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Non-determinism in GPT-4 is caused by Sparse MoE

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Re: Non-determinism in GPT-4 is caused by Sparse MoE

#51
post #14

Off topic > 3 months later, reading a paper while on board a boring flight home, I have my answer. I noticed people from hacker news routinely read scientific papers. This is a habit I envy but don't share. Any tips or sites for someone interested in picking up more science papers to read.

For just getting started I recommend collections: 1. Ideas That Created The Future[1]. It's a collection of fiftyish classic CS papers, with some commentary. 2. Wikipedia's list[2]. 3. Test of Time awards[3]. These are papers that have been around for a while and people still think are important. 4. Best paper awards[4]. Less useful than ToT as not every best paper is actually that good or important, and sometimes th…

I'd like to disagree with this. In particular, about [1]: It is a collection of papers in many different topics. There is little technical overlap between Alan Turing's Entscheidungsproblem paper, for instance, and Hoare's paper on axiomatic semantics. Also, the papers are all from the 70s. They're uniformly influential papers, and have shaped the field, but the fields and the vernacular used by working researchers is very different. At best, the papers approximate a four year undergrad curriculum in CS, and at worst, are a recipe to get distracted and overwhelmed. The link to Wikipedia [2] is somewhat better in that the papers appear to be more modern, but suffers even more from the problem of diversity.

A somewhat similar problem arises with test-of-time and best paper awards. To elaborate on my complaint, imagine the exaggerated case of someone trying to understand modern science by intensely focusing on the work of researchers who won the Nobel Prize. Clearly all very important work, but understanding the 1990 Physics Nobel Prize (on electron-proton scattering) is of no use to understanding the work for which 1991 Nobel was awarded (complex systems and polymers).

There are two things that (I'm assuming the OP's field of interest is computing) a CS education provides: At the undergrad and in the early stages of grad school, breadth of topics, and their modern synthesis. You don't spend much time reading papers (at least in an undergraduate education), but you understand the basics, and get a feel for the problems considered and the sensibilities of researchers. In an intermediate-level graduate seminar, you pick a narrow topic, and focus on papers in that topic. The first papers in the area (like Dijkstra's papers on distributed computing), the best / most important papers in the area, and the latest papers on topical interests (like Merkle trees and blockchains). There is thematic and technical continuity from one paper to the next, and you start to understand the the story being told. Then, late in graduate school, and in the rest of one's professional career, one starts reviewing papers that haven't even been published. At this point, you see the story being written: the steps and the missteps, and the memorable and not-so-memorable papers in a field. To truly understand a field, one needs to read not just the great papers, but also the middling ones.

And one needs to concentrate on a topic. The thing about a forum such as HackerNews is that for every topic of interest, there's likely a person here who's an expert in the area, but it is easy to confuse that observation with the much stronger claim that there's a person here who's an expert on every topic. The last of those people died in the mid-20th century, if they ever existed.

Re: Non-determinism in GPT-4 is caused by Sparse MoE

#52
post #29
post #12

_If_ 3.5 is a MoE model, doesn't that give a lot of hope to open source movements? Once a good open source MoE model comes out, maybe even some type of variation of the decoder models available(I don't know whether MoE models have to be trained from scratch), that implies a lot more can be done with a lot less.

It would be bad for single-consumer-GPU inference setups.

Could this work well with distributed solutions like petals?

https://github.com/bigscience-workshop/petals

I don't understand how petals can work though. I thought LLMs were typically quite monolithic.

Re: Non-determinism in GPT-4 is caused by Sparse MoE

#53

Earlier quoted context omitted.

> I've been adamantly against MoE for a set of reasons Such as?

It was completely unsubstantiated, based on rumours from a blog, but everyone repeated it as fact.

I think it is pretty compelling that almost all of the people doing research into switch transformers at Google were hired into OAI. I am not sure if that is ouboicly reported but once Ghotz leaked those details about the models, I went to check where the authirs of those papers are now and.... yep

Re: Non-determinism in GPT-4 is caused by Sparse MoE

#54
post #14

Off topic > 3 months later, reading a paper while on board a boring flight home, I have my answer. I noticed people from hacker news routinely read scientific papers. This is a habit I envy but don't share. Any tips or sites for someone interested in picking up more science papers to read.

read textbooks instead most papers are obtuse and poorly written even famous ones. you can find them in wikipedia footnotes

Re: Non-determinism in GPT-4 is caused by Sparse MoE

#55
post #34

Not sure I understand the excerpt from the referenced paper. Is it saying that part of its more-efficient inferencing relies on mixing tokens from completely-separate inputs – eg, from other users? And then, depending on what other inputs chance into the same grouping, the relative assignment-to-'experts' varies, and thus the eventual completions? If so, I'd see that as not just introducing non-determinism, but also…

this seems like a plausible outcome, and if true could spell disaster for OpenAI models relative to the competition and open source models. Currently, reliability is one of the core obstacles preventing widespread adoption of LLMs in many business critical workflows. And if these rumors, that GPT-4 is inherently un-deterministic and unreliable, are true then most enterprises are better off finetuning open source LLMs…

> domain-specific models will always outperform generalist ones

That's only true assuming you habe enough data to train a domain-specific model / expertise to train it and test it correctly.

I've encountered cases where an image recognition task could be accomplished well with a very general model like CLIP, but people still fine-tuned another model on their own small data set because that's considered better.

A domain specific model might be more likely to fail on weird outliers not present in the small domain specific training data.

> could spell disaster for OpenAI

Nah I don't think so. They are not all in on one specific model architecture. If the current architecture is found to have serious unfixable flaws then they'll just change architecture.

Re: Non-determinism in GPT-4 is caused by Sparse MoE

#56
post #29
post #12

_If_ 3.5 is a MoE model, doesn't that give a lot of hope to open source movements? Once a good open source MoE model comes out, maybe even some type of variation of the decoder models available(I don't know whether MoE models have to be trained from scratch), that implies a lot more can be done with a lot less.

It would be bad for single-consumer-GPU inference setups.

Not an expert (no pun intended), but MoE where each expert is actually just a LoRA adaptor on top of the base model gets me pretty excited. Since LoRA adaptors can be swapped in and out at runtime, it might be possible to get decent performance without a lot of extra memory pressure.

Re: Non-determinism in GPT-4 is caused by Sparse MoE

#58
post #14

Off topic > 3 months later, reading a paper while on board a boring flight home, I have my answer. I noticed people from hacker news routinely read scientific papers. This is a habit I envy but don't share. Any tips or sites for someone interested in picking up more science papers to read.

Check out the papers and talks from Papers We Love, a "repository of academic computer science papers and a community who loves reading them":

https://paperswelove.org/

Re: Non-determinism in GPT-4 is caused by Sparse MoE

#59
post #14

Off topic > 3 months later, reading a paper while on board a boring flight home, I have my answer. I noticed people from hacker news routinely read scientific papers. This is a habit I envy but don't share. Any tips or sites for someone interested in picking up more science papers to read.

I usually just read the abstract and synthesize that with the comments on HN to get the gist (and legit-ness) of the research.

Re: Non-determinism in GPT-4 is caused by Sparse MoE

#60
post #14

Off topic > 3 months later, reading a paper while on board a boring flight home, I have my answer. I noticed people from hacker news routinely read scientific papers. This is a habit I envy but don't share. Any tips or sites for someone interested in picking up more science papers to read.

Step 1. Find papers you're interested in Step 2. Open them Step 3. read them

Semantic Scholar for search. Scihub for any paywalled papers. Libgen for books. Zotero to organize.
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