[jvm-packages] throw exception when tree_method=approx and device=cuda (dmlc#9478)

---------

Co-authored-by: Jiaming Yuan <[email protected]>

Author: wbo4958

jvm-packages/xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/XGBoost.scala

@@ -93,12 +93,14 @@ private[this] class XGBoostExecutionParamsFactory(rawParams: Map[String, Any], s
 
   private val overridedParams = overrideParams(rawParams, sc)
 
+  validateSparkSslConf()
+
   /**
    * Check to see if Spark expects SSL encryption (`spark.ssl.enabled` set to true).
    * If so, throw an exception unless this safety measure has been explicitly overridden
    * via conf `xgboost.spark.ignoreSsl`.
    */
-  private def validateSparkSslConf: Unit = {
+  private def validateSparkSslConf(): Unit = {
     val (sparkSslEnabled: Boolean, xgboostSparkIgnoreSsl: Boolean) =
       SparkSession.getActiveSession match {
         case Some(ss) =>
@@ -148,55 +150,59 @@ private[this] class XGBoostExecutionParamsFactory(rawParams: Map[String, Any], s
     overridedParams
   }
 
+  /**
+   * The Map parameters accepted by estimator's constructor may have string type,
+   * Eg, Map("num_workers" -> "6", "num_round" -> 5), we need to convert these
+   * kind of parameters into the correct type in the function.
+   *
+   * @return XGBoostExecutionParams
+   */
   def buildXGBRuntimeParams: XGBoostExecutionParams = {
+
+    val obj = overridedParams.getOrElse("custom_obj", null).asInstanceOf[ObjectiveTrait]
+    val eval = overridedParams.getOrElse("custom_eval", null).asInstanceOf[EvalTrait]
+    if (obj != null) {
+      require(overridedParams.get("objective_type").isDefined, "parameter \"objective_type\" " +
+        "is not defined, you have to specify the objective type as classification or regression" +
+        " with a customized objective function")
+    }
+
+    var trainTestRatio = 1.0
+    if (overridedParams.contains("train_test_ratio")) {
+      logger.warn("train_test_ratio is deprecated since XGBoost 0.82, we recommend to explicitly" +
+        " pass a training and multiple evaluation datasets by passing 'eval_sets' and " +
+        "'eval_set_names'")
+      trainTestRatio = overridedParams.get("train_test_ratio").get.asInstanceOf[Double]
+    }
+
     val nWorkers = overridedParams("num_workers").asInstanceOf[Int]
     val round = overridedParams("num_round").asInstanceOf[Int]
     val useExternalMemory = overridedParams
       .getOrElse("use_external_memory", false).asInstanceOf[Boolean]
-    val obj = overridedParams.getOrElse("custom_obj", null).asInstanceOf[ObjectiveTrait]
-    val eval = overridedParams.getOrElse("custom_eval", null).asInstanceOf[EvalTrait]
+
     val missing = overridedParams.getOrElse("missing", Float.NaN).asInstanceOf[Float]
     val allowNonZeroForMissing = overridedParams
                                  .getOrElse("allow_non_zero_for_missing", false)
                                  .asInstanceOf[Boolean]
-    validateSparkSslConf
-    var treeMethod: Option[String] = None
-    if (overridedParams.contains("tree_method")) {
-      require(overridedParams("tree_method") == "hist" ||
-        overridedParams("tree_method") == "approx" ||
-        overridedParams("tree_method") == "auto" ||
-        overridedParams("tree_method") == "gpu_hist", "xgboost4j-spark only supports tree_method" +
-        " as 'hist', 'approx', 'gpu_hist', and 'auto'")
-      treeMethod = Some(overridedParams("tree_method").asInstanceOf[String])
-    }
 
+    val treeMethod: Option[String] = overridedParams.get("tree_method").map(_.toString)
     // back-compatible with "gpu_hist"
     val device: Option[String] = if (treeMethod.exists(_ == "gpu_hist")) {
       Some("cuda")
     } else overridedParams.get("device").map(_.toString)
 
-    if (overridedParams.contains("train_test_ratio")) {
-      logger.warn("train_test_ratio is deprecated since XGBoost 0.82, we recommend to explicitly" +
-        " pass a training and multiple evaluation datasets by passing 'eval_sets' and " +
-        "'eval_set_names'")
-    }
-    require(nWorkers > 0, "you must specify more than 0 workers")
-    if (obj != null) {
-      require(overridedParams.get("objective_type").isDefined, "parameter \"objective_type\" " +
-        "is not defined, you have to specify the objective type as classification or regression" +
-        " with a customized objective function")
-    }
+    require(!(treeMethod.exists(_ == "approx") && device.exists(_ == "cuda")),
+      "The tree method \"approx\" is not yet supported for Spark GPU cluster")
+
     val trackerConf = overridedParams.get("tracker_conf") match {
       case None => TrackerConf()
       case Some(conf: TrackerConf) => conf
       case _ => throw new IllegalArgumentException("parameter \"tracker_conf\" must be an " +
         "instance of TrackerConf.")
     }
-    val checkpointParam =
-      ExternalCheckpointParams.extractParams(overridedParams)
 
-    val trainTestRatio = overridedParams.getOrElse("train_test_ratio", 1.0)
-      .asInstanceOf[Double]
+    val checkpointParam = ExternalCheckpointParams.extractParams(overridedParams)
+
     val seed = overridedParams.getOrElse("seed", System.nanoTime()).asInstanceOf[Long]
     val inputParams = XGBoostExecutionInputParams(trainTestRatio, seed)
 

jvm-packages/xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/params/LearningTaskParams.scala

@@ -68,11 +68,13 @@ private[spark] trait LearningTaskParams extends Params {
   /**
    * Fraction of training points to use for testing.
    */
+  @Deprecated
   final val trainTestRatio = new DoubleParam(this, "trainTestRatio",
     "fraction of training points to use for testing",
     ParamValidators.inRange(0, 1))
   setDefault(trainTestRatio, 1.0)
 
+  @Deprecated
   final def getTrainTestRatio: Double = $(trainTestRatio)
 
   /**

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

@@ -92,4 +92,15 @@ class ParameterSuite extends AnyFunSuite with PerTest with BeforeAndAfterAll {
       classifier.getBaseScore
     }
   }
+
+  test("approx can't be used for gpu train") {
+    val paramMap = Map("tree_method" -> "approx", "device" -> "cuda")
+    val trainingDF = buildDataFrame(MultiClassification.train)
+    val xgb = new XGBoostClassifier(paramMap)
+    val thrown = intercept[IllegalArgumentException] {
+      xgb.fit(trainingDF)
+    }
+    assert(thrown.getMessage.contains("The tree method \"approx\" is not yet supported " +
+      "for Spark GPU cluster"))
+  }
 }