Multi-Token Attention
The "Multi-Token Attention" paper introduces a new attention mechanism for large language models, improving performance in language modeling and information retrieval by conditioning on multiple query and key vectors simultaneously.
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The paper titled "Multi-Token Attention" by Olga Golovneva and colleagues introduces a novel attention mechanism designed to enhance the performance of large language models (LLMs). Traditional soft attention relies on single token attention, where attention weights are determined by the similarity between a single query and key token vector. This approach limits the amount of information utilized for distinguishing relevant context. The proposed Multi-Token Attention (MTA) method addresses this limitation by allowing LLMs to condition attention weights on multiple query and key vectors simultaneously. This is achieved through convolution operations that enable nearby queries and keys to influence each other's attention weights, resulting in a more nuanced understanding of context. The authors demonstrate that MTA significantly improves performance on various benchmarks, particularly in language modeling tasks and scenarios requiring information retrieval from long contexts. The findings suggest that MTA's ability to leverage richer information leads to superior outcomes compared to traditional Transformer models.
- Multi-Token Attention (MTA) enhances attention mechanisms in large language models.
- MTA allows simultaneous conditioning on multiple query and key vectors.
- The method improves performance on language modeling and information retrieval tasks.
- MTA outperforms traditional Transformer models in various benchmarks.
- The approach utilizes convolution operations for more nuanced attention weight determination.
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- Several commenters discuss the integration of convolution operations with attention mechanisms, noting its potential benefits and challenges.
- There are concerns about the practicality and efficiency of the proposed method, especially regarding compatibility with existing optimized attention libraries.
- Some users question the necessity of reintroducing local windows in attention, suggesting it may contradict the original purpose of addressing long-range dependencies.
- Comparisons are made to other models, such as the Byte Latent Transformer, highlighting different approaches to attention and embedding.
- There is a general interest in moving beyond tokenization to enhance model capabilities, with some advocating for innovative solutions in AI development.
10 commentsThere's pytorch's FlexAttention which could maybe make this practical, but currently it's just way too buggy.
1. https://ai.meta.com/research/publications/byte-latent-transf...
I think we've already got a bit of a bottleneck in terms of memory bandwidth utilization.
I have been working on a classification problem on audio data (with context size somewhere between 1000 and 3000 with potential to expand later). I have been experimenting with adding attention onto a CNN for a classification task I have been working on.
I tried training a vanilla transformer but in the sizes that I am aiming for (5-30M parameters), the training is incredibly unstable and doesn't achieve the performance of an LSTM.
So I went back to CNNs which are fast to train but don't achieve the losses of LSTMs (which are much slower to train,and for higher context sizes you get into the vanishing gradient problem). The CNN-GRU hubrid a worked much better, giving me my best result.
The GRU layer I used had a size of 512. For increasing context sizes, I'd have to make the convolutional layers deeper so as not to increase the GRU size too large. Instead, I decided to swap out the GRU with a MultiHeadAttention layer. The results are great - better than the CNN-GRU (my previous best). Plus, for equivalent sizes the model is faster to train though it hogs a lot of memory.
Cool to see convolutions making such a comeback lately in the llm world. See also the recent striped hyena2 architecture, which uses the conv-based hyena operator to great success:
Put all the GPUs in cloud/s controlled by international scientists (now you can use your GPU on any device, can earn money by renting it when you don’t need it, nothing changes except you need to be online to us it, but we’ll have 5G and better worldwide. You can develop, sell or release free math-proven safe AI models in this cloud “AI App Store”, etc).
Because the main risk is an AI agent botnet - current GPUs are like nukes that are 100% unprotected - any hacker can make a virus with AI agent component just to steal money, this AI will be not aligned at all, will become a per perpetual and eventually autonomous botnet.
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