5 ms·

Starting from scratch: Training a 30M Topological Transformer

144 points by tuned 9mo ago

lostmsu 9mo ago
Comparison with vanilla of the same size/flops budget?
- Lerc 9mo ago
  I'm not sure if that is the right calculation. Provided the flops are not prohibitive. Output quality per model bytes might be better. In general people run the largest model they can. I certainly think trading speed for quality at the same size is worth looking at. Especially if it uses methods that can benefit from the efforts of others to improve speed in general. That said performance difference at 30M may not be representative of performance difference at 30B There are probably a lot of really good ideas out there waiting for someone to drop a few million in training to reveal how good they are on large sizes.
  - lostmsu 9mo ago
    So no comparison?
    - tuned 9mo ago
      comparisons will be run when the quality of generation will be on pair with other available models. It is useless to have preformance if the quality is not at lease on par. The paper runs a bench (code and bench in the paper) to compare the performance with a causal attention GPT-2 model (nanoGPT) at inference (20% faster) and at training (equivalent for T and D larger than a threshold).
- oofbey 9mo ago
  The big bet with this technique is in having a fixed (non learned) matrix which converts the tokens latent space to the linear attention space. So you can kinda cheat and say your model is small because a bunch of the smarts are in this fixed big graph laplacian matrix L. So how do you scale this up from a toy problem? Well that L would Have to get bigger. And it’s hard to imagine it being useful if L is not trained. Then it starts to look a lot more like a conventional transformer, but probably harder to train, with the benefit of smaller KV caches. (Half the size - not a massive win.) So overall doesn’t seem to me like it’s gonna amount to anything.
  - tuned 9mo ago
    the idea is to have a lot of "narrow" models to work with RAG instead of one model for all the knowledge domains or also distil the metadata that is currently in enterprise Knowledge Graphs
  - tuned 9mo ago
    also: precomputing a sparse Laplacian for N vectors at dimension D (NxD) is infinitely cheaper (if using `arrowspace`, my previous paper) than computing distances on the same full dense vectors billions of times. There are published tests that compute a Laplacian on 300Kx384 space in 500 secs on a laptop on CPU. So it is a trade-off: potentially few minutes of pretaining or hours of dot-product on dense matrices
keyle 9mo ago
Does this make any sense, to anyone?
- kannanvijayan 9mo ago
  I think this is an attempt to try to enrich the locality model in transformers. One of the weird things you do in transformers is add a position vector which captures the distance between the token being attended to the some other token. This is obviously not powerful enough to express non-linear relationships - like graph relationships. This person seems to be experimenting with doing pre-processing of the input token set, to linearly reorder it by some other heuristic that might map more closely to the actual underlying relationship between each token.
  - adroniser 9mo ago
    Adding the position vector is basic sure, but it's naive to think the model doesn't develop its own positional system bootstrapping on top of the barebones one.
    - thesz 9mo ago
      For some reason people are still adding position encodings into embeddings. As if they are not relying on the model's ability to develop its own "positional system bootstrapping on top of the barebones one."
  - thesz 9mo ago
    > like graph relationships Once upon a time during me being language modeling researcher I built and finetuned a big (at the time - about 5 billions parameters) Sparse Non-Negative Matrix Language Model [1]. [1] https://aclanthology.org/Q16-1024/ https://aclanthology.org/Q16-1024/ As this model allows for mix-and-match of various contexts, one thing that I did is to have a word-sorted context. This effectively transforms position-based context into a word-set based context, where "you and me", "me and you" and "and me you" are the same. This allowed for longer contexts and better prediction.
ashirviskas 9mo ago
I wonder what if we just crammed more into the "tokens"? I am running an experiment of replacing discrete tokens with embeddings + small byte encoder/decoder. That way you can use embedding space much more efficiently and have it contain much more nuance. Experiments I want to build on top of it: 1. Adding lsp context to the embeddings - that way the model could _see_ the syntax better, closer to how we use IDEs and would not need to read/grep 25k of lines just to find where something is used. 2. Experiments with different "compression" ratios. Each embedding could encode a different amount of bytes and we would not rely on a huge static token dictionary. I'm aware that papers exist that explore these ideas, but so far no popular/good open source models employ this. Unless someone can prove me wrong.
- appplication 9mo ago
  Not an expert in the space, but I’m not sure you need to modify tokens to get the model to see syntax, you basically get that exact association from attention.
  - ashirviskas 9mo ago
