test implementation based on kohaku diag-oft implementation

import torch

import network

from einops import rearrange

class ModuleTypeOFT(network.ModuleType):

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

    def merge_weight(self, R_weight, org_weight):

        R_weight = R_weight.to(org_weight.device, dtype=org_weight.dtype)

        if org_weight.dim() == 4:

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

        else:

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

        return weight

    # def merge_weight(self, R_weight, org_weight):

    #     R_weight = R_weight.to(org_weight.device, dtype=org_weight.dtype)

    #     if org_weight.dim() == 4:

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

    #     else:

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

    #     weight = torch.einsum(

    #         "k n m, k n ... -> k m ...", 

    #         self.oft_diag * scale + torch.eye(self.block_size, device=device), 

    #         org_weight

    #     )

    #     return weight

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

        constraint = self.constraint.to(oft_blocks.device, dtype=oft_blocks.dtype)

        # constraint = self.constraint.to(oft_blocks.device, dtype=oft_blocks.dtype)

        block_Q = oft_blocks - oft_blocks.transpose(1, 2)

        norm_Q = torch.norm(block_Q.flatten())

        new_norm_Q = torch.clamp(norm_Q, max=constraint)

        block_Q = block_Q * ((new_norm_Q + 1e-8) / (norm_Q + 1e-8))

        m_I = torch.eye(self.block_size, device=oft_blocks.device).unsqueeze(0).repeat(self.num_blocks, 1, 1)

        block_R = torch.matmul(m_I + block_Q, (m_I - block_Q).inverse())

        # block_Q = oft_blocks - oft_blocks.transpose(1, 2)

        # norm_Q = torch.norm(block_Q.flatten())

        # new_norm_Q = torch.clamp(norm_Q, max=constraint)

        # block_Q = block_Q * ((new_norm_Q + 1e-8) / (norm_Q + 1e-8))

        # m_I = torch.eye(self.block_size, device=oft_blocks.device).unsqueeze(0).repeat(self.num_blocks, 1, 1)

        # block_R = torch.matmul(m_I + block_Q, (m_I - block_Q).inverse())

        block_R_weighted = multiplier * block_R + (1 - multiplier) * m_I

        R = torch.block_diag(*block_R_weighted)

        # block_R_weighted = multiplier * block_R + (1 - multiplier) * m_I

        # R = torch.block_diag(*block_R_weighted)

        #return R

        return self.oft_blocks

        return R

    def calc_updown(self, orig_weight):

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

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

        merged_weight = self.merge_weight(R, orig_weight)

        updown = merged_weight.to(orig_weight.device, dtype=orig_weight.dtype) - orig_weight

        #R = self.get_weight(self.oft_blocks, multiplier)

        R = self.oft_blocks.to(orig_weight.device, dtype=orig_weight.dtype)

        #merged_weight = self.merge_weight(R, orig_weight)

        orig_weight = rearrange(orig_weight, '(k n) ... -> k n ...', k=self.num_blocks, n=self.block_size)

        weight = torch.einsum(

            'k n m, k n ... -> k m ...',

            R * multiplier + torch.eye(self.block_size, device=orig_weight.device),

            orig_weight

        )

        weight = rearrange(weight, 'k m ... -> (k m) ...')

        #updown = merged_weight.to(orig_weight.device, dtype=orig_weight.dtype) - orig_weight

        updown = weight.to(orig_weight.device, dtype=orig_weight.dtype) - orig_weight

        output_shape = orig_weight.shape

        orig_weight = orig_weight