【FLCE】the bias in the previous layer

[Precola]

I see the function LigerFusedLinearCrossEntropyLoss, I think bias and lin_weight are from the previous layer. If so, maybe def forward(self, _input, target, lin_weight, bias=None): is easy to understand.

    def forward(self, lin_weight, _input, target, bias=None):
        loss, z_loss = LigerFusedLinearCrossEntropyFunction.apply(
            _input,
            lin_weight,
            target,
            bias,
            self.ce_weight,
            self.ignore_index,
            self.lse_square_scale,
            self.label_smoothing,
            self.reduction,
            self.softcap,
            self.return_z_loss,
            self.accum_dtype,
        )
        if not self.return_z_loss:
            return loss
        return loss, z_loss