Model Compression

🎒 Model Compression: Making Your Neural Network Travel Light

Imagine you have a giant stuffed bear that’s too big to fit in your backpack. What if you could squeeze it smaller—without losing its softness—so it fits perfectly? That’s exactly what Model Compression does to deep learning models!

🌟 The Big Picture

Your PyTorch model is like a giant toy robot. It’s smart and powerful, but:

• 📦 Too heavy to carry on your phone
• 🐢 Too slow to play games with
• 🔋 Eats too much battery

Model Compression is our magic shrinking machine! It makes the robot smaller, faster, and lighter—while keeping it almost just as smart.

There are 3 main ways to compress a model:

graph TD
    A["🤖 Big Model"] --> B["Model Compression"]
    B --> C["⚡ Quantization"]
    B --> D["🎓 Quantization-Aware Training"]
    B --> E["✂️ Pruning"]
    C --> F["🎒 Smaller Model"]
    D --> F
    E --> F

⚡ 1. Model Quantization: Counting with Fewer Fingers

What Is It?

Imagine you’re counting apples. You could say:

• “I have 3.14159265358979 apples” 🤯
• OR simply “I have 3 apples” ✅

Both are close enough! Quantization does this for neural networks.

The Simple Story

Your model stores numbers (called weights) using 32 “memory fingers” (32-bit floating point). But do you really need all 32?

Real Example

import torch

# Your big model
model = MyBigModel()

# Make it smaller! 🎉
quantized_model = torch.quantization.quantize_dynamic(
    model,
    {torch.nn.Linear},
    dtype=torch.qint8
)
# That's it! 4x smaller!

Why Does This Work?

Think of a photograph:

• Original: 10 million colors 🌈
• Compressed: 256 colors 🎨

Can you tell the difference? Usually not! Same with neural networks—they don’t need perfect precision to work well.

✅ Quantization Benefits

• 🚀 4× smaller model size
• ⚡ 2-4× faster inference
• 🔋 Less battery drain
• 📱 Fits on phones and edge devices

🎓 2. Quantization-Aware Training (QAT): Teaching with Training Wheels

What Is It?

Remember learning to ride a bike? You used training wheels first. QAT is like that!

Instead of compressing AFTER training, we train the model knowing it will be compressed. The model learns to be accurate even with fewer “fingers” to count with.

The Simple Story

graph TD
    A["Regular Training"] --> B["Train with 32-bit precision"]
    B --> C["Compress to 8-bit LATER"]
    C --> D["😟 Some accuracy lost"]

    E["QAT Training"] --> F["Train while SIMULATING 8-bit"]
    F --> G["Model learns to handle it"]
    G --> H["😊 Better accuracy!"]

Why Is QAT Better?

Normal quantization is like:

“Here’s your new tiny backpack. Good luck fitting everything!”

QAT is like:

“Practice packing with this tiny backpack while learning. You’ll get really good at it!”

Real Example

import torch

# Step 1: Prepare your model
model = MyModel()
model.qconfig = torch.quantization.get_default_qat_qconfig('fbgemm')

# Step 2: Add "fake" quantization
model_prepared = torch.quantization.prepare_qat(model)

# Step 3: Train normally!
for epoch in range(10):
    train(model_prepared)  # It learns to handle quantization!

# Step 4: Convert for real
model_quantized = torch.quantization.convert(model_prepared)

The Magic Inside

During QAT, PyTorch adds fake quantization nodes:

• They simulate 8-bit math during training
• But actually compute in 32-bit (so gradients work)
• Model learns to be robust to rounding errors

✅ QAT Benefits

✂️ 3. Model Pruning: Cutting the Fat

What Is It?

Imagine a tree with thousands of branches. Some branches:

• 🌿 Are healthy and important
• 🥀 Are dead and useless

Pruning means cutting off the useless branches so the tree grows better!

The Simple Story

Neural networks have millions of connections (weights). But studies show:

🔬 “Up to 90% of weights can be removed with almost no accuracy loss!”

It’s like discovering most wires in your robot do nothing important!

graph LR
    A["Dense Network"] -->|Pruning| B["Sparse Network"]
    A -->|"10M weights"| C["100%"]
    B -->|"1M weights"| D["90% smaller!"]

Types of Pruning

🎯 Unstructured Pruning (Random Haircut)

Remove individual weights anywhere:

import torch.nn.utils.prune as prune

# Remove 50% of smallest weights
prune.l1_unstructured(
    module=model.layer1,
    name='weight',
    amount=0.5
)

📦 Structured Pruning (Remove Whole Sections)

Remove entire neurons or channels:

# Remove 30% of channels
prune.ln_structured(
    module=model.conv1,
    name='weight',
    amount=0.3,
    n=2,
    dim=0
)

Which Weights to Remove?

The model keeps important weights and removes lazy ones:

Real Example: Pruning Step by Step

import torch
import torch.nn.utils.prune as prune

# 1. Create model
model = torch.nn.Linear(100, 10)

# 2. Check original weights
print(f"Before: {model.weight.numel()} weights")

# 3. Prune 70% of smallest weights
prune.l1_unstructured(model, name='weight', amount=0.7)

# 4. Count zeros (pruned weights)
zeros = (model.weight == 0).sum()
print(f"After: {zeros} weights are now zero!")

# 5. Make pruning permanent
prune.remove(model, 'weight')

✅ Pruning Benefits

• 📉 50-90% fewer parameters
• 🚀 Faster inference (especially structured)
• 💾 Less memory usage
• 🔋 Lower energy consumption

🎯 Combining Techniques: The Ultimate Shrink Ray

The pros don’t use just one technique—they use ALL of them!

graph TD
    A["Original Model<br/>100MB, 100ms"] --> B["Pruning<br/>Remove 80% weights"]
    B --> C["QAT<br/>Train for quantization"]
    C --> D["Quantization<br/>FP32 → INT8"]
    D --> E["Final Model<br/>5MB, 10ms! 🎉"]

Comparison Table

🌈 Quick Recap

🚀 When to Use What?

graph TD
    A["Need smaller model?"] --> B{How much accuracy<br/>can you lose?}
    B -->|Some ~1%| C["Quantization"]
    B -->|Very little <0.5%| D["QAT"]
    B -->|Need maximum<br/>compression| E["Pruning + QAT"]

    C --> F["Quick &amp; Easy!"]
    D --> G["Best accuracy!"]
    E --> H["Smallest size!"]

💡 Key Takeaways

1. Quantization = Use smaller numbers (32-bit → 8-bit)
2. QAT = Train with quantization in mind for better accuracy
3. Pruning = Remove weights that don’t matter
4. Combine all three = Get tiny, fast models for mobile devices!

🎒 Remember: Your giant stuffed bear (model) can now fit in your pocket (phone) and still give the best hugs (predictions)!

Now you’re ready to make your PyTorch models travel light! Go compress something! 🚀