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Remove accidental SparsePage copies (#3583)

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This commit is contained in:
Rory Mitchell 2018-08-13 12:49:38 +12:00 committed by Philip Hyunsu Cho
parent 0b607fb884
commit 645996b12f
16 changed files with 28 additions and 28 deletions
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4
src/common/hist_util.cc
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@ -36,7 +36,7 @@ void HistCutMatrix::Init(DMatrix* p_fmat, uint32_t max_num_bins) {
auto iter = p_fmat->RowIterator();
iter->BeforeFirst();
while (iter->Next()) {
auto batch = iter->Value();
auto &batch = iter->Value();
#pragma omp parallel num_threads(nthread)
{
CHECK_EQ(nthread, omp_get_num_threads());
@ -137,7 +137,7 @@ void GHistIndexMatrix::Init(DMatrix* p_fmat, int max_num_bins) {
iter->BeforeFirst();
row_ptr.push_back(0);
while (iter->Next()) {
auto batch = iter->Value();
auto &batch = iter->Value();
const size_t rbegin = row_ptr.size() - 1;
for (size_t i = 0; i < batch.Size(); ++i) {
row_ptr.push_back(batch[i].length + row_ptr.back());

2
src/data/simple_dmatrix.cc
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@ -67,7 +67,7 @@ void SimpleDMatrix::MakeOneBatch(SparsePage* pcol, bool sorted) {
iter->BeforeFirst();
while (iter->Next()) {
auto batch = iter->Value();
auto &batch = iter->Value();
#pragma omp parallel for schedule(static)
for (long i = 0; i < static_cast<long>(batch.Size()); ++i) { // NOLINT(*)
int tid = omp_get_thread_num();

2
src/data/sparse_page_dmatrix.cc
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@ -185,7 +185,7 @@ void SparsePageDMatrix::InitColAccess(
while (true) {
if (batch_ptr != batch_top) {
auto batch = iter->Value();
auto &batch = iter->Value();
CHECK_EQ(batch_top, batch.Size());
for (size_t i = batch_ptr; i < batch_top; ++i) {
auto ridx = static_cast<bst_uint>(batch.base_rowid + i);

4
src/gbm/gblinear.cc
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@ -155,7 +155,7 @@ class GBLinear : public GradientBooster {
auto iter = p_fmat->RowIterator();
iter->BeforeFirst();
while (iter->Next()) {
auto batch = iter->Value();
auto &batch = iter->Value();
// parallel over local batch
const auto nsize = static_cast<bst_omp_uint>(batch.Size());
#pragma omp parallel for schedule(static)
@ -207,7 +207,7 @@ class GBLinear : public GradientBooster {
const int ngroup = model_.param.num_output_group;
preds.resize(p_fmat->Info().num_row_ * ngroup);
while (iter->Next()) {
auto batch = iter->Value();
auto &batch = iter->Value();
// output convention: nrow * k, where nrow is number of rows
// k is number of group
// parallel over local batch

2
src/gbm/gbtree.cc
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@ -441,7 +441,7 @@ class Dart : public GBTree {
auto* self = static_cast<Derived*>(this);
iter->BeforeFirst();
while (iter->Next()) {
auto batch = iter->Value();
auto &batch = iter->Value();
// parallel over local batch
constexpr int kUnroll = 8;
const auto nsize = static_cast<bst_omp_uint>(batch.Size());

