文章首页的图片主要解释MInference在大海捞针任务上的有效性,以及优于Flash Attention的效率。Insights部分 Attention is Dynamically Sparse这里先说下他这个Attention Recall,严谨点说就是:对于给定的注意力模式(如Block-Sparse, Vertical-Slash, A-shape),使用这些模式能够捕捉到的真实注意力权重的比例。换句话说,它是...
它的思路是,将单卡内部做的分块优化扩展到多卡上,通过跨卡点对点的传递 K,V 向量,来实现完整注意力,在不做近似地情况下完成超长上下文的计算。 这样我们通过 Ring Attention 解决了显存瓶颈,通过 Sparse Attention 解决了计算瓶颈,二者结合,就实现了线性内存扩展与序列长度的匹配优化,从而完成了高效的实现。 所以当...
Deep learning models, particularly in the field of Natural Language Processing (NLP), have greatly benefited from the introduction of the attention mechanism. However, as these models grow larger and tackle more complex tasks, the computational cost of attention, which is quadratic in the sequence ...
The DeepSeek team recently released a technical paper titled "Native Sparse Attention: Hardware Alignment and Native Training Sparse Attention Mechanism", introducing their proposed NSA (Naturally Sparse Attention) mechanism. NSA combines algorithm innov
Implementation of the sparse attention pattern proposed by the Deepseek team in their Native Sparse Attention paper Install $ pip install native-sparse-attention-pytorch Usage import torch from native_sparse_attention_pytorch import SparseAttention attn = SparseAttention( dim = 512, dim_head = 64, ...
Note: We don't have the ability to review paper PubDate: Jun 2022 Teams: Technical University of Denmark;Ecole des Ponts;Meta Writers: Frederik Warburg, Michael Ramamonjisoa, Manuel López-Antequera PDF:SparseFormer: Attention-based Depth Completion Network ...
attention sparsity in language-based tasks. This paper argues that attention sparsity should be learned rather than predefined. To this end, we design SeerAttention, a new Attention mechanism that augments the conventional attention with a learnable gate that adaptively selects significant block...
长文本的优化方向可谓是百花齐放,包含针对attention kernel,KV cache,位置编码,上下文的前/后处理等。相关论文综述可参考下图这篇论文: https://arxiv.org/pdf/2311.12351v2 Fig 1 本文将着眼于Sparse Attention(也可理解为KV Pruning) 这个路径中的代表性的文章StreamingLLM,LongLoRA和DuoAttention。它们的主要作者来...
This practice falls short to fully capture the dynamic nature of attention sparsity in language-based tasks. This paper argues that attention sparsity should be learned rather than predefined. To this end, we design SeerAttention, a new Attention mechanism that augments the conventional attention ...
Therefore, in this paper, we propose \textsc{sparseKT}, a simple yet effective framework to improve the robustness and generalization of the attention based DLKT approaches. Specifically, we incorporate a k-selection module to only pick items with the highest attention scores. We propose two ...