JNI wrapper for the collective communicator (#8242)

jvm-packages/xgboost4j-spark/src/test/scala/ml/dmlc/xgboost4j/scala/spark/CommunicatorRobustnessSuite.scala

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+/*
+ Copyright (c) 2014-2022 by Contributors
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+ */
+
+package ml.dmlc.xgboost4j.scala.spark
+
+import java.util.concurrent.LinkedBlockingDeque
+
+import scala.util.Random
+
+import ml.dmlc.xgboost4j.java.{Communicator, RabitTracker => PyRabitTracker}
+import ml.dmlc.xgboost4j.java.IRabitTracker.TrackerStatus
+import ml.dmlc.xgboost4j.scala.rabit.{RabitTracker => ScalaRabitTracker}
+import ml.dmlc.xgboost4j.scala.DMatrix
+import org.scalatest.FunSuite
+
+class CommunicatorRobustnessSuite extends FunSuite with PerTest {
+
+  private def getXGBoostExecutionParams(paramMap: Map[String, Any]): XGBoostExecutionParams = {
+    val classifier = new XGBoostClassifier(paramMap)
+    val xgbParamsFactory = new XGBoostExecutionParamsFactory(classifier.MLlib2XGBoostParams, sc)
+    xgbParamsFactory.buildXGBRuntimeParams
+  }
+
+  test("Customize host ip and python exec for Rabit tracker") {
+    val hostIp = "192.168.22.111"
+    val pythonExec = "/usr/bin/python3"
+
+    val paramMap = Map(
+      "num_workers" -> numWorkers,
+      "tracker_conf" -> TrackerConf(0L, "python", hostIp))
+    val xgbExecParams = getXGBoostExecutionParams(paramMap)
+    val tracker = XGBoost.getTracker(xgbExecParams.numWorkers, xgbExecParams.trackerConf)
+    tracker match {
+      case pyTracker: PyRabitTracker =>
+        val cmd = pyTracker.getRabitTrackerCommand
+        assert(cmd.contains(hostIp))
+        assert(cmd.startsWith("python"))
+      case _ => assert(false, "expected python tracker implementation")
+    }
+
+    val paramMap1 = Map(
+      "num_workers" -> numWorkers,
+      "tracker_conf" -> TrackerConf(0L, "python", "", pythonExec))
+    val xgbExecParams1 = getXGBoostExecutionParams(paramMap1)
+    val tracker1 = XGBoost.getTracker(xgbExecParams1.numWorkers, xgbExecParams1.trackerConf)
+    tracker1 match {
+      case pyTracker: PyRabitTracker =>
+        val cmd = pyTracker.getRabitTrackerCommand
+        assert(cmd.startsWith(pythonExec))
+        assert(!cmd.contains(hostIp))
+      case _ => assert(false, "expected python tracker implementation")
+    }
+
+    val paramMap2 = Map(
+      "num_workers" -> numWorkers,
+      "tracker_conf" -> TrackerConf(0L, "python", hostIp, pythonExec))
+    val xgbExecParams2 = getXGBoostExecutionParams(paramMap2)
+    val tracker2 = XGBoost.getTracker(xgbExecParams2.numWorkers, xgbExecParams2.trackerConf)
+    tracker2 match {
+      case pyTracker: PyRabitTracker =>
+        val cmd = pyTracker.getRabitTrackerCommand
+        assert(cmd.startsWith(pythonExec))
+        assert(cmd.contains(s" --host-ip=${hostIp}"))
+      case _ => assert(false, "expected python tracker implementation")
+    }
+  }
+
+  test("training with Scala-implemented Rabit tracker") {
+    val eval = new EvalError()
+    val training = buildDataFrame(Classification.train)
+    val testDM = new DMatrix(Classification.test.iterator)
+    val paramMap = Map("eta" -> "1", "max_depth" -> "6",
+      "objective" -> "binary:logistic", "num_round" -> 5, "num_workers" -> numWorkers,
+      "tracker_conf" -> TrackerConf(60 * 60 * 1000, "scala"))
+    val model = new XGBoostClassifier(paramMap).fit(training)
+    assert(eval.eval(model._booster.predict(testDM, outPutMargin = true), testDM) < 0.1)
+  }
+
+  test("test Communicator allreduce to validate Scala-implemented Rabit tracker") {
+    val vectorLength = 100
+    val rdd = sc.parallelize(
