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?
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 allows the model to predict a weight of exactly zero for some of the values, while keeping a reasonable gradient flowing through.
So the model already knows what is noise, but a single softmax makes it harder to exclude it.
Moreover, with a single softmax the output of all heads is forced to stay in the convex hull of the value vectors, whereas with this variant each head can choose its own lambda, thus shifting the "range" of the outputs outside the convex hull pre-determined by the values. This makes the model as a whole more expressive.
Everything I am seeing in this paper is related to reduced size and noise, which implies a reduction in expressiveness.
The improvement in needle and a haystack, benchmarks on multi-hop questions of in corpus data and multishot in-context learning points to this.
This is a wonderful thing if robustness is more important than generality, but it doesn't address trimming away activations that may be spurious in the general use case but may improve an individual domain specificity.
Context would dramatically impact what tradeoffs and more desireble, and noise is probably never desirable. But the ability of this paper to enable bit size for inference points to a reduction in expressiveness.
Perhaps I am too focused on generalization?
Also, where is each softmax happening here? For each attention head?
If we’re subtracting one attention matrix from another, we’d end up with attention scores between -1 and 1, with a probability of effectively 0 for any single entry to exactly equal 0.
What’s more, the learnable parameter \lambda allows for negative values. This would allow the model to learn to actually add the attention scores, making a score of exactly 0 impossible.
I wonder if a different approach without that issue exists. For instance, using max(0, exp(x)-1) instead of exp(x) in the softmax attention formula. That way when the query is orthogonal to the key (or worse), it does not contribute.
Wouldn’t this be pretty unlikely, though?
The better example is the differential signalling used in professional audio and many digital signaling protocols like Ethernet, HDMI and USB.
Instead of using one wire, referencing to ground, they send the signal as the difference between both wires. Both wires end up carrying the same signal with inverted polarity. Because both wires are running next to each-other any external noice will be applied to both equally.
The voltage will change, but the difference in voltage between both wires is untouched. And when you subtract the two voltages at the receiver end, any noise simply gets subtracted out.
It sort of feels closer to heterodyning and "demodulating" the signal encoded in the softmax. Those tiny little errors we're trying to denoise with this technique are almost closer to carrier waves (when encoded to softmax) than noise imo. This wouldn't get rid of noise in the training data or noise in the dimensionality of the key / value space. It's really only removing noise introduced by the process itself.
The simple way of doing this would be to just remove the softmax or use a sigmoid instead, but in practice a softmax works better it seems.
to eli5:
rope is the modern strategy used to give information to the model about how far a query and a key are apart when doing attention. It's the best strategy we have now, but has a major downside, where it makes some connections between tokens that are far apart much stronger than you would like them to be. Xpos (https://arxiv.org/pdf/2212.10554) is another paper by microsoft tackling issues with rope and you can see figure 1 on page 4 to get a visual interpretation of the sinusoidal attention strength (you would like it to be smooth).
I think a big reason differential transformers is working so well, especially on long sequence stuff, because when both q1 and q2 don't match a token, the rope relative strength will still have the same value and the noise will cancel out. Leaving intended matches, but at the cost of somewhat dampening the original value rope brought.
Just a hypothesis though. It would be easy to test by running this experiment against a baseline where both use alibi attention (https://arxiv.org/pdf/2108.12409) which has a different set of tradeoffs this wouldn't mitigate, but still a really interesting result.
If that is the case, then the "signal" in this case would be the softmax that encodes the dimensions captured by the query / key space. Since the noise ideally is the same in both softmax encodings, subtracting them should "cancel out" the noise.
For example, if you are trying to send a +1V signal on one wire, and a -1V signal on the other and a +0.5V noise exists, one wire will have +1.5V and the other will have -0.5V,
Take the difference and divide by 2:
(+1.5V - -0.5V) / 2 = +1V or, if your setup is different (-0.5V - +1.5V) / 2 = -1V
The analogy I can think of is when you're paying attention to a variety of things and you actively avoid concentrating on something because it will distract you. You don't give it zero attention, you give it negative attention.
I'm wondering if there's any effect of "creativity", or ability to interpolate between concepts. Hallucination and creativity feel very related to me. I understand hallucinating as simply being misaligned with the space humans feel appropriate to interpolate between
Why? I see them as just sampling errors.
Sure a mistake can spark inspiration sometimes, but creativity is much more than mistakes.
> I understand hallucinating as simply being misaligned with the space humans feel appropriate to interpolate between
These language models are next-token predictors. The way the next token is predicted is by sampling a probability space outputted by the model.
That sampling process can be non deterministic.
Hallucinations are when that sampling results in tokens that come together to create a false or otherwise unintended statement.
You can just as well think of everything a model outputs as a hallucination, but we train the model to output a space what we want them to hallucinate is more likely. Otherwise it just outputs meaningless noise.
