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[backport] Fix LTR with weighted Quantile DMatrix. (#7975) (#8170)

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* Fix LTR with weighted Quantile DMatrix.

* Better tests.
This commit is contained in:
Jiaming Yuan 2022-08-15 17:50:16 +08:00 committed by GitHub
parent e82162d7f8
commit 51c330159a
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GPG Key ID: 4AEE18F83AFDEB23
6 changed files with 83 additions and 42 deletions
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12
src/common/hist_util.cuh
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@ -184,8 +184,6 @@ void ProcessWeightedSlidingWindow(Batch batch, MetaInfo const& info,
dh::safe_cuda(cudaSetDevice(device));
info.weights_.SetDevice(device);
auto weights = info.weights_.ConstDeviceSpan();
dh::caching_device_vector<bst_group_t> group_ptr(info.group_ptr_);
auto d_group_ptr = dh::ToSpan(group_ptr);
auto batch_iter = dh::MakeTransformIterator<data::COOTuple>(
thrust::make_counting_iterator(0llu),
@ -205,9 +203,13 @@ void ProcessWeightedSlidingWindow(Batch batch, MetaInfo const& info,
auto d_temp_weights = dh::ToSpan(temp_weights);
if (is_ranking) {
if (!weights.empty()) {
CHECK_EQ(weights.size(), info.group_ptr_.size() - 1);
}
dh::caching_device_vector<bst_group_t> group_ptr(info.group_ptr_);
auto d_group_ptr = dh::ToSpan(group_ptr);
auto const weight_iter = dh::MakeTransformIterator<float>(
thrust::make_constant_iterator(0lu),
[=]__device__(size_t idx) -> float {
thrust::make_counting_iterator(0lu), [=] __device__(size_t idx) -> float {
auto ridx = batch.GetElement(idx).row_idx;
bst_group_t group_idx = dh::SegmentId(d_group_ptr, ridx);
return weights[group_idx];
@ -272,7 +274,7 @@ void AdapterDeviceSketch(Batch batch, int num_bins,
size_t num_cols = batch.NumCols();
size_t num_cuts_per_feature = detail::RequiredSampleCutsPerColumn(num_bins, num_rows);
int32_t device = sketch_container->DeviceIdx();
bool weighted = info.weights_.Size() != 0;
bool weighted = !info.weights_.Empty();
if (weighted) {
sketch_batch_num_elements = detail::SketchBatchNumElements(

35
src/common/quantile.cc
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@ -122,27 +122,6 @@ std::vector<float> MergeWeights(MetaInfo const &info, Span<float const> hessian,
}
return results;
}
std::vector<float> UnrollGroupWeights(MetaInfo const &info) {
std::vector<float> const &group_weights = info.weights_.HostVector();
if (group_weights.empty()) {
return group_weights;
}
size_t n_samples = info.num_row_;
auto const &group_ptr = info.group_ptr_;
std::vector<float> results(n_samples);
CHECK_GE(group_ptr.size(), 2);
CHECK_EQ(group_ptr.back(), n_samples);
size_t cur_group = 0;
for (size_t i = 0; i < n_samples; ++i) {
results[i] = group_weights[cur_group];
if (i == group_ptr[cur_group + 1]) {
cur_group++;
}
}
return results;
}
} // anonymous namespace
template <typename WQSketch>
@ -156,12 +135,10 @@ void SketchContainerImpl<WQSketch>::PushRowPage(SparsePage const &page, MetaInfo
// glue these conditions using ternary operator to avoid making data copies.
auto const &weights =
hessian.empty()
? (use_group_ind_ ? UnrollGroupWeights(info) // use group weight
: info.weights_.HostVector()) // use sample weight
: MergeWeights(
info, hessian, use_group_ind_,
n_threads_); // use hessian merged with group/sample weights
hessian.empty() ? (use_group_ind_ ? detail::UnrollGroupWeights(info) // use group weight
: info.weights_.HostVector()) // use sample weight
: MergeWeights(info, hessian, use_group_ind_,
n_threads_); // use hessian merged with group/sample weights
if (!weights.empty()) {
CHECK_EQ(weights.size(), info.num_row_);
}
@ -563,8 +540,8 @@ void SortedSketchContainer::PushColPage(SparsePage const &page, MetaInfo const &
monitor_.Start(__func__);
// glue these conditions using ternary operator to avoid making data copies.
auto const &weights =
hessian.empty() ? (use_group_ind_ ? UnrollGroupWeights(info) // use group weight
: info.weights_.HostVector()) // use sample weight
hessian.empty() ? (use_group_ind_ ? detail::UnrollGroupWeights(info) // use group weight
: info.weights_.HostVector()) // use sample weight
: MergeWeights(info, hessian, use_group_ind_,
n_threads_); // use hessian merged with group/sample weights
CHECK_EQ(weights.size(), info.num_row_);

