[GitHub] [spark] srowen commented on a change in pull request #26722: [SPARK-24666][ML] Fix infinity vectors produced by Word2Vec when numIterations are large
srowen commented on a change in pull request #26722: [SPARK-24666][ML] Fix
infinity vectors produced by Word2Vec when numIterations are large
URL: https://github.com/apache/spark/pull/26722#discussion_r353439839
##
File path: mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala
##
@@ -439,9 +439,21 @@ class Word2Vec extends Serializable with Logging {
}
}.flatten
}
- val synAgg = partial.reduceByKey { case (v1, v2) =>
- blas.saxpy(vectorSize, 1.0f, v2, 1, v1, 1)
- v1
+ // SPARK-24666: do normalization for aggregating weights from partitions.
+ // Original Word2Vec either single-thread or multi-thread which do
Hogwild-style aggregation.
+ // Our approach needs to do extra normalization, otherwise adding
weights continuously may
+ // cause overflow on float and lead to infinity/-infinity weights.
+ val synAgg = partial.mapPartitions { iter =>
+iter.map { case (id, vec) =>
+ (id, (vec, 1))
+}
+ }.reduceByKey { case ((v1, count1), (v2, count2)) =>
+blas.saxpy(vectorSize, 1.0f, v2, 1, v1, 1)
+(v1, count1 + count2)
+ }.map { case (id, (vec, count)) =>
+val averagedVec = Array.fill[Float](vectorSize)(0.0f)
+blas.saxpy(vectorSize, 1.0f / count, vec, 1, averagedVec, 1)
Review comment:
Yes right. I'm wondering if that's a good thing. Well, this is just done
once for each aggregate so hardly matters.
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[GitHub] [spark] srowen commented on a change in pull request #26722: [SPARK-24666][ML] Fix infinity vectors produced by Word2Vec when numIterations are large
srowen commented on a change in pull request #26722: [SPARK-24666][ML] Fix
infinity vectors produced by Word2Vec when numIterations are large
URL: https://github.com/apache/spark/pull/26722#discussion_r353159435
##
File path: mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala
##
@@ -439,9 +439,21 @@ class Word2Vec extends Serializable with Logging {
}
}.flatten
}
- val synAgg = partial.reduceByKey { case (v1, v2) =>
- blas.saxpy(vectorSize, 1.0f, v2, 1, v1, 1)
- v1
+ // SPARK-24666: do normalization for aggregating weights from partitions.
+ // Original Word2Vec either single-thread or multi-thread which do
Hogwild-style aggregation.
+ // Our approach needs to do extra normalization, otherwise adding
weights continuously may
+ // cause overflow on float and lead to infinity/-infinity weights.
+ val synAgg = partial.mapPartitions { iter =>
+iter.map { case (id, vec) =>
+ (id, (vec, 1))
+}
+ }.reduceByKey { case ((v1, count1), (v2, count2)) =>
+blas.saxpy(vectorSize, 1.0f, v2, 1, v1, 1)
+(v1, count1 + count2)
+ }.map { case (id, (vec, count)) =>
+val averagedVec = Array.fill[Float](vectorSize)(0.0f)
+blas.saxpy(vectorSize, 1.0f / count, vec, 1, averagedVec, 1)
Review comment:
Can this just use sscal, in place, or does it need a copy? it seems like it
can just modify in place but I may be forgetting the semantics here.
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[GitHub] [spark] srowen commented on a change in pull request #26722: [SPARK-24666][ML] Fix infinity vectors produced by Word2Vec when numIterations are large
srowen commented on a change in pull request #26722: [SPARK-24666][ML] Fix
infinity vectors produced by Word2Vec when numIterations are large
URL: https://github.com/apache/spark/pull/26722#discussion_r352943063
##
File path: mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala
##
@@ -438,11 +438,23 @@ class Word2Vec extends Serializable with Logging {
None
}
}.flatten
- }
- val synAgg = partial.reduceByKey { case (v1, v2) =>
- blas.saxpy(vectorSize, 1.0f, v2, 1, v1, 1)
- v1
+ }.persist()
+ // SPARK-24666: do normalization for aggregating weights from partitions.
