量化算法实现细节：如何正确实现对称量化

对称量化实现细节

对称量化是模型压缩中的核心技术，本文将详细介绍如何正确实现对称量化。

核心原理

对称量化假设权重分布关于零点对称，通过以下公式实现：

quantized_value = round(value / scale)
real_value = quantized_value * scale

其中scale = max(|weight|) / 127。

PyTorch实现示例

import torch
import torch.nn as nn

class SymmetricQuantizer:
    def __init__(self, bit=8):
        self.bit = bit
        self.scale = None
        
    def forward(self, weight):
        # 计算scale
        max_val = torch.max(torch.abs(weight))
        self.scale = max_val / ((2 ** (self.bit - 1)) - 1)
        
        # 量化
        quantized = torch.round(weight / self.scale)
        
        # 反量化
        dequantized = quantized * self.scale
        return dequantized

# 使用示例
weight = torch.randn(100, 100)
quantizer = SymmetricQuantizer(bit=8)
quantized_weight = quantizer(weight)

效果评估

通过MSE和精度损失评估：

mse = torch.mean((weight - quantized_weight) ** 2)
accuracy_loss = calculate_accuracy_loss(model, quantized_model)
print(f'MSE: {mse:.6f}, Accuracy Loss: {accuracy_loss:.4f}')

实际测试表明，对称量化在8位量化下可保持95%以上精度。