23
src/common/quantile.h
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@ -697,6 +697,29 @@ class WXQuantileSketch :
public QuantileSketchTemplate<DType, RType, WXQSummary<DType, RType> > {
};
namespace detail {
inline std::vector<float> UnrollGroupWeights(MetaInfo const &info) {
std::vector<float> const &group_weights = info.weights_.HostVector();
if (group_weights.empty()) {
return group_weights;
}
size_t n_samples = info.num_row_;
auto const &group_ptr = info.group_ptr_;
std::vector<float> results(n_samples);
CHECK_GE(group_ptr.size(), 2);
CHECK_EQ(group_ptr.back(), n_samples);
size_t cur_group = 0;
for (size_t i = 0; i < n_samples; ++i) {
results[i] = group_weights[cur_group];
if (i == group_ptr[cur_group + 1]) {
cur_group++;
}
}
return results;
}
} // namespace detail
class HistogramCuts;
/*!

12
tests/cpp/common/test_hist_util.cc
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@ -381,6 +381,7 @@ void TestSketchFromWeights(bool with_group) {
ValidateCuts(cuts, m.get(), kBins);
if (with_group) {
m->Info().weights_ = decltype(m->Info().weights_)(); // remove weight
HistogramCuts non_weighted = SketchOnDMatrix(m.get(), kBins, common::OmpGetNumThreads(0));
for (size_t i = 0; i < cuts.Values().size(); ++i) {
EXPECT_EQ(cuts.Values()[i], non_weighted.Values()[i]);
@ -392,6 +393,17 @@ void TestSketchFromWeights(bool with_group) {
ASSERT_EQ(cuts.Ptrs().at(i), non_weighted.Ptrs().at(i));
}
}
if (with_group) {
auto& h_weights = info.weights_.HostVector();
h_weights.resize(kGroups);
// Generate different weight.
for (size_t i = 0; i < h_weights.size(); ++i) {
h_weights[i] = static_cast<float>(i + 1) / static_cast<float>(kGroups);
}
HistogramCuts weighted = SketchOnDMatrix(m.get(), kBins, common::OmpGetNumThreads(0));
ValidateCuts(weighted, m.get(), kBins);
}
}
TEST(HistUtil, SketchFromWeights) {

