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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',
is_cp = False,
) -> 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)
if lora_rank>=out_ch/2 and not is_cp:
return weight, 'full'
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), 'low rank'
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)
if lora_rank>=out_ch/2:
return weight, 'full'
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), 'low rank'
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"
]
UNET_TARGET_REPLACE_NAME = [
"conv_in",
"conv_out",
"time_embedding.linear_1",
"time_embedding.linear_2",
]
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,
target_replace_names = []
):
loras = {}
temp = {}
temp_name = {}
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
elif name in target_replace_names:
temp_name[name] = 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 in {'Linear', 'Conv2d'}:
root_weight = child_module.weight
if torch.allclose(root_weight, weights[child_name]):
continue
if layer == 'Linear':
weight, decompose_mode = extract_linear(
(child_module.weight - weights[child_name]),
mode,
linear_mode_param,
device = extract_device,
)
if decompose_mode == 'low rank':
extract_a, extract_b, diff = weight
elif layer == 'Conv2d':
is_linear = (child_module.weight.shape[2] == 1
and child_module.weight.shape[3] == 1)
weight, decompose_mode = extract_conv(
(child_module.weight - weights[child_name]),
mode,
linear_mode_param if is_linear else conv_mode_param,
device = extract_device,
)
if decompose_mode == 'low rank':
extract_a, extract_b, diff = weight
if small_conv and not is_linear and decompose_mode == 'low rank':
dim = extract_a.size(0)
(extract_c, extract_a, _), _ = extract_conv(
extract_a.transpose(0, 1),
'fixed', dim,
extract_device, True
)
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
if decompose_mode == 'low rank':
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
elif decompose_mode == 'full':
loras[f'{lora_name}.diff'] = weight.detach().cpu().contiguous().half()
else:
raise NotImplementedError
elif name in temp_name:
weights = temp_name[name]
lora_name = prefix + '.' + name
lora_name = lora_name.replace('.', '_')
layer = module.__class__.__name__
if layer in {'Linear', 'Conv2d'}:
root_weight = module.weight
if torch.allclose(root_weight, weights):
continue
if layer == 'Linear':
weight, decompose_mode = extract_linear(
(root_weight - weights),
mode,
linear_mode_param,
device = extract_device,
)
if decompose_mode == 'low rank':
extract_a, extract_b, diff = weight
elif layer == 'Conv2d':
is_linear = (
root_weight.shape[2] == 1
and root_weight.shape[3] == 1
)
weight, decompose_mode = extract_conv(
(root_weight - weights),
mode,
linear_mode_param if is_linear else conv_mode_param,
device = extract_device,
)
if decompose_mode == 'low rank':
extract_a, extract_b, diff = weight
if small_conv and not is_linear and decompose_mode == 'low rank':
dim = extract_a.size(0)
(extract_c, extract_a, _), _ = extract_conv(
extract_a.transpose(0, 1),
'fixed', dim,
extract_device, True
)
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 = root_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
if decompose_mode == 'low rank':
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
elif decompose_mode == 'full':
loras[f'{lora_name}.diff'] = weight.detach().cpu().contiguous().half()
else:
raise NotImplementedError
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,
UNET_TARGET_REPLACE_NAME
)
print(len(text_encoder_loras), len(unet_loras))
return text_encoder_loras|unet_loras
def get_module(
lyco_state_dict: Dict,
lora_name
):
if f'{lora_name}.lora_up.weight' in lyco_state_dict:
up = lyco_state_dict[f'{lora_name}.lora_up.weight']
down = lyco_state_dict[f'{lora_name}.lora_down.weight']
mid = lyco_state_dict.get(f'{lora_name}.lora_mid.weight', None)
alpha = lyco_state_dict.get(f'{lora_name}.alpha', None)
return 'locon', (up, down, mid, alpha)
elif f'{lora_name}.hada_w1_a' in lyco_state_dict:
w1a = lyco_state_dict[f'{lora_name}.hada_w1_a']
w1b = lyco_state_dict[f'{lora_name}.hada_w1_b']
w2a = lyco_state_dict[f'{lora_name}.hada_w2_a']
w2b = lyco_state_dict[f'{lora_name}.hada_w2_b']
t1 = lyco_state_dict.get(f'{lora_name}.hada_t1', None)
t2 = lyco_state_dict.get(f'{lora_name}.hada_t2', None)
alpha = lyco_state_dict.get(f'{lora_name}.alpha', None)
return 'hada', (w1a, w1b, w2a, w2b, t1, t2, alpha)
elif f'{lora_name}.weight' in lyco_state_dict:
weight = lyco_state_dict[f'{lora_name}.weight']
on_input = lyco_state_dict.get(f'{lora_name}.on_input', False)
return 'ia3', (weight, on_input)
elif (f'{lora_name}.lokr_w1' in lyco_state_dict
or f'{lora_name}.lokr_w1_a' in lyco_state_dict):
w1 = lyco_state_dict.get(f'{lora_name}.lokr_w1', None)
