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Kohya S
2024-02-24 20:00:57 +09:00
parent 24092e6f21
commit fb9110bac1
1 changed files with 231 additions and 197 deletions
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168
networks/resize_lora.py
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@@ -9,21 +9,24 @@ from tqdm import tqdm
from library import train_util, model_util
import numpy as np
from library.utils import setup_logging
setup_logging()
import logging
logger = logging.getLogger(__name__)
MIN_SV = 1e-6
# Model save and load functions
def load_state_dict(file_name, dtype):
if model_util.is_safetensors(file_name):
sd = load_file(file_name)
with safe_open(file_name, framework="pt") as f:
metadata = f.metadata()
else:
sd = torch.load(file_name, map_location='cpu')
sd = torch.load(file_name, map_location="cpu")
metadata = None
for key in list(sd.keys()):
@@ -47,11 +50,12 @@ def save_to_file(file_name, model, state_dict, dtype, metadata):
# Indexing functions
def index_sv_cumulative(S, target):
original_sum = float(torch.sum(S))
cumulative_sums = torch.cumsum(S, dim=0)/original_sum
cumulative_sums = torch.cumsum(S, dim=0) / original_sum
index = int(torch.searchsorted(cumulative_sums, target)) + 1
index = max(1, min(index, len(S)-1))
index = max(1, min(index, len(S) - 1))
return index
@@ -59,18 +63,18 @@ def index_sv_cumulative(S, target):
def index_sv_fro(S, target):
S_squared = S.pow(2)
s_fro_sq = float(torch.sum(S_squared))
sum_S_squared = torch.cumsum(S_squared, dim=0)/s_fro_sq
sum_S_squared = torch.cumsum(S_squared, dim=0) / s_fro_sq
index = int(torch.searchsorted(sum_S_squared, target**2)) + 1
index = max(1, min(index, len(S)-1))
index = max(1, min(index, len(S) - 1))
return index
def index_sv_ratio(S, target):
max_sv = S[0]
min_sv = max_sv/target
min_sv = max_sv / target
index = int(torch.sum(S > min_sv).item())
index = max(1, min(index, len(S)-1))
index = max(1, min(index, len(S) - 1))
return index
@@ -142,35 +146,34 @@ def merge_linear(lora_down, lora_up, device):
# Calculate new rank
def rank_resize(S, rank, dynamic_method, dynamic_param, scale=1):
param_dict = {}
if dynamic_method=="sv_ratio":
if dynamic_method == "sv_ratio":
# Calculate new dim and alpha based off ratio
new_rank = index_sv_ratio(S, dynamic_param) + 1
new_alpha = float(scale*new_rank)
new_alpha = float(scale * new_rank)
elif dynamic_method=="sv_cumulative":
elif dynamic_method == "sv_cumulative":
# Calculate new dim and alpha based off cumulative sum
new_rank = index_sv_cumulative(S, dynamic_param) + 1
new_alpha = float(scale*new_rank)
new_alpha = float(scale * new_rank)
elif dynamic_method=="sv_fro":
elif dynamic_method == "sv_fro":
# Calculate new dim and alpha based off sqrt sum of squares
new_rank = index_sv_fro(S, dynamic_param) + 1
new_alpha = float(scale*new_rank)
new_alpha = float(scale * new_rank)
else:
new_rank = rank
new_alpha = float(scale*new_rank)
new_alpha = float(scale * new_rank)
if S[0] <= MIN_SV: # Zero matrix, set dim to 1
new_rank = 1
new_alpha = float(scale*new_rank)
new_alpha = float(scale * new_rank)
elif new_rank > rank: # cap max rank at rank
new_rank = rank
new_alpha = float(scale*new_rank)
new_alpha = float(scale * new_rank)
# Calculate resize info
s_sum = torch.sum(torch.abs(S))
@@ -179,13 +182,13 @@ def rank_resize(S, rank, dynamic_method, dynamic_param, scale=1):
S_squared = S.pow(2)
s_fro = torch.sqrt(torch.sum(S_squared))
s_red_fro = torch.sqrt(torch.sum(S_squared[:new_rank]))
fro_percent = float(s_red_fro/s_fro)
fro_percent = float(s_red_fro / s_fro)
param_dict["new_rank"] = new_rank
param_dict["new_alpha"] = new_alpha
param_dict["sum_retained"] = (s_rank)/s_sum
param_dict["sum_retained"] = (s_rank) / s_sum
param_dict["fro_retained"] = fro_percent
param_dict["max_ratio"] = S[0]/S[new_rank - 1]
param_dict["max_ratio"] = S[0] / S[new_rank - 1]
return param_dict
@@ -198,19 +201,21 @@ def resize_lora_model(lora_sd, new_rank, save_dtype, device, dynamic_method, dyn
# Extract loaded lora dim and alpha
for key, value in lora_sd.items():
if network_alpha is None and 'alpha' in key:
if network_alpha is None and "alpha" in key:
network_alpha = value
if network_dim is None and 'lora_down' in key and len(value.size()) == 2:
