)->GetBatches<xgboost::SparsePage>()) {
ASSERT_EQ(batch[0][0].fvalue, 0.0f);
ASSERT_EQ(batch[0][1].fvalue, -4.0f);
ASSERT_EQ(batch[2][0].fvalue, 3.0f);
ASSERT_EQ(batch[2][1].fvalue, 0.0f);
auto page = batch.GetView();
ASSERT_EQ(page[0][0].fvalue, 0.0f);
ASSERT_EQ(page[0][1].fvalue, -4.0f);
ASSERT_EQ(page[2][0].fvalue, 3.0f);
ASSERT_EQ(page[2][1].fvalue, 0.0f);
}
delete dmat;
@ -62,8 +63,9 @@ TEST(CAPI, XGDMatrixCreateFromMatOmp) {
ASSERT_EQ(info.num_nonzero_, num_cols * row - num_missing);
for (const auto &batch : (*dmat)->GetBatches<xgboost::SparsePage>()) {
auto page = batch.GetView();
for (size_t i = 0; i < batch.Size(); i++) {
auto inst = batch[i];
auto inst = page[i];
for (auto e : inst) {
ASSERT_EQ(e.fvalue, 1.5);
}

3
tests/cpp/common/test_hist_util.h
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@ -176,9 +176,10 @@ inline void ValidateCuts(const HistogramCuts& cuts, DMatrix* dmat,
// Collect data into columns
std::vector<std::vector<float>> columns(dmat->Info().num_col_);
for (auto& batch : dmat->GetBatches<SparsePage>()) {
auto page = batch.GetView();
ASSERT_GT(batch.Size(), 0ul);
for (auto i = 0ull; i < batch.Size(); i++) {
for (auto e : batch[i]) {
for (auto e : page[i]) {
columns[e.index].push_back(e.fvalue);
}
}

5
tests/cpp/data/test_adapter.cc
View File

@ -47,7 +47,8 @@ TEST(Adapter, CSCAdapterColsMoreThanRows) {
EXPECT_EQ(dmat.Info().num_nonzero_, 8);
auto &batch = *dmat.GetBatches<SparsePage>().begin();
auto inst = batch[0];
auto page = batch.GetView();
auto inst = page[0];
EXPECT_EQ(inst[0].fvalue, 1);
EXPECT_EQ(inst[0].index, 0);
EXPECT_EQ(inst[1].fvalue, 3);
@ -57,7 +58,7 @@ TEST(Adapter, CSCAdapterColsMoreThanRows) {
EXPECT_EQ(inst[3].fvalue, 7);
EXPECT_EQ(inst[3].index, 3);
inst = batch[1];
inst = page[1];
EXPECT_EQ(inst[0].fvalue, 2);
EXPECT_EQ(inst[0].index, 0);
EXPECT_EQ(inst[1].fvalue, 4);

