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quantile.cc
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quantile.cc
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/*!
* Copyright 2020-2022 by XGBoost Contributors
*/
#include "quantile.h"
#include <limits>
#include <utility>
#include "categorical.h"
#include "hist_util.h"
#include "rabit/rabit.h"
namespace xgboost {
namespace common {
template <typename WQSketch>
SketchContainerImpl<WQSketch>::SketchContainerImpl(std::vector<bst_row_t> columns_size,
int32_t max_bins,
Span<FeatureType const> feature_types,
bool use_group, int32_t n_threads)
: feature_types_(feature_types.cbegin(), feature_types.cend()),
columns_size_{std::move(columns_size)},
max_bins_{max_bins},
use_group_ind_{use_group},
n_threads_{n_threads} {
monitor_.Init(__func__);
CHECK_NE(columns_size_.size(), 0);
sketches_.resize(columns_size_.size());
CHECK_GE(n_threads_, 1);
categories_.resize(columns_size_.size());
has_categorical_ = std::any_of(feature_types_.cbegin(), feature_types_.cend(), IsCatOp{});
}
template <typename WQSketch>
std::vector<bst_row_t> SketchContainerImpl<WQSketch>::CalcColumnSize(SparsePage const &batch,
bst_feature_t const n_columns,
size_t const nthreads) {
auto page = batch.GetView();
std::vector<std::vector<bst_row_t>> column_sizes(nthreads);
for (auto &column : column_sizes) {
column.resize(n_columns, 0);
}
ParallelFor(page.Size(), nthreads, [&](omp_ulong i) {
auto &local_column_sizes = column_sizes.at(omp_get_thread_num());
auto row = page[i];
auto const *p_row = row.data();
for (size_t j = 0; j < row.size(); ++j) {
local_column_sizes.at(p_row[j].index)++;
}
});
std::vector<bst_row_t> entries_per_columns(n_columns, 0);
ParallelFor(n_columns, nthreads, [&](bst_omp_uint i) {
for (auto const &thread : column_sizes) {
entries_per_columns[i] += thread[i];
}
});
return entries_per_columns;
}
template <typename WQSketch>
std::vector<bst_feature_t> SketchContainerImpl<WQSketch>::LoadBalance(SparsePage const &batch,
bst_feature_t n_columns,
size_t const nthreads) {
/* Some sparse datasets have their mass concentrating on small number of features. To
* avoid waiting for a few threads running forever, we here distribute different number
* of columns to different threads according to number of entries.
*/
auto page = batch.GetView();
size_t const total_entries = page.data.size();
size_t const entries_per_thread = DivRoundUp(total_entries, nthreads);
std::vector<std::vector<bst_row_t>> column_sizes(nthreads);
for (auto& column : column_sizes) {
column.resize(n_columns, 0);
}
std::vector<bst_row_t> entries_per_columns =
CalcColumnSize(batch, n_columns, nthreads);
std::vector<bst_feature_t> cols_ptr(nthreads + 1, 0);
size_t count {0};
size_t current_thread {1};
for (auto col : entries_per_columns) {
cols_ptr.at(current_thread)++; // add one column to thread
count += col;
CHECK_LE(count, total_entries);
if (count > entries_per_thread) {
current_thread++;
count = 0;
cols_ptr.at(current_thread) = cols_ptr[current_thread-1];
}
}
// Idle threads.
for (; current_thread < cols_ptr.size() - 1; ++current_thread) {
cols_ptr[current_thread+1] = cols_ptr[current_thread];
}
return cols_ptr;
}
namespace {
// Function to merge hessian and sample weights
std::vector<float> MergeWeights(MetaInfo const &info, Span<float const> hessian, bool use_group,
int32_t n_threads) {
CHECK_EQ(hessian.size(), info.num_row_);
std::vector<float> results(hessian.size());
auto const &group_ptr = info.group_ptr_;
auto const& weights = info.weights_.HostVector();
auto get_weight = [&](size_t i) { return weights.empty() ? 1.0f : weights[i]; };
if (use_group) {
CHECK_GE(group_ptr.size(), 2);
CHECK_EQ(group_ptr.back(), hessian.size());
size_t cur_group = 0;
for (size_t i = 0; i < hessian.size(); ++i) {
results[i] = hessian[i] * get_weight(cur_group);
if (i == group_ptr[cur_group + 1]) {
cur_group++;
}
}
} else {
ParallelFor(hessian.size(), n_threads, Sched::Auto(),
[&](auto i) { results[i] = hessian[i] * get_weight(i); });
}
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>
void SketchContainerImpl<WQSketch>::PushRowPage(SparsePage const &page, MetaInfo const &info,
Span<float const> hessian) {
monitor_.Start(__func__);
bst_feature_t n_columns = info.num_col_;
auto is_dense = info.num_nonzero_ == info.num_col_ * info.num_row_;
CHECK_GE(n_threads_, 1);
CHECK_EQ(sketches_.size(), n_columns);
// 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
if (!weights.empty()) {
CHECK_EQ(weights.size(), info.num_row_);
}
auto batch = page.GetView();
// Parallel over columns. Each thread owns a set of consecutive columns.
