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Unify CPU hist sketching (#5880)

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Jiaming Yuan 2020-08-12 01:33:06 +08:00 committed by GitHub
parent bd6b7f4aa7
commit ee70a2380b
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18 changed files with 648 additions and 677 deletions
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1
amalgamation/xgboost-all0.cc
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@ -70,6 +70,7 @@
#include "../src/common/common.cc"
#include "../src/common/charconv.cc"
#include "../src/common/timer.cc"
#include "../src/common/quantile.cc"
#include "../src/common/host_device_vector.cc"
#include "../src/common/hist_util.cc"
#include "../src/common/json.cc"

20
include/xgboost/data.h
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@ -239,6 +239,21 @@ struct BatchParam {
}
};
struct HostSparsePageView {
using Inst = common::Span<Entry const>;
common::Span<bst_row_t const> offset;
common::Span<Entry const> data;
Inst operator[](size_t i) const {
auto size = *(offset.data() + i + 1) - *(offset.data() + i);
return {data.data() + *(offset.data() + i),
static_cast<Inst::index_type>(size)};
}
size_t Size() const { return offset.size() == 0 ? 0 : offset.size() - 1; }
};
/*!
* \brief In-memory storage unit of sparse batch, stored in CSR format.
*/
@ -270,6 +285,11 @@ class SparsePage {
static_cast<Inst::index_type>(size)};
}
HostSparsePageView GetView() const {
return {offset.ConstHostSpan(), data.ConstHostSpan()};
}
/*! \brief constructor */
SparsePage() {
this->Clear();

