[
https://issues.apache.org/jira/browse/SYSTEMML-1762?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Fei Hu updated SYSTEMML-1762:
-----------------------------
Description:
When running the [distributed MNIST LeNet example |
https://github.com/apache/systemml/blob/master/scripts/nn/examples/mnist_lenet_distrib_sgd.dml],
it works well in the hybrid mode. But in the Spark mode, there are some errors
about
{{java.lang.NullPointerException}} and
{{java.lang.ArrayIndexOutOfBoundsException: 1000}} when reshaping the matrix.
The involved functions are
{{org.apache.sysml.runtime.matrix.data.LibMatrixReorg#reshapeSparse}} and
{{org.apache.sysml.runtime.matrix.data.LibMatrixReorg#reshapeDense(org.apache.sysml.runtime.matrix.data.MatrixBlock,
long, long,
java.util.HashMap<org.apache.sysml.runtime.matrix.data.MatrixIndexes,org.apache.sysml.runtime.matrix.data.MatrixBlock>,
long, long, long, long, int, int, boolean)}}. The reason is that the output
matrix index computed by
{{org.apache.sysml.runtime.matrix.data.LibMatrixReorg#computeResultBlockIndex}}
does not match the keys in the {{HashMap<MatrixIndexes,MatrixBlock> rix}}.
To reproduce the error, the attached scala file {{MNIST_Distrib_Sgd.scala}}
could be used to run the distributed MNIST example.
In addition, if adding some codes to ignore the null output matrix block from
{{MatrixBlock out = rix.get(ixtmp)}}, the distributed MNIST example could run
in the Spark mode, but the result may not be right.
was:
When running the [distributed MNIST LeNet example |
https://github.com/apache/systemml/blob/master/scripts/nn/examples/mnist_lenet_distrib_sgd.dml],
it works well in the hybrid mode. But in the Spark mode, there are some errors
about
{{java.lang.NullPointerException}} and
{{java.lang.ArrayIndexOutOfBoundsException: 1000}} when reshaping the sparse
matrix. The involved function is
{{org.apache.sysml.runtime.matrix.data.LibMatrixReorg#reshapeSparse}} . The
reason is that the output matrix index computed by
{{org.apache.sysml.runtime.matrix.data.LibMatrixReorg#computeResultBlockIndex}}
does not match the keys in the {{HashMap<MatrixIndexes,MatrixBlock> rix}}.
To reproduce the error, the attached scala file {{MNIST_Distrib_Sgd.scala}}
could be used to run the distributed MNIST example.
In addition, if adding some codes to ignore the null output matrix block from
{{MatrixBlock out = rix.get(ixtmp)}}, the distributed MNIST example could run
in the Spark mode, but the result may not be right.
> Fix the matrix reshape function for the Spark mode
> --------------------------------------------------
>
> Key: SYSTEMML-1762
> URL: https://issues.apache.org/jira/browse/SYSTEMML-1762
> Project: SystemML
> Issue Type: Bug
> Components: Algorithms, ParFor, Runtime
> Reporter: Fei Hu
> Assignee: Fei Hu
> Attachments: MNIST_Distrib_Sgd.scala
>
>
> When running the [distributed MNIST LeNet example |
> https://github.com/apache/systemml/blob/master/scripts/nn/examples/mnist_lenet_distrib_sgd.dml],
> it works well in the hybrid mode. But in the Spark mode, there are some
> errors about
> {{java.lang.NullPointerException}} and
> {{java.lang.ArrayIndexOutOfBoundsException: 1000}} when reshaping the matrix.
> The involved functions are
> {{org.apache.sysml.runtime.matrix.data.LibMatrixReorg#reshapeSparse}} and
> {{org.apache.sysml.runtime.matrix.data.LibMatrixReorg#reshapeDense(org.apache.sysml.runtime.matrix.data.MatrixBlock,
> long, long,
> java.util.HashMap<org.apache.sysml.runtime.matrix.data.MatrixIndexes,org.apache.sysml.runtime.matrix.data.MatrixBlock>,
> long, long, long, long, int, int, boolean)}}. The reason is that the output
> matrix index computed by
> {{org.apache.sysml.runtime.matrix.data.LibMatrixReorg#computeResultBlockIndex}}
> does not match the keys in the {{HashMap<MatrixIndexes,MatrixBlock> rix}}.
> To reproduce the error, the attached scala file {{MNIST_Distrib_Sgd.scala}}
> could be used to run the distributed MNIST example.
> In addition, if adding some codes to ignore the null output matrix block from
> {{MatrixBlock out = rix.get(ixtmp)}}, the distributed MNIST example could
> run in the Spark mode, but the result may not be right.
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