443 lines
17 KiB
C++
443 lines
17 KiB
C++
/**
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* Copyright 2020-2024 by XGBoost contributors
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*/
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#include <gtest/gtest.h>
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#include <oneapi/dpl/random>
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#include "../../../plugin/sycl/tree/hist_updater.h"
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#include "../../../plugin/sycl/device_manager.h"
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#include "../helpers.h"
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namespace xgboost::sycl::tree {
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// Use this class to test the protected methods of HistUpdater
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template <typename GradientSumT>
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class TestHistUpdater : public HistUpdater<GradientSumT> {
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public:
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TestHistUpdater(const Context* ctx,
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::sycl::queue qu,
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const xgboost::tree::TrainParam& param,
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std::unique_ptr<TreeUpdater> pruner,
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FeatureInteractionConstraintHost int_constraints_,
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DMatrix const* fmat) : HistUpdater<GradientSumT>(ctx, qu, param,
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std::move(pruner),
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int_constraints_, fmat) {}
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void TestInitSampling(const USMVector<GradientPair, MemoryType::on_device> &gpair,
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USMVector<size_t, MemoryType::on_device>* row_indices) {
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HistUpdater<GradientSumT>::InitSampling(gpair, row_indices);
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}
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auto* TestInitData(const common::GHistIndexMatrix& gmat,
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const USMVector<GradientPair, MemoryType::on_device> &gpair,
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const DMatrix& fmat,
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const RegTree& tree) {
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HistUpdater<GradientSumT>::InitData(gmat, gpair, fmat, tree);
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return &(HistUpdater<GradientSumT>::row_set_collection_);
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}
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const auto* TestBuildHistogramsLossGuide(ExpandEntry entry,
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const common::GHistIndexMatrix &gmat,
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RegTree *p_tree,
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const USMVector<GradientPair, MemoryType::on_device> &gpair) {
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HistUpdater<GradientSumT>::BuildHistogramsLossGuide(entry, gmat, p_tree, gpair);
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return &(HistUpdater<GradientSumT>::hist_);
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}
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auto TestInitNewNode(int nid,
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const common::GHistIndexMatrix& gmat,
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const USMVector<GradientPair, MemoryType::on_device> &gpair,
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const DMatrix& fmat,
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const RegTree& tree) {
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HistUpdater<GradientSumT>::InitNewNode(nid, gmat, gpair, fmat, tree);
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return HistUpdater<GradientSumT>::snode_host_[nid];
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}
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auto TestEvaluateSplits(const std::vector<ExpandEntry>& nodes_set,
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const common::GHistIndexMatrix& gmat,
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const RegTree& tree) {
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HistUpdater<GradientSumT>::EvaluateSplits(nodes_set, gmat, tree);
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return HistUpdater<GradientSumT>::snode_host_;
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}
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};
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void GenerateRandomGPairs(::sycl::queue* qu, GradientPair* gpair_ptr, size_t num_rows, bool has_neg_hess) {
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qu->submit([&](::sycl::handler& cgh) {
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cgh.parallel_for<>(::sycl::range<1>(::sycl::range<1>(num_rows)),
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[=](::sycl::item<1> pid) {
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uint64_t i = pid.get_linear_id();
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constexpr uint32_t seed = 777;
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oneapi::dpl::minstd_rand engine(seed, i);
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GradientPair::ValueT smallest_hess_val = has_neg_hess ? -1. : 0.;
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oneapi::dpl::uniform_real_distribution<GradientPair::ValueT> distr(smallest_hess_val, 1.);
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gpair_ptr[i] = {distr(engine), distr(engine)};
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});
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});
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qu->wait();
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}
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template <typename GradientSumT>
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void TestHistUpdaterSampling(const xgboost::tree::TrainParam& param) {
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const size_t num_rows = 1u << 12;
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const size_t num_columns = 1;
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Context ctx;
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ctx.UpdateAllowUnknown(Args{{"device", "sycl"}});
