Summary HyperAttention Long-context Attention in Near-Linear Time arxiv.org
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Yale and Google developed HyperAttention, an improved attention mechanism utilizing Locality Sensitive Hashing that surpasses FlashAttention.
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Key Points
- Researchers have developed an approximate attention mechanism called "HyperAttention" to address computational challenges faced by large language models when dealing with long contexts.
- HyperAttention outperforms existing methods like FlashAttention, providing significant speed improvements.
- The algorithm achieves a practical and efficient near-linear time guarantee for attention approximation without requiring bounded entries or stable rank assumptions.
- HyperAttention maintains performance levels comparable to exact computation and has the potential to scale self-attention for both inference and training in significantly long sequences.
- The paper discusses a new method for accelerating vision transformers using a linear Taylor attention mechanism, combining low-rank and sparse approximation techniques.
Summaries
16 word summary
Yale and Google created HyperAttention, an approximate attention mechanism that surpasses FlashAttention using Locality Sensitive Hashing.
77 word summary
Yale University and Google Research have developed HyperAttention, an approximate attention mechanism that outperforms existing methods like FlashAttention. It uses Locality Sensitive Hashing (LSH) to identify large entries, providing significant speed improvements. HyperAttention achieves near-linear time guarantee for attention approximation, supports causal masking, and does not require bounded entries or stable rank assumptions. It demonstrates over a 50x acceleration in forward and backward propagation for sequence lengths of 131k while maintaining performance levels similar to exact computation.
130 word summary
Yale University and Google Research have developed HyperAttention, an approximate attention mechanism that addresses computational challenges in large language models dealing with long contexts. HyperAttention outperforms existing methods like FlashAttention by using Locality Sensitive Hashing (LSH) to identify large entries, providing significant speed improvements. It achieves a near-linear time guarantee for attention approximation, supports causal masking, and does not require bounded entries or stable rank assumptions. The algorithm involves finding large entries of the attention matrix through a black box method and using fine-grained parameters to analyze time complexity. HyperAttention demonstrates over a 50x acceleration in forward and backward propagation for sequence lengths of 131k while maintaining performance levels similar to exact computation. It significantly improves inference and training speeds with minimal performance degradation and offers versatility in various tasks.
427 word summary
Yale University and Google Research have developed an approximate attention mechanism called "HyperAttention" to address computational challenges in large language models dealing with long contexts. HyperAttention features a modular design that integrates other fast low-level implementations, including FlashAttention. Using Locality Sensitive Hashing (LSH) to identify large entries, HyperAttention outperforms existing methods like FlashAttention, providing significant speed improvements. The researchers validated its performance on various long-context length datasets, demonstrating faster inference times while maintaining comparable performance levels.
Transformers face scalability limitations due to the quadratic complexity of their attention layers. This work presents an algorithm called HyperAttention that achieves a practical and efficient near-linear time guarantee for attention approximation. It supports causal masking and does not require bounded entries or stable rank assumptions. The algorithm involves finding large entries of the attention matrix through a black box method and using fine-grained parameters to analyze time complexity. Empirically, HyperAttention demonstrates significant speed improvements, achieving over a 50x acceleration in forward and backward propagation for sequence lengths of 131k while maintaining performance levels similar to exact computation.
The robustness of HyperAttention on different tasks is investigated, and it is discovered that summarization and code completion tasks are more resilient to approximate attention layers than question answering. The concept of sortLSH, a variant of angular locality-sensitive hashing, is introduced to efficiently identify dominant entries within the attention matrix. The algorithm sorts keys and queries based on their hash buckets, allowing large entries to be captured by computing equal-sized blocks along the diagonal.
To approximate the diagonal scaling matrix D, a two-step procedure is proposed. First, sortLSH is used to identify dominant entries within the attention matrix, and then a small subset of keys is randomly selected. This simple approach provides a spectral approximation guarantee for the estimated matrix D. To approximate the matrix product between D ?1 A and the value matrix V, sampling based on the squared row norms of V is used.
The performance of HyperAttention is evaluated by integrating it into existing LLMs, measuring perplexity and speedup. HyperAttention significantly improves inference and training speeds with minimal performance degradation. The speedup compared to exact computation using FlashAttention is up to 54x faster runtime without causal masking and 5.4x faster runtime with causal masking for sequence lengths up to 131k.
In conclusion, HyperAttention is an efficient algorithm that provides a near-linear time approximation for attention mechanisms in large language models. It offers significant speed improvements while maintaining performance levels comparable to exact computation. The algorithm is versatile, supports causal masking, and does not require bounded entries or stable rank assumptions.
