535 lines
22 KiB
C++
535 lines
22 KiB
C++
/*!
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* Copyright by Contributors 2017-2021
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*/
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#include <dmlc/omp.h>
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#include <dmlc/any.h>
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#include <cstddef>
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#include <limits>
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#include <mutex>
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#include "xgboost/base.h"
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#include "xgboost/data.h"
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#include "xgboost/predictor.h"
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#include "xgboost/tree_model.h"
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#include "xgboost/tree_updater.h"
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#include "xgboost/logging.h"
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#include "xgboost/host_device_vector.h"
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#include "predict_fn.h"
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#include "../data/adapter.h"
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#include "../common/math.h"
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#include "../common/threading_utils.h"
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#include "../common/categorical.h"
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#include "../gbm/gbtree_model.h"
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namespace xgboost {
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namespace predictor {
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DMLC_REGISTRY_FILE_TAG(cpu_predictor);
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template <bool has_missing, bool has_categorical>
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bst_node_t GetLeafIndex(RegTree const &tree, const RegTree::FVec &feat,
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RegTree::CategoricalSplitMatrix const& cats) {
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bst_node_t nid = 0;
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while (!tree[nid].IsLeaf()) {
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unsigned split_index = tree[nid].SplitIndex();
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auto fvalue = feat.GetFvalue(split_index);
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nid = GetNextNode<has_missing, has_categorical>(
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tree[nid], nid, fvalue, has_missing && feat.IsMissing(split_index), cats);
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}
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return nid;
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}
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bst_float PredValue(const SparsePage::Inst &inst,
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const std::vector<std::unique_ptr<RegTree>> &trees,
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const std::vector<int> &tree_info, int bst_group,
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RegTree::FVec *p_feats, unsigned tree_begin,
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unsigned tree_end) {
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bst_float psum = 0.0f;
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p_feats->Fill(inst);
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for (size_t i = tree_begin; i < tree_end; ++i) {
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if (tree_info[i] == bst_group) {
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auto const &tree = *trees[i];
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bool has_categorical = tree.HasCategoricalSplit();
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auto cats = tree.GetCategoriesMatrix();
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bst_node_t nidx = -1;
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if (has_categorical) {
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nidx = GetLeafIndex<true, true>(tree, *p_feats, cats);
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} else {
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nidx = GetLeafIndex<true, false>(tree, *p_feats, cats);
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}
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psum += (*trees[i])[nidx].LeafValue();
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}
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}
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p_feats->Drop(inst);
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return psum;
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}
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template <bool has_categorical>
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bst_float
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PredValueByOneTree(const RegTree::FVec &p_feats, RegTree const &tree,
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RegTree::CategoricalSplitMatrix const& cats) {
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const bst_node_t leaf = p_feats.HasMissing() ?
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GetLeafIndex<true, has_categorical>(tree, p_feats, cats) :
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GetLeafIndex<false, has_categorical>(tree, p_feats, cats);
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return tree[leaf].LeafValue();
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}
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void PredictByAllTrees(gbm::GBTreeModel const &model, const size_t tree_begin,
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const size_t tree_end, std::vector<bst_float> *out_preds,
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const size_t predict_offset, const size_t num_group,
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const std::vector<RegTree::FVec> &thread_temp,
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const size_t offset, const size_t block_size) {
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std::vector<bst_float> &preds = *out_preds;
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for (size_t tree_id = tree_begin; tree_id < tree_end; ++tree_id) {
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const size_t gid = model.tree_info[tree_id];
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auto const &tree = *model.trees[tree_id];
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auto const& cats = tree.GetCategoriesMatrix();
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auto has_categorical = tree.HasCategoricalSplit();
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if (has_categorical) {
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for (size_t i = 0; i < block_size; ++i) {
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preds[(predict_offset + i) * num_group + gid] +=
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PredValueByOneTree<true>(thread_temp[offset + i], tree, cats);
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}
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} else {
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for (size_t i = 0; i < block_size; ++i) {
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preds[(predict_offset + i) * num_group + gid] +=