349
src/common/hist_util.cc
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@ -113,346 +113,12 @@ void GHistIndexMatrix::ResizeIndex(const size_t rbegin, const SparsePage& batch,
}
HistogramCuts::HistogramCuts() {
monitor_.Init(__FUNCTION__);
cut_ptrs_.HostVector().emplace_back(0);
}
// Dispatch to specific builder.
void HistogramCuts::Build(DMatrix* dmat, uint32_t const max_num_bins) {
auto const& info = dmat->Info();
size_t const total = info.num_row_ * info.num_col_;
size_t const nnz = info.num_nonzero_;
float const sparsity = static_cast<float>(nnz) / static_cast<float>(total);
// Use a small number to avoid calling `dmat->GetColumnBatches'.
float constexpr kSparsityThreshold = 0.0005;
// FIXME(trivialfis): Distributed environment is not supported.
if (sparsity < kSparsityThreshold && (!rabit::IsDistributed())) {
LOG(INFO) << "Building quantile cut on a sparse dataset.";
SparseCuts cuts(this);
cuts.Build(dmat, max_num_bins);
} else {
LOG(INFO) << "Building quantile cut on a dense dataset or distributed environment.";
DenseCuts cuts(this);
cuts.Build(dmat, max_num_bins);
}
LOG(INFO) << "Total number of hist bins: " << cut_ptrs_.HostVector().back();
}
bool CutsBuilder::UseGroup(DMatrix* dmat) {
auto& info = dmat->Info();
return CutsBuilder::UseGroup(info);
}
bool CutsBuilder::UseGroup(MetaInfo const& info) {
size_t const num_groups = info.group_ptr_.size() == 0 ?
0 : info.group_ptr_.size() - 1;
// Use group index for weights?
bool const use_group_ind = num_groups != 0 &&
(info.weights_.Size() != info.num_row_);
return use_group_ind;
}
void SparseCuts::SingleThreadBuild(SparsePage const& page, MetaInfo const& info,
uint32_t max_num_bins,
bool const use_group_ind,
uint32_t beg_col, uint32_t end_col,
uint32_t thread_id) {
CHECK_GE(end_col, beg_col);
// Data groups, used in ranking.
std::vector<bst_uint> const& group_ptr = info.group_ptr_;
auto &local_min_vals = p_cuts_->min_vals_.HostVector();
auto &local_cuts = p_cuts_->cut_values_.HostVector();
auto &local_ptrs = p_cuts_->cut_ptrs_.HostVector();
local_min_vals.resize(end_col - beg_col, 0);
for (uint32_t col_id = beg_col; col_id < page.Size() && col_id < end_col; ++col_id) {
// Using a local variable makes things easier, but at the cost of memory trashing.
WQSketch sketch;
common::Span<xgboost::Entry const> const column = page[col_id];
uint32_t const n_bins = std::min(static_cast<uint32_t>(column.size()),
max_num_bins);
if (n_bins == 0) {
// cut_ptrs_ is initialized with a zero, so there's always an element at the back
CHECK_GE(local_ptrs.size(), 1);
local_ptrs.emplace_back(local_ptrs.back());
continue;
}
sketch.Init(info.num_row_, 1.0 / (n_bins * WQSketch::kFactor));
for (auto const& entry : column) {
uint32_t weight_ind = 0;
if (use_group_ind) {
auto row_idx = entry.index;
uint32_t group_ind =
this->SearchGroupIndFromRow(group_ptr, page.base_rowid + row_idx);
weight_ind = group_ind;
} else {
weight_ind = entry.index;
}
sketch.Push(entry.fvalue, info.GetWeight(weight_ind));
}
WQSketch::SummaryContainer out_summary;
sketch.GetSummary(&out_summary);
WQSketch::SummaryContainer summary;
summary.Reserve(n_bins + 1);
summary.SetPrune(out_summary, n_bins + 1);
// Can be use data[1] as the min values so that we don't need to
// store another array?
float mval = summary.data[0].value;
local_min_vals[col_id - beg_col] = mval - (fabs(mval) + 1e-5);
this->AddCutPoint(summary, max_num_bins);
bst_float cpt = (summary.size > 0) ?
summary.data[summary.size - 1].value :
local_min_vals[col_id - beg_col];
cpt += fabs(cpt) + 1e-5;
local_cuts.emplace_back(cpt);
local_ptrs.emplace_back(local_cuts.size());
}
}
std::vector<size_t> SparseCuts::LoadBalance(SparsePage const& page,
size_t const nthreads) {
/* Some sparse datasets have their mass concentrating on small
* number of features. To avoid wating for a few threads running
* forever, we here distirbute different number of columns to
* different threads according to number of entries. */
size_t const total_entries = page.data.Size();
size_t const entries_per_thread = common::DivRoundUp(total_entries, nthreads);
std::vector<size_t> cols_ptr(nthreads+1, 0);
size_t count {0};
size_t current_thread {1};
for (size_t col_id = 0; col_id < page.Size(); ++col_id) {
auto const column = page[col_id];
cols_ptr[current_thread]++; // add one column to thread
count += column.size();
if (count > entries_per_thread + 1) {
current_thread++;
count = 0;
cols_ptr[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;
}
void SparseCuts::Build(DMatrix* dmat, uint32_t const max_num_bins) {
monitor_.Start(__FUNCTION__);
// Use group index for weights?
auto use_group = UseGroup(dmat);
uint32_t nthreads = omp_get_max_threads();
CHECK_GT(nthreads, 0);
std::vector<HistogramCuts> cuts_containers(nthreads);
std::vector<std::unique_ptr<SparseCuts>> sparse_cuts(nthreads);
for (size_t i = 0; i < nthreads; ++i) {
sparse_cuts[i].reset(new SparseCuts(&cuts_containers[i]));
}
for (auto const& page : dmat->GetBatches<CSCPage>()) {
CHECK_LE(page.Size(), dmat->Info().num_col_);
monitor_.Start("Load balance");
std::vector<size_t> col_ptr = LoadBalance(page, nthreads);
monitor_.Stop("Load balance");
// We here decouples the logic between build and parallelization
// to simplify things a bit.
#pragma omp parallel for num_threads(nthreads) schedule(static)
for (omp_ulong i = 0; i < nthreads; ++i) {
common::Monitor t_monitor;
t_monitor.Init("SingleThreadBuild: " + std::to_string(i));
t_monitor.Start(std::to_string(i));
sparse_cuts[i]->SingleThreadBuild(page, dmat->Info(), max_num_bins, use_group,
col_ptr[i], col_ptr[i+1], i);
t_monitor.Stop(std::to_string(i));
}
this->Concat(sparse_cuts, dmat->Info().num_col_);
}
monitor_.Stop(__FUNCTION__);
}
void SparseCuts::Concat(
std::vector<std::unique_ptr<SparseCuts>> const& cuts, uint32_t n_cols) {
monitor_.Start(__FUNCTION__);
uint32_t nthreads = omp_get_max_threads();
auto &local_min_vals = p_cuts_->min_vals_.HostVector();
auto &local_cuts = p_cuts_->cut_values_.HostVector();
auto &local_ptrs = p_cuts_->cut_ptrs_.HostVector();
local_min_vals.resize(n_cols, std::numeric_limits<float>::max());
size_t min_vals_tail = 0;
for (uint32_t t = 0; t < nthreads; ++t) {
auto& thread_min_vals = cuts[t]->p_cuts_->min_vals_.HostVector();
auto& thread_cuts = cuts[t]->p_cuts_->cut_values_.HostVector();
auto& thread_ptrs = cuts[t]->p_cuts_->cut_ptrs_.HostVector();
// concat csc pointers.
size_t const old_ptr_size = local_ptrs.size();
local_ptrs.resize(
thread_ptrs.size() + local_ptrs.size() - 1);
size_t const new_icp_size = local_ptrs.size();
auto tail = local_ptrs[old_ptr_size-1];
for (size_t j = old_ptr_size; j < new_icp_size; ++j) {
local_ptrs[j] = tail + thread_ptrs[j-old_ptr_size+1];
}
// concat csc values
size_t const old_iv_size = local_cuts.size();
local_cuts.resize(
thread_cuts.size() + local_cuts.size());
size_t const new_iv_size = local_cuts.size();
for (size_t j = old_iv_size; j < new_iv_size; ++j) {
local_cuts[j] = thread_cuts[j-old_iv_size];
}
// merge min values
for (size_t j = 0; j < thread_min_vals.size(); ++j) {
local_min_vals.at(min_vals_tail + j) =
std::min(local_min_vals.at(min_vals_tail + j), thread_min_vals.at(j));
}
min_vals_tail += thread_min_vals.size();
}
monitor_.Stop(__FUNCTION__);
}
void DenseCuts::Build(DMatrix* p_fmat, uint32_t max_num_bins) {
monitor_.Start(__FUNCTION__);
const MetaInfo& info = p_fmat->Info();
// safe factor for better accuracy
std::vector<WQSketch> sketchs;
const int nthread = omp_get_max_threads();
unsigned const nstep =
static_cast<unsigned>((info.num_col_ + nthread - 1) / nthread);
unsigned const ncol = static_cast<unsigned>(info.num_col_);
sketchs.resize(info.num_col_);
for (auto& s : sketchs) {
s.Init(info.num_row_, 1.0 / (max_num_bins * WQSketch::kFactor));
}
// Data groups, used in ranking.
std::vector<bst_uint> const& group_ptr = info.group_ptr_;
size_t const num_groups = group_ptr.size() == 0 ? 0 : group_ptr.size() - 1;
// Use group index for weights?
bool const use_group = UseGroup(p_fmat);
const bool isDense = p_fmat->IsDense();
for (const auto &batch : p_fmat->GetBatches<SparsePage>()) {
size_t group_ind = 0;
if (use_group) {
group_ind = this->SearchGroupIndFromRow(group_ptr, batch.base_rowid);
}
#pragma omp parallel num_threads(nthread) firstprivate(group_ind, use_group)
{
CHECK_EQ(nthread, omp_get_num_threads());
auto tid = static_cast<unsigned>(omp_get_thread_num());
unsigned begin = std::min(nstep * tid, ncol);
unsigned end = std::min(nstep * (tid + 1), ncol);
// 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) { // NOLINT(*)
size_t const ridx = batch.base_rowid + i;
SparsePage::Inst const inst = batch[i];
if (use_group &&
group_ptr[group_ind] == ridx &&
// maximum equals to weights.size() - 1
group_ind < num_groups - 1) {
// move to next group
group_ind++;
}
size_t w_idx = use_group ? group_ind : ridx;
auto w = info.GetWeight(w_idx);
if (isDense) {
auto data = inst.data();
for (size_t ii = begin; ii < end; ii++) {
sketchs[ii].Push(data[ii].fvalue, w);
}
} else {
for (auto const& entry : inst) {
if (entry.index >= begin && entry.index < end) {
sketchs[entry.index].Push(entry.fvalue, w);
}
}
}
}
}
}
}
Init(&sketchs, max_num_bins, info.num_row_);
monitor_.Stop(__FUNCTION__);
}
/**
* \param [in,out] in_sketchs
* \param max_num_bins The maximum number bins.
* \param max_rows Number of rows in this DMatrix.
*/
void DenseCuts::Init
(std::vector<WQSketch>* in_sketchs, uint32_t max_num_bins, size_t max_rows) {
monitor_.Start(__func__);
std::vector<WQSketch>& sketchs = *in_sketchs;
// Compute how many cuts samples we need at each node
// Do not require more than the number of total rows in training data
// This allows efficient training on wide data
size_t global_max_rows = max_rows;
rabit::Allreduce<rabit::op::Sum>(&global_max_rows, 1);
size_t intermediate_num_cuts =
std::min(global_max_rows, static_cast<size_t>(max_num_bins * WQSketch::kFactor));
// gather the histogram data
rabit::SerializeReducer<WQSketch::SummaryContainer> sreducer;
std::vector<WQSketch::SummaryContainer> summary_array;
summary_array.resize(sketchs.size());
for (size_t i = 0; i < sketchs.size(); ++i) {
WQSketch::SummaryContainer out;
sketchs[i].GetSummary(&out);
summary_array[i].Reserve(intermediate_num_cuts);
summary_array[i].SetPrune(out, intermediate_num_cuts);
}
CHECK_EQ(summary_array.size(), in_sketchs->size());
size_t nbytes = WQSketch::SummaryContainer::CalcMemCost(intermediate_num_cuts);
// TODO(chenqin): rabit failure recovery assumes no boostrap onetime call after loadcheckpoint
// we need to move this allreduce before loadcheckpoint call in future
sreducer.Allreduce(dmlc::BeginPtr(summary_array), nbytes, summary_array.size());
p_cuts_->min_vals_.HostVector().resize(sketchs.size());
for (size_t fid = 0; fid < summary_array.size(); ++fid) {
WQSketch::SummaryContainer a;
a.Reserve(max_num_bins + 1);
a.SetPrune(summary_array[fid], max_num_bins + 1);
const bst_float mval = a.data[0].value;
p_cuts_->min_vals_.HostVector()[fid] = mval - (fabs(mval) + 1e-5);
AddCutPoint(a, max_num_bins);
// push a value that is greater than anything
const bst_float cpt
= (a.size > 0) ? a.data[a.size - 1].value : p_cuts_->min_vals_.HostVector()[fid];
// this must be bigger than last value in a scale
const bst_float last = cpt + (fabs(cpt) + 1e-5);
p_cuts_->cut_values_.HostVector().push_back(last);
// Ensure that every feature gets at least one quantile point
CHECK_LE(p_cuts_->cut_values_.HostVector().size(), std::numeric_limits<uint32_t>::max());
auto cut_size = static_cast<uint32_t>(p_cuts_->cut_values_.HostVector().size());
CHECK_GT(cut_size, p_cuts_->cut_ptrs_.HostVector().back());
p_cuts_->cut_ptrs_.HostVector().push_back(cut_size);
}
monitor_.Stop(__func__);
}
void GHistIndexMatrix::Init(DMatrix* p_fmat, int max_bins) {
cut.Build(p_fmat, max_bins);
cut = SketchOnDMatrix(p_fmat, max_bins);
max_num_bins = max_bins;
const int32_t nthread = omp_get_max_threads();
const uint32_t nbins = cut.Ptrs().back();
@ -1048,12 +714,11 @@ void BuildHistKernel(const std::vector<GradientPair>& gpair,
}
}
template<typename GradientSumT>
void GHistBuilder<GradientSumT>::BuildHist(const std::vector<GradientPair>& gpair,
const RowSetCollection::Elem row_indices,
const GHistIndexMatrix& gmat,
GHistRowT hist,
bool isDense) {
template <typename GradientSumT>
void GHistBuilder<GradientSumT>::BuildHist(
const std::vector<GradientPair> &gpair,
const RowSetCollection::Elem row_indices, const GHistIndexMatrix &gmat,
GHistRowT hist, bool isDense) {
const size_t nrows = row_indices.Size();
const size_t no_prefetch_size = Prefetch::NoPrefetchSize(nrows);

