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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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23
tests/cpp/predictor/test_cpu_predictor.cc
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@@ -1,4 +1,6 @@
// Copyright by Contributors
/*!
* Copyright 2017-2020 XGBoost contributors
*/
#include <dmlc/filesystem.h>
#include <gtest/gtest.h>
#include <xgboost/predictor.h>
@@ -9,9 +11,8 @@
namespace xgboost {
TEST(CpuPredictor, Basic) {
auto lparam = CreateEmptyGenericParam(GPUIDX);
auto cache = std::make_shared<std::unordered_map<DMatrix*, PredictionCacheEntry>>();
std::unique_ptr<Predictor> cpu_predictor =
std::unique_ptr<Predictor>(Predictor::Create("cpu_predictor", &lparam, cache));
std::unique_ptr<Predictor>(Predictor::Create("cpu_predictor", &lparam));
int kRows = 5;
int kCols = 5;
@@ -26,10 +27,11 @@ TEST(CpuPredictor, Basic) {
auto dmat = CreateDMatrix(kRows, kCols, 0);
// Test predict batch
HostDeviceVector<float> out_predictions;
PredictionCacheEntry out_predictions;
cpu_predictor->PredictBatch((*dmat).get(), &out_predictions, model, 0);
std::vector<float>& out_predictions_h = out_predictions.HostVector();
for (size_t i = 0; i < out_predictions.Size(); i++) {
ASSERT_EQ(model.trees.size(), out_predictions.version);
std::vector<float>& out_predictions_h = out_predictions.predictions.HostVector();
for (size_t i = 0; i < out_predictions.predictions.Size(); i++) {
ASSERT_EQ(out_predictions_h[i], 1.5);
}
@@ -81,10 +83,9 @@ TEST(CpuPredictor, ExternalMemory) {
std::string filename = tmpdir.path + "/big.libsvm";
std::unique_ptr<DMatrix> dmat = CreateSparsePageDMatrix(12, 64, filename);
auto lparam = CreateEmptyGenericParam(GPUIDX);
auto cache = std::make_shared<std::unordered_map<DMatrix*, PredictionCacheEntry>>();
std::unique_ptr<Predictor> cpu_predictor =
std::unique_ptr<Predictor>(Predictor::Create("cpu_predictor", &lparam, cache));
std::unique_ptr<Predictor>(Predictor::Create("cpu_predictor", &lparam));
LearnerModelParam param;
param.base_score = 0;
@@ -94,10 +95,10 @@ TEST(CpuPredictor, ExternalMemory) {
gbm::GBTreeModel model = CreateTestModel(&param);
// Test predict batch
HostDeviceVector<float> out_predictions;
PredictionCacheEntry out_predictions;
cpu_predictor->PredictBatch(dmat.get(), &out_predictions, model, 0);
std::vector<float> &out_predictions_h = out_predictions.HostVector();
ASSERT_EQ(out_predictions.Size(), dmat->Info().num_row_);
std::vector<float> &out_predictions_h = out_predictions.predictions.HostVector();
ASSERT_EQ(out_predictions.predictions.Size(), dmat->Info().num_row_);
for (const auto& v : out_predictions_h) {
ASSERT_EQ(v, 1.5);
}