Revamp the rabit implementation. (#10112)

This PR replaces the original RABIT implementation with a new one, which has already been partially merged into XGBoost. The new one features:
- Federated learning for both CPU and GPU.
- NCCL.
- More data types.
- A unified interface for all the underlying implementations.
- Improved timeout handling for both tracker and workers.
- Exhausted tests with metrics (fixed a couple of bugs along the way).
- A reusable tracker for Python and JVM packages.

src/objective/adaptive.h

@@ -9,8 +9,6 @@
 #include <vector>   // std::vector
 
 #include "../collective/aggregator.h"
-#include "../collective/communicator-inl.h"
-#include "../common/common.h"
 #include "xgboost/base.h"                // bst_node_t
 #include "xgboost/context.h"             // Context
 #include "xgboost/data.h"                // MetaInfo
@@ -42,7 +40,7 @@ inline void UpdateLeafValues(Context const* ctx, std::vector<float>* p_quantiles
   auto& quantiles = *p_quantiles;
   auto const& h_node_idx = nidx;
 
-  size_t n_leaf = collective::GlobalMax(ctx, info, h_node_idx.size());
+  bst_idx_t n_leaf = collective::GlobalMax(ctx, info, static_cast<bst_idx_t>(h_node_idx.size()));
   CHECK(quantiles.empty() || quantiles.size() == n_leaf);
   if (quantiles.empty()) {
     quantiles.resize(n_leaf, std::numeric_limits<float>::quiet_NaN());