3
src/common/hist_util.cu
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@ -313,7 +313,6 @@ HistogramCuts DeviceSketch(int device, DMatrix* dmat, int max_bins,
device, num_cuts_per_feature, has_weights);
HistogramCuts cuts;
DenseCuts dense_cuts(&cuts);
SketchContainer sketch_container(max_bins, dmat->Info().num_col_,
dmat->Info().num_row_, device);
@ -324,7 +323,7 @@ HistogramCuts DeviceSketch(int device, DMatrix* dmat, int max_bins,
for (auto begin = 0ull; begin < batch_nnz; begin += sketch_batch_num_elements) {
size_t end = std::min(batch_nnz, size_t(begin + sketch_batch_num_elements));
if (has_weights) {
bool is_ranking = CutsBuilder::UseGroup(dmat);
bool is_ranking = HostSketchContainer::UseGroup(dmat->Info());
dh::caching_device_vector<uint32_t> groups(info.group_ptr_.cbegin(),
info.group_ptr_.cend());
ProcessWeightedBatch(

2
src/common/hist_util.cuh
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@ -306,7 +306,7 @@ void AdapterDeviceSketch(Batch batch, int num_bins,
size_t end = std::min(batch.Size(), size_t(begin + sketch_batch_num_elements));
ProcessWeightedSlidingWindow(batch, info,
num_cuts_per_feature,
CutsBuilder::UseGroup(info), missing, device, num_cols, begin, end,
HostSketchContainer::UseGroup(info), missing, device, num_cols, begin, end,
sketch_container);
}
} else {

120
src/common/hist_util.h
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@ -17,6 +17,7 @@
#include <map>
#include "row_set.h"
#include "common.h"
#include "threading_utils.h"
#include "../tree/param.h"
#include "./quantile.h"
@ -34,15 +35,8 @@ using GHistIndexRow = Span<uint32_t const>;
// A CSC matrix representing histogram cuts, used in CPU quantile hist.
// The cut values represent upper bounds of bins containing approximately equal numbers of elements
class HistogramCuts {
// Using friends to avoid creating a virtual class, since HistogramCuts is used as value
// object in many places.
friend class SparseCuts;
friend class DenseCuts;
friend class CutsBuilder;
protected:
using BinIdx = uint32_t;
common::Monitor monitor_;
public:
HostDeviceVector<bst_float> cut_values_; // NOLINT
@ -75,16 +69,12 @@ class HistogramCuts {
}
HistogramCuts& operator=(HistogramCuts&& that) noexcept(true) {
monitor_ = std::move(that.monitor_);
cut_ptrs_ = std::move(that.cut_ptrs_);
cut_values_ = std::move(that.cut_values_);
min_vals_ = std::move(that.min_vals_);
return *this;
}
/* \brief Build histogram cuts. */
void Build(DMatrix* dmat, uint32_t const max_num_bins);
/* \brief How many bins a feature has. */
uint32_t FeatureBins(uint32_t feature) const {
return cut_ptrs_.ConstHostVector().at(feature + 1) -
cut_ptrs_.ConstHostVector()[feature];
@ -118,86 +108,42 @@ class HistogramCuts {
}
};
/* \brief An interface for building quantile cuts.
*
* `DenseCuts' always assumes there are `max_bins` for each feature, which makes it not
* suitable for sparse dataset. On the other hand `SparseCuts' uses `GetColumnBatches',
* which doubles the memory usage, hence can not be applied to dense dataset.
*/
class CutsBuilder {
public:
using WQSketch = common::WQuantileSketch<bst_float, bst_float>;
/* \brief return whether group for ranking is used. */
static bool UseGroup(DMatrix* dmat);
static bool UseGroup(MetaInfo const& info);
protected:
HistogramCuts* p_cuts_;
public:
explicit CutsBuilder(HistogramCuts* p_cuts) : p_cuts_{p_cuts} {}
virtual ~CutsBuilder() = default;
static uint32_t SearchGroupIndFromRow(std::vector<bst_uint> const &group_ptr,
size_t const base_rowid) {
CHECK_LT(base_rowid, group_ptr.back())
<< "Row: " << base_rowid << " is not found in any group.";
auto it =
std::upper_bound(group_ptr.cbegin(), group_ptr.cend() - 1, base_rowid);
bst_group_t group_ind = it - group_ptr.cbegin() - 1;
return group_ind;
inline HistogramCuts SketchOnDMatrix(DMatrix *m, int32_t max_bins) {
HistogramCuts out;
auto const& info = m->Info();
const auto threads = omp_get_max_threads();
std::vector<std::vector<bst_row_t>> column_sizes(threads);
for (auto& column : column_sizes) {
column.resize(info.num_col_, 0);
}
void AddCutPoint(WQSketch::SummaryContainer const& summary, int max_bin) {
size_t required_cuts = std::min(summary.size, static_cast<size_t>(max_bin));
for (size_t i = 1; i < required_cuts; ++i) {
bst_float cpt = summary.data[i].value;
if (i == 1 || cpt > p_cuts_->cut_values_.ConstHostVector().back()) {
p_cuts_->cut_values_.HostVector().push_back(cpt);
for (auto const& page : m->GetBatches<SparsePage>()) {
page.data.HostVector();
page.offset.HostVector();
ParallelFor(page.Size(), threads, [&](size_t 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> reduced(info.num_col_, 0);
ParallelFor(info.num_col_, threads, [&](size_t i) {
for (auto const &thread : column_sizes) {
reduced[i] += thread[i];
}
});
HostSketchContainer container(reduced, max_bins,
HostSketchContainer::UseGroup(info));
for (auto const &page : m->GetBatches<SparsePage>()) {
container.PushRowPage(page, info);
}
/* \brief Build histogram indices. */
virtual void Build(DMatrix* dmat, uint32_t const max_num_bins) = 0;
};
/*! \brief Cut configuration for sparse dataset. */
class SparseCuts : public CutsBuilder {
/* \brief Distribute columns to each thread according to number of entries. */
static std::vector<size_t> LoadBalance(SparsePage const& page, size_t const nthreads);
Monitor monitor_;
public:
explicit SparseCuts(HistogramCuts* container) :
CutsBuilder(container) {
monitor_.Init(__FUNCTION__);
}
/* \brief Concatonate the built cuts in each thread. */
void Concat(std::vector<std::unique_ptr<SparseCuts>> const& cuts, uint32_t n_cols);
/* \brief Build histogram indices in single thread. */
void SingleThreadBuild(SparsePage const& page, MetaInfo const& info,
uint32_t max_num_bins,
bool const use_group_ind,
uint32_t beg, uint32_t end, uint32_t thread_id);
void Build(DMatrix* dmat, uint32_t const max_num_bins) override;
};
/*! \brief Cut configuration for dense dataset. */
class DenseCuts : public CutsBuilder {
protected:
Monitor monitor_;
public:
explicit DenseCuts(HistogramCuts* container) :
CutsBuilder(container) {
monitor_.Init(__FUNCTION__);
}
void Init(std::vector<WQSketch>* sketchs, uint32_t max_num_bins, size_t max_rows);
void Build(DMatrix* p_fmat, uint32_t max_num_bins) override;
};
container.MakeCuts(&out);
return out;
}
enum BinTypeSize {
kUint8BinsTypeSize = 1,

