Spaces:
Runtime error
Runtime error
File size: 9,251 Bytes
30a7879 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 | import os
import torch
import torch.nn as nn
from typing import Dict
from .layers import LoRALayer, PlainMultiheadAttentionLoRA
INDEX_POSITIONS_TEXT = {
'top1': [11],
'top2': [10, 11],
'top3': [9, 10, 11],
'bottom': [0, 1, 2, 3],
'mid': [4, 5, 6, 7],
'up': [8, 9, 10, 11],
'half-up': [6, 7, 8, 9, 10, 11],
'half-bottom': [0, 1, 2, 3, 4, 5],
'all': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]}
INDEX_POSITIONS_VISION = {
'ViT-B/16': {
'top': [11],
'top3': [9, 10, 11],
'bottom': [0, 1, 2, 3],
'mid': [4, 5, 6, 7],
'up': [8, 9, 10, 11],
'half-up': [6, 7, 8, 9, 10, 11],
'half-bottom': [0, 1, 2, 3, 4, 5],
'all': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]},
'ViT-B/32': {
'bottom': [0, 1, 2, 3],
'mid': [4, 5, 6, 7],
'up': [8, 9, 10, 11],
'half-up': [6, 7, 8, 9, 10, 11],
'half-bottom': [0, 1, 2, 3, 4, 5],
'all': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]},
'ViT-L/14': {
'half-up': [12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23],
'half-bottom': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11],
'all': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]}
}
def mark_only_lora_as_trainable(model: nn.Module, bias: str = 'none') -> None:
for n, p in model.named_parameters():
if 'lora_' not in n:
p.requires_grad = False
if bias == 'none':
return
elif bias == 'all':
for n, p in model.named_parameters():
if 'bias' in n:
p.requires_grad = True
elif bias == 'lora_only':
for m in model.modules():
if isinstance(m, LoRALayer) and \
hasattr(m, 'bias') and \
m.bias is not None:
m.bias.requires_grad = True
else:
raise NotImplementedError
def lora_state_dict(model: nn.Module, bias: str = 'none') -> Dict[str, torch.Tensor]:
my_state_dict = model.state_dict()
if bias == 'none':
return {k: my_state_dict[k] for k in my_state_dict if 'lora_' in k}
elif bias == 'all':
return {k: my_state_dict[k] for k in my_state_dict if 'lora_' in k or 'bias' in k}
elif bias == 'lora_only':
to_return = {}
for k in my_state_dict:
if 'lora_' in k:
to_return[k] = my_state_dict[k]
bias_name = k.split('lora_')[0]+'bias'
if bias_name in my_state_dict:
to_return[bias_name] = my_state_dict[bias_name]
return to_return
else:
raise NotImplementedError
def get_lora_parameters(model, bias='none'):
params = []
for name, param in model.named_parameters():
if bias == 'none':
if 'lora_' in name:
params.append(param)
elif bias == 'all':
if 'lora_' in name or 'bias' in name:
params.append(param)
elif bias == 'lora_only':
if 'lora_' in name:
params.append(param)
bias_name = name.split('lora_')[0] + 'bias'
if bias_name in model.state_dict():
bias_param = dict(model.named_parameters())[bias_name]
params.append(bias_param)
else:
raise NotImplementedError
return params
def apply_lora(args, clip_model):
list_lora_layers = []
if args.encoder == 'text' or args.encoder == 'both':
indices = INDEX_POSITIONS_TEXT[args.position]
text_encoder = clip_model.transformer
for i, block in enumerate(text_encoder.resblocks):
print(f"Residual Attention Block {i}: {block}")
if i in indices:
for name, submodule in block.named_children():
if isinstance(submodule, nn.MultiheadAttention):
new_multi_head_lora = PlainMultiheadAttentionLoRA(
submodule, enable_lora=args.params, r=args.r, lora_alpha=args.alpha, dropout_rate=args.dropout_rate)
setattr(block, name, new_multi_head_lora)
list_lora_layers.append(new_multi_head_lora)
if args.encoder == 'vision' or args.encoder == 'both':
indices = INDEX_POSITIONS_VISION[args.backbone][args.position]
vision_encoder = clip_model.visual.transformer