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DeviceManager device_manager;
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auto qu = device_manager.GetQueue(ctx.Device());
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ObjInfo task{ObjInfo::kRegression};
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auto p_fmat = RandomDataGenerator{num_rows, num_columns, 0.0}.GenerateDMatrix();
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FeatureInteractionConstraintHost int_constraints;
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std::unique_ptr<TreeUpdater> pruner{TreeUpdater::Create("prune", &ctx, &task)};
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TestHistUpdater<GradientSumT> updater(&ctx, qu, param, std::move(pruner), int_constraints, p_fmat.get());
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USMVector<size_t, MemoryType::on_device> row_indices_0(&qu, num_rows);
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USMVector<size_t, MemoryType::on_device> row_indices_1(&qu, num_rows);
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USMVector<GradientPair, MemoryType::on_device> gpair(&qu, num_rows);
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GenerateRandomGPairs(&qu, gpair.Data(), num_rows, true);
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updater.TestInitSampling(gpair, &row_indices_0);
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size_t n_samples = row_indices_0.Size();
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// Half of gpairs have neg hess
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ASSERT_LT(n_samples, num_rows * 0.5 * param.subsample * 1.2);
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ASSERT_GT(n_samples, num_rows * 0.5 * param.subsample / 1.2);
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// Check if two lanunches generate different realisations:
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updater.TestInitSampling(gpair, &row_indices_1);
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if (row_indices_1.Size() == n_samples) {
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std::vector<size_t> row_indices_0_host(n_samples);
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std::vector<size_t> row_indices_1_host(n_samples);
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qu.memcpy(row_indices_0_host.data(), row_indices_0.Data(), n_samples * sizeof(size_t)).wait();
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qu.memcpy(row_indices_1_host.data(), row_indices_1.Data(), n_samples * sizeof(size_t)).wait();
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// The order in row_indices_0 and row_indices_1 can be different
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std::set<size_t> rows;
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for (auto row : row_indices_0_host) {
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rows.insert(row);
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}
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size_t num_diffs = 0;
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for (auto row : row_indices_1_host) {
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if (rows.count(row) == 0) num_diffs++;
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}
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ASSERT_NE(num_diffs, 0);
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}
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}
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template <typename GradientSumT>
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void TestHistUpdaterInitData(const xgboost::tree::TrainParam& param, bool has_neg_hess) {
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const size_t num_rows = 1u << 8;
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const size_t num_columns = 1;
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const size_t n_bins = 32;
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Context ctx;
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ctx.UpdateAllowUnknown(Args{{"device", "sycl"}});
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DeviceManager device_manager;
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auto qu = device_manager.GetQueue(ctx.Device());
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ObjInfo task{ObjInfo::kRegression};
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auto p_fmat = RandomDataGenerator{num_rows, num_columns, 0.0}.GenerateDMatrix();
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FeatureInteractionConstraintHost int_constraints;
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std::unique_ptr<TreeUpdater> pruner{TreeUpdater::Create("prune", &ctx, &task)};
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TestHistUpdater<GradientSumT> updater(&ctx, qu, param, std::move(pruner), int_constraints, p_fmat.get());
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USMVector<GradientPair, MemoryType::on_device> gpair(&qu, num_rows);
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GenerateRandomGPairs(&qu, gpair.Data(), num_rows, has_neg_hess);
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DeviceMatrix dmat;
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dmat.Init(qu, p_fmat.get());
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common::GHistIndexMatrix gmat;
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gmat.Init(qu, &ctx, dmat, n_bins);
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RegTree tree;
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auto* row_set_collection = updater.TestInitData(gmat, gpair, *p_fmat, tree);
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auto& row_indices = row_set_collection->Data();
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std::vector<size_t> row_indices_host(row_indices.Size());
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qu.memcpy(row_indices_host.data(), row_indices.DataConst(), row_indices.Size()*sizeof(size_t)).wait();
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if (!has_neg_hess) {
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for (size_t i = 0; i < num_rows; ++i) {
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ASSERT_EQ(row_indices_host[i], i);
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}
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} else {
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std::vector<GradientPair> gpair_host(num_rows);
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qu.memcpy(gpair_host.data(), gpair.Data(), num_rows*sizeof(GradientPair)).wait();
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std::set<size_t> rows;