auto const ncol = static_cast<bst_feature_t>(info.num_col_);
auto thread_columns_ptr = LoadBalance(page, info.num_col_, n_threads_);
dmlc::OMPException exc;
#pragma omp parallel num_threads(n_threads_)
{
exc.Run([&]() {
auto tid = static_cast<uint32_t>(omp_get_thread_num());
auto const begin = thread_columns_ptr[tid];
auto const end = thread_columns_ptr[tid + 1];
// do not iterate if no columns are assigned to the thread
if (begin < end && end <= ncol) {
for (size_t i = 0; i < batch.Size(); ++i) {
size_t const ridx = page.base_rowid + i;
SparsePage::Inst const inst = batch[i];
auto w = weights.empty() ? 1.0f : weights[ridx];
auto p_inst = inst.data();
if (is_dense) {
for (size_t ii = begin; ii < end; ii++) {
if (IsCat(feature_types_, ii)) {
categories_[ii].emplace(p_inst[ii].fvalue);
} else {
sketches_[ii].Push(p_inst[ii].fvalue, w);
}
}
} else {
for (size_t i = 0; i < inst.size(); ++i) {
auto const& entry = p_inst[i];
if (entry.index >= begin && entry.index < end) {
if (IsCat(feature_types_, entry.index)) {
categories_[entry.index].emplace(entry.fvalue);
} else {
sketches_[entry.index].Push(entry.fvalue, w);
}
}
}
}
}
}
});
}
exc.Rethrow();
monitor_.Stop(__func__);
}
namespace {
/**
* \brief A view over gathered sketch values.
*/
template <typename T>
struct QuantileAllreduce {
common::Span<T> global_values;
common::Span<size_t> worker_indptr;
common::Span<size_t> feature_indptr;
size_t n_features{0};
/**
* \brief Get sketch values of the a feature from a worker.
*
* \param rank rank of target worker
* \param fidx feature idx
*/
auto Values(int32_t rank, bst_feature_t fidx) const {
// get span for worker
auto wsize = worker_indptr[rank + 1] - worker_indptr[rank];
auto worker_values = global_values.subspan(worker_indptr[rank], wsize);
auto psize = n_features + 1;
auto worker_feat_indptr = feature_indptr.subspan(psize * rank, psize);
// get span for feature
auto feat_beg = worker_feat_indptr[fidx];
auto feat_size = worker_feat_indptr[fidx + 1] - feat_beg;
return worker_values.subspan(feat_beg, feat_size);
}
};
/**
* \brief Merge all categories from other workers.
*/
void AllreduceCategories(Span<FeatureType const> feature_types, int32_t n_threads,
std::vector<std::set<float>> *p_categories) {
auto &categories = *p_categories;
auto world_size = rabit::GetWorldSize();
auto rank = rabit::GetRank();
if (world_size == 1) {
return;
}
// CSC indptr to each feature
std::vector<size_t> feature_ptr(categories.size() + 1, 0);
for (size_t i = 0; i < categories.size(); ++i) {
auto const &feat = categories[i];
feature_ptr[i + 1] = feat.size();
}
std::partial_sum(feature_ptr.begin(), feature_ptr.end(), feature_ptr.begin());
CHECK_EQ(feature_ptr.front(), 0);
// gather all feature ptrs from workers
std::vector<size_t> global_feat_ptrs(feature_ptr.size() * world_size, 0);
size_t feat_begin = rank * feature_ptr.size(); // pointer to current worker
std::copy(feature_ptr.begin(), feature_ptr.end(), global_feat_ptrs.begin() + feat_begin);
rabit::Allreduce<rabit::op::Sum>(global_feat_ptrs.data(), global_feat_ptrs.size());
// move all categories into a flatten vector to prepare for allreduce
size_t total = feature_ptr.back();
std::vector<float> flatten(total, 0);
auto cursor{flatten.begin()};
for (auto const &feat : categories) {
cursor = std::copy(feat.cbegin(), feat.cend(), cursor);
}
// indptr for indexing workers
std::vector<size_t> global_worker_ptr(world_size + 1, 0);
global_worker_ptr[rank + 1] = total; // shift 1 to right for constructing the indptr
rabit::Allreduce<rabit::op::Sum>(global_worker_ptr.data(), global_worker_ptr.size());
std::partial_sum(global_worker_ptr.cbegin(), global_worker_ptr.cend(), global_worker_ptr.begin());
// total number of categories in all workers with all features
auto gtotal = global_worker_ptr.back();
// categories in all workers with all features.