+      (1 to numWorkers * vectorLength).toArray.map { _ => Random.nextFloat() }, numWorkers).cache()
+
+    val tracker = new ScalaRabitTracker(numWorkers)
+    tracker.start(0)
+    val trackerEnvs = tracker.getWorkerEnvs
+    val collectedAllReduceResults = new LinkedBlockingDeque[Array[Float]]()
+
+    val rawData = rdd.mapPartitions { iter =>
+      Iterator(iter.toArray)
+    }.collect()
+
+    val maxVec = (0 until vectorLength).toArray.map { j =>
+      (0 until numWorkers).toArray.map { i => rawData(i)(j) }.max
+    }
+
+    val allReduceResults = rdd.mapPartitions { iter =>
+      Communicator.init(trackerEnvs)
+      val arr = iter.toArray
+      val results = Communicator.allReduce(arr, Communicator.OpType.MAX)
+      Communicator.shutdown()
+      Iterator(results)
+    }.cache()
+
+    val sparkThread = new Thread() {
+      override def run(): Unit = {
+        allReduceResults.foreachPartition(() => _)
+        val byPartitionResults = allReduceResults.collect()
+        assert(byPartitionResults(0).length == vectorLength)
+        collectedAllReduceResults.put(byPartitionResults(0))
+      }
+    }
+    sparkThread.start()
+    assert(tracker.waitFor(0L) == 0)
+    sparkThread.join()
+
+    assert(collectedAllReduceResults.poll().sameElements(maxVec))
+  }
+
+  test("test Java RabitTracker wrapper's exception handling: it should not hang forever.") {
+    /*
+      Deliberately create new instances of SparkContext in each unit test to avoid reusing the
+      same thread pool spawned by the local mode of Spark. As these tests simulate worker crashes
+      by throwing exceptions, the crashed worker thread never calls Rabit.shutdown, and therefore
+      corrupts the internal state of the native Rabit C++ code. Calling Rabit.init() in subsequent
+      tests on a reentrant thread will crash the entire Spark application, an undesired side-effect
+      that should be avoided.
+     */
+    val rdd = sc.parallelize(1 to numWorkers, numWorkers).cache()
+
+    val tracker = new PyRabitTracker(numWorkers)
+    tracker.start(0)
+    val trackerEnvs = tracker.getWorkerEnvs
+
+    val workerCount: Int = numWorkers
+    /*
+       Simulate worker crash events by creating dummy Rabit workers, and throw exceptions in the
+       last created worker. A cascading event chain will be triggered once the RuntimeException is
+       thrown: the thread running the dummy spark job (sparkThread) catches the exception and
+       delegates it to the UnCaughtExceptionHandler, which is the Rabit tracker itself.
+
+       The Java RabitTracker class reacts to exceptions by killing the spawned process running
+       the Python tracker. If at least one Rabit worker has yet connected to the tracker before
+       it is killed, the resulted connection failure will trigger the Rabit worker to call
+       "exit(-1);" in the native C++ code, effectively ending the dummy Spark task.
+
+       In cluster (standalone or YARN) mode of Spark, tasks are run in containers and thus are
+       isolated from each other. That is, one task calling "exit(-1);" has no effect on other tasks
+       running in separate containers. However, as unit tests are run in Spark local mode, in which
+       tasks are executed by threads belonging to the same process, one thread calling "exit(-1);"
+       ultimately kills the entire process, which also happens to host the Spark driver, causing
+       the entire Spark application to crash.
+
+       To prevent unit tests from crashing, deterministic delays were introduced to make sure that
+       the exception is thrown at last, ideally after all worker connections have been established.
