[SPARK-22949][ML] Apply CrossValidator approach to Driver/Distributed…

… memory tradeoff for TrainValidationSplit

## What changes were proposed in this pull request?

Avoid holding all models in memory for `TrainValidationSplit`.

## How was this patch tested?

Existing tests.

Author: Bago Amirbekian <[email protected]>

Closes #20143 from MrBago/trainValidMemoryFix.

(cherry picked from commit cf0aa65)
Signed-off-by: Joseph K. Bradley <[email protected]>

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

@@ -160,8 +160,10 @@ class CrossValidator @Since("1.2.0") (@Since("1.4.0") override val uid: String)
         } (executionContext)
       }
 
-      // Wait for metrics to be calculated before unpersisting validation dataset
+      // Wait for metrics to be calculated
       val foldMetrics = foldMetricFutures.map(ThreadUtils.awaitResult(_, Duration.Inf))
+
+      // Unpersist training & validation set once all metrics have been produced
       trainingDataset.unpersist()
       validationDataset.unpersist()
       foldMetrics

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

@@ -143,24 +143,13 @@ class TrainValidationSplit @Since("1.5.0") (@Since("1.5.0") override val uid: St
 
     // Fit models in a Future for training in parallel
     logDebug(s"Train split with multiple sets of parameters.")
-    val modelFutures = epm.zipWithIndex.map { case (paramMap, paramIndex) =>
-      Future[Model[_]] {
+    val metricFutures = epm.zipWithIndex.map { case (paramMap, paramIndex) =>
+      Future[Double] {
         val model = est.fit(trainingDataset, paramMap).asInstanceOf[Model[_]]
 
         if (collectSubModelsParam) {
           subModels.get(paramIndex) = model
         }
-        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.")
@@ -171,7 +160,8 @@ class TrainValidationSplit @Since("1.5.0") (@Since("1.5.0") override val uid: St
     // Wait for all metrics to be calculated
     val metrics = metricFutures.map(ThreadUtils.awaitResult(_, Duration.Inf))
 
-    // Unpersist validation set once all metrics have been produced
+    // Unpersist training & validation set once all metrics have been produced
+    trainingDataset.unpersist()
     validationDataset.unpersist()
 
     logInfo(s"Train validation split metrics: ${metrics.toSeq}")