# Copied logic from https://github.com/mit-han-lab/llm-awq/blob/f084f40bd996f3cf3a0633c1ad7d9d476c318aaa/awq/quantize/qmodule.py

import math
import torch
import torch.nn as nn
import awq_inference_engine  # with CUDA kernels


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.register_buffer('qweight', qweight)
        self.register_buffer('qzeros', qzeros)
        self.register_buffer('scales', scales)
        if bias:
            self.register_buffer('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)
    
    def extra_repr(self) -> str:
        return 'in_features={}, out_features={}, bias={}, w_bit={}, group_size={}'.format(
            self.in_features, self.out_features, self.bias is not None, self.w_bit, self.group_size
        )