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[jvm-packages] Add the new device parameter. (#9385)

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Jiaming Yuan 2023-07-17 18:40:39 +08:00 committed by GitHub
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15 changed files with 112 additions and 47 deletions
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15
doc/jvm/xgboost4j_spark_gpu_tutorial.rst
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@ -121,7 +121,7 @@ To train a XGBoost model for classification, we need to claim a XGBoostClassifie
"objective" -> "multi:softprob",
"num_class" -> 3,
"num_round" -> 100,
"tree_method" -> "gpu_hist",
"device" -> "cuda",
"num_workers" -> 1)
val featuresNames = schema.fieldNames.filter(name => name != labelName)
@ -130,15 +130,14 @@ To train a XGBoost model for classification, we need to claim a XGBoostClassifie
.setFeaturesCol(featuresNames)
.setLabelCol(labelName)
The available parameters for training a XGBoost model can be found in :doc:`here </parameter>`.
Similar to the XGBoost4J-Spark package, in addition to the default set of parameters,
XGBoost4J-Spark-GPU also supports the camel-case variant of these parameters to be
consistent with Spark's MLlib naming convention.
The ``device`` parameter is for informing XGBoost that CUDA devices should be used instead of CPU. Unlike the single-node mode, GPUs are managed by spark instead of by XGBoost. Therefore, explicitly specified device ordinal like ``cuda:1`` is not support.
The available parameters for training a XGBoost model can be found in :doc:`here </parameter>`. Similar to the XGBoost4J-Spark package, in addition to the default set of parameters, XGBoost4J-Spark-GPU also supports the camel-case variant of these parameters to be consistent with Spark's MLlib naming convention.
Specifically, each parameter in :doc:`this page </parameter>` has its equivalent form in
XGBoost4J-Spark-GPU with camel case. For example, to set ``max_depth`` for each tree, you can pass
parameter just like what we did in the above code snippet (as ``max_depth`` wrapped in a Map), or
you can do it through setters in XGBoostClassifer:
XGBoost4J-Spark-GPU with camel case. For example, to set ``max_depth`` for each tree, you
can pass parameter just like what we did in the above code snippet (as ``max_depth``
wrapped in a Map), or you can do it through setters in XGBoostClassifer:
.. code-block:: scala

15
jvm-packages/xgboost4j-example/src/main/scala/ml/dmlc/xgboost4j/scala/example/spark/SparkMLlibPipeline.scala
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@ -40,20 +40,20 @@ object SparkMLlibPipeline {
val nativeModelPath = args(1)
val pipelineModelPath = args(2)
val (treeMethod, numWorkers) = if (args.length == 4 && args(3) == "gpu") {
("gpu_hist", 1)
} else ("auto", 2)
val (device, numWorkers) = if (args.length == 4 && args(3) == "gpu") {
("cuda", 1)
} else ("cpu", 2)
val spark = SparkSession
.builder()
.appName("XGBoost4J-Spark Pipeline Example")
.getOrCreate()
run(spark, inputPath, nativeModelPath, pipelineModelPath, treeMethod, numWorkers)
run(spark, inputPath, nativeModelPath, pipelineModelPath, device, numWorkers)
.show(false)
}
private[spark] def run(spark: SparkSession, inputPath: String, nativeModelPath: String,
pipelineModelPath: String, treeMethod: String,
pipelineModelPath: String, device: String,
numWorkers: Int): DataFrame = {
// Load dataset
@ -82,13 +82,14 @@ object SparkMLlibPipeline {
.setOutputCol("classIndex")
.fit(training)
val booster = new XGBoostClassifier(
Map("eta" -> 0.1f,
Map(
"eta" -> 0.1f,
"max_depth" -> 2,
"objective" -> "multi:softprob",
"num_class" -> 3,
"num_round" -> 100,
"num_workers" -> numWorkers,
"tree_method" -> treeMethod
"device" -> device
)
)
booster.setFeaturesCol("features")

