Project 13: Dynamic Quantization for Mobile

Build a dynamic quantization pipeline optimized for mobile inference constraints.

Quick Reference

Attribute Value
Difficulty Level 4: Expert
Time Estimate 1-2 weeks
Language Python
Prerequisites Quantization basics, mobile constraints
Key Topics dynamic quantization, latency, memory

1. Learning Objectives

By completing this project, you will:

  1. Implement dynamic quantization for inference.
  2. Measure latency on CPU/mobile targets.
  3. Compare dynamic vs static quantization.
  4. Track accuracy changes.
  5. Package a mobile-friendly model.

2. Theoretical Foundation

2.1 Dynamic Quantization

Dynamic quantization quantizes weights ahead of time and activations on the fly, balancing speed and accuracy.

3. Project Specification

3.1 What You Will Build

A pipeline that applies dynamic quantization to a model and benchmarks performance on CPU-style constraints.

3.2 Functional Requirements

  1. Dynamic quantization pipeline.
  2. Latency benchmarks on CPU.
  3. Accuracy evaluation vs baseline.
  4. Model export for mobile runtime.
  5. Report on tradeoffs.

3.3 Non-Functional Requirements

  • Deterministic evaluation with fixed seeds.
  • Clear performance logs.
  • Configurable quantization settings.

4. Solution Architecture

4.1 Components

Component Responsibility
Quantizer Apply dynamic quantization
Evaluator Measure accuracy
Benchmark Measure latency
Exporter Save mobile artifacts

5. Implementation Guide

5.1 Project Structure

QUANTIZATION_DISTILLATION_INFERENCE_OPTIMIZATION_MASTERY/P13-dynamic-quant/
├── src/
│   ├── quantize.py
│   ├── eval.py
│   ├── benchmark.py
│   └── export.py

5.2 Implementation Phases

Phase 1: Quantization (4-6h)

  • Apply dynamic quantization.
  • Checkpoint: model runs on CPU.

Phase 2: Benchmarking (4-6h)

  • Measure latency vs baseline.
  • Checkpoint: report shows speed changes.

Phase 3: Export (3-5h)

  • Package model for mobile.
  • Checkpoint: exported artifacts load.

6. Testing Strategy

6.1 Test Categories

Category Purpose Examples
Unit quantization layers quantized
Integration eval model outputs valid
Regression benchmark stable latency numbers

6.2 Critical Test Cases

  1. Dynamic quantized model runs without errors.
  2. Latency improves vs fp16 baseline.
  3. Accuracy drop within threshold.

7. Common Pitfalls & Debugging

Pitfall Symptom Fix
Unsupported layers runtime errors skip or fallback
Small speed gains minimal benefit optimize batch size
Accuracy loss poor results tune quantization params

8. Extensions & Challenges

Beginner

  • Add fp16 vs int8 comparison.
  • Add CPU thread tuning.

Intermediate

  • Add quantization-aware training.
  • Add profiling on real device.

Advanced

  • Add mobile runtime integration (ONNX, TFLite).
  • Add battery usage estimates.

9. Real-World Connections

  • Mobile AI depends on dynamic quantization.
  • On-device inference requires careful tradeoffs.

10. Resources

  • Dynamic quantization docs
  • Mobile inference optimization guides

11. Self-Assessment Checklist

  • I can apply dynamic quantization.
  • I can measure latency on CPU.
  • I can package models for mobile.

12. Submission / Completion Criteria

Minimum Completion:

  • Dynamic quantization pipeline

Full Completion:

  • Benchmark + accuracy report

Excellence:

  • Mobile runtime integration
  • Real device profiling

This guide was generated from project_based_ideas/AI_AGENTS_LLM_RAG/QUANTIZATION_DISTILLATION_INFERENCE_OPTIMIZATION_MASTERY.md.