Fix wrapping GPU ID and prevent data copying. (#5160)
* Removed some data copying. * Make sure gpu_id is valid before any configuration is carried out.
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@ -41,7 +41,7 @@ BatchSet<SparsePage> SimpleDMatrix::GetRowBatches() {
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BatchSet<CSCPage> SimpleDMatrix::GetColumnBatches() {
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// column page doesn't exist, generate it
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if (!column_page_) {
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auto page = dynamic_cast<SimpleCSRSource*>(source_.get())->page_;
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auto const& page = dynamic_cast<SimpleCSRSource*>(source_.get())->page_;
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column_page_.reset(new CSCPage(page.GetTranspose(source_->info.num_col_)));
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}
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auto begin_iter =
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@ -52,7 +52,7 @@ BatchSet<CSCPage> SimpleDMatrix::GetColumnBatches() {
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BatchSet<SortedCSCPage> SimpleDMatrix::GetSortedColumnBatches() {
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// Sorted column page doesn't exist, generate it
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if (!sorted_column_page_) {
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auto page = dynamic_cast<SimpleCSRSource*>(source_.get())->page_;
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auto const& page = dynamic_cast<SimpleCSRSource*>(source_.get())->page_;
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sorted_column_page_.reset(
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new SortedCSCPage(page.GetTranspose(source_->info.num_col_)));
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sorted_column_page_->SortRows();
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@ -354,7 +354,6 @@ class SparsePageSource : public DataSource<T> {
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writer.Alloc(&page);
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page->Clear();
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MetaInfo info = src->Info();
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size_t bytes_write = 0;
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double tstart = dmlc::GetTime();
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for (auto& batch : src->GetBatches<SparsePage>()) {
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@ -275,7 +275,8 @@ class LearnerImpl : public Learner {
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// `verbosity` in logger is not saved, we should move it into generic_param_.
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// FIXME(trivialfis): Make eval_metric a training parameter.
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if (kv.first != "num_feature" && kv.first != "verbosity" &&
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kv.first != "num_class" && kv.first != kEvalMetric) {
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kv.first != "num_class" && kv.first != "num_output_group" &&
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kv.first != kEvalMetric) {
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provided.push_back(kv.first);
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}
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}
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@ -399,6 +400,8 @@ class LearnerImpl : public Learner {
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}
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fromJson(learner_parameters.at("generic_param"), &generic_parameters_);
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// make sure the GPU ID is valid in new environment before start running configure.
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generic_parameters_.ConfigureGpuId(false);
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this->need_configuration_ = true;
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}
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@ -51,14 +51,14 @@ TEST(SparsePageDMatrix, ColAccess) {
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EXPECT_EQ(dmat->GetColDensity(1), 0.5);
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// Loop over the batches and assert the data is as expected
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for (auto col_batch : dmat->GetBatches<xgboost::SortedCSCPage>()) {
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for (auto const& col_batch : dmat->GetBatches<xgboost::SortedCSCPage>()) {
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EXPECT_EQ(col_batch.Size(), dmat->Info().num_col_);
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EXPECT_EQ(col_batch[1][0].fvalue, 10.0f);
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EXPECT_EQ(col_batch[1].size(), 1);
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}
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// Loop over the batches and assert the data is as expected
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for (auto col_batch : dmat->GetBatches<xgboost::CSCPage>()) {
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for (auto const& col_batch : dmat->GetBatches<xgboost::CSCPage>()) {
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EXPECT_EQ(col_batch.Size(), dmat->Info().num_col_);
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EXPECT_EQ(col_batch[1][0].fvalue, 10.0f);
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EXPECT_EQ(col_batch[1].size(), 1);
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@ -223,7 +223,7 @@ TEST(SparsePageDMatrix, FromFile) {
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const std::string tmp_file = tempdir.path + "/simple.libsvm";
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data::SparsePageDMatrix dmat(
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&adapter, std::numeric_limits<float>::quiet_NaN(), -1, tmp_file, 1);
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for (auto &batch : dmat.GetBatches<SparsePage>()) {
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std::vector<bst_row_t> expected_offset(batch.Size() + 1);
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int n = -3;
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@ -46,7 +46,7 @@ void CheckObjFunctionImpl(std::unique_ptr<xgboost::ObjFunction> const& obj,
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std::vector<xgboost::bst_float> preds,
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std::vector<xgboost::bst_float> labels,
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std::vector<xgboost::bst_float> weights,
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xgboost::MetaInfo info,
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xgboost::MetaInfo const& info,
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std::vector<xgboost::bst_float> out_grad,
