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Dmatrix refactor stage 2 (#3395)

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* DMatrix refactor 2

* Remove buffered rowset usage where possible

* Transition to c++11 style iterators for row access

* Transition column iterators to C++ 11
This commit is contained in:
Rory Mitchell
2018-10-01 01:29:03 +13:00
committed by GitHub
parent b50bc2c1d4
commit 70d208d68c
36 changed files with 459 additions and 846 deletions
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16
src/gbm/gblinear.cc
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@@ -83,13 +83,6 @@ class GBLinear : public GradientBooster {
ObjFunction* obj) override {
monitor_.Start("DoBoost");
if (!p_fmat->HaveColAccess(false)) {
monitor_.Start("InitColAccess");
std::vector<bool> enabled(p_fmat->Info().num_col_, true);
p_fmat->InitColAccess(param_.max_row_perbatch, false);
monitor_.Stop("InitColAccess");
}
model_.LazyInitModel();
this->LazySumWeights(p_fmat);
@@ -152,10 +145,7 @@ class GBLinear : public GradientBooster {
// make sure contributions is zeroed, we could be reusing a previously allocated one
std::fill(contribs.begin(), contribs.end(), 0);
// start collecting the contributions
auto iter = p_fmat->RowIterator();
iter->BeforeFirst();
while (iter->Next()) {
auto &batch = iter->Value();
for (const auto &batch : p_fmat->GetRowBatches()) {
// parallel over local batch
const auto nsize = static_cast<bst_omp_uint>(batch.Size());
#pragma omp parallel for schedule(static)
@@ -203,11 +193,9 @@ class GBLinear : public GradientBooster {
std::vector<bst_float> &preds = *out_preds;
const auto& base_margin = p_fmat->Info().base_margin_.ConstHostVector();
// start collecting the prediction
auto iter = p_fmat->RowIterator();
const int ngroup = model_.param.num_output_group;
preds.resize(p_fmat->Info().num_row_ * ngroup);
while (iter->Next()) {
auto &batch = iter->Value();
for (const auto &batch : p_fmat->GetRowBatches()) {
// output convention: nrow * k, where nrow is number of rows
// k is number of group
// parallel over local batch

6
src/gbm/gbtree.cc
View File

@@ -438,12 +438,8 @@ class Dart : public GBTree {
<< "size_leaf_vector is enforced to 0 so far";
CHECK_EQ(preds.size(), p_fmat->Info().num_row_ * num_group);
// start collecting the prediction
auto iter = p_fmat->RowIterator();
auto* self = static_cast<Derived*>(this);
iter->BeforeFirst();
while (iter->Next()) {
auto &batch = iter->Value();
// parallel over local batch
for (const auto &batch : p_fmat->GetRowBatches()) {
constexpr int kUnroll = 8;
const auto nsize = static_cast<bst_omp_uint>(batch.Size());
const bst_omp_uint rest = nsize % kUnroll;