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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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32
src/linear/coordinate_common.h
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@@ -65,9 +65,7 @@ inline std::pair<double, double> GetGradient(int group_idx, int num_group, int f
const std::vector<GradientPair> &gpair,
DMatrix *p_fmat) {
double sum_grad = 0.0, sum_hess = 0.0;
auto iter = p_fmat->ColIterator();
while (iter->Next()) {
auto &batch = iter->Value();
for (const auto &batch : p_fmat->GetColumnBatches()) {
auto col = batch[fidx];
const auto ndata = static_cast<bst_omp_uint>(col.size());
for (bst_omp_uint j = 0; j < ndata; ++j) {
@@ -96,9 +94,7 @@ inline std::pair<double, double> GetGradientParallel(int group_idx, int num_grou
const std::vector<GradientPair> &gpair,
DMatrix *p_fmat) {
double sum_grad = 0.0, sum_hess = 0.0;
auto iter = p_fmat->ColIterator();
while (iter->Next()) {
auto &batch = iter->Value();
for (const auto &batch : p_fmat->GetColumnBatches()) {
auto col = batch[fidx];
const auto ndata = static_cast<bst_omp_uint>(col.size());
#pragma omp parallel for schedule(static) reduction(+ : sum_grad, sum_hess)
@@ -126,12 +122,11 @@ inline std::pair<double, double> GetGradientParallel(int group_idx, int num_grou
inline std::pair<double, double> GetBiasGradientParallel(int group_idx, int num_group,
const std::vector<GradientPair> &gpair,
DMatrix *p_fmat) {
const RowSet &rowset = p_fmat->BufferedRowset();
double sum_grad = 0.0, sum_hess = 0.0;
const auto ndata = static_cast<bst_omp_uint>(rowset.Size());
const auto ndata = static_cast<bst_omp_uint>(p_fmat->Info().num_row_);
#pragma omp parallel for schedule(static) reduction(+ : sum_grad, sum_hess)
for (bst_omp_uint i = 0; i < ndata; ++i) {
auto &p = gpair[rowset[i] * num_group + group_idx];
auto &p = gpair[i * num_group + group_idx];
if (p.GetHess() >= 0.0f) {
sum_grad += p.GetGrad();
sum_hess += p.GetHess();
@@ -154,9 +149,7 @@ inline void UpdateResidualParallel(int fidx, int group_idx, int num_group,
float dw, std::vector<GradientPair> *in_gpair,
DMatrix *p_fmat) {
if (dw == 0.0f) return;
auto iter = p_fmat->ColIterator();
while (iter->Next()) {
auto &batch = iter->Value();
for (const auto &batch : p_fmat->GetColumnBatches()) {
auto col = batch[fidx];
// update grad value
const auto num_row = static_cast<bst_omp_uint>(col.size());
@@ -182,11 +175,10 @@ inline void UpdateBiasResidualParallel(int group_idx, int num_group, float dbias
std::vector<GradientPair> *in_gpair,
DMatrix *p_fmat) {
if (dbias == 0.0f) return;
const RowSet &rowset = p_fmat->BufferedRowset();
const auto ndata = static_cast<bst_omp_uint>(p_fmat->Info().num_row_);
#pragma omp parallel for schedule(static)
for (bst_omp_uint i = 0; i < ndata; ++i) {
GradientPair &g = (*in_gpair)[rowset[i] * num_group + group_idx];
GradientPair &g = (*in_gpair)[i * num_group + group_idx];
if (g.GetHess() < 0.0f) continue;
g += GradientPair(g.GetHess() * dbias, 0);
}
@@ -325,9 +317,7 @@ class GreedyFeatureSelector : public FeatureSelector {
const bst_omp_uint nfeat = model.param.num_feature;
// Calculate univariate gradient sums
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();
for (const auto &batch : p_fmat->GetColumnBatches()) {
#pragma omp parallel for schedule(static)
for (bst_omp_uint i = 0; i < nfeat; ++i) {
const auto col = batch[i];
@@ -392,11 +382,9 @@ class ThriftyFeatureSelector : public FeatureSelector {
}
// Calculate univariate gradient sums
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();
// column-parallel is usually faster than row-parallel
#pragma omp parallel for schedule(static)
for (const auto &batch : p_fmat->GetColumnBatches()) {
// column-parallel is usually faster than row-parallel
#pragma omp parallel for schedule(static)
for (bst_omp_uint i = 0; i < nfeat; ++i) {
const auto col = batch[i];
const bst_uint ndata = col.size();

4
src/linear/updater_gpu_coordinate.cu
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@@ -235,10 +235,8 @@ class GPUCoordinateUpdater : public LinearUpdater {
row_begin = row_end;
}
auto iter = p_fmat->ColIterator();
CHECK(p_fmat->SingleColBlock());
iter->Next();
auto &batch = iter->Value();
const auto &batch = *p_fmat->GetColumnBatches().begin();
shards.resize(n_devices);
// Create device shards

4
src/linear/updater_shotgun.cc
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@@ -80,9 +80,7 @@ class ShotgunUpdater : public LinearUpdater {
// lock-free parallel updates of weights
selector_->Setup(*model, in_gpair->ConstHostVector(), p_fmat,
param_.reg_alpha_denorm, param_.reg_lambda_denorm, 0);
auto iter = p_fmat->ColIterator();
while (iter->Next()) {
auto &batch = iter->Value();
for (const auto &batch : p_fmat->GetColumnBatches()) {
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) {