w1a = lyco_state_dict.get(f'{lora_name}.lokr_w1_a', None)
w1b = lyco_state_dict.get(f'{lora_name}.lokr_w1_b', None)
w2 = lyco_state_dict.get(f'{lora_name}.lokr_w2', None)
w2a = lyco_state_dict.get(f'{lora_name}.lokr_w2_a', None)
w2b = lyco_state_dict.get(f'{lora_name}.lokr_w2_b', None)
t1 = lyco_state_dict.get(f'{lora_name}.lokr_t1', None)
t2 = lyco_state_dict.get(f'{lora_name}.lokr_t2', None)
alpha = lyco_state_dict.get(f'{lora_name}.alpha', None)
return 'kron', (w1, w1a, w1b, w2, w2a, w2b, t1, t2, alpha)
elif f'{lora_name}.diff' in lyco_state_dict:
return 'full', lyco_state_dict[f'{lora_name}.diff']
else:
return 'None', ()
def cp_weight_from_conv(
up, down, mid
):
up = up.reshape(up.size(0), up.size(1))
down = down.reshape(down.size(0), down.size(1))
return torch.einsum('m n w h, i m, n j -> i j w h', mid, up, down)
def cp_weight(
wa, wb, t
):
temp = torch.einsum('i j k l, j r -> i r k l', t, wb)
return torch.einsum('i j k l, i r -> r j k l', temp, wa)
@torch.no_grad()
def rebuild_weight(module_type, params, orig_weight, scale=1):
if orig_weight is None:
return orig_weight
merged = orig_weight
if module_type == 'locon':
up, down, mid, alpha = params
if alpha is not None:
scale *= alpha/up.size(1)
if mid is not None:
rebuild = cp_weight_from_conv(up, down, mid)
else:
rebuild = up.reshape(up.size(0),-1) @ down.reshape(down.size(0), -1)
merged = orig_weight + rebuild.reshape(orig_weight.shape) * scale
del up, down, mid, alpha, params, rebuild
elif module_type == 'hada':
w1a, w1b, w2a, w2b, t1, t2, alpha = params
if alpha is not None:
scale *= alpha / w1b.size(0)
if t1 is not None:
rebuild1 = cp_weight(w1a, w1b, t1)
else:
rebuild1 = w1a @ w1b
if t2 is not None:
rebuild2 = cp_weight(w2a, w2b, t2)
else:
rebuild2 = w2a @ w2b
rebuild = (rebuild1 * rebuild2).reshape(orig_weight.shape)
merged = orig_weight + rebuild * scale
del w1a, w1b, w2a, w2b, t1, t2, alpha, params, rebuild, rebuild1, rebuild2
elif module_type == 'ia3':
weight, on_input = params
if not on_input:
weight = weight.reshape(-1, 1)
merged = orig_weight + weight * orig_weight * scale
del weight, on_input, params
elif module_type == 'kron':
w1, w1a, w1b, w2, w2a, w2b, t1, t2, alpha = params
if alpha is not None and (w1b is not None or w2b is not None):
scale *= alpha / (w1b.size(0) if w1b else w2b.size(0))
if w1a is not None and w1b is not None:
if t1:
w1 = cp_weight(w1a, w1b, t1)
else:
w1 = w1a @ w1b
if w2a is not None and w2b is not None:
if t2:
w2 = cp_weight(w2a, w2b, t2)
else:
w2 = w2a @ w2b
rebuild = torch.kron(w1, w2).reshape(orig_weight.shape)
merged = orig_weight + rebuild* scale
del w1, w1a, w1b, w2, w2a, w2b, t1, t2, alpha, params, rebuild
elif module_type == 'full':
rebuild = params.reshape(orig_weight.shape)
merged = orig_weight + rebuild * scale
del params, rebuild
return merged
def merge(
base_model,
lyco_state_dict,
scale: float = 1.0,
device = 'cpu'
):
UNET_TARGET_REPLACE_MODULE = [
"Transformer2DModel",
"Attention",
"ResnetBlock2D",
"Downsample2D",
"Upsample2D"
]
UNET_TARGET_REPLACE_NAME = [
"conv_in",
"conv_out",
"time_embedding.linear_1",
"time_embedding.linear_2",
]
TEXT_ENCODER_TARGET_REPLACE_MODULE = ["CLIPAttention", "CLIPMLP"]
LORA_PREFIX_UNET = 'lora_unet'
LORA_PREFIX_TEXT_ENCODER = 'lora_te'
merged = 0
def merge_state_dict(
prefix,
root_module: torch.nn.Module,
lyco_state_dict: Dict[str,torch.Tensor],
target_replace_modules,
target_replace_names = []
):
nonlocal merged
for name, module in tqdm(list(root_module.named_modules()), desc=f'Merging {prefix}'):
if module.__class__.__name__ in target_replace_modules:
for child_name, child_module in module.named_modules():
if child_module.__class__.__name__ not in {'Linear', 'Conv2d'}:
continue
lora_name = prefix + '.' + name + '.' + child_name
lora_name = lora_name.replace('.', '_')
result = rebuild_weight(*get_module(
lyco_state_dict, lora_name
), getattr(child_module, 'weight'), scale)
if result is not None:
merged += 1
child_module.requires_grad_(False)
child_module.weight.copy_(result)
elif name in target_replace_names:
lora_name = prefix + '.' + name
lora_name = lora_name.replace('.', '_')
result = rebuild_weight(*get_module(
lyco_state_dict, lora_name
), getattr(module, 'weight'), scale)
if result is not None:
merged += 1
module.requires_grad_(False)
module.weight.copy_(result)
if device == 'cpu':
for k, v in tqdm(list(lyco_state_dict.items()), desc='Converting Dtype'):
lyco_state_dict[k] = v.float()
merge_state_dict(
LORA_PREFIX_TEXT_ENCODER,
base_model[0],
lyco_state_dict,
TEXT_ENCODER_TARGET_REPLACE_MODULE,
UNET_TARGET_REPLACE_NAME
)
merge_state_dict(
LORA_PREFIX_UNET,
base_model[2],
lyco_state_dict,
UNET_TARGET_REPLACE_MODULE,
UNET_TARGET_REPLACE_NAME
)
print(f'{merged} Modules been merged') |