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

* 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>
dmlc · Jun 4, 2020 · 34408a7 · 34408a7
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commit 34408a7
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Showing 11 changed files with 35 additions and 33 deletions.
diff --git a/CMakeLists.txt b/CMakeLists.txt
@@ -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)

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

diff --git a/R-package/DESCRIPTION b/R-package/DESCRIPTION
@@ -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"),

diff --git a/jvm-packages/pom.xml b/jvm-packages/pom.xml
@@ -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>

diff --git a/jvm-packages/xgboost4j-example/pom.xml b/jvm-packages/xgboost4j-example/pom.xml
@@ -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>

diff --git a/jvm-packages/xgboost4j-flink/pom.xml b/jvm-packages/xgboost4j-flink/pom.xml
@@ -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>

diff --git a/jvm-packages/xgboost4j-spark/pom.xml b/jvm-packages/xgboost4j-spark/pom.xml
@@ -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>

diff --git a/jvm-packages/xgboost4j/pom.xml b/jvm-packages/xgboost4j/pom.xml
@@ -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>

diff --git a/python-package/xgboost/VERSION b/python-package/xgboost/VERSION
@@ -1 +1 @@
-1.1.0
+1.1.1
diff --git a/python-package/xgboost/core.py b/python-package/xgboost/core.py
@@ -1467,6 +1467,7 @@ def eval(self, data, name='eval', iteration=0):
         self._validate_features(data)
         return self.eval_set([(data, name)], iteration)
 
+    # pylint: disable=too-many-function-args
     def predict(self,
                 data,
                 output_margin=False,

diff --git a/src/objective/regression_obj.cu b/src/objective/regression_obj.cu
@@ -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);
@@ -64,44 +65,45 @@ 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) {
       CHECK_EQ(info.weights_.Size(), ndata)
           << "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();
     }
   }