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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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4
src/gbm/gblinear.cc
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@@ -86,7 +86,7 @@ class GBLinear : public GradientBooster {
}
param_.UpdateAllowUnknown(cfg);
param_.CheckGPUSupport();
updater_.reset(LinearUpdater::Create(param_.updater, generic_param_));
updater_.reset(LinearUpdater::Create(param_.updater, ctx_));
updater_->Configure(cfg);
monitor_.Init("GBLinear");
}
@@ -120,7 +120,7 @@ class GBLinear : public GradientBooster {
CHECK_EQ(get<String>(in["name"]), "gblinear");
FromJson(in["gblinear_train_param"], &param_);
param_.CheckGPUSupport();
updater_.reset(LinearUpdater::Create(param_.updater, generic_param_));
updater_.reset(LinearUpdater::Create(param_.updater, ctx_));
this->updater_->LoadConfig(in["updater"]);
}
void SaveConfig(Json* p_out) const override {

2
src/gbm/gbm.cc
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@@ -26,7 +26,7 @@ GradientBooster* GradientBooster::Create(
LOG(FATAL) << "Unknown gbm type " << name;
}
auto p_bst = (e->body)(learner_model_param);
p_bst->generic_param_ = generic_param;
p_bst->ctx_ = generic_param;
return p_bst;
}
} // namespace xgboost

37
src/gbm/gbtree.cc
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@@ -49,14 +49,14 @@ void GBTree::Configure(const Args& cfg) {
// configure predictors
if (!cpu_predictor_) {
cpu_predictor_ = std::unique_ptr<Predictor>(
Predictor::Create("cpu_predictor", this->generic_param_));
Predictor::Create("cpu_predictor", this->ctx_));
}
cpu_predictor_->Configure(cfg);
#if defined(XGBOOST_USE_CUDA)
auto n_gpus = common::AllVisibleGPUs();
if (!gpu_predictor_ && n_gpus != 0) {
gpu_predictor_ = std::unique_ptr<Predictor>(
Predictor::Create("gpu_predictor", this->generic_param_));
Predictor::Create("gpu_predictor", this->ctx_));
}
if (n_gpus != 0) {
gpu_predictor_->Configure(cfg);
@@ -201,16 +201,16 @@ void GPUCopyGradient(HostDeviceVector<GradientPair> const *in_gpair,
}
#endif
void CopyGradient(HostDeviceVector<GradientPair> const *in_gpair,
void CopyGradient(HostDeviceVector<GradientPair> const* in_gpair, int32_t n_threads,
bst_group_t n_groups, bst_group_t group_id,
HostDeviceVector<GradientPair> *out_gpair) {
HostDeviceVector<GradientPair>* out_gpair) {
if (in_gpair->DeviceIdx() != GenericParameter::kCpuId) {
GPUCopyGradient(in_gpair, n_groups, group_id, out_gpair);
} else {
std::vector<GradientPair> &tmp_h = out_gpair->HostVector();
auto nsize = static_cast<bst_omp_uint>(out_gpair->Size());
const auto &gpair_h = in_gpair->ConstHostVector();
common::ParallelFor(nsize, [&](bst_omp_uint i) {
common::ParallelFor(nsize, n_threads, [&](bst_omp_uint i) {
tmp_h[i] = gpair_h[i * n_groups + group_id];
});
}
@@ -228,7 +228,7 @@ void GBTree::DoBoost(DMatrix* p_fmat,
// break a lots of existing code.
auto device = tparam_.tree_method != TreeMethod::kGPUHist
? GenericParameter::kCpuId
: generic_param_->gpu_id;
: ctx_->gpu_id;
auto out = linalg::TensorView<float, 2>{
device == GenericParameter::kCpuId ? predt->predictions.HostSpan()
: predt->predictions.DeviceSpan(),
@@ -255,7 +255,7 @@ void GBTree::DoBoost(DMatrix* p_fmat,
in_gpair->DeviceIdx());
bool update_predict = true;
for (int gid = 0; gid < ngroup; ++gid) {
CopyGradient(in_gpair, ngroup, gid, &tmp);
CopyGradient(in_gpair, ctx_->Threads(), ngroup, gid, &tmp);
std::vector<std::unique_ptr<RegTree> > ret;
BoostNewTrees(&tmp, p_fmat, gid, &ret);
const size_t num_new_trees = ret.size();
@@ -310,7 +310,7 @@ void GBTree::InitUpdater(Args const& cfg) {
// create new updaters
for (const std::string& pstr : ups) {
std::unique_ptr<TreeUpdater> up(
TreeUpdater::Create(pstr.c_str(), generic_param_, model_.learner_model_param->task));
TreeUpdater::Create(pstr.c_str(), ctx_, model_.learner_model_param->task));
up->Configure(cfg);
updaters_.push_back(std::move(up));
}
@@ -396,7 +396,7 @@ void GBTree::LoadConfig(Json const& in) {
updaters_.clear();
for (auto const& kv : j_updaters) {
std::unique_ptr<TreeUpdater> up(
TreeUpdater::Create(kv.first, generic_param_, model_.learner_model_param->task));
TreeUpdater::Create(kv.first, ctx_, model_.learner_model_param->task));
up->LoadConfig(kv.second);
updaters_.push_back(std::move(up));
}
@@ -562,7 +562,7 @@ GBTree::GetPredictor(HostDeviceVector<float> const *out_pred,
auto on_device = is_ellpack || is_from_device;
// Use GPU Predictor if data is already on device and gpu_id is set.
if (on_device && generic_param_->gpu_id >= 0) {
if (on_device && ctx_->gpu_id >= 0) {
#if defined(XGBOOST_USE_CUDA)
CHECK_GE(common::AllVisibleGPUs(), 1) << "No visible GPU is found for XGBoost.";
CHECK(gpu_predictor_);
@@ -728,8 +728,8 @@ class Dart : public GBTree {
auto n_groups = model_.learner_model_param->num_output_group;
PredictionCacheEntry predts; // temporary storage for prediction
if (generic_param_->gpu_id != GenericParameter::kCpuId) {
predts.predictions.SetDevice(generic_param_->gpu_id);
if (ctx_->gpu_id != GenericParameter::kCpuId) {
predts.predictions.SetDevice(ctx_->gpu_id);
}
predts.predictions.Resize(p_fmat->Info().num_row_ * n_groups, 0);
@@ -758,11 +758,10 @@ class Dart : public GBTree {
} else {
auto &h_out_predts = p_out_preds->predictions.HostVector();
auto &h_predts = predts.predictions.HostVector();
#pragma omp parallel for
for (omp_ulong ridx = 0; ridx < p_fmat->Info().num_row_; ++ridx) {
common::ParallelFor(p_fmat->Info().num_row_, ctx_->Threads(), [&](auto ridx) {
const size_t offset = ridx * n_groups + group;
h_out_predts[offset] += (h_predts[offset] * w);
}
});
}
}
}
@@ -846,13 +845,11 @@ class Dart : public GBTree {
if (device == GenericParameter::kCpuId) {
auto &h_predts = predts.predictions.HostVector();
auto &h_out_predts = out_preds->predictions.HostVector();
#pragma omp parallel for
for (omp_ulong ridx = 0; ridx < n_rows; ++ridx) {
common::ParallelFor(n_rows, ctx_->Threads(), [&](auto ridx) {
const size_t offset = ridx * n_groups + group;
// Need to remove the base margin from individual tree.
h_out_predts[offset] +=
(h_predts[offset] - model_.learner_model_param->base_score) * w;
}
h_out_predts[offset] += (h_predts[offset] - model_.learner_model_param->base_score) * w;
});
} else {
out_preds->predictions.SetDevice(device);
predts.predictions.SetDevice(device);

