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Use ellpack for prediction only when sparsepage doesn't exist. (#5504)

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This commit is contained in:
Jiaming Yuan
2020-04-10 12:15:46 +08:00
committed by GitHub
parent ad826e913f
commit 6671b42dd4
35 changed files with 166 additions and 116 deletions
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40
tests/cpp/predictor/test_predictor.cc
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@@ -21,7 +21,7 @@ TEST(Predictor, PredictionCache) {
DMatrix* m;
// Add a cache that is immediately expired.
auto add_cache = [&]() {
auto p_dmat = RandomDataGenerator(kRows, kCols, 0).GenerateDMatix();
auto p_dmat = RandomDataGenerator(kRows, kCols, 0).GenerateDMatrix();
container.Cache(p_dmat, GenericParameter::kCpuId);
m = p_dmat.get();
};
@@ -32,17 +32,16 @@ TEST(Predictor, PredictionCache) {
EXPECT_ANY_THROW(container.Entry(m));
}
// Only run this test when CUDA is enabled.
void TestTrainingPrediction(size_t rows, std::string tree_method) {
void TestTrainingPrediction(size_t rows, std::string tree_method,
std::shared_ptr<DMatrix> p_full,
std::shared_ptr<DMatrix> p_hist) {
size_t constexpr kCols = 16;
size_t constexpr kClasses = 3;
size_t constexpr kIters = 3;
std::unique_ptr<Learner> learner;
auto train = [&](std::string predictor, HostDeviceVector<float>* out) {
auto p_m = RandomDataGenerator(rows, kCols, 0).GenerateDMatix();
auto &h_label = p_m->Info().labels_.HostVector();
auto train = [&](std::string predictor, HostDeviceVector<float> *out) {
auto &h_label = p_hist->Info().labels_.HostVector();
h_label.resize(rows);
for (size_t i = 0; i < rows; ++i) {
@@ -52,30 +51,31 @@ void TestTrainingPrediction(size_t rows, std::string tree_method) {
learner.reset(Learner::Create({}));
learner->SetParam("tree_method", tree_method);
learner->SetParam("objective", "multi:softprob");
learner->SetParam("predictor", predictor);
learner->SetParam("num_feature", std::to_string(kCols));
learner->SetParam("num_class", std::to_string(kClasses));
learner->Configure();
for (size_t i = 0; i < kIters; ++i) {
learner->UpdateOneIter(i, p_m);
learner->UpdateOneIter(i, p_hist);
}
HostDeviceVector<float> from_full;
learner->Predict(p_full, false, &from_full);
HostDeviceVector<float> from_hist;
learner->Predict(p_hist, false, &from_hist);
for (size_t i = 0; i < rows; ++i) {
EXPECT_NEAR(from_hist.ConstHostVector()[i],
from_full.ConstHostVector()[i], kRtEps);
}
learner->Predict(p_m, false, out);
};
// Alternate the predictor, CPU predictor can not use ellpack while GPU predictor can
// not use CPU histogram index. So it's guaranteed one of the following is not
// predicting from histogram index. Note: As of writing only GPU supports predicting
// from gradient index, the test is written for future portability.
HostDeviceVector<float> predictions_0;
train("cpu_predictor", &predictions_0);
HostDeviceVector<float> predictions_1;
train("gpu_predictor", &predictions_1);
for (size_t i = 0; i < rows; ++i) {
EXPECT_NEAR(predictions_1.ConstHostVector()[i],
predictions_0.ConstHostVector()[i], kRtEps);
}
}
void TestInplacePrediction(dmlc::any x, std::string predictor,
@@ -83,7 +83,7 @@ void TestInplacePrediction(dmlc::any x, std::string predictor,
int32_t device) {
size_t constexpr kClasses { 4 };
auto gen = RandomDataGenerator{rows, cols, 0.5}.Device(device);
std::shared_ptr<DMatrix> m = gen.GenerateDMatix(true, false, kClasses);
std::shared_ptr<DMatrix> m = gen.GenerateDMatrix(true, false, kClasses);
std::unique_ptr<Learner> learner {
Learner::Create({m})