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Integration tests for interaction constraints with column-wise data split #9611

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merged 4 commits into from
Sep 27, 2023
Merged

Integration tests for interaction constraints with column-wise data split #9611

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1b2d7cc
refactor colsample tests
rongou Sep 26, 2023
7b702d4
add tests for interaction constraints
rongou Sep 26, 2023
5a78afb
refactor tests
rongou Sep 26, 2023
8325844
mark mgpu tests
rongou Sep 26, 2023
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99 changes: 59 additions & 40 deletions tests/cpp/test_learner.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -720,48 +720,39 @@ INSTANTIATE_TEST_SUITE_P(ColumnSplitObjective, TestColumnSplit,
});

namespace {
void VerifyColumnSplitColumnSampler(std::string const& tree_method, bool use_gpu,
Json const& expected_model) {
Json GetModelWithArgs(std::shared_ptr<DMatrix> dmat, std::string const& tree_method,
std::string const& device, Args const& args) {
std::unique_ptr<Learner> learner{Learner::Create({dmat})};
learner->SetParam("tree_method", tree_method);
learner->SetParam("device", device);
learner->SetParam("objective", "reg:logistic");
learner->SetParams(args);
learner->UpdateOneIter(0, dmat);
Json model{Object{}};
{
auto const world_size = collective::GetWorldSize();
auto const rank = collective::GetRank();
auto const objective = "reg:logistic";
auto p_fmat = MakeFmatForObjTest(objective);
std::shared_ptr<DMatrix> sliced{p_fmat->SliceCol(world_size, rank)};
std::unique_ptr<Learner> learner{Learner::Create({sliced})};
learner->SetParam("tree_method", tree_method);
if (use_gpu) {
auto gpu_id = common::AllVisibleGPUs() == 1 ? 0 : rank;
learner->SetParam("device", "cuda:" + std::to_string(gpu_id));
}
learner->SetParam("objective", objective);
learner->SetParam("colsample_bytree", "0.5");
learner->SetParam("colsample_bylevel", "0.6");
learner->SetParam("colsample_bynode", "0.7");
learner->UpdateOneIter(0, sliced);
learner->SaveModel(&model);
learner->SaveModel(&model);
return model;
}

void VerifyColumnSplitWithArgs(std::string const& tree_method, bool use_gpu, Args const& args,
Json const& expected_model) {
auto const world_size = collective::GetWorldSize();
auto const rank = collective::GetRank();
auto p_fmat = MakeFmatForObjTest("");
std::shared_ptr<DMatrix> sliced{p_fmat->SliceCol(world_size, rank)};
std::string device = "cpu";
if (use_gpu) {
auto gpu_id = common::AllVisibleGPUs() == 1 ? 0 : rank;
device = "cuda:" + std::to_string(gpu_id);
}
auto model = GetModelWithArgs(sliced, tree_method, device, args);
ASSERT_EQ(model, expected_model);
}

void TestColumnSplitColumnSampler(std::string const& tree_method, bool use_gpu) {
Json model{Object{}};
{
auto objective = "reg:logistic";
auto p_fmat = MakeFmatForObjTest(objective);
std::unique_ptr<Learner> learner{Learner::Create({p_fmat})};
learner->SetParam("tree_method", tree_method);
if (use_gpu) {
learner->SetParam("device", "cuda:0");
}
learner->SetParam("objective", objective);
learner->SetParam("colsample_bytree", "0.5");
learner->SetParam("colsample_bylevel", "0.6");
learner->SetParam("colsample_bynode", "0.7");
learner->UpdateOneIter(0, p_fmat);
learner->SaveModel(&model);
}
void TestColumnSplitWithArgs(std::string const& tree_method, bool use_gpu, Args const& args) {
auto p_fmat = MakeFmatForObjTest("");
std::string device = use_gpu ? "cuda:0" : "cpu";
auto model = GetModelWithArgs(p_fmat, tree_method, device, args);

auto world_size{3};
if (use_gpu) {
world_size = common::AllVisibleGPUs();
Expand All @@ -770,18 +761,46 @@ void TestColumnSplitColumnSampler(std::string const& tree_method, bool use_gpu)
world_size = 3;
}
}
RunWithInMemoryCommunicator(world_size, VerifyColumnSplitColumnSampler, tree_method, use_gpu,
RunWithInMemoryCommunicator(world_size, VerifyColumnSplitWithArgs, tree_method, use_gpu, args,
model);
}

void TestColumnSplitColumnSampler(std::string const& tree_method, bool use_gpu) {
Args args{{"colsample_bytree", "0.5"}, {"colsample_bylevel", "0.6"}, {"colsample_bynode", "0.7"}};
TestColumnSplitWithArgs(tree_method, use_gpu, args);
}

void TestColumnSplitInteractionConstraints(std::string const& tree_method, bool use_gpu) {
Args args{{"interaction_constraints", "[[0, 5, 7], [2, 8, 9], [1, 3, 6]]"}};
TestColumnSplitWithArgs(tree_method, use_gpu, args);
}
} // anonymous namespace

TEST(ColumnSplitColumnSampler, Approx) { TestColumnSplitColumnSampler("approx", false); }

TEST(ColumnSplitColumnSampler, Hist) { TestColumnSplitColumnSampler("hist", false); }

#if defined(XGBOOST_USE_CUDA)
TEST(ColumnSplitColumnSampler, GPUApprox) { TestColumnSplitColumnSampler("approx", true); }
TEST(MGPUColumnSplitColumnSampler, GPUApprox) { TestColumnSplitColumnSampler("approx", true); }

TEST(ColumnSplitColumnSampler, GPUHist) { TestColumnSplitColumnSampler("hist", true); }
TEST(MGPUColumnSplitColumnSampler, GPUHist) { TestColumnSplitColumnSampler("hist", true); }
#endif // defined(XGBOOST_USE_CUDA)

TEST(ColumnSplitInteractionConstraints, Approx) {
TestColumnSplitInteractionConstraints("approx", false);
}

TEST(ColumnSplitInteractionConstraints, Hist) {
TestColumnSplitInteractionConstraints("hist", false);
}

#if defined(XGBOOST_USE_CUDA)
TEST(MGPUColumnSplitInteractionConstraints, GPUApprox) {
TestColumnSplitInteractionConstraints("approx", true);
}

TEST(MGPUColumnSplitInteractionConstraints, GPUHist) {
TestColumnSplitInteractionConstraints("hist", true);
}
#endif // defined(XGBOOST_USE_CUDA)
} // namespace xgboost
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