TinyTorch/modules/16_compression/ABOUT.md

title, description, difficulty, time_estimate, prerequisites, next_steps, learning_objectives

title	description	difficulty	time_estimate	prerequisites	next_steps	learning_objectives
Compression - Pruning and Model Compression	Prune unnecessary weights and compress models for deployment	3	5-6 hours	Quantization	Acceleration	Implement magnitude-based pruning to remove unimportant weights Design structured pruning strategies (channel, layer-wise) Apply iterative pruning with fine-tuning for accuracy preservation Combine pruning with quantization for maximum compression Measure compression ratios and inference speedups

16. Compression

⚡ OPTIMIZATION TIER | Difficulty: ⭐⭐⭐ (3/4) | Time: 5-6 hours

Overview

Compress neural networks through pruning (removing weights) and combining with quantization. This module implements techniques to achieve 10-50× compression with minimal accuracy loss, enabling deployment on resource-constrained devices.

Learning Objectives

By completing this module, you will be able to:

1. Implement magnitude-based pruning to identify and remove unimportant weights
2. Design structured pruning strategies (channel pruning, layer-wise) for actual speedups
3. Apply iterative pruning with fine-tuning to maintain model accuracy
4. Combine pruning with quantization for maximum compression (50-100× possible)
5. Measure compression ratios and verify inference speedup vs accuracy trade-offs

Why This Matters

Production Context

Compression enables practical deployment:

• BERT Distillation (DistilBERT): 40% smaller, 60% faster, 97% accuracy retention
• MobileNet: Structured pruning + quantization for mobile deployment
• Lottery Ticket Hypothesis: Sparse networks train as well as dense ones
• GPT-3 Distillation: Smaller models approaching GPT-3 performance

Historical Context

• Pre-2015: Limited compression work; models small enough for hardware
• 2015-2017: Magnitude pruning (Han et al.); Lottery Ticket Hypothesis
• 2018-2020: Structured pruning; distillation; BERT compression
• 2020+: Extreme compression (100×); sparse transformers; efficient architectures

Compression is now standard for deployment, not optional.

Implementation Guide

Core Techniques

Magnitude Pruning

• Sort weights by absolute value
• Remove smallest X% (typically 50-90%)
• Fine-tune remaining weights
• Can achieve 10× compression with <1% accuracy loss

Structured Pruning

• Remove entire channels/neurons
• Achieves actual speedup (vs unstructured sparsity)
• Typically 2-5× compression
• More aggressive accuracy impact

Iterative Pruning

• Prune gradually (10% at a time)
• Fine-tune after each pruning step
• Better accuracy than one-shot pruning
• More training cost

Pruning + Quantization

• Prune 90% of weights → 10× reduction
• Quantize FP32 → INT8 → 4× reduction
• Combined: 40× compression

Testing

tito export 16_compression
tito test 16_compression

Where This Code Lives

tinytorch/
├── compression/
│   └── prune.py
└── __init__.py

Systems Thinking Questions

1. Lottery Ticket Hypothesis: Why can pruned networks retrain to full accuracy? What does this say about overparameterization?

2. Structured vs Unstructured: Unstructured pruning achieves better compression but no speedup. Why? When is sparse computation actually faster?

3. Distillation vs Pruning: Both compress models. When would you use each? Can you combine them?

Real-World Connections

DistilBERT: 40% smaller BERT with 97% performance MobileNetV2: Efficient architectures + pruning for mobile NVIDIA TensorRT: Automatic pruning + quantization for deployment

What's Next?

In Module 17: Memoization, you'll learn computational reuse:

• KV-caching for transformers
• Eliminate redundant computation
• 10-15× speedup for autoregressive generation
• Memory-compute trade-offs

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Ready to compress models? Open modules/16_compression/compression_dev.py and start implementing.