492 lines
18 KiB
C++
492 lines
18 KiB
C++
/**
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* Copyright 2018-2023 by Contributors
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*/
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#include <gtest/gtest.h>
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#include <xgboost/context.h> // Context
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#include <limits>
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#include "../../../../src/common/categorical.h"
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#include "../../../../src/common/row_set.h"
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#include "../../../../src/tree/hist/expand_entry.h"
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#include "../../../../src/tree/hist/histogram.h"
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#include "../../categorical_helpers.h"
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#include "../../helpers.h"
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namespace xgboost {
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namespace tree {
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namespace {
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void InitRowPartitionForTest(common::RowSetCollection *row_set, size_t n_samples, size_t base_rowid = 0) {
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auto &row_indices = *row_set->Data();
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row_indices.resize(n_samples);
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std::iota(row_indices.begin(), row_indices.end(), base_rowid);
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row_set->Init();
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}
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} // anonymous namespace
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void TestAddHistRows(bool is_distributed) {
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Context ctx;
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std::vector<CPUExpandEntry> nodes_for_explicit_hist_build_;
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std::vector<CPUExpandEntry> nodes_for_subtraction_trick_;
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int starting_index = std::numeric_limits<int>::max();
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int sync_count = 0;
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size_t constexpr kNRows = 8, kNCols = 16;
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int32_t constexpr kMaxBins = 4;
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auto p_fmat = RandomDataGenerator(kNRows, kNCols, 0.8).Seed(3).GenerateDMatrix();
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auto const &gmat =
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*(p_fmat->GetBatches<GHistIndexMatrix>(&ctx, BatchParam{kMaxBins, 0.5}).begin());
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RegTree tree;
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tree.ExpandNode(0, 0, 0, false, 0, 0, 0, 0, 0, 0, 0);
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tree.ExpandNode(tree[0].LeftChild(), 0, 0, false, 0, 0, 0, 0, 0, 0, 0);
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tree.ExpandNode(tree[0].RightChild(), 0, 0, false, 0, 0, 0, 0, 0, 0, 0);
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nodes_for_explicit_hist_build_.emplace_back(3, tree.GetDepth(3));
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nodes_for_explicit_hist_build_.emplace_back(4, tree.GetDepth(4));
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nodes_for_subtraction_trick_.emplace_back(5, tree.GetDepth(5));
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nodes_for_subtraction_trick_.emplace_back(6, tree.GetDepth(6));
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HistogramBuilder<CPUExpandEntry> histogram_builder;
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histogram_builder.Reset(gmat.cut.TotalBins(), {kMaxBins, 0.5}, omp_get_max_threads(), 1,
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is_distributed, false);
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histogram_builder.AddHistRows(&starting_index, &sync_count,
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nodes_for_explicit_hist_build_,
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nodes_for_subtraction_trick_, &tree);
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ASSERT_EQ(sync_count, 2);
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ASSERT_EQ(starting_index, 3);
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for (const CPUExpandEntry &node : nodes_for_explicit_hist_build_) {
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ASSERT_EQ(histogram_builder.Histogram().RowExists(node.nid), true);
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}
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for (const CPUExpandEntry &node : nodes_for_subtraction_trick_) {
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ASSERT_EQ(histogram_builder.Histogram().RowExists(node.nid), true);
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}
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}
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TEST(CPUHistogram, AddRows) {
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TestAddHistRows(true);
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TestAddHistRows(false);
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}
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void TestSyncHist(bool is_distributed) {
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size_t constexpr kNRows = 8, kNCols = 16;
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int32_t constexpr kMaxBins = 4;
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Context ctx;
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std::vector<CPUExpandEntry> nodes_for_explicit_hist_build_;
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std::vector<CPUExpandEntry> nodes_for_subtraction_trick_;
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int starting_index = std::numeric_limits<int>::max();
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int sync_count = 0;
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RegTree tree;
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auto p_fmat = RandomDataGenerator(kNRows, kNCols, 0.8).Seed(3).GenerateDMatrix();
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auto const &gmat =
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*(p_fmat->GetBatches<GHistIndexMatrix>(&ctx, BatchParam{kMaxBins, 0.5}).begin());
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HistogramBuilder<CPUExpandEntry> histogram;
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uint32_t total_bins = gmat.cut.Ptrs().back();
