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Offload some configurations into GBM. (#4553)

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This is part 1 of refactoring configuration.

* Move tree heuristic configurations.
* Split up declarations and definitions for GBTree.
* Implement UseGPU in gbm.
This commit is contained in:
Jiaming Yuan
2019-06-14 09:18:51 +08:00
committed by GitHub
parent a2042b685a
commit c5719cc457
12 changed files with 630 additions and 519 deletions
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49
tests/cpp/gbm/test_gbtree.cc Normal file
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@@ -0,0 +1,49 @@
#include <gtest/gtest.h>
#include <xgboost/generic_parameters.h>
#include "../helpers.h"
#include "../../../src/gbm/gbtree.h"
namespace xgboost {
TEST(GBTree, SelectTreeMethod) {
using Arg = std::pair<std::string, std::string>;
size_t constexpr kRows = 10;
size_t constexpr kCols = 10;
auto mat_ptr = CreateDMatrix(kRows, kCols, 0);
std::vector<std::shared_ptr<xgboost::DMatrix>> mat = {*mat_ptr};
LearnerTrainParam learner_param;
learner_param.InitAllowUnknown(std::vector<Arg>{Arg("n_gpus", "0")});
std::unique_ptr<GradientBooster> p_gbm{
GradientBooster::Create("gbtree", &learner_param, {}, 0)};
auto& gbtree = dynamic_cast<gbm::GBTree&> (*p_gbm);
// Test if `tree_method` can be set
std::string n_feat = std::to_string(kCols);
gbtree.Configure({Arg{"tree_method", "approx"}, Arg{"num_feature", n_feat}});
auto const& tparam = gbtree.GetTrainParam();
ASSERT_EQ(tparam.updater_seq, "grow_histmaker,prune");
gbtree.Configure({Arg("tree_method", "exact"), Arg("num_feature", n_feat)});
ASSERT_EQ(tparam.updater_seq, "grow_colmaker,prune");
gbtree.Configure({Arg("tree_method", "hist"), Arg("num_feature", n_feat)});
ASSERT_EQ(tparam.updater_seq, "grow_quantile_histmaker");
ASSERT_EQ(tparam.predictor, "cpu_predictor");
gbtree.Configure({Arg{"booster", "dart"}, Arg{"tree_method", "hist"},
Arg{"num_feature", n_feat}});
ASSERT_EQ(tparam.updater_seq, "grow_quantile_histmaker");
#ifdef XGBOOST_USE_CUDA
learner_param.InitAllowUnknown(std::vector<Arg>{Arg{"n_gpus", "1"}});
gbtree.Configure({Arg("tree_method", "gpu_exact"),
Arg("num_feature", n_feat)});
ASSERT_EQ(tparam.updater_seq, "grow_gpu,prune");
ASSERT_EQ(tparam.predictor, "gpu_predictor");
gbtree.Configure({Arg("tree_method", "gpu_hist"), Arg("num_feature", n_feat)});
ASSERT_EQ(tparam.updater_seq, "grow_gpu_hist");
ASSERT_EQ(tparam.predictor, "gpu_predictor");
gbtree.Configure({Arg{"booster", "dart"}, Arg{"tree_method", "gpu_hist"},
Arg{"num_feature", n_feat}});
ASSERT_EQ(tparam.updater_seq, "grow_gpu_hist");
#endif
delete mat_ptr;
}
} // namespace xgboost

