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Release patch release 1.1.1 with faster CPU performance (#5732)

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* Fix release degradation (#5720)

* fix release degradation, related to 5666

* less resizes

Co-authored-by: SHVETS, KIRILL <kirill.shvets@intel.com>

* Make 1.1.1 patch release

* Disable too-many-function-args pylint warning for predict()

* Fix Windows CI

* Remove cpplint

Co-authored-by: ShvetsKS <33296480+ShvetsKS@users.noreply.github.com>
Co-authored-by: SHVETS, KIRILL <kirill.shvets@intel.com>
This commit is contained in:
Philip Hyunsu Cho 2020-06-04 10:56:07 -07:00 committed by GitHub
parent f9b246f5ee
commit 34408a7fdc
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GPG Key ID: 4AEE18F83AFDEB23
11 changed files with 35 additions and 33 deletions
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2
CMakeLists.txt
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@ -1,5 +1,5 @@
cmake_minimum_required(VERSION 3.13)
project(xgboost LANGUAGES CXX C VERSION 1.1.0)
project(xgboost LANGUAGES CXX C VERSION 1.1.1)
include(cmake/Utils.cmake)
list(APPEND CMAKE_MODULE_PATH "${xgboost_SOURCE_DIR}/cmake/modules")
cmake_policy(SET CMP0022 NEW)

1
Jenkinsfile vendored
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@ -48,7 +48,6 @@ pipeline {
script {
parallel ([
'clang-tidy': { ClangTidy() },
'lint': { Lint() },
'sphinx-doc': { SphinxDoc() },
'doxygen': { Doxygen() }
])

2
R-package/DESCRIPTION
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@ -1,7 +1,7 @@
Package: xgboost
Type: Package
Title: Extreme Gradient Boosting
Version: 1.1.0.1
Version: 1.1.1.1
Date: 2020-02-21
Authors@R: c(
person("Tianqi", "Chen", role = c("aut"),

2
jvm-packages/pom.xml
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@ -6,7 +6,7 @@
<groupId>ml.dmlc</groupId>
<artifactId>xgboost-jvm_2.12</artifactId>
<version>1.1.0</version>
<version>1.1.1</version>
<packaging>pom</packaging>
<name>XGBoost JVM Package</name>
<description>JVM Package for XGBoost</description>

8
jvm-packages/xgboost4j-example/pom.xml
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@ -6,10 +6,10 @@
<parent>
<groupId>ml.dmlc</groupId>
<artifactId>xgboost-jvm_2.12</artifactId>
<version>1.1.0</version>
<version>1.1.1</version>
</parent>
<artifactId>xgboost4j-example_2.12</artifactId>
<version>1.1.0</version>
<version>1.1.1</version>
<packaging>jar</packaging>
<build>
<plugins>
@ -26,7 +26,7 @@
<dependency>
<groupId>ml.dmlc</groupId>
<artifactId>xgboost4j-spark_${scala.binary.version}</artifactId>
<version>1.1.0</version>
<version>1.1.1</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
@ -37,7 +37,7 @@
<dependency>
<groupId>ml.dmlc</groupId>
<artifactId>xgboost4j-flink_${scala.binary.version}</artifactId>
<version>1.1.0</version>
<version>1.1.1</version>
</dependency>
<dependency>
<groupId>org.apache.commons</groupId>

6
jvm-packages/xgboost4j-flink/pom.xml
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@ -6,10 +6,10 @@
<parent>
<groupId>ml.dmlc</groupId>
<artifactId>xgboost-jvm_2.12</artifactId>
<version>1.1.0</version>
<version>1.1.1</version>
</parent>
<artifactId>xgboost4j-flink_2.12</artifactId>
<version>1.1.0</version>
<version>1.1.1</version>
<build>
<plugins>
<plugin>
@ -26,7 +26,7 @@
<dependency>
<groupId>ml.dmlc</groupId>
<artifactId>xgboost4j_${scala.binary.version}</artifactId>
<version>1.1.0</version>
<version>1.1.1</version>
</dependency>
<dependency>
<groupId>org.apache.commons</groupId>