std::vector<float> global_categories(gtotal, 0);
auto rank_begin = global_worker_ptr[rank];
auto rank_size = global_worker_ptr[rank + 1] - rank_begin;
CHECK_EQ(rank_size, total);
std::copy(flatten.cbegin(), flatten.cend(), global_categories.begin() + rank_begin);
// gather values from all workers.
rabit::Allreduce<rabit::op::Sum>(global_categories.data(), global_categories.size());
QuantileAllreduce<float> allreduce_result{global_categories, global_worker_ptr, global_feat_ptrs,
categories.size()};
ParallelFor(categories.size(), n_threads, [&](auto fidx) {
if (!IsCat(feature_types, fidx)) {
return;
}
for (int32_t r = 0; r < world_size; ++r) {
if (r == rank) {
// continue if it's current worker.
continue;
}
// 1 feature of 1 worker
auto worker_feature = allreduce_result.Values(r, fidx);
for (auto c : worker_feature) {
categories[fidx].emplace(c);
}
}
});
}
} // anonymous namespace
template <typename WQSketch>
void SketchContainerImpl<WQSketch>::GatherSketchInfo(
std::vector<typename WQSketch::SummaryContainer> const &reduced,
std::vector<size_t> *p_worker_segments, std::vector<bst_row_t> *p_sketches_scan,
std::vector<typename WQSketch::Entry> *p_global_sketches) {
auto &worker_segments = *p_worker_segments;
worker_segments.resize(1, 0);
auto world = rabit::GetWorldSize();
auto rank = rabit::GetRank();
auto n_columns = sketches_.size();
// get the size of each feature.
std::vector<bst_row_t> sketch_size;
for (size_t i = 0; i < reduced.size(); ++i) {
if (IsCat(feature_types_, i)) {
sketch_size.push_back(0);
} else {
sketch_size.push_back(reduced[i].size);
}
}
// turn the size into CSC indptr
std::vector<bst_row_t> &sketches_scan = *p_sketches_scan;
sketches_scan.resize((n_columns + 1) * world, 0);
size_t beg_scan = rank * (n_columns + 1); // starting storage for current worker.
std::partial_sum(sketch_size.cbegin(), sketch_size.cend(), sketches_scan.begin() + beg_scan + 1);
// Gather all column pointers
rabit::Allreduce<rabit::op::Sum>(sketches_scan.data(), sketches_scan.size());
for (int32_t i = 0; i < world; ++i) {
size_t back = (i + 1) * (n_columns + 1) - 1;
auto n_entries = sketches_scan.at(back);
worker_segments.push_back(n_entries);
}
// Offset of sketch from each worker.
std::partial_sum(worker_segments.begin(), worker_segments.end(), worker_segments.begin());
CHECK_GE(worker_segments.size(), 1);
auto total = worker_segments.back();
auto &global_sketches = *p_global_sketches;
global_sketches.resize(total, typename WQSketch::Entry{0, 0, 0, 0});
auto worker_sketch = Span<typename WQSketch::Entry>{global_sketches}.subspan(
worker_segments[rank], worker_segments[rank + 1] - worker_segments[rank]);
auto cursor{worker_sketch.begin()};
for (size_t fidx = 0; fidx < reduced.size(); ++fidx) {
auto const &sketch = reduced[fidx];
if (IsCat(feature_types_, fidx)) {
// nothing to do if it's categorical feature, size is 0 so no need to change cursor
continue;
} else {
cursor = std::copy(sketch.data, sketch.data + sketch.size, cursor);
}
}
static_assert(sizeof(typename WQSketch::Entry) / 4 == sizeof(float),
"Unexpected size of sketch entry.");
rabit::Allreduce<rabit::op::Sum>(
reinterpret_cast<float *>(global_sketches.data()),
global_sketches.size() * sizeof(typename WQSketch::Entry) / sizeof(float));
}
template <typename WQSketch>
void SketchContainerImpl<WQSketch>::AllReduce(
std::vector<typename WQSketch::SummaryContainer> *p_reduced,
std::vector<int32_t>* p_num_cuts) {
monitor_.Start(__func__);
size_t n_columns = sketches_.size();
rabit::Allreduce<rabit::op::Max>(&n_columns, 1);
CHECK_EQ(n_columns, sketches_.size()) << "Number of columns differs across workers";
AllreduceCategories(feature_types_, n_threads_, &categories_);
auto& num_cuts = *p_num_cuts;
CHECK_EQ(num_cuts.size(), 0);
num_cuts.resize(sketches_.size());
auto &reduced = *p_reduced;
reduced.resize(sketches_.size());
// Prune the intermediate num cuts for synchronization.
