fix: use merge_weight to cache value

        self.block_size = self.out_dim // self.num_blocks

        self.org_module: list[torch.Module] = [self.sd_module]

        self.org_weight = self.org_module[0].weight.to(self.org_module[0].weight.device, copy=True)

        #self.org_weight = self.org_module[0].weight.to(self.org_module[0].weight.device, copy=True)

        init_multiplier = self.multiplier() * self.calc_scale()

        self.last_multiplier = init_multiplier

        self.R = self.get_weight(self.oft_blocks, init_multiplier)

        self.hooks = []

        self.merged_weight = self.merge_weight()

        self.apply_to()

        #self.apply_to()

        self.applied = False

        self.merged = False

    def merge_weight(self):

        R = self.R.to(self.org_weight.device, dtype=self.org_weight.dtype)

        if self.org_weight.dim() == 4:

            weight = torch.einsum("oihw, op -> pihw", self.org_weight, R)

        org_weight = self.org_module[0].weight

        R = self.R.to(org_weight.device, dtype=org_weight.dtype)

        if org_weight.dim() == 4:

            weight = torch.einsum("oihw, op -> pihw", org_weight, R)

        else:

            weight = torch.einsum("oi, op -> pi", self.org_weight, R)

            weight = torch.einsum("oi, op -> pi", org_weight, R)

        return weight

    def replace_weight(self, new_weight):

        self.merged = True

    def restore_weight(self):

        org_sd = self.org_module[0].state_dict()

        org_sd['weight'] = self.org_weight

        self.org_module[0].load_state_dict(org_sd)

        self.merged = False

        pass

        #org_sd = self.org_module[0].state_dict()

        #org_sd['weight'] = self.org_weight

        #self.org_module[0].load_state_dict(org_sd)

        #self.merged = False

    # FIXME: hook forward method of original linear, but how do we undo the hook when we are done?

    def apply_to(self):

        if not self.applied:

            self.org_forward = self.org_module[0].forward

            #self.org_module[0].forward = self.forward

        self.org_module[0].register_forward_pre_hook(self.pre_forward_hook)

        self.org_module[0].register_forward_hook(self.forward_hook)

            prehook = self.org_module[0].register_forward_pre_hook(self.pre_forward_hook)

            hook = self.org_module[0].register_forward_hook(self.forward_hook)

            self.hooks.append(prehook)

            self.hooks.append(hook)

            self.applied = True

    def remove_from(self):

        if self.applied:

            for hook in self.hooks:

                hook.remove()

            self.hooks = []

            self.applied = False

    def get_weight(self, oft_blocks, multiplier=None):

        multiplier = multiplier.to(oft_blocks.device, dtype=oft_blocks.dtype)

        return R

    def calc_updown(self, orig_weight):

        if not self.applied:

            self.apply_to()

        self.merged_weight = self.merged_weight.to(orig_weight.device, dtype=orig_weight.dtype)

        updown = torch.zeros_like(orig_weight, device=orig_weight.device, dtype=orig_weight.dtype)

        output_shape = orig_weight.shape

        orig_weight = self.merged_weight.to(orig_weight.device, dtype=orig_weight.dtype)

        orig_weight = self.merged_weight

        #output_shape = self.oft_blocks.shape

        return self.finalize_updown(updown, orig_weight, output_shape)

    def pre_forward_hook(self, module, input):

        #if not self.applied:

        #    self.apply_to()

        multiplier = self.multiplier() * self.calc_scale()

        if not multiplier==self.last_multiplier or not self.merged:

            self.merged_weight = self.merge_weight()

            self.replace_weight(self.merged_weight)

    def forward_hook(self, module, args, output):

        pass

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