Extract histogram builder from CPU Hist. (#7152)
* Extract the CPU histogram builder. * Fix tests. * Reduce number of histograms being built.
This commit is contained in:
Jiaming Yuan
GitHub
298
tests/cpp/tree/hist/test_histogram.cc
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298
tests/cpp/tree/hist/test_histogram.cc
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/*!
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* Copyright 2018-2021 by Contributors
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*/
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#include <gtest/gtest.h>
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#include "../../helpers.h"
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#include "../../../../src/tree/hist/histogram.h"
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#include "../../../../src/tree/updater_quantile_hist.h"
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namespace xgboost {
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namespace tree {
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template <typename GradientSumT>
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void TestAddHistRows(bool is_distributed) {
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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 =
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RandomDataGenerator(kNRows, kNCols, 0.8).Seed(3).GenerateDMatrix();
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auto const &gmat = *(p_fmat
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->GetBatches<GHistIndexMatrix>(
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BatchParam{GenericParameter::kCpuId, kMaxBins})
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.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), 0.0f);
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nodes_for_explicit_hist_build_.emplace_back(4, tree.GetDepth(4), 0.0f);
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nodes_for_subtraction_trick_.emplace_back(5, tree.GetDepth(5), 0.0f);
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nodes_for_subtraction_trick_.emplace_back(6, tree.GetDepth(6), 0.0f);
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HistogramBuilder<GradientSumT, CPUExpandEntry> histogram_builder;
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histogram_builder.Reset(gmat.cut.TotalBins(), kMaxBins, omp_get_max_threads(),
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is_distributed);
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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<float>(true);
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TestAddHistRows<double>(true);
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TestAddHistRows<float>(false);
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TestAddHistRows<double>(false);
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}
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template <typename GradientSumT>
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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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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 =
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RandomDataGenerator(kNRows, kNCols, 0.8).Seed(3).GenerateDMatrix();
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auto const &gmat = *(p_fmat
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->GetBatches<GHistIndexMatrix>(
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BatchParam{GenericParameter::kCpuId, kMaxBins})
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.begin());
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HistogramBuilder<GradientSumT, CPUExpandEntry> histogram;
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uint32_t total_bins = gmat.cut.Ptrs().back();
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histogram.Reset(total_bins, kMaxBins, omp_get_max_threads(), is_distributed);
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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), 0.0f);
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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(),
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tree.GetDepth(1), 0.0f);
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nodes_for_subtraction_trick_.emplace_back(tree[0].RightChild(),
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tree.GetDepth(2), 0.0f);
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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), 0.0f);
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nodes_for_subtraction_trick_.emplace_back(4, tree.GetDepth(4), 0.0f);
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nodes_for_explicit_hist_build_.emplace_back(5, tree.GetDepth(5), 0.0f);
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nodes_for_subtraction_trick_.emplace_back(6, tree.GetDepth(6), 0.0f);
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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<GradientSumT>> 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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GradientSumT *p_hist = reinterpret_cast<GradientSumT *>(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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GradientSumT *p_hist = reinterpret_cast<GradientSumT *>(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_, starting_index,
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sync_count);
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}
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using GHistRowT = common::GHistRow<GradientSumT>;
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auto check_hist = [](const GHistRowT parent, const GHistRowT left,
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const GHistRowT right, size_t begin, size_t end) {
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const GradientSumT *p_parent =
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reinterpret_cast<const GradientSumT *>(parent.data());
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const GradientSumT *p_left =
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reinterpret_cast<const GradientSumT *>(left.data());
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const GradientSumT *p_right =
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reinterpret_cast<const GradientSumT *>(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<float>(true);
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TestSyncHist<double>(true);
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TestSyncHist<float>(false);
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TestSyncHist<double>(false);
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}
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template <typename GradientSumT>
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void TestBuildHistogram(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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auto p_fmat =
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RandomDataGenerator(kNRows, kNCols, 0.8).Seed(3).GenerateDMatrix();
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auto const &gmat = *(p_fmat
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->GetBatches<GHistIndexMatrix>(
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BatchParam{GenericParameter::kCpuId, kMaxBins})
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.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<GradientSumT, CPUExpandEntry> histogram;
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histogram.Reset(total_bins, kMaxBins, omp_get_max_threads(), is_distributed);
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RegTree tree;
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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(CPUExpandEntry::kRootNid, tree.GetDepth(0), 0.0f);
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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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histogram.BuildHist(p_fmat.get(), &tree, row_set_collection_,
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nodes_for_explicit_hist_build_, {}, gpair);
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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<float>(true);
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TestBuildHistogram<double>(true);
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TestBuildHistogram<float>(false);
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TestBuildHistogram<double>(false);
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}
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} // namespace tree
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} // namespace xgboost
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