llvm dev - Oct 2019 - Google’s TensorFlow team would like to contribute MLIR to the LLVM Foundation

Hi all,

The TensorFlow team at Google has been leading the charge to build a new set of
compiler infrastructure, known as the MLIR project
<https://github.com/tensorflow/mlir>.  The initial focus has been on
machine learning infrastructure, high performance accelerators, heterogeneous
compute, and HPC-style computations.  That said, the implementation and design
of this infrastructure is state of the art, is not specific to these
applications, and is already being adopted (e.g.) by the Flang compiler
<https://llvm.org/devmtg/2019-10/talk-abstracts.html#tech19>.  If you are
interested in learning more about MLIR and the technical design, I’d encourage
you to look at the MLIR Keynote and Tutorial at the last LLVM Developer Meeting
<http://llvm.org/devmtg/2019-04/>.

MLIR is already open source on GitHub
<https://medium.com/tensorflow/mlir-a-new-intermediate-representation-and-compiler-framework-beba999ed18d>,
and includes a significant amount of code in two repositories.  “MLIR Core” is
located in github/tensorflow/mlir <https://github.com/tensorflow/mlir>,
including an application independent IR, the code generation infrastructure,
common graph transformation infrastructure, declarative operation definition and
rewrite infrastructure, polyhedral transformations etc.  The primary TensorFlow
repository at github/tensorflow/tensorflow
<https://github.com/tensorflow/tensorflow/> contains TensorFlow-specific
functionality built using MLIR Core infrastructure.

In discussions with a large number of industry partners
<https://blog.google/technology/ai/mlir-accelerating-ai-open-source-infrastructure/>,
we’ve achieved consensus that it would be best to build a shared ML compiler
infrastructure under a common umbrella with well known neutral governance.  As
such, we’d like to propose that MLIR Core join the non-profit LLVM Foundation as
a new subproject! We plan to follow the LLVM Developer Policy
<http://llvm.org/docs/DeveloperPolicy.html>, and have been following an
LLVM-style development process from the beginning - including all relevant
coding and testing styles, and we build on core LLVM infrastructure pervasively.

We think that MLIR is a nice complement to existing LLVM functionality,
providing common infrastructure for higher level optimization and transformation
problems, and dovetails naturally with LLVM IR optimizations and code
generation.  Please let us know if you have any thoughts, questions, or
concerns!

-Chris

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Hi, Chris, et al.,

I support adding MLIR as an LLVM subproject. Here are my thoughts:

 1. MLIR uses LLVM. LLVM is one of the MLIR dialects, MLIR is compiler
infrastructure, and it fits as a natural part of our ecosystem.

 2. As a community, we have a lot of different LLVM frontends, many of which
have their own IRs on which higher-level transformations are performed. We
don't currently offer much, in terms of infrastructure, to support the
development of these pre-LLVM transformations. MLIR provides a base on which
many of these kinds of implementations can be constructed, and I believe that
will add value to the overall ecosystem.

 3. As a specific example of the above, the current development of the new Flang
compiler depends on MLIR. Flang is becoming a subproject of LLVM and MLIR should
be part of LLVM.

 4. The MLIR project has developed capabilities, such as for the analysis of
multidimensional loops, that can be moved into LLVM and used by both LLVM- and
MLIR-level transformations. As we work to improve LLVM's capabilities in
loop optimizations, leveraging continuing work to improve MLIR's loop
capabilities in LLVM as well will benefit many of us.

 5. As a community, we have been moving toward increasing support for
heterogeneous computing and accelerators (and given industry trends, I expect
this to continue), and MLIR can facilitate that support in many cases (although
I expect we'll see further enhancements in the core LLVM libraries as well).

That all having been said, I think that it's going to be very important to
develop some documentation on how a frontend author looking to use LLVM backend
technology, and a developer looking to implement different kinds of
functionality, might reasonably choose whether to target or enhance MLIR
components, LLVM components, or both. I expect that this kind of advice will
evolve over time, but I'm sure we'll need it sooner rather than later.

