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
Compiler and Runtime Support for Sparse Operations
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- Templates for the Solution of Linear Systems: Building Blocks for Iterative Methods, Richard Barrett, Michael Berry, Tony F. Chan, Jack Demmel, James Donato, June Dongarra, Victor Eijkhout, Roldan Pozo, Charles Romine, and Henk van der Vorst, 1994 (SIAM) DOI: 10.1137/1.9781611971538 - Covers standard sparse matrix storage formats (CSR, CSC, COO) and fundamental sparse linear algebra algorithms, which are essential for understanding compiler/runtime implementation.
- NVIDIA Ampere Architecture Whitepaper, NVIDIA, 2020 (NVIDIA) - Provides a detailed description of the NVIDIA Ampere architecture, including the introduction and function of Sparse Tensor Cores for accelerating sparse deep learning computations.
- Deep Learning with Sparse Tensors: A Survey, Xiaoming Sun, Yining Ma, Yifei Li, Junbo Zhao, and Jianling Liu, 2020 Tsinghua Science and Technology, Vol. 25 (IEEE) DOI: 10.26599/TST.2020.9010046 - Comprehensive survey on sparse tensor operations in deep learning, covering representation, computation, and optimization techniques relevant for both compilers and runtimes.