[SPARK-19357][ML] Adding parallel model evaluation in ML tuning

docs/ml-tuning.md

@@ -55,6 +55,8 @@ for multiclass problems. The default metric used to choose the best `ParamMap` c
 method in each of these evaluators.
 
 To help construct the parameter grid, users can use the [`ParamGridBuilder`](api/scala/index.html#org.apache.spark.ml.tuning.ParamGridBuilder) utility.
+By default, sets of parameters from the parameter grid are evaluated in serial. Parameter evaluation can be done in parallel by setting `parallelism` with a value of 2 or more (a value of 1 will be serial) before running model selection with `CrossValidator` or `TrainValidationSplit` (NOTE: this is not yet supported in Python).
+The value of `parallelism` should be chosen carefully to maximize parallelism without exceeding cluster resources, and larger values may not always lead to improved performance.  Generally speaking, a value up to 10 should be sufficient for most clusters.
 
 # Cross-Validation
 

examples/src/main/java/org/apache/spark/examples/ml/JavaModelSelectionViaCrossValidationExample.java

@@ -94,7 +94,9 @@ public static void main(String[] args) {
     CrossValidator cv = new CrossValidator()
       .setEstimator(pipeline)
       .setEvaluator(new BinaryClassificationEvaluator())
-      .setEstimatorParamMaps(paramGrid).setNumFolds(2);  // Use 3+ in practice
+      .setEstimatorParamMaps(paramGrid)
+      .setNumFolds(2)  // Use 3+ in practice
+      .setParallelism(2);  // Evaluate up to 2 parameter settings in parallel
 
     // Run cross-validation, and choose the best set of parameters.
     CrossValidatorModel cvModel = cv.fit(training);

examples/src/main/java/org/apache/spark/examples/ml/JavaModelSelectionViaTrainValidationSplitExample.java

@@ -70,7 +70,8 @@ public static void main(String[] args) {
       .setEstimator(lr)
       .setEvaluator(new RegressionEvaluator())
       .setEstimatorParamMaps(paramGrid)
-      .setTrainRatio(0.8);  // 80% for training and the remaining 20% for validation
+      .setTrainRatio(0.8)  // 80% for training and the remaining 20% for validation
+      .setParallelism(2);  // Evaluate up to 2 parameter settings in parallel
 
     // Run train validation split, and choose the best set of parameters.
     TrainValidationSplitModel model = trainValidationSplit.fit(training);

examples/src/main/scala/org/apache/spark/examples/ml/ModelSelectionViaCrossValidationExample.scala

@@ -93,6 +93,7 @@ object ModelSelectionViaCrossValidationExample {
       .setEvaluator(new BinaryClassificationEvaluator)
       .setEstimatorParamMaps(paramGrid)
       .setNumFolds(2)  // Use 3+ in practice
+      .setParallelism(2)  // Evaluate up to 2 parameter settings in parallel
 
     // Run cross-validation, and choose the best set of parameters.
     val cvModel = cv.fit(training)

examples/src/main/scala/org/apache/spark/examples/ml/ModelSelectionViaTrainValidationSplitExample.scala

@@ -65,6 +65,8 @@ object ModelSelectionViaTrainValidationSplitExample {
       .setEstimatorParamMaps(paramGrid)
       // 80% of the data will be used for training and the remaining 20% for validation.
       .setTrainRatio(0.8)
+      // Evaluate up to 2 parameter settings in parallel
+      .setParallelism(2)
 
     // Run train validation split, and choose the best set of parameters.
     val model = trainValidationSplit.fit(training)