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for (size_t i = 0; i < num_rows; ++i) {
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if (gpair_host[i].GetHess() >= 0.0f) {
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rows.insert(i);
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}
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}
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ASSERT_EQ(rows.size(), row_indices_host.size());
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for (size_t row_idx : row_indices_host) {
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ASSERT_EQ(rows.count(row_idx), 1);
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}
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}
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}
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template <typename GradientSumT>
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void TestHistUpdaterBuildHistogramsLossGuide(const xgboost::tree::TrainParam& param, float sparsity) {
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const size_t num_rows = 1u << 8;
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const size_t num_columns = 1;
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const size_t n_bins = 32;
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Context ctx;
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ctx.UpdateAllowUnknown(Args{{"device", "sycl"}});
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DeviceManager device_manager;
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auto qu = device_manager.GetQueue(ctx.Device());
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ObjInfo task{ObjInfo::kRegression};
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auto p_fmat = RandomDataGenerator{num_rows, num_columns, sparsity}.GenerateDMatrix();
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FeatureInteractionConstraintHost int_constraints;
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std::unique_ptr<TreeUpdater> pruner{TreeUpdater::Create("prune", &ctx, &task)};
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TestHistUpdater<GradientSumT> updater(&ctx, qu, param, std::move(pruner), int_constraints, p_fmat.get());
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updater.SetHistSynchronizer(new BatchHistSynchronizer<GradientSumT>());
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updater.SetHistRowsAdder(new BatchHistRowsAdder<GradientSumT>());
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USMVector<GradientPair, MemoryType::on_device> gpair(&qu, num_rows);
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auto* gpair_ptr = gpair.Data();
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GenerateRandomGPairs(&qu, gpair_ptr, num_rows, false);
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DeviceMatrix dmat;
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dmat.Init(qu, p_fmat.get());
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common::GHistIndexMatrix gmat;
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gmat.Init(qu, &ctx, dmat, n_bins);
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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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ExpandEntry node0(0, tree.GetDepth(0));
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ExpandEntry node1(1, tree.GetDepth(1));
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ExpandEntry node2(2, tree.GetDepth(2));
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auto* row_set_collection = updater.TestInitData(gmat, gpair, *p_fmat, tree);
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row_set_collection->AddSplit(0, 1, 2, 42, num_rows - 42);
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updater.TestBuildHistogramsLossGuide(node0, gmat, &tree, gpair);
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const auto* hist = updater.TestBuildHistogramsLossGuide(node1, gmat, &tree, gpair);
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ASSERT_EQ((*hist)[0].Size(), n_bins);
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ASSERT_EQ((*hist)[1].Size(), n_bins);
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ASSERT_EQ((*hist)[2].Size(), n_bins);
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std::vector<xgboost::detail::GradientPairInternal<GradientSumT>> hist0_host(n_bins);
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std::vector<xgboost::detail::GradientPairInternal<GradientSumT>> hist1_host(n_bins);
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std::vector<xgboost::detail::GradientPairInternal<GradientSumT>> hist2_host(n_bins);
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qu.memcpy(hist0_host.data(), (*hist)[0].DataConst(), sizeof(xgboost::detail::GradientPairInternal<GradientSumT>) * n_bins);
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qu.memcpy(hist1_host.data(), (*hist)[1].DataConst(), sizeof(xgboost::detail::GradientPairInternal<GradientSumT>) * n_bins);
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qu.memcpy(hist2_host.data(), (*hist)[2].DataConst(), sizeof(xgboost::detail::GradientPairInternal<GradientSumT>) * n_bins);
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qu.wait();
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for (size_t idx_bin = 0; idx_bin < n_bins; ++idx_bin) {
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EXPECT_NEAR(hist0_host[idx_bin].GetGrad(), hist1_host[idx_bin].GetGrad() + hist2_host[idx_bin].GetGrad(), 1e-6);
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EXPECT_NEAR(hist0_host[idx_bin].GetHess(), hist1_host[idx_bin].GetHess() + hist2_host[idx_bin].GetHess(), 1e-6);
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}
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}
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template <typename GradientSumT>
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void TestHistUpdaterInitNewNode(const xgboost::tree::TrainParam& param, float sparsity) {
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const size_t num_rows = 1u << 8;
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const size_t num_columns = 1;
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const size_t n_bins = 32;
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Context ctx;
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ctx.UpdateAllowUnknown(Args{{"device", "sycl"}});
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DeviceManager device_manager;
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auto qu = device_manager.GetQueue(ctx.Device());
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ObjInfo task{ObjInfo::kRegression};