27
tests/cpp/data/test_data.cc
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@ -11,9 +11,9 @@ namespace xgboost {
TEST(SparsePage, PushCSC) {
std::vector<bst_row_t> offset {0};
std::vector<Entry> data;
SparsePage page;
page.offset.HostVector() = offset;
page.data.HostVector() = data;
SparsePage batch;
batch.offset.HostVector() = offset;
batch.data.HostVector() = data;
offset = {0, 1, 4};
for (size_t i = 0; i < offset.back(); ++i) {
@ -24,25 +24,26 @@ TEST(SparsePage, PushCSC) {
other.offset.HostVector() = offset;
other.data.HostVector() = data;
page.PushCSC(other);
batch.PushCSC(other);
ASSERT_EQ(page.offset.HostVector().size(), offset.size());
ASSERT_EQ(page.data.HostVector().size(), data.size());
ASSERT_EQ(batch.offset.HostVector().size(), offset.size());
ASSERT_EQ(batch.data.HostVector().size(), data.size());
for (size_t i = 0; i < offset.size(); ++i) {
ASSERT_EQ(page.offset.HostVector()[i], offset[i]);
ASSERT_EQ(batch.offset.HostVector()[i], offset[i]);
}
for (size_t i = 0; i < data.size(); ++i) {
ASSERT_EQ(page.data.HostVector()[i].index, data[i].index);
ASSERT_EQ(batch.data.HostVector()[i].index, data[i].index);
}
page.PushCSC(other);
ASSERT_EQ(page.offset.HostVector().size(), offset.size());
ASSERT_EQ(page.data.Size(), data.size() * 2);
batch.PushCSC(other);
ASSERT_EQ(batch.offset.HostVector().size(), offset.size());
ASSERT_EQ(batch.data.Size(), data.size() * 2);
for (size_t i = 0; i < offset.size(); ++i) {
ASSERT_EQ(page.offset.HostVector()[i], offset[i] * 2);
ASSERT_EQ(batch.offset.HostVector()[i], offset[i] * 2);
}
auto page = batch.GetView();
auto inst = page[0];
ASSERT_EQ(inst.size(), 2ul);
for (auto entry : inst) {
@ -78,7 +79,7 @@ TEST(SparsePage, PushCSCAfterTranspose) {
// The feature value for a feature in each row should be identical, as that is
// how the dmatrix has been created
for (size_t i = 0; i < page.Size(); ++i) {
auto inst = page[i];
auto inst = page.GetView()[i];
for (size_t j = 1; j < inst.size(); ++j) {
ASSERT_EQ(inst[0].fvalue, inst[j].fvalue);
}

30
tests/cpp/data/test_simple_dmatrix.cc
View File

@ -39,7 +39,8 @@ TEST(SimpleDMatrix, RowAccess) {
EXPECT_EQ(row_count, dmat->Info().num_row_);
// Test the data read into the first row
auto &batch = *dmat->GetBatches<xgboost::SparsePage>().begin();
auto first_row = batch[0];
auto page = batch.GetView();
auto first_row = page[0];
ASSERT_EQ(first_row.size(), 3);
EXPECT_EQ(first_row[2].index, 2);
EXPECT_EQ(first_row[2].fvalue, 20);
@ -143,8 +144,9 @@ TEST(SimpleDMatrix, FromDense) {
EXPECT_EQ(dmat.Info().num_nonzero_, 6);
for (auto &batch : dmat.GetBatches<SparsePage>()) {
