content: # Copied logic from https://github.com/mit-han-lab/llm-awq/blob/f084f40bd996f3cf3a0633c1ad7d9d476c318aaa/awq/quantize/qmodule.py
  
  import awq_inference_engine  # with CUDA kernels
  import torch
  import torch.nn as nn
  
  # class ScaledActivation(nn.Module):
  #     def __init__(self, module, scales):
  #         super().__init__()
  #         self.act = module
  #         self.scales = nn.Parameter(scales.data)
  #
  #     def forward(self, x):
  #         return self.act(x) / self.scales.view(1, 1, -1).to(x.device)
  
  
  class WQLinear(nn.Module):
      def __init__(self, w_bit, group_size, qweight, qzeros, scales, bias):
          super().__init__()
  
          if w_bit not in [4]:
              raise NotImplementedError("Only 4-bit are supported for now.")
  
          self.in_features = qweight.shape[0]
          self.out_features = qweight.shape[1] * 32 // w_bit
  
          self.w_bit = w_bit
          self.group_size = group_size if group_size != -1 else self.in_features
          # quick sanity check (make sure aligment)
          assert self.in_features % self.group_size == 0
          assert self.out_features % (32 // self.w_bit) == 0
  
          self.qweight = qweight
          self.qzeros = qzeros
          self.scales = scales
          if bias:
              self.bias = bias
          else:
              self.bias = None
  
      @torch.no_grad()
      def forward(self, x):
          out_shape = x.shape[:-1] + (self.out_features,)
          out = awq_inference_engine.gemm_forward_cuda(
              x.reshape(-1, x.shape[-1]), self.qweight, self.scales, self.qzeros, 8
          )
          out = out + self.bias if self.bias is not None else out
          return out.reshape(out_shape)
  
      @property
      def weight(self) -> torch.Tensor:
          return self.qweight
  