234 lines
8.9 KiB
C++
234 lines
8.9 KiB
C++
/**
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* Copyright 2018-2024, XGBoost Contributors
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*/
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#include <gtest/gtest.h>
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#include <xgboost/host_device_vector.h>
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#include <xgboost/tree_updater.h>
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#include <cstddef> // for size_t
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#include <string>
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#include <vector>
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#include "../../../src/tree/common_row_partitioner.h"
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#include "../../../src/tree/hist/expand_entry.h" // for MultiExpandEntry, CPUExpandEntry
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#include "../collective/test_worker.h" // for TestDistributedGlobal
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#include "../helpers.h"
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#include "test_column_split.h" // for TestColumnSplit
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#include "test_partitioner.h"
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#include "xgboost/data.h"
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namespace xgboost::tree {
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namespace {
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template <typename ExpandEntry>
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void TestPartitioner(bst_target_t n_targets) {
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std::size_t n_samples = 1024, base_rowid = 0;
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bst_feature_t n_features = 1;
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Context ctx;
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ctx.InitAllowUnknown(Args{});
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CommonRowPartitioner partitioner{&ctx, n_samples, base_rowid, false};
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ASSERT_EQ(partitioner.base_rowid, base_rowid);
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ASSERT_EQ(partitioner.Size(), 1);
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ASSERT_EQ(partitioner.Partitions()[0].Size(), n_samples);
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auto Xy = RandomDataGenerator{n_samples, n_features, 0}.GenerateDMatrix(true);
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std::vector<ExpandEntry> candidates{{0, 0}};
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candidates.front().split.loss_chg = 0.4;
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auto cuts = common::SketchOnDMatrix(&ctx, Xy.get(), 64);
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for (auto const& page : Xy->GetBatches<SparsePage>()) {
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GHistIndexMatrix gmat(page, {}, cuts, 64, true, 0.5, ctx.Threads());
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bst_feature_t const split_ind = 0;
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common::ColumnMatrix column_indices;
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column_indices.InitFromSparse(page, gmat, 0.5, ctx.Threads());
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{
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auto min_value = gmat.cut.MinValues()[split_ind];
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RegTree tree{n_targets, n_features};
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CommonRowPartitioner partitioner{&ctx, n_samples, base_rowid, false};
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if constexpr (std::is_same_v<ExpandEntry, CPUExpandEntry>) {
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GetSplit(&tree, min_value, &candidates);
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} else {
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GetMultiSplitForTest(&tree, min_value, &candidates);
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}
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partitioner.UpdatePosition<false, true>(&ctx, gmat, column_indices, candidates, &tree);
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ASSERT_EQ(partitioner.Size(), 3);
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ASSERT_EQ(partitioner[1].Size(), 0);
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ASSERT_EQ(partitioner[2].Size(), n_samples);
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}
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{
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CommonRowPartitioner partitioner{&ctx, n_samples, base_rowid, false};
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auto ptr = gmat.cut.Ptrs()[split_ind + 1];
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float split_value = gmat.cut.Values().at(ptr / 2);
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RegTree tree{n_targets, n_features};
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if constexpr (std::is_same<ExpandEntry, CPUExpandEntry>::value) {
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GetSplit(&tree, split_value, &candidates);
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} else {
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GetMultiSplitForTest(&tree, split_value, &candidates);
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}
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partitioner.UpdatePosition<false, true>(&ctx, gmat, column_indices, candidates, &tree);
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{
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auto left_nidx = tree.LeftChild(RegTree::kRoot);
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auto const& elem = partitioner[left_nidx];
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ASSERT_LT(elem.Size(), n_samples);
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ASSERT_GT(elem.Size(), 1);
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for (auto& it : elem) {
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auto value = gmat.cut.Values().at(gmat.index[it]);
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ASSERT_LE(value, split_value);
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}
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}
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{
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auto right_nidx = tree.RightChild(RegTree::kRoot);
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auto const& elem = partitioner[right_nidx];
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for (auto& it : elem) {
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auto value = gmat.cut.Values().at(gmat.index[it]);
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ASSERT_GT(value, split_value);
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}
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}
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}
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}
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}
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} // anonymous namespace
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TEST(QuantileHist, Partitioner) { TestPartitioner<CPUExpandEntry>(1); }
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TEST(QuantileHist, MultiPartitioner) { TestPartitioner<MultiExpandEntry>(3); }
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namespace {
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template <typename ExpandEntry>
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void VerifyColumnSplitPartitioner(bst_target_t n_targets, size_t n_samples,
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bst_feature_t n_features, size_t base_rowid,
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std::shared_ptr<DMatrix> Xy, float min_value, float mid_value,
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CommonRowPartitioner const& expected_mid_partitioner) {
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auto dmat =
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std::unique_ptr<DMatrix>{Xy->SliceCol(collective::GetWorldSize(), collective::GetRank())};
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Context ctx;
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ctx.InitAllowUnknown(Args{});
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std::vector<ExpandEntry> candidates{{0, 0}};
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candidates.front().split.loss_chg = 0.4;
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auto cuts = common::SketchOnDMatrix(&ctx, dmat.get(), 64);
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for (auto const& page : Xy->GetBatches<SparsePage>()) {
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GHistIndexMatrix gmat(page, {}, cuts, 64, true, 0.5, ctx.Threads());
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common::ColumnMatrix column_indices;
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column_indices.InitFromSparse(page, gmat, 0.5, ctx.Threads());
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{
