Tree Attention: Topology-Aware Decoding for Long-Context
The paper presents a new algorithm for efficient self-attention in transformers, achieving up to 8x faster decoding on GPU clusters while reducing communication volume and memory usage. Code is publicly available.
Read original article
The paper titled "Tree Attention: Topology-aware Decoding for Long-Context Attention on GPU clusters" addresses the computational challenges associated with self-attention mechanisms in transformer architectures, which exhibit quadratic complexity relative to sequence length. The authors derive a scalar energy function that facilitates the computation of self-attention blocks, providing a theoretical foundation and a Bayesian interpretation linked to energy-based models like Hopfield Networks. They propose a novel algorithm that employs tree reduction for parallelizing attention computation across multiple GPUs, achieving significant performance improvements. Their method reportedly performs cross-device decoding up to eight times faster than existing techniques such as Ring Attention, while also reducing communication volume and peak memory usage by half. The authors have made their code publicly available, contributing to the accessibility of their research.
- The paper introduces a new algorithm for efficient self-attention computation in transformers.
- It provides a theoretical basis for self-attention through a scalar energy function.
- The proposed method allows for up to 8x faster decoding on GPU clusters compared to existing methods.
- The algorithm reduces communication volume and peak memory requirements significantly.
- The authors have made their code publicly accessible for further research and application.
Related
Researchers run high-performing LLM on the energy needed to power a lightbulb
Researchers at UC Santa Cruz developed an energy-efficient method for large language models. By using custom hardware and ternary numbers, they achieved high performance with minimal power consumption, potentially revolutionizing model power efficiency.
The Illustrated Transformer
Jay Alammar's blog explores The Transformer model, highlighting its attention mechanism for faster training. It outperforms Google's NMT in some tasks, emphasizing parallelizability. The blog simplifies components like self-attention and multi-headed attention for better understanding.
FlashAttention-3: Fast and Accurate Attention with Asynchrony and Low-Precision
A new attention mechanism, FlashAttention-3, boosts Transformer speed and accuracy on Hopper GPUs by up to 75%. Leveraging asynchrony and low-precision computing, it achieves 1.5-2x faster processing, utilizing FP8 for quicker computations and reduced costs. FlashAttention-3 optimizes for new hardware features, enhancing efficiency and AI capabilities. Integration into PyTorch is planned.
Trillion-Parameter Sequential Transducers for Generative Recommendations
A new paper introduces HSTU, a trillion-parameter architecture for generative recommendations, outperforming existing models significantly in efficiency and effectiveness, with potential implications for large-scale applications and reduced carbon footprint.
Self-Compressing Neural Networks
The paper "Self-Compressing Neural Networks" presents a method to reduce neural network size, maintaining accuracy while using only 3% of bits and 18% of weights, accepted for the 2023 DL-Hardware conference.
5 commentsie., the innovation in statistical AI isn't in making the algorithms "smarter", it's finding ways to align the computation with modern GPU hardware -- this has been the story since 2012.
In the end, the function all such algs are approximating is a conditional probability. ie., the perfect answer to any prompt is to ignore training entirely, and at inference time, compute an expectation across all historical data. All training does is essentially optimally cache a large part of that computation.
This is very different to how it's typically sold/understood, in the sense that there's an appearance that at inference-time some unbounded computation is going on, ie., "thinking"/"reasoning"/etc. But at inference time for any prompt the same amount of computation is used, regardless of the question complexity. So the system will appear to reason (etc.) if it can sample convincingly from its pre-cached computation.
This means "innovation" here follows a moore's law S-curve for GPU hardware.
The announcement at https://www.zyphra.com/post/zamba2-small links to this paper, but the paper doesn't actually mention Zamba2 anywhere.
The authors claim this outperforms Ring Attention for distributed computation of self-attention over multiple GPUs.
Distributing computation is necessary whenever context is too long for self-attention's computation to fit in a single GPU's available memory.
Github link: https://github.com/Zyphra/tree_attention
Related
Researchers run high-performing LLM on the energy needed to power a lightbulb
Researchers at UC Santa Cruz developed an energy-efficient method for large language models. By using custom hardware and ternary numbers, they achieved high performance with minimal power consumption, potentially revolutionizing model power efficiency.
The Illustrated Transformer
Jay Alammar's blog explores The Transformer model, highlighting its attention mechanism for faster training. It outperforms Google's NMT in some tasks, emphasizing parallelizability. The blog simplifies components like self-attention and multi-headed attention for better understanding.
FlashAttention-3: Fast and Accurate Attention with Asynchrony and Low-Precision
A new attention mechanism, FlashAttention-3, boosts Transformer speed and accuracy on Hopper GPUs by up to 75%. Leveraging asynchrony and low-precision computing, it achieves 1.5-2x faster processing, utilizing FP8 for quicker computations and reduced costs. FlashAttention-3 optimizes for new hardware features, enhancing efficiency and AI capabilities. Integration into PyTorch is planned.
Trillion-Parameter Sequential Transducers for Generative Recommendations
A new paper introduces HSTU, a trillion-parameter architecture for generative recommendations, outperforming existing models significantly in efficiency and effectiveness, with potential implications for large-scale applications and reduced carbon footprint.
Self-Compressing Neural Networks
The paper "Self-Compressing Neural Networks" presents a method to reduce neural network size, maintaining accuracy while using only 3% of bits and 18% of weights, accepted for the 2023 DL-Hardware conference.