Deploying Quantized LLMs for Efficient Inference
Chapter 1: Advanced LLM Quantization Fundamentals
Revisiting Quantization Principles for Large Models
Low-Bit Quantization Techniques (Below INT8)
Understanding Quantization Data Types and Formats
Post-Training Quantization (PTQ) Algorithms for LLMs
Quantization-Aware Training (QAT) Considerations
Mixed-Precision Quantization Strategies
Calibration Data Selection and Preparation
Hands-on Practical: Applying GPTQ to an LLM
Chapter 2: Implementing LLM Quantization with Toolkits
Overview of LLM Quantization Libraries
Using bitsandbytes for Low-Bit Operations
Quantization with Hugging Face Transformers and Accelerate
Applying GPTQ using AutoGPTQ
Applying AWQ using AutoAWQ
Comparing Toolkit Outputs and Performance
Handling Model Compatibility Issues
Practice: Quantizing Models with Multiple Toolkits
Chapter 3: Performance Evaluation of Quantized LLMs
Metrics for Quantized Model Evaluation
Measuring Inference Latency and Throughput
Assessing Memory Consumption (Disk and Runtime)
Evaluating Accuracy Degradation
Benchmarking Frameworks and Tools
Analyzing Performance on Target Hardware
Visualizing Performance Trade-offs
Hands-on Practical: Benchmarking a Quantized LLM
Chapter 4: Optimizing and Deploying Quantized LLMs
Inference Optimization Techniques Post-Quantization
Choosing the Right Deployment Framework
Deploying with Text Generation Inference (TGI)
Leveraging vLLM for High-Throughput Inference
GPU Optimization with NVIDIA TensorRT-LLM
Deployment using ONNX Runtime
Containerization and Scaling Strategies
Monitoring Deployed Quantized Models
Hands-on Practical: Deploying via an Inference Server
Chapter 5: Addressing Advanced Challenges
Mitigating Accuracy Loss in Low-Bit Regimes
Handling Activation and Weight Outliers
Quantizing Specific LLM Components (Attention, Normalization)
Hardware Constraints and Kernel Availability
Dynamic Quantization vs. Static Quantization Trade-offs
Debugging Quantization Issues
Integrating Quantized Models into Production Pipelines
Practice: Fine-tuning Quantization Parameters
Post-Training Quantization (PTQ) Algorithms for LLMs
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- GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers, Elias Frantar, Saleh Ashkaneh, Jerry Zhao, Sabine M. Fathi, Shuo Yang, Siddarth Malreddy, Artem Gorevoy, Daniel Adiwardana, Jonathan Herzig, Daniel N. Gillman, Oleg Rybakov, Adam Roberts, David R. So, Shivani Agrawal, Sharan Narang, Michael S. Duke, William J. Dally, Hattie Zhou, James Bradbury, Matthew Buddy, Brian Catanzaro, Michael G. Mozer, Somasekhar Vemuri, Wojciech Zaremba, Alon Halevy, Robert Schapire, 2022 arXiv preprint arXiv:2210.01730 DOI: https://doi.org/10.48550/arXiv.2210.01730 - Introduces the GPTQ algorithm, a layer-wise, error-compensated post-training quantization method for LLMs, targeting low-bit precision with minimal accuracy degradation.
- AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration, Ji Lin, Jiaming Tang, Haotian Tang, Shang Yang, Wei-Ming Chen, Wei-Chen Wang, Guangxuan Xiao, Xingyu Dang, Chuang Gan, Song Han, 2023 arXiv preprint arXiv:2306.00978 DOI: 10.48550/arXiv.2306.00978 - Presents AWQ, a post-training quantization method that scales weights based on activation magnitudes to protect important weights, offering a fast and accurate solution for LLMs.
- SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models, Guangxuan Xiao, Ji Lin, Mickael Seznec, Hao Wu, Julien Demouth, Song Han, 2023 ICML 2023 DOI: 10.48550/arXiv.2211.10438 - Introduces SmoothQuant, an algorithm that addresses activation outliers in LLMs through per-channel re-scaling, enabling more accurate low-bit quantization.