+ // Original Word2Vec either single-thread or multi-thread which do
Hogwild-style aggregation.
+ // Our approach needs to do extra normalization, otherwise adding
weights continuously may
+ // cause overflow on float and lead to infinity/-infinity weights.
+ val keyCounts = partial.countByKey()
+ val synAgg = partial.mapPartitions { iter =>
+iter.map { case (id, vec) =>
+ val v1 = Array.fill[Float](vectorSize)(0.0f)
+ blas.saxpy(vectorSize, 1.0f / keyCounts(id), vec, 1, v1, 1)
+ (id, v1)
+}
+ }.reduceByKey { case (v1, v2) =>
Review comment:
I haven't though it through, but yeah if you can normalize between
iterations it should be OK. otherwise yeah you have to compute some kind of
running average, which isn't super hard.
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[GitHub] [spark] srowen commented on a change in pull request #26722: [SPARK-24666][ML] Fix infinity vectors produced by Word2Vec when numIterations are large
srowen commented on a change in pull request #26722: [SPARK-24666][ML] Fix
infinity vectors produced by Word2Vec when numIterations are large
URL: https://github.com/apache/spark/pull/26722#discussion_r352933894
##
File path: mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala
##
@@ -438,11 +438,23 @@ class Word2Vec extends Serializable with Logging {
None
}
}.flatten
- }
- val synAgg = partial.reduceByKey { case (v1, v2) =>
- blas.saxpy(vectorSize, 1.0f, v2, 1, v1, 1)
- v1
+ }.persist()
+ // SPARK-24666: do normalization for aggregating weights from partitions.
+ // Original Word2Vec either single-thread or multi-thread which do
Hogwild-style aggregation.
+ // Our approach needs to do extra normalization, otherwise adding
weights continuously may
+ // cause overflow on float and lead to infinity/-infinity weights.
+ val keyCounts = partial.countByKey()
+ val synAgg = partial.mapPartitions { iter =>
+iter.map { case (id, vec) =>
+ val v1 = Array.fill[Float](vectorSize)(0.0f)
+ blas.saxpy(vectorSize, 1.0f / keyCounts(id), vec, 1, v1, 1)
+ (id, v1)
+}
+ }.reduceByKey { case (v1, v2) =>
Review comment:
What if you emit `(id, v1, 1)` above and then sum those 1s as a count, and
then divide through after `reduceByKey`? I think it's _possible_, just not 100%
sure it's the right thing to do. But sounds quite plausible.
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[GitHub] [spark] srowen commented on a change in pull request #26722: [SPARK-24666][ML] Fix infinity vectors produced by Word2Vec when numIterations are large
srowen commented on a change in pull request #26722: [SPARK-24666][ML] Fix
infinity vectors produced by Word2Vec when numIterations are large
URL: https://github.com/apache/spark/pull/26722#discussion_r352933894
##
File path: mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala
##
@@ -438,11 +438,23 @@ class Word2Vec extends Serializable with Logging {
None
}
}.flatten
- }
- val synAgg = partial.reduceByKey { case (v1, v2) =>
- blas.saxpy(vectorSize, 1.0f, v2, 1, v1, 1)
- v1
+ }.persist()
+ // SPARK-24666: do normalization for aggregating weights from partitions.
+ // Original Word2Vec either single-thread or multi-thread which do
Hogwild-style aggregation.
+ // Our approach needs to do extra normalization, otherwise adding
weights continuously may
+ // cause overflow on float and lead to infinity/-infinity weights.
+ val keyCounts = partial.countByKey()
+ val synAgg = partial.mapPartitions { iter =>
+iter.map { case (id, vec) =>
+ val v1 = Array.fill[Float](vectorSize)(0.0f)
+ blas.saxpy(vectorSize, 1.0f / keyCounts(id), vec, 1, v1, 1)
+ (id, v1)
+}
+ }.reduceByKey { case (v1, v2) =>
Review comment:
What if you emit `(id, (v1, 1))` above and then sum those 1s as a count, and
then divide through after `reduceByKey`? I think it's _possible_, just not 100%
sure it's the right thing to do. But sounds quite plausible.
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