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ruifengz pushed a commit to branch branch-4.0
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The following commit(s) were added to refs/heads/branch-4.0 by this push:
new abe837d5df33 [SPARK-50844][ML][CONNECT] Make model be loaded by
ServiceLoader when loading
abe837d5df33 is described below
commit abe837d5df33d93da07f9786aff81b57c0f1d9ab
Author: Bobby Wang <[email protected]>
AuthorDate: Tue Jan 21 09:47:57 2025 +0800
[SPARK-50844][ML][CONNECT] Make model be loaded by ServiceLoader when
loading
### What changes were proposed in this pull request?
Currently ml connect discoveries Estimators, Evaluators and Transformers
(not including Model) by ServiceLoader which could make it more secure. This PR
adds a public no-args constructors for some Models which could make model to be
loaded by ServiceLoader too.
### Why are the changes needed?
This PR is a follow up of https://github.com/apache/spark/pull/49503, and
it will be more safe to discovery a Model by ServiceLoader when loading a model.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
The existing tests pass
### Was this patch authored or co-authored using generative AI tooling?
No
Closes #49569 from wbo4958/ml.model.serviceloader.
Authored-by: Bobby Wang <[email protected]>
Signed-off-by: Ruifeng Zheng <[email protected]>
(cherry picked from commit 79347e08e2e77d4f85faf65d7ca2016f7d8ffa99)
Signed-off-by: Ruifeng Zheng <[email protected]>
---
.../services/org.apache.spark.ml.Transformer | 20 +++++++
.../ml/classification/DecisionTreeClassifier.scala | 4 ++
.../spark/ml/classification/GBTClassifier.scala | 5 ++
.../ml/classification/LogisticRegression.scala | 4 ++
.../ml/classification/RandomForestClassifier.scala | 4 ++
.../spark/ml/clustering/BisectingKMeans.scala | 4 ++
.../org/apache/spark/ml/clustering/KMeans.scala | 5 ++
.../org/apache/spark/ml/recommendation/ALS.scala | 4 ++
.../ml/regression/DecisionTreeRegressor.scala | 4 ++
.../apache/spark/ml/regression/GBTRegressor.scala | 5 ++
.../spark/ml/regression/LinearRegression.scala | 4 ++
.../ml/regression/RandomForestRegressor.scala | 4 ++
.../main/scala/org/apache/spark/ml/tree/Node.scala | 5 ++
.../apache/spark/sql/connect/ml/MLHandler.scala | 14 +++--
.../org/apache/spark/sql/connect/ml/MLUtils.scala | 62 +++++++++++++++++-----
.../services/org.apache.spark.ml.Transformer | 6 +--
.../org/apache/spark/sql/connect/ml/MLHelper.scala | 2 +
.../org/apache/spark/sql/connect/ml/MLSuite.scala | 6 +--
18 files changed, 138 insertions(+), 24 deletions(-)
diff --git
a/mllib/src/main/resources/META-INF/services/org.apache.spark.ml.Transformer
b/mllib/src/main/resources/META-INF/services/org.apache.spark.ml.Transformer
index 004ec8aeff8c..392115be98ba 100644
--- a/mllib/src/main/resources/META-INF/services/org.apache.spark.ml.Transformer
+++ b/mllib/src/main/resources/META-INF/services/org.apache.spark.ml.Transformer
@@ -18,3 +18,23 @@
# Spark Connect ML uses ServiceLoader to find out the supported Spark Ml
non-model transformer.