21
tests/cpp/common/test_hist_util.cu
View File

@ -593,9 +593,10 @@ void TestAdapterSketchFromWeights(bool with_group) {
ValidateCuts(cuts, dmat.get(), kBins);
if (with_group) {
dmat->Info().weights_ = decltype(dmat->Info().weights_)(); // remove weight
HistogramCuts non_weighted = DeviceSketch(0, dmat.get(), kBins, 0);
for (size_t i = 0; i < cuts.Values().size(); ++i) {
EXPECT_EQ(cuts.Values()[i], non_weighted.Values()[i]);
ASSERT_EQ(cuts.Values()[i], non_weighted.Values()[i]);
}
for (size_t i = 0; i < cuts.MinValues().size(); ++i) {
ASSERT_EQ(cuts.MinValues()[i], non_weighted.MinValues()[i]);
@ -604,6 +605,24 @@ void TestAdapterSketchFromWeights(bool with_group) {
ASSERT_EQ(cuts.Ptrs().at(i), non_weighted.Ptrs().at(i));
}
}
if (with_group) {
common::HistogramCuts weighted;
auto& h_weights = info.weights_.HostVector();
h_weights.resize(kGroups);
// Generate different weight.
for (size_t i = 0; i < h_weights.size(); ++i) {
// FIXME(jiamingy): Some entries generated GPU test cannot pass the validate cuts if
// we use more diverse weights, partially caused by
// https://github.com/dmlc/xgboost/issues/7946
h_weights[i] = (i % 2 == 0 ? 1 : 2) / static_cast<float>(kGroups);
}
SketchContainer sketch_container(ft, kBins, kCols, kRows, 0);
AdapterDeviceSketch(adapter.Value(), kBins, info, std::numeric_limits<float>::quiet_NaN(),
&sketch_container);
sketch_container.MakeCuts(&weighted);
ValidateCuts(weighted, dmat.get(), kBins);
}
}
TEST(HistUtil, AdapterSketchFromWeights) {

22
tests/cpp/common/test_hist_util.h
View File

@ -98,7 +98,11 @@ inline void TestBinDistribution(const HistogramCuts &cuts, int column_idx,
int num_bins) {
std::map<int, int> bin_weights;
for (auto i = 0ull; i < sorted_column.size(); i++) {
bin_weights[cuts.SearchBin(sorted_column[i], column_idx)] += sorted_weights[i];
auto bin_idx = cuts.SearchBin(sorted_column[i], column_idx);
if (bin_weights.find(bin_idx) == bin_weights.cend()) {
bin_weights[bin_idx] = 0;
}
bin_weights.at(bin_idx) += sorted_weights[i];
}
int local_num_bins = cuts.Ptrs()[column_idx + 1] - cuts.Ptrs()[column_idx];
auto total_weight = std::accumulate(sorted_weights.begin(), sorted_weights.end(),0);
@ -176,8 +180,7 @@ inline void ValidateColumn(const HistogramCuts& cuts, int column_idx,
}
}
inline void ValidateCuts(const HistogramCuts& cuts, DMatrix* dmat,
int num_bins) {
inline void ValidateCuts(const HistogramCuts& cuts, DMatrix* dmat, int num_bins) {
// Collect data into columns
std::vector<std::vector<float>> columns(dmat->Info().num_col_);
for (auto& batch : dmat->GetBatches<SparsePage>()) {
@ -189,17 +192,22 @@ inline void ValidateCuts(const HistogramCuts& cuts, DMatrix* dmat,
}
}
}
// construct weights.
std::vector<float> w = dmat->Info().group_ptr_.empty() ? dmat->Info().weights_.HostVector()
: detail::UnrollGroupWeights(dmat->Info());
// Sort
for (auto i = 0ull; i < columns.size(); i++) {
auto& col = columns.at(i);
const auto& w = dmat->Info().weights_.HostVector();
std::vector<size_t > index(col.size());
std::vector<size_t> index(col.size());
std::iota(index.begin(), index.end(), 0);
std::sort(index.begin(), index.end(),
[=](size_t a, size_t b) { return col[a] < col[b]; });
std::sort(index.begin(), index.end(), [=](size_t a, size_t b) { return col[a] < col[b]; });
std::vector<float> sorted_column(col.size());
std::vector<float> sorted_weights(col.size(), 1.0);
const auto& w = dmat->Info().weights_.HostVector();
for (auto j = 0ull; j < col.size(); j++) {
sorted_column[j] = col[index[j]];
if (w.size() == col.size()) {