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auto p_fmat = RandomDataGenerator{num_rows, num_columns, sparsity}.GenerateDMatrix();
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FeatureInteractionConstraintHost int_constraints;
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std::unique_ptr<TreeUpdater> pruner{TreeUpdater::Create("prune", &ctx, &task)};
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TestHistUpdater<GradientSumT> updater(&ctx, qu, param, std::move(pruner), int_constraints, p_fmat.get());
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updater.SetHistSynchronizer(new BatchHistSynchronizer<GradientSumT>());
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updater.SetHistRowsAdder(new BatchHistRowsAdder<GradientSumT>());
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USMVector<GradientPair, MemoryType::on_device> gpair(&qu, num_rows);
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auto* gpair_ptr = gpair.Data();
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GenerateRandomGPairs(&qu, gpair_ptr, num_rows, false);
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DeviceMatrix dmat;
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dmat.Init(qu, p_fmat.get());
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common::GHistIndexMatrix gmat;
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gmat.Init(qu, &ctx, dmat, n_bins);
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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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ExpandEntry node(ExpandEntry::kRootNid, tree.GetDepth(ExpandEntry::kRootNid));
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auto* row_set_collection = updater.TestInitData(gmat, gpair, *p_fmat, tree);
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auto& row_idxs = row_set_collection->Data();
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const size_t* row_idxs_ptr = row_idxs.DataConst();
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updater.TestBuildHistogramsLossGuide(node, gmat, &tree, gpair);
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const auto snode = updater.TestInitNewNode(ExpandEntry::kRootNid, gmat, gpair, *p_fmat, tree);
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GradStats<GradientSumT> grad_stat;
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{
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::sycl::buffer<GradStats<GradientSumT>> buff(&grad_stat, 1);
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qu.submit([&](::sycl::handler& cgh) {
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auto buff_acc = buff.template get_access<::sycl::access::mode::read_write>(cgh);
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cgh.single_task<>([=]() {
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for (size_t i = 0; i < num_rows; ++i) {
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size_t row_idx = row_idxs_ptr[i];
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buff_acc[0] += GradStats<GradientSumT>(gpair_ptr[row_idx].GetGrad(),
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gpair_ptr[row_idx].GetHess());
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}
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});
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}).wait_and_throw();
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}
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EXPECT_NEAR(snode.stats.GetGrad(), grad_stat.GetGrad(), 1e-6 * grad_stat.GetGrad());
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EXPECT_NEAR(snode.stats.GetHess(), grad_stat.GetHess(), 1e-6 * grad_stat.GetHess());
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}
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template <typename GradientSumT>
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void TestHistUpdaterEvaluateSplits(const xgboost::tree::TrainParam& param) {
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const size_t num_rows = 1u << 8;
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const size_t num_columns = 2;
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const size_t n_bins = 32;
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Context ctx;
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ctx.UpdateAllowUnknown(Args{{"device", "sycl"}});
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DeviceManager device_manager;
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auto qu = device_manager.GetQueue(ctx.Device());
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ObjInfo task{ObjInfo::kRegression};
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auto p_fmat = RandomDataGenerator{num_rows, num_columns, 0.0f}.GenerateDMatrix();
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FeatureInteractionConstraintHost int_constraints;
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std::unique_ptr<TreeUpdater> pruner{TreeUpdater::Create("prune", &ctx, &task)};
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TestHistUpdater<GradientSumT> updater(&ctx, qu, param, std::move(pruner), int_constraints, p_fmat.get());
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updater.SetHistSynchronizer(new BatchHistSynchronizer<GradientSumT>());
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updater.SetHistRowsAdder(new BatchHistRowsAdder<GradientSumT>());
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USMVector<GradientPair, MemoryType::on_device> gpair(&qu, num_rows);
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auto* gpair_ptr = gpair.Data();
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GenerateRandomGPairs(&qu, gpair_ptr, num_rows, false);
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DeviceMatrix dmat;
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dmat.Init(qu, p_fmat.get());
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common::GHistIndexMatrix gmat;
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gmat.Init(qu, &ctx, dmat, n_bins);
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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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ExpandEntry node(ExpandEntry::kRootNid, tree.GetDepth(ExpandEntry::kRootNid));
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auto* row_set_collection = updater.TestInitData(gmat, gpair, *p_fmat, tree);
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auto& row_idxs = row_set_collection->Data();
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const size_t* row_idxs_ptr = row_idxs.DataConst();
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const auto* hist = updater.TestBuildHistogramsLossGuide(node, gmat, &tree, gpair);