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std::vector<xgboost::bst_float> out_hess) {
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xgboost::HostDeviceVector<xgboost::bst_float> in_preds(preds);
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@ -37,3 +37,15 @@ class TestLoadPickle(unittest.TestCase):
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config = json.loads(config)
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assert config['learner']['gradient_booster']['gbtree_train_param'][
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'predictor'] == 'gpu_predictor'
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def test_wrap_gpu_id(self):
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assert os.environ['CUDA_VISIBLE_DEVICES'] == '0'
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bst = load_pickle(model_path)
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config = bst.save_config()
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config = json.loads(config)
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assert config['learner']['generic_param']['gpu_id'] == '0'
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x, y = build_dataset()
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test_x = xgb.DMatrix(x)
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res = bst.predict(test_x)
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assert len(res) == 10
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@ -5,6 +5,9 @@ import numpy as np
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import subprocess
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import os
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import json
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import pytest
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import copy
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import xgboost as xgb
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from xgboost import XGBClassifier
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@ -31,6 +34,12 @@ def load_pickle(path):
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class TestPickling(unittest.TestCase):
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args_template = [
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"pytest",
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"--verbose",
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"-s",
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"--fulltrace"]
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def test_pickling(self):
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x, y = build_dataset()
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train_x = xgb.DMatrix(x, label=y)
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@ -61,13 +70,29 @@ class TestPickling(unittest.TestCase):
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assert status == 0
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os.remove(model_path)
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def test_pickled_predictor(self):
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args_templae = [
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"pytest",
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"--verbose",
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"-s",
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"--fulltrace"]
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@pytest.mark.mgpu
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def test_wrap_gpu_id(self):
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X, y = build_dataset()
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dtrain = xgb.DMatrix(X, y)
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bst = xgb.train({'tree_method': 'gpu_hist',
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'gpu_id': 1},
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dtrain, num_boost_round=6)
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model_path = 'model.pkl'
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save_pickle(bst, model_path)
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cuda_environment = {'CUDA_VISIBLE_DEVICES': '0'}
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env = os.environ.copy()
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env.update(cuda_environment)
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args = self.args_template.copy()
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args.append(
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"./tests/python-gpu/"
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"load_pickle.py::TestLoadPickle::test_wrap_gpu_id"
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)
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status = subprocess.call(args, env=env)
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assert status == 0
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def test_pickled_predictor(self):
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x, y = build_dataset()
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train_x = xgb.DMatrix(x, label=y)
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@ -80,7 +105,7 @@ class TestPickling(unittest.TestCase):
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save_pickle(bst, model_path)
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args = args_templae.copy()
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args = self.args_template.copy()
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args.append(
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"./tests/python-gpu/"
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"load_pickle.py::TestLoadPickle::test_predictor_type_is_auto")
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@ -93,7 +118,7 @@ class TestPickling(unittest.TestCase):
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status = subprocess.call(args, env=env)
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assert status == 0
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args = args_templae.copy()
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args = self.args_template.copy()
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args.append(
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"./tests/python-gpu/"
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"load_pickle.py::TestLoadPickle::test_predictor_type_is_gpu")
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@ -109,7 +134,6 @@ class TestPickling(unittest.TestCase):
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kwargs = {'tree_method': 'gpu_hist',
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'predictor': 'gpu_predictor',
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'verbosity': 1,
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'objective': 'binary:logistic',
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'n_estimators': 10}
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