Tensor Product Attention Is All You Need
The paper presents Tensor Product Attention (TPA), enhancing language model efficiency by reducing memory overhead and enabling longer sequence processing. The new T6 architecture outperforms standard Transformer models in various tasks.
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The paper titled "Tensor Product Attention Is All You Need" introduces a new attention mechanism called Tensor Product Attention (TPA) aimed at improving the efficiency of language models when handling longer input sequences. Traditional methods often require large key-value (KV) caches, leading to significant memory overhead during inference. TPA utilizes tensor decompositions to compactly represent queries, keys, and values, which reduces the size of the KV cache. By employing contextual low-rank components and integrating with Rotary Position Embedding (RoPE), TPA enhances both model quality and memory efficiency. The authors also present a new model architecture, the Tensor ProducT ATTenTion Transformer (T6), which outperforms standard Transformer baselines such as Multi-Head Attention (MHA) and others across various language modeling tasks. The empirical evaluations indicate that T6 achieves better performance metrics, including perplexity, while enabling the processing of longer sequences under fixed resource constraints. This advancement addresses a significant scalability challenge in modern language models. The code for the proposed methods is made available for further research and application.
- Tensor Product Attention (TPA) reduces memory overhead in language models.
- TPA integrates with Rotary Position Embedding (RoPE) for improved efficiency.
- The Tensor ProducT ATTenTion Transformer (T6) outperforms standard Transformer models.
- T6 enables processing of longer sequences within fixed resource limits.
- The research addresses scalability challenges in modern language modeling.
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- Many commenters express frustration with the trend of overly simplistic or misleading paper titles, suggesting alternatives for clarity.
- There is confusion regarding the relationship between memory consumption and parameter growth in relation to sequence length.
- Some users highlight the limitations of the paper, noting that it addresses memory efficiency but not decoding speed for longer context windows.
- Concerns are raised about the computational complexity associated with tensor decomposition in the context of the proposed method.
- Several comments reflect a general annoyance with the repetitive use of phrases like "X is all you need" in academic titles.
12 commentsWhen trying to deploy llms in with larger context windows constrained environments 2 things start to hurt: a) increased memory footprint for longer KV cache b) increased decode speed due to longer context window. this paper addresses a) only, which is useful, but we are still left with b) (right?)
Why do every paper has to mention this word "novel" and these titles are getting crazier day by day.
I thought the number of parameters grows quadratically with context window length - what do they mean?
I hate ads, but I’m not paying for YouTube Premium either. That’s how it goes. I get ads.
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