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Differential Transformer

arxiv.org

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121–130 of 188 posts

Re: Differential Transformer

#121

Post by skybrian »

dartos wrote: post #115
skybrian wrote: post #105

Earlier quoted context omitted.

LLM’s are too unpredictable for many practical uses so I’d guess better predictability is better. Hopefully the change the paper proposes will help! 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.

> evolution happens via “mistakes.” That’s a nice slogan, but it’s a gross oversimplification. In the natural world, you can say that mistakes in DNA replication leads to evolution, but that’s discounting the entire process of natural selection. Same with creativity. Look at Picasso. His was a technically brilliant realistic painter at 15, but his work later in life evolved to be more abstract and weird. I don’t thin…

To oversimplify, evolution is a generate-and-test process and the evaluation step is critical. Something needs to decide which variations are better. Often, with generative AI, it’s people who judge the results. Still, generating interesting examples (the brainstorming phase) plays some role in that.

I don’t know a whole lot about Picasso’s art, but I imagine the way he evaluated his own work played an important role, in being able to see that sometimes creative accidents are interesting.

Re: Differential Transformer

#122

Post by watsonmusic »

phire wrote: post #65
msoad wrote: post #7

Like 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?

Noise cancelling headphones are probably the wrong analogy here. 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…

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Re: Differential Transformer

#123

Post by robertsdionne »

freeqaz wrote: post #49
blackbear_ wrote: post #43

Earlier quoted context omitted.

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…

I'm able to follow most of what you're saying. It's unclear to me what "convex hull" means though. Also, where is each softmax happening here? For each attention head?

It means one of these things: https://en.wikipedia.org/wiki/Simplex#Standard_simplex

Re: Differential Transformer

#125

Post by watsonmusic »

aDyslecticCrow wrote: post #72
nowayno583 wrote: post #62

Does 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 r…

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Re: Differential Transformer

#126

Post by [deleted] »

Imnimo wrote: post #108

I feel like I'm missing a key insight here. I understand the problem that regular softmax attention struggles to approach assigning zero attention to irrelevant stuff. And I get that having this subtraction formula makes it possible to assign exactly (or near) zero attention weight without having crazy outlier activations. But it seems like it also makes it very easy to have negative attention weight (which is equiva…

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Re: Differential Transformer

#127

Post by chessgecko »

I wonder how much of the value here is from canceling out the positional noise rope produces. I would love to see a table comparing an alibi version of this to an alibi baseline in addition to the rope models here.

Crazy gains though congrats to the researchers

Re: Differential Transformer

#128

Post by quantadev »

machinelearning wrote: post #111

This is a good problem to solve but the approach is wrong imo. 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

Doesn't everything just get tweaked in whatever direction the back-propagation derivative says and proportionally to that "slope"? In other words, simply by having back-propagation system in effect there's never any question about which way to adjust the weights, right?

Re: Differential Transformer

#129

Post by chessgecko »

islewis wrote: post #104

> Differential attention takes the difference between two softmax attention functions to eliminate attention noise 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…

I think they mitigated the extra memory/compute from this by using half the number of overall heads and doubling V and O. Without actually checking the math I think it should be equivalent in flops, not counting the extra (cheap) multiply by const and subtract.

Re: Differential Transformer

#130

Post by Kubuxu »

islewis wrote: post #104

> Differential attention takes the difference between two softmax attention functions to eliminate attention noise 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…

It would double the size of the KV cache, which can be significant (multi-GB) at larger context sizes.
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