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Fix compiler warnings. (#8022)

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- Remove/fix unused parameters
- Remove deprecated code in rabit.
- Update dmlc-core.
This commit is contained in:
Jiaming Yuan
2022-06-22 21:29:10 +08:00
committed by GitHub
parent e44a082620
commit 142a208a90
61 changed files with 230 additions and 579 deletions
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4
tests/cpp/predictor/test_gpu_predictor.cu
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@@ -56,7 +56,7 @@ TEST(GPUPredictor, Basic) {
std::vector<float>& gpu_out_predictions_h = gpu_out_predictions.predictions.HostVector();
std::vector<float>& cpu_out_predictions_h = cpu_out_predictions.predictions.HostVector();
float abs_tolerance = 0.001;
for (int j = 0; j < gpu_out_predictions.predictions.Size(); j++) {
for (size_t j = 0; j < gpu_out_predictions.predictions.Size(); j++) {
ASSERT_NEAR(gpu_out_predictions_h[j], cpu_out_predictions_h[j], abs_tolerance);
}
}
@@ -118,7 +118,7 @@ TEST(GPUPredictor, ExternalMemoryTest) {
gpu_predictor->PredictBatch(dmat.get(), &out_predictions, model, 0);
EXPECT_EQ(out_predictions.predictions.Size(), dmat->Info().num_row_ * n_classes);
const std::vector<float> &host_vector = out_predictions.predictions.ConstHostVector();
for (int i = 0; i < host_vector.size() / n_classes; i++) {
for (size_t i = 0; i < host_vector.size() / n_classes; i++) {
ASSERT_EQ(host_vector[i * n_classes], 2.0);
ASSERT_EQ(host_vector[i * n_classes + 1], 0.5);
ASSERT_EQ(host_vector[i * n_classes + 2], 0.5);

13
tests/cpp/predictor/test_predictor.cc
View File

@@ -45,7 +45,7 @@ void TestTrainingPrediction(size_t rows, size_t bins,
size_t constexpr kIters = 3;
std::unique_ptr<Learner> learner;
auto train = [&](std::string predictor, HostDeviceVector<float> *out) {
auto train = [&](std::string predictor) {
p_hist->Info().labels.Reshape(rows, 1);
auto &h_label = p_hist->Info().labels.Data()->HostVector();
@@ -59,6 +59,7 @@ void TestTrainingPrediction(size_t rows, size_t bins,
learner->SetParam("num_feature", std::to_string(kCols));
learner->SetParam("num_class", std::to_string(kClasses));
learner->SetParam("max_bin", std::to_string(bins));
learner->SetParam("predictor", predictor);
learner->Configure();
for (size_t i = 0; i < kIters; ++i) {
@@ -77,11 +78,11 @@ void TestTrainingPrediction(size_t rows, size_t bins,
}
};
HostDeviceVector<float> predictions_0;
train("cpu_predictor", &predictions_0);
HostDeviceVector<float> predictions_1;
train("gpu_predictor", &predictions_1);
if (tree_method == "gpu_hist") {
train("gpu_predictor");
} else {
train("cpu_predictor");
}
}
void TestInplacePrediction(std::shared_ptr<DMatrix> x, std::string predictor, bst_row_t rows,