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from typing import *
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.linalg as linalg
from tqdm import tqdm
def make_sparse(t: torch.Tensor, sparsity=0.95):
abs_t = torch.abs(t)
np_array = abs_t.detach().cpu().numpy()
quan = float(np.quantile(np_array, sparsity))
sparse_t = t.masked_fill(abs_t < quan, 0)
return sparse_t
def extract_conv(
weight: Union[torch.Tensor, nn.Parameter],
mode = 'fixed',
mode_param = 0,
device = 'cpu',
) -> Tuple[nn.Parameter, nn.Parameter]:
weight = weight.to(device)
out_ch, in_ch, kernel_size, _ = weight.shape
U, S, Vh = linalg.svd(weight.reshape(out_ch, -1))
if mode=='fixed':
lora_rank = mode_param
elif mode=='threshold':
assert mode_param>=0
lora_rank = torch.sum(S>mode_param)
elif mode=='ratio':
assert 1>=mode_param>=0
min_s = torch.max(S)*mode_param
lora_rank = torch.sum(S>min_s)
elif mode=='quantile' or mode=='percentile':
assert 1>=mode_param>=0
s_cum = torch.cumsum(S, dim=0)
min_cum_sum = mode_param * torch.sum(S)
lora_rank = torch.sum(s_cum<min_cum_sum)
else:
raise NotImplementedError('Extract mode should be "fixed", "threshold", "ratio" or "quantile"')
lora_rank = max(1, lora_rank)
lora_rank = min(out_ch, in_ch, lora_rank)
U = U[:, :lora_rank]
S = S[:lora_rank]
U = U @ torch.diag(S)
Vh = Vh[:lora_rank, :]
diff = (weight - (U @ Vh).reshape(out_ch, in_ch, kernel_size, kernel_size)).detach()
extract_weight_A = Vh.reshape(lora_rank, in_ch, kernel_size, kernel_size).detach()
extract_weight_B = U.reshape(out_ch, lora_rank, 1, 1).detach()
del U, S, Vh, weight
return extract_weight_A, extract_weight_B, diff
def merge_conv(
weight_a: Union[torch.Tensor, nn.Parameter],
weight_b: Union[torch.Tensor, nn.Parameter],
device = 'cpu'
):
rank, in_ch, kernel_size, k_ = weight_a.shape
out_ch, rank_, _, _ = weight_b.shape
assert rank == rank_ and kernel_size == k_
wa = weight_a.to(device)
wb = weight_b.to(device)
if device == 'cpu':
wa = wa.float()
wb = wb.float()
merged = wb.reshape(out_ch, -1) @ wa.reshape(rank, -1)
weight = merged.reshape(out_ch, in_ch, kernel_size, kernel_size)
del wb, wa
return weight
def extract_linear(
weight: Union[torch.Tensor, nn.Parameter],
mode = 'fixed',
mode_param = 0,
device = 'cpu',
) -> Tuple[nn.Parameter, nn.Parameter]:
weight = weight.to(device)
out_ch, in_ch = weight.shape
U, S, Vh = linalg.svd(weight)
if mode=='fixed':
lora_rank = mode_param
elif mode=='threshold':
assert mode_param>=0
lora_rank = torch.sum(S>mode_param)
elif mode=='ratio':
assert 1>=mode_param>=0
min_s = torch.max(S)*mode_param
lora_rank = torch.sum(S>min_s)
elif mode=='quantile' or mode=='percentile':
assert 1>=mode_param>=0
s_cum = torch.cumsum(S, dim=0)
min_cum_sum = mode_param * torch.sum(S)
lora_rank = torch.sum(s_cum<min_cum_sum)
else:
raise NotImplementedError('Extract mode should be "fixed", "threshold", "ratio" or "quantile"')
lora_rank = max(1, lora_rank)
lora_rank = min(out_ch, in_ch, lora_rank)
U = U[:, :lora_rank]
S = S[:lora_rank]
U = U @ torch.diag(S)
Vh = Vh[:lora_rank, :]
diff = (weight - U @ Vh).detach()
extract_weight_A = Vh.reshape(lora_rank, in_ch).detach()
extract_weight_B = U.reshape(out_ch, lora_rank).detach()
del U, S, Vh, weight
return extract_weight_A, extract_weight_B, diff
def merge_linear(
weight_a: Union[torch.Tensor, nn.Parameter],
