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From "Niketan Pansare" <npan...@us.ibm.com>
Subject Re: Proof of Concept: Embedded Scala DSL
Date Wed, 28 Sep 2016 19:37:04 GMT

Hi Fred,

I would consider DMLProgram as an internal AST, which could be created by
IR (or IR could just create DML). According to me, IR should be at
abstraction to allow Python/R DSL to be a thin layer. This would maximize
code reuse and minimize bugs between DSLs. Something that Felix suggested
(i.e. Matrix class) would work best.

Thanks,

Niketan Pansare
IBM Almaden Research Center
E-mail: npansar At us.ibm.com
http://researcher.watson.ibm.com/researcher/view.php?person=us-npansar



From:	Frederick R Reiss/Almaden/IBM@IBMUS
To:	dev@systemml.incubator.apache.org
Date:	09/28/2016 12:02 PM
Subject:	Re: Proof of Concept: Embedded Scala DSL



Maybe I'm missing a subtle point here, but why not refactor the existing
class org.apache.sysml.parser.DMLProgram into our common internal
representation across DSLs? This class is already sufficiently expressive
to represent any DML or PyDML program.

Fred

Niketan Pansare---09/28/2016 11:20:11 AM---Thanks Felix for the response.
+1

From: Niketan Pansare/Almaden/IBM@IBMUS
To: dev@systemml.incubator.apache.org
Date: 09/28/2016 11:20 AM
Subject: Re: Proof of Concept: Embedded Scala DSL



Thanks Felix for the response.

+1
>> For the future design I will probably make the Matrix and Vector classes

abstract which allows for different concrete implementations. We could
then have one that is backed directly by SystemML and works similar to
the Python DSL in that it just uses mock operators and builds the DML
string that is then executed using SystemML. That way the deep embedding
would reuse the shallow embedding and we could offer the user to either
use the lazy MatrixType on the Repl or write code inside the macro.

Also, I agree that we can postpone the IR and integration of different DSLs
until the work on parallelize is completed.

Thanks,

Niketan Pansare
IBM Almaden Research Center
E-mail: npansar At us.ibm.com
http://researcher.watson.ibm.com/researcher/view.php?person=us-npansar

fschueler---09/28/2016 10:54:37 AM---Hi Niketan, thanks for your
suggestions! I thought about it a bit and here are my

From: fschueler@posteo.de
To: dev@systemml.incubator.apache.org
Date: 09/28/2016 10:54 AM
Subject: Re: Proof of Concept: Embedded Scala DSL



Hi Niketan,

thanks for your suggestions! I thought about it a bit and here are my
ideas on it:

The IR you are describing is basically already my user facing API. I am
not sure how much sense it makes to have an IR that looks exactly like
the API but with control structures renamed. A common IR for all DSLs
definitely makes sense in general but I am not sure if it should be part
of one particular DSL. For maintainability it might be better to have
that IR somewhere on the SystemML side.

Apart from that and to what Matthias suggested, I thought about how to
make the DSL more suitable for using on the Repl and I think we can find
a good compromise. Currently my API is backed by breeze for rapid
prototyping where breeze just forces evaluation of every statement. For
the future design I will probably make the Matrix and Vector classes
abstract which allows for different concrete implementations. We could
then have one that is backed directly by SystemML and works similar to
the Python DSL in that it just uses mock operators and builds the DML
string that is then executed using SystemML. That way the deep embedding
would reuse the shallow embedding and we could offer the user to either
use the lazy MatrixType on the Repl or write code inside the macro.

I haven't started playing around with this idea but let me know what you
think of it. The lazy, shallow DSL would basically do what you would
want from a seperate IR, but i don't know if you want to call that from
the python DSL.