# So register the supported transformer here if you're trying to add a new one.
org.apache.spark.ml.feature.VectorAssembler
+
+########### Model for loading
+# classification
+org.apache.spark.ml.classification.LogisticRegressionModel
+org.apache.spark.ml.classification.DecisionTreeClassificationModel
+org.apache.spark.ml.classification.RandomForestClassificationModel
+org.apache.spark.ml.classification.GBTClassificationModel
+
+# regression
+org.apache.spark.ml.regression.LinearRegressionModel
+org.apache.spark.ml.regression.DecisionTreeRegressionModel
+org.apache.spark.ml.regression.RandomForestRegressionModel
+org.apache.spark.ml.regression.GBTRegressionModel
+
+# clustering
+org.apache.spark.ml.clustering.KMeansModel
+org.apache.spark.ml.clustering.BisectingKMeansModel
+
+# recommendation
+org.apache.spark.ml.recommendation.ALSModel
diff --git
a/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala
b/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala
index c5f1d7f39b6b..761741e7f42d 100644
---
a/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala
+++
b/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala
@@ -192,6 +192,10 @@ class DecisionTreeClassificationModel private[ml] (
private[ml] def this(rootNode: Node, numFeatures: Int, numClasses: Int) =
this(Identifiable.randomUID("dtc"), rootNode, numFeatures, numClasses)
+ // For ml connect only
+ @Since("4.0.0")
+ private[ml] def this() = this(Node.dummyNode, 0, 0)
+
override def predict(features: Vector): Double = {
rootNode.predictImpl(features).prediction
}
diff --git
a/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala
b/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala
index 3910beda3d0a..8ed52d5e09e0 100644
---
a/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala
+++
b/mllib/src/main/scala/org/apache/spark/ml/classification/GBTClassifier.scala
@@ -272,6 +272,11 @@ class GBTClassificationModel private[ml](
def this(uid: String, _trees: Array[DecisionTreeRegressionModel],
_treeWeights: Array[Double]) =
this(uid, _trees, _treeWeights, -1, 2)
+ // For ml connect only
+ @Since("4.0.0")
+ private[ml] def this() = this(Identifiable.randomUID("gbtc"),
+ Array(new DecisionTreeRegressionModel), Array(0.0))
+
@Since("1.4.0")
override def trees: Array[DecisionTreeRegressionModel] = _trees
diff --git
a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
index 745cb61bb7aa..7432473e4aa9 100644
---
a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
+++
b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
@@ -1076,6 +1076,10 @@ class LogisticRegressionModel private[spark] (
this(uid, new DenseMatrix(1, coefficients.size, coefficients.toArray,
isTransposed = true),
Vectors.dense(intercept), 2, isMultinomial = false)
+ // For ml connect only
+ @Since("4.0.0")
+ private[ml] def this() = this(Identifiable.randomUID("logreg"),
Vectors.zeros(0), 0)
+
/**
* A vector of model coefficients for "binomial" logistic regression. If
this model was trained
* using the "multinomial" family then an exception is thrown.
diff --git
a/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala
b/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala
index 9295425f9d6b..0833ad0d402b 100644
---
a/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala
+++
b/mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala
@@ -255,6 +255,10 @@ class RandomForestClassificationModel private[ml] (
numClasses: Int) =
this(Identifiable.randomUID("rfc"), trees, numFeatures, numClasses)
+ // For ml connect only
+ @Since("4.0.0")
+ private[ml] def this() = this(Array(new DecisionTreeClassificationModel), 0,
0)
+
@Since("1.4.0")
override def trees: Array[DecisionTreeClassificationModel] = _trees
diff --git
a/mllib/src/main/scala/org/apache/spark/ml/clustering/BisectingKMeans.scala
b/mllib/src/main/scala/org/apache/spark/ml/clustering/BisectingKMeans.scala
index b4f1565362b0..d0e5cb42c41c 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/clustering/BisectingKMeans.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/clustering/BisectingKMeans.scala
@@ -96,6 +96,10 @@ class BisectingKMeansModel private[ml] (
extends Model[BisectingKMeansModel] with BisectingKMeansParams with
MLWritable
with HasTrainingSummary[BisectingKMeansSummary] {
+ @Since("4.0.0")
+ private[ml] def this() = this(Identifiable.randomUID("bisecting-kmeans"),
+ new MLlibBisectingKMeansModel(null))
+
@Since("3.0.0")