193
src/common/quantile.cc Normal file
View File

@ -0,0 +1,193 @@
/*!
* Copyright 2020 by XGBoost Contributors
*/
#include <limits>
#include <utility>
#include "quantile.h"
#include "hist_util.h"
namespace xgboost {
namespace common {
HostSketchContainer::HostSketchContainer(std::vector<bst_row_t> columns_size,
int32_t max_bins, bool use_group)
: columns_size_{std::move(columns_size)}, max_bins_{max_bins},
use_group_ind_{use_group} {
monitor_.Init(__func__);
CHECK_NE(columns_size_.size(), 0);
sketches_.resize(columns_size_.size());
for (size_t i = 0; i < sketches_.size(); ++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);
sketches_[i].Init(columns_size_[i], eps);
sketches_[i].inqueue.queue.resize(sketches_[i].limit_size * 2);
}
}
std::vector<bst_feature_t> LoadBalance(SparsePage const &page,
std::vector<size_t> columns_size,
size_t const nthreads) {
/* Some sparse datasets have their mass concentrating on small
* number of features. To avoid wating for a few threads running
* forever, we here distirbute different number of columns to
* different threads according to number of entries. */
size_t const total_entries = page.data.Size();
size_t const entries_per_thread = common::DivRoundUp(total_entries, nthreads);
std::vector<bst_feature_t> cols_ptr(nthreads+1, 0);
size_t count {0};
size_t current_thread {1};
for (auto col : columns_size) {
cols_ptr[current_thread]++; // add one column to thread
count += col;
if (count > entries_per_thread + 1) {
current_thread++;
count = 0;
cols_ptr[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;
}
void HostSketchContainer::PushRowPage(SparsePage const &page,
MetaInfo const &info) {
monitor_.Start(__func__);
int nthread = omp_get_max_threads();
CHECK_EQ(sketches_.size(), info.num_col_);
// Data groups, used in ranking.
std::vector<bst_uint> const &group_ptr = info.group_ptr_;
// Use group index for weights?
auto batch = page.GetView();
dmlc::OMPException exec;
// Parallel over columns. Asumming the data is dense, each thread owns a set of
// consecutive columns.
auto const ncol = static_cast<uint32_t>(info.num_col_);
auto const is_dense = info.num_nonzero_ == info.num_col_ * info.num_row_;
auto thread_columns_ptr = LoadBalance(page, columns_size_, nthread);
#pragma omp parallel num_threads(nthread)
{
exec.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];
size_t group_ind = 0;
// 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];
if (use_group_ind_) {
group_ind = this->SearchGroupIndFromRow(group_ptr, i + page.base_rowid);
}
size_t w_idx = use_group_ind_ ? group_ind : ridx;
auto w = info.GetWeight(w_idx);
auto p_inst = inst.data();
if (is_dense) {
for (size_t ii = begin; ii < end; ii++) {
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) {
sketches_[entry.index].Push(entry.fvalue, w);
}
}
}
}
}
});
}
exec.Rethrow();
monitor_.Stop(__func__);
}
void AddCutPoint(WQuantileSketch<float, float>::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 > cuts->cut_values_.ConstHostVector().back()) {
cut_values.push_back(cpt);
}
}
}
void HostSketchContainer::MakeCuts(HistogramCuts* cuts) {
monitor_.Start(__func__);
rabit::Allreduce<rabit::op::Sum>(columns_size_.data(), columns_size_.size());
std::vector<WQSketch::SummaryContainer> reduced(sketches_.size());
std::vector<int32_t> num_cuts;
size_t nbytes = 0;
for (size_t i = 0; i < sketches_.size(); ++i) {
int32_t intermediate_num_cuts = static_cast<int32_t>(std::min(
columns_size_[i], static_cast<size_t>(max_bins_ * WQSketch::kFactor)));
if (columns_size_[i] != 0) {
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.push_back(intermediate_num_cuts);
nbytes = std::max(
WQSketch::SummaryContainer::CalcMemCost(intermediate_num_cuts), nbytes);
}
if (rabit::IsDistributed()) {
// FIXME(trivialfis): This call will allocate nbytes * num_columns on rabit, which
// may generate oom error when data is sparse. To fix it, we need to:
// - gather the column offsets over all workers.
// - run rabit::allgather on sketch data to collect all data.
// - merge all gathered sketches based on worker offsets and column offsets of data
// from each worker.
// See GPU implementation for details.
rabit::SerializeReducer<WQSketch::SummaryContainer> sreducer;
sreducer.Allreduce(dmlc::BeginPtr(reduced), nbytes, reduced.size());
}
cuts->min_vals_.HostVector().resize(sketches_.size(), 0.0f);
for (size_t fid = 0; fid < reduced.size(); ++fid) {
WQSketch::SummaryContainer a;
size_t max_num_bins = std::min(num_cuts[fid], max_bins_);
a.Reserve(max_num_bins + 1);
CHECK(a.data);
if (columns_size_[fid] != 0) {
a.SetPrune(reduced[fid], max_num_bins + 1);
CHECK(a.data && reduced[fid].data);
const bst_float mval = a.data[0].value;
cuts->min_vals_.HostVector()[fid] = mval - fabs(mval) - 1e-5f;
} else {
// Empty column.
const float mval = 1e-5f;
cuts->min_vals_.HostVector()[fid] = mval;
}
AddCutPoint(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);
}
monitor_.Stop(__func__);
}
} // namespace common
} // namespace xgboost

2
src/common/quantile.cu
View File

@ -20,7 +20,7 @@
namespace xgboost {
namespace common {
using WQSketch = DenseCuts::WQSketch;
using WQSketch = HostSketchContainer::WQSketch;
using SketchEntry = WQSketch::Entry;
// Algorithm 4 in XGBoost's paper, using binary search to find i.

54
src/common/quantile.h
View File

@ -9,12 +9,15 @@
#include <dmlc/base.h>
#include <xgboost/logging.h>
#include <xgboost/data.h>
#include <cmath>
#include <vector>
#include <cstring>
#include <algorithm>
#include <iostream>
#include "timer.h"
namespace xgboost {
namespace common {
/*!
@ -682,6 +685,57 @@ template<typename DType, typename RType = unsigned>
class WXQuantileSketch :
public QuantileSketchTemplate<DType, RType, WXQSummary<DType, RType> > {
};
class HistogramCuts;
/*!
* A sketch matrix storing sketches for each feature.
*/
class HostSketchContainer {
public:
using WQSketch = WQuantileSketch<float, float>;
private:
std::vector<WQSketch> sketches_;
std::vector<bst_row_t> columns_size_;
int32_t max_bins_;
bool use_group_ind_{false};
Monitor monitor_;
public:
/* \brief Initialize necessary info.
*
* \param columns_size Size of each column.
* \param max_bins maximum number of bins for each feature.
* \param use_group whether is assigned to group to data instance.
*/
HostSketchContainer(std::vector<bst_row_t> columns_size, int32_t max_bins,
bool use_group);
static bool UseGroup(MetaInfo const &info) {
size_t const num_groups =
info.group_ptr_.size() == 0 ? 0 : info.group_ptr_.size() - 1;
// Use group index for weights?
bool const use_group_ind =
num_groups != 0 && (info.weights_.Size() != info.num_row_);
return use_group_ind;
}
static uint32_t SearchGroupIndFromRow(std::vector<bst_uint> const &group_ptr,
size_t const base_rowid) {
CHECK_LT(base_rowid, group_ptr.back())
<< "Row: " << base_rowid << " is not found in any group.";
bst_group_t group_ind =
std::upper_bound(group_ptr.cbegin(), group_ptr.cend() - 1, base_rowid) -
group_ptr.cbegin() - 1;
return group_ind;
}
/* \brief Push a CSR matrix. */
void PushRowPage(SparsePage const& page, MetaInfo const& info);
void MakeCuts(HistogramCuts* cuts);
};
} // namespace common
} // namespace xgboost
#endif // XGBOOST_COMMON_QUANTILE_H_