auto page = batch.GetView();
for (auto i = 0ull; i < batch.Size(); i++) {
auto inst = batch[i];
auto inst = page[i];
for (auto j = 0ull; j < inst.size(); j++) {
EXPECT_EQ(inst[j].fvalue, data[i * n + j]);
EXPECT_EQ(inst[j].index, j);
@ -165,19 +167,20 @@ TEST(SimpleDMatrix, FromCSC) {
EXPECT_EQ(dmat.Info().num_nonzero_, 5);
auto &batch = *dmat.GetBatches<SparsePage>().begin();
auto inst = batch[0];
auto page = batch.GetView();
auto inst = page[0];
EXPECT_EQ(inst[0].fvalue, 1);
EXPECT_EQ(inst[0].index, 0);
EXPECT_EQ(inst[1].fvalue, 2);
EXPECT_EQ(inst[1].index, 1);
inst = batch[1];
inst = page[1];
EXPECT_EQ(inst[0].fvalue, 3);
EXPECT_EQ(inst[0].index, 0);
EXPECT_EQ(inst[1].fvalue, 4);
EXPECT_EQ(inst[1].index, 1);
inst = batch[2];
inst = page[2];
EXPECT_EQ(inst[0].fvalue, 5);
EXPECT_EQ(inst[0].index, 1);
}
@ -194,11 +197,12 @@ TEST(SimpleDMatrix, FromFile) {
std::unique_ptr<dmlc::Parser<uint32_t>> parser(
dmlc::Parser<uint32_t>::Create(filename.c_str(), 0, 1, "auto"));
auto verify_batch = [kExpectedNumRow](SparsePage const &batch) {
auto verify_batch = [kExpectedNumRow](SparsePage const &page) {
auto batch = page.GetView();
EXPECT_EQ(batch.Size(), kExpectedNumRow);
EXPECT_EQ(batch.offset.HostVector(),
EXPECT_EQ(page.offset.HostVector(),
std::vector<bst_row_t>({0, 3, 6, 9, 12, 15, 15}));
EXPECT_EQ(batch.base_rowid, 0);
EXPECT_EQ(page.base_rowid, 0);
for (auto i = 0ull; i < batch.Size() - 1; i++) {
if (i % 2 == 0) {
@ -251,8 +255,10 @@ TEST(SimpleDMatrix, Slice) {
ASSERT_EQ(out->Info().labels_upper_bound_.Size(), ridxs.size());
ASSERT_EQ(out->Info().base_margin_.Size(), ridxs.size() * kClasses);
for (auto const& in_page : p_m->GetBatches<SparsePage>()) {
for (auto const &out_page : out->GetBatches<SparsePage>()) {
for (auto const& in_batch : p_m->GetBatches<SparsePage>()) {
auto in_page = in_batch.GetView();
for (auto const &out_batch : out->GetBatches<SparsePage>()) {
auto out_page = out_batch.GetView();
for (size_t i = 0; i < ridxs.size(); ++i) {
auto ridx = ridxs[i];
auto out_inst = out_page[i];
@ -305,8 +311,8 @@ TEST(SimpleDMatrix, SaveLoadBinary) {
auto row_iter = dmat->GetBatches<xgboost::SparsePage>().begin();
auto row_iter_read = dmat_read->GetBatches<xgboost::SparsePage>().begin();
// Test the data read into the first row
auto first_row = (*row_iter)[0];
auto first_row_read = (*row_iter_read)[0];
auto first_row = (*row_iter).GetView()[0];
auto first_row_read = (*row_iter_read).GetView()[0];
EXPECT_EQ(first_row.size(), first_row_read.size());
EXPECT_EQ(first_row[2].index, first_row_read[2].index);
EXPECT_EQ(first_row[2].fvalue, first_row_read[2].fvalue);