mllib/src/main/scala/org/apache/spark/ml/param/shared/HasParallelism.scala

@@ -0,0 +1,59 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You 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 org.apache.spark.ml.param.shared
+
+import scala.concurrent.ExecutionContext
+
+import org.apache.spark.ml.param.{IntParam, Params, ParamValidators}
+import org.apache.spark.util.ThreadUtils
+
+/**
+ * Trait to define a level of parallelism for algorithms that are able to use
+ * multithreaded execution, and provide a thread-pool based execution context.
+ */
+private[ml] trait HasParallelism extends Params {
+
+  /**
+   * The number of threads to use when running parallel algorithms.
+   * Default is 1 for serial execution
+   *
+   * @group expertParam
+   */
+  val parallelism = new IntParam(this, "parallelism",
+    "the number of threads to use when running parallel algorithms", ParamValidators.gtEq(1))
+
+  setDefault(parallelism -> 1)
+
+  /** @group expertGetParam */
+  def getParallelism: Int = $(parallelism)
+
+  /**
+   * Create a new execution context with a thread-pool that has a maximum number of threads
+   * set to the value of [[parallelism]]. If this param is set to 1, a same-thread executor
+   * will be used to run in serial.
+   */
+  private[ml] def getExecutionContext: ExecutionContext = {
+    getParallelism match {
+      case 1 =>
+        ThreadUtils.sameThread
+      case n =>
+        ExecutionContext.fromExecutorService(ThreadUtils
+          .newDaemonCachedThreadPool(s"${this.getClass.getSimpleName}-thread-pool", n))
+    }
+  }
+}

mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala

@@ -20,20 +20,23 @@ package org.apache.spark.ml.tuning
 import java.util.{List => JList}
 
 import scala.collection.JavaConverters._
+import scala.concurrent.Future
+import scala.concurrent.duration.Duration
 
-import com.github.fommil.netlib.F2jBLAS
 import org.apache.hadoop.fs.Path
 import org.json4s.DefaultFormats
 
 import org.apache.spark.annotation.Since
 import org.apache.spark.internal.Logging
-import org.apache.spark.ml._
+import org.apache.spark.ml.{Estimator, Model}
 import org.apache.spark.ml.evaluation.Evaluator
-import org.apache.spark.ml.param._
+import org.apache.spark.ml.param.{IntParam, ParamMap, ParamValidators}
+import org.apache.spark.ml.param.shared.HasParallelism
 import org.apache.spark.ml.util._
 import org.apache.spark.mllib.util.MLUtils
 import org.apache.spark.sql.{DataFrame, Dataset}
 import org.apache.spark.sql.types.StructType
+import org.apache.spark.util.ThreadUtils
 
 /**
  * Params for [[CrossValidator]] and [[CrossValidatorModel]].
@@ -64,13 +67,11 @@ private[ml] trait CrossValidatorParams extends ValidatorParams {
 @Since("1.2.0")
 class CrossValidator @Since("1.2.0") (@Since("1.4.0") override val uid: String)
   extends Estimator[CrossValidatorModel]
-  with CrossValidatorParams with MLWritable with Logging {
+  with CrossValidatorParams with HasParallelism with MLWritable with Logging {
 
   @Since("1.2.0")
   def this() = this(Identifiable.randomUID("cv"))
 
-  private val f2jBLAS = new F2jBLAS
-
   /** @group setParam */
   @Since("1.2.0")
   def setEstimator(value: Estimator[_]): this.type = set(estimator, value)
@@ -91,6 +92,15 @@ class CrossValidator @Since("1.2.0") (@Since("1.4.0") override val uid: String)
   @Since("2.0.0")
   def setSeed(value: Long): this.type = set(seed, value)
 
+  /**
+   * Set the mamixum level of parallelism to evaluate models in parallel.
+   * Default is 1 for serial evaluation
+   *
+   * @group expertSetParam
+   */
+  @Since("2.3.0")
+  def setParallelism(value: Int): this.type = set(parallelism, value)
+
   @Since("2.0.0")
   override def fit(dataset: Dataset[_]): CrossValidatorModel = {
     val schema = dataset.schema
@@ -99,32 +109,49 @@ class CrossValidator @Since("1.2.0") (@Since("1.4.0") override val uid: String)
     val est = $(estimator)
     val eval = $(evaluator)
     val epm = $(estimatorParamMaps)
-    val numModels = epm.length
-    val metrics = new Array[Double](epm.length)
+
+    // Create execution context based on $(parallelism)
+    val executionContext = getExecutionContext
 
     val instr = Instrumentation.create(this, dataset)
-    instr.logParams(numFolds, seed)
+    instr.logParams(numFolds, seed, parallelism)
     logTuningParams(instr)
 
