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Remove internal use of gpu_id. (#9568)

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This commit is contained in:
Jiaming Yuan
2023-09-20 23:29:51 +08:00
committed by GitHub
parent 38ac52dd87
commit 8c676c889d
121 changed files with 1012 additions and 1044 deletions
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2
tests/cpp/predictor/test_cpu_predictor.cc
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@@ -142,7 +142,7 @@ TEST(CpuPredictor, InplacePredict) {
bst_row_t constexpr kRows{128};
bst_feature_t constexpr kCols{64};
Context ctx;
auto gen = RandomDataGenerator{kRows, kCols, 0.5}.Device(ctx.gpu_id);
auto gen = RandomDataGenerator{kRows, kCols, 0.5}.Device(ctx.Device());
{
HostDeviceVector<float> data;
gen.GenerateDense(&data);

30
tests/cpp/predictor/test_gpu_predictor.cu
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@@ -34,7 +34,7 @@ TEST(GPUPredictor, Basic) {
auto dmat = RandomDataGenerator(n_row, n_col, 0).GenerateDMatrix();
auto ctx = MakeCUDACtx(0);
LearnerModelParam mparam{MakeMP(n_col, .5, 1, ctx.Ordinal())};
LearnerModelParam mparam{MakeMP(n_col, .5, 1, ctx.Device())};
gbm::GBTreeModel model = CreateTestModel(&mparam, &ctx);
// Test predict batch
@@ -70,7 +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.Ordinal())};
LearnerModelParam mparam{MakeMP(n_col, .5, 1, ctx.Device())};
gbm::GBTreeModel model = CreateTestModel(&mparam, &ctx);
// Test predict batch
@@ -98,7 +98,7 @@ TEST_F(MGPUPredictorTest, BasicColumnSplit) {
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.Ordinal())};
LearnerModelParam mparam{MakeMP(n_col, .5, 1, ctx.Device())};
gbm::GBTreeModel model = CreateTestModel(&mparam, &ctx);
// Test predict batch
@@ -119,8 +119,10 @@ TEST(GPUPredictor, EllpackBasic) {
auto ctx = MakeCUDACtx(0);
for (size_t bins = 2; bins < 258; bins += 16) {
size_t rows = bins * 16;
auto p_m =
RandomDataGenerator{rows, kCols, 0.0}.Bins(bins).Device(0).GenerateDeviceDMatrix(false);
auto p_m = RandomDataGenerator{rows, kCols, 0.0}
.Bins(bins)
.Device(DeviceOrd::CUDA(0))
.GenerateDeviceDMatrix(false);
ASSERT_FALSE(p_m->PageExists<SparsePage>());
TestPredictionFromGradientIndex<EllpackPage>(&ctx, rows, kCols, p_m);
TestPredictionFromGradientIndex<EllpackPage>(&ctx, bins, kCols, p_m);
@@ -132,11 +134,11 @@ TEST(GPUPredictor, EllpackTraining) {
size_t constexpr kRows{128}, kCols{16}, kBins{64};
auto p_ellpack = RandomDataGenerator{kRows, kCols, 0.0}
.Bins(kBins)
.Device(ctx.Ordinal())
.Device(ctx.Device())
.GenerateDeviceDMatrix(false);
HostDeviceVector<float> storage(kRows * kCols);
auto columnar =
RandomDataGenerator{kRows, kCols, 0.0}.Device(ctx.Ordinal()).GenerateArrayInterface(&storage);
RandomDataGenerator{kRows, kCols, 0.0}.Device(ctx.Device()).GenerateArrayInterface(&storage);
auto adapter = data::CupyAdapter(columnar);
std::shared_ptr<DMatrix> p_full{
DMatrix::Create(&adapter, std::numeric_limits<float>::quiet_NaN(), 1)};
@@ -151,7 +153,7 @@ TEST(GPUPredictor, ExternalMemoryTest) {
const int n_classes = 3;
Context ctx = MakeCUDACtx(0);
LearnerModelParam mparam{MakeMP(5, .5, n_classes, ctx.Ordinal())};
LearnerModelParam mparam{MakeMP(5, .5, n_classes, ctx.Device())};
gbm::GBTreeModel model = CreateTestModel(&mparam, &ctx, n_classes);
std::vector<std::unique_ptr<DMatrix>> dmats;
@@ -162,7 +164,7 @@ TEST(GPUPredictor, ExternalMemoryTest) {
for (const auto& dmat: dmats) {
dmat->Info().base_margin_ = decltype(dmat->Info().base_margin_){
{dmat->Info().num_row_, static_cast<size_t>(n_classes)}, 0};
{dmat->Info().num_row_, static_cast<size_t>(n_classes)}, DeviceOrd::CUDA(0)};
dmat->Info().base_margin_.Data()->Fill(0.5);
PredictionCacheEntry out_predictions;
gpu_predictor->InitOutPredictions(dmat->Info(), &out_predictions.predictions, model);
@@ -181,7 +183,7 @@ TEST(GPUPredictor, InplacePredictCupy) {
auto ctx = MakeCUDACtx(0);
size_t constexpr kRows{128}, kCols{64};
RandomDataGenerator gen(kRows, kCols, 0.5);
gen.Device(ctx.Ordinal());
gen.Device(ctx.Device());
HostDeviceVector<float> data;
std::string interface_str = gen.GenerateArrayInterface(&data);
std::shared_ptr<DMatrix> p_fmat{new data::DMatrixProxy};
@@ -193,7 +195,7 @@ TEST(GPUPredictor, InplacePredictCuDF) {
auto ctx = MakeCUDACtx(0);
size_t constexpr kRows{128}, kCols{64};
RandomDataGenerator gen(kRows, kCols, 0.5);
gen.Device(ctx.Ordinal());
gen.Device(ctx.Device());
std::vector<HostDeviceVector<float>> storage(kCols);
auto interface_str = gen.GenerateColumnarArrayInterface(&storage);
std::shared_ptr<DMatrix> p_fmat{new data::DMatrixProxy};
@@ -215,7 +217,7 @@ TEST(GPUPredictor, ShapStump) {
cudaSetDevice(0);
auto ctx = MakeCUDACtx(0);
LearnerModelParam mparam{MakeMP(1, .5, 1, ctx.Ordinal())};
LearnerModelParam mparam{MakeMP(1, .5, 1, ctx.Device())};
gbm::GBTreeModel model(&mparam, &ctx);
std::vector<std::unique_ptr<RegTree>> trees;
@@ -241,7 +243,7 @@ TEST(GPUPredictor, ShapStump) {
TEST(GPUPredictor, Shap) {
auto ctx = MakeCUDACtx(0);
LearnerModelParam mparam{MakeMP(1, .5, 1, ctx.Ordinal())};
LearnerModelParam mparam{MakeMP(1, .5, 1, ctx.Device())};
gbm::GBTreeModel model(&mparam, &ctx);
std::vector<std::unique_ptr<RegTree>> trees;
@@ -296,7 +298,7 @@ TEST_F(MGPUPredictorTest, CategoricalPredictionLeafColumnSplit) {
TEST(GPUPredictor, PredictLeafBasic) {
size_t constexpr kRows = 5, kCols = 5;
auto dmat = RandomDataGenerator(kRows, kCols, 0).Device(0).GenerateDMatrix();
auto dmat = RandomDataGenerator(kRows, kCols, 0).Device(DeviceOrd::CUDA(0)).GenerateDMatrix();
auto lparam = MakeCUDACtx(GPUIDX);
std::unique_ptr<Predictor> gpu_predictor =
std::unique_ptr<Predictor>(Predictor::Create("gpu_predictor", &lparam));

