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

Define the new device parameter. (#9362)

Browse Source
This commit is contained in:
Jiaming Yuan
2023-07-13 19:30:25 +08:00
committed by GitHub
parent 2d0cd2817e
commit 04aff3af8e
63 changed files with 827 additions and 477 deletions
Download Patch File Download Diff File Expand all files Collapse all files

30
tests/cpp/test_learner.cc
View File

@@ -27,7 +27,6 @@
#include "../../src/common/io.h" // for LoadSequentialFile
#include "../../src/common/linalg_op.h" // for ElementWiseTransformHost, begin, end
#include "../../src/common/random.h" // for GlobalRandom
#include "../../src/common/transform_iterator.h" // for IndexTransformIter
#include "dmlc/io.h" // for Stream
#include "dmlc/omp.h" // for omp_get_max_threads
#include "dmlc/registry.h" // for Registry
@@ -35,14 +34,13 @@
#include "helpers.h" // for GetBaseScore, RandomDataGenerator
#include "objective_helpers.h" // for MakeObjNamesForTest, ObjTestNameGenerator
#include "xgboost/base.h" // for bst_float, Args, bst_feature_t, bst_int
#include "xgboost/context.h" // for Context
#include "xgboost/context.h" // for Context, DeviceOrd
#include "xgboost/data.h" // for DMatrix, MetaInfo, DataType
#include "xgboost/host_device_vector.h" // for HostDeviceVector
#include "xgboost/json.h" // for Json, Object, get, String, IsA, opera...
#include "xgboost/linalg.h" // for Tensor, TensorView
#include "xgboost/logging.h" // for ConsoleLogger
#include "xgboost/predictor.h" // for PredictionCacheEntry
#include "xgboost/span.h" // for Span, operator!=, SpanIterator
#include "xgboost/string_view.h" // for StringView
namespace xgboost {
@@ -58,9 +56,9 @@ TEST(Learner, Basic) {
auto minor = XGBOOST_VER_MINOR;
auto patch = XGBOOST_VER_PATCH;
static_assert(std::is_integral<decltype(major)>::value, "Wrong major version type");
static_assert(std::is_integral<decltype(minor)>::value, "Wrong minor version type");
static_assert(std::is_integral<decltype(patch)>::value, "Wrong patch version type");
static_assert(std::is_integral_v<decltype(major)>, "Wrong major version type");
static_assert(std::is_integral_v<decltype(minor)>, "Wrong minor version type");
static_assert(std::is_integral_v<decltype(patch)>, "Wrong patch version type");
}
TEST(Learner, ParameterValidation) {
@@ -92,8 +90,7 @@ TEST(Learner, CheckGroup) {
size_t constexpr kNumRows = 17;
bst_feature_t constexpr kNumCols = 15;
std::shared_ptr<DMatrix> p_mat{
RandomDataGenerator{kNumRows, kNumCols, 0.0f}.GenerateDMatrix()};
std::shared_ptr<DMatrix> p_mat{RandomDataGenerator{kNumRows, kNumCols, 0.0f}.GenerateDMatrix()};
std::vector<bst_float> weight(kNumGroups, 1);
std::vector<bst_int> group(kNumGroups);
group[0] = 2;
@@ -312,35 +309,36 @@ TEST(Learner, GPUConfiguration) {
learner->SetParams({Arg{"booster", "gblinear"},
Arg{"updater", "gpu_coord_descent"}});
learner->UpdateOneIter(0, p_dmat);
ASSERT_EQ(learner->Ctx()->gpu_id, 0);
ASSERT_EQ(learner->Ctx()->Device(), DeviceOrd::CUDA(0));
}
{
std::unique_ptr<Learner> learner {Learner::Create(mat)};
std::unique_ptr<Learner> learner{Learner::Create(mat)};
learner->SetParams({Arg{"tree_method", "gpu_hist"}});
learner->Configure();
ASSERT_EQ(learner->Ctx()->Device(), DeviceOrd::CUDA(0));
learner->UpdateOneIter(0, p_dmat);
ASSERT_EQ(learner->Ctx()->gpu_id, 0);
ASSERT_EQ(learner->Ctx()->Device(), DeviceOrd::CUDA(0));
}
{
std::unique_ptr<Learner> learner {Learner::Create(mat)};
learner->SetParams({Arg{"tree_method", "gpu_hist"},
Arg{"gpu_id", "-1"}});
learner->UpdateOneIter(0, p_dmat);
ASSERT_EQ(learner->Ctx()->gpu_id, 0);
ASSERT_EQ(learner->Ctx()->Device(), DeviceOrd::CUDA(0));
}
{
// with CPU algorithm
std::unique_ptr<Learner> learner {Learner::Create(mat)};
learner->SetParams({Arg{"tree_method", "hist"}});
learner->UpdateOneIter(0, p_dmat);
ASSERT_EQ(learner->Ctx()->gpu_id, -1);
ASSERT_EQ(learner->Ctx()->Device(), DeviceOrd::CPU());
}
{
// with CPU algorithm, but `gpu_id` takes priority
std::unique_ptr<Learner> learner {Learner::Create(mat)};
learner->SetParams({Arg{"tree_method", "hist"},
Arg{"gpu_id", "0"}});
learner->SetParams({Arg{"tree_method", "hist"}, Arg{"gpu_id", "0"}});
learner->UpdateOneIter(0, p_dmat);
ASSERT_EQ(learner->Ctx()->gpu_id, 0);
ASSERT_EQ(learner->Ctx()->Device(), DeviceOrd::CUDA(0));
}
}
#endif // defined(XGBOOST_USE_CUDA)