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PredValueByOneTree<false>(thread_temp[offset + i], tree, cats);
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}
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}
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}
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}
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template <typename DataView>
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void FVecFill(const size_t block_size, const size_t batch_offset, const int num_feature,
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DataView* batch, const size_t fvec_offset, std::vector<RegTree::FVec>* p_feats) {
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for (size_t i = 0; i < block_size; ++i) {
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RegTree::FVec &feats = (*p_feats)[fvec_offset + i];
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if (feats.Size() == 0) {
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feats.Init(num_feature);
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}
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const SparsePage::Inst inst = (*batch)[batch_offset + i];
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feats.Fill(inst);
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}
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}
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template <typename DataView>
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void FVecDrop(const size_t block_size, const size_t batch_offset, DataView* batch,
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const size_t fvec_offset, std::vector<RegTree::FVec>* p_feats) {
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for (size_t i = 0; i < block_size; ++i) {
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RegTree::FVec &feats = (*p_feats)[fvec_offset + i];
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const SparsePage::Inst inst = (*batch)[batch_offset + i];
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feats.Drop(inst);
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}
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}
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template <size_t kUnrollLen = 8>
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struct SparsePageView {
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bst_row_t base_rowid;
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HostSparsePageView view;
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static size_t constexpr kUnroll = kUnrollLen;
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explicit SparsePageView(SparsePage const *p)
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: base_rowid{p->base_rowid} {
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view = p->GetView();
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}
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SparsePage::Inst operator[](size_t i) { return view[i]; }
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size_t Size() const { return view.Size(); }
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};
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template <typename Adapter, size_t kUnrollLen = 8>
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class AdapterView {
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Adapter* adapter_;
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float missing_;
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common::Span<Entry> workspace_;
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std::vector<size_t> current_unroll_;
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public:
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static size_t constexpr kUnroll = kUnrollLen;
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public:
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explicit AdapterView(Adapter *adapter, float missing,
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common::Span<Entry> workplace)
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: adapter_{adapter}, missing_{missing}, workspace_{workplace},
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current_unroll_(omp_get_max_threads() > 0 ? omp_get_max_threads() : 1, 0) {}
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SparsePage::Inst operator[](size_t i) {
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bst_feature_t columns = adapter_->NumColumns();
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auto const &batch = adapter_->Value();
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auto row = batch.GetLine(i);
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auto t = omp_get_thread_num();
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auto const beg = (columns * kUnroll * t) + (current_unroll_[t] * columns);
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size_t non_missing {beg};
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for (size_t c = 0; c < row.Size(); ++c) {
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auto e = row.GetElement(c);
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if (missing_ != e.value && !common::CheckNAN(e.value)) {
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workspace_[non_missing] =
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Entry{static_cast<bst_feature_t>(e.column_idx), e.value};
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++non_missing;
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}
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}
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auto ret = workspace_.subspan(beg, non_missing - beg);
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current_unroll_[t]++;
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if (current_unroll_[t] == kUnroll) {
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current_unroll_[t] = 0;
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}
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return ret;
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}
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size_t Size() const { return adapter_->NumRows(); }
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bst_row_t const static base_rowid = 0; // NOLINT
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};
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template <typename DataView, size_t block_of_rows_size>
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void PredictBatchByBlockOfRowsKernel(
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DataView batch, std::vector<bst_float> *out_preds,
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gbm::GBTreeModel const &model, int32_t tree_begin, int32_t tree_end,
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std::vector<RegTree::FVec> *p_thread_temp) {
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auto &thread_temp = *p_thread_temp;
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int32_t const num_group = model.learner_model_param->num_output_group;
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CHECK_EQ(model.param.size_leaf_vector, 0)
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<< "size_leaf_vector is enforced to 0 so far";
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// parallel over local batch
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const auto nsize = static_cast<bst_omp_uint>(batch.Size());
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const int num_feature = model.learner_model_param->num_feature;
