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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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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;