Hendrik/xgboost
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Rename and extract Context. (#8528)

Browse Source 
* Rename `GenericParameter` to `Context`.
* Rename header file to reflect the change.
* Rename all references.
This commit is contained in:
Jiaming Yuan
2022-12-07 04:58:54 +08:00
committed by GitHub
parent 05fc6f3ca9
commit 3e26107a9c
105 changed files with 548 additions and 574 deletions
Download Patch File Download Diff File Expand all files Collapse all files

8
tests/cpp/tree/gpu_hist/test_row_partitioner.cu
View File

@@ -2,17 +2,17 @@
* Copyright 2019-2022 by XGBoost Contributors
*/
#include <gtest/gtest.h>
#include <algorithm>
#include <vector>
#include <thrust/device_vector.h>
#include <thrust/host_vector.h>
#include <thrust/sequence.h>
#include <algorithm>
#include <vector>
#include "../../../../src/tree/gpu_hist/row_partitioner.cuh"
#include "../../helpers.h"
#include "xgboost/base.h"
#include "xgboost/generic_parameters.h"
#include "xgboost/context.h"
#include "xgboost/task.h"
#include "xgboost/tree_model.h"

4
tests/cpp/tree/test_approx.cc
View File

@@ -12,7 +12,7 @@ namespace xgboost {
namespace tree {
TEST(Approx, Partitioner) {
size_t n_samples = 1024, n_features = 1, base_rowid = 0;
GenericParameter ctx;
Context ctx;
CommonRowPartitioner partitioner{&ctx, n_samples, base_rowid};
ASSERT_EQ(partitioner.base_rowid, base_rowid);
ASSERT_EQ(partitioner.Size(), 1);
@@ -69,7 +69,7 @@ TEST(Approx, Partitioner) {
namespace {
void TestLeafPartition(size_t n_samples) {
size_t const n_features = 2, base_rowid = 0;
GenericParameter ctx;
Context ctx;
common::RowSetCollection row_set;
CommonRowPartitioner partitioner{&ctx, n_samples, base_rowid};

18
tests/cpp/tree/test_gpu_hist.cu
View File

@@ -18,7 +18,7 @@
#include "../filesystem.h" // dmlc::TemporaryDirectory
#include "../helpers.h"
#include "../histogram_helpers.h"
#include "xgboost/generic_parameters.h"
#include "xgboost/context.h"
#include "xgboost/json.h"
namespace xgboost {
@@ -170,9 +170,9 @@ void TestHistogramIndexImpl() {
// Build 2 matrices and build a histogram maker with that
GenericParameter generic_param(CreateEmptyGenericParam(0));
tree::GPUHistMaker hist_maker{&generic_param,ObjInfo{ObjInfo::kRegression}},
hist_maker_ext{&generic_param,ObjInfo{ObjInfo::kRegression}};
Context ctx(CreateEmptyGenericParam(0));
tree::GPUHistMaker hist_maker{&ctx, ObjInfo{ObjInfo::kRegression}},
hist_maker_ext{&ctx, ObjInfo{ObjInfo::kRegression}};
std::unique_ptr<DMatrix> hist_maker_dmat(
CreateSparsePageDMatrixWithRC(kNRows, kNCols, 0, true));
@@ -239,8 +239,8 @@ void UpdateTree(HostDeviceVector<GradientPair>* gpair, DMatrix* dmat,
{"sampling_method", sampling_method},
};
GenericParameter generic_param(CreateEmptyGenericParam(0));
tree::GPUHistMaker hist_maker{&generic_param,ObjInfo{ObjInfo::kRegression}};
Context ctx(CreateEmptyGenericParam(0));
tree::GPUHistMaker hist_maker{&ctx,ObjInfo{ObjInfo::kRegression}};
hist_maker.Configure(args);
std::vector<HostDeviceVector<bst_node_t>> position(1);
@@ -384,9 +384,9 @@ TEST(GpuHist, ExternalMemoryWithSampling) {
}
TEST(GpuHist, ConfigIO) {
GenericParameter generic_param(CreateEmptyGenericParam(0));
Context ctx(CreateEmptyGenericParam(0));
std::unique_ptr<TreeUpdater> updater{
TreeUpdater::Create("grow_gpu_hist", &generic_param, ObjInfo{ObjInfo::kRegression})};
TreeUpdater::Create("grow_gpu_hist", &ctx, ObjInfo{ObjInfo::kRegression})};
updater->Configure(Args{});
Json j_updater { Object() };
@@ -404,7 +404,7 @@ TEST(GpuHist, ConfigIO) {
}
TEST(GpuHist, MaxDepth) {
GenericParameter generic_param(CreateEmptyGenericParam(0));
Context ctx(CreateEmptyGenericParam(0));
size_t constexpr kRows = 16;
size_t constexpr kCols = 4;
auto p_mat = RandomDataGenerator{kRows, kCols, 0}.GenerateDMatrix();