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histogram.Reset(total_bins, {kMaxBins, 0.5}, omp_get_max_threads(), 1, is_distributed, false);
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common::RowSetCollection row_set_collection_;
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{
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row_set_collection_.Clear();
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std::vector<size_t> &row_indices = *row_set_collection_.Data();
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row_indices.resize(kNRows);
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std::iota(row_indices.begin(), row_indices.end(), 0);
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row_set_collection_.Init();
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}
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// level 0
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nodes_for_explicit_hist_build_.emplace_back(0, tree.GetDepth(0));
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histogram.AddHistRows(&starting_index, &sync_count,
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nodes_for_explicit_hist_build_,
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nodes_for_subtraction_trick_, &tree);
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tree.ExpandNode(0, 0, 0, false, 0, 0, 0, 0, 0, 0, 0);
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nodes_for_explicit_hist_build_.clear();
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nodes_for_subtraction_trick_.clear();
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// level 1
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nodes_for_explicit_hist_build_.emplace_back(tree[0].LeftChild(), tree.GetDepth(1));
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nodes_for_subtraction_trick_.emplace_back(tree[0].RightChild(), tree.GetDepth(2));
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histogram.AddHistRows(&starting_index, &sync_count,
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nodes_for_explicit_hist_build_,
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nodes_for_subtraction_trick_, &tree);
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tree.ExpandNode(tree[0].LeftChild(), 0, 0, false, 0, 0, 0, 0, 0, 0, 0);
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tree.ExpandNode(tree[0].RightChild(), 0, 0, false, 0, 0, 0, 0, 0, 0, 0);
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nodes_for_explicit_hist_build_.clear();
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nodes_for_subtraction_trick_.clear();
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// level 2
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nodes_for_explicit_hist_build_.emplace_back(3, tree.GetDepth(3));
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nodes_for_subtraction_trick_.emplace_back(4, tree.GetDepth(4));
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nodes_for_explicit_hist_build_.emplace_back(5, tree.GetDepth(5));
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nodes_for_subtraction_trick_.emplace_back(6, tree.GetDepth(6));
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histogram.AddHistRows(&starting_index, &sync_count,
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nodes_for_explicit_hist_build_,
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nodes_for_subtraction_trick_, &tree);
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const size_t n_nodes = nodes_for_explicit_hist_build_.size();
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ASSERT_EQ(n_nodes, 2ul);
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row_set_collection_.AddSplit(0, tree[0].LeftChild(), tree[0].RightChild(), 4,
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4);
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row_set_collection_.AddSplit(1, tree[1].LeftChild(), tree[1].RightChild(), 2,
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2);
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row_set_collection_.AddSplit(2, tree[2].LeftChild(), tree[2].RightChild(), 2,
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2);
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common::BlockedSpace2d space(
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n_nodes,
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[&](size_t node) {
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const int32_t nid = nodes_for_explicit_hist_build_[node].nid;
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return row_set_collection_[nid].Size();
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},
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256);
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std::vector<common::GHistRow> target_hists(n_nodes);
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for (size_t i = 0; i < nodes_for_explicit_hist_build_.size(); ++i) {
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const int32_t nid = nodes_for_explicit_hist_build_[i].nid;
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target_hists[i] = histogram.Histogram()[nid];
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}
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// set values to specific nodes hist
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std::vector<size_t> n_ids = {1, 2};
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for (size_t i : n_ids) {
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auto this_hist = histogram.Histogram()[i];
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double *p_hist = reinterpret_cast<double *>(this_hist.data());
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for (size_t bin_id = 0; bin_id < 2 * total_bins; ++bin_id) {
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p_hist[bin_id] = 2 * bin_id;
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}
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}
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n_ids[0] = 3;
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n_ids[1] = 5;
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for (size_t i : n_ids) {
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auto this_hist = histogram.Histogram()[i];
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double *p_hist = reinterpret_cast<double *>(this_hist.data());
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for (size_t bin_id = 0; bin_id < 2 * total_bins; ++bin_id) {
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p_hist[bin_id] = bin_id;
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}
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}
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histogram.Buffer().Reset(1, n_nodes, space, target_hists);
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// sync hist
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if (is_distributed) {
