Refactor configuration [Part II]. (#4577)

* Refactor configuration [Part II].

* General changes:
** Remove `Init` methods to avoid ambiguity.
** Remove `Configure(std::map<>)` to avoid redundant copying and prepare for
   parameter validation. (`std::vector` is returned from `InitAllowUnknown`).
** Add name to tree updaters for easier debugging.

* Learner changes:
** Make `LearnerImpl` the only source of configuration.

    All configurations are stored and carried out by `LearnerImpl::Configure()`.

** Remove booster in C API.

    Originally kept for "compatibility reason", but did not state why.  So here
    we just remove it.

** Add a `metric_names_` field in `LearnerImpl`.
** Remove `LazyInit`.  Configuration will always be lazy.
** Run `Configure` before every iteration.

* Predictor changes:
** Allocate both cpu and gpu predictor.
** Remove cpu_predictor from gpu_predictor.

    `GBTree` is now used to dispatch the predictor.

** Remove some GPU Predictor tests.

* IO

No IO changes.  The binary model format stability is tested by comparing
hashing value of save models between two commits

src/objective/multiclass_obj.cu

@@ -50,7 +50,11 @@ class SoftmaxMultiClassObj : public ObjFunction {
                    HostDeviceVector<GradientPair>* out_gpair) override {
     CHECK_NE(info.labels_.Size(), 0U) << "label set cannot be empty";
     CHECK(preds.Size() == (static_cast<size_t>(param_.num_class) * info.labels_.Size()))
-        << "SoftmaxMultiClassObj: label size and pred size does not match";
+        << "SoftmaxMultiClassObj: label size and pred size does not match.\n"
+        << "label.Size() * num_class: "
+        << info.labels_.Size() * static_cast<size_t>(param_.num_class) << "\n"
+        << "num_class: " << param_.num_class << "\n"
+        << "preds.Size(): " << preds.Size();
 
     const int nclass = param_.num_class;
     const auto ndata = static_cast<int64_t>(preds.Size() / nclass);

src/objective/objective.cc

@@ -14,7 +14,7 @@ DMLC_REGISTRY_ENABLE(::xgboost::ObjFunctionReg);
 
 namespace xgboost {
 // implement factory functions
-ObjFunction* ObjFunction::Create(const std::string& name, LearnerTrainParam const* tparam) {
+ObjFunction* ObjFunction::Create(const std::string& name, GenericParameter const* tparam) {
   auto *e = ::dmlc::Registry< ::xgboost::ObjFunctionReg>::Get()->Find(name);
   if (e == nullptr) {
     for (const auto& entry : ::dmlc::Registry< ::xgboost::ObjFunctionReg>::List()) {