fix: return orig weights during updown, merge weights before forward

import torch

import network

from modules import devices

class ModuleTypeOFT(network.ModuleType):

        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(devices.cpu, copy=True)

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

        self.merged_weight = self.merge_weight()

        self.apply_to()

        self.merged = False

    def merge_weight(self):

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

        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)

        else:

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

        org_sd['weight'] = weight

        # replace weight

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

        return weight

        pass

    def replace_weight(self, new_weight):

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

        org_sd['weight'] = new_weight

        self.org_module[0].load_state_dict(org_sd)

        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

    # replace forward method of original linear rather than replacing the module

    # how do we revert this to unload the weights?

    def apply_to(self):

        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)

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

        self.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=self.constraint)

        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=self.oft_blocks.device).unsqueeze(0).repeat(self.num_blocks, 1, 1)

        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) * I

        #R = torch.block_diag(*block_R_weighted)

        return R

    def calc_updown(self, orig_weight):

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

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

        R = self.get_weight(oft_blocks)

        self.R = R

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

        ##self.R = R

        #if orig_weight.dim() == 4:

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

        #else:

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

        updown = orig_weight @ R

        output_shape = self.oft_blocks.shape

        #updown = orig_weight @ R

        #updown = weight

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

        #updown = orig_weight

        output_shape = orig_weight.shape

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

        #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.merged:

            self.replace_weight(self.merged_weight)

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

        if self.merged:

            pass

            #self.restore_weight()

        #print(f'Forward hook in {self.network_key} called')

        x = output

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

        if x.dim() == 4:

            x = x.permute(0, 2, 3, 1)

            x = torch.matmul(x, R)

            x = x.permute(0, 3, 1, 2)

        else:

            x = torch.matmul(x, R)

        return x

        #x = output

        #R = self.R.to(x.device, dtype=x.dtype)

        #if x.dim() == 4:

        #    x = x.permute(0, 2, 3, 1)

        #    x = torch.matmul(x, R)

        #    x = x.permute(0, 3, 1, 2)

        #else:

        #    x = torch.matmul(x, R)

        #return x

    # def forward(self, x, y=None):

    #     x = self.org_forward(x)