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Support column split in multi-target hist (#9171)

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This commit is contained in:
Rong Ou
2023-05-26 01:56:05 -07:00
committed by GitHub
parent acd363033e
commit 5b69534b43
17 changed files with 386 additions and 96 deletions
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7
tests/cpp/plugin/helpers.h
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@@ -13,6 +13,7 @@
#include "../../../plugin/federated/federated_server.h"
#include "../../../src/collective/communicator-inl.h"
#include "../../../src/common/threading_utils.h"
namespace xgboost {
@@ -75,11 +76,7 @@ void RunWithFederatedCommunicator(int32_t world_size, std::string const& server_
xgboost::collective::Finalize();
};
#if defined(_OPENMP)
#pragma omp parallel num_threads(world_size)
{
auto rank = omp_get_thread_num();
run(rank);
}
common::ParallelFor(world_size, world_size, run);
#else
std::vector<std::thread> threads;
for (auto rank = 0; rank < world_size; rank++) {

23
tests/cpp/plugin/test_federated_learner.cc
View File

@@ -15,9 +15,9 @@
namespace xgboost {
namespace {
auto MakeModel(std::string objective, std::shared_ptr<DMatrix> dmat) {
auto MakeModel(std::string tree_method, std::string objective, std::shared_ptr<DMatrix> dmat) {
std::unique_ptr<Learner> learner{Learner::Create({dmat})};
learner->SetParam("tree_method", "approx");
learner->SetParam("tree_method", tree_method);
learner->SetParam("objective", objective);
if (objective.find("quantile") != std::string::npos) {
learner->SetParam("quantile_alpha", "0.5");
@@ -35,7 +35,7 @@ auto MakeModel(std::string objective, std::shared_ptr<DMatrix> dmat) {
}
void VerifyObjective(size_t rows, size_t cols, float expected_base_score, Json expected_model,
std::string objective) {
std::string tree_method, std::string objective) {
auto const world_size = collective::GetWorldSize();
auto const rank = collective::GetRank();
std::shared_ptr<DMatrix> dmat{RandomDataGenerator{rows, cols, 0}.GenerateDMatrix(rank == 0)};
@@ -61,7 +61,7 @@ void VerifyObjective(size_t rows, size_t cols, float expected_base_score, Json e
}
std::shared_ptr<DMatrix> sliced{dmat->SliceCol(world_size, rank)};
auto model = MakeModel(objective, sliced);
auto model = MakeModel(tree_method, objective, sliced);
auto base_score = GetBaseScore(model);
ASSERT_EQ(base_score, expected_base_score);
ASSERT_EQ(model, expected_model);
@@ -76,7 +76,7 @@ class FederatedLearnerTest : public ::testing::TestWithParam<std::string> {
void SetUp() override { server_ = std::make_unique<ServerForTest>(kWorldSize); }
void TearDown() override { server_.reset(nullptr); }
void Run(std::string objective) {
void Run(std::string tree_method, std::string objective) {
static auto constexpr kRows{16};
static auto constexpr kCols{16};
@@ -99,17 +99,22 @@ class FederatedLearnerTest : public ::testing::TestWithParam<std::string> {
}
}
auto model = MakeModel(objective, dmat);
auto model = MakeModel(tree_method, objective, dmat);
auto score = GetBaseScore(model);
RunWithFederatedCommunicator(kWorldSize, server_->Address(), &VerifyObjective, kRows, kCols,
score, model, objective);
score, model, tree_method, objective);
}
};
TEST_P(FederatedLearnerTest, Objective) {
TEST_P(FederatedLearnerTest, Approx) {
std::string objective = GetParam();
this->Run(objective);
this->Run("approx", objective);
}
TEST_P(FederatedLearnerTest, Hist) {
std::string objective = GetParam();
this->Run("hist", objective);
}
INSTANTIATE_TEST_SUITE_P(FederatedLearnerObjective, FederatedLearnerTest,