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Remove omp_get_max_threads in gbm and linear. (#7537)

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* Use ctx in gbm.

* Use ctx threads in gbm and linear.
This commit is contained in:
Jiaming Yuan
2022-01-05 03:28:52 +08:00
committed by GitHub
parent eea094e1bc
commit 28af6f9abb
12 changed files with 124 additions and 135 deletions
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91
src/linear/coordinate_common.h
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@@ -149,21 +149,21 @@ GetGradientParallel(GenericParameter const *ctx, int group_idx, int num_group,
*/
inline std::pair<double, double> GetBiasGradientParallel(int group_idx, int num_group,
const std::vector<GradientPair> &gpair,
DMatrix *p_fmat) {
double sum_grad = 0.0, sum_hess = 0.0;
DMatrix *p_fmat, int32_t n_threads) {
const auto ndata = static_cast<bst_omp_uint>(p_fmat->Info().num_row_);
dmlc::OMPException exc;
#pragma omp parallel for schedule(static) reduction(+ : sum_grad, sum_hess)
for (bst_omp_uint i = 0; i < ndata; ++i) {
exc.Run([&]() {
auto &p = gpair[i * num_group + group_idx];
if (p.GetHess() >= 0.0f) {
sum_grad += p.GetGrad();
sum_hess += p.GetHess();
}
});
}
exc.Rethrow();
std::vector<double> sum_grad_tloc(n_threads, 0);
std::vector<double> sum_hess_tloc(n_threads, 0);
common::ParallelFor(ndata, n_threads, [&](auto i) {
auto tid = omp_get_thread_num();
auto &p = gpair[i * num_group + group_idx];
if (p.GetHess() >= 0.0f) {
sum_grad_tloc[tid] += p.GetGrad();
sum_hess_tloc[tid] += p.GetHess();
}
});
double sum_grad = std::accumulate(sum_grad_tloc.cbegin(), sum_grad_tloc.cend(), 0.0);
double sum_hess = std::accumulate(sum_hess_tloc.cbegin(), sum_hess_tloc.cend(), 0.0);
return std::make_pair(sum_grad, sum_hess);
}
@@ -179,23 +179,18 @@ inline std::pair<double, double> GetBiasGradientParallel(int group_idx, int num_
*/
inline void UpdateResidualParallel(int fidx, int group_idx, int num_group,
float dw, std::vector<GradientPair> *in_gpair,
DMatrix *p_fmat) {
DMatrix *p_fmat, int32_t n_threads) {
if (dw == 0.0f) return;
for (const auto &batch : p_fmat->GetBatches<CSCPage>()) {
auto page = batch.GetView();
auto col = page[fidx];
// update grad value
const auto num_row = static_cast<bst_omp_uint>(col.size());
dmlc::OMPException exc;
#pragma omp parallel for schedule(static)
for (bst_omp_uint j = 0; j < num_row; ++j) {
exc.Run([&]() {
GradientPair &p = (*in_gpair)[col[j].index * num_group + group_idx];
if (p.GetHess() < 0.0f) return;
p += GradientPair(p.GetHess() * col[j].fvalue * dw, 0);
});
}
exc.Rethrow();
common::ParallelFor(num_row, n_threads, [&](auto j) {
GradientPair &p = (*in_gpair)[col[j].index * num_group + group_idx];
if (p.GetHess() < 0.0f) return;
p += GradientPair(p.GetHess() * col[j].fvalue * dw, 0);
});
}
}
@@ -209,20 +204,15 @@ inline void UpdateResidualParallel(int fidx, int group_idx, int num_group,
* \param p_fmat The input feature matrix.
*/
inline void UpdateBiasResidualParallel(int group_idx, int num_group, float dbias,
std::vector<GradientPair> *in_gpair,
DMatrix *p_fmat) {
std::vector<GradientPair> *in_gpair, DMatrix *p_fmat,
int32_t n_threads) {
if (dbias == 0.0f) return;
const auto ndata = static_cast<bst_omp_uint>(p_fmat->Info().num_row_);
dmlc::OMPException exc;
#pragma omp parallel for schedule(static)
for (bst_omp_uint i = 0; i < ndata; ++i) {
exc.Run([&]() {
GradientPair &g = (*in_gpair)[i * num_group + group_idx];
if (g.GetHess() < 0.0f) return;
g += GradientPair(g.GetHess() * dbias, 0);
});
}
exc.Rethrow();
common::ParallelFor(ndata, n_threads, [&](auto i) {
GradientPair &g = (*in_gpair)[i * num_group + group_idx];
if (g.GetHess() < 0.0f) return;
g += GradientPair(g.GetHess() * dbias, 0);
});
}
/**
@@ -230,9 +220,13 @@ inline void UpdateBiasResidualParallel(int group_idx, int num_group, float dbias
* in coordinate descent algorithms.
