Prepare external memory support for hist. (#7638)
This PR prepares the GHistIndexMatrix to host the column matrix which is used by the hist tree method by accepting sparse_threshold parameter. Some cleanups are made to ensure the correct batch param is being passed into DMatrix along with some additional tests for correctness of SimpleDMatrix.
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@@ -31,11 +31,11 @@ namespace {
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template <typename GradientSumT>
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auto BatchSpec(TrainParam const &p, common::Span<float> hess,
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HistEvaluator<GradientSumT, CPUExpandEntry> const &evaluator) {
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return BatchParam{GenericParameter::kCpuId, p.max_bin, hess, !evaluator.Task().const_hess};
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return BatchParam{p.max_bin, hess, !evaluator.Task().const_hess};
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}
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auto BatchSpec(TrainParam const &p, common::Span<float> hess) {
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return BatchParam{GenericParameter::kCpuId, p.max_bin, hess, false};
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return BatchParam{p.max_bin, hess, false};
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}
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} // anonymous namespace
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@@ -68,9 +68,7 @@ void QuantileHistMaker::CallBuilderUpdate(const std::unique_ptr<Builder<Gradient
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void QuantileHistMaker::Update(HostDeviceVector<GradientPair> *gpair,
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DMatrix *dmat,
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const std::vector<RegTree *> &trees) {
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auto it = dmat->GetBatches<GHistIndexMatrix>(
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BatchParam{GenericParameter::kCpuId, param_.max_bin})
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.begin();
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auto it = dmat->GetBatches<GHistIndexMatrix>(HistBatch(param_)).begin();
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auto p_gmat = it.Page();
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if (dmat != p_last_dmat_ || is_gmat_initialized_ == false) {
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updater_monitor_.Start("GmatInitialization");
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@@ -127,8 +125,8 @@ void QuantileHistMaker::Builder<GradientSumT>::InitRoot(
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nodes_for_explicit_hist_build_.push_back(node);
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size_t page_id = 0;
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for (auto const &gidx : p_fmat->GetBatches<GHistIndexMatrix>(
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{GenericParameter::kCpuId, param_.max_bin})) {
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for (auto const& gidx :
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p_fmat->GetBatches<GHistIndexMatrix>(HistBatch(param_))) {
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this->histogram_builder_->BuildHist(
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page_id, gidx, p_tree, row_set_collection_,
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nodes_for_explicit_hist_build_, nodes_for_subtraction_trick_, gpair_h);
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@@ -141,10 +139,7 @@ void QuantileHistMaker::Builder<GradientSumT>::InitRoot(
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GradientPairT grad_stat;
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if (data_layout_ == DataLayout::kDenseDataZeroBased ||
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data_layout_ == DataLayout::kDenseDataOneBased) {
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auto const &gmat = *(p_fmat
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->GetBatches<GHistIndexMatrix>(BatchParam{
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GenericParameter::kCpuId, param_.max_bin})
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.begin());
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auto const& gmat = *(p_fmat->GetBatches<GHistIndexMatrix>(HistBatch(param_)).begin());
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const std::vector<uint32_t> &row_ptr = gmat.cut.Ptrs();
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const uint32_t ibegin = row_ptr[fid_least_bins_];
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const uint32_t iend = row_ptr[fid_least_bins_ + 1];
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@@ -170,8 +165,7 @@ void QuantileHistMaker::Builder<GradientSumT>::InitRoot(
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std::vector<CPUExpandEntry> entries{node};
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builder_monitor_.Start("EvaluateSplits");
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auto ft = p_fmat->Info().feature_types.ConstHostSpan();
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for (auto const &gmat : p_fmat->GetBatches<GHistIndexMatrix>(
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BatchParam{GenericParameter::kCpuId, param_.max_bin})) {
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for (auto const& gmat : p_fmat->GetBatches<GHistIndexMatrix>(HistBatch(param_))) {
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evaluator_->EvaluateSplits(histogram_builder_->Histogram(), gmat.cut, ft,
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*p_tree, &entries);
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break;
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@@ -264,8 +258,7 @@ void QuantileHistMaker::Builder<GradientSumT>::ExpandTree(
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if (param_.max_depth == 0 || depth < param_.max_depth) {
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size_t i = 0;
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for (auto const &gidx : p_fmat->GetBatches<GHistIndexMatrix>(
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{GenericParameter::kCpuId, param_.max_bin})) {
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for (auto const& gidx : p_fmat->GetBatches<GHistIndexMatrix>(HistBatch(param_))) {
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this->histogram_builder_->BuildHist(
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i, gidx, p_tree, row_set_collection_,
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nodes_for_explicit_hist_build_, nodes_for_subtraction_trick_,
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@@ -92,6 +92,10 @@ using xgboost::common::GHistBuilder;
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using xgboost::common::ColumnMatrix;
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using xgboost::common::Column;
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inline BatchParam HistBatch(TrainParam const& param) {
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return {param.max_bin, param.sparse_threshold};
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}
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/*! \brief construct a tree using quantized feature values */
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class QuantileHistMaker: public TreeUpdater {
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public:
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