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Convert federated tests into test suite. (#9006)

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- Add specialization for learning to rank.
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Jiaming Yuan 2023-04-04 01:29:47 +08:00 committed by GitHub
parent 15e073ca9d
commit 1cf4d93246
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1 changed files with 111 additions and 56 deletions
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167
tests/cpp/test_learner.cc
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@ -1,22 +1,48 @@
/*!
* Copyright 2017-2023 by XGBoost contributors
/**
* Copyright (c) 2017-2023, XGBoost contributors
*/
#include <gtest/gtest.h>
#include <xgboost/learner.h>
#include <xgboost/objective.h> // ObjFunction
#include <xgboost/version_config.h>
#include <xgboost/learner.h> // for Learner
#include <xgboost/logging.h> // for LogCheck_NE, CHECK_NE, LogCheck_EQ
#include <xgboost/objective.h> // for ObjFunction
#include <xgboost/version_config.h> // for XGBOOST_VER_MAJOR, XGBOOST_VER_MINOR
#include <string> // std::stof, std::string
#include <thread>
#include <vector>
#include <algorithm> // for equal, transform
#include <cinttypes> // for int32_t, int64_t, uint32_t
#include <cstddef> // for size_t
#include <iosfwd> // for ofstream
#include <iterator> // for back_insert_iterator, back_inserter
#include <limits> // for numeric_limits
#include <map> // for map
#include <memory> // for unique_ptr, shared_ptr, __shared_ptr_...
#include <random> // for uniform_real_distribution
#include <string> // for allocator, basic_string, string, oper...
#include <thread> // for thread
#include <type_traits> // for is_integral
#include <utility> // for pair
#include <vector> // for vector
#include "../../src/common/api_entry.h" // XGBAPIThreadLocalEntry
#include "../../src/common/io.h"
#include "../../src/common/linalg_op.h"
#include "../../src/common/random.h"
#include "filesystem.h" // dmlc::TemporaryDirectory
#include "helpers.h"
#include "xgboost/json.h"
#include "../../src/collective/communicator-inl.h" // for GetRank, GetWorldSize
#include "../../src/common/api_entry.h" // for XGBAPIThreadLocalEntry
#include "../../src/common/io.h" // for LoadSequentialFile
#include "../../src/common/linalg_op.h" // for ElementWiseTransformHost, begin, end
#include "../../src/common/random.h" // for GlobalRandom
#include "../../src/common/transform_iterator.h" // for IndexTransformIter
#include "dmlc/io.h" // for Stream
#include "dmlc/omp.h" // for omp_get_max_threads
#include "dmlc/registry.h" // for Registry
#include "filesystem.h" // for TemporaryDirectory
#include "helpers.h" // for GetBaseScore, RandomDataGenerator
#include "xgboost/base.h" // for bst_float, Args, bst_feature_t, bst_int
#include "xgboost/context.h" // for Context
#include "xgboost/data.h" // for DMatrix, MetaInfo, DataType
#include "xgboost/host_device_vector.h" // for HostDeviceVector
#include "xgboost/json.h" // for Json, Object, get, String, IsA, opera...
#include "xgboost/linalg.h" // for Tensor, TensorView
#include "xgboost/logging.h" // for ConsoleLogger
#include "xgboost/predictor.h" // for PredictionCacheEntry
#include "xgboost/span.h" // for Span, operator!=, SpanIterator
#include "xgboost/string_view.h" // for StringView
namespace xgboost {
TEST(Learner, Basic) {
@ -608,74 +634,103 @@ TEST_F(InitBaseScore, InitWithPredict) { this->TestInitWithPredt(); }
TEST_F(InitBaseScore, UpdateProcess) { this->TestUpdateProcess(); }
void TestColumnSplit(std::shared_ptr<DMatrix> dmat, std::vector<float> const& expected_base_scores,
std::vector<Json> const& expected_models) {
auto const world_size = collective::GetWorldSize();
auto const rank = collective::GetRank();
std::shared_ptr<DMatrix> sliced{dmat->SliceCol(world_size, rank)};
class TestColumnSplit : public ::testing::TestWithParam<std::string> {
static auto MakeFmat(std::string const& obj) {
auto constexpr kRows = 10, kCols = 10;
auto p_fmat = RandomDataGenerator{kRows, kCols, 0}.GenerateDMatrix(true);
auto& h_upper = p_fmat->Info().labels_upper_bound_.HostVector();
