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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 Landscape 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

LLM Compression and Acceleration Techniques

Prerequisites: Strong foundation in deep learning, NLP, and LLMs. Experience with frameworks like PyTorch/TensorFlow. Familiarity with model training and deployment.

Level: Expert

What You'll Learn

Optimization Analysis
Analyze the complex trade-offs between various LLM compression and acceleration methodologies.
Advanced Quantization
Implement and evaluate sophisticated quantization techniques, including sub-4-bit precision and QAT.
Sophisticated Pruning
Apply and compare advanced structured and unstructured pruning strategies for LLMs.
Knowledge Distillation
Design, implement, and evaluate knowledge distillation pipelines tailored for large language models.
PEFT Methods
Utilize and adapt various Parameter-Efficient Fine-Tuning methods like LoRA and QLoRA.
Hardware Optimization
Optimize LLM inference performance targeting specific hardware architectures.
Performance Evaluation
Rigorously evaluate the performance, fidelity, and efficiency impacts of optimization techniques.
Integrated Deployment
Integrate multiple optimization techniques into practical LLM deployment workflows.

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