def forward()

in optimum/quanto/tensor/weights/awq/qbits.py [0:0]

• 20 lines of code
• 3 McCabe index (conditional complexity)

    def forward(ctx, input, other, bias):
        ctx.save_for_backward(input, other)
        if type(input) is not torch.Tensor:
            input = input.dequantize()
        out_features, in_features = other.shape
        rows = input.numel() // in_features
        output = torch.ops.quanto.gemm_f16i4_awq(
            input,
            other._data._data,
            other._scale,
            other._shift,
            rows=rows,
            out_cols=out_features,
            in_cols=in_features,
            bits=4,
            group_size=other._group_size,
        )
        if bias is not None:
            output = output + bias
        return output