“Hallucinate” is really an awful word for what it’s trying to describe.
Exactly. Don't forget that an important factor in the success of GPT3 was RLHF, which is essentially training the model to produce "hallucinations" that are more acceptable on average to human trainers.
Hallucination describes the same feature you just called "non deterministic sampling", but exclusively the cases that we don't like. It would be really convenient if we could actually draw that line, but we can't. If non-determinism is a core feature, then that feature will be present in every case; including the ones we find desirable, and the ones we find undesirable.
It looks like creativity has many steps but being able to come with novel, unprompted stuff is important, as long as you are able to discard the bullshit earlier.
"Hallucination" is only a problem if later layers (or additional networks) can't detect and remove it
But here’s a case for the other side: sure, most mistakes are just errors, but evolution happens via “mistakes.” Also, LLM’s often deliberately add add randomness at inference time.
For one, speed and memory. They have twice as many Q and K weights in the attention blocks, leading to a ~10% reduction in throughput on their H100 (table 7 in appendix A).
But Figure 1 clearly shows that it works, so I don't doubt that it is in fact possible. I'm just struggling to build a picture of how exactly the network accomplishes this.
softmax should be exp()/1+∑exp()
Notice the 1 added to the denominator.
The difference is at the negative limit, softmax can be 0, instead of some epsilon. The same could be done by adding an extra zero value in x.
Downside is, you have to retrain your model from scratch to fix this.
if you think about it, the "escape hatch" is the design of the entire transformer dictionary. if Key/Query attention misaligns with Value's weights, you get a layer head that does not attend to anything...
I didn't follow Miller's proposal quite as he wrote it though and I put the mechanism in all the layers rather than avoiding it at the end.
My test doesn't absolutely rule out usefulness-- there's always different ways of applying something, but I saw no indication of it.
> softmax should be exp()/1+∑exp()
I've also tested it on many networks and different types of attention and I've yet to see a meaningful improvement (or even an improvement), even in generalization.
It really is the training method...
As to the paper, I'm also still at a big lost and honestly, if reviewing could not accept it. The results look good, but I can't tell why and there's some "black magic" going on here.
- Figure 3 has "Transformer" and doesn't specify. Is this StableLM-3B-4E1T?
- What fucking dataset is this on? Stable has a WandB link[2] for that project and I don't see any experiment with similar (presumably entropy?) loss values (come on... this is fucking research... label your fucking graphs...)
- Where the fuck is the ablation? (Yes, I saw Fig 6 and Sec 3.8)
- How do I know that (assuming this is Stable) that the difference isn't just hyperparemeters? Or worse, GPUs! (yes, number of GPUs can change results due to sharding and this changing the statistics)
- How do I know it isn't down to 1k warmup steps instead of 5k?
- What about hidden size, layers, heads, or FFN size? Stable has 32/2560/32/? and this has 28/3072/12/8192 (these all will mess with sharding statistics too). Is the head dimension the same?
- How do I know it isn't down to the tokenizer?
- What is this magic? `0.8 - 0.6 * math.exp(-0.3 * depth)`
- Was this learned? Hand picked? This is a huge factor
- Any information about the learned parameters? Their final values? Trajectories?
- The code does not seem to be the same as whats in the algos...
[0] https://www.evanmiller.org/attention-is-off-by-one.html
[1] This is a bit convoluted but without this condition many "alternative forms" you see would be equivalent to other architectures like linear layers or gated units. Term is not well defined, but this really appears to be the only agreed upon aspect, even if only implicitly stated. This is a much longer conversation though.
[2] https://stability.wandb.io/stability-llm/stable-lm/reports/StableLM-3B-4E1T--VmlldzoyMjU4?accessToken=u3zujipenkx5g7rtcj9qojjgxpconyjktjkli2po09nffrffdhhchq045vp0wyfo
[2.1] The config: https://github.com/Stability-AI/StableLM/blob/main/configs/stablelm-3b-4e1t.ymlIn the first diagram with the attention weights, there actually are some negative scores in the noise section. But, the attention to that section is very small anyway. All the second attention map needs to do is predict the noise in the first one -- a task that can be done very accurately, because it has full access to the input of the first.
To refer back to their real-world comparison, noise-canceling headphones have access to what your ear hears through a microphone, so they can output exactly the right cancellation signal. Similarly, the second attention map knows what's being input into the first one, so it can output a corresponding cancellation signal. It's not perfect -- just as noise-canceling headphones aren't perfect -- but it still gets you 99% of the way there, which is enough to boost performance.
I mean, intuitively it would be trivial for the model to just optimise lambda to zero during training. Then you essentially have built a vanilla transformer with an overcomplicated parameter pruning mechanism. Pruning is already pretty well established in the literature as something that works surprisingly good for reducing parameter counts up to (hold on to your papers)... about 40%. In practice the model probably doesn't work exactly like that, but I wouldn't be surprised if it just approximates the normal transformer in the end anyways.