if network_dim is None and "lora_down" in key and len(value.size()) == 2:
network_dim = value.size()[0]
if network_alpha is not None and network_dim is not None:
break
if network_alpha is None:
network_alpha = network_dim
scale = network_alpha/network_dim
scale = network_alpha / network_dim
if dynamic_method:
logger.info(f"Dynamically determining new alphas and dims based off {dynamic_method}: {dynamic_param}, max rank is {new_rank}")
logger.info(
f"Dynamically determining new alphas and dims based off {dynamic_method}: {dynamic_param}, max rank is {new_rank}"
)
lora_down_weight = None
lora_up_weight = None
@@ -222,8 +227,8 @@ def resize_lora_model(lora_sd, new_rank, save_dtype, device, dynamic_method, dyn
with torch.no_grad():
for key, value in tqdm(lora_sd.items()):
weight_name = None
if 'lora_down' in key:
block_down_name = key.rsplit('.lora_down', 1)[0]
if "lora_down" in key:
block_down_name = key.rsplit(".lora_down", 1)[0]
weight_name = key.rsplit(".", 1)[-1]
lora_down_weight = value
else:
@@ -231,18 +236,18 @@ def resize_lora_model(lora_sd, new_rank, save_dtype, device, dynamic_method, dyn
# find corresponding lora_up and alpha
block_up_name = block_down_name
lora_up_weight = lora_sd.get(block_up_name + '.lora_up.' + weight_name, None)
lora_alpha = lora_sd.get(block_down_name + '.alpha', None)
lora_up_weight = lora_sd.get(block_up_name + ".lora_up." + weight_name, None)
lora_alpha = lora_sd.get(block_down_name + ".alpha", None)
weights_loaded = (lora_down_weight is not None and lora_up_weight is not None)
weights_loaded = lora_down_weight is not None and lora_up_weight is not None
if weights_loaded:
conv2d = (len(lora_down_weight.size()) == 4)
conv2d = len(lora_down_weight.size()) == 4
if lora_alpha is None:
scale = 1.0
else:
scale = lora_alpha/lora_down_weight.size()[0]
scale = lora_alpha / lora_down_weight.size()[0]
if conv2d:
full_weight_matrix = merge_conv(lora_down_weight, lora_up_weight, device)
@@ -252,24 +257,26 @@ def resize_lora_model(lora_sd, new_rank, save_dtype, device, dynamic_method, dyn
param_dict = extract_linear(full_weight_matrix, new_rank, dynamic_method, dynamic_param, device, scale)
if verbose:
max_ratio = param_dict['max_ratio']
sum_retained = param_dict['sum_retained']
fro_retained = param_dict['fro_retained']
max_ratio = param_dict["max_ratio"]
sum_retained = param_dict["sum_retained"]
fro_retained = param_dict["fro_retained"]
if not np.isnan(fro_retained):
fro_list.append(float(fro_retained))
verbose_str+=f"{block_down_name:75} | "
verbose_str+=f"sum(S) retained: {sum_retained:.1%}, fro retained: {fro_retained:.1%}, max(S) ratio: {max_ratio:0.1f}"
verbose_str += f"{block_down_name:75} | "
verbose_str += (
f"sum(S) retained: {sum_retained:.1%}, fro retained: {fro_retained:.1%}, max(S) ratio: {max_ratio:0.1f}"
)
if verbose and dynamic_method:
verbose_str+=f", dynamic | dim: {param_dict['new_rank']}, alpha: {param_dict['new_alpha']}\n"
verbose_str += f", dynamic | dim: {param_dict['new_rank']}, alpha: {param_dict['new_alpha']}\n"
else:
verbose_str+=f"\n"
verbose_str += f"\n"
new_alpha = param_dict['new_alpha']
new_alpha = param_dict["new_alpha"]
o_lora_sd[block_down_name + "." + "lora_down.weight"] = param_dict["lora_down"].to(save_dtype).contiguous()
o_lora_sd[block_up_name + "." + "lora_up.weight"] = param_dict["lora_up"].to(save_dtype).contiguous()
o_lora_sd[block_up_name + "." "alpha"] = torch.tensor(param_dict['new_alpha']).to(save_dtype)
o_lora_sd[block_up_name + "." "alpha"] = torch.tensor(param_dict["new_alpha"]).to(save_dtype)
block_down_name = None
block_up_name = None
@@ -287,23 +294,27 @@ def resize_lora_model(lora_sd, new_rank, save_dtype, device, dynamic_method, dyn
def resize(args):
if args.save_to is None or not (args.save_to.endswith('.ckpt') or args.save_to.endswith('.pt') or args.save_to.endswith('.pth') or args.save_to.endswith('.safetensors')):
if args.save_to is None or not (
args.save_to.endswith(".ckpt")
or args.save_to.endswith(".pt")
or args.save_to.endswith(".pth")
or args.save_to.endswith(".safetensors")
):
raise Exception("The --save_to argument must be specified and must be a .ckpt , .pt, .pth or .safetensors file.")