31
src/common/threading_utils.h
View File

@ -6,9 +6,9 @@
#ifndef XGBOOST_COMMON_THREADING_UTILS_H_
#define XGBOOST_COMMON_THREADING_UTILS_H_
#include <dmlc/common.h>
#include <vector>
#include <algorithm>
#include "xgboost/logging.h"
namespace xgboost {
@ -115,17 +115,32 @@ void ParallelFor2d(const BlockedSpace2d& space, int nthreads, Func func) {
nthreads = std::min(nthreads, omp_get_max_threads());
nthreads = std::max(nthreads, 1);
dmlc::OMPException omp_exc;
#pragma omp parallel num_threads(nthreads)
{
size_t tid = omp_get_thread_num();
size_t chunck_size = num_blocks_in_space / nthreads + !!(num_blocks_in_space % nthreads);
omp_exc.Run([&]() {
size_t tid = omp_get_thread_num();
size_t chunck_size =
num_blocks_in_space / nthreads + !!(num_blocks_in_space % nthreads);
size_t begin = chunck_size * tid;
size_t end = std::min(begin + chunck_size, num_blocks_in_space);
for (auto i = begin; i < end; i++) {
func(space.GetFirstDimension(i), space.GetRange(i));
}
size_t begin = chunck_size * tid;
size_t end = std::min(begin + chunck_size, num_blocks_in_space);
for (auto i = begin; i < end; i++) {
func(space.GetFirstDimension(i), space.GetRange(i));
}
});
}
omp_exc.Rethrow();
}
template <typename Func>
void ParallelFor(size_t size, size_t nthreads, Func fn) {
dmlc::OMPException omp_exc;
#pragma omp parallel for num_threads(nthreads)
for (omp_ulong i = 0; i < size; ++i) {
omp_exc.Run(fn, i);
}
omp_exc.Rethrow();
}
} // namespace common

12
src/predictor/cpu_predictor.cc
View File

@ -44,18 +44,16 @@ bst_float PredValue(const SparsePage::Inst &inst,
template <size_t kUnrollLen = 8>
struct SparsePageView {
SparsePage const* page;
bst_row_t base_rowid;
HostSparsePageView view;
static size_t constexpr kUnroll = kUnrollLen;
explicit SparsePageView(SparsePage const *p)
: page{p}, base_rowid{page->base_rowid} {
// Pull to host before entering omp block, as this is not thread safe.
page->data.HostVector();
page->offset.HostVector();
: base_rowid{p->base_rowid} {
view = p->GetView();
}
SparsePage::Inst operator[](size_t i) { return (*page)[i]; }
size_t Size() const { return page->Size(); }
SparsePage::Inst operator[](size_t i) { return view[i]; }
size_t Size() const { return view.Size(); }
};
template <typename Adapter, size_t kUnrollLen = 8>