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histogram.SyncHistogramDistributed(&tree, nodes_for_explicit_hist_build_,
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nodes_for_subtraction_trick_,
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starting_index, sync_count);
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} else {
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histogram.SyncHistogramLocal(&tree, nodes_for_explicit_hist_build_,
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nodes_for_subtraction_trick_);
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}
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using GHistRowT = common::GHistRow;
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auto check_hist = [](const GHistRowT parent, const GHistRowT left, const GHistRowT right,
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size_t begin, size_t end) {
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const double *p_parent = reinterpret_cast<const double *>(parent.data());
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const double *p_left = reinterpret_cast<const double *>(left.data());
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const double *p_right = reinterpret_cast<const double *>(right.data());
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for (size_t i = 2 * begin; i < 2 * end; ++i) {
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ASSERT_EQ(p_parent[i], p_left[i] + p_right[i]);
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}
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};
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size_t node_id = 0;
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for (const CPUExpandEntry &node : nodes_for_explicit_hist_build_) {
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auto this_hist = histogram.Histogram()[node.nid];
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const size_t parent_id = tree[node.nid].Parent();
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const size_t subtraction_node_id =
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nodes_for_subtraction_trick_[node_id].nid;
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auto parent_hist = histogram.Histogram()[parent_id];
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auto sibling_hist = histogram.Histogram()[subtraction_node_id];
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check_hist(parent_hist, this_hist, sibling_hist, 0, total_bins);
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++node_id;
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}
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node_id = 0;
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for (const CPUExpandEntry &node : nodes_for_subtraction_trick_) {
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auto this_hist = histogram.Histogram()[node.nid];
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const size_t parent_id = tree[node.nid].Parent();
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const size_t subtraction_node_id =
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nodes_for_explicit_hist_build_[node_id].nid;
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auto parent_hist = histogram.Histogram()[parent_id];
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auto sibling_hist = histogram.Histogram()[subtraction_node_id];
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check_hist(parent_hist, this_hist, sibling_hist, 0, total_bins);
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++node_id;
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}
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}
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TEST(CPUHistogram, SyncHist) {
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TestSyncHist(true);
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TestSyncHist(false);
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}
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void TestBuildHistogram(bool is_distributed, bool force_read_by_column, bool is_col_split) {
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size_t constexpr kNRows = 8, kNCols = 16;
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int32_t constexpr kMaxBins = 4;
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Context ctx;
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auto p_fmat =
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RandomDataGenerator(kNRows, kNCols, 0.8).Seed(3).GenerateDMatrix();
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if (is_col_split) {
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p_fmat = std::shared_ptr<DMatrix>{
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p_fmat->SliceCol(collective::GetWorldSize(), collective::GetRank())};
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}
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auto const &gmat =
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*(p_fmat->GetBatches<GHistIndexMatrix>(&ctx, BatchParam{kMaxBins, 0.5}).begin());
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uint32_t total_bins = gmat.cut.Ptrs().back();
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static double constexpr kEps = 1e-6;
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std::vector<GradientPair> gpair = {
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{0.23f, 0.24f}, {0.24f, 0.25f}, {0.26f, 0.27f}, {0.27f, 0.28f},
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{0.27f, 0.29f}, {0.37f, 0.39f}, {0.47f, 0.49f}, {0.57f, 0.59f}};
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bst_node_t nid = 0;
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HistogramBuilder<CPUExpandEntry> histogram;
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histogram.Reset(total_bins, {kMaxBins, 0.5}, omp_get_max_threads(), 1, is_distributed,
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is_col_split);
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RegTree tree;
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common::RowSetCollection row_set_collection;
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row_set_collection.Clear();
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std::vector<size_t> &row_indices = *row_set_collection.Data();
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row_indices.resize(kNRows);
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std::iota(row_indices.begin(), row_indices.end(), 0);
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row_set_collection.Init();
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CPUExpandEntry node{RegTree::kRoot, tree.GetDepth(0)};
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std::vector<CPUExpandEntry> nodes_for_explicit_hist_build;
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nodes_for_explicit_hist_build.push_back(node);
