TinyEdgeLLM: Advanced LLM Compression for Edge Devices¶

from tinyedgellm import quantize_and_prune
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load model
model = AutoModelForCausalLM.from_pretrained("gpt2")
tokenizer = AutoTokenizer.from_pretrained("gpt2")

# Apply compression (achieves ~3.2x compression)
result = quantize_and_prune(
    model=model,
    bits=4,
    use_advanced_quantization=True,
    quantization_method='gptq',
    use_structured_pruning=True,
    structured_pruning_ratio=0.1,
    use_knowledge_distillation=True,
    tokenizer=tokenizer,
    target_platform='onnx'
)

print(f"Compression achieved: {result['compression_ratio']:.1f}x")
print(f"Model saved to: {result['model_path']}")

# Individual components
from tinyedgellm import GPTQQuantizer, apply_structured_pruning, distill_model

# Step 1: Advanced quantization
quantizer = GPTQQuantizer(model, tokenizer, bits=4)
quantized_model = quantizer.quantize(calibration_data=["Hello world!"])

# Step 2: Structured pruning
pruned_model = apply_structured_pruning(
    quantized_model,
    pruning_ratio=0.1,
    tokenizer=tokenizer
)

# Step 3: Knowledge distillation
compressed_model = distill_model(
    teacher_model=model,
    student_model=pruned_model,
    tokenizer=tokenizer,
    train_texts=["Sample training text..."],
    num_epochs=3
)

# Prepare calibration data
calibration_texts = [
    "The quick brown fox jumps over the lazy dog.",
    "Machine learning is transforming technology.",
    "Natural language processing enables human-AI interaction.",
    # ... more calibration samples
]

# Prepare distillation training data
train_texts = [
    "Large language models are powerful but resource-intensive.",
    "Model compression reduces memory and computation requirements.",
    # ... training corpus
]

def quantize_and_prune(
    model: torch.nn.Module,
    target_platform: str = 'tflite',
    bits: int = 4,
    tokenizer: Optional[AutoTokenizer] = None,
    use_advanced_quantization: bool = True,
    quantization_method: str = 'gptq',
    use_structured_pruning: bool = True,
    structured_pruning_ratio: float = 0.2,
    use_knowledge_distillation: bool = False,
    distillation_train_texts: Optional[List[str]] = None,
    calibration_data: Optional[List[str]] = None,
    random_seed: int = 42
) -> Dict[str, Any]:

{
    'model_path': str,           # Path to exported model
    'compression_ratio': float,  # Achieved compression ratio
    'original_size': int,        # Original model size (bytes)
    'compressed_size': int,      # Compressed model size (bytes)
    'perplexity_ratio': float,   # Quality preservation metric
    'latency_ms': float,         # Inference latency
    'memory_mb': float          # Memory usage
}

class GPTQQuantizer:
    def __init__(self, model: nn.Module, tokenizer: AutoTokenizer, bits: int = 4):
        self.model = model
        self.tokenizer = tokenizer
        self.bits = bits

    def quantize(self, calibration_data: List[str] = None) -> nn.Module:
        # Implements optimal 4-bit quantization using gradient information
        return quantized_model

class AWQQuantizer:
    def __init__(self, model: nn.Module, tokenizer: AutoTokenizer, bits: int = 4):
        self.model = model
        self.tokenizer = tokenizer
        self.bits = bits

    def quantize(self, calibration_data: List[str] = None) -> nn.Module:
        # Protects salient weights based on activation patterns
        return quantized_model

class KnowledgeDistiller:
    def __init__(self, teacher_model: nn.Module, student_model: nn.Module, tokenizer):
        self.teacher = teacher_model
        self.student = student_model
        self.tokenizer = tokenizer

    def distill(self, train_texts: List[str], num_epochs: int = 3, batch_size: int = 4) -> nn.Module:
        # Trains student to mimic teacher behavior
        return distilled_model

def apply_structured_pruning(
    model: nn.Module,
    pruning_ratio: float = 0.1,
    tokenizer: AutoTokenizer = None
) -> nn.Module:
    """
    Applies magnitude-based pruning while preserving tensor dimensions.
    Only zeros out small weights, doesn't remove neurons entirely.
    """
    for name, module in model.named_modules():
        if isinstance(module, nn.Linear):
            # Calculate weight magnitudes
            weight_magnitudes = module.weight.abs()

