This is a weird post. "What the transformer is actually doing"? You can just follow the code and see what it's doing. It's not doing something more or less than that. It's not doing some other thing.
float InvSqrt(float x){
float xhalf = 0.5f * x;
int i = *(int*)&x;
i = 0x5f3759df - (i >> 1);
x = *(float*)&i;
x = x*(1.5f - xhalf*x*x);
return x;
}
In my case I don't have a huge amount of time to chase down every rabbit hole, but I'd love to accelerate intuition for LLM's. Multiple points of intuition or comparison really help. I'm also not a python expert - what you see and what I see from a line of code will be quite different.
People try to (and often do) generate intuition for architectures that will work given the lay out of data. But, the reason models are so big now is that trying to understand what the model is "doing" in a way humans understand didn't work out so well.
Sure, the code is doing what it's doing. But trying to understand it at that level of abstraction seems ... not at all promising.
Consider a question about psychology. Say: "What are people doing when they decide what to buy in a shop?".
If someone writes an article about this, drawing on some (necessarily simplified) model of human thinking and decision-making, and some experimental evidence about how people's purchasing decisions change in response to changes in price, different lighting conditions, mood, etc., ... would you say "You can just apply the laws of physics and see what the people are doing. They're not doing something more or less than that."?
I mean, it would be true. People, so far as we know, do in fact obey the laws of physics. You could, in principle, predict what someone will buy in a given situation by modelling their body and surroundings at the level of atoms or thereabouts (quantum physics is a thing, of course, but it seems likely that a basically-classical model could be good enough for this purpose). When we make decisions, we are obeying the laws of physics and not doing some other thing.
But this answer is completely useless for actually understanding what we do. If you're wondering "what would happen if the price were ten cents higher?" you've got no way to answer it other than running the whole simulation again. Maybe running thousands of versions of it since other factors could affect the results. If you're wondering "does the lighting make a difference, and what level of lighting in the shop will lead to people spending least or most?" then you've got no way to answer it other than running simulations with many different lighting conditions.
Whereas if you have a higher-level, less precise model that says things like "people mostly prefer to spend less" and "people try to predict quality on the basis of price, so sometimes they will spend more if it seems like they're getting something better that way" and "people like to feel that they're getting a bargain" and so on, you may be able to make predictions without running an impossibly detailed person-simulation zillions of times. You may be able to give general advice to someone with a spending problem who'd like to spend more wisely, or to a shopkeeper who wants to encourage their customers to spend more.
Similarly with language models and similar systems. Sure, you can find out what it does in some very specific situation by just running the code. But what if you have some broader question than that? Then simply knowing what the code does may not help you at all, because what the code does is gazillions of copies of "multiply these numbers together and add them".
Again, I make no claim about whether the particular thing linked here offers much real insight. But it makes zero sense, so far as I can see, to dismiss it on the grounds that all you need to do is read the code.
…so what’s your point? I’m not getting it from those two words.
And yet, we spend quite a lot of our time thinking about what human brains do, and sometimes it's pretty useful.
For a lot of this, we treat the actual brain as a black box and don't particularly care about how it does what it does, but knowing something about the internal workings at various levels of abstraction is useful too.
Similarly, if for whatever reason you are interested in, or spend some of your time interacting with, transformer-based language models, then you might want some intuition for what they do and how.
You'll never fit the whole thing in your brain. That's why you want simplified abstracted versions of it. Which, AIUI, is one thing that the OP is trying to do. (As I said before, I don't know how well it does it; what I'm objecting to is the idea that trying to do this is a waste of time because the only thing there is to know is that the model does what the code says it does.)
And yet, seeing Alpha Zero play has indeed led to new human chess strategies.
Trying to understand it at some level of abstraction that humans can fit in their head has been a dead end.
Evolution optimized the human brain to do things over an unbelievably long period of time. Human brains were not designed top-down with human-understandable principles. But neuroscientists, cognitive scientists, and psychologists have arguably had success with understanding the brain partially at a higher level of abstraction than just neurons, or just saying "evolution optimized these clumps of matter for spreading genes; there's nothing more to say". What do you think is the relevant difference between the human brain and current machine learning models that makes the latter just utterly incomprehensible at any higher level of abstraction, but the former worth pursuing by means of different scientific fields?
People use many abstractions in AI/ML. Just look at all the functionality you get in PyTorch as an example. But they are abstractions of pieces of a model, or pieces of the training process etc. They aren't abstractions of the function the model is trying to learn.
I understand you're emphasizing the point that the connectionist paradigm has had a lot more empirical success than the computationalist paradigm - letting AI systems learn organically, bottom-up is more effective than trying to impose human mind-like principles top-down when we design them. But I don't understand why this means understanding bottom-up systems at higher level of abstractions is necessarily impossible when we have a clear example of a bottom-up system that we've had some success in understanding at a high level of abstraction, viz. the human mind.
Think about MoE models. Each expert learns to be good at completing certain types of inputs. It sounds like a great explanation for how it works. Except, it doesn't seem to actually work that way. The mixtral paper showed that the activated routes seemed to follow basically no pattern. Maybe if they trained it differently it would? Who knows. It certainly isn't a good name regardless.
Many fields/things can be understood at higher and higher levels of abstraction. Computer science is full of good high level abstractions. Humans love it. It doesn't work everywhere.
I... don't know if the people involved in that specific stuff have really grokked they are working in high dimensional space? A lot of otherwise smart people work in macroeconomics, where for decades they haven't really made any progress because it's so complex. It seems stupid to suggest a whole field of smart people don't realize what they are up against, but sheesh it kinda seems that way doesn't it? Maybe I'll be eating my words in 10 years.
But the main thrust of what I'm saying is that we shouldn't be dismissing explanations a priori - answers to "how does a transformer work?" that go beyond descriptions of self-attention aren't necessarily nonsensical. You can think it's a waste of time (...frankly, I kind of think it's a waste of time too...), but just like any other field, it's not really fair to close our eyes and ears and dismiss proposals out of hand. I suppose > Maybe I'll be eating my words in 10 years. indicates you understand this though.
If the argument is that a model is a function approximator, then it certainly isn't approximating some function that performs worse at the task at hand, and it certainly isn't approximating a function we can describe in a few hundred words.
Through analysis, I proved it indeed counts each letter's frequency. It then combines the "overall" letter frequencies with another distribution of "first letter" frequencies. This increased its accuracy to nearly 100%.
To me, this description is much nicer than had I "followed the code" and described it as millions of multiplications and additions. (Much of this ends up being noise anyway.)
First of all, you're rejecting teleological anthropomorphizing (saying it's weird to act as if "the weights have some goal") but then in the very next line you talk about the optimizer making "an attempt" to accomplish a goal. All of which misses the point, since the question is about _explanations_ not goals and intentions.
Then you reject out of hand any other level of explanation than the one you favor, saying "it's not more or less than that" when in fact it is both more and less than that; you can climb the ladder of abstraction either way to build more or less abstract explanations. We can dig down and talk about how the optimizer adjusts weights, or how tensor math works and how it's used in this case, or about how GPUs work, or gates, or transistors, etc. Or we could climb up and talk about (as this article does) and talk about what attention heads do, and why they work, when they work, when they don't, etc.
Climbing the layer of abstraction from model weights doesn't seem to work in this field. Just saying it's so doesn't make it so.
Does that apply to you as well?
And that's why we'll never have fun things like an obfuscated code contest. Oh wait[1]...