+    // Compute metrics for each model over each split
     val splits = MLUtils.kFold(dataset.toDF.rdd, $(numFolds), $(seed))
-    splits.zipWithIndex.foreach { case ((training, validation), splitIndex) =>
+    val metrics = splits.zipWithIndex.map { case ((training, validation), splitIndex) =>
       val trainingDataset = sparkSession.createDataFrame(training, schema).cache()
       val validationDataset = sparkSession.createDataFrame(validation, schema).cache()
-      // multi-model training
       logDebug(s"Train split $splitIndex with multiple sets of parameters.")
-      val models = est.fit(trainingDataset, epm).asInstanceOf[Seq[Model[_]]]
-      trainingDataset.unpersist()
-      var i = 0
-      while (i < numModels) {
-        // TODO: duplicate evaluator to take extra params from input
-        val metric = eval.evaluate(models(i).transform(validationDataset, epm(i)))
-        logDebug(s"Got metric $metric for model trained with ${epm(i)}.")
-        metrics(i) += metric
-        i += 1
+
+      // Fit models in a Future for training in parallel
+      val modelFutures = epm.map { paramMap =>
+        Future[Model[_]] {
+          val model = est.fit(trainingDataset, paramMap)
+          model.asInstanceOf[Model[_]]
+        } (executionContext)
+      }
+
+      // Unpersist training data only when all models have trained
+      Future.sequence[Model[_], Iterable](modelFutures)(implicitly, executionContext)
+        .onComplete { _ => trainingDataset.unpersist() } (executionContext)
+
+      // Evaluate models in a Future that will calulate a metric and allow model to be cleaned up
+      val foldMetricFutures = modelFutures.zip(epm).map { case (modelFuture, paramMap) =>
+        modelFuture.map { model =>
+          // TODO: duplicate evaluator to take extra params from input
+          val metric = eval.evaluate(model.transform(validationDataset, paramMap))
+          logDebug(s"Got metric $metric for model trained with $paramMap.")
+          metric
+        } (executionContext)
       }
+
+      // Wait for metrics to be calculated before unpersisting validation dataset
+      val foldMetrics = foldMetricFutures.map(ThreadUtils.awaitResult(_, Duration.Inf))
       validationDataset.unpersist()
-    }
-    f2jBLAS.dscal(numModels, 1.0 / $(numFolds), metrics, 1)
+      foldMetrics
+    }.transpose.map(_.sum / $(numFolds)) // Calculate average metric over all splits
+
     logInfo(s"Average cross-validation metrics: ${metrics.toSeq}")
     val (bestMetric, bestIndex) =
       if (eval.isLargerBetter) metrics.zipWithIndex.maxBy(_._1)

mllib/src/main/scala/org/apache/spark/ml/tuning/TrainValidationSplit.scala

@@ -20,6 +20,8 @@ package org.apache.spark.ml.tuning
 import java.util.{List => JList}
 
 import scala.collection.JavaConverters._
+import scala.concurrent.Future
+import scala.concurrent.duration.Duration
 import scala.language.existentials
 
 import org.apache.hadoop.fs.Path
@@ -30,9 +32,11 @@ import org.apache.spark.internal.Logging
 import org.apache.spark.ml.{Estimator, Model}
 import org.apache.spark.ml.evaluation.Evaluator
 import org.apache.spark.ml.param.{DoubleParam, ParamMap, ParamValidators}
+import org.apache.spark.ml.param.shared.HasParallelism
 import org.apache.spark.ml.util._
 import org.apache.spark.sql.{DataFrame, Dataset}
 import org.apache.spark.sql.types.StructType
+import org.apache.spark.util.ThreadUtils
 
 /**
  * Params for [[TrainValidationSplit]] and [[TrainValidationSplitModel]].
@@ -62,7 +66,7 @@ private[ml] trait TrainValidationSplitParams extends ValidatorParams {
 @Since("1.5.0")
 class TrainValidationSplit @Since("1.5.0") (@Since("1.5.0") override val uid: String)
   extends Estimator[TrainValidationSplitModel]
-  with TrainValidationSplitParams with MLWritable with Logging {
+  with TrainValidationSplitParams with HasParallelism with MLWritable with Logging {
 
   @Since("1.5.0")
   def this() = this(Identifiable.randomUID("tvs"))
@@ -87,37 +91,62 @@ class TrainValidationSplit @Since("1.5.0") (@Since("1.5.0") override val uid: St
   @Since("2.0.0")
   def setSeed(value: Long): this.type = set(seed, value)
 