for i, block in enumerate(vision_encoder.resblocks):
print(f"Residual Attention Block {i}: {block}")
if i in indices:
for name, submodule in block.named_children():
if isinstance(submodule, nn.MultiheadAttention):
new_multi_head_lora = PlainMultiheadAttentionLoRA(
submodule, enable_lora=args.params, r=args.r, lora_alpha=args.alpha, dropout_rate=args.dropout_rate)
setattr(block, name, new_multi_head_lora)
list_lora_layers.append(new_multi_head_lora)
return list_lora_layers
def save_lora(args, list_lora_layers):
weights = {}
for i, layer in enumerate(list_lora_layers):
layer_weights = {}
if 'q' in args.params:
layer_weights['q_proj'] = {
'w_lora_A': layer.q_proj.w_lora_A.data,
'w_lora_B': layer.q_proj.w_lora_B.data
}
if 'k' in args.params:
layer_weights['k_proj'] = {
'w_lora_A': layer.k_proj.w_lora_A.data,
'w_lora_B': layer.k_proj.w_lora_B.data
}
if 'v' in args.params:
layer_weights['v_proj'] = {
'w_lora_A': layer.v_proj.w_lora_A.data,
'w_lora_B': layer.v_proj.w_lora_B.data
}
if 'o' in args.params:
layer_weights['proj'] = {
'w_lora_A': layer.proj.w_lora_A.data,
'w_lora_B': layer.proj.w_lora_B.data
}
weights[f'layer_{i}'] = layer_weights
metadata = {
'r': args.r,
'alpha': args.alpha,
'encoder': args.encoder,
'params': args.params,
'position': args.position
}
save_data = {
'weights': weights,
'metadata': metadata
}
# to manage names like ViT-B/16
backbone = args.backbone.replace('/', '').replace('-', '').lower()
save_dir = f'{args.save_path}/{backbone}/{args.dataset}/{args.shots}shots/seed{args.seed}'
os.makedirs(save_dir, exist_ok=True)
save_path = f'{save_dir}/{args.filename}.pt'
torch.save(save_data, save_path)
print(f'LoRA weights saved to {save_path}')
def load_lora(args, list_lora_layers):
# to manage names like ViT-B/16
backbone = args.backbone.replace('/', '').replace('-', '').lower()
load_path = f'{args.save_path}/{backbone}/{args.dataset}/{args.shots}shots/seed{args.seed}/{args.filename}.pt'
if not os.path.exists(load_path):
raise FileNotFoundError(f'File {load_path} does not exist.')
loaded_data = torch.load(load_path)
metadata = loaded_data['metadata']
if metadata['r'] != args.r:
raise ValueError(
f"r mismatch: expected {args.r}, found {metadata['r']}")
if metadata['alpha'] != args.alpha:
raise ValueError(
f"alpha mismatch: expected {args.alpha}, found {metadata['alpha']}")
if metadata['encoder'] != args.encoder:
raise ValueError(
f"Encoder mismatch: expected {args.encoder}, found {metadata['encoder']}")
if metadata['params'] != args.params:
raise ValueError(
f"Params mismatch: expected {args.params}, found {metadata['params']}")
if metadata['position'] != args.position:
raise ValueError(
f"Position mismatch: expected {args.position}, found {metadata['position']}")
weights = loaded_data['weights']
for i, layer in enumerate(list_lora_layers):
layer_weights = weights[f'layer_{i}']
if 'q' in args.params and 'q_proj' in layer_weights:
layer.q_proj.w_lora_A.data.copy_(
layer_weights['q_proj']['w_lora_A'])
layer.q_proj.w_lora_B.data.copy_(
layer_weights['q_proj']['w_lora_B'])
if 'k' in args.params and 'k_proj' in layer_weights:
layer.k_proj.w_lora_A.data.copy_(
layer_weights['k_proj']['w_lora_A'])
layer.k_proj.w_lora_B.data.copy_(
layer_weights['k_proj']['w_lora_B'])
if 'v' in args.params and 'v_proj' in layer_weights:
layer.v_proj.w_lora_A.data.copy_(
layer_weights['v_proj']['w_lora_A'])
layer.v_proj.w_lora_B.data.copy_(
layer_weights['v_proj']['w_lora_B'])
if 'o' in args.params and 'proj' in layer_weights:
layer.proj.w_lora_A.data.copy_(layer_weights['proj']['w_lora_A'])
layer.proj.w_lora_B.data.copy_(layer_weights['proj']['w_lora_B'])
print(f'LoRA weights loaded from {load_path}') |