lazy val numFeatures: Int = parentModel.clusterCenters.head.size
diff --git a/mllib/src/main/scala/org/apache/spark/ml/clustering/KMeans.scala
b/mllib/src/main/scala/org/apache/spark/ml/clustering/KMeans.scala
index 50fb18bb620a..17d34a277af2 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/clustering/KMeans.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/clustering/KMeans.scala
@@ -138,6 +138,11 @@ class KMeansModel private[ml] (
extends Model[KMeansModel] with KMeansParams with GeneralMLWritable
with HasTrainingSummary[KMeansSummary] {
+ // For ml connect only
+ @Since("4.0.0")
+ private[ml] def this() = this(Identifiable.randomUID("kmeans"),
+ new MLlibKMeansModel(clusterCenters = null))
+
@Since("3.0.0")
lazy val numFeatures: Int = parentModel.clusterCenters.head.size
diff --git a/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala
b/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala
index 5899bf891ec9..4120e16794a8 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala
@@ -280,6 +280,10 @@ class ALSModel private[ml] (
@transient val itemFactors: DataFrame)
extends Model[ALSModel] with ALSModelParams with MLWritable {
+ // For ml connect only
+ @Since("4.0.0")
+ private[ml] def this() = this(Identifiable.randomUID("als"), 0, null, null)
+
/** @group setParam */
@Since("1.4.0")
def setUserCol(value: String): this.type = set(userCol, value)
diff --git
a/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala
b/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala
index dace99f214b1..2c692d33a38d 100644
---
a/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala
+++
b/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala
@@ -187,6 +187,10 @@ class DecisionTreeRegressionModel private[ml] (
private[ml] def this(rootNode: Node, numFeatures: Int) =
this(Identifiable.randomUID("dtr"), rootNode, numFeatures)
+ // For ml connect only
+ @Since("4.0.0")
+ private[ml] def this() = this(Node.dummyNode, 0)
+
override def predict(features: Vector): Double = {
rootNode.predictImpl(features).prediction
}
diff --git
a/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala
b/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala
index 732bfcbd671e..c2c672a7fa60 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/GBTRegressor.scala
@@ -242,6 +242,11 @@ class GBTRegressionModel private[ml](
def this(uid: String, _trees: Array[DecisionTreeRegressionModel],
_treeWeights: Array[Double]) =
this(uid, _trees, _treeWeights, -1)
+ // For ml connect only
+ @Since("4.0.0")
+ private[ml] def this() = this(Identifiable.randomUID("gbtr"),
+ Array(new DecisionTreeRegressionModel), Array(0.0))
+
@Since("1.4.0")
override def trees: Array[DecisionTreeRegressionModel] = _trees
diff --git
a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
index 4f74dd734e8f..2afcb52dbb4f 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala
@@ -702,6 +702,10 @@ class LinearRegressionModel private[ml] (
private[ml] def this(uid: String, coefficients: Vector, intercept: Double) =
this(uid, coefficients, intercept, 1.0)
+ // For ml connect only
+ @Since("4.0.0")
+ private[ml] def this() = this(Identifiable.randomUID("linReg"),
Vectors.zeros(0), 0.0, 0.0)
+
override val numFeatures: Int = coefficients.size
/**
diff --git
a/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala
b/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala
index 4135afb5ed0b..b0409c916a05 100644
---
a/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala
+++
b/mllib/src/main/scala/org/apache/spark/ml/regression/RandomForestRegressor.scala
@@ -212,6 +212,10 @@ class RandomForestRegressionModel private[ml] (
private[ml] def this(trees: Array[DecisionTreeRegressionModel], numFeatures:
Int) =
this(Identifiable.randomUID("rfr"), trees, numFeatures)
+ // For ml connect only
+ @Since("4.0.0")
+ private[ml] def this() = this(Array(new DecisionTreeRegressionModel), 0)
+
@Since("1.4.0")
override def trees: Array[DecisionTreeRegressionModel] = _trees
diff --git a/mllib/src/main/scala/org/apache/spark/ml/tree/Node.scala
b/mllib/src/main/scala/org/apache/spark/ml/tree/Node.scala
index dfa4961d9ffb..697d98953839 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/tree/Node.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/tree/Node.scala
@@ -105,6 +105,11 @@ private[ml] object Node {
split = Split.fromOld(oldNode.split.get, categoricalFeatures),
impurityStats = null)
}
}
+
+ // A dummy node used for ml connect only