6
tests/cpp/tree/test_prediction_cache.cc
View File

@@ -63,12 +63,12 @@ class TestPredictionCache : public ::testing::Test {
void RunTest(std::string updater_name) {
{
omp_set_num_threads(1);
GenericParameter ctx;
Context ctx;
ctx.InitAllowUnknown(Args{{"nthread", "8"}});
if (updater_name == "grow_gpu_hist") {
ctx.gpu_id = 0;
} else {
ctx.gpu_id = GenericParameter::kCpuId;
ctx.gpu_id = Context::kCpuId;
}
std::unique_ptr<TreeUpdater> updater{
@@ -82,7 +82,7 @@ class TestPredictionCache : public ::testing::Test {
HostDeviceVector<float> out_prediction_cached;
out_prediction_cached.SetDevice(ctx.gpu_id);
out_prediction_cached.Resize(n_samples_);
auto cache = linalg::VectorView<float>{ctx.gpu_id == GenericParameter::kCpuId
auto cache = linalg::VectorView<float>{ctx.gpu_id == Context::kCpuId
? out_prediction_cached.HostSpan()
: out_prediction_cached.DeviceSpan(),
{out_prediction_cached.Size()},

4
tests/cpp/tree/test_prune.cc
View File

@@ -31,7 +31,7 @@ TEST(Updater, Prune) {
std::shared_ptr<DMatrix> p_dmat {
RandomDataGenerator{32, 10, 0}.GenerateDMatrix() };
auto lparam = CreateEmptyGenericParam(GPUIDX);
auto ctx = CreateEmptyGenericParam(GPUIDX);
// prepare tree
RegTree tree = RegTree();
@@ -39,7 +39,7 @@ TEST(Updater, Prune) {
std::vector<RegTree*> trees {&tree};
// prepare pruner
std::unique_ptr<TreeUpdater> pruner(
TreeUpdater::Create("prune", &lparam, ObjInfo{ObjInfo::kRegression}));
TreeUpdater::Create("prune", &ctx, ObjInfo{ObjInfo::kRegression}));
pruner->Configure(cfg);
// loss_chg < min_split_loss;

2
tests/cpp/tree/test_quantile_hist.cc
View File

@@ -20,7 +20,7 @@ namespace xgboost {
namespace tree {
TEST(QuantileHist, Partitioner) {
size_t n_samples = 1024, n_features = 1, base_rowid = 0;
GenericParameter ctx;
Context ctx;
ctx.InitAllowUnknown(Args{});
CommonRowPartitioner partitioner{&ctx, n_samples, base_rowid};

6
tests/cpp/tree/test_refresh.cc
View File

@@ -29,11 +29,11 @@ TEST(Updater, Refresh) {
{"reg_lambda", "1"}};
RegTree tree = RegTree();
auto lparam = CreateEmptyGenericParam(GPUIDX);
auto ctx = CreateEmptyGenericParam(GPUIDX);
tree.param.UpdateAllowUnknown(cfg);
std::vector<RegTree*> trees {&tree};
std::vector<RegTree*> trees{&tree};
std::unique_ptr<TreeUpdater> refresher(
TreeUpdater::Create("refresh", &lparam, ObjInfo{ObjInfo::kRegression}));
TreeUpdater::Create("refresh", &ctx, ObjInfo{ObjInfo::kRegression}));
tree.ExpandNode(0, 2, 0.2f, false, 0.0, 0.2f, 0.8f, 0.0f, 0.0f,
/*left_sum=*/0.0f, /*right_sum=*/0.0f);

9
tests/cpp/tree/test_tree_stat.cc
View File

@@ -67,8 +67,8 @@ class UpdaterEtaTest : public ::testing::Test {
}
void RunTest(std::string updater) {
GenericParameter ctx(updater == "grow_gpu_hist" ? CreateEmptyGenericParam(0)
: CreateEmptyGenericParam(Context::kCpuId));
Context ctx(updater == "grow_gpu_hist" ? CreateEmptyGenericParam(0)
: CreateEmptyGenericParam(Context::kCpuId));
float eta = 0.4;
auto up_0 = std::unique_ptr<TreeUpdater>{
TreeUpdater::Create(updater, &ctx, ObjInfo{ObjInfo::kClassification})};
@@ -140,9 +140,8 @@ class TestMinSplitLoss : public ::testing::Test {
// test gamma
{"gamma", std::to_string(gamma)}};
std::cout << "updater:" << updater << std::endl;
GenericParameter ctx(updater == "grow_gpu_hist" ? CreateEmptyGenericParam(0)
: CreateEmptyGenericParam(Context::kCpuId));
Context ctx(updater == "grow_gpu_hist" ? CreateEmptyGenericParam(0)
: CreateEmptyGenericParam(Context::kCpuId));
std::cout << ctx.gpu_id << std::endl;
auto up = std::unique_ptr<TreeUpdater>{
TreeUpdater::Create(updater, &ctx, ObjInfo{ObjInfo::kRegression})};