On 2/28/24 19:03, Richard Fontana wrote:
On Tue, Feb 27, 2024 at 5:58 PM Tim Flink <tfl...@fedoraproject.org> wrote:
On 2/26/24 19:06, Richard Fontana wrote:
<snip>
4. Is it acceptable to package code which downloads pre-trained weights from a
non-Fedora source upon first use post-installation by a user if that model and
its associated weights are
a. For a specific model?
What do you mean by "upon first use post-installation"? Does that mean
I install the package, and the first time I launch it or whatever, it
automatically downloads some set of pre-trained weights, or is this
something that would be controlled by the user? The example you gave
suggests the latter but I wasn't sure if I was misunderstanding.
Once the package is installed, pre-trained weights would downloaded if and only
if code written to use a specific model with pre-trained weights is run. In the
cases I'm aware of, code that would cause the weights to be downloaded is not
directly part of the packaged libraries and anything that could trigger the
downloading of pre-trained weights would have to be written by a user or
contained in a separate package. If a specific model with pre-trained weights
is not used and not executed by another library/application, the weights will
not be downloaded. With the ViT example, the vitb16 weights would be downloaded
when that code (not included in the package) is run but the vitb32 weights
would not be downloaded unless the example was changed or something else
specified a pre-trained ViT model with the vitb32 weights. Similarly, the
weights for other models (googlenet, as an example) would not be downloaded
unless code that uses that specific model in its pre-trained form is executed
post-installation.
The implementations that I'm familiar with will check for downloaded weights as
the code is initialized. When done in this way, the download is transparent to
the user and unless code using these models/weights is written in such a way
that the user a choice, there is not much a user could do to change the
download URL or prevent the weights from being downloaded. The only ways I can
think of off hand would be to modify the underlying libraries to override the
hard-coded URLs or maybe put identically named files in the cache location but
that would end up being dependant on model implementation. For the specific
libraries I used as examples, I don't know what the local download folder is
off the top of my head, nor do I know if they do any verification of downloads
so putting files into the cached location may not work if they don't match the
intended file contents.
This is just my opinion but I doubt that many people writing code that uses
pre-trained models are going to go out of their way to help users avoid
downloading pre-trained weights. I know that for code that I've written using
pre-trained models, it might be able to execute without the pre-trained weights
but the output would just be noise in that situation. I would have a hard time
justifying the work needed to make those downloads optional since it would make
the code useless for what it was intended to do.
It may also be worth noting that some models with pre-trained weights are
almost useless without those weights. For some (mostly older) models, it's
feasible to train a model from scratch but for many of the recent models, it's
just not feasible. As an example, the weights for Meta's Llama 2 took 3.3
million hours of GPU time to train [1] with a cost into the millions of USD
ignoring what it would take to obtain enough data to train a model that large.
Apologies for my verbosity but I hope that I answered your question and the
extra bits weren't entirely useless.
Tim
Richard
b. For a user-defined model which may or may not exist at the time of
packaging?
I can provide examples of any of these situations if that would be helpful.
Can you elaborate on 4a/4b with examples?
There are 2 simple examples for the two cases I mentioned (4a and 4b) at the
bottom of this email
Tim
-----------------------------------------------------------------
4a - code that downloads pre-trained weights for a specific model
-----------------------------------------------------------------
torchvision [1] is a pytorch adjacent library which contains "Datasets, Transforms
and Models specific to Computer Vision". torchvision contains code to implement
several pre-defined model structures which can be used with or without pre-trained
weights [2]. torchvision is distributed under a BSD 3-clause license [3] and is currently
packaged in Fedora as python-torchvision but all of the specific model code is removed at
package build time and not distributed as a Fedora package.
As an example, to instantiate a vision transformer (ViT) base model variant
with 16x16 input patch size and download pre-trained weights, the following
python code could be used:
```
import torchvision
vitb16 = torchvision.models.vit_b_16()
```
The code describing the vit_b_16 model is included in torchvision but the
weights are downloaded from an external site when the model is first used. At
the time I write this, the weights are downloaded from
https://download.pytorch.org/models/vit_b_16-c867db91.pth
In this case and for all the other models contained in torchvision, the exact
links to the pretrained weights are all contained within the torchvision code.
Something worthy of note is that the weights for vit_b_16 are from Facebook's
SWAG project [4] which is distributed as CC-BY-NC-4.0 [5] and would not be
acceptable for use in a Fedora package. For the other models in torchvision,
some of the pre-trained weights have an explicit license (like ViT) but many of
them are not distributed under any explicit license (ResNet[6] as an example).
[1] https://github.com/pytorch/vision
[2] https://github.com/pytorch/vision/tree/main/torchvision/models
[3] https://github.com/pytorch/vision/blob/main/LICENSE
[4] https://github.com/facebookresearch/SWAG
[5] https://github.com/facebookresearch/SWAG/blob/main/LICENSE
[6] https://pytorch.org/hub/pytorch_vision_resnet/
----------------------------------------------------
4b - code that downloads an somewhat arbitrary model
----------------------------------------------------
One of the newer features of pytorch (which is still considered to be in beta) is the
ability to interface with "PyTorch Hub" [7] to use pre-defined and pre-trained
models which have been uploaded by other users. At the time of this writing, the pytorch
hub appears to be moderated by the pytorch team but the underlying code which supports
loading of semi-arbitrary models from user-defined locations at runtime.
As an example, this code loads a MiDaS v3 large model with pre-trained weights
directly from intel's github repo [8].
```
model_type = "DPT_Large"
midas = torch.hub.load("intel-isl/MiDaS", model_type)
```
Similar to the ViT example above, this model will download weights from a url
(https://github.com/isl-org/MiDaS/releases/download/v3/dpt_large_384.pt at the
time of this writing) but unlike the ViT example, the definitions of the model
and where the weights are located are determined by code contained in the
github repository specified by the user [9] and downloaded at runtime to
determine the exact link to any code and pre-trained weights. The MiDaS
repository is distributed under an MIT license [10].
[7] https://pytorch.org/hub/
[8] https://github.com/isl-org/MiDaS
[9] https://github.com/isl-org/MiDaS/blob/master/hubconf.py#L218
[10] https://github.com/isl-org/MiDaS/blob/master/LICENSE
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