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Refactor configuration [Part II]. (#4577)

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* Refactor configuration [Part II].

* General changes:
** Remove `Init` methods to avoid ambiguity.
** Remove `Configure(std::map<>)` to avoid redundant copying and prepare for
   parameter validation. (`std::vector` is returned from `InitAllowUnknown`).
** Add name to tree updaters for easier debugging.

* Learner changes:
** Make `LearnerImpl` the only source of configuration.

    All configurations are stored and carried out by `LearnerImpl::Configure()`.

** Remove booster in C API.

    Originally kept for "compatibility reason", but did not state why.  So here
    we just remove it.

** Add a `metric_names_` field in `LearnerImpl`.
** Remove `LazyInit`.  Configuration will always be lazy.
** Run `Configure` before every iteration.

* Predictor changes:
** Allocate both cpu and gpu predictor.
** Remove cpu_predictor from gpu_predictor.

    `GBTree` is now used to dispatch the predictor.

** Remove some GPU Predictor tests.

* IO

No IO changes.  The binary model format stability is tested by comparing
hashing value of save models between two commits
This commit is contained in:
Jiaming Yuan
2019-07-20 08:34:56 -04:00
committed by GitHub
parent ad1192e8a3
commit f0064c07ab
69 changed files with 669 additions and 761 deletions
Download Patch File Download Diff File Expand all files Collapse all files

