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import torch
import network
from lyco_helpers import factorization
from einops import rearrange
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"]) or all(x in weights.w for x in ["oft_diag"]):
return NetworkModuleOFT(net, weights)
return None
# adapted from kohya-ss' implementation https://github.com/kohya-ss/sd-scripts/blob/main/networks/oft.py
# and KohakuBlueleaf's implementation https://github.com/KohakuBlueleaf/LyCORIS/blob/dev/lycoris/modules/diag_oft.py
class NetworkModuleOFT(network.NetworkModule):
def __init__(self, net: network.Network, weights: network.NetworkWeights):
super().__init__(net, weights)
self.lin_module = None
self.org_module: list[torch.Module] = [self.sd_module]
# kohya-ss
if "oft_blocks" in weights.w.keys():
self.is_kohya = True
self.oft_blocks = weights.w["oft_blocks"] # (num_blocks, block_size, block_size)
self.alpha = weights.w["alpha"]
self.dim = self.oft_blocks.shape[0] # lora dim
#self.oft_blocks = rearrange(self.oft_blocks, 'k m ... -> (k m) ...')
elif "oft_diag" in weights.w.keys():
self.is_kohya = False
self.oft_blocks = weights.w["oft_diag"] # (num_blocks, block_size, block_size)
# alpha is rank if alpha is 0 or None
if self.alpha is None:
pass
self.dim = self.oft_blocks.shape[1] # FIXME: almost certainly incorrect, assumes tensor is shape [*, m, n]
else:
raise ValueError("oft_blocks or oft_diag must be in weights dict")
is_linear = type(self.sd_module) in [torch.nn.Linear, torch.nn.modules.linear.NonDynamicallyQuantizableLinear]
is_conv = type(self.sd_module) in [torch.nn.Conv2d]
is_other_linear = type(self.sd_module) in [torch.nn.MultiheadAttention]
if is_linear:
self.out_dim = self.sd_module.out_features
elif is_other_linear:
self.out_dim = self.sd_module.embed_dim
elif is_conv:
self.out_dim = self.sd_module.out_channels
else:
raise ValueError("sd_module must be Linear or Conv")
if self.is_kohya:
#self.num_blocks = self.dim
#self.block_size = self.out_dim // self.num_blocks
#self.block_size = self.dim
#self.num_blocks = self.out_dim // self.block_size
self.constraint = self.alpha * self.out_dim
self.num_blocks, self.block_size = factorization(self.out_dim, self.dim)
else:
self.constraint = None
self.block_size, self.num_blocks = factorization(self.out_dim, self.dim)
if is_other_linear:
self.lin_module = self.create_module(weights.w, "oft_diag", none_ok=True)
def create_module(self, weights, key, none_ok=False):
weight = weights.get(key)
if weight is None and none_ok:
return None
is_linear = type(self.sd_module) in [torch.nn.Linear, torch.nn.modules.linear.NonDynamicallyQuantizableLinear, torch.nn.MultiheadAttention]
is_conv = type(self.sd_module) in [torch.nn.Conv2d]
if is_linear:
weight = weight.reshape(weight.shape[0], -1)
module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False)
elif is_conv and key == "lora_down.weight" or key == "dyn_up":
if len(weight.shape) == 2:
weight = weight.reshape(weight.shape[0], -1, 1, 1)
if weight.shape[2] != 1 or weight.shape[3] != 1:
module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], self.sd_module.kernel_size, self.sd_module.stride, self.sd_module.padding, bias=False)
else:
module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (1, 1), bias=False)
elif is_conv and key == "lora_mid.weight":
module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], self.sd_module.kernel_size, self.sd_module.stride, self.sd_module.padding, bias=False)
elif is_conv and key == "lora_up.weight" or key == "dyn_down":
module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (1, 1), bias=False)
else:
raise AssertionError(f'Lora layer {self.network_key} matched a layer with unsupported type: {type(self.sd_module).__name__}')
with torch.no_grad():
if weight.shape != module.weight.shape:
weight = weight.reshape(module.weight.shape)
module.weight.copy_(weight)
module.to(device=devices.cpu, dtype=devices.dtype)
module.weight.requires_grad_(False)
return 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 get_weight(self, oft_blocks, multiplier=None):
if self.constraint is not None:
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())
if self.constraint is not None:
new_norm_Q = torch.clamp(norm_Q, max=constraint)
else:
new_norm_Q = norm_Q
block_Q = block_Q * ((new_norm_Q + 1e-8) / (norm_Q + 1e-8))
m_I = torch.eye(self.num_blocks, device=oft_blocks.device).unsqueeze(0).repeat(self.block_size, 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) * m_I
R = torch.block_diag(*block_R_weighted)
return R
def calc_updown_kohya(self, orig_weight, multiplier):
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
output_shape = orig_weight.shape
orig_weight = orig_weight
return self.finalize_updown(updown, orig_weight, output_shape)
def calc_updown_kb(self, orig_weight, multiplier):
is_other_linear = type(self.sd_module) in [torch.nn.MultiheadAttention]
if not is_other_linear:
#if is_other_linear and orig_weight.shape[0] != orig_weight.shape[1]:
# orig_weight=orig_weight.permute(1, 0)
oft_blocks = self.oft_blocks.to(orig_weight.device, dtype=orig_weight.dtype)
# without this line the results are significantly worse / less accurate
oft_blocks = oft_blocks - oft_blocks.transpose(1, 2)
R = oft_blocks.to(orig_weight.device, dtype=orig_weight.dtype)
R = R * multiplier + torch.eye(self.block_size, device=orig_weight.device)
merged_weight = rearrange(orig_weight, '(k n) ... -> k n ...', k=self.num_blocks, n=self.block_size)
merged_weight = torch.einsum(
'k n m, k n ... -> k m ...',
R,
merged_weight
)
merged_weight = rearrange(merged_weight, 'k m ... -> (k m) ...')
#if is_other_linear and orig_weight.shape[0] != orig_weight.shape[1]:
# orig_weight=orig_weight.permute(1, 0)
updown = merged_weight.to(orig_weight.device, dtype=orig_weight.dtype) - orig_weight
output_shape = orig_weight.shape
else:
# FIXME: skip MultiheadAttention for now
#up = self.lin_module.weight.to(orig_weight.device, dtype=orig_weight.dtype)
updown = torch.zeros([orig_weight.shape[1], orig_weight.shape[1]], device=orig_weight.device, dtype=orig_weight.dtype)
output_shape = (orig_weight.shape[1], orig_weight.shape[1])
return self.finalize_updown(updown, orig_weight, output_shape)
def calc_updown(self, orig_weight):
multiplier = self.multiplier() * self.calc_scale()
#if self.is_kohya:
# return self.calc_updown_kohya(orig_weight, multiplier)
#else:
return self.calc_updown_kb(orig_weight, multiplier)
# override to remove the multiplier/scale factor; it's already multiplied in get_weight
def finalize_updown(self, updown, orig_weight, output_shape, ex_bias=None):
#return super().finalize_updown(updown, orig_weight, output_shape, ex_bias)
if self.bias is not None:
updown = updown.reshape(self.bias.shape)
updown += self.bias.to(orig_weight.device, dtype=orig_weight.dtype)
updown = updown.reshape(output_shape)
if len(output_shape) == 4:
updown = updown.reshape(output_shape)
if orig_weight.size().numel() == updown.size().numel():
updown = updown.reshape(orig_weight.shape)
if ex_bias is not None:
ex_bias = ex_bias * self.multiplier()
return updown, ex_bias
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