623 word summary
Yale University and Google Research have developed an approximate attention mechanism called "HyperAttention" to address computational challenges in large language models (LLMs) dealing with long contexts. Previous work has shown that quadratic time is necessary for attention layers unless the attention matrix has bounded entries or low stable rank. The researchers introduced two parameters to measure the hardness of the problem: the max column norm in the normalized attention matrix and the ratio of row norms in the unnormalized attention matrix after removing large entries. HyperAttention features a modular design that integrates other fast low-level implementations, including FlashAttention. Empirically, using Locality Sensitive Hashing (LSH) to identify large entries, HyperAttention outperforms existing methods like FlashAttention, providing significant speed improvements. The researchers validated its performance on various long-context length datasets, demonstrating faster inference times while maintaining comparable performance levels.
Transformers, successfully applied to various learning tasks, face scalability limitations due to the quadratic complexity of their attention layers. Several approaches have been explored to approximate intermediate matrices in attention layers, but they do not provide end-to-end guarantees or support causal masking. Recent theoretical bounds suggest that entry-wise approximations to the attention matrix are impossible in sub-quadratic time, except for KDEFormer, which provides provable approximation in sub-quadratic time under the assumption of bounded entries. This work presents an algorithm that achieves a practical and efficient near-linear time guarantee for attention approximation. It supports causal masking and does not require bounded entries or stable rank assumptions. The algorithm involves finding large entries of the attention matrix through a black box method and using fine-grained parameters to analyze time complexity. Empirically, HyperAttention demonstrates significant speed improvements, achieving over a 50x acceleration in forward and backward propagation for sequence lengths of 131k while maintaining performance levels similar to exact computation.
The robustness of HyperAttention on different tasks is investigated, and it is discovered that summarization and code completion tasks are more resilient to approximate attention layers than question answering. The concept of sortLSH, a variant of angular locality-sensitive hashing, is introduced to efficiently identify dominant entries within the attention matrix. The algorithm sorts keys and queries based on their hash buckets, allowing large entries to be captured by computing equal-sized blocks along the diagonal. This approach aligns with modern hardware's block-memory access patterns and can be efficiently parallelized.
To approximate the diagonal scaling matrix D, a two-step procedure is proposed. First, sortLSH is used to identify dominant entries within the attention matrix, and then a small subset of keys is randomly selected. This simple approach provides a spectral approximation guarantee for the estimated matrix D. To approximate the matrix product between D ?1 A and the value matrix V, sampling based on the squared row norms of V is used.
The performance of HyperAttention is evaluated by integrating it into existing LLMs, measuring perplexity and speedup. HyperAttention significantly improves inference and training speeds with minimal performance degradation. The speedup compared to exact computation using FlashAttention is up to 54x faster runtime without causal masking and 5.4x faster runtime with causal masking for sequence lengths up to 131k.
In conclusion, HyperAttention is an efficient algorithm that provides a near-linear time approximation for attention mechanisms in large language models. It offers significant speed improvements while maintaining performance levels comparable to exact computation. The algorithm is versatile, supports causal masking, and does not require bounded entries or stable rank assumptions. It has the potential to scale self-attention for both inference and training in significantly long sequences.
The paper also discusses a new method for accelerating vision transformers using a linear Taylor attention mechanism, providing references to relevant works in the field. It includes proofs for key lemmas and theorems, as well as corollaries that further demonstrate the effectiveness of the proposed method.
1175 word summary
Researchers from Yale University and Google Research have developed an approximate attention mechanism called "HyperAttention" to address the computational challenges faced by large language models (LLMs) when dealing with long contexts. Previous work has shown that quadratic time is necessary for attention layers unless the attention matrix has bounded entries or low stable rank. The researchers introduced two parameters to measure the hardness of the problem: the max column norm in the normalized attention matrix and the ratio of row norms in the unnormalized attention matrix after removing large entries.
HyperAttention features a modular design that can integrate other fast low-level implementations, including FlashAttention. Empirically, using Locality Sensitive Hashing (LSH) to identify large entries, HyperAttention outperforms existing methods like FlashAttention, providing significant speed improvements. The researchers validated the empirical performance of HyperAttention on various long-context length datasets, demonstrating faster inference times and maintaining performance levels comparable to the original models.
Transformers, which have been successfully applied to various learning tasks, face scalability limitations due to the quadratic complexity of their attention layers. Several approaches have been explored to approximate intermediate matrices in attention layers, but they do not provide end-to-end guarantees or support causal masking. Recent theoretical bounds suggest that entry-wise approximations to the attention matrix are impossible in sub-quadratic time. However, a recent work called KDEFormer provides provable approximation in sub-quadratic time under the assumption of bounded entries.
In this work, the researchers provide an algorithm that achieves a practical and efficient near-linear time guarantee for attention approximation. Their approach supports causal masking and does not require bounded entries or stable rank assumptions. The algorithm involves finding large entries of the attention matrix through a black box method and using fine-grained parameters to analyze time complexity. Empirically, HyperAttention demonstrates significant speed improvements, achieving over a 50x acceleration in forward and backward propagation for sequence lengths of 131k. When applied to pretrained LLMs, it maintains performance levels that closely match those of the original models.
