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Remove gpu_exact tree method (#4742)

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This commit is contained in:
Rong Ou
2019-08-06 16:43:20 -07:00
committed by Rory Mitchell
parent 2a4df8e29f
commit 851b5b3808
16 changed files with 36 additions and 971 deletions
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4
tests/cpp/gbm/test_gbtree.cc
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@@ -36,10 +36,6 @@ TEST(GBTree, SelectTreeMethod) {
ASSERT_EQ(tparam.updater_seq, "grow_quantile_histmaker");
#ifdef XGBOOST_USE_CUDA
generic_param.InitAllowUnknown(std::vector<Arg>{Arg{"n_gpus", "1"}});
gbtree.ConfigureWithKnownData({Arg("tree_method", "gpu_exact"),
Arg("num_feature", n_feat)}, p_dmat);
ASSERT_EQ(tparam.updater_seq, "grow_gpu,prune");
ASSERT_EQ(tparam.predictor, "gpu_predictor");
gbtree.ConfigureWithKnownData({Arg("tree_method", "gpu_hist"), Arg("num_feature", n_feat)},
p_dmat);
ASSERT_EQ(tparam.updater_seq, "grow_gpu_hist");

7
tests/cpp/test_learner.cc
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@@ -171,13 +171,6 @@ TEST(Learner, GPUConfiguration) {
ASSERT_EQ(learner->GetGenericParameter().gpu_id, 0);
ASSERT_EQ(learner->GetGenericParameter().n_gpus, 1);
}
{
std::unique_ptr<Learner> learner {Learner::Create(mat)};
learner->SetParams({Arg{"tree_method", "gpu_exact"}});
learner->UpdateOneIter(0, p_dmat.get());
ASSERT_EQ(learner->GetGenericParameter().gpu_id, 0);
ASSERT_EQ(learner->GetGenericParameter().n_gpus, 1);
}
{
std::unique_ptr<Learner> learner {Learner::Create(mat)};
learner->SetParams({Arg{"tree_method", "gpu_hist"}});

49
tests/cpp/tree/test_gpu_exact.cu
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@@ -1,49 +0,0 @@
#include <gtest/gtest.h>
#include <xgboost/tree_updater.h>
#include <vector>
#include <string>
#include <utility>
#include "../helpers.h"
namespace xgboost {
namespace tree {
TEST(GPUExact, Update) {
using Arg = std::pair<std::string, std::string>;
auto lparam = CreateEmptyGenericParam(0, 1);
std::vector<Arg> args{{"max_depth", "1"}};
auto* p_gpuexact_maker = TreeUpdater::Create("grow_gpu", &lparam);
p_gpuexact_maker->Configure(args);
size_t constexpr kNRows = 4;
size_t constexpr kNCols = 8;
bst_float constexpr kSparsity = 0.0f;
auto dmat = CreateDMatrix(kNRows, kNCols, kSparsity, 3);
std::vector<GradientPair> h_gpair(kNRows);
for (size_t i = 0; i < kNRows; ++i) {
h_gpair[i] = GradientPair(i % 2, 1);
}
HostDeviceVector<GradientPair> gpair (h_gpair);
RegTree tree;
p_gpuexact_maker->Update(&gpair, (*dmat).get(), {&tree});
auto const& nodes = tree.GetNodes();
ASSERT_EQ(nodes.size(), 3);
float constexpr kRtEps = 1e-6;
ASSERT_NEAR(tree.Stat(0).sum_hess, 4, kRtEps);
ASSERT_NEAR(tree.Stat(1).sum_hess, 2, kRtEps);
ASSERT_NEAR(tree.Stat(2).sum_hess, 2, kRtEps);
ASSERT_NEAR(tree.Stat(0).loss_chg, 0.8f, kRtEps);
delete dmat;
delete p_gpuexact_maker;
}
} // namespace tree
} // namespace xgboost

12
tests/python-gpu/test_gpu_updaters.py
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@@ -20,16 +20,6 @@ datasets = ["Boston", "Cancer", "Digits", "Sparse regression",
class TestGPU(unittest.TestCase):
def test_gpu_exact(self):
variable_param = {'max_depth': [2, 6, 15], }
for param in parameter_combinations(variable_param):
param['tree_method'] = 'gpu_exact'
gpu_results = run_suite(param, select_datasets=datasets)
assert_results_non_increasing(gpu_results, 1e-2)
param['tree_method'] = 'exact'
cpu_results = run_suite(param, select_datasets=datasets)
assert_gpu_results(cpu_results, gpu_results)
def test_gpu_hist(self):
test_param = parameter_combinations({'n_gpus': [1], 'max_depth': [2, 8],
'max_leaves': [255, 4],
@@ -65,7 +55,7 @@ class TestGPU(unittest.TestCase):
'max_leaves': [255, 4],
'max_bin': [2, 64],
'grow_policy': ['lossguide'],
'tree_method': ['gpu_hist', 'gpu_exact']}
'tree_method': ['gpu_hist']}
for param in parameter_combinations(variable_param):
gpu_results = run_suite(param, select_datasets=datasets)
assert_results_non_increasing(gpu_results, 1e-2)

6
tests/python/test_with_sklearn.py
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@@ -444,9 +444,9 @@ def test_sklearn_n_jobs():
def test_kwargs():
params = {'updater': 'grow_gpu', 'subsample': .5, 'n_jobs': -1}
params = {'updater': 'grow_gpu_hist', 'subsample': .5, 'n_jobs': -1}
clf = xgb.XGBClassifier(n_estimators=1000, **params)
assert clf.get_params()['updater'] == 'grow_gpu'
assert clf.get_params()['updater'] == 'grow_gpu_hist'
assert clf.get_params()['subsample'] == .5
assert clf.get_params()['n_estimators'] == 1000
@@ -472,7 +472,7 @@ def test_kwargs_grid_search():
def test_kwargs_error():
params = {'updater': 'grow_gpu', 'subsample': .5, 'n_jobs': -1}
params = {'updater': 'grow_gpu_hist', 'subsample': .5, 'n_jobs': -1}
with pytest.raises(TypeError):
clf = xgb.XGBClassifier(n_jobs=1000, **params)
assert isinstance(clf, xgb.XGBClassifier)