Init estimation for regression. (#8272)

src/collective/rabit_communicator.h

@@ -119,7 +119,7 @@ class RabitCommunicator : public Communicator {
   }
 
   template <typename DType, std::enable_if_t<std::is_floating_point<DType>::value> * = nullptr>
-  void DoBitwiseAllReduce(void *send_receive_buffer, std::size_t count, Operation op) {
+  void DoBitwiseAllReduce(void *, std::size_t, Operation) {
     LOG(FATAL) << "Floating point types do not support bitwise operations.";
   }
 

src/data/data.cc

@@ -684,7 +684,7 @@ void MetaInfo::Extend(MetaInfo const& that, bool accumulate_rows, bool check_col
   }
 }
 
-void MetaInfo::Validate(int32_t device) const {
+void MetaInfo::Validate(std::int32_t device) const {
   if (group_ptr_.size() != 0 && weights_.Size() != 0) {
     CHECK_EQ(group_ptr_.size(), weights_.Size() + 1)
         << "Size of weights must equal to number of groups when ranking "

src/data/gradient_index.h

@@ -15,7 +15,7 @@
 #include "../common/hist_util.h"
 #include "../common/numeric.h"
 #include "../common/threading_utils.h"
-#include "../common/transform_iterator.h"  // MakeIndexTransformIter
+#include "../common/transform_iterator.h"  // common::MakeIndexTransformIter
 #include "adapter.h"
 #include "proxy_dmatrix.h"
 #include "xgboost/base.h"

src/learner.cc

@@ -190,6 +190,32 @@ struct LearnerModelParamLegacy : public dmlc::Parameter<LearnerModelParamLegacy>
     }
     return dmlc::Parameter<LearnerModelParamLegacy>::UpdateAllowUnknown(kwargs);
   }
+  // sanity check
+  void Validate() {
+    if (!collective::IsDistributed()) {
+      return;
+    }
+
+    std::array<std::int32_t, 6> data;
+    std::size_t pos{0};
+    std::memcpy(data.data() + pos, &base_score, sizeof(base_score));
+    pos += 1;
+    std::memcpy(data.data() + pos, &num_feature, sizeof(num_feature));
+    pos += 1;
+    std::memcpy(data.data() + pos, &num_class, sizeof(num_class));
+    pos += 1;
+    std::memcpy(data.data() + pos, &num_target, sizeof(num_target));
+    pos += 1;
+    std::memcpy(data.data() + pos, &major_version, sizeof(major_version));
+    pos += 1;
+    std::memcpy(data.data() + pos, &minor_version, sizeof(minor_version));
+
+    std::array<std::int32_t, 6> sync;
+    std::copy(data.cbegin(), data.cend(), sync.begin());
+    collective::Broadcast(sync.data(), sync.size(), 0);
+    CHECK(std::equal(data.cbegin(), data.cend(), sync.cbegin()))
+        << "Different model parameter across workers.";
+  }
 
   // declare parameters
   DMLC_DECLARE_PARAMETER(LearnerModelParamLegacy) {
@@ -391,6 +417,7 @@ class LearnerConfiguration : public Learner {
       }
       // Update the shared model parameter
       this->ConfigureModelParamWithoutBaseScore();
+      mparam_.Validate();
     }
     CHECK(!std::isnan(mparam_.base_score));
     CHECK(!std::isinf(mparam_.base_score));

src/objective/regression_obj.cu

@@ -20,6 +20,7 @@
 #include "../common/stats.h"
 #include "../common/threading_utils.h"
 #include "../common/transform.h"
+#include "../tree/fit_stump.h"  // FitStump
 #include "./regression_loss.h"
 #include "adaptive.h"
 #include "xgboost/base.h"
@@ -53,6 +54,31 @@ void CheckRegInputs(MetaInfo const& info, HostDeviceVector<bst_float> const& pre
 }
 }  // anonymous namespace
 
+class RegInitEstimation : public ObjFunction {
+  void InitEstimation(MetaInfo const& info, linalg::Tensor<float, 1>* base_score) const override {
+    CheckInitInputs(info);
+    // Avoid altering any state in child objective.
+    HostDeviceVector<float> dummy_predt(info.labels.Size(), 0.0f, this->ctx_->gpu_id);
+    HostDeviceVector<GradientPair> gpair(info.labels.Size(), GradientPair{}, this->ctx_->gpu_id);
+
+    Json config{Object{}};
+    this->SaveConfig(&config);
+
+    std::unique_ptr<ObjFunction> new_obj{
+        ObjFunction::Create(get<String const>(config["name"]), this->ctx_)};
+    new_obj->LoadConfig(config);
+    new_obj->GetGradient(dummy_predt, info, 0, &gpair);
+    bst_target_t n_targets = this->Targets(info);
+    linalg::Vector<float> leaf_weight;
+    tree::FitStump(this->ctx_, gpair, n_targets, &leaf_weight);
+
+    // workaround, we don't support multi-target due to binary model serialization for
+    // base margin.
+    common::Mean(this->ctx_, leaf_weight, base_score);
+    this->PredTransform(base_score->Data());
+  }
+};
+
 #if defined(XGBOOST_USE_CUDA)
 DMLC_REGISTRY_FILE_TAG(regression_obj_gpu);
 #endif  // defined(XGBOOST_USE_CUDA)
@@ -67,7 +93,7 @@ struct RegLossParam : public XGBoostParameter<RegLossParam> {
 };
 
 template<typename Loss>
-class RegLossObj : public ObjFunction {
+class RegLossObj : public RegInitEstimation {
  protected:
   HostDeviceVector<float> additional_input_;
 
@@ -214,7 +240,7 @@ XGBOOST_REGISTER_OBJECTIVE(LinearRegression, "reg:linear")
     return new RegLossObj<LinearSquareLoss>(); });
 // End deprecated
 
-class PseudoHuberRegression : public ObjFunction {
+class PseudoHuberRegression : public RegInitEstimation {
   PesudoHuberParam param_;
 
  public:
@@ -289,7 +315,7 @@ struct PoissonRegressionParam : public XGBoostParameter<PoissonRegressionParam>
 };
 
 // poisson regression for count
-class PoissonRegression : public ObjFunction {
+class PoissonRegression : public RegInitEstimation {
  public:
   // declare functions
   void Configure(const std::vector<std::pair<std::string, std::string> >& args) override {
@@ -384,7 +410,7 @@ XGBOOST_REGISTER_OBJECTIVE(PoissonRegression, "count:poisson")
 
 
 // cox regression for survival data (negative values mean they are censored)
-class CoxRegression : public ObjFunction {
+class CoxRegression : public RegInitEstimation {
  public:
   void Configure(Args const&) override {}
   ObjInfo Task() const override { return ObjInfo::kRegression; }
@@ -481,7 +507,7 @@ XGBOOST_REGISTER_OBJECTIVE(CoxRegression, "survival:cox")
 .set_body([]() { return new CoxRegression(); });
 
 // gamma regression
-class GammaRegression : public ObjFunction {
+class GammaRegression : public RegInitEstimation {
  public:
   void Configure(Args const&) override {}
   ObjInfo Task() const override { return ObjInfo::kRegression; }
@@ -572,7 +598,7 @@ struct TweedieRegressionParam : public XGBoostParameter<TweedieRegressionParam>
 };
 
 // tweedie regression
-class TweedieRegression : public ObjFunction {
+class TweedieRegression : public RegInitEstimation {
  public:
   // declare functions
   void Configure(const std::vector<std::pair<std::string, std::string> >& args) override {