第9章 · Transformer 架构精讲

Transformer 是现代 AI 的基石。从 2017 年的论文到 2026 年的每一款大模型，核心架构没变，但每个组件都被反复优化过。本章讲原理，不讲推导。第 10 章讲 LLM 层面的演进。

生活比喻：Transformer 像全班同学同时传纸条——RNN 是挨个传，你传给我、我传给他，效率低还容易传丢（长句子忘开头）；Transformer 是所有人同时互传，每个人都能直接看到全班所有人的纸条内容（自注意力），然后自己决定重点关注谁的信息。这样一次传完，不用排队，速度快了上百倍。

一句话总结：Transformer = 自注意力（全局并行建模）+ 位置编码（注入顺序）+ 残差连接 + LayerNorm。2026 年标准 LLM 架构 = Decoder-Only + GQA + RoPE + FlashAttention。本章从 RNN 的困境出发，逐个拆解这些组件为什么被发明、如何工作、以及现代变体如何将其推向极致。

本章目录

graph TD
    A[Transformer 架构] --> B[核心组件]
    A --> C[架构变体]
    A --> D[2026 进化]
    B --> B1[自注意力机制]
    B --> B2[多头注意力]
    B --> B3[位置编码]
    B --> B4[前馈网络 FFN]
    B --> B5[残差连接 + LayerNorm]
    C --> C1[Encoder-Decoder]
    C --> C2[Decoder-Only]
    D --> D1[RoPE / GQA]
    D --> D2[FlashAttention]
    D --> D3[动手实现]

跨章节关联

第10章 · LLM 原理与架构演进 — Transformer 之后：LLM 的架构演进
第19章 · 多模态深度原理 — ViT：Transformer 在视觉领域的应用
第8章 · RNN 与序列模型 — Transformer 的前身：为什么需要替代 RNN
第6章 · 神经网络基础 — 注意力机制的前置：神经网络怎么学习

来源

Vaswani et al., “Attention Is All You Need”, 2017, https://arxiv.org/abs/1706.03762
Bahdanau et al., “Neural Machine Translation by Jointly Learning to Align and Translate”, 2014, https://arxiv.org/abs/1409.0473
Sutskever et al., “Sequence to Sequence Learning with Neural Networks”, 2014, https://arxiv.org/abs/1409.3215
Jay Alammar, “The Illustrated Transformer”, https://jalammar.github.io/illustrated-transformer/
Lilian Weng, “The Transformer Family”, https://lilianweng.github.io/posts/2020-04-07-the-transformer-family/
d2l.ai, “Attention Mechanisms”, https://d2l.ai/chapter_attention-mechanisms/index.html
Ranksquire, “LLM Architecture 2026”, https://ranksquire.com/2026/04/13/llm-architecture-2026/
Knight Li, “LLM Architecture Evolution 2023-2026”, https://knightli.com/en/2026/05/17/llm-architecture-evolution-2023-2026/
IBM, “What is Grouped-Query Attention?”, https://www.ibm.com/think/topics/grouped-query-attention
Developers Digest, “LLM Architecture Complexity: MoE & FlexAttention”, https://www.developersdigest.tech/blog/llm-architecture-complexity-moe-flexattention
Dao et al., “FlashAttention-1”, 2022, https://arxiv.org/abs/2205.14135
Dao, “FlashAttention-2”, 2023, https://arxiv.org/abs/2307.08691
Shah et al., “FlashAttention-3”, 2024, https://arxiv.org/abs/2407.08608
Sebastian Raschka, “LLM Research Papers 2026 Part 1”, https://magazine.sebastianraschka.com/p/llm-research-papers-2026-part1
Su et al., “RoFormer: Enhanced Transformer with Rotary Position Embedding”, 2021, https://arxiv.org/abs/2104.09864
Press et al., “Train Short, Test Long: Attention with Linear Biases”, 2022, https://arxiv.org/abs/2108.12409
Shazeer, “GLU Variants Improve Transformer”, 2020, https://arxiv.org/abs/2002.05202
Geva et al., “Transformer Feed-Forward Layers Are Key-Value Memories”, 2021, https://arxiv.org/abs/2012.14913
Meng et al., “Locating and Editing Factual Associations in GPT”, 2022, https://arxiv.org/abs/2202.05262
Shazeer et al., “Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer”, 2017, https://arxiv.org/abs/1701.06538
Fedus et al., “Switch Transformers”, 2022, https://arxiv.org/abs/2101.03961
Zhang & Sennrich, “Root Mean Square Layer Normalization”, 2019, https://arxiv.org/abs/1910.07467
Ba et al., “Layer Normalization”, 2016, https://arxiv.org/abs/1607.06450
Clark et al., “What Does BERT Look At?”, 2019, https://arxiv.org/abs/1906.04341
Michel et al., “Are Sixteen Heads Really Better than One?”, 2019, https://arxiv.org/abs/1905.10650
Voita et al., “Analyzing Multi-Head Self-Attention”, 2019, https://arxiv.org/abs/1905.09418
Peng et al., “YaRN: Efficient Context Window Extension”, 2023, https://arxiv.org/abs/2309.00071
Gu & Dao, “Mamba: Linear-Time Sequence Modeling”, 2023, https://arxiv.org/abs/2312.00752
Lieber et al., “Jamba: A Hybrid Transformer-Mamba Language Model”, 2024, https://arxiv.org/abs/2403.19887

第9章 · Transformer 架构精讲

本章目录

跨章节关联

来源

关联61

反向链接46

出链15