10
src/linear/coordinate_common.h
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@ -67,7 +67,7 @@ inline std::pair<double, double> GetGradient(int group_idx, int num_group, int f
double sum_grad = 0.0, sum_hess = 0.0;
auto iter = p_fmat->ColIterator();
while (iter->Next()) {
auto batch = iter->Value();
auto &batch = iter->Value();
auto col = batch[fidx];
const auto ndata = static_cast<bst_omp_uint>(col.length);
for (bst_omp_uint j = 0; j < ndata; ++j) {
@ -98,7 +98,7 @@ inline std::pair<double, double> GetGradientParallel(int group_idx, int num_grou
double sum_grad = 0.0, sum_hess = 0.0;
auto iter = p_fmat->ColIterator();
while (iter->Next()) {
auto batch = iter->Value();
auto &batch = iter->Value();
auto col = batch[fidx];
const auto ndata = static_cast<bst_omp_uint>(col.length);
#pragma omp parallel for schedule(static) reduction(+ : sum_grad, sum_hess)
@ -156,7 +156,7 @@ inline void UpdateResidualParallel(int fidx, int group_idx, int num_group,
if (dw == 0.0f) return;
auto iter = p_fmat->ColIterator();
while (iter->Next()) {
auto batch = iter->Value();
auto &batch = iter->Value();
auto col = batch[fidx];
// update grad value
const auto num_row = static_cast<bst_omp_uint>(col.length);
@ -327,7 +327,7 @@ class GreedyFeatureSelector : public FeatureSelector {
std::fill(gpair_sums_.begin(), gpair_sums_.end(), std::make_pair(0., 0.));
auto iter = p_fmat->ColIterator();
while (iter->Next()) {
auto batch = iter->Value();
auto &batch = iter->Value();
#pragma omp parallel for schedule(static)
for (bst_omp_uint i = 0; i < nfeat; ++i) {
const auto col = batch[i];
@ -394,7 +394,7 @@ class ThriftyFeatureSelector : public FeatureSelector {
std::fill(gpair_sums_.begin(), gpair_sums_.end(), std::make_pair(0., 0.));
auto iter = p_fmat->ColIterator();
while (iter->Next()) {
auto batch = iter->Value();
auto &batch = iter->Value();
// column-parallel is usually faster than row-parallel
#pragma omp parallel for schedule(static)
for (bst_omp_uint i = 0; i < nfeat; ++i) {

2
src/linear/updater_gpu_coordinate.cu
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@ -237,7 +237,7 @@ class GPUCoordinateUpdater : public LinearUpdater {
auto iter = p_fmat->ColIterator();
CHECK(p_fmat->SingleColBlock());
iter->Next();
auto batch = iter->Value();
auto &batch = iter->Value();
shards.resize(n_devices);
// Create device shards

2
src/linear/updater_shotgun.cc
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@ -81,7 +81,7 @@ class ShotgunUpdater : public LinearUpdater {
param_.reg_alpha_denorm, param_.reg_lambda_denorm, 0);
auto iter = p_fmat->ColIterator();
while (iter->Next()) {
auto batch = iter->Value();
auto &batch = iter->Value();
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) {

4
src/predictor/cpu_predictor.cc
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@ -236,7 +236,7 @@ class CPUPredictor : public Predictor {
auto iter = p_fmat->RowIterator();
iter->BeforeFirst();
while (iter->Next()) {
auto batch = iter->Value();
auto &batch = iter->Value();
// parallel over local batch
const auto nsize = static_cast<bst_omp_uint>(batch.Size());
#pragma omp parallel for schedule(static)
@ -285,7 +285,7 @@ class CPUPredictor : public Predictor {
const std::vector<bst_float>& base_margin = info.base_margin_;
iter->BeforeFirst();
while (iter->Next()) {
auto batch = iter->Value();
auto &batch = iter->Value();
// parallel over local batch
const auto nsize = static_cast<bst_omp_uint>(batch.Size());
#pragma omp parallel for schedule(static)

2
src/predictor/gpu_predictor.cu
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@ -64,7 +64,7 @@ struct DeviceMatrix {
iter->BeforeFirst();
size_t data_offset = 0;
while (iter->Next()) {
auto batch = iter->Value();
auto &batch = iter->Value();
// Copy row ptr
dh::safe_cuda(cudaMemcpy(
row_ptr.Data() + batch.base_rowid, batch.offset.data(),

4
src/tree/updater_basemaker-inl.h
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@ -46,7 +46,7 @@ class BaseMaker: public TreeUpdater {
auto iter = p_fmat->ColIterator();
iter->BeforeFirst();
while (iter->Next()) {
auto batch = iter->Value();
auto &batch = iter->Value();
for (bst_uint fid = 0; fid < batch.Size(); ++fid) {
auto c = batch[fid];
if (c.length != 0) {
@ -305,7 +305,7 @@ class BaseMaker: public TreeUpdater {
this->GetSplitSet(nodes, tree, &fsplits);
auto iter = p_fmat->ColIterator();
while (iter->Next()) {
auto batch = iter->Value();
auto &batch = iter->Value();
for (auto fid : fsplits) {
auto col = batch[fid];
const auto ndata = static_cast<bst_omp_uint>(col.length);

