Hendrik/xgboost
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

More tests for cpu predictor with column split (#9270)

Browse Source
This commit is contained in:
Rong Ou
2023-06-08 07:47:19 -07:00
committed by GitHub
parent 84d3fcb7ea
commit ff122d61ff
5 changed files with 243 additions and 41 deletions
Download Patch File Download Diff File Expand all files Collapse all files

224
tests/cpp/predictor/test_predictor.cc
View File

@@ -153,28 +153,32 @@ void TestInplacePrediction(std::shared_ptr<DMatrix> x, std::string predictor, bs
learner->Configure();
}
void TestPredictionWithLesserFeatures(std::string predictor_name) {
size_t constexpr kRows = 256, kTrainCols = 256, kTestCols = 4, kIters = 4;
auto m_train = RandomDataGenerator(kRows, kTrainCols, 0.5).GenerateDMatrix(true);
auto m_test = RandomDataGenerator(kRows, kTestCols, 0.5).GenerateDMatrix(false);
std::unique_ptr<Learner> learner{Learner::Create({m_train})};
for (size_t i = 0; i < kIters; ++i) {
learner->UpdateOneIter(i, m_train);
namespace {
std::unique_ptr<Learner> LearnerForTest(std::shared_ptr<DMatrix> dmat, size_t iters,
size_t forest = 1) {
std::unique_ptr<Learner> learner{Learner::Create({dmat})};
learner->SetParams(Args{{"num_parallel_tree", std::to_string(forest)}});
for (size_t i = 0; i < iters; ++i) {
learner->UpdateOneIter(i, dmat);
}
return learner;
}
void VerifyPredictionWithLesserFeatures(Learner *learner, std::string const &predictor_name,
size_t rows, std::shared_ptr<DMatrix> const &m_test,
std::shared_ptr<DMatrix> const &m_invalid) {
HostDeviceVector<float> prediction;
learner->SetParam("predictor", predictor_name);
learner->Configure();
Json config{Object()};
learner->SaveConfig(&config);
ASSERT_EQ(get<String>(config["learner"]["gradient_booster"]["gbtree_train_param"]["predictor"]), predictor_name);
ASSERT_EQ(get<String>(config["learner"]["gradient_booster"]["gbtree_train_param"]["predictor"]),
predictor_name);
learner->Predict(m_test, false, &prediction, 0, 0);
ASSERT_EQ(prediction.Size(), kRows);
ASSERT_EQ(prediction.Size(), rows);
auto m_invalid = RandomDataGenerator(kRows, kTrainCols + 1, 0.5).GenerateDMatrix(false);
ASSERT_THROW({learner->Predict(m_invalid, false, &prediction, 0, 0);}, dmlc::Error);
ASSERT_THROW({ learner->Predict(m_invalid, false, &prediction, 0, 0); }, dmlc::Error);
#if defined(XGBOOST_USE_CUDA)
HostDeviceVector<float> from_cpu;
@@ -185,13 +189,49 @@ void TestPredictionWithLesserFeatures(std::string predictor_name) {
learner->SetParam("predictor", "gpu_predictor");
learner->Predict(m_test, false, &from_cuda, 0, 0);
auto const& h_cpu = from_cpu.ConstHostVector();
auto const& h_gpu = from_cuda.ConstHostVector();
auto const &h_cpu = from_cpu.ConstHostVector();
auto const &h_gpu = from_cuda.ConstHostVector();
for (size_t i = 0; i < h_cpu.size(); ++i) {
ASSERT_NEAR(h_cpu[i], h_gpu[i], kRtEps);
}
#endif // defined(XGBOOST_USE_CUDA)
}
} // anonymous namespace
void TestPredictionWithLesserFeatures(std::string predictor_name) {
size_t constexpr kRows = 256, kTrainCols = 256, kTestCols = 4, kIters = 4;
auto m_train = RandomDataGenerator(kRows, kTrainCols, 0.5).GenerateDMatrix(true);
auto learner = LearnerForTest(m_train, kIters);
auto m_test = RandomDataGenerator(kRows, kTestCols, 0.5).GenerateDMatrix(false);
auto m_invalid = RandomDataGenerator(kRows, kTrainCols + 1, 0.5).GenerateDMatrix(false);
