Quantization Benchmark Tool

What It Does

Installation

pip install -r requirements.txt

Usage

python quantization_benchmark.py --model model.pth --methods GGUF GPTQ --dataset eval_data.json --output csv

Source Code

import os
import json
import time
import click
import torch
import numpy as np
from transformers import AutoModelForCausalLM, AutoTokenizer

def load_model(model_path):
    """Load the pre-trained model and tokenizer."""
    tokenizer = AutoTokenizer.from_pretrained(model_path)
    model = AutoModelForCausalLM.from_pretrained(model_path)
    return model, tokenizer

def apply_quantization(model, method):
    """Apply the specified quantization method to the model."""
    if method == "GGUF":
        # Placeholder for GGUF quantization logic
        return model
    elif method == "GPTQ":
        # Placeholder for GPTQ quantization logic
        return model
    elif method == "AWQ":
        # Placeholder for AWQ quantization logic
        return model
    else:
        raise ValueError(f"Unsupported quantization method: {method}")

def benchmark_model(model, tokenizer, dataset):
    """Benchmark the model for memory usage, inference speed, and accuracy."""
    # Memory usage
    memory_usage = torch.cuda.memory_allocated() if torch.cuda.is_available() else 0

    # Inference speed
    start_time = time.time()
    for sample in dataset:
        inputs = tokenizer(sample, return_tensors="pt")
        _ = model.generate(**inputs)
    inference_time = time.time() - start_time

    # Accuracy (dummy implementation for now)
    accuracy = np.random.uniform(0.8, 0.95)  # Placeholder for actual accuracy computation

    return {
        "memory_usage": memory_usage,
        "inference_time": inference_time,
        "accuracy": accuracy
    }

def load_dataset(dataset_path):
    """Load the evaluation dataset."""
    if not os.path.exists(dataset_path):
        raise FileNotFoundError(f"Dataset file not found: {dataset_path}")
    with open(dataset_path, "r") as f:
        return json.load(f)

@click.command()
@click.option('--model', required=True, type=click.Path(exists=True), help="Path to the pre-trained model.")
@click.option('--methods', required=True, multiple=True, type=click.Choice(['GGUF', 'GPTQ', 'AWQ']), help="Quantization methods to test.")
@click.option('--dataset', required=False, type=click.Path(exists=True), help="Path to the evaluation dataset (JSON file).")
@click.option('--output', required=True, type=click.Choice(['json', 'csv']), help="Output format for the summary report.")
def main(model, methods, dataset, output):
    """Quantization Benchmark Tool"""
    try:
        model, tokenizer = load_model(model)
        dataset = load_dataset(dataset) if dataset else ["Hello world!"]

        results = {}
        for method in methods:
            quantized_model = apply_quantization(model, method)
            metrics = benchmark_model(quantized_model, tokenizer, dataset)
            results[method] = metrics

        if output == "json":
            print(json.dumps(results, indent=4))
        elif output == "csv":
            print("method,memory_usage,inference_time,accuracy")
            for method, metrics in results.items():
                print(f"{method},{metrics['memory_usage']},{metrics['inference_time']},{metrics['accuracy']}")

    except Exception as e:
        click.echo(f"Error: {e}", err=True)

if __name__ == "__main__":
    main()