    You get that association that is relevant to your project only if you can cram the whole codebase. Otherwise it is making rough estimates and some of the time that seems to be where the models fail. It can only be fully resolved with either infinite context length, or doing it similar to how humans do it - add some LSP "color" to the code tokens. You can get a feel of what LLMs deal with when you try opening 3000 lines of code in a simple text editor and try to do something. May work for simple fixes, but not whole codebase refactors. Only ultra skilled humans can be productive in it (using my subjective definition of "productive")
- Yemoshino 9mo ago
  I found a few papers in this direction with perplexity like this one https://ceur-ws.org/Vol-4005/paper1.pdf https://ceur-ws.org/Vol-4005/paper1.pdf and it doesn't seem to be that relevant for now. The progress of a handful models seem to be so much better (because limited compute, we have only a handful of big ones, i presume) that these finetunings are just not yet relevant. I'm also curious if a english java + html + css + javascript only model would look like in size and speed for example. Unfortunate whenever i ask myself the question of finetunging tokens (just a few days ago this question came up again), deep diving takes too much time. Claude only got lsp support in november i think. And its not even clear to me to what extend. So despite the feeling we are moving fast, tons of basic ideas haven't even made it in yet
geoffbp 9mo ago
I dug into this a bit (with AI ofc) and it spat this out. I found it an easy way to visualise and start to understand: > Standard AI models (like GPT-4) treat data using Global Geometry. They imagine every word as a point floating in a massive, flat, high-dimensional room. To see how two words relate, they draw a straight line between them. > Local Topology changes the "room" into a landscape (a manifold). Instead of a flat void, the data exists on a curved surface that has hills, valleys, and paths.
- xtiansimon 9mo ago
  What is a "high-dimensional room"? A "room" is by definition three-dimensional in so far as we're using metaphor for description. Then to add this "high-dimensional" modifier does little for me, since the only visualizable high-dimensional cube is a tesseract, which still leaves you at 4-d. The presented counterpoint to this metaphor has the "room" change into a "landscape". The room is a "flat void" compared to a landscape with "hills, valleys, and paths". None of these landscape features evoke higher dimensionality in my imagination. Certainly not in the way, say, the metaphor of the "coastline" of Great Britain does when discussing the unusual properties of a fractal. These moves don't shift my railroad mind from one track onto another. So I wonder, if a metaphoric usage is not in some way universal, how can it be instructive?
  - inimino 9mo ago
    The metaphor works only if you already understand the maths.
    - xtiansimon 9mo ago
      Maths I’ve never heard of. Possible. Probable. And what you’re saying is the words “room” and “landscape” are _over coded_ to such an extent the natural logic of 3-d rooms and 2-d landscapes are easily overcome by scaffolds of mathematical instruction—such that the latter could be imagined as having *higher* dimensionality than the former, for example? Or whatever other idea orbiting those words counter to their nature. That’s very interesting.
kouteiheika 9mo ago
If you want to prove (i.e. show that it works and/or it's faster in a real-world scenario) a new alternative to attention without breaking the bank then one of the best ways to do that would probably be to retrain an already existing model, just with swapped attention modules. Then once you have such a model you can do apples-to-apples benchmarks. This has been done successfully in the past: https://huggingface.co/featherless-ai/QRWKV-72B https://huggingface.co/featherless-ai/QRWKV-72B Note that this is a 72B model which would be very expensive to train from scratch, but here they did the conversion for less than $2000.
- oofbey 9mo ago
  Depending on how different the attention mechanism is, that might not work. If it’s just a faster / different way of finding the tokens to attend to, sure. But I get the sense the author is implying this method uses different semantics somehow. Although tbh I didn’t follow it entry.
- andai 9mo ago
  This is interesting. Has there been more research into this architecture? I hear about it once every few years but it always seems like a niche / experimental thing. But based on the graph in their blog post you'd expect every company to be using this.
  - tuned 9mo ago
    This is a novel re-interpretation of the Transformer, based on my previous research made with a library called `arrowspace`. It is somehow what is called a "Grassmann-like flow" but without the Plucker embedding, or also similar to what is done in DavisTensor but relying on spectral Laplacian instead of purely geometric distances. The problem with a lot of stuff done before is that it focuses on dense representations. This architecture is focuses on sparse representation and provides a new approximation computation based on energy-informed graphs.
- Herring 9mo ago
  I'd say try the nanogpt speedrun. It's much easier to train, and gives you a better comparison vs optimized systems. https://github.com/KellerJordan/modded-nanogpt https://github.com/KellerJordan/modded-nanogpt
tuned 9mo ago
Thanks to all that have read. I would be glad to answer further scoped questions on the content of the post and the paper. I answered some comments that may clarify the ideas from the redesign.

