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Improve OpenMP exception handling (#6680)

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This commit is contained in:
Louis Desreumaux
2021-02-25 06:56:16 +01:00
committed by GitHub
parent c375173dca
commit 9b530e5697
26 changed files with 610 additions and 475 deletions
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21
src/predictor/cpu_predictor.cc
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@@ -18,6 +18,7 @@
#include "../data/adapter.h"
#include "../common/math.h"
#include "../common/threading_utils.h"
#include "../gbm/gbtree_model.h"
namespace xgboost {
@@ -157,8 +158,7 @@ void PredictBatchByBlockOfRowsKernel(DataView batch, std::vector<bst_float> *out
const auto nsize = static_cast<bst_omp_uint>(batch.Size());
const int num_feature = model.learner_model_param->num_feature;
const bst_omp_uint n_row_blocks = (nsize) / block_of_rows_size + !!((nsize) % block_of_rows_size);
#pragma omp parallel for schedule(static)
for (bst_omp_uint block_id = 0; block_id < n_row_blocks; ++block_id) {
common::ParallelFor(n_row_blocks, [&](bst_omp_uint block_id) {
const size_t batch_offset = block_id * block_of_rows_size;
const size_t block_size = std::min(nsize - batch_offset, block_of_rows_size);
const size_t fvec_offset = omp_get_thread_num() * block_of_rows_size;
@@ -168,7 +168,7 @@ void PredictBatchByBlockOfRowsKernel(DataView batch, std::vector<bst_float> *out
PredictByAllTrees(model, tree_begin, tree_end, out_preds, batch_offset + batch.base_rowid,
num_group, thread_temp, fvec_offset, block_size);
FVecDrop(block_size, batch_offset, &batch, fvec_offset, p_thread_temp);
}
});
}
class CPUPredictor : public Predictor {
@@ -335,8 +335,7 @@ class CPUPredictor : public Predictor {
// parallel over local batch
auto page = batch.GetView();
const auto nsize = static_cast<bst_omp_uint>(batch.Size());
#pragma omp parallel for schedule(static)
for (bst_omp_uint i = 0; i < nsize; ++i) {
common::ParallelFor(nsize, [&](bst_omp_uint i) {
const int tid = omp_get_thread_num();
auto ridx = static_cast<size_t>(batch.base_rowid + i);
RegTree::FVec &feats = feat_vecs[tid];
@@ -349,7 +348,7 @@ class CPUPredictor : public Predictor {
preds[ridx * ntree_limit + j] = static_cast<bst_float>(tid);
}
feats.Drop(page[i]);
}
});
}
}
@@ -378,18 +377,16 @@ class CPUPredictor : public Predictor {
// allocated one
std::fill(contribs.begin(), contribs.end(), 0);
// initialize tree node mean values
#pragma omp parallel for schedule(static)
for (bst_omp_uint i = 0; i < ntree_limit; ++i) {
common::ParallelFor(bst_omp_uint(ntree_limit), [&](bst_omp_uint i) {
model.trees[i]->FillNodeMeanValues();
}
});
const std::vector<bst_float>& base_margin = info.base_margin_.HostVector();
// start collecting the contributions
for (const auto &batch : p_fmat->GetBatches<SparsePage>()) {
auto page = batch.GetView();
// parallel over local batch
const auto nsize = static_cast<bst_omp_uint>(batch.Size());
#pragma omp parallel for schedule(static)
for (bst_omp_uint i = 0; i < nsize; ++i) {
common::ParallelFor(nsize, [&](bst_omp_uint i) {
auto row_idx = static_cast<size_t>(batch.base_rowid + i);
RegTree::FVec &feats = feat_vecs[omp_get_thread_num()];
if (feats.Size() == 0) {
@@ -425,7 +422,7 @@ class CPUPredictor : public Predictor {
p_contribs[ncolumns - 1] += model.learner_model_param->base_score;
}
}
}
});
}
}