236
tests/cpp/common/test_hist_util.cc
View File

@ -158,86 +158,20 @@ TEST(CutsBuilder, SearchGroupInd) {
HistogramCuts hmat;
size_t group_ind = CutsBuilder::SearchGroupIndFromRow(p_mat->Info().group_ptr_, 0);
size_t group_ind = HostSketchContainer::SearchGroupIndFromRow(p_mat->Info().group_ptr_, 0);
ASSERT_EQ(group_ind, 0);
group_ind = CutsBuilder::SearchGroupIndFromRow(p_mat->Info().group_ptr_, 5);
group_ind = HostSketchContainer::SearchGroupIndFromRow(p_mat->Info().group_ptr_, 5);
ASSERT_EQ(group_ind, 2);
EXPECT_ANY_THROW(HostSketchContainer::SearchGroupIndFromRow(p_mat->Info().group_ptr_, 17));
p_mat->Info().Validate(-1);
EXPECT_THROW(CutsBuilder::SearchGroupIndFromRow(p_mat->Info().group_ptr_, 17),
EXPECT_THROW(HostSketchContainer::SearchGroupIndFromRow(p_mat->Info().group_ptr_, 17),
dmlc::Error);
std::vector<bst_uint> group_ptr {0, 1, 2};
CHECK_EQ(CutsBuilder::SearchGroupIndFromRow(group_ptr, 1), 1);
}
TEST(SparseCuts, SingleThreadedBuild) {
size_t constexpr kRows = 267;
size_t constexpr kCols = 31;
size_t constexpr kBins = 256;
auto p_fmat = RandomDataGenerator(kRows, kCols, 0).GenerateDMatrix();
common::GHistIndexMatrix hmat;
hmat.Init(p_fmat.get(), kBins);
HistogramCuts cuts;
SparseCuts indices(&cuts);
auto const& page = *(p_fmat->GetBatches<xgboost::CSCPage>().begin());
indices.SingleThreadBuild(page, p_fmat->Info(), kBins, false, 0, page.Size(), 0);
ASSERT_EQ(hmat.cut.Ptrs().size(), cuts.Ptrs().size());
ASSERT_EQ(hmat.cut.Ptrs(), cuts.Ptrs());
ASSERT_EQ(hmat.cut.Values(), cuts.Values());
ASSERT_EQ(hmat.cut.MinValues(), cuts.MinValues());
}
TEST(SparseCuts, MultiThreadedBuild) {
size_t constexpr kRows = 17;
size_t constexpr kCols = 15;
size_t constexpr kBins = 255;
omp_ulong ori_nthreads = omp_get_max_threads();
omp_set_num_threads(16);
auto Compare =
#if defined(_MSC_VER) // msvc fails to capture
[kBins](DMatrix* p_fmat) {
#else
[](DMatrix* p_fmat) {
#endif
HistogramCuts threaded_container;
SparseCuts threaded_indices(&threaded_container);
threaded_indices.Build(p_fmat, kBins);
HistogramCuts container;
SparseCuts indices(&container);
auto const& page = *(p_fmat->GetBatches<xgboost::CSCPage>().begin());
indices.SingleThreadBuild(page, p_fmat->Info(), kBins, false, 0, page.Size(), 0);
ASSERT_EQ(container.Ptrs().size(), threaded_container.Ptrs().size());
ASSERT_EQ(container.Values().size(), threaded_container.Values().size());
for (uint32_t i = 0; i < container.Ptrs().size(); ++i) {
ASSERT_EQ(container.Ptrs()[i], threaded_container.Ptrs()[i]);
}
for (uint32_t i = 0; i < container.Values().size(); ++i) {
ASSERT_EQ(container.Values()[i], threaded_container.Values()[i]);
}
};
{
auto p_fmat = RandomDataGenerator(kRows, kCols, 0).GenerateDMatrix();
Compare(p_fmat.get());
}
{
auto p_fmat = RandomDataGenerator(kRows, kCols, 0.0001).GenerateDMatrix();
Compare(p_fmat.get());
}
omp_set_num_threads(ori_nthreads);
CHECK_EQ(HostSketchContainer::SearchGroupIndFromRow(group_ptr, 1), 1);
}
TEST(HistUtil, DenseCutsCategorical) {
@ -250,9 +184,7 @@ TEST(HistUtil, DenseCutsCategorical) {
std::vector<float> x_sorted(x);
std::sort(x_sorted.begin(), x_sorted.end());
auto dmat = GetDMatrixFromData(x, n, 1);
HistogramCuts cuts;
DenseCuts dense(&cuts);
dense.Build(dmat.get(), num_bins);
HistogramCuts cuts = SketchOnDMatrix(dmat.get(), num_bins);
auto cuts_from_sketch = cuts.Values();
EXPECT_LT(cuts.MinValues()[0], x_sorted.front());
EXPECT_GT(cuts_from_sketch.front(), x_sorted.front());
@ -264,15 +196,14 @@ TEST(HistUtil, DenseCutsCategorical) {
TEST(HistUtil, DenseCutsAccuracyTest) {
int bin_sizes[] = {2, 16, 256, 512};
int sizes[] = {100, 1000, 1500};
int sizes[] = {100};
// omp_set_num_threads(1);
int num_columns = 5;
for (auto num_rows : sizes) {
auto x = GenerateRandom(num_rows, num_columns);
auto dmat = GetDMatrixFromData(x, num_rows, num_columns);
for (auto num_bins : bin_sizes) {
HistogramCuts cuts;
DenseCuts dense(&cuts);
dense.Build(dmat.get(), num_bins);
HistogramCuts cuts = SketchOnDMatrix(dmat.get(), num_bins);
ValidateCuts(cuts, dmat.get(), num_bins);
}
}
@ -288,9 +219,7 @@ TEST(HistUtil, DenseCutsAccuracyTestWeights) {
auto w = GenerateRandomWeights(num_rows);
dmat->Info().weights_.HostVector() = w;
for (auto num_bins : bin_sizes) {
HistogramCuts cuts;
DenseCuts dense(&cuts);
dense.Build(dmat.get(), num_bins);
HistogramCuts cuts = SketchOnDMatrix(dmat.get(), num_bins);
ValidateCuts(cuts, dmat.get(), num_bins);
}
}
@ -306,65 +235,7 @@ TEST(HistUtil, DenseCutsExternalMemory) {
auto dmat =
GetExternalMemoryDMatrixFromData(x, num_rows, num_columns, 50, tmpdir);
for (auto num_bins : bin_sizes) {
HistogramCuts cuts;
DenseCuts dense(&cuts);
dense.Build(dmat.get(), num_bins);
ValidateCuts(cuts, dmat.get(), num_bins);
}
}
}
TEST(HistUtil, SparseCutsAccuracyTest) {
int bin_sizes[] = {2, 16, 256, 512};
int sizes[] = {100, 1000, 1500};
int num_columns = 5;
for (auto num_rows : sizes) {
auto x = GenerateRandom(num_rows, num_columns);
auto dmat = GetDMatrixFromData(x, num_rows, num_columns);
for (auto num_bins : bin_sizes) {
HistogramCuts cuts;
SparseCuts sparse(&cuts);
sparse.Build(dmat.get(), num_bins);
ValidateCuts(cuts, dmat.get(), num_bins);
}
}
}
TEST(HistUtil, SparseCutsCategorical) {
int categorical_sizes[] = {2, 6, 8, 12};
int num_bins = 256;
int sizes[] = {25, 100, 1000};
for (auto n : sizes) {
for (auto num_categories : categorical_sizes) {
auto x = GenerateRandomCategoricalSingleColumn(n, num_categories);
std::vector<float> x_sorted(x);
std::sort(x_sorted.begin(), x_sorted.end());
auto dmat = GetDMatrixFromData(x, n, 1);
HistogramCuts cuts;
SparseCuts sparse(&cuts);
sparse.Build(dmat.get(), num_bins);
auto cuts_from_sketch = cuts.Values();
EXPECT_LT(cuts.MinValues()[0], x_sorted.front());
EXPECT_GT(cuts_from_sketch.front(), x_sorted.front());
EXPECT_GE(cuts_from_sketch.back(), x_sorted.back());
EXPECT_EQ(cuts_from_sketch.size(), num_categories);
}
}
}
TEST(HistUtil, SparseCutsExternalMemory) {
int bin_sizes[] = {2, 16, 256, 512};
int sizes[] = {100, 1000, 1500};
int num_columns = 5;
for (auto num_rows : sizes) {
auto x = GenerateRandom(num_rows, num_columns);
dmlc::TemporaryDirectory tmpdir;
auto dmat =
GetExternalMemoryDMatrixFromData(x, num_rows, num_columns, 50, tmpdir);
for (auto num_bins : bin_sizes) {
HistogramCuts cuts;
SparseCuts dense(&cuts);
dense.Build(dmat.get(), num_bins);
HistogramCuts cuts = SketchOnDMatrix(dmat.get(), num_bins);
ValidateCuts(cuts, dmat.get(), num_bins);
}
}
@ -391,25 +262,6 @@ TEST(HistUtil, IndexBinBound) {
}
}
TEST(HistUtil, SparseIndexBinBound) {
uint64_t bin_sizes[] = { static_cast<uint64_t>(std::numeric_limits<uint8_t>::max()) + 1,
static_cast<uint64_t>(std::numeric_limits<uint16_t>::max()) + 1,
static_cast<uint64_t>(std::numeric_limits<uint16_t>::max()) + 2 };
BinTypeSize expected_bin_type_sizes[] = { kUint32BinsTypeSize,
kUint32BinsTypeSize,
kUint32BinsTypeSize };
size_t constexpr kRows = 100;
size_t constexpr kCols = 10;
size_t bin_id = 0;
for (auto max_bin : bin_sizes) {
auto p_fmat = RandomDataGenerator(kRows, kCols, 0.2).GenerateDMatrix();
common::GHistIndexMatrix hmat;
hmat.Init(p_fmat.get(), max_bin);
EXPECT_EQ(expected_bin_type_sizes[bin_id++], hmat.index.GetBinTypeSize());
}
}
template <typename T>
void CheckIndexData(T* data_ptr, uint32_t* offsets,
const common::GHistIndexMatrix& hmat, size_t n_cols) {
@ -448,25 +300,61 @@ TEST(HistUtil, IndexBinData) {
}
}
TEST(HistUtil, SparseIndexBinData) {
uint64_t bin_sizes[] = { static_cast<uint64_t>(std::numeric_limits<uint8_t>::max()) + 1,
static_cast<uint64_t>(std::numeric_limits<uint16_t>::max()) + 1,
static_cast<uint64_t>(std::numeric_limits<uint16_t>::max()) + 2 };
size_t constexpr kRows = 100;
size_t constexpr kCols = 10;
void TestSketchFromWeights(bool with_group) {
size_t constexpr kRows = 300, kCols = 20, kBins = 256;
size_t constexpr kGroups = 10;
auto m =
RandomDataGenerator{kRows, kCols, 0}.Device(0).GenerateDMatrix();
common::HistogramCuts cuts = SketchOnDMatrix(m.get(), kBins);
for (auto max_bin : bin_sizes) {
auto p_fmat = RandomDataGenerator(kRows, kCols, 0.2).GenerateDMatrix();
common::GHistIndexMatrix hmat;
hmat.Init(p_fmat.get(), max_bin);
EXPECT_EQ(hmat.index.Offset(), nullptr);
MetaInfo info;
auto& h_weights = info.weights_.HostVector();
if (with_group) {
h_weights.resize(kGroups);
} else {
h_weights.resize(kRows);
}
std::fill(h_weights.begin(), h_weights.end(), 1.0f);
uint32_t* data_ptr = hmat.index.data<uint32_t>();
for (size_t i = 0; i < hmat.index.Size(); ++i) {
EXPECT_EQ(data_ptr[i], hmat.index[i]);
std::vector<bst_group_t> groups(kGroups);
if (with_group) {
for (size_t i = 0; i < kGroups; ++i) {
groups[i] = kRows / kGroups;
}
info.SetInfo("group", groups.data(), DataType::kUInt32, kGroups);
}
info.num_row_ = kRows;
info.num_col_ = kCols;
// Assign weights.
if (with_group) {
m->Info().SetInfo("group", groups.data(), DataType::kUInt32, kGroups);
}
m->Info().SetInfo("weight", h_weights.data(), DataType::kFloat32, h_weights.size());
m->Info().num_col_ = kCols;
m->Info().num_row_ = kRows;
ASSERT_EQ(cuts.Ptrs().size(), kCols + 1);
ValidateCuts(cuts, m.get(), kBins);
if (with_group) {
HistogramCuts non_weighted = SketchOnDMatrix(m.get(), kBins);
for (size_t i = 0; i < cuts.Values().size(); ++i) {
EXPECT_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]);
}
for (size_t i = 0; i < cuts.Ptrs().size(); ++i) {
ASSERT_EQ(cuts.Ptrs().at(i), non_weighted.Ptrs().at(i));
}
}
}
TEST(HistUtil, SketchFromWeights) {
TestSketchFromWeights(true);
TestSketchFromWeights(false);
}
} // namespace common
} // namespace xgboost