12
jvm-packages/xgboost4j-example/src/main/scala/ml/dmlc/xgboost4j/scala/example/spark/SparkTraining.scala
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@ -31,18 +31,18 @@ object SparkTraining {
sys.exit(1)
}
val (treeMethod, numWorkers) = if (args.length == 2 && args(1) == "gpu") {
("gpu_hist", 1)
} else ("auto", 2)
val (device, numWorkers) = if (args.length == 2 && args(1) == "gpu") {
("cuda", 1)
} else ("cpu", 2)
val spark = SparkSession.builder().getOrCreate()
val inputPath = args(0)
val results: DataFrame = run(spark, inputPath, treeMethod, numWorkers)
val results: DataFrame = run(spark, inputPath, device, numWorkers)
results.show()
}
private[spark] def run(spark: SparkSession, inputPath: String,
treeMethod: String, numWorkers: Int): DataFrame = {
device: String, numWorkers: Int): DataFrame = {
val schema = new StructType(Array(
StructField("sepal length", DoubleType, true),
StructField("sepal width", DoubleType, true),
@ -80,7 +80,7 @@ private[spark] def run(spark: SparkSession, inputPath: String,
"num_class" -> 3,
"num_round" -> 100,
"num_workers" -> numWorkers,
"tree_method" -> treeMethod,
"device" -> device,
"eval_sets" -> Map("eval1" -> eval1, "eval2" -> eval2))
val xgbClassifier = new XGBoostClassifier(xgbParam).
setFeaturesCol("features").

4
jvm-packages/xgboost4j-example/src/test/scala/ml/dmlc/xgboost4j/scala/example/spark/SparkExamplesTest.scala
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@ -104,7 +104,7 @@ class SparkExamplesTest extends AnyFunSuite with BeforeAndAfterAll {
test("Smoke test for SparkMLlibPipeline example") {
SparkMLlibPipeline.run(spark, pathToTestDataset.toString, "target/native-model",
"target/pipeline-model", "auto", 2)
"target/pipeline-model", "cpu", 2)
}
test("Smoke test for SparkTraining example") {
@ -118,6 +118,6 @@ class SparkExamplesTest extends AnyFunSuite with BeforeAndAfterAll {
.config("spark.task.cpus", 1)
.getOrCreate()
SparkTraining.run(spark, pathToTestDataset.toString, "auto", 2)
SparkTraining.run(spark, pathToTestDataset.toString, "cpu", 2)
}
}

3
jvm-packages/xgboost4j-gpu/src/test/java/ml/dmlc/xgboost4j/gpu/java/BoosterTest.java
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@ -77,7 +77,8 @@ public class BoosterTest {
put("objective", "binary:logistic");
put("num_round", round);
put("num_workers", 1);
put("tree_method", "gpu_hist");
put("tree_method", "hist");
put("device", "cuda");
put("max_bin", maxBin);
}
};

8
jvm-packages/xgboost4j-spark-gpu/src/main/scala/ml/dmlc/xgboost4j/scala/rapids/spark/GpuPreXGBoost.scala
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@ -137,8 +137,12 @@ object GpuPreXGBoost extends PreXGBoostProvider {
val (Seq(labelName, weightName, marginName), feturesCols, groupName, evalSets) =
estimator match {
case est: XGBoostEstimatorCommon =>
require(est.isDefined(est.treeMethod) && est.getTreeMethod.equals("gpu_hist"),
s"GPU train requires tree_method set to gpu_hist")
require(
est.isDefined(est.device) &&
(est.getDevice.equals("cuda") || est.getDevice.equals("gpu")) ||
est.isDefined(est.treeMethod) && est.getTreeMethod.equals("gpu_hist"),
s"GPU train requires `device` set to `cuda` or `gpu`."
)
val groupName = estimator match {
case regressor: XGBoostRegressor => if (regressor.isDefined(regressor.groupCol)) {
regressor.getGroupCol } else ""

