stable-diffusion-webui/extensions-builtin/Lora/network_oft.py

import torch
import network


class ModuleTypeOFT(network.ModuleType):
    def create_module(self, net: network.Network, weights: network.NetworkWeights):
        if all(x in weights.w for x in ["oft_blocks"]):
            return NetworkModuleOFT(net, weights)

        return None

# adapted from kohya's implementation https://github.com/kohya-ss/sd-scripts/blob/main/networks/oft.py
class NetworkModuleOFT(network.NetworkModule):
    def __init__(self,  net: network.Network, weights: network.NetworkWeights):

        super().__init__(net, weights)

        self.oft_blocks = weights.w["oft_blocks"]
        self.alpha = weights.w["alpha"]
        self.dim = self.oft_blocks.shape[0]
        self.num_blocks = self.dim

        if "Linear" in self.sd_module.__class__.__name__:
            self.out_dim = self.sd_module.out_features
        elif "Conv" in self.sd_module.__class__.__name__:
            self.out_dim = self.sd_module.out_channels

        self.constraint = self.alpha * self.out_dim
        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)

        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.applied = False
        self.merged = False

    def merge_weight(self):
        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", org_weight, R)
        return weight

    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):
        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
            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)
        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_R_weighted = multiplier * block_R + (1 - multiplier) * m_I
        R = torch.block_diag(*block_R_weighted)

        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
        #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.R = self.get_weight(self.oft_blocks, multiplier)
            self.last_multiplier = multiplier
            self.merged_weight = self.merge_weight()
            self.replace_weight(self.merged_weight)

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