srowen commented on a change in pull request #30468:
URL: https://github.com/apache/spark/pull/30468#discussion_r528775009
##########
File path:
mllib/src/main/scala/org/apache/spark/ml/recommendation/TopByKeyAggregator.scala
##########
@@ -57,3 +57,57 @@ private[recommendation] class TopByKeyAggregator[K1:
TypeTag, K2: TypeTag, V: Ty
override def outputEncoder: Encoder[Array[(K2, V)]] =
ExpressionEncoder[Array[(K2, V)]]()
}
+
+
+/**
+ * Works on rows of the form (ScrId, DstIds, Scores).
+ * Finds the top `num` DstIds and Scores.
+ */
+private[recommendation] class TopKArrayAggregator(num: Int)
+ extends Aggregator[
+ (Int, Array[Int], Array[Float]),
+ (Array[Int], Array[Float]),
+ Array[(Int, Float)]] {
+
+ override def zero: (Array[Int], Array[Float]) = {
+ (Array.emptyIntArray, Array.emptyFloatArray)
+ }
+
+ override def reduce(
+ b: (Array[Int], Array[Float]),
+ a: (Int, Array[Int], Array[Float])): (Array[Int], Array[Float]) = {
+ merge(b, (a._2, a._3))
+ }
+
+ def merge(
+ b1: (Array[Int], Array[Float]),
+ b2: (Array[Int], Array[Float])): (Array[Int], Array[Float]) = {
+ val (ids1, scores1) = b1
+ val (ids2, socres2) = b2
Review comment:
Nit: typo in socres2
##########
File path: mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala
##########
@@ -456,34 +457,42 @@ class ALSModel private[ml] (
num: Int,
blockSize: Int): DataFrame = {
import srcFactors.sparkSession.implicits._
+ import ALSModel.TopSelector
val srcFactorsBlocked = blockify(srcFactors.as[(Int, Array[Float])],
blockSize)
val dstFactorsBlocked = blockify(dstFactors.as[(Int, Array[Float])],
blockSize)
- val ratings = srcFactorsBlocked.crossJoin(dstFactorsBlocked)
- .as[(Seq[(Int, Array[Float])], Seq[(Int, Array[Float])])]
- .flatMap { case (srcIter, dstIter) =>
- val m = srcIter.size
- val n = math.min(dstIter.size, num)
- val output = new Array[(Int, Int, Float)](m * n)
- var i = 0
- val pq = new BoundedPriorityQueue[(Int, Float)](num)(Ordering.by(_._2))
- srcIter.foreach { case (srcId, srcFactor) =>
- dstIter.foreach { case (dstId, dstFactor) =>
- // We use F2jBLAS which is faster than a call to native BLAS for
vector dot product
- val score = BLAS.f2jBLAS.sdot(rank, srcFactor, 1, dstFactor, 1)
- pq += dstId -> score
+ val partialRecs = srcFactorsBlocked.crossJoin(dstFactorsBlocked)
+ .as[(Array[Int], Array[Float], Array[Int], Array[Float])]
+ .mapPartitions { iter =>
+ var buffer: Array[Float] = null
+ var selector: TopSelector = null
+ iter.flatMap { case (srcIds, srcMat, dstIds, dstMat) =>
+ require(srcMat.length == srcIds.length * rank)
+ require(dstMat.length == dstIds.length * rank)
+ val m = srcIds.length
+ val n = dstIds.length
+ if (buffer == null || buffer.length < n) {
+ buffer = Array.ofDim[Float](n)
+ selector = new TopSelector(buffer)
}
- pq.foreach { case (dstId, score) =>
- output(i) = (srcId, dstId, score)
- i += 1
+
+ Iterator.tabulate(m) { i =>
+ // buffer = i-th vec in srcMat * dstMat
+ BLAS.f2jBLAS.sgemv("T", rank, n, 1.0F, dstMat, 0, rank,
+ srcMat, i * rank, 1, 0.0F, buffer, 0, 1)
+ val indices = selector.selectTopKIndices(Iterator.range(0, n), num)
+ (srcIds(i), indices.map(dstIds), indices.map(buffer))
}
- pq.clear()
+ } ++ {
Review comment:
I'm a little confused by this part - why null these out? the flatMap in
which they are declared is done here. Maybe I misread
##########
File path: mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala
##########
@@ -551,6 +563,21 @@ object ALSModel extends MLReadable[ALSModel] {
model
}
}
+
+ /** select top indices based on values. */
+ private[recommendation] class TopSelector(val values: Array[Float]) {
Review comment:
Does this need to be a class? looks like this code is called once. May
be less code/indirection
##########
File path:
mllib/src/main/scala/org/apache/spark/ml/recommendation/TopByKeyAggregator.scala
##########
@@ -57,3 +57,57 @@ private[recommendation] class TopByKeyAggregator[K1:
TypeTag, K2: TypeTag, V: Ty
override def outputEncoder: Encoder[Array[(K2, V)]] =
ExpressionEncoder[Array[(K2, V)]]()
}
+
+
+/**
+ * Works on rows of the form (ScrId, DstIds, Scores).
+ * Finds the top `num` DstIds and Scores.
+ */
+private[recommendation] class TopKArrayAggregator(num: Int)
Review comment:
I don't love the copy-paste - are the other uses of the class above able
to use your new idea?
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