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)
pythonimport 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
pythonfrom 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)
pythondef 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
pythondef 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
pythondef 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
pythondef 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
Complete Pruning Pipeline
pythonfrom transformers import Trainer, TrainingArguments def production_pruning_pipeline( model_name="meta-llama/Llama-2-7b-hf", target_sparsity=0.5, method="wanda", # or "sparsegpt" ): # 1. Load model model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16) tokenizer = AutoTokenizer.from_pretrained(model_name) # 2. Load calibration data calib_dataset = load_dataset("wikitext", "wikitext-2-raw-v1", split="train[:1000]") # 3. Apply pruning if method == "wanda": pruned_model = wanda_prune(model, calib_dataset, sparsity=target_sparsity) elif method == "sparsegpt": pruner = SparseGPT(model) pruned_model = pruner.prune(calib_dataset, sparsity=target_sparsity) # 4. (Optional) Fine-tune to recover accuracy training_args = TrainingArguments( output_dir="./pruned-model", num_train_epochs=1, per_device_train_batch_size=4, learning_rate=1e-5, bf16=True, ) trainer = Trainer( model=pruned_model, args=training_args, train_dataset=finetune_dataset, ) trainer.train() # 5. Save pruned_model.save_pretrained("./pruned-llama-7b-50") tokenizer.save_pretrained("./pruned-llama-7b-50") return pruned_model # Usage pruned_model = production_pruning_pipeline( model_name="meta-llama/Llama-2-7b-hf", target_sparsity=0.5, method="wanda" )
Evaluation
pythonfrom lm_eval import evaluator # Evaluate pruned vs original model original_results = evaluator.simple_evaluate( model="hf", model_args="pretrained=meta-llama/Llama-2-7b-hf", tasks=["arc_easy", "hellaswag", "winogrande"], ) pruned_results = evaluator.simple_evaluate( model="hf", model_args="pretrained=./pruned-llama-7b-50", tasks=["arc_easy", "hellaswag", "winogrande"], ) # Compare print(f"Original: {original_results['results']['arc_easy']['acc']:.3f}") print(f"Pruned: {pruned_results['results']['arc_easy']['acc']:.3f}") print(f"Degradation: {(original_results - pruned_results):.3f}") # Typical results at 50% sparsity: # - Wanda: <1% accuracy loss # - SparseGPT: <0.5% accuracy loss # - Magnitude: 2-3% accuracy loss
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/
