Merge branch 'master' of https://github.com/dmlc/xgboost into optimiz…

…ation_part_applysplit

NEWS.md

@@ -3,6 +3,19 @@ XGBoost Change Log
 
 This file records the changes in xgboost library in reverse chronological order.
 
+## v1.6.1 (2022 May 9)
+This is a patch release for bug fixes and Spark barrier mode support. The R package is unchanged.
+
+### Experimental support for categorical data
+- Fix segfault when the number of samples is smaller than the number of categories. (https://github.com/dmlc/xgboost/pull/7853)
+- Enable partition-based split for all model types. (https://github.com/dmlc/xgboost/pull/7857)
+
+### JVM packages
+We replaced the old parallelism tracker with spark barrier mode to improve the robustness of the JVM package and fix the GPU training pipeline.
+- Fix GPU training pipeline quantile synchronization. (#7823, #7834)
+- Use barrier model in spark package. (https://github.com/dmlc/xgboost/pull/7836, https://github.com/dmlc/xgboost/pull/7840, https://github.com/dmlc/xgboost/pull/7845, https://github.com/dmlc/xgboost/pull/7846)
+- Fix shared object loading on some platforms. (https://github.com/dmlc/xgboost/pull/7844)
+
 ## v1.6.0 (2022 Apr 16)
 
 After a long period of development, XGBoost v1.6.0 is packed with many new features and

demo/nvflare/README.md

@@ -0,0 +1,55 @@
+# Experimental Support of Federated XGBoost using NVFlare
+
+This directory contains a demo of Federated Learning using
+[NVFlare](https://nvidia.github.io/NVFlare/).
+
+To run the demo, first build XGBoost with the federated learning plugin enabled (see the
+[README](../../plugin/federated/README.md)).
+
+Install NVFlare (note that currently NVFlare only supports Python 3.8):
+```shell
+pip install nvflare
+```
+
+Prepare the data:
+```shell
+./prepare_data.sh
+```
+
+Start the NVFlare federated server:
+```shell
+./poc/server/startup/start.sh
+```
+
+In another terminal, start the first worker:
+```shell
+./poc/site-1/startup/start.sh
+```
+
+And the second worker:
+```shell
+./poc/site-2/startup/start.sh
+```
+
+Then start the admin CLI, using `admin/admin` as username/password:
+```shell
+./poc/admin/startup/fl_admin.sh
+```
+
+In the admin CLI, run the following commands:
+```shell
+upload_app hello-xgboost
+set_run_number 1
+deploy_app hello-xgboost all
+start_app all
+```
+
+Once the training finishes, the model file should be written into
+`./poc/site-1/run_1/test.model.json` and `./poc/site-2/run_1/test.model.json`
+respectively.
+
+Finally, shutdown everything from the admin CLI:
+```shell
+shutdown client
+shutdown server
+```

demo/nvflare/config/config_fed_client.json

@@ -0,0 +1,22 @@
+{
+  "format_version": 2,
+  "executors": [
+    {
+      "tasks": [
+        "train"
+      ],
+      "executor": {
+        "path": "trainer.XGBoostTrainer",
+        "args": {
+          "server_address": "localhost:9091",
+          "world_size": 2,
+          "server_cert_path": "server-cert.pem",
+          "client_key_path": "client-key.pem",
+          "client_cert_path": "client-cert.pem"
+        }
+      }
+    }
+  ],
+  "task_result_filters": [],
+  "task_data_filters": []
+}

demo/nvflare/config/config_fed_server.json

@@ -0,0 +1,22 @@
+{
+  "format_version": 2,
+  "server": {
+    "heart_beat_timeout": 600
+  },
+  "task_data_filters": [],
+  "task_result_filters": [],
+  "workflows": [
+    {
+      "id": "server_workflow",
+      "path": "controller.XGBoostController",
+      "args": {
+        "port": 9091,
+        "world_size": 2,
+        "server_key_path": "server-key.pem",
+        "server_cert_path": "server-cert.pem",
+        "client_cert_path": "client-cert.pem"
+      }
+    }
+  ],
+  "components": []
+}

