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https://issues.apache.org/jira/browse/SYSTEMML-1561?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Glenn Weidner updated SYSTEMML-1561:
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Fix Version/s: (was: SystemML 1.0)
SystemML 0.15
> Improve constant folding during compilation
> -------------------------------------------
>
> Key: SYSTEMML-1561
> URL: https://issues.apache.org/jira/browse/SYSTEMML-1561
> Project: SystemML
> Issue Type: Improvement
> Reporter: Mike Dusenberry
> Assignee: Mike Dusenberry
> Fix For: SystemML 0.15
>
> Attachments: scenario1_plan.txt, scenario1.py, scenario2_plan.txt,
> scenario2.py
>
>
> In our `nn` library, our convolution and pooling layers have to pass around
> the spatial dimensions (height and width) of the images that are stretched
> out into rows of the input/output matrices. These output dimensions are
> computed within the forward functions of the above layers as small scalar
> equations. From a mathematical standpoint, these sizes can be determined at
> compile time, and it is nice to have these size equations in DML (v.s. hiding
> them inside the engine within built-in functions). However, we do not
> currently evaluate these expressions during compilation, and thus we are left
> with unknown sizes even during recompilation. This naturally leads to max
> memory estimates and thus often leads to unnecessary distributed runtime ops
> rather than simple CP ones.
> I have two related scenarios for which this is a problem. They both involve
> the {{Houtc1}} & {{Woutc1}} values that are returned from a
> `conv2d::forward(...)` function. These represent the spatial dimensions of
> the volume with each of the rows of the output {{outc1}} of the function, and
> the third dimension is {{F1}}. Thus, {{outc1}} has a number of columns equal
> to {{F1*Houtc1*Wouc1}}.
> In the first scenario ({{scenario1.py}}), a random matrix {{doutc1}} is
> created that should have the same dimensions as {{outc1}}. For the columns,
> if I use {{cols=ncol(outc1)}} in this rand statement, the size will be
> propagated and CP ops will be compiled and run. I I instead use
> {{cols=F1*Houtc1*Woutc1}}, the size will forever be unknown, even during
> recompilation, and thus Spark ops will be compiled and run. I have included
> the recompile hops plan ({{scenario1_plan.txt}}).
> In the second scenario ({{scenario2.py}}), a {{max_pool2d::forward(...)}}
> function is inserted after the {{conv2d::forward(...)}} function that
> requires the {{Houtc1}} and {{Woutc1}} variables to be supplied as arguments.
> Since those latter variables are not executed during compilation time, the
> max pooling sizes remain unknown, even during recompilation, and thus Spark
> ops will be compiled and run. I have included the recompile hops plan
> ({{scenario2_plan.txt}}).
> We should either improve or fix our constant folding rewrites so that these
> scenarios are fixed, as they are necessary for performant deep learning
> applications. Note too that this issue will be present in other non-deep
> learning scenarios as well.
> Mailing list thread:
> https://www.mail-archive.com/dev@systemml.incubator.apache.org/msg01657.html
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