Anyone remember siamese networks?
Differential Transformer
71–80 of 188 posts
Re: Differential Transformer
#72Does anyone understand why they are taking the difference between transformers instead of the sum? It seems to me that in a noise reducing solution we would be more interested in the sum, as random noise would cancel out and signal would be constructive. Of course, even if I'm right proper training would account to that by inverting signs where appropriate. Still, it seems weird to present it as the difference, espec…
The noise isn't truly random; it's just a matrix of small values that shouldn't be taken into account. Subtracting them cancels them out.
As pointed out by a different comment, it's actually the attention we are interested in that is cancelled out *if they are both equal*. This is what the paper mentions in its abstract;
> promoting the emergence of sparse attention patterns
In theory, it is quite clever, and their results seem to back it up.
Re: Differential Transformer
#73Does anyone understand why they are taking the difference between transformers instead of the sum? It seems to me that in a noise reducing solution we would be more interested in the sum, as random noise would cancel out and signal would be constructive. Of course, even if I'm right proper training would account to that by inverting signs where appropriate. Still, it seems weird to present it as the difference, espec…
I suspect that plus vs minus is arbitrary in this case (as you said, due to being able to learn a simple negation during training), but they are presenting it in this way because it is more intuitive. Indeed, adding two sources that are noisy in the same way just doubles the noise, whereas subtracting cancels it out. It's how balanced audio cables work, for example.
But with noise cancelling headphones, we don't sum anything directly---we emit an inverted sound, and to the human ear, this sounds like a subtraction of the two signals. (Audio from the audio source, and noise from the microphone.)
Re: Differential Transformer
#74Like most things in this new world of Machine Learning, I'm really confused why this works? The analogy to noise-cancelling headphones is helpful but in that case we clearly know which is signal and which is noise. Here, if we knew why would we even bother to the noise-cancelling work?
With a single softmax you cannot predict exactly 0, but only very small numbers. When you have a large number of values to add up, this "poisons" the output with a lot of irrelevant stuff (the noise mentioned in the paper). To make things worse, low attention values will have very low gradient, thus needing a lot of weight updates to undo that kind of mistakes. On the other hand, subtracting the output of two softmax…
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Re: Differential Transformer
#75Earlier quoted context omitted.
It matches the training data. Whether the training data matches truth (and whether it's correctly understood - sarcasm included) is a completely separate thing. > The training does not perfectly align the model with truth, but 'orthogonal' Nitpicky, but the more dimensions you have, the easier it is for almost everything to be orthogonal. ( https://softwaredoug.com/blog/2022/12/26/surpries-at-hi-dime... ) That's why…
I went to school to learn about the world and the overwhelming majority of that learning was from professors and textbooks. Whether the professors' beliefs and the textbooks' contents reflected the true properties of the world was a completely separate thing, entirely outside of my control. But I did come away with a better understanding of the world and few would say that education is orthogonal to that goal. If you…
> But I did come away with a better understanding of the world and few would say that education is orthogonal to that goal.
That's due to the reward function / environment. But even outside extremes like North Korea, lots of education environments value conformity over independent analysis.
Re: Differential Transformer
#76Earlier quoted context omitted.
It's a different attention mechanism with a different map setup, so fundamentally a different type of model
Looks like it is a drop in replacement for attention, but models will need to be retrained for this one, yes.
It may not need to be entirely retrained. The value spans and input are the same, and no extra weights are needed. You may be able to tune an existing model with this attention mechanism and get some of the benefits.
But overall... it's mainly a training change, so training is needed to make a difference.
Re: Differential Transformer
#77Is the thing that's going on here that softmax can't push a value to 0, but by subtracting 2 softmax maps we can output 0s?
Or negatives
Re: Differential Transformer
#78Very clever. I like this kind of nitty-gritty detail work, and the change is small enough to be adapted easily by others. Bravo!
I'm a little concerned about the last sentence of the section introduction of "2 Differential Transformer". It mentions using improvements from previous papers, but in the grammatical context, it's unclear if this improvement is added to both the normal transformer and their diff transformer. This would otherwise sully the comparisons. It's the "main difference" wording in the previous sentence that raised a flag for me.
Of course, a good-faith researcher would know this and may not feel the need to clarify. But you can never be too careful about some published research in this field.
Re: Differential Transformer
#79The key bit I didn't understand at first was what happens if the two groups of attention learn the same thing; because their attention masks are subtracted from one another if they both output similar values the attention across the board will drop to zero and this will lead to high loss. So the only way to reduce loss is if they learn to attend to different things. One of the simplest strategies they could learn (an…
An interesting aspect is that they don't do a plain subtraction, but rather subtract a portion of the second softmax.
This makes sense, if one considers that the two copies are identical then the softmax outputs would be identical and the difference is zero everywhere. However, by subtracting a scaled copy, the normalization of the difference seems to really boost the signal value(s) over the "noise", making the signal stand out compared to pre-normalization.
Re: Differential Transformer
#80The key bit I didn't understand at first was what happens if the two groups of attention learn the same thing; because their attention masks are subtracted from one another if they both output similar values the attention across the board will drop to zero and this will lead to high loss. So the only way to reduce loss is if they learn to attend to different things. One of the simplest strategies they could learn (an…
Maybe the loss function could penalize them learning the same thing?