def str_to_dtype(p):
if p == 'float':
if p == "float":
return torch.float
if p == 'fp16':
if p == "fp16":
return torch.float16
if p == 'bf16':
if p == "bf16":
return torch.bfloat16
return None
if args.dynamic_method and not args.dynamic_param:
raise Exception("If using dynamic_method, then dynamic_param is required")
merge_dtype = str_to_dtype('float') # matmul method above only seems to work in float32
merge_dtype = str_to_dtype("float") # matmul method above only seems to work in float32
save_dtype = str_to_dtype(args.save_precision)
if save_dtype is None:
save_dtype = merge_dtype
@@ -312,7 +323,9 @@ def resize(args):
lora_sd, metadata = load_state_dict(args.model, merge_dtype)
logger.info("Resizing Lora...")
state_dict, old_dim, new_alpha = resize_lora_model(lora_sd, args.new_rank, save_dtype, args.device, args.dynamic_method, args.dynamic_param, args.verbose)
state_dict, old_dim, new_alpha = resize_lora_model(
lora_sd, args.new_rank, save_dtype, args.device, args.dynamic_method, args.dynamic_param, args.verbose
)
# update metadata
if metadata is None:
@@ -325,9 +338,11 @@ def resize(args):
metadata["ss_network_dim"] = str(args.new_rank)
metadata["ss_network_alpha"] = str(new_alpha)
else:
metadata["ss_training_comment"] = f"Dynamic resize with {args.dynamic_method}: {args.dynamic_param} from {old_dim}; {comment}"
metadata["ss_network_dim"] = 'Dynamic'
metadata["ss_network_alpha"] = 'Dynamic'
metadata["ss_training_comment"] = (
f"Dynamic resize with {args.dynamic_method}: {args.dynamic_param} from {old_dim}; {comment}"
)
metadata["ss_network_dim"] = "Dynamic"
metadata["ss_network_alpha"] = "Dynamic"
model_hash, legacy_hash = train_util.precalculate_safetensors_hashes(state_dict, metadata)
metadata["sshs_model_hash"] = model_hash
@@ -340,26 +355,45 @@ def resize(args):
def setup_parser() -> argparse.ArgumentParser:
parser = argparse.ArgumentParser()
parser.add_argument("--save_precision", type=str, default=None,
choices=[None, "float", "fp16", "bf16"], help="precision in saving, float if omitted / 保存時の精度、未指定時はfloat")
parser.add_argument("--new_rank", type=int, default=4,
help="Specify rank of output LoRA / 出力するLoRAのrank (dim)")
parser.add_argument("--save_to", type=str, default=None,
help="destination file name: ckpt or safetensors file / 保存先のファイル名、ckptまたはsafetensors")
parser.add_argument("--model", type=str, default=None,
help="LoRA model to resize at to new rank: ckpt or safetensors file / 読み込むLoRAモデル、ckptまたはsafetensors")
parser.add_argument("--device", type=str, default=None, help="device to use, cuda for GPU / 計算を行うデバイス、cuda でGPUを使う")
parser.add_argument("--verbose", action="store_true",
help="Display verbose resizing information / rank変更時の詳細情報を出力する")
parser.add_argument("--dynamic_method", type=str, default=None, choices=[None, "sv_ratio", "sv_fro", "sv_cumulative"],
help="Specify dynamic resizing method, --new_rank is used as a hard limit for max rank")
parser.add_argument("--dynamic_param", type=float, default=None,
help="Specify target for dynamic reduction")
parser.add_argument(
"--save_precision",
type=str,
default=None,
choices=[None, "float", "fp16", "bf16"],
help="precision in saving, float if omitted / 保存時の精度、未指定時はfloat",
)
parser.add_argument("--new_rank", type=int, default=4, help="Specify rank of output LoRA / 出力するLoRAのrank (dim)")
parser.add_argument(
"--save_to",
type=str,
default=None,
help="destination file name: ckpt or safetensors file / 保存先のファイル名、ckptまたはsafetensors",
)
parser.add_argument(
"--model",
type=str,
default=None,
help="LoRA model to resize at to new rank: ckpt or safetensors file / 読み込むLoRAモデル、ckptまたはsafetensors",
)
parser.add_argument(
"--device", type=str, default=None, help="device to use, cuda for GPU / 計算を行うデバイス、cuda でGPUを使う"
)
parser.add_argument(
"--verbose", action="store_true", help="Display verbose resizing information / rank変更時の詳細情報を出力する"
)
parser.add_argument(
"--dynamic_method",
type=str,
default=None,
choices=[None, "sv_ratio", "sv_fro", "sv_cumulative"],
help="Specify dynamic resizing method, --new_rank is used as a hard limit for max rank",
)
parser.add_argument("--dynamic_param", type=float, default=None, help="Specify target for dynamic reduction")
return parser
if __name__ == '__main__':
if __name__ == "__main__":
parser = setup_parser()
args = parser.parse_args()