VerifyPredictionWithLesserFeatures(learner.get(), predictor_name, kRows, m_test, m_invalid);
}
namespace {
void VerifyPredictionWithLesserFeaturesColumnSplit(Learner *learner,
std::string const &predictor_name, size_t rows,
std::shared_ptr<DMatrix> m_test,
std::shared_ptr<DMatrix> m_invalid) {
auto const world_size = collective::GetWorldSize();
auto const rank = collective::GetRank();
std::shared_ptr<DMatrix> sliced_test{m_test->SliceCol(world_size, rank)};
std::shared_ptr<DMatrix> sliced_invalid{m_invalid->SliceCol(world_size, rank)};
VerifyPredictionWithLesserFeatures(learner, predictor_name, rows, sliced_test, sliced_invalid);
}
} // anonymous namespace
void TestPredictionWithLesserFeaturesColumnSplit(std::string predictor_name) {
size_t constexpr kRows = 256, kTrainCols = 256, kTestCols = 4, kIters = 4;
auto m_train = RandomDataGenerator(kRows, kTrainCols, 0.5).GenerateDMatrix(true);
auto learner = LearnerForTest(m_train, kIters);
auto m_test = RandomDataGenerator(kRows, kTestCols, 0.5).GenerateDMatrix(false);
auto m_invalid = RandomDataGenerator(kRows, kTrainCols + 1, 0.5).GenerateDMatrix(false);
auto constexpr kWorldSize = 2;
RunWithInMemoryCommunicator(kWorldSize, VerifyPredictionWithLesserFeaturesColumnSplit,
learner.get(), predictor_name, kRows, m_test, m_invalid);
}
void GBTreeModelForTest(gbm::GBTreeModel *model, uint32_t split_ind,
bst_cat_t split_cat, float left_weight,
@@ -212,7 +252,7 @@ void GBTreeModelForTest(gbm::GBTreeModel *model, uint32_t split_ind,
model->CommitModelGroup(std::move(trees), 0);
}
void TestCategoricalPrediction(std::string name) {
void TestCategoricalPrediction(std::string name, bool is_column_split) {
size_t constexpr kCols = 10;
PredictionCacheEntry out_predictions;
@@ -236,6 +276,9 @@ void TestCategoricalPrediction(std::string name) {
std::vector<FeatureType> types(10, FeatureType::kCategorical);
m->Info().feature_types.HostVector() = types;
if (is_column_split) {
m = std::shared_ptr<DMatrix>{m->SliceCol(collective::GetWorldSize(), collective::GetRank())};
}
predictor->InitOutPredictions(m->Info(), &out_predictions.predictions, model);
predictor->PredictBatch(m.get(), &out_predictions, model, 0);
@@ -246,13 +289,21 @@ void TestCategoricalPrediction(std::string name) {
row[split_ind] = split_cat + 1;
m = GetDMatrixFromData(row, 1, kCols);
if (is_column_split) {
m = std::shared_ptr<DMatrix>{m->SliceCol(collective::GetWorldSize(), collective::GetRank())};
}
out_predictions.version = 0;
predictor->InitOutPredictions(m->Info(), &out_predictions.predictions, model);
predictor->PredictBatch(m.get(), &out_predictions, model, 0);
ASSERT_EQ(out_predictions.predictions.HostVector()[0], left_weight + score);
}
void TestCategoricalPredictLeaf(StringView name) {
void TestCategoricalPredictionColumnSplit(std::string name) {
auto constexpr kWorldSize = 2;
RunWithInMemoryCommunicator(kWorldSize, TestCategoricalPrediction, name, true);
}
void TestCategoricalPredictLeaf(StringView name, bool is_column_split) {
size_t constexpr kCols = 10;
PredictionCacheEntry out_predictions;
@@ -275,6 +326,9 @@ void TestCategoricalPredictLeaf(StringView name) {
std::vector<float> row(kCols);
row[split_ind] = split_cat;
auto m = GetDMatrixFromData(row, 1, kCols);
if (is_column_split) {
m = std::shared_ptr<DMatrix>{m->SliceCol(collective::GetWorldSize(), collective::GetRank())};
