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Avoid regenerating the gradient index for approx. (#7591)

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Jiaming Yuan 2022-01-26 21:41:30 +08:00 committed by GitHub
parent 5d7818e75d
commit e060519d4f
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7 changed files with 40 additions and 18 deletions
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3
src/data/gradient_index.cc
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@ -17,8 +17,7 @@ void GHistIndexMatrix::PushBatch(SparsePage const &batch,
// block is parallelized on anything other than the batch/block size,
// it should be reassigned
const size_t batch_threads =
std::max(size_t(1), std::min(batch.Size(),
static_cast<size_t>(n_threads)));
std::max(static_cast<size_t>(1), std::min(batch.Size(), static_cast<size_t>(n_threads)));
auto page = batch.GetView();
common::MemStackAllocator<size_t, 128> partial_sums(batch_threads);
size_t *p_part = partial_sums.Get();

11
src/data/gradient_index.h
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@ -108,5 +108,16 @@ class GHistIndexMatrix {
std::vector<size_t> hit_count_tloc_;
bool isDense_;
};
/**
* \brief Should we regenerate the gradient index?
*
* \param old Parameter stored in DMatrix.
* \param p New parameter passed in by caller.
*/
inline bool RegenGHist(BatchParam old, BatchParam p) {
// parameter is renewed or caller requests a regen
return p.regen || (old.gpu_id != p.gpu_id || old.max_bin != p.max_bin);
}
} // namespace xgboost
#endif // XGBOOST_DATA_GRADIENT_INDEX_H_

3
src/data/simple_dmatrix.cc
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@ -94,7 +94,8 @@ BatchSet<GHistIndexMatrix> SimpleDMatrix::GetGradientIndex(const BatchParam& par
if (!(batch_param_ != BatchParam{})) {
CHECK(param != BatchParam{}) << "Batch parameter is not initialized.";
}
if (!gradient_index_ || (batch_param_ != param && param != BatchParam{}) || param.regen) {
if (!gradient_index_ || RegenGHist(batch_param_, param)) {
LOG(INFO) << "Generating new Gradient Index.";
CHECK_GE(param.max_bin, 2);
CHECK_EQ(param.gpu_id, -1);
// Used only by approx.

6
src/data/sparse_page_dmatrix.cc
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@ -157,7 +157,7 @@ BatchSet<SortedCSCPage> SparsePageDMatrix::GetSortedColumnBatches() {
return BatchSet<SortedCSCPage>(BatchIterator<SortedCSCPage>(begin_iter));
}
BatchSet<GHistIndexMatrix> SparsePageDMatrix::GetGradientIndex(const BatchParam& param) {
BatchSet<GHistIndexMatrix> SparsePageDMatrix::GetGradientIndex(const BatchParam &param) {
CHECK_GE(param.max_bin, 2);
if (param.hess.empty() && !param.regen) {
// hist method doesn't support full external memory implementation, so we concatenate
@ -176,10 +176,10 @@ BatchSet<GHistIndexMatrix> SparsePageDMatrix::GetGradientIndex(const BatchParam&
auto id = MakeCache(this, ".gradient_index.page", cache_prefix_, &cache_info_);
this->InitializeSparsePage();
if (!cache_info_.at(id)->written || (batch_param_ != param && param != BatchParam{}) ||
param.regen) {
if (!cache_info_.at(id)->written || RegenGHist(batch_param_, param)) {
cache_info_.erase(id);
MakeCache(this, ".gradient_index.page", cache_prefix_, &cache_info_);
LOG(INFO) << "Generating new Gradient Index.";
// Use sorted sketch for approx.
auto sorted_sketch = param.regen;
auto cuts =

2
src/data/sparse_page_dmatrix.cu
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@ -14,7 +14,7 @@ BatchSet<EllpackPage> SparsePageDMatrix::GetEllpackBatches(const BatchParam& par
auto id = MakeCache(this, ".ellpack.page", cache_prefix_, &cache_info_);
size_t row_stride = 0;
this->InitializeSparsePage();
if (!cache_info_.at(id)->written || (batch_param_ != param && param != BatchParam{})) {
if (!cache_info_.at(id)->written || RegenGHist(batch_param_, param)) {
// reinitialize the cache
cache_info_.erase(id);
MakeCache(this, ".ellpack.page", cache_prefix_, &cache_info_);

