[Breaking] Switch from rabit to the collective communicator (#8257)

* Switch from rabit to the collective communicator

* fix size_t specialization

* really fix size_t

* try again

* add include

* more include

* fix lint errors

* remove rabit includes

* fix pylint error

* return dict from communicator context

* fix communicator shutdown

* fix dask test

* reset communicator mocklist

* fix distributed tests

* do not save device communicator

* fix jvm gpu tests

* add python test for federated communicator

* Update gputreeshap submodule

Co-authored-by: Hyunsu Philip Cho <chohyu01@cs.washington.edu>

demo/nvflare/custom/trainer.py

@@ -52,15 +52,15 @@ class XGBoostTrainer(Executor):
     def _do_training(self, fl_ctx: FLContext):
         client_name = fl_ctx.get_prop(FLContextKey.CLIENT_NAME)
         rank = int(client_name.split('-')[1]) - 1
-        rabit_env = [
-            f'federated_server_address={self._server_address}',
-            f'federated_world_size={self._world_size}',
-            f'federated_rank={rank}',
-            f'federated_server_cert={self._server_cert_path}',
-            f'federated_client_key={self._client_key_path}',
-            f'federated_client_cert={self._client_cert_path}'
-        ]
-        with xgb.rabit.RabitContext([e.encode() for e in rabit_env]):
+        communicator_env = {
+            'federated_server_address': self._server_address,
+            'federated_world_size': self._world_size,
+            'federated_rank': rank,
+            'federated_server_cert': self._server_cert_path,
+            'federated_client_key': self._client_key_path,
+            'federated_client_cert': self._client_cert_path
+        }
+        with xgb.collective.CommunicatorContext(**communicator_env):
             # Load file, file will not be sharded in federated mode.
             dtrain = xgb.DMatrix('agaricus.txt.train')
             dtest = xgb.DMatrix('agaricus.txt.test')
@@ -86,4 +86,4 @@ class XGBoostTrainer(Executor):
             run_number = fl_ctx.get_prop(FLContextKey.CURRENT_RUN)
             run_dir = workspace.get_run_dir(run_number)
             bst.save_model(os.path.join(run_dir, "test.model.json"))
-            xgb.rabit.tracker_print("Finished training\n")
+            xgb.collective.communicator_print("Finished training\n")