Thanks again,

Hal

On 9/9/19 10:30 AM, Chris Lattner via llvm-dev wrote:
Hi all,

The TensorFlow team at Google has been leading the charge to build a new set of
compiler infrastructure, known as the MLIR
project<https://github.com/tensorflow/mlir>.  The initial focus has been
on machine learning infrastructure, high performance accelerators, heterogeneous
compute, and HPC-style computations.  That said, the implementation and design
of this infrastructure is state of the art, is not specific to these
applications, and is already being adopted (e.g.) by the Flang
compiler<https://llvm.org/devmtg/2019-10/talk-abstracts.html#tech19>.  If
you are interested in learning more about MLIR and the technical design, I’d
encourage you to look at the MLIR Keynote and Tutorial at the last LLVM
Developer Meeting<http://llvm.org/devmtg/2019-04/>.

MLIR is already open source on
GitHub<https://medium.com/tensorflow/mlir-a-new-intermediate-representation-and-compiler-framework-beba999ed18d>,
and includes a significant amount of code in two repositories.  “MLIR Core” is
located in github/tensorflow/mlir<https://github.com/tensorflow/mlir>,
including an application independent IR, the code generation infrastructure,
common graph transformation infrastructure, declarative operation definition and
rewrite infrastructure, polyhedral transformations etc.  The primary TensorFlow
repository at
github/tensorflow/tensorflow<https://github.com/tensorflow/tensorflow/>
contains TensorFlow-specific functionality built using MLIR Core infrastructure.

In discussions with a large number of industry
partners<https://blog.google/technology/ai/mlir-accelerating-ai-open-source-infrastructure/>,
we’ve achieved consensus that it would be best to build a shared ML compiler
infrastructure under a common umbrella with well known neutral governance.  As
such, we’d like to propose that MLIR Core join the non-profit LLVM Foundation as
a new subproject! We plan to follow the LLVM Developer
Policy<http://llvm.org/docs/DeveloperPolicy.html>, and have been following
an LLVM-style development process from the beginning - including all relevant
coding and testing styles, and we build on core LLVM infrastructure pervasively.

We think that MLIR is a nice complement to existing LLVM functionality,
providing common infrastructure for higher level optimization and transformation
problems, and dovetails naturally with LLVM IR optimizations and code
generation.  Please let us know if you have any thoughts, questions, or
concerns!

-Chris




_______________________________________________
LLVM Developers mailing list
llvm-dev at lists.llvm.org<mailto:llvm-dev at lists.llvm.org>
https://lists.llvm.org/cgi-bin/mailman/listinfo/llvm-dev


--
Hal Finkel
Lead, Compiler Technology and Programming Languages
Leadership Computing Facility
Argonne National Laboratory
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Overall, I think it will be a good move.

Maintenance wise, I'm expecting the existing community to move into
LLVM (if not all in already), so I don't foresee any additional costs.

Though, Hal's points are spot on...

On Mon, 9 Sep 2019 at 18:47, Finkel, Hal J. via llvm-dev
<llvm-dev at lists.llvm.org> wrote:
>  3. As a specific example of the above, the current development of the new
Flang compiler depends on MLIR.
Who knows, one day, Clang can, too! :)
>  5. As a community, we have been moving toward increasing support for
heterogeneous computing and accelerators (and given industry trends, I expect
this to continue), and MLIR can facilitate that support in many cases (although
I expect we'll see further enhancements in the core LLVM libraries as well).
Yes, and yes! MLIR can become a simpler entry point into LLVM, from
other languages, frameworks and optimisation plugins. A more abstract
representation and a more stable IR generation from it, could make
maintenance of external projects much easier than direct connections
of today. This could benefit research as much as enterprise, and by
consequence, the LLVM project.
> That all having been said, I think that it's going to be very important
to develop some documentation on how a frontend author looking to use LLVM
backend technology, and a developer looking to implement different kinds of
functionality, might reasonably choose whether to target or enhance MLIR
components, LLVM components, or both. I expect that this kind of advice will
evolve over time, but I'm sure we'll need it sooner rather than later.
Right, I'm also worried that it's too broad in respect to what it can
do on paper, versus what LLVM can handle on code.

