Model Pruning

---
name: model-pruning
description: Reduce LLM size and accelerate inference using pruning techniques like Wanda and SparseGPT. Use when compressing models without retraining, achieving 50% sparsity with minimal accuracy loss, or enabling faster inference on hardware accelerators. Covers unstructured pruning, structured pruning, N:M sparsity, magnitude pruning, and one-shot methods.
license: MIT
metadata:
  version: 1.0.0
  category: multimodal-and-emerging
  maintainer: Kalaris Labs
  tags: Emerging Techniques, Model Pruning, Wanda, SparseGPT, Sparsity, Model Compression, N:M Sparsity, One-Shot Pruning, Structured Pruning, Unstructured Pruning, Fast Inference
  dependencies: transformers, torch
---

# 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

```bash
# 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)

```python
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")
```

### SparseGPT (Second-Order Pruning)

**Source**: arXiv 2301.00774

```python
from sparsegpt import SparseGPT

# Load model
model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf")

# Initialize SparseGPT
pruner = SparseGPT(model)

# Calibration data
calib_data = load_calibration_data()  # ~128 samples

# Prune (one-shot, layer-wise reconstruction)
pruned_model = pruner.prune(
    calib_data=calib_data,
    sparsity=0.5,           # 50% sparsity
    prunen=0,               # Unstructured (0) or N:M structured
    prunem=0,
    percdamp=0.01,          # Damping for Hessian inverse
)

# Results: Near-lossless pruning at 50% sparsity
```

### N:M Structured Pruning (Hardware Accelerator)

```python
def nm_prune(weight, n=2, m=4):
    """
    N:M pruning: Keep N weights per M consecutive weights.
    Example: 2:4 = keep 2 out of every 4 weights.

    Compatible with NVIDIA sparse tensor cores (2:4, 4:8).
    """
    # Reshape weight into groups of M
    shape = weight.shape
    weight_flat = weight.flatten()

    # Pad to multiple of M
    pad_size = (m - weight_flat.numel() % m) % m
    weight_padded = F.pad(weight_flat, (0, pad_size))

    # Reshape into (num_groups, m)
    weight_grouped = weight_padded.reshape(-1, m)

    # Find top-N in each group
    _, indices = torch.topk(weight_grouped.abs(), n, dim=-1)

    # Create mask
    mask = torch.zeros_like(weight_grouped)
    mask.scatter_(1, indices, 1.0)

    # Apply mask
    weight_pruned = weight_grouped * mask

    # Reshape back
    weight_pruned = weight_pruned.flatten()[:weight_flat.numel()]
    return weight_pruned.reshape(shape)

# Apply 2:4 sparsity (NVIDIA hardware)
for name, module in model.named_modules():
    if isinstance(module, torch.nn.Linear):
        module.weight.data = nm_prune(module.weight.data, n=2, m=4)

# 50% sparsity, 2× speedup on A100 with sparse tensor cores
```

## Core Concepts

### 1. Pruning Criteria

**Magnitude Pruning** (baseline):
```python
# Prune weights with smallest absolute values
importance = weight.abs()
threshold = torch.quantile(importance, sparsity)
mask = importance >= threshold
```

**Wanda** (weights × activations):
```python
# Importance = |weight| × input_activation
importance = weight.abs() * activation
# Better than magnitude alone (considers usage)
```

**SparseGPT** (second-order):
```python
# Uses Hessian (second derivative) for importance
# More accurate but computationally expensive
importance = weight^2 / diag(Hessian)
```

### 2. Structured vs Unstructured

**Unstructured** (fine-grained):
- Prune individual weights
- Higher quality (better accuracy)
- No hardware speedup (irregular sparsity)

**Structured** (coarse-grained):
- Prune entire neurons, heads, or layers
- Lower quality (more accuracy loss)
- Hardware speedup (regular sparsity)

**Semi-structured (N:M)**:
- Best of both worlds
- 50% sparsity (2:4) → 2× speedup on NVIDIA GPUs
- Minimal accuracy loss

### 3. Sparsity Patterns

```python
# Unstructured (random)
# [1, 0, 1, 0, 1, 1, 0, 0]
# Pros: Flexible, high quality
# Cons: No speedup

# Structured (block)
# [1, 1, 0, 0, 1, 1, 0, 0]
# Pros: Hardware friendly
# Cons: More accuracy loss

# N:M (semi-structured)
# [1, 0, 1, 0] [1, 1, 0, 0]  (2:4 pattern)
# Pros: Hardware speedup + good quality
# Cons: Requires specific hardware (NVIDIA)
```

## Pruning Strategies

### Strategy 1: Gradual Magnitude Pruning

```python
def gradual_prune(model, initial_sparsity=0.0, final_sparsity=0.5, num_steps=100):
    """Gradually increase sparsity during training."""
    for step in range(num_steps):
        # Current sparsity
        current_sparsity = initial_sparsity + (final_sparsity - initial_sparsity) * (step / num_steps)

        # Prune at current sparsity
        for module in model.modules():
            if isinstance(module, torch.nn.Linear):
                weight = module.weight.data
                threshold = torch.quantile(weight.abs().flatten(), current_sparsity)
                mask = weight.abs() >= threshold
                weight *= mask.float()

