architectures including Multi-head Latent Attention (MLA) and DeepSeekMoE. MLA guarantees efficient inference through significantly compressing the Key-Value (KV) cache into a latent vector, while DeepSeekMoE Introduction 4 2 Architecture 6 2.1 Multi-Head Latent Attention: Boosting Inference Efficiency 6 2.1.1 Preliminaries: Standard Multi-Head Attention 6 2.1.2 Low-Rank Key-Value Joint Compression 16B Model Equipped with MLA and DeepSeekMoE 29 B.1 Model Description ..... 29 B.2 Performance Evaluation ..... 30 C Full Formulas of MLA ..... 31 D Ablation of Attention Mechanisms ..... 31 D.1

[Image](/uploads/documents/a/b/7/b/ab7b254a5c187d70765c98d89cffb40d/p6_1.jpg) ### 1 \.Transformer介绍 ## Attention注意力机制 在介绍什么是注意力机制之前，先让大家看一张图片。当大家看到下面图片，会首先看到什么内容？当过载信息映入眼帘时，我们的大脑会把注意力放在主要的信息上，这就是大脑的注意力机制。 ! [Image](/uploads/documents/a/b/7/b/ab7b254a5c187d70765c98d89cffb40d/p7_1.jpg) ### 1 \.Transformer介绍 ## 每个词的Attention计算 ## 每个词的Q会跟整个序列中每一个K计算得分，然后基于得分再分配特征 Q: query，要去查询的 K: key，等着被查的 V: value，实际的特征信息 ![Image Transformer介绍 ## Attention的优点 1. 参数少：相比于 CNN、RNN，其复杂度更小，参数也更少。所以对算力的要求也就更小。 2.速度快：Attention 解决了 RNN 及其变体模型不能并行计算的问题。Attention 机制每一步计算不依赖于上一步的计算结果，因此可以和 CNN 一样并行处理。 3. 效果好：在Attention 机制引入之前，有一个问题大家一直

del-rel 其中，Qwen 系列凭借灵活的多尺寸选项，强大的多语言支持以及友好的模型授权功能，赢得了社区开发者的高度评价。DeepSeek 通过引入多头潜在注意力（Multi-head Latent Attention，MLA）技术，在性能和成本上实现了革命性突破，开创高性价比的 AI 新纪元。 智谱的 CogVideoX 系列文生视频模型，成为全球首批开源的文生视频模型之一，不仅在技术方面

upgrades in architecture and optimization: (1) a hybrid attention architecture that combines Compressed Sparse Attention (CSA) and Heavily Compressed Attention (HCA) to improve long-context efficiency; (2) Manifold- Manifold-Constrained Hyper-Connections 7 2.3 Hybrid Attention with CSA and HCA 9 2.3.1 Compressed Sparse Attention 9 2.3.2 Heavily Compressed Attention 11 2.3.3 Other Details 12 2.3.4 Efficiency Discussion Cost-Effective and Memory-Efficient Implementation of mHC 21 3.5.3 Contextual Parallelism for Long-Context Attention 21 3.5.4 Extended Automatic Differentiation for Flexible Activation Checkpointing 21 3.6 Inference

Transformer 是谷歌在论文《Attention is all you need》 $ ^{[1]} $ 中提出来解决 Sequence to Sequence 问题的模型，其本质上是一个编解码（Encoder-Decoder）结构，编码器 Encoder 由 6 个编码 block 组成，Encoder 中的每个 block 包含 Multi-Head Attention 和 FFN（Feed-Forward FFN（Feed-Forward Network）；同样解码器 Decoder 也是 6 个解码 block 组成，每个 block 包含 Multi-Head Attention、Encoder-Decoder Attention 和FFN。具体结构如图1所示，其详细的介绍可参考文献 $ [1,6] $ 。 ![Image](/uploads/documents/4/2/c/4/42c4fbcf187df0f62 Transformer 的编码层，这里先对它做简单的介绍。它主要由以下两部分组成： Multi-Head Attention Multi-Head Attention 实际上是 h 个 Self-Attention 的集成，h 代表头的个数。其中 Self-Attention 的计算公式如下： $$ Attention\left(\mathbf{K},\mathbf{Q},\mathbf{V}\r

