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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
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