/
gradient_index.cc
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/
gradient_index.cc
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/*!
* Copyright 2017-2022 by XGBoost Contributors
* \brief Data type for fast histogram aggregation.
*/
#include "gradient_index.h"
#include <algorithm>
#include <limits>
#include <memory>
#include <utility> // std::forward
#include "../common/column_matrix.h"
#include "../common/hist_util.h"
#include "../common/numeric.h"
#include "../common/threading_utils.h"
namespace xgboost {
GHistIndexMatrix::GHistIndexMatrix() : columns_{std::make_unique<common::ColumnMatrix>()} {}
GHistIndexMatrix::GHistIndexMatrix(DMatrix *p_fmat, bst_bin_t max_bins_per_feat,
double sparse_thresh, bool sorted_sketch, int32_t n_threads,
common::Span<float> hess) {
CHECK(p_fmat->SingleColBlock());
// We use sorted sketching for approx tree method since it's more efficient in
// computation time (but higher memory usage).
cut = common::SketchOnDMatrix(p_fmat, max_bins_per_feat, n_threads, sorted_sketch, hess);
max_num_bins = max_bins_per_feat;
const uint32_t nbins = cut.Ptrs().back();
hit_count.resize(nbins, 0);
hit_count_tloc_.resize(n_threads * nbins, 0);
size_t new_size = 1;
for (const auto &batch : p_fmat->GetBatches<SparsePage>()) {
new_size += batch.Size();
}
row_ptr.resize(new_size);
row_ptr[0] = 0;
const bool isDense = p_fmat->IsDense();
this->isDense_ = isDense;
auto ft = p_fmat->Info().feature_types.ConstHostSpan();
for (const auto &batch : p_fmat->GetBatches<SparsePage>()) {
this->PushBatch(batch, ft, n_threads);
}
this->columns_ = std::make_unique<common::ColumnMatrix>();
// hessian is empty when hist tree method is used or when dataset is empty
if (hess.empty() && !std::isnan(sparse_thresh)) {
// hist
CHECK(!sorted_sketch);
for (auto const &page : p_fmat->GetBatches<SparsePage>()) {
this->columns_->Init(page, *this, sparse_thresh, n_threads);
}
}
}
GHistIndexMatrix::GHistIndexMatrix(MetaInfo const &info, common::HistogramCuts &&cuts,
bst_bin_t max_bin_per_feat)
: row_ptr(info.num_row_ + 1, 0),
hit_count(cuts.TotalBins(), 0),
cut{std::forward<common::HistogramCuts>(cuts)},
max_num_bins(max_bin_per_feat),
isDense_{info.num_col_ * info.num_row_ == info.num_nonzero_} {}
GHistIndexMatrix::~GHistIndexMatrix() = default;
void GHistIndexMatrix::PushBatch(SparsePage const &batch, common::Span<FeatureType const> ft,
int32_t n_threads) {
auto page = batch.GetView();
auto it = common::MakeIndexTransformIter([&](size_t ridx) { return page[ridx].size(); });
common::PartialSum(n_threads, it, it + page.Size(), static_cast<size_t>(0), row_ptr.begin());
data::SparsePageAdapterBatch adapter_batch{page};
auto is_valid = [](auto) { return true; }; // SparsePage always contains valid entries
PushBatchImpl(n_threads, adapter_batch, 0, is_valid, ft);
}
GHistIndexMatrix::GHistIndexMatrix(SparsePage const &batch, common::Span<FeatureType const> ft,
common::HistogramCuts const &cuts, int32_t max_bins_per_feat,
bool isDense, double sparse_thresh, int32_t n_threads) {
CHECK_GE(n_threads, 1);
base_rowid = batch.base_rowid;
isDense_ = isDense;
cut = cuts;
max_num_bins = max_bins_per_feat;
CHECK_EQ(row_ptr.size(), 0);
// The number of threads is pegged to the batch size. If the OMP
// block is parallelized on anything other than the batch/block size,
// it should be reassigned
row_ptr.resize(batch.Size() + 1, 0);
const uint32_t nbins = cut.Ptrs().back();
