//  [python]
def fasterquant(self, blocksize=128, percdamp=0.01, group_size=-1, actorder=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()

        tick = time.time()

        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

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

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

        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 (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

                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()
        logger.info(f"duration: {(time.time() - tick)}")
        logger.info(f"avg loss: {torch.sum(Losses).item() / self.nsamples}")

        group_size = group_size if group_size != -1 else self.columns
        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:
            invperm = torch.argsort(perm)
            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