Project 1: The Linear Quantizer (The First Principles)

Build a linear quantizer from scratch to understand scaling, zero-points, and quantization error.

Quick Reference

Attribute Value
Difficulty Level 2: Intermediate
Time Estimate 6-10 hours
Language Python
Prerequisites Numeric precision basics
Key Topics quantization, scaling, error analysis

1. Learning Objectives

By completing this project, you will:

  1. Implement symmetric and asymmetric quantization.
  2. Compute scale and zero-point values.
  3. Measure quantization error across ranges.
  4. Visualize error distribution.
  5. Compare int8 vs fp16 precision.

2. Theoretical Foundation

2.1 Linear Quantization

Linear quantization maps floating point values into discrete integer bins with a scale and zero-point.

3. Project Specification

3.1 What You Will Build

A quantizer library that takes tensors, quantizes them, and reports error metrics.

3.2 Functional Requirements

  1. Quantize/dequantize functions.
  2. Scale + zero-point computation.
  3. Error metrics (MSE, max error).
  4. Visualization of quantization error.
  5. Comparison of symmetric vs asymmetric.

3.3 Non-Functional Requirements

  • Deterministic tests with fixed inputs.
  • Clear numeric reports.
  • Reusable API for later projects.

4. Solution Architecture

4.1 Components

Component Responsibility
Quantizer Apply quantization
Analyzer Compute error metrics
Visualizer Plot error distributions

5. Implementation Guide

5.1 Project Structure

QUANTIZATION_DISTILLATION_INFERENCE_OPTIMIZATION_MASTERY/P01-linear-quantizer/
├── src/
│   ├── quantize.py
│   ├── metrics.py
│   └── plot.py

5.2 Implementation Phases

Phase 1: Quantization functions (3-4h)

  • Implement scale/zero-point.
  • Checkpoint: quantize/dequantize works.

Phase 2: Error metrics (2-3h)

  • Compute MSE and max error.
  • Checkpoint: metrics reported.

Phase 3: Visualization (1-3h)

  • Plot errors across ranges.
  • Checkpoint: error distribution visualized.

6. Testing Strategy

6.1 Test Categories

Category Purpose Examples
Unit quantize known values
Integration dequantize round-trip error
Regression metrics stable MSE

6.2 Critical Test Cases

  1. Round-trip error within tolerance.
  2. Scale and zero-point computed correctly.
  3. Error metrics reported for different ranges.

7. Common Pitfalls & Debugging

Pitfall Symptom Fix
Clipping distorted values expand range or use per-channel
Wrong scale huge error verify min/max handling
Overflow int saturation clamp values before cast

8. Extensions & Challenges

Beginner

  • Add fp16 baseline.
  • Add per-tensor histograms.

Intermediate

  • Add per-channel quantization.
  • Add calibration dataset support.

Advanced

  • Add non-linear quantization.
  • Compare to hardware quantization results.

9. Real-World Connections

  • Inference cost depends on quantization tradeoffs.
  • Hardware accelerators require scale/zero-point logic.

10. Resources

  • Quantization tutorials
  • Numeric precision references

11. Self-Assessment Checklist

  • I can implement linear quantization.
  • I can compute and visualize error.
  • I can compare quantization strategies.

12. Submission / Completion Criteria

Minimum Completion:

  • Quantize/dequantize pipeline

Full Completion:

  • Error metrics + visualization

Excellence:

  • Per-channel quantization
  • Calibration support

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