serving. vLLM is fast with: - State-of-the-art serving throughput - Efficient management of attention key and value memory with PagedAttention - Continuous batching of incoming requests - Fast model tested version ## 1. Install flash attention for ROCm Install ROCm's flash attention (v2.0.4) following the instructions from ROCmSoftwarePlatform/flash attention Note: - If you are using rocm5.7 ROCm flash attention directly. - If you fail to install ROCmSoftwarePlatform/flash-attention, try cloning from the commit 6fd2f8e572805681cd67ef8596c7e2ce521ed3c6. - ROCm's Flash-attention-2 (v2.0.4)

AutoHensGNN 框架 ## 多类别层次化图模型优化： （1）候选图模型的生成：现实世界中的图通常是多种属性的组合，这些属性信息很难只用一种方法捕捉完全，因此，我们使用了基于谱域、空域、Attention机制等多种不同类型的模型来捕捉多种属性关系。不同模型在不同数据集上效果差异较大，为了防止后续模型融合时加入效果较差的模型，会对GCN、GAT、APPNP、TAGC、DNA、GraphSAGE、 网络注重于细粒度刻画空间信息，源于不同的球面距离，不同的区块位置影响大，需要多重信息深度建模。更多详情，大家可参考团队的 CIKM 论文：Trilateral Spatiotemporal Attention Network for User Behavior Modeling in Location-based Search $ ^{[23]} $ 。 ![Image](/uploads/doc M. xgboost: Extreme Gradient Boosting[J]. 2016. [23] Qi, Yi, et al. “Trilateral Spatiotemporal Attention Network for User Behavior Modeling in Location-based Search”, CIKM 2021. [24] 广告深度预估技术在美团到店场景下的突破与畅想

serving. vLLM is fast with: - State-of-the-art serving throughput - Efficient management of attention key and value memory with PagedAttention - Continuous batching of incoming requests - Fast model BUILD_FA: specifies whether to build CK flash-attention. The default is 1. For Radeon RX 7900 series (gfx1100), this should be set to 0 before flash-attention supports this target. - FX_GFX_ARCHS: specifies flash-attention, for example, gfx90a;gfx942 for MI200 and MI300. The default is gfx90a;gfx942 - FA_BRANCH: specifies the branch used to build the CK flash-attention in ROCm's flash-attention repo.

Succeeded✓ Succeeded✓ Succeeded✓ SucceededHUAWEI HUAWEI MLA-AL10✓ Succeeded✓ Succeeded✓ Succeeded✓ Succeeded✓ Suc 30.99°C| |---|---|---|---|---|---|---| |✗ Vivo vivo Y66 522s|✗ OPPO OPPO A59m 3.25s|✗ HUAWEI HUAWEI MLA-AL10 6.21%|✗ MI MI 6X 264.29MB|✗ Samsung SM-N9500 0fps|✗ MI Redmi Note 4X 2,605.5KB|✗ OPPO OPPO R11s |Vivo|vivo Y66|1.4|3,072|522| |Vivo|vivo Y75s|1.8|4,096|503| |MEIZU|m3 note|1.8|2,048|503| |HUAWEI|HUAWEI MLA-AL10|2|4,096|497| |Smartisan|OS103|1.6|6,144|496| ![Image](/uploads/documents/5/e/6/f/5e6f968ae29

serving. vLLM is fast with: - State-of-the-art serving throughput - Efficient management of attention key and value memory with PagedAttention - Continuous batching of incoming requests - Fast model BUILD_FA: specifies whether to build CK flash-attention. The default is 1. For Radeon RX 7900 series (gfx1100), this should be set to 0 before flash-attention supports this target. - FX_GFX_ARCHS: specifies flash-attention, for example, gfx90a;gfx942 for MI200 and MI300. The default is gfx90a;gfx942 - FA_BRANCH: specifies the branch used to build the CK flash-attention in ROCm's flash-attention repo.