std::vector<bst_row_t> global_column_size(columns_size_);
rabit::Allreduce<rabit::op::Sum>(global_column_size.data(), global_column_size.size());
ParallelFor(sketches_.size(), n_threads_, [&](size_t i) {
int32_t intermediate_num_cuts = static_cast<int32_t>(
std::min(global_column_size[i], static_cast<size_t>(max_bins_ * WQSketch::kFactor)));
if (global_column_size[i] == 0) {
return;
}
if (IsCat(feature_types_, i)) {
intermediate_num_cuts = categories_[i].size();
} else {
typename WQSketch::SummaryContainer out;
sketches_[i].GetSummary(&out);
reduced[i].Reserve(intermediate_num_cuts);
CHECK(reduced[i].data);
reduced[i].SetPrune(out, intermediate_num_cuts);
}
num_cuts[i] = intermediate_num_cuts;
});
auto world = rabit::GetWorldSize();
if (world == 1) {
monitor_.Stop(__func__);
return;
}
std::vector<size_t> worker_segments(1, 0); // CSC pointer to sketches.
std::vector<bst_row_t> sketches_scan((n_columns + 1) * world, 0);
std::vector<typename WQSketch::Entry> global_sketches;
this->GatherSketchInfo(reduced, &worker_segments, &sketches_scan, &global_sketches);
std::vector<typename WQSketch::SummaryContainer> final_sketches(n_columns);
ParallelFor(n_columns, n_threads_, [&](auto fidx) {
// gcc raises subobject-linkage warning if we put allreduce_result as lambda capture
QuantileAllreduce<typename WQSketch::Entry> allreduce_result{global_sketches, worker_segments,
sketches_scan, n_columns};
int32_t intermediate_num_cuts = num_cuts[fidx];
auto nbytes = WQSketch::SummaryContainer::CalcMemCost(intermediate_num_cuts);
if (IsCat(feature_types_, fidx)) {
return;
}
for (int32_t r = 0; r < world; ++r) {
// 1 feature of 1 worker
auto worker_feature = allreduce_result.Values(r, fidx);
CHECK(worker_feature.data());
typename WQSketch::Summary summary(worker_feature.data(), worker_feature.size());
auto &out = final_sketches.at(fidx);
out.Reduce(summary, nbytes);
}
reduced.at(fidx).Reserve(intermediate_num_cuts);
reduced.at(fidx).SetPrune(final_sketches.at(fidx), intermediate_num_cuts);
});
monitor_.Stop(__func__);
}
template <typename SketchType>
void AddCutPoint(typename SketchType::SummaryContainer const &summary, int max_bin,
HistogramCuts *cuts) {
size_t required_cuts = std::min(summary.size, static_cast<size_t>(max_bin));
auto &cut_values = cuts->cut_values_.HostVector();
for (size_t i = 1; i < required_cuts; ++i) {
bst_float cpt = summary.data[i].value;
if (i == 1 || cpt > cut_values.back()) {
cut_values.push_back(cpt);
}
}
}
auto AddCategories(std::set<float> const &categories, HistogramCuts *cuts) {
if (std::any_of(categories.cbegin(), categories.cend(), InvalidCat)) {
InvalidCategory();
}
auto &cut_values = cuts->cut_values_.HostVector();
auto max_cat = *std::max_element(categories.cbegin(), categories.cend());
CheckMaxCat(max_cat, categories.size());
for (bst_cat_t i = 0; i <= AsCat(max_cat); ++i) {
cut_values.push_back(i);
}
return max_cat;
}
template <typename WQSketch>
void SketchContainerImpl<WQSketch>::MakeCuts(HistogramCuts* cuts) {
monitor_.Start(__func__);
std::vector<typename WQSketch::SummaryContainer> reduced;
std::vector<int32_t> num_cuts;
this->AllReduce(&reduced, &num_cuts);
cuts->min_vals_.HostVector().resize(sketches_.size(), 0.0f);
std::vector<typename WQSketch::SummaryContainer> final_summaries(reduced.size());
ParallelFor(reduced.size(), n_threads_, Sched::Guided(), [&](size_t fidx) {
if (IsCat(feature_types_, fidx)) {
return;
}
typename WQSketch::SummaryContainer &a = final_summaries[fidx];
size_t max_num_bins = std::min(num_cuts[fidx], max_bins_);
a.Reserve(max_num_bins + 1);
CHECK(a.data);
if (num_cuts[fidx] != 0) {
a.SetPrune(reduced[fidx], max_num_bins + 1);
CHECK(a.data && reduced[fidx].data);
const bst_float mval = a.data[0].value;
cuts->min_vals_.HostVector()[fidx] = mval - fabs(mval) - 1e-5f;
} else {
// Empty column.