+  /**
+   * Set the mamixum level of parallelism to evaluate models in parallel.
+   * Default is 1 for serial evaluation
+   *
+   * @group expertSetParam
+   */
+  @Since("2.3.0")
+  def setParallelism(value: Int): this.type = set(parallelism, value)
+
   @Since("2.0.0")
   override def fit(dataset: Dataset[_]): TrainValidationSplitModel = {
     val schema = dataset.schema
     transformSchema(schema, logging = true)
     val est = $(estimator)
     val eval = $(evaluator)
     val epm = $(estimatorParamMaps)
-    val numModels = epm.length
-    val metrics = new Array[Double](epm.length)
+
+    // Create execution context based on $(parallelism)
+    val executionContext = getExecutionContext
 
     val instr = Instrumentation.create(this, dataset)
-    instr.logParams(trainRatio, seed)
+    instr.logParams(trainRatio, seed, parallelism)
     logTuningParams(instr)
 
     val Array(trainingDataset, validationDataset) =
       dataset.randomSplit(Array($(trainRatio), 1 - $(trainRatio)), $(seed))
     trainingDataset.cache()
     validationDataset.cache()
 
-    // multi-model training
+    // Fit models in a Future for training in parallel
     logDebug(s"Train split with multiple sets of parameters.")
-    val models = est.fit(trainingDataset, epm).asInstanceOf[Seq[Model[_]]]
-    trainingDataset.unpersist()
-    var i = 0
-    while (i < numModels) {
-      // TODO: duplicate evaluator to take extra params from input
-      val metric = eval.evaluate(models(i).transform(validationDataset, epm(i)))
-      logDebug(s"Got metric $metric for model trained with ${epm(i)}.")
-      metrics(i) += metric
-      i += 1
+    val modelFutures = epm.map { paramMap =>
+      Future[Model[_]] {
+        val model = est.fit(trainingDataset, paramMap)
+        model.asInstanceOf[Model[_]]
+      } (executionContext)
     }
+
+    // Unpersist training data only when all models have trained
+    Future.sequence[Model[_], Iterable](modelFutures)(implicitly, executionContext)
+      .onComplete { _ => trainingDataset.unpersist() } (executionContext)
+
+    // Evaluate models in a Future that will calulate a metric and allow model to be cleaned up
+    val metricFutures = modelFutures.zip(epm).map { case (modelFuture, paramMap) =>
+      modelFuture.map { model =>
+        // TODO: duplicate evaluator to take extra params from input
+        val metric = eval.evaluate(model.transform(validationDataset, paramMap))
+        logDebug(s"Got metric $metric for model trained with $paramMap.")
+        metric
+      } (executionContext)
+    }
+
+    // Wait for all metrics to be calculated
+    val metrics = metricFutures.map(ThreadUtils.awaitResult(_, Duration.Inf))
+
+    // Unpersist validation set once all metrics have been produced
     validationDataset.unpersist()
 
     logInfo(s"Train validation split metrics: ${metrics.toSeq}")

mllib/src/test/scala/org/apache/spark/ml/tuning/CrossValidatorSuite.scala

@@ -120,6 +120,33 @@ class CrossValidatorSuite
     }
   }
 
+  test("cross validation with parallel evaluation") {
+    val lr = new LogisticRegression
+    val lrParamMaps = new ParamGridBuilder()
+      .addGrid(lr.regParam, Array(0.001, 1000.0))
+      .addGrid(lr.maxIter, Array(0, 3))
+      .build()
+    val eval = new BinaryClassificationEvaluator
+    val cv = new CrossValidator()
+      .setEstimator(lr)
+      .setEstimatorParamMaps(lrParamMaps)
+      .setEvaluator(eval)
+      .setNumFolds(2)
+      .setParallelism(1)
+    val cvSerialModel = cv.fit(dataset)
+    cv.setParallelism(2)
+    val cvParallelModel = cv.fit(dataset)
+
+    val serialMetrics = cvSerialModel.avgMetrics.sorted
+    val parallelMetrics = cvParallelModel.avgMetrics.sorted
+    assert(serialMetrics === parallelMetrics)
+
+    val parentSerial = cvSerialModel.bestModel.parent.asInstanceOf[LogisticRegression]
+    val parentParallel = cvParallelModel.bestModel.parent.asInstanceOf[LogisticRegression]
+    assert(parentSerial.getRegParam === parentParallel.getRegParam)
+    assert(parentSerial.getMaxIter === parentParallel.getMaxIter)
+  }
+
   test("read/write: CrossValidator with simple estimator") {
     val lr = new LogisticRegression().setMaxIter(3)
     val evaluator = new BinaryClassificationEvaluator()