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for (auto const &gidx : p_fmat->GetBatches<GHistIndexMatrix>(&ctx, {kMaxBins, 0.5})) {
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histogram.BuildHist(0, gidx, &tree, row_set_collection, nodes_for_explicit_hist_build, {},
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gpair, force_read_by_column);
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}
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// Check if number of histogram bins is correct
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ASSERT_EQ(histogram.Histogram()[nid].size(), gmat.cut.Ptrs().back());
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std::vector<GradientPairPrecise> histogram_expected(histogram.Histogram()[nid].size());
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// Compute the correct histogram (histogram_expected)
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CHECK_EQ(gpair.size(), kNRows);
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for (size_t rid = 0; rid < kNRows; ++rid) {
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const size_t ibegin = gmat.row_ptr[rid];
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const size_t iend = gmat.row_ptr[rid + 1];
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for (size_t i = ibegin; i < iend; ++i) {
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const size_t bin_id = gmat.index[i];
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histogram_expected[bin_id] += GradientPairPrecise(gpair[rid]);
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}
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}
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// Now validate the computed histogram returned by BuildHist
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for (size_t i = 0; i < histogram.Histogram()[nid].size(); ++i) {
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GradientPairPrecise sol = histogram_expected[i];
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ASSERT_NEAR(sol.GetGrad(), histogram.Histogram()[nid][i].GetGrad(), kEps);
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ASSERT_NEAR(sol.GetHess(), histogram.Histogram()[nid][i].GetHess(), kEps);
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}
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}
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TEST(CPUHistogram, BuildHist) {
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TestBuildHistogram(true, false, false);
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TestBuildHistogram(false, false, false);
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TestBuildHistogram(true, true, false);
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TestBuildHistogram(false, true, false);
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}
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TEST(CPUHistogram, BuildHistColSplit) {
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auto constexpr kWorkers = 4;
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RunWithInMemoryCommunicator(kWorkers, TestBuildHistogram, true, true, true);
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RunWithInMemoryCommunicator(kWorkers, TestBuildHistogram, true, false, true);
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}
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namespace {
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template <typename GradientSumT>
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void ValidateCategoricalHistogram(size_t n_categories,
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common::Span<GradientSumT> onehot,
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common::Span<GradientSumT> cat) {
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auto cat_sum = std::accumulate(cat.cbegin(), cat.cend(), GradientPairPrecise{});
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for (size_t c = 0; c < n_categories; ++c) {
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auto zero = onehot[c * 2];
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auto one = onehot[c * 2 + 1];
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auto chosen = cat[c];
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auto not_chosen = cat_sum - chosen;
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ASSERT_LE(RelError(zero.GetGrad(), not_chosen.GetGrad()), kRtEps);
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ASSERT_LE(RelError(zero.GetHess(), not_chosen.GetHess()), kRtEps);
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ASSERT_LE(RelError(one.GetGrad(), chosen.GetGrad()), kRtEps);
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ASSERT_LE(RelError(one.GetHess(), chosen.GetHess()), kRtEps);
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}
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}
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void TestHistogramCategorical(size_t n_categories, bool force_read_by_column) {
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size_t constexpr kRows = 340;
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int32_t constexpr kBins = 256;
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auto x = GenerateRandomCategoricalSingleColumn(kRows, n_categories);
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auto cat_m = GetDMatrixFromData(x, kRows, 1);
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cat_m->Info().feature_types.HostVector().push_back(FeatureType::kCategorical);
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Context ctx;
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BatchParam batch_param{0, static_cast<int32_t>(kBins)};
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RegTree tree;
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CPUExpandEntry node{RegTree::kRoot, tree.GetDepth(0)};
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std::vector<CPUExpandEntry> nodes_for_explicit_hist_build;
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nodes_for_explicit_hist_build.push_back(node);
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auto gpair = GenerateRandomGradients(kRows, 0, 2);
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common::RowSetCollection row_set_collection;
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row_set_collection.Clear();
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std::vector<size_t> &row_indices = *row_set_collection.Data();
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row_indices.resize(kRows);
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std::iota(row_indices.begin(), row_indices.end(), 0);
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row_set_collection.Init();
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/**
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* Generate hist with cat data.