1
src/tree/hist/evaluate_splits.h
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@ -351,6 +351,7 @@ template <typename GradientSumT, typename ExpandEntry> class HistEvaluator {
auto Evaluator() const { return tree_evaluator_.GetEvaluator(); }
auto const& Stats() const { return snode_; }
auto Task() const { return task_; }
float InitRoot(GradStats const& root_sum) {
snode_.resize(1);

32
src/tree/updater_approx.cc
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@ -26,6 +26,19 @@ namespace tree {
DMLC_REGISTRY_FILE_TAG(updater_approx);
namespace {
// Return the BatchParam used by DMatrix.
template <typename GradientSumT>
auto BatchSpec(TrainParam const &p, common::Span<float> hess,
HistEvaluator<GradientSumT, CPUExpandEntry> const &evaluator) {
return BatchParam{GenericParameter::kCpuId, p.max_bin, hess, !evaluator.Task().const_hess};
}
auto BatchSpec(TrainParam const &p, common::Span<float> hess) {
return BatchParam{GenericParameter::kCpuId, p.max_bin, hess, false};
}
} // anonymous namespace
template <typename GradientSumT>
class GloablApproxBuilder {
protected:
@ -46,12 +59,13 @@ class GloablApproxBuilder {
public:
void InitData(DMatrix *p_fmat, common::Span<float> hess) {
monitor_->Start(__func__);
n_batches_ = 0;
int32_t n_total_bins = 0;
partitioner_.clear();
// Generating the GHistIndexMatrix is quite slow, is there a way to speed it up?
for (auto const &page : p_fmat->GetBatches<GHistIndexMatrix>(
{GenericParameter::kCpuId, param_.max_bin, hess, true})) {
for (auto const &page :
p_fmat->GetBatches<GHistIndexMatrix>(BatchSpec(param_, hess, evaluator_))) {
if (n_total_bins == 0) {
n_total_bins = page.cut.TotalBins();
feature_values_ = page.cut;
@ -62,9 +76,8 @@ class GloablApproxBuilder {
n_batches_++;
}
histogram_builder_.Reset(n_total_bins,
BatchParam{GenericParameter::kCpuId, param_.max_bin, hess},
ctx_->Threads(), n_batches_, rabit::IsDistributed());
histogram_builder_.Reset(n_total_bins, BatchSpec(param_, hess), ctx_->Threads(), n_batches_,
rabit::IsDistributed());
monitor_->Stop(__func__);
}
@ -82,8 +95,7 @@ class GloablApproxBuilder {
std::vector<CPUExpandEntry> nodes{best};
size_t i = 0;
auto space = this->ConstructHistSpace(nodes);
for (auto const &page :
p_fmat->GetBatches<GHistIndexMatrix>({GenericParameter::kCpuId, param_.max_bin, hess})) {
for (auto const &page : p_fmat->GetBatches<GHistIndexMatrix>(BatchSpec(param_, hess))) {
histogram_builder_.BuildHist(i, space, page, p_tree, partitioner_.at(i).Partitions(), nodes,
{}, gpair);
i++;
@ -175,8 +187,7 @@ class GloablApproxBuilder {
size_t i = 0;
auto space = this->ConstructHistSpace(nodes_to_build);
for (auto const &page :
p_fmat->GetBatches<GHistIndexMatrix>({GenericParameter::kCpuId, param_.max_bin, hess})) {
for (auto const &page : p_fmat->GetBatches<GHistIndexMatrix>(BatchSpec(param_, hess))) {
histogram_builder_.BuildHist(i, space, page, p_tree, partitioner_.at(i).Partitions(),
nodes_to_build, nodes_to_sub, gpair);
i++;
@ -225,8 +236,7 @@ class GloablApproxBuilder {
monitor_->Start("UpdatePosition");
size_t i = 0;
for (auto const &page :
p_fmat->GetBatches<GHistIndexMatrix>({GenericParameter::kCpuId, param_.max_bin, hess})) {
for (auto const &page : p_fmat->GetBatches<GHistIndexMatrix>(BatchSpec(param_, hess))) {
partitioner_.at(i).UpdatePosition(ctx_, page, applied, p_tree);
i++;
}