86
tests/cpp/test_learner.cc
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@@ -14,7 +14,7 @@ TEST(Learner, Basic) {
auto mat_ptr = CreateDMatrix(10, 10, 0);
std::vector<std::shared_ptr<xgboost::DMatrix>> mat = {*mat_ptr};
auto learner = std::unique_ptr<Learner>(Learner::Create(mat));
learner->Configure(args);
learner->SetParams(args);
delete mat_ptr;
}
@@ -46,9 +46,7 @@ TEST(Learner, CheckGroup) {
std::vector<std::shared_ptr<xgboost::DMatrix>> mat = {p_mat};
auto learner = std::unique_ptr<Learner>(Learner::Create(mat));
learner->Configure({Arg{"objective", "rank:pairwise"}});
learner->InitModel();
learner->SetParams({Arg{"objective", "rank:pairwise"}});
EXPECT_NO_THROW(learner->UpdateOneIter(0, p_mat.get()));
group.resize(kNumGroups+1);
@@ -77,11 +75,34 @@ TEST(Learner, SLOW_CheckMultiBatch) {
dmat->Info().SetInfo("label", labels.data(), DataType::kFloat32, num_row);
std::vector<std::shared_ptr<DMatrix>> mat{dmat};
auto learner = std::unique_ptr<Learner>(Learner::Create(mat));
learner->Configure({Arg{"objective", "binary:logistic"}});
learner->InitModel();
learner->SetParams({Arg{"objective", "binary:logistic"}, Arg{"verbosity", "3"}});
learner->UpdateOneIter(0, dmat.get());
}
TEST(Learner, Configuration) {
std::string const emetric = "eval_metric";
{
std::unique_ptr<Learner> learner { Learner::Create({nullptr}) };
learner->SetParam(emetric, "auc");
learner->SetParam(emetric, "rmsle");
learner->SetParam("foo", "bar");
// eval_metric is not part of configuration
auto attr_names = learner->GetConfigurationArguments();
ASSERT_EQ(attr_names.size(), 1);
ASSERT_EQ(attr_names.find(emetric), attr_names.cend());
ASSERT_EQ(attr_names.at("foo"), "bar");
}
{
std::unique_ptr<Learner> learner { Learner::Create({nullptr}) };
learner->SetParams({{"foo", "bar"}, {emetric, "auc"}, {emetric, "entropy"}, {emetric, "KL"}});
auto attr_names = learner->GetConfigurationArguments();
ASSERT_EQ(attr_names.size(), 1);
ASSERT_EQ(attr_names.at("foo"), "bar");
}
}
#if defined(XGBOOST_USE_CUDA)
TEST(Learner, IO) {
@@ -98,13 +119,12 @@ TEST(Learner, IO) {
std::vector<std::shared_ptr<DMatrix>> mat {p_dmat};
std::unique_ptr<Learner> learner {Learner::Create(mat)};
learner->Configure({Arg{"tree_method", "auto"},
learner->SetParams({Arg{"tree_method", "auto"},
Arg{"predictor", "gpu_predictor"},
Arg{"n_gpus", "-1"}});
learner->InitModel();
learner->UpdateOneIter(0, p_dmat.get());
ASSERT_EQ(learner->GetLearnerTrainParameter().gpu_id, 0);
ASSERT_EQ(learner->GetLearnerTrainParameter().n_gpus, -1);
ASSERT_EQ(learner->GetGenericParameter().gpu_id, 0);
ASSERT_EQ(learner->GetGenericParameter().n_gpus, -1);
dmlc::TemporaryDirectory tempdir;
const std::string fname = tempdir.path + "/model.bst";
@@ -117,8 +137,8 @@ TEST(Learner, IO) {
std::unique_ptr<dmlc::Stream> fi(dmlc::Stream::Create(fname.c_str(), "r"));
learner->Load(fi.get());
ASSERT_EQ(learner->GetLearnerTrainParameter().gpu_id, 0);
ASSERT_EQ(learner->GetLearnerTrainParameter().n_gpus, 0);
ASSERT_EQ(learner->GetGenericParameter().gpu_id, 0);
ASSERT_EQ(learner->GetGenericParameter().n_gpus, 0);
delete pp_dmat;
}
@@ -137,59 +157,53 @@ TEST(Learner, GPUConfiguration) {
p_dmat->Info().labels_.HostVector() = labels;
{
std::unique_ptr<Learner> learner {Learner::Create(mat)};
learner->Configure({Arg{"booster", "gblinear"},
learner->SetParams({Arg{"booster", "gblinear"},
Arg{"updater", "gpu_coord_descent"}});
learner->InitModel();
learner->UpdateOneIter(0, p_dmat.get());
ASSERT_EQ(learner->GetLearnerTrainParameter().gpu_id, 0);
ASSERT_EQ(learner->GetLearnerTrainParameter().n_gpus, 1);
ASSERT_EQ(learner->GetGenericParameter().gpu_id, 0);
ASSERT_EQ(learner->GetGenericParameter().n_gpus, 1);
}
{
std::unique_ptr<Learner> learner {Learner::Create(mat)};
learner->Configure({Arg{"tree_method", "gpu_exact"}});
learner->InitModel();
learner->SetParams({Arg{"tree_method", "gpu_exact"}});
learner->UpdateOneIter(0, p_dmat.get());
ASSERT_EQ(learner->GetLearnerTrainParameter().gpu_id, 0);
ASSERT_EQ(learner->GetLearnerTrainParameter().n_gpus, 1);
ASSERT_EQ(learner->GetGenericParameter().gpu_id, 0);
ASSERT_EQ(learner->GetGenericParameter().n_gpus, 1);
}
{
std::unique_ptr<Learner> learner {Learner::Create(mat)};
learner->Configure({Arg{"tree_method", "gpu_hist"}});
learner->InitModel();
learner->SetParams({Arg{"tree_method", "gpu_hist"}});
learner->UpdateOneIter(0, p_dmat.get());
ASSERT_EQ(learner->GetLearnerTrainParameter().gpu_id, 0);
ASSERT_EQ(learner->GetLearnerTrainParameter().n_gpus, 1);
ASSERT_EQ(learner->GetGenericParameter().gpu_id, 0);
ASSERT_EQ(learner->GetGenericParameter().n_gpus, 1);
}
{
// with CPU algorithm
std::unique_ptr<Learner> learner {Learner::Create(mat)};
learner->Configure({Arg{"tree_method", "hist"}});
learner->InitModel();
learner->SetParams({Arg{"tree_method", "hist"}});
learner->UpdateOneIter(0, p_dmat.get());
ASSERT_EQ(learner->GetLearnerTrainParameter().gpu_id, 0);
ASSERT_EQ(learner->GetLearnerTrainParameter().n_gpus, 0);
ASSERT_EQ(learner->GetGenericParameter().gpu_id, 0);
ASSERT_EQ(learner->GetGenericParameter().n_gpus, 0);
}
{
// with CPU algorithm, but `n_gpus` takes priority
std::unique_ptr<Learner> learner {Learner::Create(mat)};
learner->Configure({Arg{"tree_method", "hist"},
learner->SetParams({Arg{"tree_method", "hist"},
Arg{"n_gpus", "1"}});
learner->InitModel();
learner->UpdateOneIter(0, p_dmat.get());
ASSERT_EQ(learner->GetLearnerTrainParameter().gpu_id, 0);
ASSERT_EQ(learner->GetLearnerTrainParameter().n_gpus, 1);
ASSERT_EQ(learner->GetGenericParameter().gpu_id, 0);
ASSERT_EQ(learner->GetGenericParameter().n_gpus, 1);
}
{
// With CPU algorithm but GPU Predictor, this is to simulate when
// XGBoost is only used for prediction, so tree method is not
// specified.
std::unique_ptr<Learner> learner {Learner::Create(mat)};
learner->Configure({Arg{"tree_method", "hist"},
learner->SetParams({Arg{"tree_method", "hist"},
Arg{"predictor", "gpu_predictor"}});
learner->InitModel();
learner->UpdateOneIter(0, p_dmat.get());
ASSERT_EQ(learner->GetLearnerTrainParameter().gpu_id, 0);
ASSERT_EQ(learner->GetLearnerTrainParameter().n_gpus, 1);
ASSERT_EQ(learner->GetGenericParameter().gpu_id, 0);
ASSERT_EQ(learner->GetGenericParameter().n_gpus, 1);
}
delete pp_dmat;