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")
SparseGPT (Second-Order Pruning)
Source: arXiv 2301.00774
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)
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):
# Prune weights with smallest absolute values
importance = weight.abs()
threshold = torch.quantile(importance, sparsity)
mask = importance >= threshold
Wanda (weights × activations):
# Importance = |weight| × input_activation
importance = weight.abs() * activation
# Better than magnitude alone (considers usage)
SparseGPT (second-order):
# 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
# 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
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
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
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. Read it when this step applies.
Best Practices
1. Sparsity Selection
# 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
# 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
# ❌ 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
- Check the environment. Confirm GPU memory, model checkpoint and licence, framework versions and input formats.
- 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.
- Run a small version first. Run inference on a single sample at low resolution or short length first.
- Execute the full task using the instructions and references above.
- Validate the result. Inspect outputs qualitatively and with task metrics; check licence restrictions for generated content.
- 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.