Re: Guidelines for pre-trained ML model weight binaries (Was re: Where should we put machine learning model parameters?)

[Simon Tournier <zimon.toutoune@gmail.com>]

Hi,

On Sun, 02 Jul 2023 at 21:51, Ludovic Courtès <ludo@gnu.org> wrote:

> Someone™ has to invest time in studying this specific case, look at what
> others like Debian are doing, and seek consensus on a way forward.

Hum, I am probably not this Someone™ but here the result of my looks. :-)


First, please note that the Debian thread [1] is about,

    Concerns to software freedom when packaging deep-learning based appications

and not about specifically Leela Zero.  The thread asked the general
question keeping in mind the packaging of leela-zero [2] – now available
with Debian [3].  Back on 2019, patch#36071 [4] also introduced
leela-zero in Guix.  The issues about Machine Learning that this package
raises are not gone, although this specific package had been included,
because we are lacking some guideline. :-) Quoting the Debian package
description [3], it reads,

        Recomputing the AlphaGo Zero weights will take about 1700 years
        on commodity hardware. Upstream is running a public, distributed
        effort to repeat this work. Working together, and especially
        when starting on a smaller scale, it will take less than 1700
        years to get a good network (which you can feed into this
        program, suddenly making it strong). To help with this effort,
        run the leelaz-autogtp binary provided in this package. The
        best-known network weights file is at
        http://zero.sjeng.org/best-network

For instance, this message [5] from patch#36071,

        We need to ensure that the software necessary to train the
        networks is included.  Is this the case?

        Back to this patch: I think it’s fine to accept it as long as the
        software necessary for training is included.

would suggest that the training software should be part of the package
for inclusion although it would not be affordable to recompute this
training.  Well, applying this “criteria”, then GNU Backgamon (package
gnubg included since a while) should be rejected since there is no
training software for the neural network weights.  For the inclusion of
leela-zero in Guix, the argument is from [6] quoting [7]:

            So this is basically a distributed computing client as well as a
            Go engine that runs with the results of that distributed
            computing effort.

        If that's true, there is no separate ‘training software’ to
        worry about.

which draws the line: free client vs the “database”; as pointed in [8].

Somehow, we have to distinguish cases depending on the weights.

    If the weights are clearly separated, as with leela-zero, then the
    code (neural network) itself can be included.  Else if the weights
    are tangled with the code, then we distribute them only if their
    license is compliant with FSDG as any other data, as with GNU
    Backgamon, IIUC.

Well, I do not see any difference between pre-trained weights and icons
or sound or good fitted-parameters (e.g., the package
python-scikit-learn has a lot ;-)).  As I said elsewhere, I do not see
the difference between pre-trained neural network weights and genomic
references (e.g., the package r-bsgenome-hsapiens-1000genomes-hs37d5).

The only question for inclusion or not is about the license, IMHO.  For
sure, it is far better if we are able to recompute the weights.
However, similarly as debootstrapping is a strong recommendation, the
ability to recompute the pre-trained neural network weights must just be
a recommendation.

Please note this message [11] from Nicolas about VOSK models and
patch#62443 [12] already merged; the weights are separated as with the
package leela-zero.

All that said.  Second, please note Debian thread dates from 2018, as
well as the LWN article [13]; and I am not aware of something new since
then.  Third, I have never read something on this topic produced by GNU
or related; and the fact that GNU Backgamon [14] distributes the weights
without the way to reproduce them draws one line.  Fourth, we – at least
I – are still waiting an answer from licensing@fsf.org; on FSF side, I
am only aware about this [15] and also these white papers [16] about the
very specific case of Copilot.

On Debian side, I am only aware of [16,17]:

    Unofficial Policy for Debian & Machine Learning


1: https://lists.debian.org/debian-devel/2018/07/msg00153.html
2: https://bugs.debian.org/cgi-bin/bugreport.cgi?bug=903634
3: https://packages.debian.org/trixie/leela-zero
4: https://issues.guix.gnu.org/36071
5: https://issues.guix.gnu.org/36071#5
6: https://issues.guix.gnu.org/36071#6
7: https://lwn.net/Articles/760483/
8: https://issues.guix.gnu.org/36071#4-lineno34
9: https://yhetil.org/guix/87v8gtzvu3.fsf@gmail.com

11: https://yhetil.org/guix/87jzyshpyr.fsf@ngraves.fr
12: https://issues.guix.gnu.org/62443

10: https://lwn.net/Articles/760142/
11: https://www.gnu.org/software/gnubg/
12: https://www.fsf.org/bulletin/2022/spring/unjust-algorithms/
13: https://www.fsf.org/news/publication-of-the-fsf-funded-white-papers-on-questions-around-copilot


16: https://salsa.debian.org/deeplearning-team/ml-policy
17: https://people.debian.org/~lumin/debian-dl.html

Cheers,
simon