LLM Compression and Acceleration Techniques
Chapter 1: Foundations of LLM Efficiency Challenges
Scaling Laws and Computational Costs of LLMs
Memory Bandwidth and Compute Bottlenecks in LLM Inference
Architectural Considerations for Efficiency
Metrics for Evaluating LLM Compression and Latency
Hardware for LLM Deployment
Theoretical Limits of Compression and Acceleration
Chapter 2: Advanced Quantization Techniques
Quantization Fundamentals Revisited
Post-Training Quantization (PTQ)
Quantization-Aware Training (QAT)
Extreme Quantization
Mixed-Precision Quantization Strategies
Hardware Acceleration for Quantized Operations
Evaluating Fidelity and Performance of Quantized LLMs
Hands-on Practical: Implementing PTQ and QAT
Chapter 3: Sophisticated Pruning Methodologies
Unstructured vs. Structured Pruning
Magnitude-Based Pruning
Movement Pruning and Dynamic Sparsity
Structured Pruning Techniques
Integrating Pruning with Quantization
Compiler and Runtime Support for Sparse Operations
Analyzing the Effects of Pruning on LLM Capabilities
Practice: Applying Structured Pruning
Chapter 4: Knowledge Distillation for Large Models
Principles of Knowledge Distillation
Distillation Objectives
Self-Distillation and Data Augmentation Strategies
Task-Specific vs. Task-Agnostic Distillation
Distilling Large Models into Smaller Models
Challenges in Distilling Generative Models
Evaluating Distilled Model Performance
Hands-on Practical: Distilling a Generative LLM
Chapter 5: Parameter-Efficient Fine-Tuning (PEFT) and Adaptation
Motivation for PEFT
Adapter Modules
Prefix Tuning, Prompt Tuning, and P-Tuning
Low-Rank Adaptation (LoRA)
Quantized LoRA (QLoRA)
Combining PEFT Methods
Performance Analysis of PEFT Techniques
Practice: Fine-tuning with LoRA and QLoRA
Chapter 6: Hardware Acceleration and Systems Optimization
Mapping LLM Operations to Hardware Architectures
Memory Management Techniques for Large Models
Optimized Kernels for LLM Layers
Compiler Optimizations for LLMs
Distributed Inference Strategies
Advanced Inference Optimization Algorithms
Benchmarking LLM Performance on Diverse Hardware
Hands-on Practical: Optimizing Inference with Runtimes
Chapter 7: Integrated Optimization Strategies and Advanced Topics
Combining Multiple Optimization Techniques
Neural Architecture Search (NAS) for Efficient LLMs
Conditional Computation and Mixture-of-Experts (MoE)
Continual Learning with Optimized Models
Measuring Impact on Fairness and Robustness
Research Frontiers in LLM Efficiency
Practice: Designing an End-to-End Optimized Pipeline
Self-Distillation and Data Augmentation Strategies
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- Distilling the Knowledge in a Neural Network, Geoffrey Hinton, Oriol Vinyals, Jeff Dean, 2015 NIPS 2014 Deep Learning Workshop DOI: 10.48550/arXiv.1503.02531 - Introduces the concept of knowledge distillation, using soft targets from a teacher model to train a student model.
- Born Again Neural Networks, Tommaso Furlanello, Zachary Lipton, Michael Tschannen, Laurent Itti, Anima Anandkumar, 2018 Proceedings of the 35th International Conference on Machine Learning, Vol. 80 (PMLR) - Presents self-distillation, where a model trains itself by using its own predictions from a previous state as a teacher, demonstrating performance improvements.
- Unsupervised Data Augmentation for Consistency Training, Qizhe Xie, Zihang Dai, Eduard Hovy, Minh-Thang Luong, Quoc V. Le, 2020 Advances in Neural Information Processing Systems (NeurIPS), Vol. 33 (Curran Associates Inc.) DOI: 10.48550/arXiv.1904.12848 - Explores data augmentation combined with consistency training, a strategy highly relevant for distillation-aware augmentation where the model learns invariances from augmented inputs.