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https://issues.apache.org/jira/browse/SYSTEMML-652?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15260484#comment-15260484
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Mike Dusenberry edited comment on SYSTEMML-652 at 4/27/16 5:04 PM:
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[~mboehm7] Yeah I encountered this with the latest master yesterday. Thanks
for looking into it!
I definitely think we need to allow for function calls in expressions, and I
think we also need to place more focus on the integration of functions with the
rest of the optimizer - i.e. to avoid bugs such as this one, as well as to
apply valid rewrites such as the update-in-place in SYSTEMML-633 to functions.
Otherwise, it makes it quite difficult to build libraries that have any
reasonable performance, and I think that hurts the project and the idea of
declarative ML overall.
was (Author: mwdus...@us.ibm.com):
[~mboehm7], yeah I encountered this with the latest master yesterday. Glad
that the problem was determined.
I definitely think we need to allow for function calls in expressions, and I
think we also need to place more focus on the integration of functions with the
rest of the optimizer - i.e. to avoid bugs such as this one, as well as to
apply valid rewrites such as the update-in-place in SYSTEMML-633 to functions.
Otherwise, it makes it quite difficult to build libraries that have any
reasonable performance, and I think that hurts the project and the idea of
declarative ML overall.
> Left-Indexing With Result of DML Function Changes Matrix Size
> -------------------------------------------------------------
>
> Key: SYSTEMML-652
> URL: https://issues.apache.org/jira/browse/SYSTEMML-652
> Project: SystemML
> Issue Type: Bug
> Components: Compiler
> Affects Versions: SystemML 0.9, SystemML 0.10
> Reporter: Mike Dusenberry
>
> I've found a bug in which assigning the result of a DML function to a portion
> of a matrix with left-indexing results in the left-hand matrix being reduced
> in size dimensionally. This bug was encountered while working on the deep
> learning DML library, and the following simplified example aims to provide a
> simple, reproducible example.
> Given the following code,
> {code}
> N = 3
> M = 5
> forward = function(matrix[double] X) return (matrix[double] out) {
> out = 1 / (1 + exp(-X))
> }
> X = rand(rows=N, cols=M)
> X[,1:2] = forward(X[,1:2])
> print("X1: " + nrow(X) + "x" + ncol(X))
> if(1==1){}
> X = rand(rows=N, cols=M)
> temp = forward(X[,1:2])
> X[,1:2] = temp
> print("X2: " + nrow(X) + "x" + ncol(X))
> if(1==1){}
> print("")
> {code}
> , notice that {{X}} should always be a {{3x5}} matrix, as both cases are
> equivalent. However, in the first case, {{X}} is truncated to a {{3x2}}
> matrix:
> {code}
> X1: 3x2
> X2: 3x5
> {code}
> Note: The {{if(1==1){}}} statements are included because otherwise the print
> statements are executed out of order.
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