144 points by tuned 9mo ago

lostmsu 9mo ago

Comparison with vanilla of the same size/flops budget?

Lerc 9mo ago
I'm not sure if that is the right calculation. Provided the flops are not prohibitive. Output quality per model bytes might be better. In general people run the largest model they can. I certainly think trading speed for quality at the same size is worth looking at. Especially if it uses methods that can benefit from the efforts of others to improve speed in general. That said performance difference at 30M may not be representative of performance difference at 30B There are probably a lot of really good ideas out there waiting for someone to drop a few million in training to reveal how good they are on large sizes.
- lostmsu 9mo ago
  So no comparison?
  - tuned 9mo ago
    comparisons will be run when the quality of generation will be on pair with other available models. It is useless to have preformance if the quality is not at lease on par. The paper runs a bench (code and bench in the paper) to compare the performance with a causal attention GPT-2 model (nanoGPT) at inference (20% faster) and at training (equivalent for T and D larger than a threshold).
oofbey 9mo ago
The big bet with this technique is in having a fixed (non learned) matrix which converts the tokens latent space to the linear attention space. So you can kinda cheat and say your model is small because a bunch of the smarts are in this fixed big graph laplacian matrix L. So how do you scale this up from a toy problem? Well that L would Have to get bigger. And it’s hard to imagine it being useful if L is not trained. Then it starts to look a lot more like a conventional transformer, but probably harder to train, with the benefit of smaller KV caches. (Half the size - not a massive win.) So overall doesn’t seem to me like it’s gonna amount to anything.
- tuned 9mo ago
  the idea is to have a lot of "narrow" models to work with RAG instead of one model for all the knowledge domains or also distil the metadata that is currently in enterprise Knowledge Graphs
- tuned 9mo ago
  also: precomputing a sparse Laplacian for N vectors at dimension D (NxD) is infinitely cheaper (if using `arrowspace`, my previous paper) than computing distances on the same full dense vectors billions of times. There are published tests that compute a Laplacian on 300Kx384 space in 500 secs on a laptop on CPU. So it is a trade-off: potentially few minutes of pretaining or hours of dot-product on dense matrices

keyle 9mo ago

Does this make any sense, to anyone?

kannanvijayan 9mo ago
I think this is an attempt to try to enrich the locality model in transformers. One of the weird things you do in transformers is add a position vector which captures the distance between the token being attended to the some other token. This is obviously not powerful enough to express non-linear relationships - like graph relationships. This person seems to be experimenting with doing pre-processing of the input token set, to linearly reorder it by some other heuristic that might map more closely to the actual underlying relationship between each token.
- adroniser 9mo ago
  Adding the position vector is basic sure, but it's naive to think the model doesn't develop its own positional system bootstrapping on top of the barebones one.
  - thesz 9mo ago
    For some reason people are still adding position encodings into embeddings. As if they are not relying on the model's ability to develop its own "positional system bootstrapping on top of the barebones one."
- thesz 9mo ago
  > like graph relationships Once upon a time during me being language modeling researcher I built and finetuned a big (at the time - about 5 billions parameters) Sparse Non-Negative Matrix Language Model [1]. [1] https://aclanthology.org/Q16-1024/ https://aclanthology.org/Q16-1024/ As this model allows for mix-and-match of various contexts, one thing that I did is to have a word-sorted context. This effectively transforms position-based context into a word-set based context, where "you and me", "me and you" and "and me you" are the same. This allowed for longer contexts and better prediction.

ashirviskas 9mo ago

I wonder what if we just crammed more into the "tokens"? I am running an experiment of replacing discrete tokens with embeddings + small byte encoder/decoder. That way you can use embedding space much more efficiently and have it contain much more nuance. Experiments I want to build on top of it: 1. Adding lsp context to the embeddings - that way the model could _see_ the syntax better, closer to how we use IDEs and would not need to read/grep 25k of lines just to find where something is used. 2. Experiments with different "compression" ratios. Each embedding could encode a different amount of bytes and we would not rely on a huge static token dictionary. I'm aware that papers exist that explore these ideas, but so far no popular/good open source models employ this. Unless someone can prove me wrong.