14
tests/cpp/predictor/test_predictor.cc
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@@ -34,7 +34,7 @@ TEST(Predictor, PredictionCache) {
// Add a cache that is immediately expired.
auto add_cache = [&]() {
auto p_dmat = RandomDataGenerator(kRows, kCols, 0).GenerateDMatrix();
container.Cache(p_dmat, Context::kCpuId);
container.Cache(p_dmat, DeviceOrd::CPU());
m = p_dmat.get();
};
@@ -93,7 +93,7 @@ void TestTrainingPrediction(Context const *ctx, size_t rows, size_t bins,
void TestInplacePrediction(Context const *ctx, std::shared_ptr<DMatrix> x, bst_row_t rows,
bst_feature_t cols) {
std::size_t constexpr kClasses { 4 };
auto gen = RandomDataGenerator{rows, cols, 0.5}.Device(ctx->gpu_id);
auto gen = RandomDataGenerator{rows, cols, 0.5}.Device(ctx->Device());
std::shared_ptr<DMatrix> m = gen.GenerateDMatrix(true, false, kClasses);
std::unique_ptr<Learner> learner {
@@ -192,7 +192,7 @@ void TestPredictionDeviceAccess() {
HostDeviceVector<float> from_cpu;
{
ASSERT_EQ(from_cpu.DeviceIdx(), Context::kCpuId);
ASSERT_TRUE(from_cpu.Device().IsCPU());
Context cpu_ctx;
learner->SetParam("device", cpu_ctx.DeviceName());
learner->Predict(m_test, false, &from_cpu, 0, 0);
@@ -206,7 +206,7 @@ void TestPredictionDeviceAccess() {
Context cuda_ctx = MakeCUDACtx(0);
learner->SetParam("device", cuda_ctx.DeviceName());
learner->Predict(m_test, false, &from_cuda, 0, 0);
ASSERT_EQ(from_cuda.DeviceIdx(), 0);
ASSERT_EQ(from_cuda.Device(), DeviceOrd::CUDA(0));
ASSERT_TRUE(from_cuda.DeviceCanWrite());
ASSERT_FALSE(from_cuda.HostCanRead());
}
@@ -351,7 +351,7 @@ void TestCategoricalPredictLeaf(bool use_gpu, bool is_column_split) {
void TestIterationRange(Context const* ctx) {
size_t constexpr kRows = 1000, kCols = 20, kClasses = 4, kForest = 3, kIters = 10;
auto dmat = RandomDataGenerator(kRows, kCols, 0)
.Device(ctx->gpu_id)
.Device(ctx->Device())
.GenerateDMatrix(true, true, kClasses);
auto learner = LearnerForTest(ctx, dmat, kIters, kForest);
@@ -522,7 +522,7 @@ void TestSparsePrediction(Context const *ctx, float sparsity) {
if (ctx->IsCUDA()) {
learner->SetParam("tree_method", "gpu_hist");
learner->SetParam("gpu_id", std::to_string(ctx->gpu_id));
learner->SetParam("device", ctx->Device().Name());
}
learner->Predict(Xy, false, &sparse_predt, 0, 0);
@@ -620,7 +620,7 @@ void TestVectorLeafPrediction(Context const *ctx) {
size_t constexpr kCols = 5;
LearnerModelParam mparam{static_cast<bst_feature_t>(kCols),
linalg::Vector<float>{{0.5}, {1}, Context::kCpuId}, 1, 3,
linalg::Vector<float>{{0.5}, {1}, DeviceOrd::CPU()}, 1, 3,
MultiStrategy::kMultiOutputTree};
std::vector<std::unique_ptr<RegTree>> trees;