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
Efficient Attention Implementations (FlashAttention)
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- FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Aware Operators, Tri Dao, Daniel Y. Fu, Stefano Ermon, Atri Rudra, Christopher Ré, 2022 Advances in Neural Information Processing Systems (NeurIPS 2022) DOI: 10.48550/arXiv.2205.14135 - The foundational paper introducing FlashAttention, detailing its core techniques of kernel fusion, tiling, and online softmax for exact, I/O-aware attention optimization.
- FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning, Tri Dao, 2023 arXiv preprint arXiv:2307.08691 DOI: 10.48550/arXiv.2307.08691 - Describes improvements in FlashAttention-2, focusing on enhanced parallelism and work partitioning for further speedups and memory efficiency on modern GPUs.
- FlashAttention: Faster and More Efficient Attention for Transformers, Tri Dao, Daniel Y. Fu, Stefano Ermon, Atri Rudra, Christopher Ré, 2022 arXiv (arXiv) DOI: 2205.14135 - An accessible explanation by the original authors that details the practical implications and underlying hardware principles of FlashAttention's memory and speed optimizations.