one about llm quantization
In one of my interviews for ML Engineer position, I was asked,
“What is quantization and how does it help with LLM inference?”
Here is how you can answer:
This can be the approach you follow:
Understand the Basics
Definition: Quantization reduces the precision of model weights and activations (e.g., from 32-bit floating point to 8-bit integers).
Purpose: Decreases model size and memory usage, speeds up inference, and reduces computational costs.
Trade-offs: May slightly impact accuracy but enables deployment on resource-constrained devices.
Types of Quantization
Post-Training Quantization (PTQ): Apply quantization after model training without retraining. Simpler but may have more accuracy loss.
Quantization-Aware Training (QAT): Train the model with quantization in mind, simulating low-precision during training. Better accuracy preservation but requires more effort.
Dynamic vs Static: Dynamic quantization determines scale factors at runtime; static quantization calculates them beforehand.
Common Precision Levels
FP32 (Full Precision): Standard 32-bit floating point, highest accuracy but largest size.
FP16 (Half Precision): 16-bit floating point, reduces memory by 50% with minimal accuracy loss.
INT8 (8-bit Integer): Reduces size by 75%, faster inference, widely supported by hardware accelerators.
INT4/INT2: Extreme quantization for maximum compression, requires careful calibration.
Implementation Techniques
Weight-Only Quantization: Quantize only weights, keep activations in higher precision. Good balance of performance and accuracy.
Weight and Activation Quantization: Quantize both for maximum speedup but more complex calibration needed.
Mixed Precision: Use different precision levels for different layers based on sensitivity analysis.
Tools and Frameworks
Popular Libraries: GPTQ, AWQ, bitsandbytes, GGUF format for llama.cpp.
Framework Support: PyTorch (torch.quantization), TensorFlow Lite, ONNX Runtime.
Calibration: Use representative dataset to determine optimal scaling factors and minimize accuracy degradation.
These are the points you can keep in mind before framing your answer.
