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Theoretically transformers are fundamentally more powerful than stuff like Mamba and RWKV because they don't "forget"; the newest token can attend perfectly to

Theoretically transformers are fundamentally more powerful than stuff like Mamba and RWKV because they don't "forget"; the newest token can attend perfectly to the oldest token if it wants. Mamba and RWKV on the other hand compress the old state, based on information available at the time of compression. If it turns out the new token wants some information from the old token that during the compression was excluded due to the compression thinking it wouldn't be needed, the new token has no way to access it.

nickpsecurity 3y ago
I didn’t know those alternatives had those weaknesses. I did have a whole list of alternatives to check out. Having already looked at some, do you know any transformer alternatives that don’t forget things but were still sub-quadratic? What rabbit holes are worth digging deeper into?
- deepnotderp 3y ago
  Doesn't sub-quadratic attention fundamentally imply forgetting things?
evnc 3y ago
This is fair -- the newest token can attend perfectly to the oldest token, within the context window. but also, on a broader scale, if a transformer model is presented with a long input that does not fit in its context (e.g.: you are building a chatbot, and you have a very long chat history), it must "compress" or "forget" some of that information (e.g.: repeatedly summarizing historical messages, dropping them and appending the summary at the beginning of the input). Mamba/RWKV/other "recurrent" architectures, can theoretically operate on unbounded input lengths; they "forget" information from earlier tokens over time, but is that not comparable to what a transformer must do with input lengths greater than their context window?
D0TheMath 3y ago
The jury very much still seems out on this. Computationally speaking, I believe Mamba is Turing complete, while transformers aren't (they can't do loops), so technically Mamba is more expressive. But of course, the question is always whether it ends up with lower loss.
- D0TheMath 3y ago
  (well actually its not turing complete, but it definitely seems closer)
visarga 3y ago
Yet this all-to-all approach of transformers is bottlenecked on compute, while Mamba can take in a context length of a million tokens, that might have a positive effect on long range tasks.
- maleldil 3y ago
  What use is to have infinite context if new tokens can only access a lossy compression of past tokens? There is definitely a use for this kind of model, but this also shows why Transformers are still the main architecture we use today.
  - seo-speedwagon 3y ago
    I know almost nothing about this space but “lossy compression of millions of past tokens” feels a lot more like how actual human memory works than “perfect access to a small number of recent tokens”.
    - throwup238 3y ago
      > feels a lot more like how actual human memory works Beware of feel-good traps like this. If you were to map the human connectome to a computational neural network down to the ion channel, it'd be at least 500 quadrillion parameters*. That's at least 5-6 orders of magnitude beyond what is currently possible with SOTA ML which means that even if the human brain was 99% devoted to compressing those tokens, that 1% that could actually do work with them is still a thousand times bigger than GPT4. There be emergent dragons. * This is a fascile argument to begin with since biological neuron signals aren't quantized and ion channels are far too complex to map to a single static parameter
      - fwip 3y ago
        > biological neuron signals aren't quantized Perhaps, but they inherently have imprecision due the nature of being biological. Expose the same neuron to the same input N times, and you'll get a range of output. The effect of noisy analog data is, broadly speaking, similar to the effect of low-resolution digital data.

nickpsecurity 3y ago

I didn’t know those alternatives had those weaknesses. I did have a whole list of alternatives to check out. Having already looked at some, do you know any transformer alternatives that don’t forget things but were still sub-quadratic? What rabbit holes are worth digging deeper into?

deepnotderp 3y ago
Doesn't sub-quadratic attention fundamentally imply forgetting things?

evnc 3y ago

This is fair -- the newest token can attend perfectly to the oldest token, within the context window. but also, on a broader scale, if a transformer model is presented with a long input that does not fit in its context (e.g.: you are building a chatbot, and you have a very long chat history), it must "compress" or "forget" some of that information (e.g.: repeatedly summarizing historical messages, dropping them and appending the summary at the beginning of the input). Mamba/RWKV/other "recurrent" architectures, can theoretically operate on unbounded input lengths; they "forget" information from earlier tokens over time, but is that not comparable to what a transformer must do with input lengths greater than their context window?

D0TheMath 3y ago

The jury very much still seems out on this. Computationally speaking, I believe Mamba is Turing complete, while transformers aren't (they can't do loops), so technically Mamba is more expressive. But of course, the question is always whether it ends up with lower loss.

D0TheMath 3y ago
(well actually its not turing complete, but it definitely seems closer)

visarga 3y ago

Yet this all-to-all approach of transformers is bottlenecked on compute, while Mamba can take in a context length of a million tokens, that might have a positive effect on long range tasks.

maleldil 3y ago
What use is to have infinite context if new tokens can only access a lossy compression of past tokens? There is definitely a use for this kind of model, but this also shows why Transformers are still the main architecture we use today.
- seo-speedwagon 3y ago
  I know almost nothing about this space but “lossy compression of millions of past tokens” feels a lot more like how actual human memory works than “perfect access to a small number of recent tokens”.
  - throwup238 3y ago
    > feels a lot more like how actual human memory works Beware of feel-good traps like this. If you were to map the human connectome to a computational neural network down to the ion channel, it'd be at least 500 quadrillion parameters*. That's at least 5-6 orders of magnitude beyond what is currently possible with SOTA ML which means that even if the human brain was 99% devoted to compressing those tokens, that 1% that could actually do work with them is still a thousand times bigger than GPT4. There be emergent dragons. * This is a fascile argument to begin with since biological neuron signals aren't quantized and ion channels are far too complex to map to a single static parameter
    - fwip 3y ago
      > biological neuron signals aren't quantized Perhaps, but they inherently have imprecision due the nature of being biological. Expose the same neuron to the same input N times, and you'll get a range of output. The effect of noisy analog data is, broadly speaking, similar to the effect of low-resolution digital data.