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omp_ulong n_blocks = common::DivRoundUp(nsize, block_of_rows_size);
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common::ParallelFor(n_blocks, [&](bst_omp_uint block_id) {
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const size_t batch_offset = block_id * block_of_rows_size;
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const size_t block_size =
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std::min(nsize - batch_offset, block_of_rows_size);
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const size_t fvec_offset = omp_get_thread_num() * block_of_rows_size;
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FVecFill(block_size, batch_offset, num_feature, &batch, fvec_offset,
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p_thread_temp);
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// process block of rows through all trees to keep cache locality
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PredictByAllTrees(model, tree_begin, tree_end, out_preds,
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batch_offset + batch.base_rowid, num_group, thread_temp,
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fvec_offset, block_size);
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FVecDrop(block_size, batch_offset, &batch, fvec_offset, p_thread_temp);
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});
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}
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class CPUPredictor : public Predictor {
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protected:
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// init thread buffers
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static void InitThreadTemp(int nthread, int num_feature, std::vector<RegTree::FVec>* out) {
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int prev_thread_temp_size = out->size();
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if (prev_thread_temp_size < nthread) {
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out->resize(nthread, RegTree::FVec());
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}
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}
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void PredictDMatrix(DMatrix *p_fmat, std::vector<bst_float> *out_preds,
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gbm::GBTreeModel const &model, int32_t tree_begin,
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int32_t tree_end) const {
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const int threads = omp_get_max_threads();
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std::vector<RegTree::FVec> feat_vecs;
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InitThreadTemp(threads * kBlockOfRowsSize,
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model.learner_model_param->num_feature, &feat_vecs);
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for (auto const& batch : p_fmat->GetBatches<SparsePage>()) {
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CHECK_EQ(out_preds->size(),
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p_fmat->Info().num_row_ * model.learner_model_param->num_output_group);
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size_t constexpr kUnroll = 8;
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PredictBatchByBlockOfRowsKernel<SparsePageView<kUnroll>,
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kBlockOfRowsSize>(SparsePageView<kUnroll>{&batch},
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out_preds, model, tree_begin,
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tree_end, &feat_vecs);
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}
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}
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void InitOutPredictions(const MetaInfo& info,
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HostDeviceVector<bst_float>* out_preds,
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const gbm::GBTreeModel& model) const override {
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CHECK_NE(model.learner_model_param->num_output_group, 0);
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size_t n = model.learner_model_param->num_output_group * info.num_row_;
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const auto& base_margin = info.base_margin_.HostVector();
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out_preds->Resize(n);
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std::vector<bst_float>& out_preds_h = out_preds->HostVector();
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if (base_margin.size() == n) {
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CHECK_EQ(out_preds->Size(), n);
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std::copy(base_margin.begin(), base_margin.end(), out_preds_h.begin());
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} else {
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if (!base_margin.empty()) {
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std::ostringstream oss;
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oss << "Ignoring the base margin, since it has incorrect length. "
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<< "The base margin must be an array of length ";
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if (model.learner_model_param->num_output_group > 1) {
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oss << "[num_class] * [number of data points], i.e. "
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<< model.learner_model_param->num_output_group << " * " << info.num_row_
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<< " = " << n << ". ";
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} else {
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oss << "[number of data points], i.e. " << info.num_row_ << ". ";
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}
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oss << "Instead, all data points will use "
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<< "base_score = " << model.learner_model_param->base_score;
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LOG(WARNING) << oss.str();
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}
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std::fill(out_preds_h.begin(), out_preds_h.end(),
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model.learner_model_param->base_score);
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}
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}
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public:
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explicit CPUPredictor(GenericParameter const* generic_param) :
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Predictor::Predictor{generic_param} {}
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void PredictBatch(DMatrix *dmat, PredictionCacheEntry *predts,
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const gbm::GBTreeModel &model, uint32_t tree_begin,
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uint32_t tree_end = 0) const override {
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auto* out_preds = &predts->predictions;
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// This is actually already handled in gbm, but large amount of tests rely on the
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// behaviour.
