339 lines
12 KiB
Python
339 lines
12 KiB
Python
#
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# File from: https://raw.githubusercontent.com/mgz-dev/sd-scripts/main/networks/resize_lora.py
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#
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# Convert LoRA to different rank approximation (should only be used to go to lower rank)
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# This code is based off the extract_lora_from_models.py file which is based on https://github.com/cloneofsimo/lora/blob/develop/lora_diffusion/cli_svd.py
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# Thanks to cloneofsimo and kohya
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import argparse
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import torch
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from safetensors.torch import load_file, save_file, safe_open
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from tqdm import tqdm
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from library import train_util, model_util
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import numpy as np
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MIN_SV = 1e-6
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def load_state_dict(file_name, dtype):
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if model_util.is_safetensors(file_name):
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sd = load_file(file_name)
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with safe_open(file_name, framework="pt") as f:
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metadata = f.metadata()
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else:
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sd = torch.load(file_name, map_location='cpu')
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metadata = None
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for key in list(sd.keys()):
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if type(sd[key]) == torch.Tensor:
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sd[key] = sd[key].to(dtype)
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return sd, metadata
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def save_to_file(file_name, model, state_dict, dtype, metadata):
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if dtype is not None:
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for key in list(state_dict.keys()):
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if type(state_dict[key]) == torch.Tensor:
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state_dict[key] = state_dict[key].to(dtype)
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if model_util.is_safetensors(file_name):
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save_file(model, file_name, metadata)
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else:
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torch.save(model, file_name)
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def index_sv_cumulative(S, target):
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original_sum = float(torch.sum(S))
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cumulative_sums = torch.cumsum(S, dim=0)/original_sum
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index = int(torch.searchsorted(cumulative_sums, target)) + 1
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if index >= len(S):
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index = len(S) - 1
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return index
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def index_sv_fro(S, target):
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S_squared = S.pow(2)
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s_fro_sq = float(torch.sum(S_squared))
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sum_S_squared = torch.cumsum(S_squared, dim=0)/s_fro_sq
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index = int(torch.searchsorted(sum_S_squared, target**2)) + 1
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if index >= len(S):
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index = len(S) - 1
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return index
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# Modified from Kohaku-blueleaf's extract/merge functions
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def extract_conv(weight, lora_rank, dynamic_method, dynamic_param, device, scale=1):
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out_size, in_size, kernel_size, _ = weight.size()
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U, S, Vh = torch.linalg.svd(weight.reshape(out_size, -1).to(device))
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param_dict = rank_resize(S, lora_rank, dynamic_method, dynamic_param, scale)
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lora_rank = param_dict["new_rank"]
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U = U[:, :lora_rank]
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S = S[:lora_rank]
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U = U @ torch.diag(S)
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Vh = Vh[:lora_rank, :]
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param_dict["lora_down"] = Vh.reshape(lora_rank, in_size, kernel_size, kernel_size).cpu()
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param_dict["lora_up"] = U.reshape(out_size, lora_rank, 1, 1).cpu()
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del U, S, Vh, weight
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return param_dict
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def extract_linear(weight, lora_rank, dynamic_method, dynamic_param, device, scale=1):
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out_size, in_size = weight.size()
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U, S, Vh = torch.linalg.svd(weight.to(device))
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param_dict = rank_resize(S, lora_rank, dynamic_method, dynamic_param, scale)
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lora_rank = param_dict["new_rank"]
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U = U[:, :lora_rank]
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S = S[:lora_rank]
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U = U @ torch.diag(S)
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Vh = Vh[:lora_rank, :]
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param_dict["lora_down"] = Vh.reshape(lora_rank, in_size).cpu()
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param_dict["lora_up"] = U.reshape(out_size, lora_rank).cpu()
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del U, S, Vh, weight
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return param_dict
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def merge_conv(lora_down, lora_up, device):
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in_rank, in_size, kernel_size, k_ = lora_down.shape
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out_size, out_rank, _, _ = lora_up.shape
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assert in_rank == out_rank and kernel_size == k_, f"rank {in_rank} {out_rank} or kernel {kernel_size} {k_} mismatch"
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lora_down = lora_down.to(device)
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lora_up = lora_up.to(device)
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merged = lora_up.reshape(out_size, -1) @ lora_down.reshape(in_rank, -1)
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weight = merged.reshape(out_size, in_size, kernel_size, kernel_size)
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del lora_up, lora_down
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return weight
