d5992dd881
* Replaced std::vector-based interfaces with HostDeviceVector-based interfaces. - replacement was performed in the learner, boosters, predictors, updaters, and objective functions - only interfaces used in training were replaced; interfaces like PredictInstance() still use std::vector - refactoring necessary for replacement of interfaces was also performed, such as using HostDeviceVector in prediction cache * HostDeviceVector-based interfaces for custom objective function example plugin.
79 lines
2.9 KiB
Plaintext
79 lines
2.9 KiB
Plaintext
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/*!
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* Copyright 2017 XGBoost contributors
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*/
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#include <xgboost/c_api.h>
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#include <xgboost/predictor.h>
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#include "gtest/gtest.h"
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#include "../helpers.h"
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namespace xgboost {
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namespace predictor {
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TEST(gpu_predictor, Test) {
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std::unique_ptr<Predictor> gpu_predictor =
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std::unique_ptr<Predictor>(Predictor::Create("gpu_predictor"));
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std::unique_ptr<Predictor> cpu_predictor =
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std::unique_ptr<Predictor>(Predictor::Create("cpu_predictor"));
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gpu_predictor->Init({}, {});
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cpu_predictor->Init({}, {});
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std::vector<std::unique_ptr<RegTree>> trees;
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trees.push_back(std::unique_ptr<RegTree>(new RegTree()));
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trees.back()->InitModel();
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(*trees.back())[0].set_leaf(1.5f);
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(*trees.back()).stat(0).sum_hess = 1.0f;
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gbm::GBTreeModel model(0.5);
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model.CommitModel(std::move(trees), 0);
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model.param.num_output_group = 1;
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int n_row = 5;
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int n_col = 5;
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auto dmat = CreateDMatrix(n_row, n_col, 0);
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// Test predict batch
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HostDeviceVector<float> gpu_out_predictions;
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HostDeviceVector<float> cpu_out_predictions;
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gpu_predictor->PredictBatch(dmat.get(), &gpu_out_predictions, model, 0);
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cpu_predictor->PredictBatch(dmat.get(), &cpu_out_predictions, model, 0);
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std::vector<float>& gpu_out_predictions_h = gpu_out_predictions.data_h();
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std::vector<float>& cpu_out_predictions_h = cpu_out_predictions.data_h();
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float abs_tolerance = 0.001;
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for (int i = 0; i < gpu_out_predictions.size(); i++) {
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ASSERT_LT(std::abs(gpu_out_predictions_h[i] - cpu_out_predictions_h[i]),
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abs_tolerance);
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}
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// Test predict instance
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auto batch = dmat->RowIterator()->Value();
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for (int i = 0; i < batch.size; i++) {
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std::vector<float> gpu_instance_out_predictions;
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std::vector<float> cpu_instance_out_predictions;
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cpu_predictor->PredictInstance(batch[i], &cpu_instance_out_predictions,
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model);
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gpu_predictor->PredictInstance(batch[i], &gpu_instance_out_predictions,
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model);
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ASSERT_EQ(gpu_instance_out_predictions[0], cpu_instance_out_predictions[0]);
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}
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// Test predict leaf
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std::vector<float> gpu_leaf_out_predictions;
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std::vector<float> cpu_leaf_out_predictions;
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cpu_predictor->PredictLeaf(dmat.get(), &cpu_leaf_out_predictions, model);
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gpu_predictor->PredictLeaf(dmat.get(), &gpu_leaf_out_predictions, model);
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for (int i = 0; i < gpu_leaf_out_predictions.size(); i++) {
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ASSERT_EQ(gpu_leaf_out_predictions[i], cpu_leaf_out_predictions[i]);
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}
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// Test predict contribution
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std::vector<float> gpu_out_contribution;
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std::vector<float> cpu_out_contribution;
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cpu_predictor->PredictContribution(dmat.get(), &cpu_out_contribution, model);
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gpu_predictor->PredictContribution(dmat.get(), &gpu_out_contribution, model);
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for (int i = 0; i < gpu_out_contribution.size(); i++) {
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ASSERT_EQ(gpu_out_contribution[i], cpu_out_contribution[i]);
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}
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}
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} // namespace predictor
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} // namespace xgboost
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