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
Relative Positional Encodings
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- Self-Attention with Relative Position Representations, Peter Shaw, Jakob Uszkoreit, Ashish Vaswani, 2018 Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers) (Association for Computational Linguistics) DOI: 10.18653/v1/N18-2074 - This paper introduces one of the earliest explicit formulations for incorporating relative positional information into the self-attention mechanism by adding learned relative position embeddings to keys and values.
- Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context, Zihang Dai, Zhilin Yang, Yiming Yang, Jaime Carbonell, Quoc Le, Ruslan Salakhutdinov, 2019 Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Association for Computational Linguistics) DOI: 10.18653/v1/P19-1285 - This work presents an efficient relative positional encoding scheme that reformulates the attention score calculation to decompose it into content and relative position terms, which helps with better handling of longer sequences.
- Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer, Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu, 2020 JMLR, Vol. 21 (JMLR) - This paper describes the T5 model, which employs a simplified variant of relative positional encodings, showcasing their effectiveness in large-scale pre-training for various natural language processing tasks.
- DeBERTa: Decoding-enhanced BERT with Disentangled Attention, Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen, 2021 International Conference on Learning Representations (ICLR) DOI: 10.48550/arXiv.2006.03654 - This research introduces a disentangled attention mechanism that refines relative positional encoding by treating content and relative position embeddings as separate vectors, leading to strong performance in many NLP benchmarks.