weight_b: Union[torch.Tensor, nn.Parameter],
device = 'cpu'
):
rank, in_ch = weight_a.shape
out_ch, rank_ = weight_b.shape
assert rank == rank_
wa = weight_a.to(device)
wb = weight_b.to(device)
if device == 'cpu':
wa = wa.float()
wb = wb.float()
weight = wb @ wa
del wb, wa
return weight
def extract_diff(
base_model,
db_model,
mode = 'fixed',
linear_mode_param = 0,
conv_mode_param = 0,
extract_device = 'cpu',
use_bias = False,
sparsity = 0.98,
small_conv = True
):
UNET_TARGET_REPLACE_MODULE = [
"Transformer2DModel",
"Attention",
"ResnetBlock2D",
"Downsample2D",
"Upsample2D"
]
TEXT_ENCODER_TARGET_REPLACE_MODULE = ["CLIPAttention", "CLIPMLP"]
LORA_PREFIX_UNET = 'lora_unet'
LORA_PREFIX_TEXT_ENCODER = 'lora_te'
def make_state_dict(
prefix,
root_module: torch.nn.Module,
target_module: torch.nn.Module,
target_replace_modules
):
loras = {}
temp = {}
for name, module in root_module.named_modules():
if module.__class__.__name__ in target_replace_modules:
temp[name] = {}
for child_name, child_module in module.named_modules():
if child_module.__class__.__name__ not in {'Linear', 'Conv2d'}:
continue
temp[name][child_name] = child_module.weight
for name, module in tqdm(list(target_module.named_modules())):
if name in temp:
weights = temp[name]
for child_name, child_module in module.named_modules():
lora_name = prefix + '.' + name + '.' + child_name
lora_name = lora_name.replace('.', '_')
layer = child_module.__class__.__name__
if layer == 'Linear':
extract_a, extract_b, diff = extract_linear(
(child_module.weight - weights[child_name]),
mode,
linear_mode_param,
device = extract_device,
)
elif layer == 'Conv2d':
is_linear = (child_module.weight.shape[2] == 1
and child_module.weight.shape[3] == 1)
extract_a, extract_b, diff = extract_conv(
(child_module.weight - weights[child_name]),
mode,
linear_mode_param if is_linear else conv_mode_param,
device = extract_device,
)
if small_conv and not is_linear:
dim = extract_a.size(0)
extract_c, extract_a, _ = extract_conv(
extract_a.transpose(0, 1),
'fixed', dim,
extract_device
)
extract_a = extract_a.transpose(0, 1)
extract_c = extract_c.transpose(0, 1)
loras[f'{lora_name}.lora_mid.weight'] = extract_c.detach().cpu().contiguous().half()
diff = child_module.weight - torch.einsum(
'i j k l, j r, p i -> p r k l',
extract_c, extract_a.flatten(1, -1), extract_b.flatten(1, -1)
).detach().cpu().contiguous()
del extract_c
else:
continue
loras[f'{lora_name}.lora_down.weight'] = extract_a.detach().cpu().contiguous().half()
loras[f'{lora_name}.lora_up.weight'] = extract_b.detach().cpu().contiguous().half()
loras[f'{lora_name}.alpha'] = torch.Tensor([extract_a.shape[0]]).half()
if use_bias:
diff = diff.detach().cpu().reshape(extract_b.size(0), -1)
sparse_diff = make_sparse(diff, sparsity).to_sparse().coalesce()
indices = sparse_diff.indices().to(torch.int16)
values = sparse_diff.values().half()
loras[f'{lora_name}.bias_indices'] = indices
loras[f'{lora_name}.bias_values'] = values
loras[f'{lora_name}.bias_size'] = torch.tensor(diff.shape).to(torch.int16)
del extract_a, extract_b, diff
return loras
text_encoder_loras = make_state_dict(
LORA_PREFIX_TEXT_ENCODER,
base_model[0], db_model[0],
TEXT_ENCODER_TARGET_REPLACE_MODULE
)
unet_loras = make_state_dict(
LORA_PREFIX_UNET,
base_model[2], db_model[2],
UNET_TARGET_REPLACE_MODULE