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if (tree_end == 0) {
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tree_end = model.trees.size();
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}
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this->PredictDMatrix(dmat, &out_preds->HostVector(), model, tree_begin,
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tree_end);
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}
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template <typename Adapter>
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void DispatchedInplacePredict(dmlc::any const &x, std::shared_ptr<DMatrix> p_m,
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const gbm::GBTreeModel &model, float missing,
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PredictionCacheEntry *out_preds,
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uint32_t tree_begin, uint32_t tree_end) const {
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auto threads = omp_get_max_threads();
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auto m = dmlc::get<std::shared_ptr<Adapter>>(x);
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CHECK_EQ(m->NumColumns(), model.learner_model_param->num_feature)
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<< "Number of columns in data must equal to trained model.";
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if (p_m) {
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p_m->Info().num_row_ = m->NumRows();
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this->InitOutPredictions(p_m->Info(), &(out_preds->predictions), model);
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} else {
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MetaInfo info;
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info.num_row_ = m->NumRows();
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this->InitOutPredictions(info, &(out_preds->predictions), model);
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}
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std::vector<Entry> workspace(m->NumColumns() * 8 * threads);
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auto &predictions = out_preds->predictions.HostVector();
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std::vector<RegTree::FVec> thread_temp;
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InitThreadTemp(threads * kBlockOfRowsSize,
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model.learner_model_param->num_feature, &thread_temp);
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PredictBatchByBlockOfRowsKernel<AdapterView<Adapter>, kBlockOfRowsSize>(
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AdapterView<Adapter>(m.get(), missing, common::Span<Entry>{workspace}),
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&predictions, model, tree_begin, tree_end, &thread_temp);
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}
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bool InplacePredict(dmlc::any const &x, std::shared_ptr<DMatrix> p_m,
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const gbm::GBTreeModel &model, float missing,
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PredictionCacheEntry *out_preds, uint32_t tree_begin,
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unsigned tree_end) const override {
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if (x.type() == typeid(std::shared_ptr<data::DenseAdapter>)) {
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this->DispatchedInplacePredict<data::DenseAdapter>(
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x, p_m, model, missing, out_preds, tree_begin, tree_end);
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} else if (x.type() == typeid(std::shared_ptr<data::CSRAdapter>)) {
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this->DispatchedInplacePredict<data::CSRAdapter>(
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x, p_m, model, missing, out_preds, tree_begin, tree_end);
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} else if (x.type() == typeid(std::shared_ptr<data::ArrayAdapter>)) {
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this->DispatchedInplacePredict<data::ArrayAdapter> (
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x, p_m, model, missing, out_preds, tree_begin, tree_end);
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} else if (x.type() == typeid(std::shared_ptr<data::CSRArrayAdapter>)) {
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this->DispatchedInplacePredict<data::CSRArrayAdapter> (
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x, p_m, model, missing, out_preds, tree_begin, tree_end);
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} else {
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return false;
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}
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return true;
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}
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void PredictInstance(const SparsePage::Inst& inst,
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std::vector<bst_float>* out_preds,
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const gbm::GBTreeModel& model, unsigned ntree_limit) const override {
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std::vector<RegTree::FVec> feat_vecs;
