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Define the new device parameter. (#9362)

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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
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49
tests/cpp/predictor/test_gpu_predictor.cu
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@@ -33,9 +33,8 @@ TEST(GPUPredictor, Basic) {
int n_row = i, n_col = i;
auto dmat = RandomDataGenerator(n_row, n_col, 0).GenerateDMatrix();
Context ctx;
ctx.gpu_id = 0;
LearnerModelParam mparam{MakeMP(n_col, .5, 1, ctx.gpu_id)};
auto ctx = MakeCUDACtx(0);
LearnerModelParam mparam{MakeMP(n_col, .5, 1, ctx.Ordinal())};
gbm::GBTreeModel model = CreateTestModel(&mparam, &ctx);
// Test predict batch
@@ -71,7 +70,7 @@ void VerifyBasicColumnSplit(std::array<std::vector<float>, 32> const& expected_r
auto dmat = RandomDataGenerator(n_row, n_col, 0).GenerateDMatrix();
std::unique_ptr<DMatrix> sliced{dmat->SliceCol(world_size, rank)};
LearnerModelParam mparam{MakeMP(n_col, .5, 1, ctx.gpu_id)};
LearnerModelParam mparam{MakeMP(n_col, .5, 1, ctx.Ordinal())};
gbm::GBTreeModel model = CreateTestModel(&mparam, &ctx);
// Test predict batch
@@ -102,7 +101,7 @@ TEST(GPUPredictor, MGPUBasicColumnSplit) {
size_t n_row = i, n_col = i;
auto dmat = RandomDataGenerator(n_row, n_col, 0).GenerateDMatrix();
LearnerModelParam mparam{MakeMP(n_col, .5, 1, ctx.gpu_id)};
LearnerModelParam mparam{MakeMP(n_col, .5, 1, ctx.Ordinal())};
gbm::GBTreeModel model = CreateTestModel(&mparam, &ctx);
// Test predict batch
@@ -132,18 +131,19 @@ TEST(GPUPredictor, EllpackBasic) {
}
TEST(GPUPredictor, EllpackTraining) {
size_t constexpr kRows { 128 }, kCols { 16 }, kBins { 64 };
auto p_ellpack =
RandomDataGenerator{kRows, kCols, 0.0}.Bins(kBins).Device(0).GenerateDeviceDMatrix(false);
auto ctx = MakeCUDACtx(0);
size_t constexpr kRows{128}, kCols{16}, kBins{64};
auto p_ellpack = RandomDataGenerator{kRows, kCols, 0.0}
.Bins(kBins)
.Device(ctx.Ordinal())
.GenerateDeviceDMatrix(false);
HostDeviceVector<float> storage(kRows * kCols);
auto columnar = RandomDataGenerator{kRows, kCols, 0.0}
.Device(0)
.GenerateArrayInterface(&storage);
auto columnar =
RandomDataGenerator{kRows, kCols, 0.0}.Device(ctx.Ordinal()).GenerateArrayInterface(&storage);
auto adapter = data::CupyAdapter(columnar);
std::shared_ptr<DMatrix> p_full {
DMatrix::Create(&adapter, std::numeric_limits<float>::quiet_NaN(), 1)
};
TestTrainingPrediction(kRows, kBins, "gpu_hist", p_full, p_ellpack);
std::shared_ptr<DMatrix> p_full{
DMatrix::Create(&adapter, std::numeric_limits<float>::quiet_NaN(), 1)};
TestTrainingPrediction(&ctx, kRows, kBins, p_full, p_ellpack);
}
TEST(GPUPredictor, ExternalMemoryTest) {
@@ -153,9 +153,8 @@ TEST(GPUPredictor, ExternalMemoryTest) {
gpu_predictor->Configure({});
const int n_classes = 3;
Context ctx;
ctx.gpu_id = 0;
LearnerModelParam mparam{MakeMP(5, .5, n_classes, ctx.gpu_id)};
Context ctx = MakeCUDACtx(0);
LearnerModelParam mparam{MakeMP(5, .5, n_classes, ctx.Ordinal())};
gbm::GBTreeModel model = CreateTestModel(&mparam, &ctx, n_classes);
std::vector<std::unique_ptr<DMatrix>> dmats;
@@ -185,7 +184,7 @@ TEST(GPUPredictor, InplacePredictCupy) {
auto ctx = MakeCUDACtx(0);
size_t constexpr kRows{128}, kCols{64};
RandomDataGenerator gen(kRows, kCols, 0.5);
gen.Device(ctx.gpu_id);
gen.Device(ctx.Ordinal());
HostDeviceVector<float> data;
std::string interface_str = gen.GenerateArrayInterface(&data);
std::shared_ptr<DMatrix> p_fmat{new data::DMatrixProxy};
@@ -197,7 +196,7 @@ TEST(GPUPredictor, InplacePredictCuDF) {
auto ctx = MakeCUDACtx(0);
size_t constexpr kRows{128}, kCols{64};
RandomDataGenerator gen(kRows, kCols, 0.5);
gen.Device(ctx.gpu_id);
gen.Device(ctx.Ordinal());
std::vector<HostDeviceVector<float>> storage(kCols);
auto interface_str = gen.GenerateColumnarArrayInterface(&storage);
std::shared_ptr<DMatrix> p_fmat{new data::DMatrixProxy};
@@ -214,9 +213,8 @@ TEST(GpuPredictor, LesserFeatures) {
TEST(GPUPredictor, ShapStump) {
cudaSetDevice(0);
Context ctx;
ctx.gpu_id = 0;
LearnerModelParam mparam{MakeMP(1, .5, 1, ctx.gpu_id)};
auto ctx = MakeCUDACtx(0);
LearnerModelParam mparam{MakeMP(1, .5, 1, ctx.Ordinal())};
gbm::GBTreeModel model(&mparam, &ctx);
std::vector<std::unique_ptr<RegTree>> trees;
@@ -241,9 +239,8 @@ TEST(GPUPredictor, ShapStump) {
}
TEST(GPUPredictor, Shap) {
Context ctx;
ctx.gpu_id = 0;
LearnerModelParam mparam{MakeMP(1, .5, 1, ctx.gpu_id)};
auto ctx = MakeCUDACtx(0);
LearnerModelParam mparam{MakeMP(1, .5, 1, ctx.Ordinal())};
gbm::GBTreeModel model(&mparam, &ctx);
std::vector<std::unique_ptr<RegTree>> trees;