The researchers also investigate the robustness of HyperAttention on different tasks and discover that summarization and code completion tasks are more resilient to approximate attention layers than question answering. They introduce the concept of sortLSH, a variant of angular locality-sensitive hashing, to efficiently identify dominant entries within the attention matrix. The algorithm sorts keys and queries based on their hash buckets, allowing large entries to be captured by computing equal-sized blocks along the diagonal. This approach aligns with modern hardware's block-memory access patterns and can be efficiently parallelized.
To approximate the diagonal scaling matrix D, the researchers propose a two-step procedure. First, they use sortLSH to identify dominant entries within the attention matrix, and then they randomly select a small subset of keys. This simple approach provides a spectral approximation guarantee for the estimated matrix D. To approximate the matrix product between D ?1 A and the value matrix V, they use sampling based on the squared row norms of V.
The researchers evaluate the performance of HyperAttention by monkey patching it into existing LLMs and measuring perplexity and speedup. They find that HyperAttention can significantly improve inference and training speeds with minimal performance degradation. They also measure the speedup of HyperAttention compared to exact computation using FlashAttention and observe up to 54x faster runtime without causal masking and 5.4x faster runtime with causal masking for sequence lengths up to 131k.
In conclusion, HyperAttention is an efficient algorithm that provides a near-linear time approximation for attention mechanisms in large language models. It offers significant speed improvements while maintaining performance levels comparable to exact computation. The algorithm is versatile, supports causal masking, and does not require bounded entries or stable rank assumptions. It has the potential to scale self-attention for both inference and training in significantly long sequences.
The paper titled "HyperAttention Long-context Attention in Near-Linear Time" discusses a new method for accelerating vision transformers using a linear Taylor attention mechanism. The authors propose a unified approach that combines low-rank and sparse approximation techniques to improve the efficiency of vision transformers.
The paper references several related works in the field, including Vitality by Yingyan Lin, which also focuses on accelerating vision transformers. Other notable references include BERT by Jacob Devlin et al., LongNet by Jiayu Ding et al., An Image is Worth 16x16 Words by Alexey Dosovitskiy et al., and Transformers are RNNs by Angelos Katharopoulos et al. These works provide insights into pre-training deep bidirectional transformers, scaling transformers to large token sizes, and applying transformers to image recognition tasks.
The authors also mention the use of fast Monte-Carlo algorithms for approximate matrix multiplication by Petros Drineas and Ravi Kannan, which is relevant to the efficient computation of attention mechanisms. They reference the GLM language model pretraining method by Zhengxiao Du et al. and the LM-infinite model by Chi Han et al., both of which contribute to improving language models.
Additionally, the paper cites works such as Reformer by Nikita Kitaev et al., which focuses on efficient transformers, and Heavy Hitters via Cluster-Preserving Clustering by Kasper Green Larsen et al., which discusses clustering algorithms. The authors also reference Faster Algorithms for Rectangular Matrix Multiplication by François Le Gall, which is relevant to matrix multiplication operations.
Other works mentioned include Learning the Positions in CountSketch by Yi Li et al., Textbooks Are All You Need II: phi-1.5 Technical Report by Yuanzhi Li et al., Concentration by Colin McDiarmid, Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer by Colin Raffel et al., Efficient Content-Based Sparse Attention with Routing Transformers by Aurko Roy et al., Sparse Attention with Learning to Hash by Zhiqing Sun et al., Attention Is All You Need by Ashish Vaswani et al., XLNet by Zhilin Yang et al., Big Bird: Transformers for Longer Sequences by Manzil Zaheer et al., KDEformer: Accelerating Transformers via Kernel Density Estimation by Amir Zandieh et al., and Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting by Haoyi Zhou et al.
The paper includes omitted proofs, specifically the proof of Lemma 1. The lemma shows that the calculated value in line 3 of Algorithm D2 is close to the maximum row sum of a matrix. It also defines subsets and sets bounds on their cardinalities. The proof concludes by bounding the operator norm of the error.
The paper also includes the proof of Theorem 1, which demonstrates the spectral approximation guarantee for the proposed method. The proof relies on Lemma 1 and provides a bound on the stable rank of a matrix. The runtime of the algorithm is analyzed, and it is shown that the computation time is dominated by matrix multiplication and attention matrix calculations.
The paper concludes with two corollaries. Corollary 1 discusses collision probabilities in hash functions, and Corollary 2 presents a method for constructing a mask matrix based on the support of certain columns.
In summary, the paper introduces a novel approach for accelerating vision transformers using a linear Taylor attention mechanism. It references relevant works in the field, presents proofs for key lemmas and theorems, and includes corollaries that further demonstrate the effectiveness of the proposed method.