4
src/tree/updater_colmaker.cc
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@ -731,7 +731,7 @@ class ColMaker: public TreeUpdater {
fsplits.resize(std::unique(fsplits.begin(), fsplits.end()) - fsplits.begin());
auto iter = p_fmat->ColIterator();
while (iter->Next()) {
auto batch = iter->Value();
auto &batch = iter->Value();
for (auto fid : fsplits) {
auto col = batch[fid];
const auto ndata = static_cast<bst_omp_uint>(col.length);
@ -862,7 +862,7 @@ class DistColMaker : public ColMaker {
}
auto iter = p_fmat->ColIterator();
while (iter->Next()) {
auto batch = iter->Value();
auto &batch = iter->Value();
for (auto fid : fsplits) {
auto col = batch[fid];
const auto ndata = static_cast<bst_omp_uint>(col.length);

2
src/tree/updater_gpu.cu
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@ -666,7 +666,7 @@ class GPUMaker : public TreeUpdater {
auto iter = dmat->ColIterator();
iter->BeforeFirst();
while (iter->Next()) {
auto batch = iter->Value();
auto &batch = iter->Value();
for (int i = 0; i < batch.Size(); i++) {
auto col = batch[i];
for (const Entry* it = col.data; it != col.data + col.length;

8
src/tree/updater_histmaker.cc
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@ -347,7 +347,7 @@ class CQHistMaker: public HistMaker<TStats> {
auto iter = p_fmat->ColIterator();
iter->BeforeFirst();
while (iter->Next()) {
auto batch = iter->Value();
auto &batch = iter->Value();
// start enumeration
const auto nsize = static_cast<bst_omp_uint>(fset.size());
#pragma omp parallel for schedule(dynamic, 1)
@ -429,7 +429,7 @@ class CQHistMaker: public HistMaker<TStats> {
auto iter = p_fmat->ColIterator();
iter->BeforeFirst();
while (iter->Next()) {
auto batch = iter->Value();
auto &batch = iter->Value();
// TWOPASS: use the real set + split set in the column iteration.
this->CorrectNonDefaultPositionByBatch(batch, fsplit_set_, tree);
@ -717,7 +717,7 @@ class GlobalProposalHistMaker: public CQHistMaker<TStats> {
auto iter = p_fmat->ColIterator();
iter->BeforeFirst();
while (iter->Next()) {
auto batch = iter->Value();
auto &batch = iter->Value();
// TWOPASS: use the real set + split set in the column iteration.
this->CorrectNonDefaultPositionByBatch(batch, this->fsplit_set_, tree);
@ -775,7 +775,7 @@ class QuantileHistMaker: public HistMaker<TStats> {
auto iter = p_fmat->RowIterator();
iter->BeforeFirst();
while (iter->Next()) {
auto batch = iter->Value();
auto &batch = iter->Value();
// parallel convert to column major format
common::ParallelGroupBuilder<Entry>
builder(&col_ptr_, &col_data_, &thread_col_ptr_);

2
src/tree/updater_refresh.cc
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@ -60,7 +60,7 @@ class TreeRefresher: public TreeUpdater {
auto *iter = p_fmat->RowIterator();
iter->BeforeFirst();
while (iter->Next()) {
auto batch = iter->Value();
auto &batch = iter->Value();
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)

2
src/tree/updater_skmaker.cc
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@ -147,7 +147,7 @@ class SketchMaker: public BaseMaker {
auto iter = p_fmat->ColIterator();
iter->BeforeFirst();
while (iter->Next()) {
auto batch = iter->Value();
auto &batch = iter->Value();
// start enumeration
const auto nsize = static_cast<bst_omp_uint>(batch.Size());
#pragma omp parallel for schedule(dynamic, 1)