33
tests/cpp/data/test_simple_dmatrix.cu
View File

@ -35,8 +35,9 @@ TEST(SimpleDMatrix, FromColumnarDenseBasic) {
void TestDenseColumn(DMatrix* dmat, size_t n_rows, size_t n_cols) {
for (auto& batch : dmat->GetBatches<SparsePage>()) {
auto page = batch.GetView();
for (auto i = 0ull; i < batch.Size(); i++) {
auto inst = batch[i];
auto inst = page[i];
for (auto j = 0ull; j < inst.size(); j++) {
EXPECT_EQ(inst[j].fvalue, i * 2);
EXPECT_EQ(inst[j].index, j);
@ -162,8 +163,9 @@ TEST(SimpleDMatrix, FromColumnarWithEmptyRows) {
-1);
for (auto& batch : dmat.GetBatches<SparsePage>()) {
auto page = batch.GetView();
for (auto i = 0ull; i < batch.Size(); i++) {
auto inst = batch[i];
auto inst = page[i];
for (auto j = 0ull; j < inst.size(); j++) {
EXPECT_EQ(inst[j].fvalue, i);
EXPECT_EQ(inst[j].index, j);
@ -257,8 +259,9 @@ TEST(SimpleCSRSource, FromColumnarSparse) {
data::CudfAdapter adapter(str);
data::SimpleDMatrix dmat(&adapter, 2.0, -1);
for (auto& batch : dmat.GetBatches<SparsePage>()) {
auto page = batch.GetView();
for (auto i = 0ull; i < batch.Size(); i++) {
auto inst = batch[i];
auto inst = page[i];
for (auto e : inst) {
ASSERT_NE(e.fvalue, 2.0);
}
@ -304,8 +307,9 @@ TEST(SimpleDMatrix, FromColumnarSparseBasic) {
EXPECT_EQ(dmat.Info().num_nonzero_, 32);
for (auto& batch : dmat.GetBatches<SparsePage>()) {
auto page = batch.GetView();
for (auto i = 0ull; i < batch.Size(); i++) {
auto inst = batch[i];
auto inst = page[i];
for (auto j = 0ull; j < inst.size(); j++) {
EXPECT_EQ(inst[j].fvalue, i * 2);
EXPECT_EQ(inst[j].index, j);
@ -329,8 +333,9 @@ TEST(SimpleDMatrix, FromCupy){
EXPECT_EQ(dmat.Info().num_nonzero_, rows*cols);
for (auto& batch : dmat.GetBatches<SparsePage>()) {
auto page = batch.GetView();
for (auto i = 0ull; i < batch.Size(); i++) {
auto inst = batch[i];
auto inst = page[i];
for (auto j = 0ull; j < inst.size(); j++) {
EXPECT_EQ(inst[j].fvalue, i * cols + j);
EXPECT_EQ(inst[j].index, j);
@ -354,12 +359,14 @@ TEST(SimpleDMatrix, FromCupySparse){
EXPECT_EQ(dmat.Info().num_row_, rows);
EXPECT_EQ(dmat.Info().num_nonzero_, rows * cols - 2);
auto& batch = *dmat.GetBatches<SparsePage>().begin();
auto inst0 = batch[0];
auto inst1 = batch[1];
EXPECT_EQ(batch[0].size(), 1);
EXPECT_EQ(batch[1].size(), 1);
EXPECT_EQ(batch[0][0].fvalue, 0.0f);
EXPECT_EQ(batch[0][0].index, 0);
EXPECT_EQ(batch[1][0].fvalue, 3.0f);
EXPECT_EQ(batch[1][0].index, 1);
auto page = batch.GetView();
auto inst0 = page[0];
auto inst1 = page[1];
EXPECT_EQ(page[0].size(), 1);
EXPECT_EQ(page[1].size(), 1);
EXPECT_EQ(page[0][0].fvalue, 0.0f);
EXPECT_EQ(page[0][0].index, 0);
EXPECT_EQ(page[1][0].fvalue, 3.0f);
EXPECT_EQ(page[1][0].index, 1);
}