43
jvm-packages/xgboost4j-spark-gpu/src/test/scala/ml/dmlc/xgboost4j/scala/rapids/spark/GpuXGBoostGeneralSuite.scala
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@ -1,5 +1,5 @@
/*
Copyright (c) 2021-2022 by Contributors
Copyright (c) 2021-2023 by Contributors
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
@ -50,9 +50,12 @@ class GpuXGBoostGeneralSuite extends GpuTestSuite {
withGpuSparkSession() { spark =>
import spark.implicits._
val trainingDf = trainingData.toDF(allColumnNames: _*)
val xgbParam = Map("eta" -> 0.1f, "max_depth" -> 2, "objective" -> "multi:softprob",
"num_class" -> 3, "num_round" -> 5, "num_workers" -> 1, "tree_method" -> "gpu_hist",
"features_cols" -> featureNames, "label_col" -> labelName)
val xgbParam = Map(
"eta" -> 0.1f, "max_depth" -> 2, "objective" -> "multi:softprob",
"num_class" -> 3, "num_round" -> 5, "num_workers" -> 1,
"tree_method" -> "hist", "device" -> "cuda",
"features_cols" -> featureNames, "label_col" -> labelName
)
new XGBoostClassifier(xgbParam)
.fit(trainingDf)
}
@ -65,8 +68,11 @@ class GpuXGBoostGeneralSuite extends GpuTestSuite {
trainingDf = trainingDf.select(labelName, "f2", weightName, "f3", baseMarginName, "f1")
val xgbParam = Map("eta" -> 0.1f, "max_depth" -> 2, "objective" -> "multi:softprob",
"num_class" -> 3, "num_round" -> 5, "num_workers" -> 1, "tree_method" -> "gpu_hist")
val xgbParam = Map(
"eta" -> 0.1f, "max_depth" -> 2, "objective" -> "multi:softprob",
"num_class" -> 3, "num_round" -> 5, "num_workers" -> 1,
"tree_method" -> "hist", "device" -> "cuda"
)
new XGBoostClassifier(xgbParam)
.setFeaturesCol(featureNames)
.setLabelCol(labelName)
@ -127,7 +133,7 @@ class GpuXGBoostGeneralSuite extends GpuTestSuite {
}
}
test("Throw exception when tree method is not set to gpu_hist") {
test("Throw exception when device is not set to cuda") {
withGpuSparkSession() { spark =>
import spark.implicits._
val trainingDf = trainingData.toDF(allColumnNames: _*)
@ -139,12 +145,11 @@ class GpuXGBoostGeneralSuite extends GpuTestSuite {
.setLabelCol(labelName)
.fit(trainingDf)
}
assert(thrown.getMessage.contains("GPU train requires tree_method set to gpu_hist"))
assert(thrown.getMessage.contains("GPU train requires `device` set to `cuda`"))
}
}
test("Train with eval") {
withGpuSparkSession() { spark =>
import spark.implicits._
val Array(trainingDf, eval1, eval2) = trainingData.toDF(allColumnNames: _*)
@ -184,4 +189,24 @@ class GpuXGBoostGeneralSuite extends GpuTestSuite {
}
}
test("device ordinal should not be specified") {
withGpuSparkSession() { spark =>
import spark.implicits._
val trainingDf = trainingData.toDF(allColumnNames: _*)
val params = Map(
"objective" -> "multi:softprob",
"num_class" -> 3,
"num_round" -> 5,
"num_workers" -> 1
)
val thrown = intercept[IllegalArgumentException] {
new XGBoostClassifier(params)
.setFeaturesCol(featureNames)
.setLabelCol(labelName)
.setDevice("cuda:1")
.fit(trainingDf)
}
assert(thrown.getMessage.contains("`cuda` or `gpu`"))
}
}
}

