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
Magnitude-Based Pruning
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- Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding, Song Han, Huizi Mao, William J. Dally, 2016 International Conference on Learning Representations 2016 (OpenReview) DOI: 10.48550/arXiv.1510.00149 - A seminal work on neural network compression, it introduces iterative magnitude-based pruning as a fundamental technique alongside quantization and Huffman coding to achieve high sparsity and reduced model size.
- The Lottery Ticket Hypothesis: Finding Sparse, Trainable Subnetworks, Jonathan Frankle, Michael Carbin, 2019 ICLR 2019 DOI: 10.48550/arXiv.1803.03635 - This paper proposes the 'Lottery Ticket Hypothesis,' which offers a theoretical justification for the effectiveness of iterative magnitude pruning, demonstrating that dense, randomly initialized networks contain sparse subnetworks that can be trained in isolation to achieve comparable accuracy.
- Rethinking the Value of Network Pruning, Zhuang Liu, Mingxing Tan, Edward Albert, Quoc V. Le, 2018 Proceedings of Machine Learning Research, Volume 97 (ICLR 2019), Vol. 97 (PMLR (Proceedings of Machine Learning Research)) DOI: 10.48550/arXiv.1810.05270 - This research critically evaluates existing pruning techniques, including magnitude-based methods, and discusses the discrepancy between sparsity and actual inference speedup, emphasizing the challenges of unstructured sparsity on general-purpose hardware.
- Sparse Training: A Survey, Thomas Gale, Erich Elsen, Sara Hooker, 2020 arXiv preprint arXiv:1912.06733 DOI: 10.48550/arXiv.1912.06733 - A comprehensive survey that reviews various methods for sparse neural network training, including magnitude-based pruning as a foundational approach, offering a broad overview of the field and its challenges.