+       For the same reason, the Java RabitTracker class delays the killing of the Python tracker
+       process to ensure that pending worker connections are handled.
+     */
+    val dummyTasks = rdd.mapPartitions { iter =>
+      Communicator.init(trackerEnvs)
+      val index = iter.next()
+      Thread.sleep(100 + index * 10)
+      if (index == workerCount) {
+        // kill the worker by throwing an exception
+        throw new RuntimeException("Worker exception.")
+      }
+      Communicator.shutdown()
+      Iterator(index)
+    }.cache()
+
+    val sparkThread = new Thread() {
+      override def run(): Unit = {
+        // forces a Spark job.
+        dummyTasks.foreachPartition(() => _)
+      }
+    }
+
+    sparkThread.setUncaughtExceptionHandler(tracker)
+    sparkThread.start()
+    assert(tracker.waitFor(0) != 0)
+  }
+
+  test("test Scala RabitTracker's exception handling: it should not hang forever.") {
+    val rdd = sc.parallelize(1 to numWorkers, numWorkers).cache()
+
+    val tracker = new ScalaRabitTracker(numWorkers)
+    tracker.start(0)
+    val trackerEnvs = tracker.getWorkerEnvs
+
+    val workerCount: Int = numWorkers
+    val dummyTasks = rdd.mapPartitions { iter =>
+      Communicator.init(trackerEnvs)
+      val index = iter.next()
+      Thread.sleep(100 + index * 10)
+      if (index == workerCount) {
+        // kill the worker by throwing an exception
+        throw new RuntimeException("Worker exception.")
+      }
+      Communicator.shutdown()
+      Iterator(index)
+    }.cache()
+
+    val sparkThread = new Thread() {
+      override def run(): Unit = {
+        // forces a Spark job.
+        dummyTasks.foreachPartition(() => _)
+      }
+    }
+    sparkThread.setUncaughtExceptionHandler(tracker)
+    sparkThread.start()
+    assert(tracker.waitFor(0L) == TrackerStatus.FAILURE.getStatusCode)
+  }
+
+  test("test Scala RabitTracker's workerConnectionTimeout") {
+    val rdd = sc.parallelize(1 to numWorkers, numWorkers).cache()
+
+    val tracker = new ScalaRabitTracker(numWorkers)
+    tracker.start(500)
+    val trackerEnvs = tracker.getWorkerEnvs
+
+    val dummyTasks = rdd.mapPartitions { iter =>
+      val index = iter.next()
+      // simulate that the first worker cannot connect to tracker due to network issues.
+      if (index != 1) {
+        Communicator.init(trackerEnvs)
+        Thread.sleep(1000)
+        Communicator.shutdown()
+      }
+
+      Iterator(index)
+    }.cache()
+
+    val sparkThread = new Thread() {
+      override def run(): Unit = {
+        // forces a Spark job.
+        dummyTasks.foreachPartition(() => _)
+      }
+    }
+    sparkThread.setUncaughtExceptionHandler(tracker)
+    sparkThread.start()
+    // should fail due to connection timeout
+    assert(tracker.waitFor(0L) == TrackerStatus.FAILURE.getStatusCode)
+  }
+
+  test("should allow the dataframe containing communicator calls to be partially evaluated for" +
+    " multiple times (ISSUE-4406)") {
+    val paramMap = Map(
+      "eta" -> "1",
+      "max_depth" -> "6",
+      "silent" -> "1",
+      "objective" -> "binary:logistic")
+    val trainingDF = buildDataFrame(Classification.train)
+    val model = new XGBoostClassifier(paramMap ++ Array("num_round" -> 10,
+      "num_workers" -> numWorkers)).fit(trainingDF)
+    val prediction = model.transform(trainingDF)
+    // a partial evaluation of dataframe will cause rabit initialized but not shutdown in some
+    // threads
+    prediction.show()
+    // a full evaluation here will re-run init and shutdown all rabit proxy
+    // expecting no error
+    prediction.collect()
+  }
+}