With MLIR as a separate project, that point is interesting, at most.
When it becomes part of the LLVM umbrella, then we need to make sure
that MLIR and LLVM IR interact within known boundaries and expected
behaviour.

I'm not saying MLIR can't be used for anything else after the move,
just saying that, by being inside the repo, and maintained by our
community, LLVM IR would end up as the *primary* target, and there
will be a minimum stability/functionality requirements.

But perhaps more importantly, as Hal states clearly, is the need for
an official specification, similar to the one for LLVM IR, as well as
a formal document with the expected semantics into LLVM IR. Sooner,
indeed.

cheers,
--renato

On behalf of the LLVM Foundation board of directors, we accept MLIR as a project
into LLVM. This is based upon the responses that the community is supportive and
is in favor of this. We will provide services and support on our side.

Welcome MLIR!

Thanks,
Tanya Lattner
President, LLVM Foundation
> On Sep 9, 2019, at 8:30 AM, Chris Lattner via llvm-dev <llvm-dev at
lists.llvm.org> wrote:
> 
> Hi all,
> 
> The TensorFlow team at Google has been leading the charge to build a new
set of compiler infrastructure, known as the MLIR project
<https://github.com/tensorflow/mlir>.  The initial focus has been on
machine learning infrastructure, high performance accelerators, heterogeneous
compute, and HPC-style computations.  That said, the implementation and design
of this infrastructure is state of the art, is not specific to these
applications, and is already being adopted (e.g.) by the Flang compiler
<https://llvm.org/devmtg/2019-10/talk-abstracts.html#tech19>.  If you are
interested in learning more about MLIR and the technical design, I’d encourage
you to look at the MLIR Keynote and Tutorial at the last LLVM Developer Meeting
<http://llvm.org/devmtg/2019-04/>.
> 
> MLIR is already open source on GitHub
<https://medium.com/tensorflow/mlir-a-new-intermediate-representation-and-compiler-framework-beba999ed18d>,
and includes a significant amount of code in two repositories.  “MLIR Core” is
located in github/tensorflow/mlir <https://github.com/tensorflow/mlir>,
including an application independent IR, the code generation infrastructure,
common graph transformation infrastructure, declarative operation definition and
rewrite infrastructure, polyhedral transformations etc.  The primary TensorFlow
repository at github/tensorflow/tensorflow
<https://github.com/tensorflow/tensorflow/> contains TensorFlow-specific
functionality built using MLIR Core infrastructure.
> 
> In discussions with a large number of industry partners
<https://blog.google/technology/ai/mlir-accelerating-ai-open-source-infrastructure/>,
we’ve achieved consensus that it would be best to build a shared ML compiler
infrastructure under a common umbrella with well known neutral governance.  As
such, we’d like to propose that MLIR Core join the non-profit LLVM Foundation as
a new subproject! We plan to follow the LLVM Developer Policy
<http://llvm.org/docs/DeveloperPolicy.html>, and have been following an
LLVM-style development process from the beginning - including all relevant
coding and testing styles, and we build on core LLVM infrastructure pervasively.
> 
> We think that MLIR is a nice complement to existing LLVM functionality,
providing common infrastructure for higher level optimization and transformation
problems, and dovetails naturally with LLVM IR optimizations and code
generation.  Please let us know if you have any thoughts, questions, or
concerns!
> 
> -Chris
> 
> _______________________________________________
> LLVM Developers mailing list
> llvm-dev at lists.llvm.org
> https://lists.llvm.org/cgi-bin/mailman/listinfo/llvm-dev
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Fantastic, thank you!

