模型压缩：让大模型无处不在

从FP32到INT4的性能对比

import torch
import torch.nn as nn
from transformers import AutoModel

class QuantizationEngine:
    """模型量化引擎"""
    
    def quantize_model(self, model, quantization_config):
        """执行模型量化"""
        
        # 1. 校准数据收集
        calibration_data = self.collect_calibration_data(model)
        
        # 2. 计算量化参数
        quant_params = self.calculate_quantization_params(
            model, 
            calibration_data,
            method=quantization_config['method']  # 'symmetric' or 'asymmetric'
        )
        
        # 3. 应用量化
        if quantization_config['bits'] == 8:
            quantized_model = self.int8_quantization(model, quant_params)
        elif quantization_config['bits'] == 4:
            quantized_model = self.int4_quantization(model, quant_params)
        
        # 4. 量化感知微调（可选）
        if quantization_config.get('qat', False):
            quantized_model = self.quantization_aware_training(
                quantized_model,
                training_data=quantization_config['training_data']
            )
        
        return quantized_model
    
    def benchmark_quantization(self, original_model, quantized_model):
        """性能基准测试"""
        results = {
            'model_size': {
                'original': self.get_model_size(original_model),  # 13GB
                'quantized': self.get_model_size(quantized_model), # 3.2GB
                'compression_ratio': '4.1x'
            },
            'inference_speed': {
                'original': self.measure_latency(original_model),   # 245ms
                'quantized': self.measure_latency(quantized_model), # 62ms
                'speedup': '3.9x'
            },
            'accuracy': {
                'original': self.evaluate_accuracy(original_model),   # 92.5%
                'quantized': self.evaluate_accuracy(quantized_model), # 91.8%
                'degradation': '-0.7%'
            }
        }
        return results