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*/
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HistogramBuilder<CPUExpandEntry> cat_hist;
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for (auto const &gidx : cat_m->GetBatches<GHistIndexMatrix>(&ctx, {kBins, 0.5})) {
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auto total_bins = gidx.cut.TotalBins();
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cat_hist.Reset(total_bins, {kBins, 0.5}, omp_get_max_threads(), 1, false, false);
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cat_hist.BuildHist(0, gidx, &tree, row_set_collection, nodes_for_explicit_hist_build, {},
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gpair.HostVector(), force_read_by_column);
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}
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/**
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* Generate hist with one hot encoded data.
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*/
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auto x_encoded = OneHotEncodeFeature(x, n_categories);
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auto encode_m = GetDMatrixFromData(x_encoded, kRows, n_categories);
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HistogramBuilder<CPUExpandEntry> onehot_hist;
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for (auto const &gidx : encode_m->GetBatches<GHistIndexMatrix>(&ctx, {kBins, 0.5})) {
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auto total_bins = gidx.cut.TotalBins();
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onehot_hist.Reset(total_bins, {kBins, 0.5}, omp_get_max_threads(), 1, false, false);
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onehot_hist.BuildHist(0, gidx, &tree, row_set_collection, nodes_for_explicit_hist_build, {},
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gpair.HostVector(), force_read_by_column);
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}
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auto cat = cat_hist.Histogram()[0];
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auto onehot = onehot_hist.Histogram()[0];
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ValidateCategoricalHistogram(n_categories, onehot, cat);
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}
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} // anonymous namespace
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TEST(CPUHistogram, Categorical) {
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for (size_t n_categories = 2; n_categories < 8; ++n_categories) {
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TestHistogramCategorical(n_categories, false);
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}
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for (size_t n_categories = 2; n_categories < 8; ++n_categories) {
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TestHistogramCategorical(n_categories, true);
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}
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}
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namespace {
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void TestHistogramExternalMemory(Context const *ctx, BatchParam batch_param, bool is_approx,
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bool force_read_by_column) {
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size_t constexpr kEntries = 1 << 16;
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auto m = CreateSparsePageDMatrix(kEntries, "cache");
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std::vector<float> hess(m->Info().num_row_, 1.0);
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if (is_approx) {
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batch_param.hess = hess;
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}
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std::vector<size_t> partition_size(1, 0);
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size_t total_bins{0};
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size_t n_samples{0};
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auto gpair = GenerateRandomGradients(m->Info().num_row_, 0.0, 1.0);
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auto const &h_gpair = gpair.HostVector();
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RegTree tree;
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std::vector<CPUExpandEntry> nodes;