}
predictor->PredictLeaf(m.get(), &out_predictions.predictions, model);
CHECK_EQ(out_predictions.predictions.Size(), 1);
@@ -283,25 +337,25 @@ void TestCategoricalPredictLeaf(StringView name) {
row[split_ind] = split_cat + 1;
m = GetDMatrixFromData(row, 1, kCols);
if (is_column_split) {
m = std::shared_ptr<DMatrix>{m->SliceCol(collective::GetWorldSize(), collective::GetRank())};
}
out_predictions.version = 0;
predictor->InitOutPredictions(m->Info(), &out_predictions.predictions, model);
predictor->PredictLeaf(m.get(), &out_predictions.predictions, model);
ASSERT_EQ(out_predictions.predictions.HostVector()[0], 1);
}
void TestCategoricalPredictLeafColumnSplit(StringView name) {
auto constexpr kWorldSize = 2;
RunWithInMemoryCommunicator(kWorldSize, TestCategoricalPredictLeaf, name, true);
}
void TestIterationRange(std::string name) {
size_t constexpr kRows = 1000, kCols = 20, kClasses = 4, kForest = 3;
size_t constexpr kRows = 1000, kCols = 20, kClasses = 4, kForest = 3, kIters = 10;
auto dmat = RandomDataGenerator(kRows, kCols, 0).GenerateDMatrix(true, true, kClasses);
std::unique_ptr<Learner> learner{Learner::Create({dmat})};
learner->SetParams(Args{{"num_parallel_tree", std::to_string(kForest)},
{"predictor", name}});
size_t kIters = 10;
for (size_t i = 0; i < kIters; ++i) {
learner->UpdateOneIter(i, dmat);
}
auto learner = LearnerForTest(dmat, kIters, kForest);
learner->SetParams(Args{{"predictor", name}});
bool bound = false;
std::unique_ptr<Learner> sliced {learner->Slice(0, 3, 1, &bound)};
@@ -363,15 +417,82 @@ void TestIterationRange(std::string name) {
}
}
void TestSparsePrediction(float sparsity, std::string predictor) {
size_t constexpr kRows = 512, kCols = 128;
auto Xy = RandomDataGenerator(kRows, kCols, sparsity).GenerateDMatrix(true);
std::unique_ptr<Learner> learner{Learner::Create({Xy})};
learner->Configure();
for (size_t i = 0; i < 4; ++i) {
learner->UpdateOneIter(i, Xy);
namespace {
void VerifyIterationRangeColumnSplit(DMatrix *dmat, Learner *learner, Learner *sliced,
std::vector<float> const &expected_margin_ranged,
std::vector<float> const &expected_margin_sliced,
std::vector<float> const &expected_leaf_ranged,
std::vector<float> const &expected_leaf_sliced) {
auto const world_size = collective::GetWorldSize();
auto const rank = collective::GetRank();
std::shared_ptr<DMatrix> Xy{dmat->SliceCol(world_size, rank)};
HostDeviceVector<float> out_predt_sliced;
HostDeviceVector<float> out_predt_ranged;
// margin
{
sliced->Predict(Xy, true, &out_predt_sliced, 0, 0, false, false, false, false, false);
learner->Predict(Xy, true, &out_predt_ranged, 0, 3, false, false, false, false, false);
auto const &h_sliced = out_predt_sliced.HostVector();
auto const &h_range = out_predt_ranged.HostVector();
ASSERT_EQ(h_sliced.size(), expected_margin_sliced.size());
ASSERT_EQ(h_sliced, expected_margin_sliced);
ASSERT_EQ(h_range.size(), expected_margin_ranged.size());
ASSERT_EQ(h_range, expected_margin_ranged);
}
// Leaf
{
sliced->Predict(Xy, false, &out_predt_sliced, 0, 0, false, true, false, false, false);
learner->Predict(Xy, false, &out_predt_ranged, 0, 3, false, true, false, false, false);
auto const &h_sliced = out_predt_sliced.HostVector();
auto const &h_range = out_predt_ranged.HostVector();
ASSERT_EQ(h_sliced.size(), expected_leaf_sliced.size());
