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Refactor configuration [Part II]. (#4577)

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* Refactor configuration [Part II].

* General changes:
** Remove `Init` methods to avoid ambiguity.
** Remove `Configure(std::map<>)` to avoid redundant copying and prepare for
   parameter validation. (`std::vector` is returned from `InitAllowUnknown`).
** Add name to tree updaters for easier debugging.

* Learner changes:
** Make `LearnerImpl` the only source of configuration.

    All configurations are stored and carried out by `LearnerImpl::Configure()`.

** Remove booster in C API.

    Originally kept for "compatibility reason", but did not state why.  So here
    we just remove it.

** Add a `metric_names_` field in `LearnerImpl`.
** Remove `LazyInit`.  Configuration will always be lazy.
** Run `Configure` before every iteration.

* Predictor changes:
** Allocate both cpu and gpu predictor.
** Remove cpu_predictor from gpu_predictor.

    `GBTree` is now used to dispatch the predictor.

** Remove some GPU Predictor tests.

* IO

No IO changes.  The binary model format stability is tested by comparing
hashing value of save models between two commits
This commit is contained in:
Jiaming Yuan
2019-07-20 08:34:56 -04:00
committed by GitHub
parent ad1192e8a3
commit f0064c07ab
69 changed files with 669 additions and 761 deletions
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85
tests/cpp/predictor/test_gpu_predictor.cu
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@@ -2,10 +2,12 @@
/*!
* Copyright 2017-2019 XGBoost contributors
*/
#include <dmlc/logging.h>
#include <dmlc/filesystem.h>
#include <xgboost/c_api.h>
#include <xgboost/predictor.h>
#include <xgboost/logging.h>
#include <xgboost/learner.h>
#include <string>
#include "gtest/gtest.h"
#include "../helpers.h"
@@ -20,8 +22,14 @@ inline void CheckCAPICall(int ret) {
} // namespace anonymous
#endif
extern const std::map<std::string, std::string>&
QueryBoosterConfigurationArguments(BoosterHandle handle);
const std::map<std::string, std::string>&
QueryBoosterConfigurationArguments(BoosterHandle handle) {
CHECK_NE(handle, static_cast<void*>(nullptr));
auto* bst = static_cast<xgboost::Learner*>(handle);
bst->Configure();
return bst->GetConfigurationArguments();
}
namespace xgboost {
namespace predictor {
@@ -35,8 +43,8 @@ TEST(gpu_predictor, Test) {
std::unique_ptr<Predictor> cpu_predictor =
std::unique_ptr<Predictor>(Predictor::Create("cpu_predictor", &cpu_lparam));
gpu_predictor->Init({}, {});
cpu_predictor->Init({}, {});
gpu_predictor->Configure({}, {});
cpu_predictor->Configure({}, {});
int n_row = 5;
int n_col = 5;
@@ -56,35 +64,6 @@ TEST(gpu_predictor, Test) {
for (int i = 0; i < gpu_out_predictions.Size(); i++) {
ASSERT_NEAR(gpu_out_predictions_h[i], cpu_out_predictions_h[i], abs_tolerance);
}
// Test predict instance
const auto &batch = *(*dmat)->GetRowBatches().begin();
for (int i = 0; i < batch.Size(); i++) {
std::vector<float> gpu_instance_out_predictions;
std::vector<float> cpu_instance_out_predictions;
cpu_predictor->PredictInstance(batch[i], &cpu_instance_out_predictions,
model);
gpu_predictor->PredictInstance(batch[i], &gpu_instance_out_predictions,
model);
ASSERT_EQ(gpu_instance_out_predictions[0], cpu_instance_out_predictions[0]);
}
// Test predict leaf
std::vector<float> gpu_leaf_out_predictions;
std::vector<float> cpu_leaf_out_predictions;
cpu_predictor->PredictLeaf((*dmat).get(), &cpu_leaf_out_predictions, model);
gpu_predictor->PredictLeaf((*dmat).get(), &gpu_leaf_out_predictions, model);
for (int i = 0; i < gpu_leaf_out_predictions.size(); i++) {
ASSERT_EQ(gpu_leaf_out_predictions[i], cpu_leaf_out_predictions[i]);
}
// Test predict contribution
std::vector<float> gpu_out_contribution;
std::vector<float> cpu_out_contribution;
cpu_predictor->PredictContribution((*dmat).get(), &cpu_out_contribution, model);
gpu_predictor->PredictContribution((*dmat).get(), &gpu_out_contribution, model);
for (int i = 0; i < gpu_out_contribution.size(); i++) {
ASSERT_EQ(gpu_out_contribution[i], cpu_out_contribution[i]);
}
delete dmat;
}
@@ -93,7 +72,7 @@ TEST(gpu_predictor, ExternalMemoryTest) {
auto lparam = CreateEmptyGenericParam(0, 1);
std::unique_ptr<Predictor> gpu_predictor =
std::unique_ptr<Predictor>(Predictor::Create("gpu_predictor", &lparam));
gpu_predictor->Init({}, {});
gpu_predictor->Configure({}, {});
gbm::GBTreeModel model = CreateTestModel();
int n_col = 3;
model.param.num_feature = n_col;
@@ -108,38 +87,6 @@ TEST(gpu_predictor, ExternalMemoryTest) {
for (const auto& v : out_predictions.HostVector()) {
ASSERT_EQ(v, 1.5);
}
// Test predict leaf
std::vector<float> leaf_out_predictions;
gpu_predictor->PredictLeaf(dmat.get(), &leaf_out_predictions, model);
EXPECT_EQ(leaf_out_predictions.size(), dmat->Info().num_row_);
for (const auto& v : leaf_out_predictions) {
ASSERT_EQ(v, 0);
}
// Test predict contribution
std::vector<float> out_contribution;
gpu_predictor->PredictContribution(dmat.get(), &out_contribution, model);
EXPECT_EQ(out_contribution.size(), dmat->Info().num_row_ * (n_col + 1));
for (int i = 0; i < out_contribution.size(); i++) {
if (i % (n_col + 1) == n_col) {
ASSERT_EQ(out_contribution[i], 1.5);
} else {
ASSERT_EQ(out_contribution[i], 0);
}
}
// Test predict contribution (approximate method)
std::vector<float> out_contribution_approximate;
gpu_predictor->PredictContribution(dmat.get(), &out_contribution_approximate, model, true);
EXPECT_EQ(out_contribution.size(), dmat->Info().num_row_ * (n_col + 1));
for (int i = 0; i < out_contribution.size(); i++) {
if (i % (n_col + 1) == n_col) {
ASSERT_EQ(out_contribution[i], 1.5);
} else {
ASSERT_EQ(out_contribution[i], 0);
}
}
}
#if defined(XGBOOST_USE_NCCL)
@@ -231,7 +178,7 @@ TEST(gpu_predictor, MGPU_Test) {
std::unique_ptr<Predictor> cpu_predictor =
std::unique_ptr<Predictor>(Predictor::Create("cpu_predictor", &cpu_lparam));
cpu_predictor->Init({}, {});
cpu_predictor->Configure({}, {});
for (size_t i = 1; i < 33; i *= 2) {
int n_row = i, n_col = i;
@@ -263,7 +210,7 @@ TEST(gpu_predictor, MGPU_ExternalMemoryTest) {
std::unique_ptr<Predictor> gpu_predictor =
std::unique_ptr<Predictor>(Predictor::Create("gpu_predictor", &gpu_lparam));
gpu_predictor->Init({}, {});
gpu_predictor->Configure({}, {});
gbm::GBTreeModel model = CreateTestModel();
model.param.num_feature = 3;