hit_count.resize(nbins, 0);
hit_count_tloc_.resize(n_threads * nbins, 0);
this->PushBatch(batch, ft, n_threads);
this->columns_ = std::make_unique<common::ColumnMatrix>();
if (!std::isnan(sparse_thresh)) {
this->columns_->Init(batch, *this, sparse_thresh, n_threads);
}
}
template <typename Batch>
void GHistIndexMatrix::PushAdapterBatchColumns(Context const *ctx, Batch const &batch,
float missing, size_t rbegin) {
CHECK(columns_);
this->columns_->PushBatch(ctx->Threads(), batch, missing, *this, rbegin);
}
#define INSTANTIATION_PUSH(BatchT) \
template void GHistIndexMatrix::PushAdapterBatchColumns<BatchT>( \
Context const *ctx, BatchT const &batch, float missing, size_t rbegin);
INSTANTIATION_PUSH(data::CSRArrayAdapterBatch)
INSTANTIATION_PUSH(data::ArrayAdapterBatch)
INSTANTIATION_PUSH(data::SparsePageAdapterBatch)
#undef INSTANTIATION_PUSH
void GHistIndexMatrix::ResizeIndex(const size_t n_index, const bool isDense) {
if ((max_num_bins - 1 <= static_cast<int>(std::numeric_limits<uint8_t>::max())) && isDense) {
// compress dense index to uint8
index.SetBinTypeSize(common::kUint8BinsTypeSize);
index.Resize((sizeof(uint8_t)) * n_index);
} else if ((max_num_bins - 1 > static_cast<int>(std::numeric_limits<uint8_t>::max()) &&
max_num_bins - 1 <= static_cast<int>(std::numeric_limits<uint16_t>::max())) &&
isDense) {
// compress dense index to uint16
index.SetBinTypeSize(common::kUint16BinsTypeSize);
index.Resize((sizeof(uint16_t)) * n_index);
} else {
index.SetBinTypeSize(common::kUint32BinsTypeSize);
index.Resize((sizeof(uint32_t)) * n_index);
}
}
common::ColumnMatrix const &GHistIndexMatrix::Transpose() const {
CHECK(columns_);
return *columns_;
}
float GHistIndexMatrix::GetFvalue(size_t ridx, size_t fidx, bool is_cat) const {
auto const &values = cut.Values();
auto const &mins = cut.MinValues();
auto const &ptrs = cut.Ptrs();
if (is_cat) {
auto f_begin = ptrs[fidx];
auto f_end = ptrs[fidx + 1];
auto begin = RowIdx(ridx);
auto end = RowIdx(ridx + 1);
auto gidx = BinarySearchBin(begin, end, index, f_begin, f_end);
if (gidx == -1) {
return std::numeric_limits<float>::quiet_NaN();
}
return values[gidx];
}
auto lower = static_cast<bst_bin_t>(cut.Ptrs()[fidx]);
auto get_bin_idx = [&](auto &column) {
auto bin_idx = column[ridx];
if (bin_idx == common::DenseColumnIter<uint8_t, true>::kMissingId) {
return std::numeric_limits<float>::quiet_NaN();
}
if (bin_idx == lower) {
return mins[fidx];
}
return values[bin_idx - 1];
};
if (columns_->GetColumnType(fidx) == common::kDenseColumn) {
if (columns_->AnyMissing()) {
return common::DispatchBinType(columns_->GetTypeSize(), [&](auto dtype) {
auto column = columns_->DenseColumn<decltype(dtype), true>(fidx);
return get_bin_idx(column);
});
} else {
return common::DispatchBinType(columns_->GetTypeSize(), [&](auto dtype) {
auto column = columns_->DenseColumn<decltype(dtype), false>(fidx);
return get_bin_idx(column);
});
}
} else {
return common::DispatchBinType(columns_->GetTypeSize(), [&](auto dtype) {
auto column = columns_->SparseColumn<decltype(dtype)>(fidx, 0);
return get_bin_idx(column);
});
}
SPAN_CHECK(false);
return std::numeric_limits<float>::quiet_NaN();
}
bool GHistIndexMatrix::ReadColumnPage(dmlc::SeekStream *fi) {
return this->columns_->Read(fi, this->cut.Ptrs().data());
}
size_t GHistIndexMatrix::WriteColumnPage(dmlc::Stream *fo) const {
return this->columns_->Write(fo);
}
} // namespace xgboost