import torch import network from modules import devices 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) #self.org_weight = self.org_module[0].weight.to(devices.cpu, 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.merged_weight = self.merge_weight() self.apply_to() self.merged = False # weights_backup = getattr(self.org_module[0], 'network_weights_backup', None) # if weights_backup is None: # self.org_module[0].network_weights_backup = self.org_weight 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): 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) #R = torch.block_diag(*block_R) return R def calc_updown(self, orig_weight): #oft_blocks = self.oft_blocks.to(orig_weight.device, dtype=orig_weight.dtype) #R = self.R.to(orig_weight.device, dtype=orig_weight.dtype) ##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 #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): multiplier = self.multiplier() * self.calc_scale() if not multiplier==self.last_multiplier or not self.merged: #if 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) #elif not self.merged: # self.replace_weight(self.merged_weight) def forward_hook(self, module, args, output): pass #output = output * self.multiplier() * self.calc_scale() #if len(args) > 0: # y = args[0] # output = output + y #return 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 # def forward(self, x, y=None): # x = self.org_forward(x) # if self.multiplier() == 0.0: # return x # # calculating R here is excruciatingly slow # #R = self.get_weight().to(x.device, dtype=x.dtype) # 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