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Mixture-of-Depths: Dynamically allocating compute in transformers

arxiv.org

51–60 of 89 posts

Re: Mixture-of-Depths: Dynamically allocating compute in transformers

#51

I think more complicated routing is absolutely going to become more common. Specifically, I think at some point we are going to move to recursive routing, ie. pass back through a set of experts again. In the future, 'chain-of-thought' will happen internal to the model recursively

Most of the original MoE implementations around LLMs were in fact recursive

Re: Mixture-of-Depths: Dynamically allocating compute in transformers

#52

I think more complicated routing is absolutely going to become more common. Specifically, I think at some point we are going to move to recursive routing, ie. pass back through a set of experts again. In the future, 'chain-of-thought' will happen internal to the model recursively

Most of the original MoE implementations around LLMs were in fact recursive

Could you please elaborate?

Re: Mixture-of-Depths: Dynamically allocating compute in transformers

#53

I think more complicated routing is absolutely going to become more common. Specifically, I think at some point we are going to move to recursive routing, ie. pass back through a set of experts again. In the future, 'chain-of-thought' will happen internal to the model recursively

We can name these hypothetical objects Recursive Neural Networks.

We did: https://en.m.wikipedia.org/wiki/Recursive_neural_network

Re: Mixture-of-Depths: Dynamically allocating compute in transformers

#54

Earlier quoted context omitted.

To playfully invite for you to participate in conversation further, so that I may humbly learn from you. "I don't know what you're talking about" seemed too spartan and austere and aggressive, and you reciprocated politely, if again sparsely, when the other person playfully invited you to elaborate.

Well, you've now made your original intent specific, but in case you didn't draw the requisite lesson I'll make that explicit. Because text has less bandwidth than almost any other medium, certain forms of humor are much more likely to be understood (in this case, your "gentle playfulness" was taken to be snark, sarcasm, and point scoring). If you insist on using this and similar forms of humor that, ordinarily, depe…

[dead]

Re: Mixture-of-Depths: Dynamically allocating compute in transformers

#55

Earlier quoted context omitted.

We can name these hypothetical objects Recursive Neural Networks.

i know you're jesting but RNNs are recursive along the sequence length where I am describing recursion along the depth.

Depthwise RNN?

Re: Mixture-of-Depths: Dynamically allocating compute in transformers

#56

Earlier quoted context omitted.

To playfully invite for you to participate in conversation further, so that I may humbly learn from you. "I don't know what you're talking about" seemed too spartan and austere and aggressive, and you reciprocated politely, if again sparsely, when the other person playfully invited you to elaborate.

Well, you've now made your original intent specific, but in case you didn't draw the requisite lesson I'll make that explicit. Because text has less bandwidth than almost any other medium, certain forms of humor are much more likely to be understood (in this case, your "gentle playfulness" was taken to be snark, sarcasm, and point scoring). If you insist on using this and similar forms of humor that, ordinarily, depe…

"blah, blah, blah" can be an expression of scornful boredom or the utterance of a vampire.

Re: Mixture-of-Depths: Dynamically allocating compute in transformers

#57

Simplified Intro Version: Imagine you have a smart assistant that can understand and process the words you say to it. Usually, this assistant pays equal attention to every word you say, no matter how important or unimportant each word is to the overall meaning of your message. Now, imagine that we found a way to teach the assistant to be smarter about how it uses its "brain power." Instead of giving equal attention t…

I understand this is ELI5, but doesn’t attention already do this, in the way you described? It pays specific focus to the most contextual words in the prior sequence.

Re: Mixture-of-Depths: Dynamically allocating compute in transformers

#58
Most important paper of 2024.

The idea that we want models not to have to use the same amount of compute for every token has been around for a while. This is the first compelling mechanism I've seen for doing it.

> Equipped with these new methods, we can sample autoregressively by choosing to route tokens to or around a block based on the router’s output, which does not depend on any information from future tokens. We provide empirical evidence that this is a relatively easy auxiliary task that quickly achieves 99% accuracy.

Does anyone else find this is a bit surprising?

Re: Mixture-of-Depths: Dynamically allocating compute in transformers

#59

Earlier quoted context omitted.

To playfully invite for you to participate in conversation further, so that I may humbly learn from you. "I don't know what you're talking about" seemed too spartan and austere and aggressive, and you reciprocated politely, if again sparsely, when the other person playfully invited you to elaborate.

Well, you've now made your original intent specific, but in case you didn't draw the requisite lesson I'll make that explicit. Because text has less bandwidth than almost any other medium, certain forms of humor are much more likely to be understood (in this case, your "gentle playfulness" was taken to be snark, sarcasm, and point scoring). If you insist on using this and similar forms of humor that, ordinarily, depe…

You are painfully boring

Re: Mixture-of-Depths: Dynamically allocating compute in transformers

#60
post #57

Simplified Intro Version: Imagine you have a smart assistant that can understand and process the words you say to it. Usually, this assistant pays equal attention to every word you say, no matter how important or unimportant each word is to the overall meaning of your message. Now, imagine that we found a way to teach the assistant to be smarter about how it uses its "brain power." Instead of giving equal attention t…

I understand this is ELI5, but doesn’t attention already do this, in the way you described? It pays specific focus to the most contextual words in the prior sequence.

The way I understood it is that for each token, the attention mechanism itself consumes a fixed amount of processor time.

The innovation here is to prioritize tokens so that some tokens have more or less processor time.

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