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def merge_linear(lora_down, lora_up, device):
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in_rank, in_size = lora_down.shape
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out_size, out_rank = lora_up.shape
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assert in_rank == out_rank, f"rank {in_rank} {out_rank} mismatch"
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lora_down = lora_down.to(device)
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lora_up = lora_up.to(device)
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weight = lora_up @ lora_down
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del lora_up, lora_down
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return weight
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def rank_resize(S, rank, dynamic_method, dynamic_param, scale=1):
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param_dict = {}
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if dynamic_method=="sv_ratio":
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# Calculate new dim and alpha based off ratio
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max_sv = S[0]
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min_sv = max_sv/dynamic_param
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new_rank = max(torch.sum(S > min_sv).item(),1)
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new_alpha = float(scale*new_rank)
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elif dynamic_method=="sv_cumulative":
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# Calculate new dim and alpha based off cumulative sum
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new_rank = index_sv_cumulative(S, dynamic_param)
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new_rank = max(new_rank, 1)
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new_alpha = float(scale*new_rank)
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elif dynamic_method=="sv_fro":
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# Calculate new dim and alpha based off sqrt sum of squares
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new_rank = index_sv_fro(S, dynamic_param)
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new_rank = min(max(new_rank, 1), len(S)-1)
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new_alpha = float(scale*new_rank)
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else:
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new_rank = rank
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new_alpha = float(scale*new_rank)
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if S[0] <= MIN_SV: # Zero matrix, set dim to 1
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new_rank = 1
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new_alpha = float(scale*new_rank)
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elif new_rank > rank: # cap max rank at rank
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new_rank = rank
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new_alpha = float(scale*new_rank)
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# Calculate resize info
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s_sum = torch.sum(torch.abs(S))
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s_rank = torch.sum(torch.abs(S[:new_rank]))
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S_squared = S.pow(2)
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s_fro = torch.sqrt(torch.sum(S_squared))
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s_red_fro = torch.sqrt(torch.sum(S_squared[:new_rank]))
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fro_percent = float(s_red_fro/s_fro)
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param_dict["new_rank"] = new_rank
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param_dict["new_alpha"] = new_alpha
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param_dict["sum_retained"] = (s_rank)/s_sum
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param_dict["fro_retained"] = fro_percent
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param_dict["max_ratio"] = S[0]/S[new_rank]
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return param_dict
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def resize_lora_model(lora_sd, new_rank, save_dtype, device, dynamic_method, dynamic_param, verbose):
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network_alpha = None
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network_dim = None
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verbose_str = "\n"
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fro_list = []
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# Extract loaded lora dim and alpha
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for key, value in lora_sd.items():
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if network_alpha is None and 'alpha' in key:
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network_alpha = value
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if network_dim is None and 'lora_down' in key and len(value.size()) == 2:
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network_dim = value.size()[0]
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if network_alpha is not None and network_dim is not None:
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break
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if network_alpha is None:
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network_alpha = network_dim
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scale = network_alpha/network_dim
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if dynamic_method:
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print(f"Dynamically determining new alphas and dims based off {dynamic_method}: {dynamic_param}, max rank is {new_rank}")
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lora_down_weight = None
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lora_up_weight = None
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o_lora_sd = lora_sd.copy()
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block_down_name = None
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block_up_name = None
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with torch.no_grad():
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for key, value in tqdm(lora_sd.items()):
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if 'lora_down' in key:
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block_down_name = key.split(".")[0]
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lora_down_weight = value
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if 'lora_up' in key:
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block_up_name = key.split(".")[0]
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lora_up_weight = value
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weights_loaded = (lora_down_weight is not None and lora_up_weight is not None)
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if (block_down_name == block_up_name) and weights_loaded:
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conv2d = (len(lora_down_weight.size()) == 4)
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if conv2d:
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full_weight_matrix = merge_conv(lora_down_weight, lora_up_weight, device)
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param_dict = extract_conv(full_weight_matrix, new_rank, dynamic_method, dynamic_param, device, scale)
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else:
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full_weight_matrix = merge_linear(lora_down_weight, lora_up_weight, device)
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param_dict = extract_linear(full_weight_matrix, new_rank, dynamic_method, dynamic_param, device, scale)