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.
I wonder if there's a metaphor here for our own experience and utility in "surprise".
Like if one attention head is surprised by what another learns, up-weight it. But if they both find the same, assume it's not very surprising and down-weight it.
Admittedly, "surprise" is something that has a big section of my knowledgebase[1][2][3] (both as a subjective feeling and adaptive function of our minds, one of the most complex adaptive system we know of)
[1] https://plus.maths.org/content/information-surprise
[2] https://blakeelias.name/papers/Multi-Agent-Cooperation-Intri...
[3] https://complexity.simplecast.com/episodes/81/transcript
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.
In any event, I'd imagine that this will get widely adopted if the numbers hold up; like I said, this seems to be basically no downside, and should be easy to replicate.
https://github.com/microsoft/unilm/blob/master/Diff-Transfor...
If I understand correctly, this architecture trades twice as much attention memory in exchange for either a higher quality model, or less parameters at a similar quality.
> According to the fitted curves, 6.8B-size DIFF Transformer achieves a validation loss comparable to 11B-size Transformer, requiring only 62.2% of parameters
This raises a few questions for me:
- Would having only 60% of the parameters negate the double space for attention, leaving a similar memory profile as a traditional transformer?
- Does that tradeoff change noticeably between training and inference?
Here's the bit from the paper:
> We set the number of heads h = dmodel/2d, where d is equal to the head dimension of Transformer. So we can align the parameter counts and computational complexity.
In other words, they make up for it by having only half as many attention heads per layer.
I wonder about the story behind that formula...
(Although it seems the author do not discuss this choice anywhere in the paper?)
edit: not fully but it gives promising results. quiet an improvement actually.
The fundamental issue is that most of the time LLMs are going to be combining statistics derived from many training samples when generating a single continuation, and there is just no guarantee that this will result in a semantically coherent response. Of course the model's depth of parsing and semantic analysis usually means that each generated word is highly plausible, but this isn't the same as being factually correct, especially so in these cases where the model is drawing on multiple sources to create a mashup response, which is the normal mode of operation.
The root problem is simply that the model doesn't capture reality, just an approximation. What we are incorrectly calling "hallucination" is just the best the model has to offer.
I'm imagining a smaller model examining the output tokens of a larger model and metaphorically slapping it on the wrist with a ruler if the output tokens start drifting off topic. Not quite the same, but an entertaining thought nonetheless.
I’m very interested in this claim. I was under the impression that hallucination is unavoidable in these kinds of models. IIRC proof for that was trending on HN a couple weeks ago.
Most of it should be happening when there's no data to draw conclusions from. E.g. STT models make up words in silence, vision models find things in lens cap noise, LLMs make up explanations when they have no data to pull from.
The real solution would be more along the lines of training models to specifically ignore these cases, or in the case of LLMs to just know when to say "I don't know".
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, especially seeing as they compare this directly to noise cancelling headphones, where we sum both microphones inputs.
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.
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.)
Simplified differential T. looks like: (softmax(Q₁K₁) − λ softmax(Q₂K₂)) V
You can factor this into:
x = softmax(Q₁K₁)V
x += -λ softmax(Q₂K₂)V
It has to be done in a hierarchical way to know what you attended to + full context.
If the differential vector is being computed with the same input as the attention vector how do you know how to modify the attention vector correctly
They're effectively doing softmax with a fixed temperature, but it's unclear that this work is going to do better than just learning a per-head temperature parameter.
c.f. https://arxiv.org/abs/2010.04245 which shows an improvement by learning per-head temperature.
The other way to think about this is that it looks like a hacked-up kinda-sorta gated attention. If that's the case, then doing softmax(alphaq_1k_1^T - log_sigmoid(betaq_2k_2^T)) might be better? (where alpha,beta are learned temperatures).
Crazy gains though congrats to the researchers
Then we would know how much this transformer innovation helps by itself.
[...] Specifically, we partition the query and key vectors into two groups and compute two separate softmax attention maps. Then the result of subtracting these two maps is regarded as attention scores.
[...] The approach is analogous to noise-canceling headphones and differential amplifiers in electrical engineering, where the difference between two signals cancels out common-mode noise.
Simple change, with seemingly decent improvements across the board.
"The scaling curves indicate that Diff Transformer requires only about 65% of model size or training tokens needed by Transformer to achieve comparable language modeling performance."
"Diff Transformer retains high performance even at reduced bit-widths, ranging from 16 bits to 6 bits. In comparison, Transformer’s accuracy significantly drops with 6-bit quantization. The 4-bit Diff Transformer achieves comparable accuracy as the 6-bit Transformer, and outperforms the 4-bit Transformer by about 25% in accuracy."