)
print(len(text_encoder_loras), len(unet_loras))
return text_encoder_loras|unet_loras
def merge_locon(
base_model,
locon_state_dict: Dict[str, torch.TensorType],
scale: float = 1.0,
device = 'cpu'
):
UNET_TARGET_REPLACE_MODULE = [
"Transformer2DModel",
"Attention",
"ResnetBlock2D",
"Downsample2D",
"Upsample2D"
]
TEXT_ENCODER_TARGET_REPLACE_MODULE = ["CLIPAttention", "CLIPMLP"]
LORA_PREFIX_UNET = 'lora_unet'
LORA_PREFIX_TEXT_ENCODER = 'lora_te'
def merge(
prefix,
root_module: torch.nn.Module,
target_replace_modules
):
temp = {}
for name, module in tqdm(list(root_module.named_modules())):
if module.__class__.__name__ in target_replace_modules:
temp[name] = {}
for child_name, child_module in module.named_modules():
layer = child_module.__class__.__name__
if layer not in {'Linear', 'Conv2d'}:
continue
lora_name = prefix + '.' + name + '.' + child_name
lora_name = lora_name.replace('.', '_')
down = locon_state_dict[f'{lora_name}.lora_down.weight'].float()
up = locon_state_dict[f'{lora_name}.lora_up.weight'].float()
alpha = locon_state_dict[f'{lora_name}.alpha'].float()
rank = down.shape[0]
if layer == 'Conv2d':
delta = merge_conv(down, up, device)
child_module.weight.requires_grad_(False)
child_module.weight += (alpha.to(device)/rank * scale * delta).cpu()
del delta
elif layer == 'Linear':
delta = merge_linear(down, up, device)
child_module.weight.requires_grad_(False)
child_module.weight += (alpha.to(device)/rank * scale * delta).cpu()
del delta
merge(
LORA_PREFIX_TEXT_ENCODER,
base_model[0],
TEXT_ENCODER_TARGET_REPLACE_MODULE
)
merge(
LORA_PREFIX_UNET,
base_model[2],
UNET_TARGET_REPLACE_MODULE
)
def merge_loha(
base_model,
loha_state_dict: Dict[str, torch.TensorType],
scale: float = 1.0,
device = 'cpu'
):
UNET_TARGET_REPLACE_MODULE = [
"Transformer2DModel",
"Attention",
"ResnetBlock2D",
"Downsample2D",
"Upsample2D"
]
TEXT_ENCODER_TARGET_REPLACE_MODULE = ["CLIPAttention", "CLIPMLP"]
LORA_PREFIX_UNET = 'lora_unet'
LORA_PREFIX_TEXT_ENCODER = 'lora_te'
def merge(
prefix,
root_module: torch.nn.Module,
target_replace_modules
):
temp = {}
for name, module in tqdm(list(root_module.named_modules())):
if module.__class__.__name__ in target_replace_modules:
temp[name] = {}
for child_name, child_module in module.named_modules():
layer = child_module.__class__.__name__
if layer not in {'Linear', 'Conv2d'}:
continue
lora_name = prefix + '.' + name + '.' + child_name
lora_name = lora_name.replace('.', '_')
w1a = loha_state_dict[f'{lora_name}.hada_w1_a'].float().to(device)
w1b = loha_state_dict[f'{lora_name}.hada_w1_b'].float().to(device)
w2a = loha_state_dict[f'{lora_name}.hada_w2_a'].float().to(device)
w2b = loha_state_dict[f'{lora_name}.hada_w2_b'].float().to(device)
alpha = loha_state_dict[f'{lora_name}.alpha'].float().to(device)
dim = w1b.shape[0]
delta = (w1a @ w1b) * (w2a @ w2b)
delta = delta.reshape(child_module.weight.shape)
if layer == 'Conv2d':
child_module.weight.requires_grad_(False)
child_module.weight += (alpha.to(device)/dim * scale * delta).cpu()
elif layer == 'Linear':
child_module.weight.requires_grad_(False)
child_module.weight += (alpha.to(device)/dim * scale * delta).cpu()
del delta
merge(
LORA_PREFIX_TEXT_ENCODER,
base_model[0],
TEXT_ENCODER_TARGET_REPLACE_MODULE
)
merge(
LORA_PREFIX_UNET,
base_model[2],
UNET_TARGET_REPLACE_MODULE
) |