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https://issues.apache.org/jira/browse/SYSTEMML-512?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15152761#comment-15152761
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Mike Dusenberry commented on SYSTEMML-512:
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[~mboehm7] Confirmed -- the OOM issue is indeed related to the young generation
heap size. Setting -Xmn=100M with driver memory still set to 1G allows the
script to run. Is there anything we can do internally to avoid this?
For clarity to anyone else reading this, the long runtime issue is still
present.
> DML Script With UDFs Results In Out Of Memory Error As Compared to Without
> UDFs
> -------------------------------------------------------------------------------
>
> Key: SYSTEMML-512
> URL: https://issues.apache.org/jira/browse/SYSTEMML-512
> Project: SystemML
> Issue Type: Bug
> Reporter: Mike Dusenberry
> Attachments: test1.scala, test2.scala
>
>
> Currently, the following script for running a simple version of Poisson
> non-negative matrix factorization (PNMF) runs in linear time as desired:
> {code}
> # data & args
> X = read($X)
> X = X+1 # change product IDs to be 1-based, rather than 0-based
> V = table(X[,1], X[,2])
> V = V[1:$size,1:$size]
> max_iteration = as.integer($maxiter)
> rank = as.integer($rank)
> # run PNMF
> n = nrow(V)
> m = ncol(V)
> range = 0.01
> W = Rand(rows=n, cols=rank, min=0, max=range, pdf="uniform")
> H = Rand(rows=rank, cols=m, min=0, max=range, pdf="uniform")
> i=0
> while(i < max_iteration) {
> H = (H * (t(W) %*% (V/(W%*%H))))/t(colSums(W))
> W = (W * ((V/(W%*%H)) %*% t(H)))/t(rowSums(H))
> i = i + 1;
> }
> # compute negative log-likelihood
> negloglik_temp = -1 * (sum(V*log(W%*%H)) - as.scalar(colSums(W)%*%rowSums(H)))
> # write outputs
> negloglik = matrix(negloglik_temp, rows=1, cols=1)
> write(negloglik, $negloglikout)
> write(W, $Wout)
> write(H, $Hout)
> {code}
> However, a small refactoring of this same script to pull the core PNMF
> algorithm and the negative log-likelihood computation out into separate UDFs
> results in non-linear runtime and a Java out of memory heap error on the same
> dataset.
> {code}
> pnmf = function(matrix[double] V, integer max_iteration, integer rank) return
> (matrix[double] W, matrix[double] H) {
> n = nrow(V)
> m = ncol(V)
>
> range = 0.01
> W = Rand(rows=n, cols=rank, min=0, max=range, pdf="uniform")
> H = Rand(rows=rank, cols=m, min=0, max=range, pdf="uniform")
>
> i=0
> while(i < max_iteration) {
> H = (H * (t(W) %*% (V/(W%*%H))))/t(colSums(W))
> W = (W * ((V/(W%*%H)) %*% t(H)))/t(rowSums(H))
> i = i + 1;
> }
> }
> negloglikfunc = function(matrix[double] V, matrix[double] W, matrix[double]
> H) return (double negloglik) {
> negloglik = -1 * (sum(V*log(W%*%H)) - as.scalar(colSums(W)%*%rowSums(H)))
> }
> # data & args
> X = read($X)
> X = X+1 # change product IDs to be 1-based, rather than 0-based
> V = table(X[,1], X[,2])
> V = V[1:$size,1:$size]
> max_iteration = as.integer($maxiter)
> rank = as.integer($rank)
> # run PNMF and evaluate
> [W, H] = pnmf(V, max_iteration, rank)
> negloglik_temp = negloglikfunc(V, W, H)
> # write outputs
> negloglik = matrix(negloglik_temp, rows=1, cols=1)
> write(negloglik, $negloglikout)
> write(W, $Wout)
> write(H, $Hout)
> {code}
> The expectation would be that such modularization at the DML level should be
> allowed without any impact on performance.
> Details:
> - Data: Amazon product co-purchasing dataset from Stanford
> [http://snap.stanford.edu/data/amazon0601.html |
> http://snap.stanford.edu/data/amazon0601.html]
> - Execution mode: Spark {{MLContext}}, but should be applicable to
> command-line invocation as well.
> - Error message:
> {code}
> java.lang.OutOfMemoryError: Java heap space
> at
> org.apache.sysml.runtime.matrix.data.MatrixBlock.allocateDenseBlock(MatrixBlock.java:415)
> at
> org.apache.sysml.runtime.matrix.data.MatrixBlock.sparseToDense(MatrixBlock.java:1212)
> at
> org.apache.sysml.runtime.matrix.data.MatrixBlock.examSparsity(MatrixBlock.java:1103)
> at
> org.apache.sysml.runtime.instructions.cp.MatrixMatrixArithmeticCPInstruction.processInstruction(MatrixMatrixArithmeticCPInstruction.java:60)
> at
> org.apache.sysml.runtime.controlprogram.ProgramBlock.executeSingleInstruction(ProgramBlock.java:309)
> at
> org.apache.sysml.runtime.controlprogram.ProgramBlock.executeInstructions(ProgramBlock.java:227)
> at
> org.apache.sysml.runtime.controlprogram.ProgramBlock.execute(ProgramBlock.java:169)
> at
> org.apache.sysml.runtime.controlprogram.WhileProgramBlock.execute(WhileProgramBlock.java:183)
> at
> org.apache.sysml.runtime.controlprogram.FunctionProgramBlock.execute(FunctionProgramBlock.java:115)
> at
> org.apache.sysml.runtime.instructions.cp.FunctionCallCPInstruction.processInstruction(FunctionCallCPInstruction.java:177)
> at
> org.apache.sysml.runtime.controlprogram.ProgramBlock.executeSingleInstruction(ProgramBlock.java:309)
> at
> org.apache.sysml.runtime.controlprogram.ProgramBlock.executeInstructions(ProgramBlock.java:227)
> at
> org.apache.sysml.runtime.controlprogram.ProgramBlock.execute(ProgramBlock.java:169)
> at
> org.apache.sysml.runtime.controlprogram.Program.execute(Program.java:146)
> at
> org.apache.sysml.api.MLContext.executeUsingSimplifiedCompilationChain(MLContext.java:1387)
> at
> org.apache.sysml.api.MLContext.compileAndExecuteScript(MLContext.java:1252)
> at org.apache.sysml.api.MLContext.executeScript(MLContext.java:1184)
> at org.apache.sysml.api.MLContext.executeScript(MLContext.java:1165)
> at
> $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$anonfun$1.apply(<console>:113)
> at
> $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$anonfun$1.apply(<console>:103)
> at
> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
> at
> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
> at scala.collection.immutable.Range.foreach(Range.scala:141)
> at scala.collection.TraversableLike$class.map(TraversableLike.scala:244)
> at scala.collection.AbstractTraversable.map(Traversable.scala:105)
> at
> $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:103)
> at
> $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:135)
> at
> $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:137)
> at
> $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:139)
> at
> $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:141)
> at
> $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:143)
> at
> $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:145)
> {code}
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