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feat_vecs.resize(1, RegTree::FVec());
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feat_vecs[0].Init(model.learner_model_param->num_feature);
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ntree_limit *= model.learner_model_param->num_output_group;
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if (ntree_limit == 0 || ntree_limit > model.trees.size()) {
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ntree_limit = static_cast<unsigned>(model.trees.size());
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}
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out_preds->resize(model.learner_model_param->num_output_group *
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(model.param.size_leaf_vector + 1));
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// loop over output groups
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for (uint32_t gid = 0; gid < model.learner_model_param->num_output_group; ++gid) {
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(*out_preds)[gid] = PredValue(inst, model.trees, model.tree_info, gid,
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&feat_vecs[0], 0, ntree_limit) +
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model.learner_model_param->base_score;
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}
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}
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void PredictLeaf(DMatrix* p_fmat, HostDeviceVector<bst_float>* out_preds,
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const gbm::GBTreeModel& model, unsigned ntree_limit) const override {
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const int nthread = omp_get_max_threads();
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std::vector<RegTree::FVec> feat_vecs;
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const int num_feature = model.learner_model_param->num_feature;
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InitThreadTemp(nthread, num_feature, &feat_vecs);
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const MetaInfo& info = p_fmat->Info();
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// number of valid trees
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if (ntree_limit == 0 || ntree_limit > model.trees.size()) {
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ntree_limit = static_cast<unsigned>(model.trees.size());
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}
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std::vector<bst_float>& preds = out_preds->HostVector();
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preds.resize(info.num_row_ * ntree_limit);
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// start collecting the prediction
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for (const auto &batch : p_fmat->GetBatches<SparsePage>()) {
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// parallel over local batch
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auto page = batch.GetView();
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const auto nsize = static_cast<bst_omp_uint>(batch.Size());
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common::ParallelFor(nsize, [&](bst_omp_uint i) {
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const int tid = omp_get_thread_num();
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auto ridx = static_cast<size_t>(batch.base_rowid + i);
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RegTree::FVec &feats = feat_vecs[tid];
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if (feats.Size() == 0) {
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feats.Init(num_feature);
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}
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feats.Fill(page[i]);
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for (unsigned j = 0; j < ntree_limit; ++j) {
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auto const& tree = *model.trees[j];
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auto const& cats = tree.GetCategoriesMatrix();
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bst_node_t tid = GetLeafIndex<true, true>(tree, feats, cats);
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preds[ridx * ntree_limit + j] = static_cast<bst_float>(tid);
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}
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feats.Drop(page[i]);
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});
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}
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}
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void PredictContribution(DMatrix* p_fmat, HostDeviceVector<float>* out_contribs,
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const gbm::GBTreeModel& model, uint32_t ntree_limit,
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std::vector<bst_float>* tree_weights,
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bool approximate, int condition,
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unsigned condition_feature) const override {
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const int nthread = omp_get_max_threads();
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const int num_feature = model.learner_model_param->num_feature;
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std::vector<RegTree::FVec> feat_vecs;
