Advanced Transformer Architecture
Chapter 1: Revisiting Sequence Modeling Limitations
Sequential Computation in Recurrent Networks
The Vanishing and Exploding Gradient Problems
Long Short-Term Memory (LSTM) Gating Mechanisms
Gated Recurrent Units (GRUs) Architecture
Challenges with Long-Range Dependencies
Parallelization Constraints in Recurrent Models
Chapter 2: The Attention Mechanism: Core Concepts
Motivation: Overcoming Fixed-Length Context Vectors
General Framework: Query, Value Abstraction
Mathematical Formulation of Dot-Product Attention
Scaled Dot-Product Attention
The Softmax Function for Attention Weights
Computational Aspects and Matrix Operations
Practice: Implementing Scaled Dot-Product Attention
Chapter 3: Multi-Head Self-Attention
Self-Attention: Queries, Keys, Values from the Same Source
Limitations of Single Attention Head
Introducing Multiple Attention Heads
Linear Projections for Q, K, V per Head
Parallel Attention Computations
Concatenation and Final Linear Projection
Analysis of What Different Heads Learn
Hands-on Practical: Building a Multi-Head Attention Layer
Chapter 4: Positional Encoding and Embedding Layer
The Need for Positional Information
Input Embedding Layer Transformation
Sinusoidal Positional Encoding: Formulation
Properties of Sinusoidal Encodings
Combining Embeddings and Positional Encodings
Alternative: Learned Positional Embeddings
Comparison: Sinusoidal vs. Learned Embeddings
Practice: Generating and Visualizing Positional Encodings
Chapter 5: Encoder and Decoder Stacks
Overall Transformer Architecture Overview
Encoder Layer Structure
Decoder Layer Structure
Masked Self-Attention in Decoders
Encoder-Decoder Cross-Attention
Position-wise Feed-Forward Networks (FFN)
Residual Connections (Add)
Layer Normalization (Norm)
Stacking Multiple Layers
Final Linear Layer and Softmax Output
Hands-on Practical: Constructing an Encoder Block
Chapter 6: Advanced Architectural Variants and Analysis
Computational Complexity of Self-Attention
Sparse Attention Mechanisms
Approximating Attention: Linear Transformers
Kernel-Based Attention Approximation (Performers)
Low-Rank Projection Methods (Linformer)
Transformer-XL: Segment-Level Recurrence
Relative Positional Encodings
Pre-Normalization vs Post-Normalization (Pre-LN vs Post-LN)
Scaling Laws for Neural Language Models
Parameter Efficiency and Sharing Techniques
Chapter 7: Implementation Details and Optimization
Choosing a Framework (PyTorch, TensorFlow, JAX)
Weight Initialization Strategies
Optimizers for Transformers (Adam, AdamW)
Learning Rate Scheduling (Warmup, Decay)
Regularization Techniques (Dropout, Label Smoothing)
Gradient Clipping
Mixed-Precision Training
Efficient Attention Implementations (FlashAttention)
Model Parallelism and Data Parallelism Strategies
Practice: Analyzing Attention Weight Distributions
Low-Rank Projection Methods (Linformer)
Was this section helpful?
- Linformer: Self-Attention with Linear Complexity, Sinong Wang, Belinda Z. Li, Madian Khabsa, Han Fang, Hao Ma, 2020 Advances in Neural Information Processing Systems (NeurIPS), Vol. 33 (Curran Associates, Inc.) DOI: 10.48550/arXiv.2006.04768 - Introduces the Linformer model, which achieves linear complexity in self-attention by projecting keys and values to a lower dimension.
- Nyströmformer: A Nyström-Algorithm-based Efficient Transformer, Yuqi Xiong, Ye Li, Bo Li, Zhanpeng Zeng, Eytan Hu, Lizhen Nie, Chao Zhang, Mohan Teng, Xiao Zhang, Hao Ma, 2021 International Conference on Machine Learning (ICML), Vol. 139 DOI: 10.48550/arXiv.2102.03906 - Presents an alternative efficient Transformer that uses the Nyström method for low-rank approximation of attention matrices.
- A Survey of Efficient Transformers, Yi Tay, Mostafa Dehghani, Dara Bahri, Donald Metzler, 2022 ACM Computing Surveys, Vol. 35 (ACM) DOI: 10.48550/arXiv.2009.06732 - Offers an overview and taxonomy of various efficient Transformer architectures, including Linformer and other linear attention models.
- Attention Is All You Need, Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, Illia Polosukhin, 2017 Advances in Neural Information Processing Systems (NeurIPS), Vol. 30 (Curran Associates, Inc.) DOI: 10.48550/arXiv.1706.03762 - The foundational paper that introduced the Transformer architecture and the self-attention mechanism, highlighting the quadratic complexity bottleneck that Linformer addresses.