auto& h_lower = p_fmat->Info().labels_lower_bound_.HostVector();
h_lower.resize(kRows);
h_upper.resize(kRows);
for (size_t i = 0; i < kRows; ++i) {
h_lower[i] = 1;
h_upper[i] = 10;
}
if (obj.find("rank:") != std::string::npos) {
auto h_label = p_fmat->Info().labels.HostView();
std::size_t k = 0;
for (auto& v : h_label) {
v = k % 2 == 0;
++k;
}
}
return p_fmat;
};
auto i = 0;
for (auto const* entry : ::dmlc::Registry<::xgboost::ObjFunctionReg>::List()) {
void TestBaseScore(std::string objective, float expected_base_score, Json expected_model) {
auto const world_size = collective::GetWorldSize();
auto const rank = collective::GetRank();
auto p_fmat = MakeFmat(objective);
std::shared_ptr<DMatrix> sliced{p_fmat->SliceCol(world_size, rank)};
std::unique_ptr<Learner> learner{Learner::Create({sliced})};
learner->SetParam("tree_method", "approx");
learner->SetParam("objective", entry->name);
if (entry->name.find("quantile") != std::string::npos) {
learner->SetParam("objective", objective);
if (objective.find("quantile") != std::string::npos) {
learner->SetParam("quantile_alpha", "0.5");
}
if (entry->name.find("multi") != std::string::npos) {
if (objective.find("multi") != std::string::npos) {
learner->SetParam("num_class", "3");
}
learner->UpdateOneIter(0, sliced);
Json config{Object{}};
learner->SaveConfig(&config);
auto base_score = GetBaseScore(config);
ASSERT_EQ(base_score, expected_base_scores[i]);
ASSERT_EQ(base_score, expected_base_score);
Json model{Object{}};
learner->SaveModel(&model);
ASSERT_EQ(model, expected_models[i]);
i++;
}
}
TEST(ColumnSplit, Objectives) {
auto constexpr kRows = 10, kCols = 10;
std::shared_ptr<DMatrix> dmat{RandomDataGenerator{kRows, kCols, 0}.GenerateDMatrix(true)};
auto& h_upper = dmat->Info().labels_upper_bound_.HostVector();
auto& h_lower = dmat->Info().labels_lower_bound_.HostVector();
h_lower.resize(kRows);
h_upper.resize(kRows);
for (size_t i = 0; i < kRows; ++i) {
h_lower[i] = 1;
h_upper[i] = 10;
ASSERT_EQ(model, expected_model);
}
std::vector<float> base_scores;
std::vector<Json> models;
for (auto const* entry : ::dmlc::Registry<::xgboost::ObjFunctionReg>::List()) {
std::unique_ptr<Learner> learner{Learner::Create({dmat})};
public:
void Run(std::string objective) {
auto p_fmat = MakeFmat(objective);
std::unique_ptr<Learner> learner{Learner::Create({p_fmat})};
learner->SetParam("tree_method", "approx");
learner->SetParam("objective", entry->name);
if (entry->name.find("quantile") != std::string::npos) {
learner->SetParam("objective", objective);
if (objective.find("quantile") != std::string::npos) {
learner->SetParam("quantile_alpha", "0.5");
}
if (entry->name.find("multi") != std::string::npos) {
if (objective.find("multi") != std::string::npos) {
learner->SetParam("num_class", "3");
}
learner->UpdateOneIter(0, dmat);
learner->UpdateOneIter(0, p_fmat);
Json config{Object{}};
learner->SaveConfig(&config);
base_scores.emplace_back(GetBaseScore(config));
Json model{Object{}};
learner->SaveModel(&model);
models.emplace_back(model);
}
auto constexpr kWorldSize{3};
RunWithInMemoryCommunicator(kWorldSize, &TestColumnSplit, dmat, base_scores, models);
auto constexpr kWorldSize{3};
auto call = [this, &objective](auto&... args) { TestBaseScore(objective, args...); };
auto score = GetBaseScore(config);
RunWithInMemoryCommunicator(kWorldSize, call, score, model);
}
};
TEST_P(TestColumnSplit, Objective) {
std::string objective = GetParam();
this->Run(objective);
}
auto MakeValues() {
auto list = ::dmlc::Registry<::xgboost::ObjFunctionReg>::List();
std::vector<std::string> names;
std::transform(list.cbegin(), list.cend(), std::back_inserter(names),
[](auto const* entry) { return entry->name; });
return names;
}
INSTANTIATE_TEST_SUITE_P(ColumnSplitObjective, TestColumnSplit, ::testing::ValuesIn(MakeValues()),
[](const ::testing::TestParamInfo<TestColumnSplit::ParamType>& info) {
auto name = std::string{info.param};
// Name must be a valid c++ symbol
auto it = std::find(name.cbegin(), name.cend(), ':');
if (it != name.cend()) {
name[std::distance(name.cbegin(), it)] = '_';
}
return name;
});
} // namespace xgboost