Project 7: Benchmarking Quantization Loss (The PPL Tool)

Build a perplexity (PPL) benchmarking tool to quantify quality loss after quantization.

Quick Reference

Attribute Value
Difficulty Level 3: Advanced
Time Estimate 10-16 hours
Language Python
Prerequisites Language model evaluation basics
Key Topics perplexity, evaluation, quantization impact

1. Learning Objectives

By completing this project, you will:

  1. Implement a PPL evaluation pipeline.
  2. Compare fp16 vs quantized models.
  3. Measure quality loss across datasets.
  4. Generate benchmark reports.
  5. Automate regression detection.

2. Theoretical Foundation

2.1 Perplexity as Quality Proxy

Perplexity provides a measurable proxy for model quality and is commonly used to compare quantized models.

3. Project Specification

3.1 What You Will Build

A tool that evaluates perplexity for different quantization configs and produces comparative reports.

3.2 Functional Requirements

  1. Dataset loader for text corpora.
  2. PPL evaluator for models.
  3. Comparison reports for fp16 vs int8.
  4. Automation for multiple configs.
  5. Regression alerts when PPL spikes.

3.3 Non-Functional Requirements

  • Deterministic runs with fixed seeds.
  • Clear logging for evaluation runs.
  • Configurable datasets.

4. Solution Architecture

4.1 Components

Component Responsibility
Loader Read evaluation data
Evaluator Compute PPL
Reporter Compare configs
Alerting Detect regressions

5. Implementation Guide

5.1 Project Structure

QUANTIZATION_DISTILLATION_INFERENCE_OPTIMIZATION_MASTERY/P07-ppl-tool/
├── src/
│   ├── data.py
│   ├── eval.py
│   ├── report.py
│   └── alert.py

5.2 Implementation Phases

Phase 1: PPL evaluator (4-6h)

  • Compute PPL on a dataset.
  • Checkpoint: baseline PPL reported.

Phase 2: Comparison (3-5h)

  • Evaluate quantized models.
  • Checkpoint: report shows deltas.

Phase 3: Automation (3-5h)

  • Run multiple configs and detect regressions.
  • Checkpoint: alert triggers on PPL spikes.

6. Testing Strategy

6.1 Test Categories

Category Purpose Examples
Unit PPL calc known outputs
Integration eval model runs
Regression alerts detect PPL spikes

6.2 Critical Test Cases

  1. PPL computed consistently across runs.
  2. Quantized model shows higher PPL.
  3. Alert triggers when threshold exceeded.

7. Common Pitfalls & Debugging

Pitfall Symptom Fix
Inconsistent PPL random results fix seeds and batching
Dataset mismatch wrong comparisons use same eval set
Slow eval long runtime reduce dataset or batch size

8. Extensions & Challenges

Beginner

  • Add CSV export.
  • Add small default dataset.

Intermediate

  • Add multi-dataset benchmarking.
  • Add PPL trend charts.

Advanced

  • Add token-level loss analysis.
  • Integrate with CI pipelines.

9. Real-World Connections

  • Quantization pipelines rely on PPL regression checks.
  • Model releases use PPL as a quality gate.

10. Resources

  • Language model evaluation references
  • Quantization benchmarking guides

11. Self-Assessment Checklist

  • I can compute PPL for a model.
  • I can compare quantization configs.
  • I can detect regressions.

12. Submission / Completion Criteria

Minimum Completion:

  • PPL evaluation for fp16 + int8

Full Completion:

  • Comparative reports

Excellence:

  • Multi-dataset trends
  • CI integration

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