33
tests/cpp/predictor/test_gpu_predictor.cu
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@@ -145,25 +145,30 @@ TEST(gpu_predictor, MGPU_PicklingTest) {
}
// Load data matrix
CheckCAPICall(XGDMatrixCreateFromFile(tmp_file.c_str(), 0, &dmat[0]));
CheckCAPICall(XGDMatrixSetFloatInfo(dmat[0], "label", label.data(), 200));
ASSERT_EQ(XGDMatrixCreateFromFile(
tmp_file.c_str(), 0, &dmat[0]), 0) << XGBGetLastError();
ASSERT_EQ(XGDMatrixSetFloatInfo(
dmat[0], "label", label.data(), 200), 0) << XGBGetLastError();
// Create booster
CheckCAPICall(XGBoosterCreate(dmat, 1, &bst));
ASSERT_EQ(XGBoosterCreate(dmat, 1, &bst), 0) << XGBGetLastError();
// Set parameters
CheckCAPICall(XGBoosterSetParam(bst, "seed", "0"));
CheckCAPICall(XGBoosterSetParam(bst, "base_score", "0.5"));
CheckCAPICall(XGBoosterSetParam(bst, "booster", "gbtree"));
CheckCAPICall(XGBoosterSetParam(bst, "learning_rate", "0.01"));
CheckCAPICall(XGBoosterSetParam(bst, "max_depth", "8"));
CheckCAPICall(XGBoosterSetParam(bst, "objective", "binary:logistic"));
CheckCAPICall(XGBoosterSetParam(bst, "seed", "123"));
CheckCAPICall(XGBoosterSetParam(bst, "tree_method", "gpu_hist"));
CheckCAPICall(XGBoosterSetParam(bst, "n_gpus", std::to_string(ngpu).c_str()));
CheckCAPICall(XGBoosterSetParam(bst, "predictor", "gpu_predictor"));
ASSERT_EQ(XGBoosterSetParam(bst, "seed", "0"), 0) << XGBGetLastError();
ASSERT_EQ(XGBoosterSetParam(bst, "base_score", "0.5"), 0) << XGBGetLastError();
ASSERT_EQ(XGBoosterSetParam(bst, "booster", "gbtree"), 0) << XGBGetLastError();
ASSERT_EQ(XGBoosterSetParam(bst, "learning_rate", "0.01"), 0) << XGBGetLastError();
ASSERT_EQ(XGBoosterSetParam(bst, "max_depth", "8"), 0) << XGBGetLastError();
ASSERT_EQ(XGBoosterSetParam(
bst, "objective", "binary:logistic"), 0) << XGBGetLastError();
ASSERT_EQ(XGBoosterSetParam(bst, "seed", "123"), 0) << XGBGetLastError();
ASSERT_EQ(XGBoosterSetParam(
bst, "tree_method", "gpu_hist"), 0) << XGBGetLastError();
ASSERT_EQ(XGBoosterSetParam(
bst, "n_gpus", std::to_string(ngpu).c_str()), 0) << XGBGetLastError();
ASSERT_EQ(XGBoosterSetParam(bst, "predictor", "gpu_predictor"), 0) << XGBGetLastError();
// Run boosting iterations
for (int i = 0; i < 10; ++i) {
CheckCAPICall(XGBoosterUpdateOneIter(bst, i, dmat[0]));
ASSERT_EQ(XGBoosterUpdateOneIter(bst, i, dmat[0]), 0) << XGBGetLastError();
}
// Delete matrix

34
tests/cpp/test_learner.cc
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@@ -19,40 +19,6 @@ TEST(Learner, Basic) {
delete mat_ptr;
}
TEST(Learner, SelectTreeMethod) {
using Arg = std::pair<std::string, std::string>;
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));
// Test if `tree_method` can be set
learner->Configure({Arg("tree_method", "approx")});
ASSERT_EQ(learner->GetConfigurationArguments().at("updater"),
"grow_histmaker,prune");
learner->Configure({Arg("tree_method", "exact")});
ASSERT_EQ(learner->GetConfigurationArguments().at("updater"),
"grow_colmaker,prune");
learner->Configure({Arg("tree_method", "hist")});
ASSERT_EQ(learner->GetConfigurationArguments().at("updater"),
"grow_quantile_histmaker");
learner->Configure({Arg{"booster", "dart"}, Arg{"tree_method", "hist"}});
ASSERT_EQ(learner->GetConfigurationArguments().at("updater"),
"grow_quantile_histmaker");
#ifdef XGBOOST_USE_CUDA
learner->Configure({Arg("tree_method", "gpu_exact")});
ASSERT_EQ(learner->GetConfigurationArguments().at("updater"),
"grow_gpu,prune");
learner->Configure({Arg("tree_method", "gpu_hist")});
ASSERT_EQ(learner->GetConfigurationArguments().at("updater"),
"grow_gpu_hist");
learner->Configure({Arg{"booster", "dart"}, Arg{"tree_method", "gpu_hist"}});
ASSERT_EQ(learner->GetConfigurationArguments().at("updater"),
"grow_gpu_hist");
#endif
delete mat_ptr;
}
TEST(Learner, CheckGroup) {
using Arg = std::pair<std::string, std::string>;
size_t constexpr kNumGroups = 4;