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Move prediction cache to Learner. (#5220)

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* Move prediction cache into Learner.

* Clean-ups

- Remove duplicated cache in Learner and GBM.
- Remove ad-hoc fix of invalid cache.
- Remove `PredictFromCache` in predictors.
- Remove prediction cache for linear altogether, as it's only moving the
  prediction into training process but doesn't provide any actual overall speed
  gain.
- The cache is now unique to Learner, which means the ownership is no longer
  shared by any other components.

* Changes

- Add version to prediction cache.
- Use weak ptr to check expired DMatrix.
- Pass shared pointer instead of raw pointer.
This commit is contained in:
Jiaming Yuan
2020-02-14 13:04:23 +08:00
committed by GitHub
parent 24ad9dec0b
commit c35cdecddd
19 changed files with 457 additions and 372 deletions
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30
tests/cpp/predictor/test_gpu_predictor.cu
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@@ -1,6 +1,5 @@
/*!
* Copyright 2017-2019 XGBoost contributors
* Copyright 2017-2020 XGBoost contributors
*/
#include <dmlc/filesystem.h>
#include <xgboost/c_api.h>
@@ -19,12 +18,11 @@ namespace predictor {
TEST(GpuPredictor, Basic) {
auto cpu_lparam = CreateEmptyGenericParam(-1);
auto gpu_lparam = CreateEmptyGenericParam(0);
auto cache = std::make_shared<std::unordered_map<DMatrix*, PredictionCacheEntry>>();
std::unique_ptr<Predictor> gpu_predictor =
std::unique_ptr<Predictor>(Predictor::Create("gpu_predictor", &gpu_lparam, cache));
std::unique_ptr<Predictor>(Predictor::Create("gpu_predictor", &gpu_lparam));
std::unique_ptr<Predictor> cpu_predictor =
std::unique_ptr<Predictor>(Predictor::Create("cpu_predictor", &cpu_lparam, cache));
std::unique_ptr<Predictor>(Predictor::Create("cpu_predictor", &cpu_lparam));
gpu_predictor->Configure({});
cpu_predictor->Configure({});
@@ -41,16 +39,17 @@ TEST(GpuPredictor, Basic) {
gbm::GBTreeModel model = CreateTestModel(&param);
// Test predict batch
HostDeviceVector<float> gpu_out_predictions;
HostDeviceVector<float> cpu_out_predictions;
PredictionCacheEntry gpu_out_predictions;
PredictionCacheEntry cpu_out_predictions;
gpu_predictor->PredictBatch((*dmat).get(), &gpu_out_predictions, model, 0);
ASSERT_EQ(model.trees.size(), gpu_out_predictions.version);
cpu_predictor->PredictBatch((*dmat).get(), &cpu_out_predictions, model, 0);
std::vector<float>& gpu_out_predictions_h = gpu_out_predictions.HostVector();
std::vector<float>& cpu_out_predictions_h = cpu_out_predictions.HostVector();
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.Size(); j++) {
for (int j = 0; j < gpu_out_predictions.predictions.Size(); j++) {
ASSERT_NEAR(gpu_out_predictions_h[j], cpu_out_predictions_h[j], abs_tolerance);
}
delete dmat;
@@ -59,9 +58,8 @@ TEST(GpuPredictor, Basic) {
TEST(gpu_predictor, ExternalMemoryTest) {
auto lparam = CreateEmptyGenericParam(0);
auto cache = std::make_shared<std::unordered_map<DMatrix*, PredictionCacheEntry>>();
std::unique_ptr<Predictor> gpu_predictor =
std::unique_ptr<Predictor>(Predictor::Create("gpu_predictor", &lparam, cache));
std::unique_ptr<Predictor>(Predictor::Create("gpu_predictor", &lparam));
gpu_predictor->Configure({});
LearnerModelParam param;
@@ -70,7 +68,7 @@ TEST(gpu_predictor, ExternalMemoryTest) {
param.num_output_group = n_classes;
param.base_score = 0.5;
gbm::GBTreeModel model = CreateTestModel(&param);
gbm::GBTreeModel model = CreateTestModel(&param, n_classes);
std::vector<std::unique_ptr<DMatrix>> dmats;
dmlc::TemporaryDirectory tmpdir;
std::string file0 = tmpdir.path + "/big_0.libsvm";
@@ -82,10 +80,10 @@ TEST(gpu_predictor, ExternalMemoryTest) {
for (const auto& dmat: dmats) {
dmat->Info().base_margin_.Resize(dmat->Info().num_row_ * n_classes, 0.5);
HostDeviceVector<float> out_predictions;
PredictionCacheEntry out_predictions;
gpu_predictor->PredictBatch(dmat.get(), &out_predictions, model, 0);
EXPECT_EQ(out_predictions.Size(), dmat->Info().num_row_ * n_classes);
const std::vector<float> &host_vector = out_predictions.ConstHostVector();
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++) {
ASSERT_EQ(host_vector[i * n_classes], 2.0);
ASSERT_EQ(host_vector[i * n_classes + 1], 0.5);