5
src/gbm/gbtree.h
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@@ -413,10 +413,9 @@ class GBTree : public GradientBooster {
p_fmat, out_contribs, model_, tree_end, nullptr, approximate);
}
std::vector<std::string> DumpModel(const FeatureMap& fmap,
bool with_stats,
std::vector<std::string> DumpModel(const FeatureMap& fmap, bool with_stats,
std::string format) const override {
return model_.DumpModel(fmap, with_stats, format);
return model_.DumpModel(fmap, with_stats, this->ctx_->Threads(), format);
}
protected:

7
src/gbm/gbtree_model.h
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@@ -109,12 +109,11 @@ struct GBTreeModel : public Model {
void SaveModel(Json* p_out) const override;
void LoadModel(Json const& p_out) override;
std::vector<std::string> DumpModel(const FeatureMap &fmap, bool with_stats,
std::vector<std::string> DumpModel(const FeatureMap& fmap, bool with_stats, int32_t n_threads,
std::string format) const {
std::vector<std::string> dump(trees.size());
common::ParallelFor(static_cast<omp_ulong>(trees.size()), [&](size_t i) {
dump[i] = trees[i]->DumpModel(fmap, with_stats, format);
});
common::ParallelFor(trees.size(), n_threads,
[&](size_t i) { dump[i] = trees[i]->DumpModel(fmap, with_stats, format); });
return dump;
}
void CommitModel(std::vector<std::unique_ptr<RegTree> >&& new_trees,