Submitted by drinkingsomuchcoffee t3_113m1ly in MachineLearning
drinkingsomuchcoffee OP t1_j8unlkp wrote
Reply to comment by fasttosmile in [D] HuggingFace considered harmful to the community. /rant by drinkingsomuchcoffee
Alright, I have a bit of time so I'll address a few things.
>You need to understand that there is a trade-off between centralizing [...] verses keeping it hackable that is unavoidable.
I don't know what hackable means. You haven't defined it. I'm going to use the most generous interpretation to mean, you can modify it without impacting other places. Well you can do that if it's centralized, just copy paste it into your file and then edit it- that's no excuse to completely ban centralization! Alternatively decompose the centralized function more and only use the pieces you need.
Now onto the blog post.
>If a bug is found in one of the model files, we want to make it as easy as possible for the finder to fix it. There is little that is more demotivating than fixing a bug only to see that it caused 100 failures of other models.
Maybe it should cause 100s of failures if it's a breaking change (a bug). That's a pretty good sign you really did screw something up.
>Similarly, it's easier to add new modeling code and review the corresponding PR if only a single new model file is added.
No it's not. If new code uses a battle tested core, I don't have to review those parts as thoroughly. If it's copy pasted, I still have to review it and make sure they didn't copy an old version with bugs or slightly modified it and broke something. Sounds like this is common as many people have complained about dozens of bugs!
>We assume that a significant amount of users of the Transformers library not only read the documentation, but also look into the actual modeling code and potentially modify it. This hypothesis is backed by the Transformers library being forked over 10,000 times and the Transformers paper being cited over a thousand times.
Maybe you should check your assumptions before you make a fundamental decision (you know, basic engineering). There's plenty of forked libraries that are not modified and are forked for archival purposes. Nor should you cater to a small minority if most people _aren't_ doing this.
> Providing all the necessary logical components in order in a single modeling file helps a lot to achieve improved readability and adaptability.
It can _sometimes_. But not always. Having one massive file named `main.py` is not more readable than a well split program. This seems like basic common sense to me, but here's an actual paper on the subject: http://www.catb.org/esr/writings/taoup/html/ch04s01.html
>Every time we would have to have asked ourselves whether the "standard" attention function should be adapted or whether it would have been better to add a new attention function to attention.py. But then how do we name it? attention_with_positional_embd, reformer_attention, deberta_attention?
Yep, you've identified a place where you shouldn't try to fit every idea under a single "Attention" class. That's just common sense programming, not an argument against writing good shared functions or classes.
>Once a machine learning model is published, it is rarely adapted or changed afterward.
Then why does the Bert module have changes as recent as this week with changes from dozens of authors going back years?
https://github.com/huggingface/transformers/tree/main/src/transformers/models/bert
This is irrefutable hard evidence against your argument.
> Sylvain Gugger, found a great mechanism that respects both the single file policy and keeps maintainability cost in bounds. This mechanism, loosely called "the copying mechanism", allows us to mark logical components, such as an attention layer function, with a # Copied from <predecessor_model>.<function> statement
Ok so the programmer you mentioned before is going to "break 100s of tests" when she changes this ad-hoc C-preprocessor knock off. You're still doing "DRY" you're just doing it how C programmers did it 30 years ago, in a much more complicated manner.
If anyone here works at HuggingFace, please forward this to the author of that article.
hpstring t1_j8x5iyh wrote
I'm a beginner in this field and I was wondering what it means for code to be "centralized" and "dry". Does "centralized" mean putting a lot of code in a single file and "dry" means raw code that is not very easy to read but is efficient or have some other advantages?
baffo32 t1_j8zbmua wrote
dry is a very basic software engineering principle that means to include only one copy of every sequence of code. it looks like machine learning people did not learn this as they weren’t trained as software engineers. DRY stands for “don’t repeat yourself”, and if not respected then it gets harder and slower more and more to maintain, improve, or bugfix software, the larger and older it gets.
baffo32 t1_j8zbuup wrote
i think by centralized they mean what they imagine dry looking like, putting code in one place rather than spreading it out. it’s not usually used that way. it’s a reasonable expression though; people usually centralize components so there is one organized place to go to in order to access them.
hpstring t1_j8zw7i5 wrote
Lots of thanks! I didn't receive training from software engineering perspective, which seems to be an important aspect in machine learning.