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if verbose:
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max_ratio = param_dict['max_ratio']
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sum_retained = param_dict['sum_retained']
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fro_retained = param_dict['fro_retained']
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if not np.isnan(fro_retained):
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fro_list.append(float(fro_retained))
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verbose_str+=f"{block_down_name:75} | "
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verbose_str+=f"sum(S) retained: {sum_retained:.1%}, fro retained: {fro_retained:.1%}, max(S) ratio: {max_ratio:0.1f}"
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if verbose and dynamic_method:
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verbose_str+=f", dynamic | dim: {param_dict['new_rank']}, alpha: {param_dict['new_alpha']}\n"
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else:
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verbose_str+=f"\n"
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new_alpha = param_dict['new_alpha']
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o_lora_sd[block_down_name + "." + "lora_down.weight"] = param_dict["lora_down"].to(save_dtype).contiguous()
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o_lora_sd[block_up_name + "." + "lora_up.weight"] = param_dict["lora_up"].to(save_dtype).contiguous()
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o_lora_sd[block_up_name + "." "alpha"] = torch.tensor(param_dict['new_alpha']).to(save_dtype)
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block_down_name = None
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block_up_name = None
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lora_down_weight = None
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lora_up_weight = None
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weights_loaded = False
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del param_dict
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if verbose:
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print(verbose_str)
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print(f"Average Frobenius norm retention: {np.mean(fro_list):.2%} | std: {np.std(fro_list):0.3f}")
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print("resizing complete")
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return o_lora_sd, network_dim, new_alpha
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def resize(args):
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def str_to_dtype(p):
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if p == 'float':
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return torch.float
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if p == 'fp16':
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return torch.float16
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if p == 'bf16':
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return torch.bfloat16
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return None
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if args.dynamic_method and not args.dynamic_param:
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raise Exception("If using dynamic_method, then dynamic_param is required")
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merge_dtype = str_to_dtype('float') # matmul method above only seems to work in float32
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save_dtype = str_to_dtype(args.save_precision)
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if save_dtype is None:
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save_dtype = merge_dtype
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print("loading Model...")
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lora_sd, metadata = load_state_dict(args.model, merge_dtype)
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print("Resizing Lora...")
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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)
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# update metadata
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if metadata is None:
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metadata = {}
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comment = metadata.get("ss_training_comment", "")
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if not args.dynamic_method:
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metadata["ss_training_comment"] = f"dimension is resized from {old_dim} to {args.new_rank}; {comment}"
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metadata["ss_network_dim"] = str(args.new_rank)
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metadata["ss_network_alpha"] = str(new_alpha)
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else:
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metadata["ss_training_comment"] = f"Dynamic resize with {args.dynamic_method}: {args.dynamic_param} from {old_dim}; {comment}"
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metadata["ss_network_dim"] = 'Dynamic'
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metadata["ss_network_alpha"] = 'Dynamic'
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model_hash, legacy_hash = train_util.precalculate_safetensors_hashes(state_dict, metadata)
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metadata["sshs_model_hash"] = model_hash
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metadata["sshs_legacy_hash"] = legacy_hash
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print(f"saving model to: {args.save_to}")
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save_to_file(args.save_to, state_dict, state_dict, save_dtype, metadata)
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument("--save_precision", type=str, default=None,
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choices=[None, "float", "fp16", "bf16"], help="precision in saving, float if omitted / 保存時の精度、未指定時はfloat")
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parser.add_argument("--new_rank", type=int, default=4,
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help="Specify rank of output LoRA / 出力するLoRAのrank (dim)")
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parser.add_argument("--save_to", type=str, default=None,
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help="destination file name: ckpt or safetensors file / 保存先のファイル名、ckptまたはsafetensors")
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parser.add_argument("--model", type=str, default=None,
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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")
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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")
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parser.add_argument("--verbose", action="store_true",
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help="Display verbose resizing information / rank変更時の詳細情報を出力する")
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parser.add_argument("--dynamic_method", type=str, default=None, choices=[None, "sv_ratio", "sv_fro", "sv_cumulative"],
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help="Specify dynamic resizing method, --new_rank is used as a hard limit for max rank")
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parser.add_argument("--dynamic_param", type=float, default=None,
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help="Specify target for dynamic reduction")
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args = parser.parse_args()
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resize(args) |