demo/nvflare/custom/controller.py

@@ -0,0 +1,68 @@
+"""
+Example of training controller with NVFlare
+===========================================
+"""
+import multiprocessing
+
+import xgboost.federated
+from nvflare.apis.client import Client
+from nvflare.apis.fl_context import FLContext
+from nvflare.apis.impl.controller import Controller, Task
+from nvflare.apis.shareable import Shareable
+from nvflare.apis.signal import Signal
+
+from trainer import SupportedTasks
+
+
+class XGBoostController(Controller):
+    def __init__(self, port: int, world_size: int, server_key_path: str,
+                 server_cert_path: str, client_cert_path: str):
+        """Controller for federated XGBoost.
+
+        Args:
+            port: the port for the gRPC server to listen on.
+            world_size: the number of sites.
+            server_key_path: the path to the server key file.
+            server_cert_path: the path to the server certificate file.
+            client_cert_path: the path to the client certificate file.
+        """
+        super().__init__()
+        self._port = port
+        self._world_size = world_size
+        self._server_key_path = server_key_path
+        self._server_cert_path = server_cert_path
+        self._client_cert_path = client_cert_path
+        self._server = None
+
+    def start_controller(self, fl_ctx: FLContext):
+        self._server = multiprocessing.Process(
+            target=xgboost.federated.run_federated_server,
+            args=(self._port, self._world_size, self._server_key_path,
+                  self._server_cert_path, self._client_cert_path))
+        self._server.start()
+
+    def stop_controller(self, fl_ctx: FLContext):
+        if self._server:
+            self._server.terminate()
+
+    def process_result_of_unknown_task(self, client: Client, task_name: str,
+                                       client_task_id: str, result: Shareable,
+                                       fl_ctx: FLContext):
+        self.log_warning(fl_ctx, f"Unknown task: {task_name} from client {client.name}.")
+
+    def control_flow(self, abort_signal: Signal, fl_ctx: FLContext):
+        self.log_info(fl_ctx, "XGBoost training control flow started.")
+        if abort_signal.triggered:
+            return
+        task = Task(name=SupportedTasks.TRAIN, data=Shareable())
+        self.broadcast_and_wait(
+            task=task,
+            min_responses=self._world_size,
+            fl_ctx=fl_ctx,
+            wait_time_after_min_received=1,
+            abort_signal=abort_signal,
+        )
+        if abort_signal.triggered:
+            return
+
+        self.log_info(fl_ctx, "XGBoost training control flow finished.")

demo/nvflare/custom/trainer.py

@@ -0,0 +1,84 @@
+import os
+
+from nvflare.apis.executor import Executor
+from nvflare.apis.fl_constant import ReturnCode, FLContextKey
+from nvflare.apis.fl_context import FLContext
+from nvflare.apis.shareable import Shareable, make_reply
+from nvflare.apis.signal import Signal
+
+import xgboost as xgb
+from xgboost import callback
+
+
+class SupportedTasks(object):
+    TRAIN = "train"
+
+
+class XGBoostTrainer(Executor):
+    def __init__(self, server_address: str, world_size: int, server_cert_path: str,
+                 client_key_path: str, client_cert_path: str):
+        """Trainer for federated XGBoost.
+
+        Args:
+            server_address: address for the gRPC server to connect to.
+            world_size: the number of sites.
+            server_cert_path: the path to the server certificate file.
+            client_key_path: the path to the client key file.
+            client_cert_path: the path to the client certificate file.
+        """
+        super().__init__()
+        self._server_address = server_address
+        self._world_size = world_size
+        self._server_cert_path = server_cert_path
+        self._client_key_path = client_key_path
+        self._client_cert_path = client_cert_path
+
+    def execute(self, task_name: str, shareable: Shareable, fl_ctx: FLContext,
+                abort_signal: Signal) -> Shareable:
+        self.log_info(fl_ctx, f"Executing {task_name}")
+        try:
+            if task_name == SupportedTasks.TRAIN:
+                self._do_training(fl_ctx)
+                return make_reply(ReturnCode.OK)
+            else:
+                self.log_error(fl_ctx, f"{task_name} is not a supported task.")
+                return make_reply(ReturnCode.TASK_UNKNOWN)
+        except BaseException as e:
+            self.log_exception(fl_ctx,
+                               f"Task {task_name} failed. Exception: {e.__str__()}")
+            return make_reply(ReturnCode.EXECUTION_EXCEPTION)
+
+    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]):
+            # Load file, file will not be sharded in federated mode.
+            dtrain = xgb.DMatrix('agaricus.txt.train')
+            dtest = xgb.DMatrix('agaricus.txt.test')
+
+            # Specify parameters via map, definition are same as c++ version
+            param = {'max_depth': 2, 'eta': 1, 'objective': 'binary:logistic'}
+
+            # Specify validations set to watch performance
+            watchlist = [(dtest, 'eval'), (dtrain, 'train')]
+            num_round = 20
+
+            # Run training, all the features in training API is available.
+            bst = xgb.train(param, dtrain, num_round, evals=watchlist,
+                            early_stopping_rounds=2, verbose_eval=False,
+                            callbacks=[callback.EvaluationMonitor(rank=rank)])
+
+            # Save the model.
+            workspace = fl_ctx.get_prop(FLContextKey.WORKSPACE_OBJECT)
+            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")