Felix

Am 24.09.2016 19:39 schrieb Niketan Pansare:
> Hi Felix,
>
> Thanks for the summary. The document is extremely useful. I
> particularly like the idea of parallelizing the code with 'breeze'
> library. I would like to pitch in few ideas which would enable your
> code to be reused by other DSLs:
> 1. Scala DSL/parallelize macro remains the same as described in your
> documentation, but instead of generating DML directly, we call an
> intermediate representation (IR). This IR then generates DML (instead
> of generating DML directly by parallelize). This IR will be then
> reused by Python DSL and R DSL.
> 2. As an example, IR could be a lazy Matrix class (which would be part
> of SystemML). It could have awkward syntax/mechanism for pushing down
> control structures for example: beginWhile and endWhile. Since IR will
> not be exposed to the end-user, it should be fine.
>
> Example:
>
https://github.com/apache/incubator-systemml/blob/master/src/main/python/systemml/defmatrix.py#L537

> [1] will call IR's add() method. At the end of parallelize or when the
> user wants result (i.e. eval() ), IR could generate DML code and
> execute it.
>
> Again, this is just a proposal and am fine dropping the idea of
> integrating different DSL if it makes the implementation of Scala DSL
> complicated. Also, please feel free to correct me if I am missing
> anything.
>
> Thanks,
>
> Niketan Pansare
> IBM Almaden Research Center
> E-mail: npansar At us.ibm.com
> http://researcher.watson.ibm.com/researcher/view.php?person=us-npansar
> [2]
>
> Matthias Boehm---09/24/2016 01:11:36 AM---thanks for sharing the
> summary - this is very nice. While looking over the example, I had the
> follow
>
> From: Matthias Boehm/Almaden/IBM@IBMUS
> To: dev@systemml.incubator.apache.org
> Date: 09/24/2016 01:11 AM
> Subject: Re: Proof of Concept: Embedded Scala DSL
>
> -------------------------
>
> thanks for sharing the summary - this is very nice. While looking over
> the example, I had the following questions:
>
> 1) Output handling: It would be great to see an example how the
> results of Algorithm.execute() are consumed. Do you intend to hand out
> our binary matrix representation or MLContext's Matrix from which the
> user then requests specific output formats? Also if there are multiple
> Algorithm instances, how is the MLContext (with its internal state of
> lazily evaluated intermediates) reused?
>
> 2) Scala-breeze prototyping: How do you intend to support operations
> that are not supported in breeze? Examples are removeEmpty, table,
> aggregate, rowIndexMax, quantile/centralmoment, cummin/cummax, and DNN
> operations?
>
> 3) Frame data type and operations: Do you also intend to add a frame
> type and its operations? I think for this initial prototype it is not
> necessarily required but please make the scope explicit.
>
> Regards,
> Matthias
>
> fschueler---09/23/2016 04:36:14 PM---As discussed in the related Jira
> (SYSTEMML-451) I have started to implement a prototype/proof of co
>
> From: fschueler@posteo.de
> To: dev@systemml.incubator.apache.org
> Date: 09/23/2016 04:36 PM
> Subject: Proof of Concept: Embedded Scala DSL
>
> -------------------------
>
> As discussed in the related Jira (SYSTEMML-451) I have started to
> implement a prototype/proof of concept for an embedded DSL in Scala.
>
> I have summarized the current approach in a short document that you
> can
> find on github together with the code:
> https://github.com/fschueler/emma/blob/sysml-dsl/emma-sysml-dsl/README.md
> [3]
> Please note that current development happens in the Emma project but
> will move to an independent module in the SystemML project once the
> necessary additions to Emma are merged. By having the DSL in a
> separate
> module, we can include Scala and Emma dependencies only for the users
> that actually want to use the Scala DSL.
>
> The current code serves as a proof of concept to discuss further
> development with the SystemML community. I especially welcome input
> from
> SystemML Scala users on the usability of the API design.
> Next steps will include the translation from Scala code to DML with
> support of all features currently supported in DML, including control
> flow structures.
> Also, a coherent way of executing the generated scripts from Scala and
>
> the interaction with outside data formats (such as Spark Dataframes)
> will be integrated.
>
> I am happy to answer your questions and discuss the described approach
>
> here!
>
> Felix
>
>
>
> Links:
> ------
> [1]
>
https://github.com/apache/incubator-systemml/blob/master/src/main/python/systemml/defmatrix.py#L537

> [2]
> http://researcher.watson.ibm.com/researcher/view.php?person=us-npansar
> [3]
> https://github.com/fschueler/emma/blob/sysml-dsl/emma-sysml-dsl/README.md








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