-Chris
> On Oct 7, 2019, at 1:17 AM, Tanya Lattner via llvm-dev <llvm-dev at
lists.llvm.org> wrote:
> 
> On behalf of the LLVM Foundation board of directors, we accept MLIR as a
project into LLVM. This is based upon the responses that the community is
supportive and is in favor of this. We will provide services and support on our
side.
> 
> Welcome MLIR!
> 
> Thanks,
> Tanya Lattner
> President, LLVM Foundation
> 
>> On Sep 9, 2019, at 8:30 AM, Chris Lattner via llvm-dev <llvm-dev at
lists.llvm.org <mailto:llvm-dev at lists.llvm.org>> wrote:
>> 
>> Hi all,
>> 
>> The TensorFlow team at Google has been leading the charge to build a
new set of compiler infrastructure, known as the MLIR project
<https://github.com/tensorflow/mlir>.  The initial focus has been on
machine learning infrastructure, high performance accelerators, heterogeneous
compute, and HPC-style computations.  That said, the implementation and design
of this infrastructure is state of the art, is not specific to these
applications, and is already being adopted (e.g.) by the Flang compiler
<https://llvm.org/devmtg/2019-10/talk-abstracts.html#tech19>.  If you are
interested in learning more about MLIR and the technical design, I’d encourage
you to look at the MLIR Keynote and Tutorial at the last LLVM Developer Meeting
<http://llvm.org/devmtg/2019-04/>.
>> 
>> MLIR is already open source on GitHub
<https://medium.com/tensorflow/mlir-a-new-intermediate-representation-and-compiler-framework-beba999ed18d>,
and includes a significant amount of code in two repositories.  “MLIR Core” is
located in github/tensorflow/mlir <https://github.com/tensorflow/mlir>,
including an application independent IR, the code generation infrastructure,
common graph transformation infrastructure, declarative operation definition and
rewrite infrastructure, polyhedral transformations etc.  The primary TensorFlow
repository at github/tensorflow/tensorflow
<https://github.com/tensorflow/tensorflow/> contains TensorFlow-specific
functionality built using MLIR Core infrastructure.
>> 
>> In discussions with a large number of industry partners
<https://blog.google/technology/ai/mlir-accelerating-ai-open-source-infrastructure/>,
we’ve achieved consensus that it would be best to build a shared ML compiler
infrastructure under a common umbrella with well known neutral governance.  As
such, we’d like to propose that MLIR Core join the non-profit LLVM Foundation as
a new subproject! We plan to follow the LLVM Developer Policy
<http://llvm.org/docs/DeveloperPolicy.html>, and have been following an
LLVM-style development process from the beginning - including all relevant
coding and testing styles, and we build on core LLVM infrastructure pervasively.
>> 
>> We think that MLIR is a nice complement to existing LLVM functionality,
providing common infrastructure for higher level optimization and transformation
problems, and dovetails naturally with LLVM IR optimizations and code
generation.  Please let us know if you have any thoughts, questions, or
concerns!
>> 
>> -Chris
>> 
>> _______________________________________________
>> LLVM Developers mailing list
>> llvm-dev at lists.llvm.org <mailto:llvm-dev at lists.llvm.org>
>> https://lists.llvm.org/cgi-bin/mailman/listinfo/llvm-dev
> 
> _______________________________________________
> LLVM Developers mailing list
> llvm-dev at lists.llvm.org
> https://lists.llvm.org/cgi-bin/mailman/listinfo/llvm-dev
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Fantastic news, indeed! Thank you for accepting MLIR as an LLVM project!

