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[SPARK-19746][ML] Faster indexing for logistic aggregator

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## What changes were proposed in this pull request?

JIRA: [SPARK-19746](https://issues.apache.org/jira/browse/SPARK-19746)

The following code is inefficient:

````scala
    val localCoefficients: Vector = bcCoefficients.value

    features.foreachActive { (index, value) =>
      val stdValue = value / localFeaturesStd(index)
      var j = 0
      while (j < numClasses) {
        margins(j) += localCoefficients(index * numClasses + j) * stdValue
        j += 1
      }
    }
````

`localCoefficients(index * numClasses + j)` calls `Vector.apply` which creates a new Breeze vector and indexes that. Even if it is not that slow to create the object, we will generate a lot of extra garbage that may result in longer GC pauses. This is a hot inner loop, so we should optimize wherever possible.

## How was this patch tested?

I don't think there's a great way to test this patch. It's purely performance related, so unit tests should guarantee that we haven't made any unwanted changes. Empirically I observed between 10-40% speedups just running short local tests. I suspect the big differences will be seen when large data/coefficient sizes have to pause for GC more often. I welcome other ideas for testing.

Author: sethah <seth.hendrickson16@gmail.com>

Closes #17078 from sethah/logistic_agg_indexing.
This commit is contained in:
sethah 2017-02-28 00:34:38 +00:00 committed by DB Tsai
parent 8a5a58506c
commit 16d8472f74
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2 changed files with 34 additions and 3 deletions
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11
mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
View file

@ -1431,7 +1431,12 @@ private class LogisticAggregator(
private var weightSum = 0.0
private var lossSum = 0.0
private val gradientSumArray = Array.fill[Double](coefficientSize)(0.0D)
@transient private lazy val coefficientsArray: Array[Double] = bcCoefficients.value match {
case DenseVector(values) => values
case _ => throw new IllegalArgumentException(s"coefficients only supports dense vector but " +
s"got type ${bcCoefficients.value.getClass}.)")
}
private val gradientSumArray = new Array[Double](coefficientSize)
if (multinomial && numClasses <= 2) {
logInfo(s"Multinomial logistic regression for binary classification yields separate " +
@ -1447,7 +1452,7 @@ private class LogisticAggregator(
label: Double): Unit = {
val localFeaturesStd = bcFeaturesStd.value
val localCoefficients = bcCoefficients.value
val localCoefficients = coefficientsArray
val localGradientArray = gradientSumArray
val margin = - {
var sum = 0.0
@ -1491,7 +1496,7 @@ private class LogisticAggregator(
logistic regression without pivoting.
*/
val localFeaturesStd = bcFeaturesStd.value
val localCoefficients = bcCoefficients.value
val localCoefficients = coefficientsArray
val localGradientArray = gradientSumArray
// marginOfLabel is margins(label) in the formula

26
mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala
View file

@ -456,6 +456,32 @@ class LogisticRegressionSuite
assert(blrModel.intercept !== 0.0)
}
test("sparse coefficients in LogisticAggregator") {
val bcCoefficientsBinary = spark.sparkContext.broadcast(Vectors.sparse(2, Array(0), Array(1.0)))
val bcFeaturesStd = spark.sparkContext.broadcast(Array(1.0))
val binaryAgg = new LogisticAggregator(bcCoefficientsBinary, bcFeaturesStd, 2,
fitIntercept = true, multinomial = false)
val thrownBinary = withClue("binary logistic aggregator cannot handle sparse coefficients") {
intercept[IllegalArgumentException] {
binaryAgg.add(Instance(1.0, 1.0, Vectors.dense(1.0)))
}
}
assert(thrownBinary.getMessage.contains("coefficients only supports dense"))
val bcCoefficientsMulti = spark.sparkContext.broadcast(Vectors.sparse(6, Array(0), Array(1.0)))
val multinomialAgg = new LogisticAggregator(bcCoefficientsMulti, bcFeaturesStd, 3,
fitIntercept = true, multinomial = true)
val thrown = withClue("multinomial logistic aggregator cannot handle sparse coefficients") {
intercept[IllegalArgumentException] {
multinomialAgg.add(Instance(1.0, 1.0, Vectors.dense(1.0)))
}
}
assert(thrown.getMessage.contains("coefficients only supports dense"))
bcCoefficientsBinary.destroy(blocking = false)
bcFeaturesStd.destroy(blocking = false)
bcCoefficientsMulti.destroy(blocking = false)
}
test("overflow prediction for multiclass") {
val model = new LogisticRegressionModel("mLogReg",
Matrices.dense(3, 2, Array(0.0, 0.0, 0.0, 1.0, 2.0, 3.0)),