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RegTree tree{n_targets, n_features};
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CommonRowPartitioner partitioner{&ctx, n_samples, base_rowid, true};
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if constexpr (std::is_same<ExpandEntry, CPUExpandEntry>::value) {
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GetSplit(&tree, min_value, &candidates);
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} else {
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GetMultiSplitForTest(&tree, min_value, &candidates);
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}
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partitioner.UpdatePosition<false, true>(&ctx, gmat, column_indices, candidates, &tree);
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ASSERT_EQ(partitioner.Size(), 3);
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ASSERT_EQ(partitioner[1].Size(), 0);
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ASSERT_EQ(partitioner[2].Size(), n_samples);
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}
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{
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RegTree tree{n_targets, n_features};
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CommonRowPartitioner partitioner{&ctx, n_samples, base_rowid, true};
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if constexpr (std::is_same<ExpandEntry, CPUExpandEntry>::value) {
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GetSplit(&tree, mid_value, &candidates);
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} else {
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GetMultiSplitForTest(&tree, mid_value, &candidates);
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}
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auto left_nidx = tree.LeftChild(RegTree::kRoot);
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partitioner.UpdatePosition<false, true>(&ctx, gmat, column_indices, candidates, &tree);
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{
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auto const& elem = partitioner[left_nidx];
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ASSERT_LT(elem.Size(), n_samples);
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ASSERT_GT(elem.Size(), 1);
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auto const& expected_elem = expected_mid_partitioner[left_nidx];
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ASSERT_EQ(elem.Size(), expected_elem.Size());
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for (auto it = elem.begin(), eit = expected_elem.begin(); it != elem.end(); ++it, ++eit) {
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ASSERT_EQ(*it, *eit);
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}
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}
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{
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auto right_nidx = tree.RightChild(RegTree::kRoot);
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auto const& elem = partitioner[right_nidx];
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auto const& expected_elem = expected_mid_partitioner[right_nidx];
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ASSERT_EQ(elem.Size(), expected_elem.Size());
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for (auto it = elem.begin(), eit = expected_elem.begin(); it != elem.end(); ++it, ++eit) {
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ASSERT_EQ(*it, *eit);
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}
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}
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}
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}
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}
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template <typename ExpandEntry>
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void TestColumnSplitPartitioner(bst_target_t n_targets) {
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std::size_t n_samples = 1024, base_rowid = 0;
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bst_feature_t n_features = 16;
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auto Xy = RandomDataGenerator{n_samples, n_features, 0}.GenerateDMatrix(true);
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std::vector<ExpandEntry> candidates{{0, 0}};
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candidates.front().split.loss_chg = 0.4;
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Context ctx;
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ctx.InitAllowUnknown(Args{});
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auto cuts = common::SketchOnDMatrix(&ctx, Xy.get(), 64);
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float min_value, mid_value;
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CommonRowPartitioner mid_partitioner{&ctx, n_samples, base_rowid, false};
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for (auto const& page : Xy->GetBatches<SparsePage>()) {
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GHistIndexMatrix gmat(page, {}, cuts, 64, true, 0.5, ctx.Threads());
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bst_feature_t const split_ind = 0;
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common::ColumnMatrix column_indices;
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column_indices.InitFromSparse(page, gmat, 0.5, ctx.Threads());
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min_value = gmat.cut.MinValues()[split_ind];
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auto ptr = gmat.cut.Ptrs()[split_ind + 1];
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mid_value = gmat.cut.Values().at(ptr / 2);
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RegTree tree{n_targets, n_features};
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if constexpr (std::is_same<ExpandEntry, CPUExpandEntry>::value) {
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GetSplit(&tree, mid_value, &candidates);
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} else {
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GetMultiSplitForTest(&tree, mid_value, &candidates);
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}
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mid_partitioner.UpdatePosition<false, true>(&ctx, gmat, column_indices, candidates, &tree);
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}
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auto constexpr kWorkers = 4;
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collective::TestDistributedGlobal(kWorkers, [&] {
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VerifyColumnSplitPartitioner<ExpandEntry>(n_targets, n_samples, n_features, base_rowid, Xy,
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min_value, mid_value, mid_partitioner);
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});
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}
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} // anonymous namespace
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TEST(QuantileHist, PartitionerColSplit) { TestColumnSplitPartitioner<CPUExpandEntry>(1); }
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TEST(QuantileHist, MultiPartitionerColSplit) { TestColumnSplitPartitioner<MultiExpandEntry>(3); }
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namespace {
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class TestHistColSplit : public ::testing::TestWithParam<std::tuple<bst_target_t, bool, float>> {
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public:
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void Run() {
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auto [n_targets, categorical, sparsity] = GetParam();
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TestColumnSplit(n_targets, categorical, "grow_quantile_histmaker", sparsity);
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}
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};
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} // anonymous namespace
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TEST_P(TestHistColSplit, Basic) { this->Run(); }
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INSTANTIATE_TEST_SUITE_P(ColumnSplit, TestHistColSplit, ::testing::ValuesIn([]() {
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std::vector<std::tuple<bst_target_t, bool, float>> params;
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for (auto categorical : {true, false}) {
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for (auto sparsity : {0.0f, 0.6f}) {
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for (bst_target_t n_targets : {1u, 3u}) {
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params.emplace_back(n_targets, categorical, sparsity);
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}
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}
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}
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return params;
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}()));
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} // namespace xgboost::tree
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