3136185bc5
* Add saving/loading JSON configuration. * Implement Python pickle interface with new IO routines. * Basic tests for training continuation.
98 lines
3.4 KiB
Plaintext
98 lines
3.4 KiB
Plaintext
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/*!
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* Copyright 2017-2019 XGBoost contributors
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*/
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#include <dmlc/filesystem.h>
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#include <xgboost/c_api.h>
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#include <xgboost/predictor.h>
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#include <xgboost/logging.h>
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#include <xgboost/learner.h>
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#include <string>
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#include "gtest/gtest.h"
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#include "../helpers.h"
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#include "../../../src/gbm/gbtree_model.h"
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namespace xgboost {
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namespace predictor {
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TEST(GpuPredictor, Basic) {
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auto cpu_lparam = CreateEmptyGenericParam(-1);
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auto gpu_lparam = CreateEmptyGenericParam(0);
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auto cache = std::make_shared<std::unordered_map<DMatrix*, PredictionCacheEntry>>();
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std::unique_ptr<Predictor> gpu_predictor =
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std::unique_ptr<Predictor>(Predictor::Create("gpu_predictor", &gpu_lparam, cache));
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std::unique_ptr<Predictor> cpu_predictor =
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std::unique_ptr<Predictor>(Predictor::Create("cpu_predictor", &cpu_lparam, cache));
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gpu_predictor->Configure({});
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cpu_predictor->Configure({});
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for (size_t i = 1; i < 33; i *= 2) {
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int n_row = i, n_col = i;
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auto dmat = CreateDMatrix(n_row, n_col, 0);
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LearnerModelParam param;
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param.num_feature = n_col;
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param.num_output_group = 1;
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param.base_score = 0.5;
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gbm::GBTreeModel model = CreateTestModel(¶m);
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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.HostVector();
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std::vector<float>& cpu_out_predictions_h = cpu_out_predictions.HostVector();
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float abs_tolerance = 0.001;
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for (int j = 0; j < gpu_out_predictions.Size(); j++) {
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ASSERT_NEAR(gpu_out_predictions_h[j], cpu_out_predictions_h[j], abs_tolerance);
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}
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delete dmat;
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}
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}
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TEST(gpu_predictor, ExternalMemoryTest) {
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auto lparam = CreateEmptyGenericParam(0);
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auto cache = std::make_shared<std::unordered_map<DMatrix*, PredictionCacheEntry>>();
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std::unique_ptr<Predictor> gpu_predictor =
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std::unique_ptr<Predictor>(Predictor::Create("gpu_predictor", &lparam, cache));
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gpu_predictor->Configure({});
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LearnerModelParam param;
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param.num_feature = 2;
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const int n_classes = 3;
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param.num_output_group = n_classes;
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param.base_score = 0.5;
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gbm::GBTreeModel model = CreateTestModel(¶m);
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std::vector<std::unique_ptr<DMatrix>> dmats;
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dmlc::TemporaryDirectory tmpdir;
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std::string file0 = tmpdir.path + "/big_0.libsvm";
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std::string file1 = tmpdir.path + "/big_1.libsvm";
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std::string file2 = tmpdir.path + "/big_2.libsvm";
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dmats.push_back(CreateSparsePageDMatrix(9, 64UL, file0));
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dmats.push_back(CreateSparsePageDMatrix(128, 128UL, file1));
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dmats.push_back(CreateSparsePageDMatrix(1024, 1024UL, file2));
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for (const auto& dmat: dmats) {
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dmat->Info().base_margin_.Resize(dmat->Info().num_row_ * n_classes, 0.5);
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HostDeviceVector<float> out_predictions;
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gpu_predictor->PredictBatch(dmat.get(), &out_predictions, model, 0);
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EXPECT_EQ(out_predictions.Size(), dmat->Info().num_row_ * n_classes);
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const std::vector<float> &host_vector = out_predictions.ConstHostVector();
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for (int i = 0; i < host_vector.size() / n_classes; i++) {
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ASSERT_EQ(host_vector[i * n_classes], 2.0);
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ASSERT_EQ(host_vector[i * n_classes + 1], 0.5);
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ASSERT_EQ(host_vector[i * n_classes + 2], 0.5);
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}
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}
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}
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} // namespace predictor
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} // namespace xgboost
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