Pass infomation about objective to tree methods. (#7385)

* Define the `ObjInfo` and pass it down to every tree updater.

src/objective/aft_obj.cu

@@ -38,6 +38,8 @@ class AFTObj : public ObjFunction {
     param_.UpdateAllowUnknown(args);
   }
 
+  ObjInfo Task() const override { return {ObjInfo::kSurvival, false}; }
+
   template <typename Distribution>
   void GetGradientImpl(const HostDeviceVector<bst_float> &preds,
                        const MetaInfo &info,

src/objective/hinge.cu

@@ -27,6 +27,8 @@ class HingeObj : public ObjFunction {
   void Configure(
       const std::vector<std::pair<std::string, std::string> > &args) override {}
 
+  ObjInfo Task() const override { return {ObjInfo::kRegression, false}; }
+
   void GetGradient(const HostDeviceVector<bst_float> &preds,
                    const MetaInfo &info,
                    int iter,

src/objective/multiclass_obj.cu

@@ -45,6 +45,9 @@ class SoftmaxMultiClassObj : public ObjFunction {
   void Configure(Args const& args) override {
     param_.UpdateAllowUnknown(args);
   }
+
+  ObjInfo Task() const override { return {ObjInfo::kClassification, false}; }
+
   void GetGradient(const HostDeviceVector<bst_float>& preds,
                    const MetaInfo& info,
                    int iter,

src/objective/rank_obj.cu

@@ -754,6 +754,8 @@ class LambdaRankObj : public ObjFunction {
     param_.UpdateAllowUnknown(args);
   }
 
+  ObjInfo Task() const override { return {ObjInfo::kRanking, false}; }
+
   void GetGradient(const HostDeviceVector<bst_float>& preds,
                    const MetaInfo& info,
                    int iter,

src/objective/regression_loss.h

@@ -7,6 +7,8 @@
 #include <dmlc/omp.h>
 #include <xgboost/logging.h>
 #include <algorithm>
+
+#include "xgboost/task.h"
 #include "../common/math.h"
 
 namespace xgboost {
@@ -36,6 +38,7 @@ struct LinearSquareLoss {
   static const char* DefaultEvalMetric() { return "rmse"; }
 
   static const char* Name() { return "reg:squarederror"; }
+  static ObjInfo Info() { return {ObjInfo::kRegression, true}; }
 };
 
 struct SquaredLogError {
@@ -61,6 +64,8 @@ struct SquaredLogError {
   static const char* DefaultEvalMetric() { return "rmsle"; }
 
   static const char* Name() { return "reg:squaredlogerror"; }
+
+  static ObjInfo Info() { return {ObjInfo::kRegression, false}; }
 };
 
 // logistic loss for probability regression task
@@ -96,6 +101,8 @@ struct LogisticRegression {
   static const char* DefaultEvalMetric() { return "rmse"; }
 
   static const char* Name() { return "reg:logistic"; }
+
+  static ObjInfo Info() { return {ObjInfo::kRegression, false}; }
 };
 
 struct PseudoHuberError {
@@ -127,12 +134,14 @@ struct PseudoHuberError {
   static const char* Name() {
     return "reg:pseudohubererror";
   }
+  static ObjInfo Info() { return {ObjInfo::kRegression, false}; }
 };
 
 // logistic loss for binary classification task
 struct LogisticClassification : public LogisticRegression {
   static const char* DefaultEvalMetric() { return "logloss"; }
   static const char* Name() { return "binary:logistic"; }
+  static ObjInfo Info() { return {ObjInfo::kBinary, false}; }
 };
 
 // logistic loss, but predict un-transformed margin
@@ -168,6 +177,8 @@ struct LogisticRaw : public LogisticRegression {
   static const char* DefaultEvalMetric() { return "logloss"; }
 
   static const char* Name() { return "binary:logitraw"; }
+
+  static ObjInfo Info() { return {ObjInfo::kRegression, false}; }
 };
 
 }  // namespace obj

src/objective/regression_obj.cu

@@ -52,6 +52,10 @@ class RegLossObj : public ObjFunction {
     param_.UpdateAllowUnknown(args);
   }
 
+  struct ObjInfo Task() const override {
+    return Loss::Info();
+  }
+
   void GetGradient(const HostDeviceVector<bst_float>& preds,
                    const MetaInfo &info, int,
                    HostDeviceVector<GradientPair>* out_gpair) override {
@@ -207,6 +211,10 @@ class PoissonRegression : public ObjFunction {
     param_.UpdateAllowUnknown(args);
   }
 
+  struct ObjInfo Task() const override {
+    return {ObjInfo::kRegression, false};
+  }
+
   void GetGradient(const HostDeviceVector<bst_float>& preds,
                    const MetaInfo &info, int,
                    HostDeviceVector<GradientPair> *out_gpair) override {
@@ -298,6 +306,10 @@ class CoxRegression : public ObjFunction {
   void Configure(
       const std::vector<std::pair<std::string, std::string> >&) override {}
 
+  struct ObjInfo Task() const override {
+    return {ObjInfo::kRegression, false};
+  }
+
   void GetGradient(const HostDeviceVector<bst_float>& preds,
                    const MetaInfo &info, int,
                    HostDeviceVector<GradientPair> *out_gpair) override {
@@ -395,6 +407,10 @@ class GammaRegression : public ObjFunction {
   void Configure(
       const std::vector<std::pair<std::string, std::string> >&) override {}
 
+  struct ObjInfo Task() const override {
+    return {ObjInfo::kRegression, false};
+  }
+
   void GetGradient(const HostDeviceVector<bst_float> &preds,
                    const MetaInfo &info, int,
                    HostDeviceVector<GradientPair> *out_gpair) override {
@@ -491,6 +507,10 @@ class TweedieRegression : public ObjFunction {
     metric_ = os.str();
   }
 
+  struct ObjInfo Task() const override {
+    return {ObjInfo::kRegression, false};
+  }
+
   void GetGradient(const HostDeviceVector<bst_float>& preds,
                    const MetaInfo &info, int,
                    HostDeviceVector<GradientPair> *out_gpair) override {