+ val dummyNode: Node = {
+ new LeafNode(0.0, 0.0, ImpurityCalculator.getCalculator("gini",
Array.empty, 0))
+ }
}
/**
diff --git
a/sql/connect/server/src/main/scala/org/apache/spark/sql/connect/ml/MLHandler.scala
b/sql/connect/server/src/main/scala/org/apache/spark/sql/connect/ml/MLHandler.scala
index cc89079aeca3..c66a2e7004b9 100644
---
a/sql/connect/server/src/main/scala/org/apache/spark/sql/connect/ml/MLHandler.scala
+++
b/sql/connect/server/src/main/scala/org/apache/spark/sql/connect/ml/MLHandler.scala
@@ -204,7 +204,7 @@ private[connect] object MLHandler extends Logging {
val path = mlCommand.getRead.getPath
if (operator.getType == proto.MlOperator.OperatorType.MODEL) {
- val model = MLUtils.load(sessionHolder, name,
path).asInstanceOf[Model[_]]
+ val model = MLUtils.loadTransformer(sessionHolder, name, path)
val id = mlCache.register(model)
proto.MlCommandResult
.newBuilder()
@@ -218,15 +218,21 @@ private[connect] object MLHandler extends Logging {
} else if (operator.getType == proto.MlOperator.OperatorType.ESTIMATOR
||
operator.getType == proto.MlOperator.OperatorType.EVALUATOR) {
- val operator = MLUtils.load(sessionHolder, name,
path).asInstanceOf[Params]
+ val mlOperator = {
+ if (operator.getType == proto.MlOperator.OperatorType.ESTIMATOR) {
+ MLUtils.loadEstimator(sessionHolder, name,
path).asInstanceOf[Params]
+ } else {
+ MLUtils.loadEvaluator(sessionHolder, name,
path).asInstanceOf[Params]
+ }
+ }
proto.MlCommandResult
.newBuilder()
.setOperatorInfo(
proto.MlCommandResult.MlOperatorInfo
.newBuilder()
.setName(name)
- .setUid(operator.uid)
- .setParams(Serializer.serializeParams(operator)))
+ .setUid(mlOperator.uid)
+ .setParams(Serializer.serializeParams(mlOperator)))
.build()
} else {
throw MlUnsupportedException(s"${operator.getType} read not
supported")
diff --git
a/sql/connect/server/src/main/scala/org/apache/spark/sql/connect/ml/MLUtils.scala
b/sql/connect/server/src/main/scala/org/apache/spark/sql/connect/ml/MLUtils.scala
index 000a01c232bd..86dd013b9d98 100644
---
a/sql/connect/server/src/main/scala/org/apache/spark/sql/connect/ml/MLUtils.scala
+++
b/sql/connect/server/src/main/scala/org/apache/spark/sql/connect/ml/MLUtils.scala
@@ -29,13 +29,13 @@ import org.apache.spark.ml.{Estimator, Transformer}
import org.apache.spark.ml.evaluation.Evaluator
import org.apache.spark.ml.linalg._
import org.apache.spark.ml.param.Params
-import org.apache.spark.ml.util.{MLReadable, MLWritable}
+import org.apache.spark.ml.util.MLWritable
import org.apache.spark.sql.{DataFrame, Dataset}
import org.apache.spark.sql.connect.common.{DataTypeProtoConverter,
LiteralValueProtoConverter}
import org.apache.spark.sql.connect.planner.SparkConnectPlanner
import org.apache.spark.sql.connect.plugin.SparkConnectPluginRegistry
import org.apache.spark.sql.connect.service.SessionHolder
-import org.apache.spark.util.{SparkClassUtils, Utils}
+import org.apache.spark.util.Utils
private[ml] object MLUtils {
@@ -352,28 +352,62 @@ private[ml] object MLUtils {
}
/**
- * Call "load" function on the ML operator given the operator name
+ * Load an ML component (Estimator, Transformer, or Evaluator) from the
given path.
*
+ * @param sessionHolder
+ * the session holder
* @param className
* the ML operator name
* @param path
* the path to be loaded
+ * @param operatorClass
+ * the class type of the ML operator (Estimator, Transformer, or Evaluator)
+ * @tparam T
+ * the type of the ML operator
* @return
- * the ML instance
+ * the instance of the ML operator
*/
- def load(sessionHolder: SessionHolder, className: String, path: String):
Object = {
+ private def loadOperator[T](
+ sessionHolder: SessionHolder,
+ className: String,
+ path: String,
+ operatorClass: Class[T]): T = {
val name = replaceOperator(sessionHolder, className)
-
- // It's the companion object of the corresponding spark operators to load.
- val objectCls = SparkClassUtils.classForName(name + "$")
- val mlReadableClass = classOf[MLReadable[_]]
- // Make sure it is implementing MLReadable
- if (!mlReadableClass.isAssignableFrom(objectCls)) {
- throw MlUnsupportedException(s"$name must implement MLReadable.")
+ val operators = loadOperators(operatorClass)
+ if (operators.isEmpty || !operators.contains(name)) {
+ throw MlUnsupportedException(s"Unsupported read for $name")
}
+ operators(name)
+ .getMethod("load", classOf[String])
+ .invoke(null, path)
+ .asInstanceOf[T]
+ }
+
+ /**
+ * Load an estimator from the specified path.