4
jvm-packages/xgboost4j-spark/pom.xml
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@ -6,7 +6,7 @@
<parent>
<groupId>ml.dmlc</groupId>
<artifactId>xgboost-jvm_2.12</artifactId>
<version>1.1.0</version>
<version>1.1.1</version>
</parent>
<artifactId>xgboost4j-spark_2.12</artifactId>
<build>
@ -24,7 +24,7 @@
<dependency>
<groupId>ml.dmlc</groupId>
<artifactId>xgboost4j_${scala.binary.version}</artifactId>
<version>1.1.0</version>
<version>1.1.1</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>

4
jvm-packages/xgboost4j/pom.xml
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@ -6,10 +6,10 @@
<parent>
<groupId>ml.dmlc</groupId>
<artifactId>xgboost-jvm_2.12</artifactId>
<version>1.1.0</version>
<version>1.1.1</version>
</parent>
<artifactId>xgboost4j_2.12</artifactId>
<version>1.1.0</version>
<version>1.1.1</version>
<packaging>jar</packaging>
<dependencies>

2
python-package/xgboost/VERSION
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@ -1 +1 @@
1.1.0
1.1.1

1
python-package/xgboost/core.py
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@ -1467,6 +1467,7 @@ class Booster(object):
self._validate_features(data)
return self.eval_set([(data, name)], iteration)
# pylint: disable=too-many-function-args
def predict(self,
data,
output_margin=False,

36
src/objective/regression_obj.cu
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@ -41,10 +41,11 @@ struct RegLossParam : public XGBoostParameter<RegLossParam> {
template<typename Loss>
class RegLossObj : public ObjFunction {
protected:
HostDeviceVector<int> label_correct_;
HostDeviceVector<float> additional_input_;
public:
RegLossObj() = default;
// 0 - label_correct flag, 1 - scale_pos_weight, 2 - is_null_weight
RegLossObj(): additional_input_(3) {}
void Configure(const std::vector<std::pair<std::string, std::string> >& args) override {
param_.UpdateAllowUnknown(args);
@ -64,8 +65,7 @@ class RegLossObj : public ObjFunction {
size_t const ndata = preds.Size();
out_gpair->Resize(ndata);
auto device = tparam_->gpu_id;
label_correct_.Resize(1);
label_correct_.Fill(1);
additional_input_.HostVector().begin()[0] = 1; // Fill the label_correct flag
bool is_null_weight = info.weights_.Size() == 0;
if (!is_null_weight) {
@ -73,35 +73,37 @@ class RegLossObj : public ObjFunction {
<< "Number of weights should be equal to number of data points.";
}
auto scale_pos_weight = param_.scale_pos_weight;
common::Transform<>::Init(
[=] XGBOOST_DEVICE(size_t _idx,
common::Span<int> _label_correct,
additional_input_.HostVector().begin()[1] = scale_pos_weight;
additional_input_.HostVector().begin()[2] = is_null_weight;
common::Transform<>::Init([] XGBOOST_DEVICE(size_t _idx,
common::Span<float> _additional_input,
common::Span<GradientPair> _out_gpair,
common::Span<const bst_float> _preds,
common::Span<const bst_float> _labels,
common::Span<const bst_float> _weights) {
const float _scale_pos_weight = _additional_input[1];
const bool _is_null_weight = _additional_input[2];
bst_float p = Loss::PredTransform(_preds[_idx]);
bst_float w = is_null_weight ? 1.0f : _weights[_idx];
bst_float w = _is_null_weight ? 1.0f : _weights[_idx];
bst_float label = _labels[_idx];
if (label == 1.0f) {
w *= scale_pos_weight;
w *= _scale_pos_weight;
}
if (!Loss::CheckLabel(label)) {
// If there is an incorrect label, the host code will know.
_label_correct[0] = 0;
_additional_input[0] = 0;
}
_out_gpair[_idx] = GradientPair(Loss::FirstOrderGradient(p, label) * w,
Loss::SecondOrderGradient(p, label) * w);
},
common::Range{0, static_cast<int64_t>(ndata)}, device).Eval(
&label_correct_, out_gpair, &preds, &info.labels_, &info.weights_);
&additional_input_, out_gpair, &preds, &info.labels_, &info.weights_);
// copy "label correct" flags back to host
std::vector<int>& label_correct_h = label_correct_.HostVector();
for (auto const flag : label_correct_h) {
if (flag == 0) {
LOG(FATAL) << Loss::LabelErrorMsg();
}
auto const flag = additional_input_.HostVector().begin()[0];
if (flag == 0) {
LOG(FATAL) << Loss::LabelErrorMsg();
}
}