//  [python]
def fasterquant(
        self, blocksize=128, percdamp=.01, group_size=-1, actorder=False, static_groups=False
    ):
        W = self.layer.weight.data.clone()
        if isinstance(self.layer, nn.Conv2d):
            W = W.flatten(1)
        if isinstance(self.layer, transformers.Conv1D):
            W = W.t()
        W = W.float()

        if not self.quantizer.ready():
            self.quantizer.find_params(W, weight=True)

        H = self.H
        del self.H
        dead = torch.diag(H) == 0
        H[dead, dead] = 1
        W[:, dead] = 0

        g_idx = []
        scale = []
        zero = []
        now_idx = 1

        if static_groups:
            import copy
            groups = []
            for i in range(0, self.columns, group_size):
                quantizer = copy.deepcopy(self.quantizer)
                quantizer.find_params(W[:, i:(i + group_size)], weight=True)
                scale.append(quantizer.scale)
                zero.append(quantizer.zero)
                groups.append(quantizer)

        if actorder:
            perm = torch.argsort(torch.diag(H), descending=True)
            W = W[:, perm]
            H = H[perm][:, perm]
            invperm = torch.argsort(perm)

        Losses = torch.zeros_like(W)
        Q = torch.zeros_like(W)

        damp = percdamp * torch.mean(torch.diag(H))
        diag = torch.arange(self.columns, device=self.dev)
        H[diag, diag] += damp
        H = torch.linalg.cholesky(H)
        H = torch.cholesky_inverse(H)
        H = torch.linalg.cholesky(H, upper=True)
        Hinv = H

        for i1 in range(0, self.columns, blocksize):
            i2 = min(i1 + blocksize, self.columns)
            count = i2 - i1

            W1 = W[:, i1:i2].clone()
            Q1 = torch.zeros_like(W1)
            Err1 = torch.zeros_like(W1)
            Losses1 = torch.zeros_like(W1)
            Hinv1 = Hinv[i1:i2, i1:i2]

            for i in range(count):
                w = W1[:, i]
                d = Hinv1[i, i]

                if group_size != -1:
                    if not static_groups:
                        if (i1 + i) % group_size == 0:
                            self.quantizer.find_params(W[:, (i1 + i):(i1 + i + group_size)], weight=True)
                            
                        if ((i1 + i) // group_size) - now_idx == -1:
                            scale.append(self.quantizer.scale)
                            zero.append(self.quantizer.zero)
                            now_idx += 1
                    else:
                        idx = i1 + i
                        if actorder:
                            idx = perm[idx]
                        self.quantizer = groups[idx // group_size]
                        
                q = self.quantizer.quantize(w.unsqueeze(1)).flatten()
                Q1[:, i] = q
                Losses1[:, i] = (w - q) ** 2 / d ** 2

                err1 = (w - q) / d
                W1[:, i:] -= err1.unsqueeze(1).matmul(Hinv1[i, i:].unsqueeze(0))
                Err1[:, i] = err1

            Q[:, i1:i2] = Q1
            Losses[:, i1:i2] = Losses1 / 2

            W[:, i2:] -= Err1.matmul(Hinv[i1:i2, i2:])

            if os.environ.get("DEBUG"):
                self.layer.weight.data[:, :i2] = Q[:, :i2]
                self.layer.weight.data[:, i2:] = W[:, i2:]
                logger.debug(torch.sum((self.layer(self.inp1) - self.out1) ** 2))
                logger.debug(torch.sum(Losses))

        torch.cuda.synchronize()

        group_size = group_size if group_size != -1 else self.columns
        if static_groups and actorder:
            g_idx = [perm[i] // group_size for i in range(self.columns)]
        else:
            g_idx = [i // group_size for i in range(self.columns)]
        g_idx = torch.tensor(g_idx, dtype=torch.int32, device=Q.device)
        if actorder:
            Q = Q[:, invperm]
            g_idx = g_idx[invperm]

        if isinstance(self.layer, transformers.Conv1D):
            Q = Q.t()
        self.layer.weight.data = Q.reshape(self.layer.weight.shape).type_as(self.layer.weight.data)
        if os.environ.get("DEBUG"):
            logger.debug(torch.sum((self.layer(self.inp1) - self.out1) ** 2))

        if scale == []:
            scale.append(self.quantizer.scale)
            zero.append(self.quantizer.zero)
        scale = torch.cat(scale, dim=1)
        zero = torch.cat(zero, dim=1)
        return scale, zero, g_idx