        # Train one step
        train_step(model)

    return model
```

### Strategy 2: Layer-wise Pruning

```python
def layer_wise_prune(model, sparsity_per_layer):
    """Different sparsity for different layers."""
    # Early layers: Less pruning (more important)
    # Late layers: More pruning (less critical)

    sparsity_schedule = {
        "layer.0": 0.3,   # 30% sparsity
        "layer.1": 0.4,
        "layer.2": 0.5,
        "layer.3": 0.6,   # 60% sparsity
    }

    for name, module in model.named_modules():
        if isinstance(module, torch.nn.Linear):
            # Find layer index
            for layer_name, sparsity in sparsity_schedule.items():
                if layer_name in name:
                    # Prune at layer-specific sparsity
                    prune_layer(module, sparsity)
                    break

    return model
```

### Strategy 3: Iterative Pruning + Fine-tuning

```python
def iterative_prune_finetune(model, target_sparsity=0.5, iterations=5):
    """Prune gradually with fine-tuning between iterations."""
    current_sparsity = 0.0
    sparsity_increment = target_sparsity / iterations

    for i in range(iterations):
        # Increase sparsity
        current_sparsity += sparsity_increment

        # Prune
        prune_model(model, sparsity=current_sparsity)

        # Fine-tune (recover accuracy)
        fine_tune(model, epochs=2, lr=1e-5)

    return model

# Results: Better accuracy than one-shot at high sparsity
```

## Production Deployment

Details, code examples and parameter tables: [references/production-deployment.md](references/production-deployment.md). Read it when this step applies.

## Best Practices

### 1. Sparsity Selection

```python
# Conservative (safe)
sparsity = 0.3  # 30%, <0.5% loss

# Balanced (recommended)
sparsity = 0.5  # 50%, ~1% loss

# Aggressive (risky)
sparsity = 0.7  # 70%, 2-5% loss

# Extreme (model-dependent)
sparsity = 0.9  # 90%, significant degradation
```

### 2. Method Selection

```python
# One-shot, no retraining → Wanda or SparseGPT
if no_retraining_budget:
    use_method = "wanda"  # Faster

# Best quality → SparseGPT
if need_best_quality:
    use_method = "sparsegpt"  # More accurate

# Hardware speedup → N:M structured
if need_speedup:
    use_method = "nm_prune"  # 2:4 or 4:8
```

### 3. Avoid Common Pitfalls

```python
# ❌ Bad: Pruning without calibration data
prune_random(model)  # No activation statistics

# ✅ Good: Use calibration data
prune_wanda(model, calib_data)

# ❌ Bad: Too high sparsity in one shot
prune(model, sparsity=0.9)  # Massive accuracy loss

# ✅ Good: Gradual or iterative
iterative_prune(model, target=0.9, steps=10)
```

## Performance Comparison

**Pruning methods at 50% sparsity** (LLaMA-7B):

| Method | Accuracy Loss | Speed | Memory | Retraining Needed |
|--------|---------------|-------|---------|-------------------|
| **Magnitude** | -2.5% | 1.0× | -50% | No |
| **Wanda** | -0.8% | 1.0× | -50% | No |
| **SparseGPT** | -0.4% | 1.0× | -50% | No |
| **N:M (2:4)** | -1.0% | 2.0× | -50% | No |
| **Structured** | -3.0% | 2.0× | -50% | No |

**Source**: Wanda paper (ICLR 2024), SparseGPT paper

## Resources

- **Wanda Paper (ICLR 2024)**: https://arxiv.org/abs/2306.11695
- **Wanda GitHub**: https://github.com/locuslab/wanda
- **SparseGPT Paper**: https://arxiv.org/abs/2301.00774
- **SparseGPT GitHub**: https://github.com/IST-DASLab/sparsegpt
- **NVIDIA Sparse Tensor Cores**: https://developer.nvidia.com/blog/accelerating-inference-with-sparsity-using-ampere-and-tensorrt/

## Agent operating procedure

1. **Check the environment.** Confirm GPU memory, model checkpoint and licence, framework versions and input formats.
2. **Pin down the inputs.** Confirm formats, identifiers and parameters from the data or the user. Ask rather than guess any value that changes the result.
3. **Run a small version first.** Run inference on a single sample at low resolution or short length first.
4. **Execute the full task** using the instructions and references above.
5. **Validate the result.** Inspect outputs qualitatively and with task metrics; check licence restrictions for generated content.
6. **Report.** State what was run (versions, commands, parameters), what was checked, and what is still uncertain.

| If this happens | Do this |
|---|---|
| Out-of-memory on large inputs | Reduce resolution or sequence length, use half precision, or tile the input. |
| A function, flag or endpoint in these instructions is missing in the installed version | Check the installed version's own documentation (`help()`, `--help`, official docs), adapt, and tell the user. Never invent an API. |
| A required input, identifier or parameter is ambiguous | Ask the user, or state the assumption explicitly before running. |

**Integrity rules**

- Never fabricate results, parameters, identifiers, citations or statistics. If something cannot be run or verified, say so plainly.
- Check model and dataset licences before commercial or published use.
- Treat version-specific details here as possibly outdated: confirm them against the official documentation for the installed version.
- Ask before actions that cost money, consume shared GPUs or cloud quota, touch personal or patient data, or cannot be undone.

## Related skills

- `awq-quantization`: Activation-aware weight quantization for 4-bit LLM compression with 3x speedup and minimal accuracy loss.
- `quantizing-models-bitsandbytes`: Quantizes LLMs to 8-bit or 4-bit for 50-75% memory reduction with minimal accuracy loss.
- `speculative-decoding`: Accelerate LLM inference using speculative decoding, Medusa multiple heads, and lookahead decoding techniques.