+ */
+ def loadEstimator(
+ sessionHolder: SessionHolder,
+ className: String,
+ path: String): Estimator[_] = {
+ loadOperator(sessionHolder, className, path, classOf[Estimator[_]])
+ }
- val loadedMethod = SparkClassUtils.classForName(name).getMethod("load",
classOf[String])
- loadedMethod.invoke(null, path)
+ /**
+ * Load a transformer from the specified path.
+ */
+ def loadTransformer(
+ sessionHolder: SessionHolder,
+ className: String,
+ path: String): Transformer = {
+ loadOperator(sessionHolder, className, path, classOf[Transformer])
+ }
+
+ /**
+ * Load an evaluator from the specified path.
+ */
+ def loadEvaluator(sessionHolder: SessionHolder, className: String, path:
String): Evaluator = {
+ loadOperator(sessionHolder, className, path, classOf[Evaluator])
}
// Since we're using reflection way to get the attribute, in order not to
diff --git
a/mllib/src/main/resources/META-INF/services/org.apache.spark.ml.Transformer
b/sql/connect/server/src/test/resources/META-INF/services/org.apache.spark.ml.Transformer
similarity index 84%
copy from
mllib/src/main/resources/META-INF/services/org.apache.spark.ml.Transformer
copy to
sql/connect/server/src/test/resources/META-INF/services/org.apache.spark.ml.Transformer
index 004ec8aeff8c..92d3a7018054 100644
--- a/mllib/src/main/resources/META-INF/services/org.apache.spark.ml.Transformer
+++
b/sql/connect/server/src/test/resources/META-INF/services/org.apache.spark.ml.Transformer
@@ -15,6 +15,6 @@
# limitations under the License.
#
-# Spark Connect ML uses ServiceLoader to find out the supported Spark Ml
non-model transformer.
-# So register the supported transformer here if you're trying to add a new one.
-org.apache.spark.ml.feature.VectorAssembler
+# Spark Connect ML uses ServiceLoader to find out the supported Spark Ml
estimators.
+# So register the supported estimator here if you're trying to add a new one.
+org.apache.spark.sql.connect.ml.MyLogisticRegressionModel
diff --git
a/sql/connect/server/src/test/scala/org/apache/spark/sql/connect/ml/MLHelper.scala
b/sql/connect/server/src/test/scala/org/apache/spark/sql/connect/ml/MLHelper.scala
index 844e85fa03b6..ef5b8a59a58b 100644
---
a/sql/connect/server/src/test/scala/org/apache/spark/sql/connect/ml/MLHelper.scala
+++
b/sql/connect/server/src/test/scala/org/apache/spark/sql/connect/ml/MLHelper.scala
@@ -149,6 +149,8 @@ class MyLogisticRegressionModel(
with HasFakedParam
with DefaultParamsWritable {
+ private[spark] def this() = this("MyLogisticRegressionModel", 1.0f, 1.0f)
+
def setFakeParam(v: Int): this.type = set(fakeParam, v)
def setMaxIter(v: Int): this.type = set(maxIter, v)
diff --git
a/sql/connect/server/src/test/scala/org/apache/spark/sql/connect/ml/MLSuite.scala
b/sql/connect/server/src/test/scala/org/apache/spark/sql/connect/ml/MLSuite.scala
index 49dcd7dbe9ad..aee0759d0d3a 100644
---
a/sql/connect/server/src/test/scala/org/apache/spark/sql/connect/ml/MLSuite.scala
+++
b/sql/connect/server/src/test/scala/org/apache/spark/sql/connect/ml/MLSuite.scala
@@ -322,7 +322,7 @@ class MLSuite extends MLHelper {
}
}
- test("ML operator must implement MLReadable for loading") {
+ test("Model must be registered into ServiceLoader when loading") {
val thrown = intercept[MlUnsupportedException] {
val sessionHolder = SparkConnectTestUtils.createDummySessionHolder(spark)
val readCmd = proto.MlCommand
@@ -339,8 +339,8 @@ class MLSuite extends MLHelper {
MLHandler.handleMlCommand(sessionHolder, readCmd)
}
assert(
-
thrown.message.contains("org.apache.spark.sql.connect.ml.NotImplementingMLReadble
" +
- "must implement MLReadable"))
+ thrown.message.contains("Unsupported read for " +
+ "org.apache.spark.sql.connect.ml.NotImplementingMLReadble"))
}
test("RegressionEvaluator works") {
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