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nodes.emplace_back(0, tree.GetDepth(0));
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common::GHistRow multi_page;
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HistogramBuilder<CPUExpandEntry> multi_build;
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{
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/**
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* Multi page
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*/
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std::vector<common::RowSetCollection> rows_set;
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for (auto const &page : m->GetBatches<GHistIndexMatrix>(ctx, batch_param)) {
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CHECK_LT(page.base_rowid, m->Info().num_row_);
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auto n_rows_in_node = page.Size();
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partition_size[0] = std::max(partition_size[0], n_rows_in_node);
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total_bins = page.cut.TotalBins();
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n_samples += n_rows_in_node;
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|
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rows_set.emplace_back();
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InitRowPartitionForTest(&rows_set.back(), n_rows_in_node, page.base_rowid);
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}
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ASSERT_EQ(n_samples, m->Info().num_row_);
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|
|
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common::BlockedSpace2d space{
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1, [&](size_t nidx_in_set) { return partition_size.at(nidx_in_set); },
|
|
256};
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|
|
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multi_build.Reset(total_bins, batch_param, ctx->Threads(), rows_set.size(), false, false);
|
|
|
|
size_t page_idx{0};
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|
for (auto const &page : m->GetBatches<GHistIndexMatrix>(ctx, batch_param)) {
|
|
multi_build.BuildHist(page_idx, space, page, &tree, rows_set.at(page_idx), nodes, {}, h_gpair,
|
|
force_read_by_column);
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|
++page_idx;
|
|
}
|
|
ASSERT_EQ(page_idx, 2);
|
|
multi_page = multi_build.Histogram()[0];
|
|
}
|
|
|
|
HistogramBuilder<CPUExpandEntry> single_build;
|
|
common::GHistRow single_page;
|
|
{
|
|
/**
|
|
* Single page
|
|
*/
|
|
common::RowSetCollection row_set_collection;
|
|
InitRowPartitionForTest(&row_set_collection, n_samples);
|
|
|
|
single_build.Reset(total_bins, batch_param, ctx->Threads(), 1, false, false);
|
|
SparsePage concat;
|
|
std::vector<float> hess(m->Info().num_row_, 1.0f);
|
|
for (auto const& page : m->GetBatches<SparsePage>()) {
|
|
concat.Push(page);
|
|
}
|
|
|
|
auto cut = common::SketchOnDMatrix(ctx, m.get(), batch_param.max_bin, false, hess);
|
|
GHistIndexMatrix gmat(concat, {}, cut, batch_param.max_bin, false,
|
|
std::numeric_limits<double>::quiet_NaN(), ctx->Threads());
|
|
single_build.BuildHist(0, gmat, &tree, row_set_collection, nodes, {}, h_gpair, force_read_by_column);
|
|
single_page = single_build.Histogram()[0];
|
|
}
|
|
|
|
for (size_t i = 0; i < single_page.size(); ++i) {
|
|
ASSERT_NEAR(single_page[i].GetGrad(), multi_page[i].GetGrad(), kRtEps);
|
|
ASSERT_NEAR(single_page[i].GetHess(), multi_page[i].GetHess(), kRtEps);
|
|
}
|
|
}
|
|
} // anonymous namespace
|
|
|
|
TEST(CPUHistogram, ExternalMemory) {
|
|
int32_t constexpr kBins = 256;
|
|
Context ctx;
|
|
|
|
TestHistogramExternalMemory(&ctx, BatchParam{kBins, common::Span<float>{}, false}, true, false);
|
|
TestHistogramExternalMemory(&ctx, BatchParam{kBins, common::Span<float>{}, false}, true, true);
|
|
|
|
float sparse_thresh{0.5};
|
|
TestHistogramExternalMemory(&ctx, {kBins, sparse_thresh}, false, false);
|
|
TestHistogramExternalMemory(&ctx, {kBins, sparse_thresh}, false, true);
|
|
sparse_thresh = std::numeric_limits<float>::quiet_NaN();
|
|
TestHistogramExternalMemory(&ctx, {kBins, sparse_thresh}, false, false);
|
|
TestHistogramExternalMemory(&ctx, {kBins, sparse_thresh}, false, true);
|
|
}
|
|
} // namespace tree
|
|
} // namespace xgboost
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