14
tests/cpp/common/test_hist_util.cu
View File

@ -24,10 +24,8 @@ namespace common {
template <typename AdapterT>
HistogramCuts GetHostCuts(AdapterT *adapter, int num_bins, float missing) {
HistogramCuts cuts;
DenseCuts builder(&cuts);
data::SimpleDMatrix dmat(adapter, missing, 1);
builder.Build(&dmat, num_bins);
HistogramCuts cuts = SketchOnDMatrix(&dmat, num_bins);
return cuts;
}
@ -39,9 +37,7 @@ TEST(HistUtil, DeviceSketch) {
auto dmat = GetDMatrixFromData(x, num_rows, num_columns);
auto device_cuts = DeviceSketch(0, dmat.get(), num_bins);
HistogramCuts host_cuts;
DenseCuts builder(&host_cuts);
builder.Build(dmat.get(), num_bins);
HistogramCuts host_cuts = SketchOnDMatrix(dmat.get(), num_bins);
EXPECT_EQ(device_cuts.Values(), host_cuts.Values());
EXPECT_EQ(device_cuts.Ptrs(), host_cuts.Ptrs());
@ -460,7 +456,11 @@ void TestAdapterSketchFromWeights(bool with_group) {
&storage);
MetaInfo info;
auto& h_weights = info.weights_.HostVector();
h_weights.resize(kRows);
if (with_group) {
h_weights.resize(kGroups);
} else {
h_weights.resize(kRows);
}
std::fill(h_weights.begin(), h_weights.end(), 1.0f);
std::vector<bst_group_t> groups(kGroups);

77
tests/cpp/common/test_quantile.cc Normal file
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@ -0,0 +1,77 @@
#include <gtest/gtest.h>
#include "test_quantile.h"
#include "../../../src/common/quantile.h"
#include "../../../src/common/hist_util.h"
namespace xgboost {
namespace common {
TEST(Quantile, SameOnAllWorkers) {
std::string msg{"Skipping Quantile AllreduceBasic test"};
size_t constexpr kWorkers = 4;
InitRabitContext(msg, kWorkers);
auto world = rabit::GetWorldSize();
if (world != 1) {
CHECK_EQ(world, kWorkers);
} else {
return;
}
constexpr size_t kRows = 1000, kCols = 100;
RunWithSeedsAndBins(
kRows, [=](int32_t seed, size_t n_bins, MetaInfo const &info) {
auto rank = rabit::GetRank();
HostDeviceVector<float> storage;
auto m = RandomDataGenerator{kRows, kCols, 0}
.Device(0)
.Seed(rank + seed)
.GenerateDMatrix();
auto cuts = SketchOnDMatrix(m.get(), n_bins);
std::vector<float> cut_values(cuts.Values().size() * world, 0);
std::vector<
typename std::remove_reference_t<decltype(cuts.Ptrs())>::value_type>
cut_ptrs(cuts.Ptrs().size() * world, 0);
std::vector<float> cut_min_values(cuts.MinValues().size() * world, 0);
size_t value_size = cuts.Values().size();
rabit::Allreduce<rabit::op::Max>(&value_size, 1);
size_t ptr_size = cuts.Ptrs().size();
rabit::Allreduce<rabit::op::Max>(&ptr_size, 1);
CHECK_EQ(ptr_size, kCols + 1);
size_t min_value_size = cuts.MinValues().size();
rabit::Allreduce<rabit::op::Max>(&min_value_size, 1);
CHECK_EQ(min_value_size, kCols);
size_t value_offset = value_size * rank;
std::copy(cuts.Values().begin(), cuts.Values().end(),
cut_values.begin() + value_offset);
size_t ptr_offset = ptr_size * rank;
std::copy(cuts.Ptrs().cbegin(), cuts.Ptrs().cend(),
cut_ptrs.begin() + ptr_offset);
size_t min_values_offset = min_value_size * rank;
std::copy(cuts.MinValues().cbegin(), cuts.MinValues().cend(),
cut_min_values.begin() + min_values_offset);
rabit::Allreduce<rabit::op::Sum>(cut_values.data(), cut_values.size());
rabit::Allreduce<rabit::op::Sum>(cut_ptrs.data(), cut_ptrs.size());
rabit::Allreduce<rabit::op::Sum>(cut_min_values.data(), cut_min_values.size());
for (int32_t i = 0; i < world; i++) {
for (size_t j = 0; j < value_size; ++j) {
size_t idx = i * value_size + j;
ASSERT_NEAR(cuts.Values().at(j), cut_values.at(idx), kRtEps);
}
for (size_t j = 0; j < ptr_size; ++j) {
size_t idx = i * ptr_size + j;
ASSERT_EQ(cuts.Ptrs().at(j), cut_ptrs.at(idx));
}
for (size_t j = 0; j < min_value_size; ++j) {
size_t idx = i * min_value_size + j;
ASSERT_EQ(cuts.MinValues().at(j), cut_min_values.at(idx));
}
}
});
}
} // namespace common
} // namespace xgboost

52
tests/cpp/common/test_quantile.cu
View File

@ -1,4 +1,5 @@
#include <gtest/gtest.h>
#include "test_quantile.h"
#include "../helpers.h"
#include "../../../src/common/hist_util.cuh"
#include "../../../src/common/quantile.cuh"
@ -16,32 +17,6 @@ TEST(GPUQuantile, Basic) {
ASSERT_EQ(sketch.Data().size(), 0);
}
template <typename Fn> void RunWithSeedsAndBins(size_t rows, Fn fn) {
std::vector<int32_t> seeds(4);
SimpleLCG lcg;
SimpleRealUniformDistribution<float> dist(3, 1000);
std::generate(seeds.begin(), seeds.end(), [&](){ return dist(&lcg); });
std::vector<size_t> bins(8);
for (size_t i = 0; i < bins.size() - 1; ++i) {
bins[i] = i * 35 + 2;
}
bins.back() = rows + 80; // provide a bin number greater than rows.
std::vector<MetaInfo> infos(2);
auto& h_weights = infos.front().weights_.HostVector();
h_weights.resize(rows);
std::generate(h_weights.begin(), h_weights.end(), [&]() { return dist(&lcg); });
for (auto seed : seeds) {
for (auto n_bin : bins) {
for (auto const& info : infos) {
fn(seed, n_bin, info);
}
}
}
}
void TestSketchUnique(float sparsity) {
constexpr size_t kRows = 1000, kCols = 100;
RunWithSeedsAndBins(kRows, [kRows, kCols, sparsity](int32_t seed, size_t n_bins, MetaInfo const& info) {
@ -297,31 +272,12 @@ TEST(GPUQuantile, MergeDuplicated) {
}
}
void InitRabitContext(std::string msg) {
auto n_gpus = AllVisibleGPUs();
auto port = std::getenv("DMLC_TRACKER_PORT");
std::string port_str;
if (port) {
port_str = port;
} else {
LOG(WARNING) << msg << " as `DMLC_TRACKER_PORT` is not set up.";
return;
}
std::vector<std::string> envs{
"DMLC_TRACKER_PORT=" + port_str,
"DMLC_TRACKER_URI=127.0.0.1",
"DMLC_NUM_WORKER=" + std::to_string(n_gpus)};
char* c_envs[] {&(envs[0][0]), &(envs[1][0]), &(envs[2][0])};
rabit::Init(3, c_envs);
}
TEST(GPUQuantile, AllReduceBasic) {
// This test is supposed to run by a python test that setups the environment.
std::string msg {"Skipping AllReduce test"};
#if defined(__linux__) && defined(XGBOOST_USE_NCCL)
InitRabitContext(msg);
auto n_gpus = AllVisibleGPUs();
InitRabitContext(msg, n_gpus);
auto world = rabit::GetWorldSize();
if (world != 1) {
ASSERT_EQ(world, n_gpus);
@ -407,9 +363,9 @@ TEST(GPUQuantile, AllReduceBasic) {
TEST(GPUQuantile, SameOnAllWorkers) {
std::string msg {"Skipping SameOnAllWorkers test"};
#if defined(__linux__) && defined(XGBOOST_USE_NCCL)
InitRabitContext(msg);
auto world = rabit::GetWorldSize();
auto n_gpus = AllVisibleGPUs();
InitRabitContext(msg, n_gpus);
auto world = rabit::GetWorldSize();
if (world != 1) {
ASSERT_EQ(world, n_gpus);
} else {

