91537e7353
* Fix #3342 and h2oai/h2o4gpu#625: Save predictor parameters in model file This allows pickled models to retain predictor attributes, such as 'predictor' (whether to use CPU or GPU) and 'n_gpu' (number of GPUs to use). Related: h2oai/h2o4gpu#625 Closes #3342. TODO. Write a test. * Fix lint * Do not load GPU predictor into CPU-only XGBoost * Add a test for pickling GPU predictors * Make sample data big enough to pass multi GPU test * Update test_gpu_predictor.cu
49 lines
1.6 KiB
C++
49 lines
1.6 KiB
C++
// Copyright by Contributors
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#include <gtest/gtest.h>
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#include <vector>
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#include "helpers.h"
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#include "xgboost/learner.h"
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namespace xgboost {
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TEST(learner, Test) {
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typedef std::pair<std::string, std::string> arg;
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auto args = {arg("tree_method", "exact")};
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auto mat_ptr = CreateDMatrix(10, 10, 0);
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std::vector<std::shared_ptr<xgboost::DMatrix>> mat = {*mat_ptr};
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auto learner = std::unique_ptr<Learner>(Learner::Create(mat));
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learner->Configure(args);
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delete mat_ptr;
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}
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TEST(learner, SelectTreeMethod) {
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using arg = std::pair<std::string, std::string>;
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auto mat_ptr = CreateDMatrix(10, 10, 0);
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std::vector<std::shared_ptr<xgboost::DMatrix>> mat = {*mat_ptr};
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auto learner = std::unique_ptr<Learner>(Learner::Create(mat));
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// Test if `tree_method` can be set
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learner->Configure({arg("tree_method", "approx")});
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ASSERT_EQ(learner->GetConfigurationArguments().at("updater"),
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"grow_histmaker,prune");
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learner->Configure({arg("tree_method", "exact")});
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ASSERT_EQ(learner->GetConfigurationArguments().at("updater"),
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"grow_colmaker,prune");
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learner->Configure({arg("tree_method", "hist")});
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ASSERT_EQ(learner->GetConfigurationArguments().at("updater"),
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"grow_fast_histmaker");
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#ifdef XGBOOST_USE_CUDA
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learner->Configure({arg("tree_method", "gpu_exact")});
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ASSERT_EQ(learner->GetConfigurationArguments().at("updater"),
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"grow_gpu,prune");
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learner->Configure({arg("tree_method", "gpu_hist")});
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ASSERT_EQ(learner->GetConfigurationArguments().at("updater"),
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"grow_gpu_hist");
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#endif
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delete mat_ptr;
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}
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} // namespace xgboost
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