32
jvm-packages/xgboost4j-spark-gpu/src/test/scala/ml/dmlc/xgboost4j/scala/rapids/spark/GpuXGBoostRegressorSuite.scala
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@ -1,5 +1,5 @@
/*
Copyright (c) 2021-2022 by Contributors
Copyright (c) 2021-2023 by Contributors
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
@ -40,7 +40,7 @@ class GpuXGBoostRegressorSuite extends GpuTestSuite {
test("The transform result should be same for several runs on same model") {
withGpuSparkSession(enableCsvConf()) { spark =>
val xgbParam = Map("eta" -> 0.1f, "max_depth" -> 2, "objective" -> "reg:squarederror",
"num_round" -> 10, "num_workers" -> 1, "tree_method" -> "gpu_hist",
"num_round" -> 10, "num_workers" -> 1, "tree_method" -> "hist", "device" -> "cuda",
"features_cols" -> featureNames, "label_col" -> labelName)
val Array(originalDf, testDf) = spark.read.option("header", "true").schema(schema)
.csv(getResourcePath("/rank.train.csv")).randomSplit(Array(0.7, 0.3), seed = 1)
@ -54,10 +54,30 @@ class GpuXGBoostRegressorSuite extends GpuTestSuite {
}
}
test("Tree method gpu_hist still works") {
withGpuSparkSession(enableCsvConf()) { spark =>
val params = Map(
"tree_method" -> "gpu_hist",
"features_cols" -> featureNames,
"label_col" -> labelName,
"num_round" -> 10,
"num_workers" -> 1
)
val Array(originalDf, testDf) = spark.read.option("header", "true").schema(schema)
.csv(getResourcePath("/rank.train.csv")).randomSplit(Array(0.7, 0.3), seed = 1)
// Get a model
val model = new XGBoostRegressor(params).fit(originalDf)
val left = model.transform(testDf).collect()
val right = model.transform(testDf).collect()
// The left should be same with right
assert(compareResults(true, 0.000001, left, right))
}
}
test("use weight") {
withGpuSparkSession(enableCsvConf()) { spark =>
val xgbParam = Map("eta" -> 0.1f, "max_depth" -> 2, "objective" -> "reg:squarederror",
"num_round" -> 10, "num_workers" -> 1, "tree_method" -> "gpu_hist",
"num_round" -> 10, "num_workers" -> 1, "tree_method" -> "hist", "device" -> "cuda",
"features_cols" -> featureNames, "label_col" -> labelName)
val Array(originalDf, testDf) = spark.read.option("header", "true").schema(schema)
.csv(getResourcePath("/rank.train.csv")).randomSplit(Array(0.7, 0.3), seed = 1)
@ -88,7 +108,8 @@ class GpuXGBoostRegressorSuite extends GpuTestSuite {
val classifier = new XGBoostRegressor(xgbParam)
.setFeaturesCol(featureNames)
.setLabelCol(labelName)
.setTreeMethod("gpu_hist")
.setTreeMethod("hist")
.setDevice("cuda")
(classifier.fit(rawInput), testDf)
}
@ -175,7 +196,7 @@ class GpuXGBoostRegressorSuite extends GpuTestSuite {
val classifier = new XGBoostRegressor(xgbParam)
.setFeaturesCol(featureNames)
.setLabelCol(labelName)
.setTreeMethod("gpu_hist")
.setDevice("cuda")
classifier.fit(rawInput)
}
@ -234,5 +255,4 @@ class GpuXGBoostRegressorSuite extends GpuTestSuite {
assert(testDf.count() === ret.length)
}
}
}

11
jvm-packages/xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/XGBoost.scala
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@ -73,7 +73,7 @@ private[scala] case class XGBoostExecutionParams(
xgbInputParams: XGBoostExecutionInputParams,
earlyStoppingParams: XGBoostExecutionEarlyStoppingParams,
cacheTrainingSet: Boolean,
treeMethod: Option[String],
device: Option[String],
isLocal: Boolean,
featureNames: Option[Array[String]],
featureTypes: Option[Array[String]]) {
@ -180,6 +180,10 @@ private[this] class XGBoostExecutionParamsFactory(rawParams: Map[String, Any], s
" as 'hist', 'approx', 'gpu_hist', and 'auto'")
treeMethod = Some(overridedParams("tree_method").asInstanceOf[String])
}
val device: Option[String] = overridedParams.get("device") match {
case None => None
case Some(dev: String) => if (treeMethod == "gpu_hist") Some("cuda") else Some(dev)
}
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 " +
@ -228,7 +232,7 @@ private[this] class XGBoostExecutionParamsFactory(rawParams: Map[String, Any], s
inputParams,
xgbExecEarlyStoppingParams,
cacheTrainingSet,
treeMethod,
device,
isLocal,
featureNames,
featureTypes
@ -318,7 +322,7 @@ object XGBoost extends Serializable {
val externalCheckpointParams = xgbExecutionParam.checkpointParam
var params = xgbExecutionParam.toMap
if (xgbExecutionParam.treeMethod.exists(m => m == "gpu_hist")) {
if (xgbExecutionParam.device.exists(m => (m == "cuda" || m == "gpu"))) {
val gpuId = if (xgbExecutionParam.isLocal) {
// For local mode, force gpu id to primary device
0
@ -328,6 +332,7 @@ object XGBoost extends Serializable {
logger.info("Leveraging gpu device " + gpuId + " to train")
params = params + ("device" -> s"cuda:$gpuId")
}
val booster = if (makeCheckpoint) {
SXGBoost.trainAndSaveCheckpoint(
watches.toMap("train"), params, numRounds,