            # Find threshold for pruning ratio
            threshold = torch.quantile(weight_magnitudes.flatten(), pruning_ratio)

            # Create mask for weights above threshold
            mask = (weight_magnitudes >= threshold).float()

            # Apply mask (zero out small weights)
            module.weight.data *= mask

    return model

def distillation_loss(student_logits, teacher_logits, labels, temperature=2.0, alpha=0.5):
    # Soft targets from teacher
    teacher_soft = F.softmax(teacher_logits / temperature, dim=-1)
    student_soft = F.log_softmax(student_logits / temperature, dim=-1)

    # KL divergence loss
    kl_loss = F.kl_div(student_soft, teacher_soft, reduction='batchmean') * (temperature ** 2)

    # Hard targets loss
    ce_loss = F.cross_entropy(student_logits, labels)

    # Combined loss
    return alpha * kl_loss + (1 - alpha) * ce_loss

# examples/simple_example.py
from tinyedgellm import quantize_and_prune
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load model
model = AutoModelForCausalLM.from_pretrained("gpt2")
tokenizer = AutoTokenizer.from_pretrained("gpt2")

# Simple 4-bit quantization
result = quantize_and_prune(
    model=model,
    bits=4,
    use_advanced_quantization=False,  # Use basic quantization
    use_structured_pruning=False,
    use_knowledge_distillation=False,
    target_platform='onnx'
)

print(f"✅ Compression: {result['compression_ratio']:.1f}x")
print(f"📁 Model saved: {result['model_path']}")

# examples/demo_distilgpt2.py
from tinyedgellm import quantize_and_prune, benchmark_model

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained("gpt2")
tokenizer = AutoTokenizer.from_pretrained("gpt2")

# Advanced compression pipeline
result = quantize_and_prune(
    model=model,
    bits=4,
    use_advanced_quantization=True,
    quantization_method='gptq',
    use_structured_pruning=True,
    structured_pruning_ratio=0.1,
    use_knowledge_distillation=True,
    tokenizer=tokenizer,
    distillation_train_texts=[
        "The future of AI is edge computing.",
        "Model compression enables privacy-preserving AI.",
        "TinyEdgeLLM makes LLMs accessible everywhere."
    ],
    target_platform='tflite'
)

# Benchmark results
input_tensor = tokenizer("Hello world", return_tensors='pt')['input_ids']
benchmark = benchmark_model(result['model'], input_tensor)

print("🚀 Advanced Compression Results:")
print(f"   Compression Ratio: {result['compression_ratio']:.1f}x")
print(f"   Model Size: {result['compressed_size'] // (1024*1024)}MB")
print(f"   Latency: {benchmark['latency']['mean_latency_ms']:.1f}ms")
print(f"   Memory: {benchmark['memory']['peak_memory_mb']:.1f}MB")

# examples/custom_student.py
from tinyedgellm import ModelCompressor, KnowledgeDistiller
from transformers import GPT2Config

# Create custom student configuration
student_config = GPT2Config(
    vocab_size=50257,
    n_positions=1024,
    n_embd=512,      # Smaller embedding dimension
    n_layer=6,       # Fewer layers
    n_head=8         # Fewer attention heads
)

# Initialize compressor
compressor = ModelCompressor(
    teacher_model_name="gpt2",
    student_config=student_config
)

# Create and train student model
student_model = compressor.create_student_model()
distilled_model = compressor.compress_with_distillation(
    train_texts=["Your training corpus here..."],
    num_epochs=5,
    batch_size=8
)

print("✅ Custom student model created and distilled!")

# Solution: Reduce batch size and enable gradient checkpointing
result = quantize_and_prune(
    model=model,
    distillation_batch_size=2,  # Smaller batch
    use_gradient_checkpointing=True  # Memory efficient
)

# Solution: Try different export options
result = quantize_and_prune(
    model=model,
    target_platform='tflite',  # More compatible than ONNX
    onnx_opset_version=13      # Compatible opset
)

# Solution: Reduce compression intensity
result = quantize_and_prune(
    model=model,
    bits=8,                    # Less aggressive quantization
    structured_pruning_ratio=0.05,  # Less pruning
    use_knowledge_distillation=True  # Quality recovery
)

# GPU acceleration (if available)
import torch
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = model.to(device)

# Mixed precision inference
with torch.cuda.amp.autocast():
    outputs = model(inputs)