[deleted] t1_j8xphu8 wrote
[deleted]
fasttosmile t1_j8v03xu wrote
> I don't know what hackable means. You haven't defined it. I'm going to use the most generous interpretation to mean, you can modify it without impacting other places. Well you can do that if it's centralized, just copy paste it into your file and then edit it- that's no excuse to completely ban centralization! Alternatively decompose the centralized function more and only use the pieces you need.
Your definition of hackable is almost it. What’s missing is that being decentralized makes things much, much easier to understand because the code is very straightforward and doesn’t have to take 10 different things into account.
You cant just copy paste a file if it’s centralized, you’ll have to copy paste multiple, and the main issue is it’s gonna take a while to understand which ones (and you'll have to modify the imports etc., unless you copy the entire repo! are you seriously suggesting that lmao) and what’s safe to modify inside of them. Decomposing is just going to make things more complicated for no gain.
Deep learning is about the details, and whenever you start breaking things apart and putting the details in different corners that’s how you end up with code that is hard to understand and people making mistakes and not understanding what’s going on.
> Maybe it should cause 100s of failures if it's a breaking change (a bug). That's a pretty good sign you really did screw something up.
It's a syntax/interface/some-other-not-fundamental bug. A real bug would have already been spotted when checking the test-set performance .
> No it's not. If new code uses a battle tested core, I don't have to review those parts as thoroughly. If it's copy pasted, I still have to review it and make sure they didn't copy an old version with bugs or slightly modified it and broke something. Sounds like this is common as many people have complained about dozens of bugs!
The way code is shown to be correct is by getting SOTA results. If it does that it is "battle tested". If it didn't do that no one would even think of merging it in the first place.
> Yep, you've identified a place where you shouldn't try to fit every idea under a single "Attention" class. That's just common sense programming, not an argument against writing good shared functions or classes.
It is an argument against having shared classes. At the same time, sure you can have some shared code, Huggingface does that.
> It can sometimes. But not always. Having one massive file named main.py is not more readable than a well split program. This seems like basic common sense to me, but here's an actual paper on the subject:
There is an important distinction that you're ignoring here. Having semantically separate objects in one file is indeed confusing. But if put everything related to the model in one file that simplifies things and reduces the working memory people require to read your code.
> Then why does the Bert module have changes as recent as this week with changes from dozens of authors going back years?
The recent change for Bert is some inference Interfaxe code which has to be kept common across all models. That’s their decision, I wouldn’t even do that, just make kwargs mandatory imo.
> Maybe you should check your assumptions before you make a fundamental decision (you know, basic engineering). There's plenty of forked libraries that are not modified and are forked for archival purposes. Nor should you cater to a small minority if most people aren't doing this.
Everyone in deep learning likes to gamble on making some tweaks to the model hoping they’ll get the next ICLR oral. Why else would they care about modifying the model code?
--
I suggest you go read some modeling code from different frameworks, one example is fairseq. I like fairseq, I think it's well done considering it's aims and constraints. But you're crazy if you think it's easier to understand and modify the code for some specific model than in huggingface. Here's the link to fairseq's roberta, you'll need to understand look at a dozen files to see what's happening. In constrast, huggingface is one file.
Spent too much time on this already, not gonna reply anymore.
drinkingsomuchcoffee OP t1_j8v3y80 wrote
>You cant just copy paste a file if it’s centralized, you’ll have to copy paste multiple, and the main issue is it’s gonna take a while to understand which ones (and you'll have to modify the imports etc., unless you copy the entire repo! are you seriously suggesting that lmao)
Yep apparently they themselves claim to do this for every module. Thank you for pointing out how crazy this is and proving my point.
>Your definition of hackable is almost it. What’s missing is that being decentralized makes things much, much easier to understand because the code is very straightforward and doesn’t have to take 10 different things into account.
Oh really? I think those files depend on pytorch functions and also numpy. Should they copy those entire libraries into the file to be more "hackable"? Lmao
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