*/
class FeatureSelector {
protected:
int32_t n_threads_{-1};
public:
explicit FeatureSelector(int32_t n_threads) : n_threads_{n_threads} {}
/*! \brief factory method */
static FeatureSelector *Create(int choice);
static FeatureSelector *Create(int choice, int32_t n_threads);
/*! \brief virtual destructor */
virtual ~FeatureSelector() = default;
/**
@@ -274,6 +268,7 @@ class FeatureSelector {
*/
class CyclicFeatureSelector : public FeatureSelector {
public:
using FeatureSelector::FeatureSelector;
int NextFeature(int iteration, const gbm::GBLinearModel &model,
int , const std::vector<GradientPair> &,
DMatrix *, float, float) override {
@@ -287,6 +282,7 @@ class CyclicFeatureSelector : public FeatureSelector {
*/
class ShuffleFeatureSelector : public FeatureSelector {
public:
using FeatureSelector::FeatureSelector;
void Setup(const gbm::GBLinearModel &model,
const std::vector<GradientPair>&,
DMatrix *, float, float, int) override {
@@ -313,6 +309,7 @@ class ShuffleFeatureSelector : public FeatureSelector {
*/
class RandomFeatureSelector : public FeatureSelector {
public:
using FeatureSelector::FeatureSelector;
int NextFeature(int, const gbm::GBLinearModel &model,
int, const std::vector<GradientPair> &,
DMatrix *, float, float) override {
@@ -331,6 +328,7 @@ class RandomFeatureSelector : public FeatureSelector {
*/
class GreedyFeatureSelector : public FeatureSelector {
public:
using FeatureSelector::FeatureSelector;
void Setup(const gbm::GBLinearModel &model,
const std::vector<GradientPair> &,
DMatrix *, float, float, int param) override {
@@ -360,7 +358,7 @@ class GreedyFeatureSelector : public FeatureSelector {
std::fill(gpair_sums_.begin(), gpair_sums_.end(), std::make_pair(0., 0.));
for (const auto &batch : p_fmat->GetBatches<CSCPage>()) {
auto page = batch.GetView();
common::ParallelFor(nfeat, [&](bst_omp_uint i) {
common::ParallelFor(nfeat, this->n_threads_, [&](bst_omp_uint i) {
const auto col = page[i];
const bst_uint ndata = col.size();
auto &sums = gpair_sums_[group_idx * nfeat + i];
@@ -407,6 +405,7 @@ class GreedyFeatureSelector : public FeatureSelector {
*/
class ThriftyFeatureSelector : public FeatureSelector {
public:
using FeatureSelector::FeatureSelector;
void Setup(const gbm::GBLinearModel &model,
const std::vector<GradientPair> &gpair,
DMatrix *p_fmat, float alpha, float lambda, int param) override {
@@ -426,7 +425,7 @@ class ThriftyFeatureSelector : public FeatureSelector {
for (const auto &batch : p_fmat->GetBatches<CSCPage>()) {
auto page = batch.GetView();
// column-parallel is usually fastaer than row-parallel
common::ParallelFor(nfeat, [&](bst_omp_uint i) {
common::ParallelFor(nfeat, this->n_threads_, [&](auto i) {
const auto col = page[i];
const bst_uint ndata = col.size();
for (bst_uint gid = 0u; gid < ngroup; ++gid) {
@@ -483,18 +482,18 @@ class ThriftyFeatureSelector : public FeatureSelector {
std::vector<std::pair<double, double>> gpair_sums_;
};
inline FeatureSelector *FeatureSelector::Create(int choice) {
inline FeatureSelector *FeatureSelector::Create(int choice, int32_t n_threads) {
switch (choice) {
case kCyclic:
return new CyclicFeatureSelector();
return new CyclicFeatureSelector(n_threads);
case kShuffle:
return new ShuffleFeatureSelector();
return new ShuffleFeatureSelector(n_threads);
case kThrifty:
return new ThriftyFeatureSelector();
return new ThriftyFeatureSelector(n_threads);
case kGreedy:
return new GreedyFeatureSelector();
return new GreedyFeatureSelector(n_threads);
case kRandom:
return new RandomFeatureSelector();
return new RandomFeatureSelector(n_threads);
default:
LOG(FATAL) << "unknown coordinate selector: " << choice;
}