2
jvm-packages/xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/XGBoostClassifier.scala
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@ -93,6 +93,8 @@ class XGBoostClassifier (
def setTreeMethod(value: String): this.type = set(treeMethod, value)
def setDevice(value: String): this.type = set(device, value)
def setGrowPolicy(value: String): this.type = set(growPolicy, value)
def setMaxBins(value: Int): this.type = set(maxBins, value)

2
jvm-packages/xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/XGBoostRegressor.scala
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@ -95,6 +95,8 @@ class XGBoostRegressor (
def setTreeMethod(value: String): this.type = set(treeMethod, value)
def setDevice(value: String): this.type = set(device, value)
def setGrowPolicy(value: String): this.type = set(growPolicy, value)
def setMaxBins(value: Int): this.type = set(maxBins, value)

8
jvm-packages/xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/params/BoosterParams.scala
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@ -154,6 +154,14 @@ private[spark] trait BoosterParams extends Params {
(value: String) => BoosterParams.supportedTreeMethods.contains(value))
final def getTreeMethod: String = $(treeMethod)
/**
* The device for running XGBoost algorithms, options: cpu, cuda
*/
final val device = new Param[String](
this, "device", "The device for running XGBoost algorithms, options: cpu, cuda"
)
final def getDevice: String = $(device)
/**
* growth policy for fast histogram algorithm

2
jvm-packages/xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/params/GeneralParams.scala
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@ -284,7 +284,7 @@ private[spark] trait ParamMapFuncs extends Params {
(paramName == "updater" && paramValue != "grow_histmaker,prune" &&
paramValue != "grow_quantile_histmaker" && paramValue != "grow_gpu_hist")) {
throw new IllegalArgumentException(s"you specified $paramName as $paramValue," +
s" XGBoost-Spark only supports gbtree as booster type and grow_histmaker,prune or" +
s" XGBoost-Spark only supports gbtree as booster type and grow_histmaker or" +
s" grow_quantile_histmaker or grow_gpu_hist as the updater type")
}
val name = CaseFormat.LOWER_UNDERSCORE.to(CaseFormat.LOWER_CAMEL, paramName)

1
jvm-packages/xgboost4j-spark/src/test/scala/ml/dmlc/xgboost4j/scala/spark/XGBoostClassifierSuite.scala
View File

@ -469,7 +469,6 @@ class XGBoostClassifierSuite extends AnyFunSuite with PerTest with TmpFolderPerS
.setFeatureTypes(featureTypes)
val model = xgb.fit(trainingDF)
val modelStr = new String(model._booster.toByteArray("json"))
System.out.println(modelStr)
val jsonModel = parseJson(modelStr)
implicit val formats: Formats = DefaultFormats
val featureNamesInModel = (jsonModel \ "learner" \ "feature_names").extract[List[String]]

1
jvm-packages/xgboost4j/src/test/java/ml/dmlc/xgboost4j/java/BoosterImplTest.java
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@ -143,7 +143,6 @@ public class BoosterImplTest {
booster.saveModel(temp.getAbsolutePath());
String modelString = new String(booster.toByteArray("json"));
System.out.println(modelString);
Booster bst2 = XGBoost.loadModel(temp.getAbsolutePath());
assert (Arrays.equals(bst2.toByteArray("ubj"), booster.toByteArray("ubj")));