Tatiana

On Mon, Oct 7, 2019 at 9:16 PM Reid Tatge <tatge at google.com> wrote:
> This is great news!   Congratulations everyone!
>
> On Mon, Oct 7, 2019 at 9:14 PM Tatiana Shpeisman <shpeisman at
google.com>
> wrote:
>
>> Congratulations, everybody!
>>
>> On Mon, Oct 7, 2019 at 3:55 PM Chris Lattner <clattner at
google.com> wrote:
>>
>>> Fantastic, thank you!
>>>
>>> -Chris
>>>
>>> On Oct 7, 2019, at 1:17 AM, Tanya Lattner via llvm-dev <
>>> llvm-dev at lists.llvm.org> wrote:
>>>
>>> On behalf of the LLVM Foundation board of directors, we accept MLIR
as a
>>> project into LLVM. This is based upon the responses that the
community is
>>> supportive and is in favor of this. We will provide services and
support on
>>> our side.
>>>
>>> Welcome MLIR!
>>>
>>> Thanks,
>>> Tanya Lattner
>>> President, LLVM Foundation
>>>
>>> On Sep 9, 2019, at 8:30 AM, Chris Lattner via llvm-dev <
>>> llvm-dev at lists.llvm.org> wrote:
>>>
>>> Hi all,
>>>
>>> The TensorFlow team at Google has been leading the charge to build
a new
>>> set of compiler infrastructure, known as the MLIR project
>>> <https://github.com/tensorflow/mlir>.  The initial focus has
been on
>>> machine learning infrastructure, high performance accelerators,
>>> heterogeneous compute, and HPC-style computations.  That said, the
>>> implementation and design of this infrastructure is state of the
art, is
>>> not specific to these applications, and is already being adopted
(e.g.) by the
>>> Flang compiler
>>> <https://llvm.org/devmtg/2019-10/talk-abstracts.html#tech19>.
If you
>>> are interested in learning more about MLIR and the technical
design, I’d
>>> encourage you to look at the MLIR Keynote and Tutorial at the last
LLVM
>>> Developer Meeting <http://llvm.org/devmtg/2019-04/>.
>>>
>>> MLIR is already open source on GitHub
>>>
<https://medium.com/tensorflow/mlir-a-new-intermediate-representation-and-compiler-framework-beba999ed18d>,
>>> and includes a significant amount of code in two repositories. 
“MLIR Core”
>>> is located in github/tensorflow/mlir
>>> <https://github.com/tensorflow/mlir>, including an
application
>>> independent IR, the code generation infrastructure, common graph
>>> transformation infrastructure, declarative operation definition and
rewrite
>>> infrastructure, polyhedral transformations etc.  The primary
TensorFlow
>>> repository at github/tensorflow/tensorflow
>>> <https://github.com/tensorflow/tensorflow/> contains
>>> TensorFlow-specific functionality built using MLIR Core
infrastructure.
>>>
>>> In discussions with a large number of industry partners
>>>
<https://blog.google/technology/ai/mlir-accelerating-ai-open-source-infrastructure/>,
>>> we’ve achieved consensus that it would be best to build a shared ML
>>> compiler infrastructure under a common umbrella with well known
neutral
>>> governance.  As such, we’d like to propose that MLIR Core join the
>>> non-profit LLVM Foundation as a new subproject! We plan to follow
the LLVM
>>> Developer Policy <http://llvm.org/docs/DeveloperPolicy.html>,
and have
>>> been following an LLVM-style development process from the beginning
-
>>> including all relevant coding and testing styles, and we build on
core LLVM
>>> infrastructure pervasively.
>>>
>>> We think that MLIR is a nice complement to existing LLVM
functionality,
>>> providing common infrastructure for higher level optimization and
>>> transformation problems, and dovetails naturally with LLVM IR
optimizations
>>> and code generation.  Please let us know if you have any thoughts,
>>> questions, or concerns!
>>>
>>> -Chris
>>>
>>> _______________________________________________
>>> LLVM Developers mailing list
>>> llvm-dev at lists.llvm.org
>>> https://lists.llvm.org/cgi-bin/mailman/listinfo/llvm-dev
>>>
>>>
>>> _______________________________________________
>>> LLVM Developers mailing list
>>> llvm-dev at lists.llvm.org
>>> https://lists.llvm.org/cgi-bin/mailman/listinfo/llvm-dev
>>>
>>>
>>>
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