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InitThreadTemp(nthread, num_feature, &feat_vecs);
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const MetaInfo& info = p_fmat->Info();
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// number of valid trees
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if (ntree_limit == 0 || ntree_limit > model.trees.size()) {
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ntree_limit = static_cast<unsigned>(model.trees.size());
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}
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const int ngroup = model.learner_model_param->num_output_group;
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CHECK_NE(ngroup, 0);
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size_t const ncolumns = num_feature + 1;
|
|
CHECK_NE(ncolumns, 0);
|
|
// allocate space for (number of features + bias) times the number of rows
|
|
std::vector<bst_float>& contribs = out_contribs->HostVector();
|
|
contribs.resize(info.num_row_ * ncolumns * model.learner_model_param->num_output_group);
|
|
// make sure contributions is zeroed, we could be reusing a previously
|
|
// allocated one
|
|
std::fill(contribs.begin(), contribs.end(), 0);
|
|
// initialize tree node mean values
|
|
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());
|
|
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) {
|
|
feats.Init(num_feature);
|
|
}
|
|
std::vector<bst_float> this_tree_contribs(ncolumns);
|
|
// loop over all classes
|
|
for (int gid = 0; gid < ngroup; ++gid) {
|
|
bst_float* p_contribs = &contribs[(row_idx * ngroup + gid) * ncolumns];
|
|
feats.Fill(page[i]);
|
|
// calculate contributions
|
|
for (unsigned j = 0; j < ntree_limit; ++j) {
|
|
std::fill(this_tree_contribs.begin(), this_tree_contribs.end(), 0);
|
|
if (model.tree_info[j] != gid) {
|
|
continue;
|
|
}
|
|
if (!approximate) {
|
|
model.trees[j]->CalculateContributions(feats, &this_tree_contribs[0],
|
|
condition, condition_feature);
|
|
} else {
|
|
model.trees[j]->CalculateContributionsApprox(feats, &this_tree_contribs[0]);
|
|
}
|
|
for (size_t ci = 0 ; ci < ncolumns ; ++ci) {
|
|
p_contribs[ci] += this_tree_contribs[ci] *
|
|
(tree_weights == nullptr ? 1 : (*tree_weights)[j]);
|
|
}
|
|
}
|
|
feats.Drop(page[i]);
|
|
// add base margin to BIAS
|
|
if (base_margin.size() != 0) {
|
|
p_contribs[ncolumns - 1] += base_margin[row_idx * ngroup + gid];
|
|
} else {
|
|
p_contribs[ncolumns - 1] += model.learner_model_param->base_score;
|
|
}
|
|
}
|
|
});
|
|
}
|
|
}
|
|
|
|
void PredictInteractionContributions(DMatrix* p_fmat, HostDeviceVector<bst_float>* out_contribs,
|
|
const gbm::GBTreeModel& model, unsigned ntree_limit,
|
|
std::vector<bst_float>* tree_weights,
|
|
bool approximate) const override {
|
|
const MetaInfo& info = p_fmat->Info();
|
|
const int ngroup = model.learner_model_param->num_output_group;
|
|
size_t const ncolumns = model.learner_model_param->num_feature;
|
|
const unsigned row_chunk = ngroup * (ncolumns + 1) * (ncolumns + 1);
|
|
const unsigned mrow_chunk = (ncolumns + 1) * (ncolumns + 1);
|
|
const unsigned crow_chunk = ngroup * (ncolumns + 1);
|
|
|
|
// allocate space for (number of features^2) times the number of rows and tmp off/on contribs
|
|
std::vector<bst_float>& contribs = out_contribs->HostVector();
|
|
contribs.resize(info.num_row_ * ngroup * (ncolumns + 1) * (ncolumns + 1));
|
|
HostDeviceVector<bst_float> contribs_off_hdv(info.num_row_ * ngroup * (ncolumns + 1));
|
|
auto &contribs_off = contribs_off_hdv.HostVector();
|
|
HostDeviceVector<bst_float> contribs_on_hdv(info.num_row_ * ngroup * (ncolumns + 1));
|
|
auto &contribs_on = contribs_on_hdv.HostVector();
|
|
HostDeviceVector<bst_float> contribs_diag_hdv(info.num_row_ * ngroup * (ncolumns + 1));
|
|
auto &contribs_diag = contribs_diag_hdv.HostVector();
|
|
|
|
// Compute the difference in effects when conditioning on each of the features on and off
|
|
// see: Axiomatic characterizations of probabilistic and
|
|
// cardinal-probabilistic interaction indices
|
|
PredictContribution(p_fmat, &contribs_diag_hdv, model, ntree_limit,
|
|
tree_weights, approximate, 0, 0);
|
|
for (size_t i = 0; i < ncolumns + 1; ++i) {
|
|
PredictContribution(p_fmat, &contribs_off_hdv, model, ntree_limit,
|
|
tree_weights, approximate, -1, i);
|
|
PredictContribution(p_fmat, &contribs_on_hdv, model, ntree_limit,
|
|
tree_weights, approximate, 1, i);
|
|
|
|
for (size_t j = 0; j < info.num_row_; ++j) {
|
|
for (int l = 0; l < ngroup; ++l) {
|
|
const unsigned o_offset = j * row_chunk + l * mrow_chunk + i * (ncolumns + 1);
|
|
const unsigned c_offset = j * crow_chunk + l * (ncolumns + 1);
|
|
contribs[o_offset + i] = 0;
|
|
for (size_t k = 0; k < ncolumns + 1; ++k) {
|
|
// fill in the diagonal with additive effects, and off-diagonal with the interactions
|
|
if (k == i) {
|
|
contribs[o_offset + i] += contribs_diag[c_offset + k];
|
|
} else {
|
|
contribs[o_offset + k] = (contribs_on[c_offset + k] - contribs_off[c_offset + k])/2.0;
|
|
contribs[o_offset + i] -= contribs[o_offset + k];
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
private:
|
|
static size_t constexpr kBlockOfRowsSize = 64;
|
|
};
|
|
|
|
XGBOOST_REGISTER_PREDICTOR(CPUPredictor, "cpu_predictor")
|
|
.describe("Make predictions using CPU.")
|
|
.set_body([](GenericParameter const* generic_param) {
|
|
return new CPUPredictor(generic_param);
|
|
});
|
|
} // namespace predictor
|
|
} // namespace xgboost
|