ASSERT_EQ(h_sliced, expected_leaf_sliced);
ASSERT_EQ(h_range.size(), expected_leaf_ranged.size());
ASSERT_EQ(h_range, expected_leaf_ranged);
}
}
} // anonymous namespace
void TestIterationRangeColumnSplit(std::string name) {
size_t constexpr kRows = 1000, kCols = 20, kClasses = 4, kForest = 3, kIters = 10;
auto dmat = RandomDataGenerator(kRows, kCols, 0).GenerateDMatrix(true, true, kClasses);
auto learner = LearnerForTest(dmat, kIters, kForest);
learner->SetParams(Args{{"predictor", name}});
bool bound = false;
std::unique_ptr<Learner> sliced{learner->Slice(0, 3, 1, &bound)};
ASSERT_FALSE(bound);
// margin
HostDeviceVector<float> margin_predt_sliced;
HostDeviceVector<float> margin_predt_ranged;
sliced->Predict(dmat, true, &margin_predt_sliced, 0, 0, false, false, false, false, false);
learner->Predict(dmat, true, &margin_predt_ranged, 0, 3, false, false, false, false, false);
auto const &margin_sliced = margin_predt_sliced.HostVector();
auto const &margin_ranged = margin_predt_ranged.HostVector();
// Leaf
HostDeviceVector<float> leaf_predt_sliced;
HostDeviceVector<float> leaf_predt_ranged;
sliced->Predict(dmat, false, &leaf_predt_sliced, 0, 0, false, true, false, false, false);
learner->Predict(dmat, false, &leaf_predt_ranged, 0, 3, false, true, false, false, false);
auto const &leaf_sliced = leaf_predt_sliced.HostVector();
auto const &leaf_ranged = leaf_predt_ranged.HostVector();
auto constexpr kWorldSize = 2;
RunWithInMemoryCommunicator(kWorldSize, VerifyIterationRangeColumnSplit, dmat.get(),
learner.get(), sliced.get(), margin_ranged, margin_sliced,
leaf_ranged, leaf_sliced);
}
void TestSparsePrediction(float sparsity, std::string predictor) {
size_t constexpr kRows = 512, kCols = 128, kIters = 4;
auto Xy = RandomDataGenerator(kRows, kCols, sparsity).GenerateDMatrix(true);
auto learner = LearnerForTest(Xy, kIters);
HostDeviceVector<float> sparse_predt;
Json model{Object{}};
@@ -419,6 +540,43 @@ void TestSparsePrediction(float sparsity, std::string predictor) {
}
}
namespace {
void VerifySparsePredictionColumnSplit(DMatrix *dmat, Learner *learner,
std::vector<float> const &expected_predt) {
std::shared_ptr<DMatrix> sliced{
dmat->SliceCol(collective::GetWorldSize(), collective::GetRank())};
HostDeviceVector<float> sparse_predt;
learner->Predict(sliced, false, &sparse_predt, 0, 0);
auto const &predt = sparse_predt.HostVector();
ASSERT_EQ(predt.size(), expected_predt.size());
for (size_t i = 0; i < predt.size(); ++i) {
ASSERT_FLOAT_EQ(predt[i], expected_predt[i]);
}
}
} // anonymous namespace
void TestSparsePredictionColumnSplit(float sparsity, std::string predictor) {
size_t constexpr kRows = 512, kCols = 128, kIters = 4;
auto Xy = RandomDataGenerator(kRows, kCols, sparsity).GenerateDMatrix(true);
auto learner = LearnerForTest(Xy, kIters);
HostDeviceVector<float> sparse_predt;
Json model{Object{}};
learner->SaveModel(&model);
learner.reset(Learner::Create({Xy}));
learner->LoadModel(model);
learner->SetParam("predictor", predictor);
learner->Predict(Xy, false, &sparse_predt, 0, 0);
auto constexpr kWorldSize = 2;
RunWithInMemoryCommunicator(kWorldSize, VerifySparsePredictionColumnSplit, Xy.get(),
learner.get(), sparse_predt.HostVector());
}
void TestVectorLeafPrediction(Context const *ctx) {
std::unique_ptr<Predictor> cpu_predictor =
std::unique_ptr<Predictor>(Predictor::Create("cpu_predictor", ctx));