Replaced std::vector with HostDeviceVector in MetaInfo and SparsePage. (#3446)

* Replaced std::vector with HostDeviceVector in MetaInfo and SparsePage.

- added distributions to HostDeviceVector
- using HostDeviceVector for labels, weights and base margings in MetaInfo
- using HostDeviceVector for offset and data in SparsePage
- other necessary refactoring

* Added const version of HostDeviceVector API calls.

- const versions added to calls that can trigger data transfers, e.g. DevicePointer()
- updated the code that uses HostDeviceVector
- objective functions now accept const HostDeviceVector<bst_float>& for predictions

* Updated src/linear/updater_gpu_coordinate.cu.

* Added read-only state for HostDeviceVector sync.

- this means no copies are performed if both host and devices access
  the HostDeviceVector read-only

* Fixed linter and test errors.

- updated the lz4 plugin
- added ConstDeviceSpan to HostDeviceVector
- using device % dh::NVisibleDevices() for the physical device number,
  e.g. in calls to cudaSetDevice()

* Fixed explicit template instantiation errors for HostDeviceVector.

- replaced HostDeviceVector<unsigned int> with HostDeviceVector<int>

* Fixed HostDeviceVector tests that require multiple GPUs.

- added a mock set device handler; when set, it is called instead of cudaSetDevice()

src/learner.cc

@@ -386,7 +386,7 @@ class LearnerImpl : public Learner {
     this->PredictRaw(train, &preds_);
     monitor_.Stop("PredictRaw");
     monitor_.Start("GetGradient");
-    obj_->GetGradient(&preds_, train->Info(), iter, &gpair_);
+    obj_->GetGradient(preds_, train->Info(), iter, &gpair_);
     monitor_.Stop("GetGradient");
     gbm_->DoBoost(train, &gpair_, obj_.get());
     monitor_.Stop("UpdateOneIter");
@@ -416,7 +416,8 @@ class LearnerImpl : public Learner {
       obj_->EvalTransform(&preds_);
       for (auto& ev : metrics_) {
         os << '\t' << data_names[i] << '-' << ev->Name() << ':'
-           << ev->Eval(preds_.HostVector(), data_sets[i]->Info(), tparam_.dsplit == 2);
+           << ev->Eval(preds_.ConstHostVector(), data_sets[i]->Info(),
+                       tparam_.dsplit == 2);
       }
     }
 
@@ -459,7 +460,8 @@ class LearnerImpl : public Learner {
     this->PredictRaw(data, &preds_);
     obj_->EvalTransform(&preds_);
     return std::make_pair(metric,
-                          ev->Eval(preds_.HostVector(), data->Info(), tparam_.dsplit == 2));
+                          ev->Eval(preds_.ConstHostVector(), data->Info(),
+                                   tparam_.dsplit == 2));
   }
 
   void Predict(DMatrix* data, bool output_margin,