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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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Mike Dusenberry updated SYSTEMML-1561:
--------------------------------------
Description:
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
was:
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.
> 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
> 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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