demo/nvflare/prepare_data.sh

@@ -0,0 +1,25 @@
+#!/bin/bash
+
+set -e
+
+rm -fr ./agaricus* ./*.pem ./poc
+
+world_size=2
+
+# Generate server and client certificates.
+openssl req -x509 -newkey rsa:2048 -days 7 -nodes -keyout server-key.pem -out server-cert.pem -subj "/C=US/CN=localhost"
+openssl req -x509 -newkey rsa:2048 -days 7 -nodes -keyout client-key.pem -out client-cert.pem -subj "/C=US/CN=localhost"
+
+# Split train and test files manually to simulate a federated environment.
+split -n l/${world_size} --numeric-suffixes=1 -a 1 ../data/agaricus.txt.train agaricus.txt.train-site-
+split -n l/${world_size} --numeric-suffixes=1 -a 1 ../data/agaricus.txt.test agaricus.txt.test-site-
+
+poc -n 2
+mkdir -p poc/admin/transfer/hello-xgboost
+cp -fr config custom poc/admin/transfer/hello-xgboost
+cp server-*.pem client-cert.pem poc/server/
+for id in $(eval echo "{1..$world_size}"); do
+  cp server-cert.pem client-*.pem poc/site-"$id"/
+  cp agaricus.txt.train-site-"$id" poc/site-"$id"/agaricus.txt.train
+  cp agaricus.txt.test-site-"$id" poc/site-"$id"/agaricus.txt.test
+done

doc/model.schema

@@ -207,7 +207,7 @@
         }
       }
     },
-    "pseduo_huber_param": {
+    "pseudo_huber_param": {
       "type": "object",
       "properties": {
         "huber_slope": {

plugin/federated/engine_federated.cc

@@ -111,9 +111,7 @@ class FederatedEngine : public IEngine {
 
   void TrackerPrint(const std::string &msg) override {
     // simply print information into the tracker
-    if (GetRank() == 0) {
-      utils::Printf("%s", msg.c_str());
-    }
+    utils::Printf("%s", msg.c_str());
   }
 
  private:

python-package/xgboost/dask.py

@@ -224,25 +224,16 @@ def _assert_dask_support() -> None:
         LOGGER.warning(msg)
 
 
-class RabitContext:
+class RabitContext(rabit.RabitContext):
     """A context controlling rabit initialization and finalization."""
 
     def __init__(self, args: List[bytes]) -> None:
-        self.args = args
+        super().__init__(args)
         worker = distributed.get_worker()
         self.args.append(
             ("DMLC_TASK_ID=[xgboost.dask]:" + str(worker.address)).encode()
         )
 
-    def __enter__(self) -> None:
-        rabit.init(self.args)
-        assert rabit.is_distributed()
-        LOGGER.debug("-------------- rabit say hello ------------------")
-
-    def __exit__(self, *args: List) -> None:
-        rabit.finalize()
-        LOGGER.debug("--------------- rabit say bye ------------------")
-
 
 def concat(value: Any) -> Any:  # pylint: disable=too-many-return-statements
     """To be replaced with dask builtin."""

python-package/xgboost/rabit.py

@@ -1,13 +1,16 @@
 """Distributed XGBoost Rabit related API."""
 import ctypes
 from enum import IntEnum, unique
+import logging
 import pickle
 from typing import Any, TypeVar, Callable, Optional, cast, List, Union
 
 import numpy as np
 
 from .core import _LIB, c_str, _check_call
 
+LOGGER = logging.getLogger("[xgboost.rabit]")
+
 
 def _init_rabit() -> None:
     """internal library initializer."""
@@ -224,5 +227,21 @@ def version_number() -> int:
     return ret
 
 
+class RabitContext:
+    """A context controlling rabit initialization and finalization."""
+
+    def __init__(self, args: List[bytes]) -> None:
+        self.args = args
+
+    def __enter__(self) -> None:
+        init(self.args)
+        assert is_distributed()
+        LOGGER.debug("-------------- rabit say hello ------------------")
+
+    def __exit__(self, *args: List) -> None:
+        finalize()
+        LOGGER.debug("--------------- rabit say bye ------------------")
+
+
 # initialization script
 _init_rabit()