# vLLM中的量化实现
from vllm.model_executor.quantization_utils import QuantizationConfig
from vllm.model_executor.layers.linear import LinearMethod

class QuantizedLinear:
    def __init__(self, weight, bias=None, bits=8):
        self.bits = bits
        self.scale = self._calculate_scale(weight)
        self.zero_point = self._calculate_zero_point(weight)
        
        # 量化权重
        self.quantized_weight = self._quantize(weight)
        self.bias = bias
    
    def _quantize(self, tensor):
        """量化张量"""
        return torch.round(tensor / self.scale + self.zero_point).clamp(
            0, 2**self.bits - 1
        ).to(torch.uint8)
    
    def _dequantize(self, quantized_tensor):
        """反量化张量"""
        return (quantized_tensor.float() - self.zero_point) * self.scale
    
    def forward(self, x):
        # 反量化权重
        weight = self._dequantize(self.quantized_weight)
        return torch.matmul(x, weight.t()) + self.bias