3760cede0f
- Use context in all tests. - Use context in R. - Use context in C API DMatrix initialization. (0 threads is used as dft).
126 lines
3.7 KiB
C++
126 lines
3.7 KiB
C++
/**
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* Copyright 2022-2023 XGBoost contributors
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*/
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#include <gtest/gtest.h>
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#include "../../../src/data/adapter.h"
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#include "../../../src/data/simple_dmatrix.h"
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#include "../helpers.h"
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#include "xgboost/context.h"
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namespace xgboost {
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namespace {
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class DMatrixForTest : public data::SimpleDMatrix {
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size_t n_regen_{0};
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public:
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using SimpleDMatrix::SimpleDMatrix;
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BatchSet<GHistIndexMatrix> GetGradientIndex(const BatchParam& param) override {
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auto backup = this->gradient_index_;
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auto iter = SimpleDMatrix::GetGradientIndex(param);
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n_regen_ += (backup != this->gradient_index_);
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return iter;
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}
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BatchSet<EllpackPage> GetEllpackBatches(const BatchParam& param) override {
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auto backup = this->ellpack_page_;
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auto iter = SimpleDMatrix::GetEllpackBatches(param);
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n_regen_ += (backup != this->ellpack_page_);
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return iter;
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}
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auto NumRegen() const { return n_regen_; }
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void Reset() {
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this->gradient_index_.reset();
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this->ellpack_page_.reset();
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n_regen_ = 0;
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}
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};
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/**
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* \brief Test for whether the gradient index is correctly regenerated.
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*/
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class RegenTest : public ::testing::Test {
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protected:
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std::shared_ptr<DMatrix> p_fmat_;
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void SetUp() override {
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size_t constexpr kRows = 256, kCols = 10;
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HostDeviceVector<float> storage;
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auto dense = RandomDataGenerator{kRows, kCols, 0.5}.GenerateArrayInterface(&storage);
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auto adapter = data::ArrayAdapter(StringView{dense});
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p_fmat_ = std::shared_ptr<DMatrix>(new DMatrixForTest{
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&adapter, std::numeric_limits<float>::quiet_NaN(), AllThreadsForTest()});
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p_fmat_->Info().labels.Reshape(256, 1);
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auto labels = p_fmat_->Info().labels.Data();
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RandomDataGenerator{kRows, 1, 0}.GenerateDense(labels);
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}
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auto constexpr Iter() const { return 4; }
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template <typename Page>
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size_t TestTreeMethod(std::string tree_method, std::string obj, bool reset = true) const {
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auto learner = std::unique_ptr<Learner>{Learner::Create({p_fmat_})};
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learner->SetParam("tree_method", tree_method);
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learner->SetParam("objective", obj);
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learner->Configure();
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for (auto i = 0; i < Iter(); ++i) {
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learner->UpdateOneIter(i, p_fmat_);
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}
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auto for_test = dynamic_cast<DMatrixForTest*>(p_fmat_.get());
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CHECK(for_test);
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auto backup = for_test->NumRegen();
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for_test->GetBatches<Page>(BatchParam{});
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CHECK_EQ(for_test->NumRegen(), backup);
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if (reset) {
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for_test->Reset();
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}
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return backup;
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}
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};
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} // anonymous namespace
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TEST_F(RegenTest, Approx) {
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auto n = this->TestTreeMethod<GHistIndexMatrix>("approx", "reg:squarederror");
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ASSERT_EQ(n, 1);
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n = this->TestTreeMethod<GHistIndexMatrix>("approx", "reg:logistic");
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ASSERT_EQ(n, this->Iter());
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}
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TEST_F(RegenTest, Hist) {
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auto n = this->TestTreeMethod<GHistIndexMatrix>("hist", "reg:squarederror");
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ASSERT_EQ(n, 1);
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n = this->TestTreeMethod<GHistIndexMatrix>("hist", "reg:logistic");
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ASSERT_EQ(n, 1);
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}
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TEST_F(RegenTest, Mixed) {
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auto n = this->TestTreeMethod<GHistIndexMatrix>("hist", "reg:squarederror", false);
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ASSERT_EQ(n, 1);
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n = this->TestTreeMethod<GHistIndexMatrix>("approx", "reg:logistic", true);
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ASSERT_EQ(n, this->Iter() + 1);
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n = this->TestTreeMethod<GHistIndexMatrix>("approx", "reg:logistic", false);
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ASSERT_EQ(n, this->Iter());
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n = this->TestTreeMethod<GHistIndexMatrix>("hist", "reg:squarederror", true);
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ASSERT_EQ(n, this->Iter() + 1);
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}
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#if defined(XGBOOST_USE_CUDA)
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TEST_F(RegenTest, GpuHist) {
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auto n = this->TestTreeMethod<EllpackPage>("gpu_hist", "reg:squarederror");
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ASSERT_EQ(n, 1);
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n = this->TestTreeMethod<EllpackPage>("gpu_hist", "reg:logistic", false);
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ASSERT_EQ(n, 1);
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n = this->TestTreeMethod<EllpackPage>("hist", "reg:logistic");
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ASSERT_EQ(n, 2);
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}
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#endif // defined(XGBOOST_USE_CUDA)
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} // namespace xgboost
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