40
tests/cpp/data/test_sparse_page_dmatrix.cc
View File

@ -39,7 +39,8 @@ TEST(SparsePageDMatrix, RowAccess) {
// Test the data read into the first row
auto &batch = *dmat->GetBatches<xgboost::SparsePage>().begin();
auto first_row = batch[0];
auto page = batch.GetView();
auto first_row = page[0];
ASSERT_EQ(first_row.size(), 3ul);
EXPECT_EQ(first_row[2].index, 2u);
EXPECT_EQ(first_row[2].fvalue, 20);
@ -54,16 +55,18 @@ TEST(SparsePageDMatrix, ColAccess) {
// Loop over the batches and assert the data is as expected
for (auto const &col_batch : dmat->GetBatches<xgboost::SortedCSCPage>()) {
EXPECT_EQ(col_batch.Size(), dmat->Info().num_col_);
EXPECT_EQ(col_batch[1][0].fvalue, 10.0f);
EXPECT_EQ(col_batch[1].size(), 1);
auto col_page = col_batch.GetView();
EXPECT_EQ(col_page.Size(), dmat->Info().num_col_);
EXPECT_EQ(col_page[1][0].fvalue, 10.0f);
EXPECT_EQ(col_page[1].size(), 1);
}
// Loop over the batches and assert the data is as expected
for (auto const &col_batch : dmat->GetBatches<xgboost::CSCPage>()) {
EXPECT_EQ(col_batch.Size(), dmat->Info().num_col_);
EXPECT_EQ(col_batch[1][0].fvalue, 10.0f);
EXPECT_EQ(col_batch[1].size(), 1);
auto col_page = col_batch.GetView();
EXPECT_EQ(col_page.Size(), dmat->Info().num_col_);
EXPECT_EQ(col_page[1][0].fvalue, 10.0f);
EXPECT_EQ(col_page[1].size(), 1);
}
EXPECT_TRUE(FileExists(tmp_file + ".cache"));
@ -238,8 +241,9 @@ TEST(SparsePageDMatrix, FromDense) {
EXPECT_EQ(dmat.Info().num_nonzero_, 6);
for (auto &batch : dmat.GetBatches<SparsePage>()) {
auto page = batch.GetView();
for (auto i = 0ull; i < batch.Size(); i++) {
auto inst = batch[i];
auto inst = page[i];
for (auto j = 0ull; j < inst.size(); j++) {
EXPECT_EQ(inst[j].fvalue, data[i * n + j]);
EXPECT_EQ(inst[j].index, j);
@ -262,19 +266,20 @@ TEST(SparsePageDMatrix, FromCSC) {
EXPECT_EQ(dmat.Info().num_nonzero_, 5);
auto &batch = *dmat.GetBatches<SparsePage>().begin();
auto inst = batch[0];
auto page = batch.GetView();
auto inst = page[0];
EXPECT_EQ(inst[0].fvalue, 1);
EXPECT_EQ(inst[0].index, 0);
EXPECT_EQ(inst[1].fvalue, 2);
EXPECT_EQ(inst[1].index, 1);
inst = batch[1];
inst = page[1];
EXPECT_EQ(inst[0].fvalue, 3);
EXPECT_EQ(inst[0].index, 0);
EXPECT_EQ(inst[1].fvalue, 4);
EXPECT_EQ(inst[1].index, 1);
inst = batch[2];
inst = page[2];
EXPECT_EQ(inst[0].fvalue, 5);
EXPECT_EQ(inst[0].index, 1);
}
@ -294,19 +299,20 @@ TEST(SparsePageDMatrix, FromFile) {
for (auto &batch : dmat.GetBatches<SparsePage>()) {
std::vector<bst_row_t> expected_offset(batch.Size() + 1);
auto page = batch.GetView();
int n = -3;
std::generate(expected_offset.begin(), expected_offset.end(),
[&n] { return n += 3; });
EXPECT_EQ(batch.offset.HostVector(), expected_offset);
if (batch.base_rowid % 2 == 0) {
EXPECT_EQ(batch[0][0].index, 0);
EXPECT_EQ(batch[0][1].index, 1);
EXPECT_EQ(batch[0][2].index, 2);
EXPECT_EQ(page[0][0].index, 0);
EXPECT_EQ(page[0][1].index, 1);
EXPECT_EQ(page[0][2].index, 2);
} else {
EXPECT_EQ(batch[0][0].index, 0);
EXPECT_EQ(batch[0][1].index, 3);
EXPECT_EQ(batch[0][2].index, 4);
EXPECT_EQ(page[0][0].index, 0);
EXPECT_EQ(page[0][1].index, 3);
EXPECT_EQ(page[0][2].index, 4);
}
}
}

3
tests/cpp/predictor/test_cpu_predictor.cc
View File

@ -39,9 +39,10 @@ TEST(CpuPredictor, Basic) {
// Test predict instance
auto const &batch = *dmat->GetBatches<xgboost::SparsePage>().begin();
auto page = batch.GetView();
for (size_t i = 0; i < batch.Size(); i++) {
std::vector<float> instance_out_predictions;
cpu_predictor->PredictInstance(batch[i], &instance_out_predictions, model);
cpu_predictor->PredictInstance(page[i], &instance_out_predictions, model);
ASSERT_EQ(instance_out_predictions[0], 1.5);
}

3
tests/cpp/tree/test_quantile_hist.cc
View File

@ -72,12 +72,13 @@ class QuantileHistMock : public QuantileHistMaker {
ASSERT_LT(*std::max_element(gmat.index.begin(), gmat.index.end()),
gmat.cut.Ptrs().back());
for (const auto& batch : p_fmat->GetBatches<xgboost::SparsePage>()) {
auto page = batch.GetView();
for (size_t i = 0; i < batch.Size(); ++i) {
const size_t rid = batch.base_rowid + i;
ASSERT_LT(rid, num_row);
const size_t gmat_row_offset = gmat.row_ptr[rid];
ASSERT_LT(gmat_row_offset, gmat.index.Size());
SparsePage::Inst inst = batch[i];
SparsePage::Inst inst = page[i];
ASSERT_EQ(gmat.row_ptr[rid] + inst.size(), gmat.row_ptr[rid + 1]);
for (size_t j = 0; j < inst.size(); ++j) {
// Each entry of GHistIndexMatrix represents a bin ID