const float mval = 1e-5f;
cuts->min_vals_.HostVector()[fidx] = mval;
}
});
float max_cat{-1.f};
for (size_t fid = 0; fid < reduced.size(); ++fid) {
size_t max_num_bins = std::min(num_cuts[fid], max_bins_);
typename WQSketch::SummaryContainer const& a = final_summaries[fid];
if (IsCat(feature_types_, fid)) {
max_cat = std::max(max_cat, AddCategories(categories_.at(fid), cuts));
} else {
AddCutPoint<WQSketch>(a, max_num_bins, cuts);
// push a value that is greater than anything
const bst_float cpt = (a.size > 0) ? a.data[a.size - 1].value
: cuts->min_vals_.HostVector()[fid];
// this must be bigger than last value in a scale
const bst_float last = cpt + (fabs(cpt) + 1e-5f);
cuts->cut_values_.HostVector().push_back(last);
}
// Ensure that every feature gets at least one quantile point
CHECK_LE(cuts->cut_values_.HostVector().size(), std::numeric_limits<uint32_t>::max());
auto cut_size = static_cast<uint32_t>(cuts->cut_values_.HostVector().size());
CHECK_GT(cut_size, cuts->cut_ptrs_.HostVector().back());
cuts->cut_ptrs_.HostVector().push_back(cut_size);
}
cuts->SetCategorical(this->has_categorical_, max_cat);
monitor_.Stop(__func__);
}
template class SketchContainerImpl<WQuantileSketch<float, float>>;
template class SketchContainerImpl<WXQuantileSketch<float, float>>;
HostSketchContainer::HostSketchContainer(int32_t max_bins, MetaInfo const &info,
std::vector<size_t> columns_size, bool use_group,
Span<float const> hessian, int32_t n_threads)
: SketchContainerImpl{columns_size, max_bins, info.feature_types.ConstHostSpan(), use_group,
n_threads} {
monitor_.Init(__func__);
ParallelFor(sketches_.size(), n_threads_, Sched::Auto(), [&](auto i) {
auto n_bins = std::min(static_cast<size_t>(max_bins_), columns_size_[i]);
n_bins = std::max(n_bins, static_cast<decltype(n_bins)>(1));
auto eps = 1.0 / (static_cast<float>(n_bins) * WQSketch::kFactor);
if (!IsCat(this->feature_types_, i)) {
sketches_[i].Init(columns_size_[i], eps);
sketches_[i].inqueue.queue.resize(sketches_[i].limit_size * 2);
}
});
}
void SortedSketchContainer::PushColPage(SparsePage const &page, MetaInfo const &info,
Span<float const> hessian) {
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
: MergeWeights(info, hessian, use_group_ind_,
n_threads_); // use hessian merged with group/sample weights
CHECK_EQ(weights.size(), info.num_row_);
auto view = page.GetView();
ParallelFor(view.Size(), n_threads_, [&](size_t fidx) {
auto column = view[fidx];
auto &sketch = sketches_[fidx];
sketch.Init(max_bins_);
// first pass
sketch.sum_total = 0.0;
for (auto c : column) {
sketch.sum_total += weights[c.index];
}
// second pass
if (IsCat(feature_types_, fidx)) {
for (auto c : column) {
categories_[fidx].emplace(c.fvalue);
}
} else {
for (auto c : column) {
sketch.Push(c.fvalue, weights[c.index], max_bins_);
}
}
if (!IsCat(feature_types_, fidx) && !column.empty()) {
sketch.Finalize(max_bins_);
}
});
monitor_.Stop(__func__);
}
} // namespace common
} // namespace xgboost