54
tests/cpp/common/test_quantile.h Normal file
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@ -0,0 +1,54 @@
#include <rabit/rabit.h>
#include <algorithm>
#include <string>
#include <vector>
#include "../helpers.h"
namespace xgboost {
namespace common {
inline void InitRabitContext(std::string msg, size_t n_workers) {
auto port = std::getenv("DMLC_TRACKER_PORT");
std::string port_str;
if (port) {
port_str = port;
} else {
LOG(WARNING) << msg << " as `DMLC_TRACKER_PORT` is not set up.";
return;
}
std::vector<std::string> envs{
"DMLC_TRACKER_PORT=" + port_str,
"DMLC_TRACKER_URI=127.0.0.1",
"DMLC_NUM_WORKER=" + std::to_string(n_workers)};
char* c_envs[] {&(envs[0][0]), &(envs[1][0]), &(envs[2][0])};
rabit::Init(3, c_envs);
}
template <typename Fn> void RunWithSeedsAndBins(size_t rows, Fn fn) {
std::vector<int32_t> seeds(4);
SimpleLCG lcg;
SimpleRealUniformDistribution<float> dist(3, 1000);
std::generate(seeds.begin(), seeds.end(), [&](){ return dist(&lcg); });
std::vector<size_t> bins(8);
for (size_t i = 0; i < bins.size() - 1; ++i) {
bins[i] = i * 35 + 2;
}
bins.back() = rows + 80; // provide a bin number greater than rows.
std::vector<MetaInfo> infos(2);
auto& h_weights = infos.front().weights_.HostVector();
h_weights.resize(rows);
std::generate(h_weights.begin(), h_weights.end(), [&]() { return dist(&lcg); });
for (auto seed : seeds) {
for (auto n_bin : bins) {
for (auto const& info : infos) {
fn(seed, n_bin, info);
}
}
}
}
} // namespace common
} // namespace xgboost

2
tests/python-gpu/test_gpu_with_dask.py
View File

@ -233,12 +233,14 @@ class TestDistributedGPU(unittest.TestCase):
assert ret.returncode == 0, msg
@pytest.mark.skipif(**tm.no_dask())
@pytest.mark.skipif(**tm.no_dask_cuda())
@pytest.mark.mgpu
@pytest.mark.gtest
def test_quantile_basic(self):
self.run_quantile('AllReduceBasic')
@pytest.mark.skipif(**tm.no_dask())
@pytest.mark.skipif(**tm.no_dask_cuda())
@pytest.mark.mgpu
@pytest.mark.gtest
def test_quantile_same_on_all_workers(self):

103
tests/python/test_with_dask.py
View File

@ -1,11 +1,16 @@
import testing as tm
import pytest
import unittest
import xgboost as xgb
import sys
import numpy as np
import json
import asyncio
from sklearn.datasets import make_classification
import os
import subprocess
from hypothesis import given, strategies, settings, note
from test_updaters import hist_parameter_strategy, exact_parameter_strategy
if sys.platform.startswith("win"):
pytest.skip("Skipping dask tests on Windows", allow_module_level=True)
@ -14,12 +19,16 @@ pytestmark = pytest.mark.skipif(**tm.no_dask())
try:
from distributed import LocalCluster, Client
from distributed.utils_test import client, loop, cluster_fixture
import dask.dataframe as dd
import dask.array as da
from xgboost.dask import DaskDMatrix
except ImportError:
LocalCluster = None
Client = None
client = None
loop = None
cluster_fixture = None
dd = None
da = None
DaskDMatrix = None
@ -461,3 +470,97 @@ def test_with_asyncio():
asyncio.run(run_dask_regressor_asyncio(address))
asyncio.run(run_dask_classifier_asyncio(address))
class TestWithDask:
def run_updater_test(self, client, params, num_rounds, dataset,
tree_method):
params['tree_method'] = tree_method
params = dataset.set_params(params)
# multi class doesn't handle empty dataset well (empty
# means at least 1 worker has data).
if params['objective'] == "multi:softmax":
return
# It doesn't make sense to distribute a completely
# empty dataset.
if dataset.X.shape[0] == 0:
return
chunk = 128
X = da.from_array(dataset.X,
chunks=(chunk, dataset.X.shape[1]))
y = da.from_array(dataset.y, chunks=(chunk, ))
if dataset.w is not None:
w = da.from_array(dataset.w, chunks=(chunk, ))
else:
w = None
m = xgb.dask.DaskDMatrix(
client, data=X, label=y, weight=w)
history = xgb.dask.train(client, params=params, dtrain=m,
num_boost_round=num_rounds,
evals=[(m, 'train')])['history']
note(history)
assert tm.non_increasing(history['train'][dataset.metric])
@given(params=hist_parameter_strategy,
num_rounds=strategies.integers(10, 20),
dataset=tm.dataset_strategy)
@settings(deadline=None)
def test_hist(self, params, num_rounds, dataset, client):
self.run_updater_test(client, params, num_rounds, dataset, 'hist')
@given(params=exact_parameter_strategy,
num_rounds=strategies.integers(10, 20),
dataset=tm.dataset_strategy)
@settings(deadline=None)
def test_approx(self, client, params, num_rounds, dataset):
self.run_updater_test(client, params, num_rounds, dataset, 'approx')
def run_quantile(self, name):
if sys.platform.startswith("win"):
pytest.skip("Skipping dask tests on Windows")
exe = None
for possible_path in {'./testxgboost', './build/testxgboost',
'../build/testxgboost',
'../cpu-build/testxgboost',
'../gpu-build/testxgboost'}:
if os.path.exists(possible_path):
exe = possible_path
if exe is None:
return
test = "--gtest_filter=Quantile." + name
def runit(worker_addr, rabit_args):
port = None
# setup environment for running the c++ part.
for arg in rabit_args:
if arg.decode('utf-8').startswith('DMLC_TRACKER_PORT'):
port = arg.decode('utf-8')
port = port.split('=')
env = os.environ.copy()
env[port[0]] = port[1]
return subprocess.run([exe, test], env=env, stdout=subprocess.PIPE)
with LocalCluster(n_workers=4) as cluster:
with Client(cluster) as client:
workers = list(xgb.dask._get_client_workers(client).keys())
rabit_args = client.sync(
xgb.dask._get_rabit_args, workers, client)
futures = client.map(runit,
workers,
pure=False,
workers=workers,
rabit_args=rabit_args)
results = client.gather(futures)
for ret in results:
msg = ret.stdout.decode('utf-8')
assert msg.find('1 test from Quantile') != -1, msg
assert ret.returncode == 0, msg
@pytest.mark.skipif(**tm.no_dask())
@pytest.mark.gtest
def test_quantile_basic(self):
self.run_quantile('SameOnAllWorkers')