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const auto snode_init = updater.TestInitNewNode(ExpandEntry::kRootNid, gmat, gpair, *p_fmat, tree);
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const auto snode_updated = updater.TestEvaluateSplits({node}, gmat, tree);
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auto best_loss_chg = snode_updated[0].best.loss_chg;
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auto stats = snode_init.stats;
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auto root_gain = snode_init.root_gain;
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// Check all splits manually. Save the best one and compare with the ans
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TreeEvaluator<GradientSumT> tree_evaluator(qu, param, num_columns);
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auto evaluator = tree_evaluator.GetEvaluator();
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const uint32_t* cut_ptr = gmat.cut_device.Ptrs().DataConst();
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const size_t size = gmat.cut_device.Ptrs().Size();
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int n_better_splits = 0;
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const auto* hist_ptr = (*hist)[0].DataConst();
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std::vector<bst_float> best_loss_chg_des(1, -1);
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{
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::sycl::buffer<bst_float> best_loss_chg_buff(best_loss_chg_des.data(), 1);
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qu.submit([&](::sycl::handler& cgh) {
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auto best_loss_chg_acc = best_loss_chg_buff.template get_access<::sycl::access::mode::read_write>(cgh);
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cgh.single_task<>([=]() {
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for (size_t i = 1; i < size; ++i) {
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GradStats<GradientSumT> left(0, 0);
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GradStats<GradientSumT> right = stats - left;
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for (size_t j = cut_ptr[i-1]; j < cut_ptr[i]; ++j) {
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auto loss_change = evaluator.CalcSplitGain(0, i - 1, left, right) - root_gain;
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if (loss_change > best_loss_chg_acc[0]) {
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best_loss_chg_acc[0] = loss_change;
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}
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left.Add(hist_ptr[j].GetGrad(), hist_ptr[j].GetHess());
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right = stats - left;
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}
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}
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});
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}).wait();
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}
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ASSERT_NEAR(best_loss_chg_des[0], best_loss_chg, 1e-6);
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}
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TEST(SyclHistUpdater, Sampling) {
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xgboost::tree::TrainParam param;
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param.UpdateAllowUnknown(Args{{"subsample", "0.7"}});
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TestHistUpdaterSampling<float>(param);
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TestHistUpdaterSampling<double>(param);
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}
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TEST(SyclHistUpdater, InitData) {
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xgboost::tree::TrainParam param;
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param.UpdateAllowUnknown(Args{{"subsample", "1"}});
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TestHistUpdaterInitData<float>(param, true);
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TestHistUpdaterInitData<float>(param, false);
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TestHistUpdaterInitData<double>(param, true);
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TestHistUpdaterInitData<double>(param, false);
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}
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TEST(SyclHistUpdater, BuildHistogramsLossGuide) {
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xgboost::tree::TrainParam param;
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param.UpdateAllowUnknown(Args{{"max_depth", "3"}});
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TestHistUpdaterBuildHistogramsLossGuide<float>(param, 0.0);
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TestHistUpdaterBuildHistogramsLossGuide<float>(param, 0.5);
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TestHistUpdaterBuildHistogramsLossGuide<double>(param, 0.0);
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TestHistUpdaterBuildHistogramsLossGuide<double>(param, 0.5);
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}
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TEST(SyclHistUpdater, InitNewNode) {
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xgboost::tree::TrainParam param;
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param.UpdateAllowUnknown(Args{{"max_depth", "3"}});
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TestHistUpdaterInitNewNode<float>(param, 0.0);
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TestHistUpdaterInitNewNode<float>(param, 0.5);
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TestHistUpdaterInitNewNode<double>(param, 0.0);
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TestHistUpdaterInitNewNode<double>(param, 0.5);
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}
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TEST(SyclHistUpdater, EvaluateSplits) {
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xgboost::tree::TrainParam param;
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param.UpdateAllowUnknown(Args{{"max_depth", "3"}});
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TestHistUpdaterEvaluateSplits<float>(param);
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TestHistUpdaterEvaluateSplits<double>(param);
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}
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} // namespace xgboost::sycl::tree
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