appplication 9mo ago
Not an expert in the space, but I’m not sure you need to modify tokens to get the model to see syntax, you basically get that exact association from attention.
- ashirviskas 9mo ago
  You get that association that is relevant to your project only if you can cram the whole codebase. Otherwise it is making rough estimates and some of the time that seems to be where the models fail. It can only be fully resolved with either infinite context length, or doing it similar to how humans do it - add some LSP "color" to the code tokens. You can get a feel of what LLMs deal with when you try opening 3000 lines of code in a simple text editor and try to do something. May work for simple fixes, but not whole codebase refactors. Only ultra skilled humans can be productive in it (using my subjective definition of "productive")
Yemoshino 9mo ago
I found a few papers in this direction with perplexity like this one https://ceur-ws.org/Vol-4005/paper1.pdf https://ceur-ws.org/Vol-4005/paper1.pdf and it doesn't seem to be that relevant for now. The progress of a handful models seem to be so much better (because limited compute, we have only a handful of big ones, i presume) that these finetunings are just not yet relevant. I'm also curious if a english java + html + css + javascript only model would look like in size and speed for example. Unfortunate whenever i ask myself the question of finetunging tokens (just a few days ago this question came up again), deep diving takes too much time. Claude only got lsp support in november i think. And its not even clear to me to what extend. So despite the feeling we are moving fast, tons of basic ideas haven't even made it in yet

geoffbp 9mo ago

I dug into this a bit (with AI ofc) and it spat this out. I found it an easy way to visualise and start to understand: > Standard AI models (like GPT-4) treat data using Global Geometry. They imagine every word as a point floating in a massive, flat, high-dimensional room. To see how two words relate, they draw a straight line between them. > Local Topology changes the "room" into a landscape (a manifold). Instead of a flat void, the data exists on a curved surface that has hills, valleys, and paths.

xtiansimon 9mo ago
What is a "high-dimensional room"? A "room" is by definition three-dimensional in so far as we're using metaphor for description. Then to add this "high-dimensional" modifier does little for me, since the only visualizable high-dimensional cube is a tesseract, which still leaves you at 4-d. The presented counterpoint to this metaphor has the "room" change into a "landscape". The room is a "flat void" compared to a landscape with "hills, valleys, and paths". None of these landscape features evoke higher dimensionality in my imagination. Certainly not in the way, say, the metaphor of the "coastline" of Great Britain does when discussing the unusual properties of a fractal. These moves don't shift my railroad mind from one track onto another. So I wonder, if a metaphoric usage is not in some way universal, how can it be instructive?
- inimino 9mo ago
  The metaphor works only if you already understand the maths.
  - xtiansimon 9mo ago
    Maths I’ve never heard of. Possible. Probable. And what you’re saying is the words “room” and “landscape” are _over coded_ to such an extent the natural logic of 3-d rooms and 2-d landscapes are easily overcome by scaffolds of mathematical instruction—such that the latter could be imagined as having *higher* dimensionality than the former, for example? Or whatever other idea orbiting those words counter to their nature. That’s very interesting.

kouteiheika 9mo ago

If you want to prove (i.e. show that it works and/or it's faster in a real-world scenario) a new alternative to attention without breaking the bank then one of the best ways to do that would probably be to retrain an already existing model, just with swapped attention modules. Then once you have such a model you can do apples-to-apples benchmarks. This has been done successfully in the past: https://huggingface.co/featherless-ai/QRWKV-72B https://huggingface.co/featherless-ai/QRWKV-72B Note that this is a 72B model which would be very expensive to train from scratch, but here they did the conversion for less than $2000.

oofbey 9mo ago
Depending on how different the attention mechanism is, that might not work. If it’s just a faster / different way of finding the tokens to attend to, sure. But I get the sense the author is implying this method uses different semantics somehow. Although tbh I didn’t follow it entry.
andai 9mo ago
This is interesting. Has there been more research into this architecture? I hear about it once every few years but it always seems like a niche / experimental thing. But based on the graph in their blog post you'd expect every company to be using this.
- tuned 9mo ago
  This is a novel re-interpretation of the Transformer, based on my previous research made with a library called `arrowspace`. It is somehow what is called a "Grassmann-like flow" but without the Plucker embedding, or also similar to what is done in DavisTensor but relying on spectral Laplacian instead of purely geometric distances. The problem with a lot of stuff done before is that it focuses on dense representations. This architecture is focuses on sparse representation and provides a new approximation computation based on energy-informed graphs.
Herring 9mo ago
I'd say try the nanogpt speedrun. It's much easier to train, and gives you a better comparison vs optimized systems. https://github.com/KellerJordan/modded-nanogpt https://github.com/KellerJordan/modded-nanogpt

tuned 9mo ago

Thanks to all that have read. I would be glad to answer further scoped questions on the content of the post and the paper. I answered some comments that may clarify the ideas from the redesign.