emerging-techniques-model-pruning

Model Pruning: Compressing LLMs

When to Use This Skill

Use Model Pruning when you need to:

• Reduce model size by 40-60% with <1% accuracy loss
• Accelerate inference using hardware-friendly sparsity (2-4× speedup)
• Deploy on constrained hardware (mobile, edge devices)
• Compress without retraining using one-shot methods
• Enable efficient serving with reduced memory footprint

Key Techniques: Wanda (weights × activations), SparseGPT (second-order), structured pruning, N:M sparsity

Papers: Wanda ICLR 2024 (arXiv 2306.11695), SparseGPT (arXiv 2301.00774)

Installation

# Wanda implementation
git clone https://github.com/locuslab/wanda
cd wanda
pip install -r requirements.txt

# Optional: SparseGPT
git clone https://github.com/IST-DASLab/sparsegpt
cd sparsegpt
pip install -e .

# Dependencies
pip install torch transformers accelerate

Quick Start

Wanda Pruning (One-Shot, No Retraining)

Source: ICLR 2024 (arXiv 2306.11695)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load model
model = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Llama-2-7b-hf",
    torch_dtype=torch.float16,
    device_map="cuda"
)
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-hf")

# Calibration data (small dataset for activation statistics)
calib_data = [
    "The quick brown fox jumps over the lazy dog.",
    "Machine learning is transforming the world.",
    "Artificial intelligence powers modern applications.",
]

# Wanda pruning function
def wanda_prune(model, calib_data, sparsity=0.5):
    """
    Wanda: Prune by weight magnitude × input activation.

    Args:
        sparsity: Fraction of weights to prune (0.5 = 50%)
    """
    # 1. Collect activation statistics
    activations = {}

    def hook_fn(name):
        def hook(module, input, output):
            # Store input activation norms
            activations[name] = input[0].detach().abs().mean(dim=0)
        return hook

    # Register hooks for all linear layers
    hooks = []
    for name, module in model.named_modules():
        if isinstance(module, torch.nn.Linear):
            hooks.append(module.register_forward_hook(hook_fn(name)))

    # Run calibration data
    model.eval()
    with torch.no_grad():
        for text in calib_data:
            inputs = tokenizer(text, return_tensors="pt").to(model.device)
            model(**inputs)

    # Remove hooks
    for hook in hooks:
        hook.remove()

    # 2. Prune weights based on |weight| × activation
    for name, module in model.named_modules():
        if isinstance(module, torch.nn.Linear) and name in activations:
            W = module.weight.data
            act = activations[name]

            # Compute importance: |weight| × activation
            importance = W.abs() * act.unsqueeze(0)

            # Flatten and find threshold
            threshold = torch.quantile(importance.flatten(), sparsity)

            # Create mask
            mask = importance >= threshold

            # Apply mask (prune)
            W *= mask.float()

    return model

# Apply Wanda pruning (50% sparsity, one-shot, no retraining)
pruned_model = wanda_prune(model, calib_data, sparsity=0.5)

# Save
pruned_model.save_pretrained("./llama-2-7b-wanda-50")