File size: 17,666 Bytes
0c6166f 0f9dd39 1a04d3c 0c6166f 1a04d3c 0f9dd39 0c6166f 0f9dd39 1a04d3c 0c6166f 1a04d3c 0f9dd39 0c6166f 1a04d3c 0f9dd39 0c6166f 0f9dd39 1a04d3c 0c6166f 0f9dd39 1a04d3c 0f9dd39 0c6166f 0f9dd39 0c6166f 1a04d3c 0c6166f 1a04d3c 0c6166f 1a04d3c 0c6166f 0f9dd39 0c6166f 1a04d3c 0c6166f |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 |
from collections import OrderedDict
from typing import Dict
import typing
from rwkv.model import RWKV as RWKV_UPSTREAM
import types, gc, os, time, re
import torch
from torch.nn import functional as F
# valid_filter_pattern = r"(((\d+\.\d+\*)?(\d+)(-\d+)?(/\S+)?|(/\S+))(\s+|$))+"
def get_filter_keys_and_merge_coef(layer_filter):
if layer_filter:
layers = []
layer_coef = {}
layer_remove_patterns = {}
for layer in layer_filter.split(' '):
if '/' in layer: #过滤pattern，需要写成正则表达式
layer,_,remove_pattern = layer.partition('/')
remove_pattern = re.compile(remove_pattern)
else:
remove_pattern = None
if layer=='':
layer_remove_patterns['global']=remove_pattern
continue
if '*' in layer:
coef,_,layer = layer.partition('*')
coef = float(coef)
else:
coef = 1
if layer.isdecimal():
layers.append(int(layer))
layer_coef[int(layer)]=coef
layer_remove_patterns[int(layer)]=remove_pattern
elif '-' in layer:
start,_,end = layer.partition('-')
start,end = int(start),int(end)
layers.extend(range(start,end+1))
for l in range(start,end+1):
layer_coef[l] = coef
layer_remove_patterns[l]=remove_pattern
else:
raise NotImplementedError("layer_filter Not implemented:",layer_filter)
layers = sorted(set(layers))
# layer_prefixes = tuple(f"blocks.{l}." for l in layers)
def filter_keys(keys):
new_keys = []
for key in keys:
if layer_remove_patterns.get("global") and layer_remove_patterns['global'].search(key):
continue #符合全局去除规则
if key.startswith("blocks."): #过滤掉blocks开头，且不在允许范围内的权重
l = int(key.split('.')[1])
if l not in layers: #不在允许层，过滤掉
continue
if layer_remove_patterns[l] and layer_remove_patterns[l].search(key): #符合对应层的去除规则，过滤掉
continue
# if not key.startswith(layer_prefixes):
# continue
new_keys.append(key)
return new_keys
def merge_coef(key):
if key.startswith('blocks.') and int(key.split('.')[1]) in layer_coef:
return layer_coef[int(key.split('.')[1])]
else:
return 1
else:
def filter_keys(keys):
return keys
def merge_coef(key):
return 1
return filter_keys,merge_coef
def lora_merge(base_model,lora,lora_alpha,device="cuda",layer_filter=None,):
print(f"Loading LoRA: {lora}")
print(f"LoRA alpha={lora_alpha}, layer_filter={layer_filter}")
filter_keys,merge_coef = get_filter_keys_and_merge_coef(layer_filter)
w: Dict[str, torch.Tensor] = torch.load(base_model, map_location='cpu')
# merge LoRA-only slim checkpoint into the main weights
w_lora: Dict[str, torch.Tensor] = torch.load(lora, map_location='cpu')
# pdb.set_trace() #DEBUG
for k in filter_keys(w_lora.keys()): #处理time_mixing之类的融合
if k in w:
print(f"replacing {k}")
w[k] = w_lora[k]
output_w: typing.OrderedDict[str, torch.Tensor] = OrderedDict()
# merge LoRA weights
keys = list(w.keys())
for k in keys:
if k.endswith('.weight'):
prefix = k[:-len('.weight')]
lora_A = prefix + '.lora_A'
lora_B = prefix + '.lora_B'
if lora_A in keys:
assert lora_B in keys
print(f'merging {lora_A} and {lora_B} into {k}')
assert w[lora_B].shape[1] == w[lora_A].shape[0]
lora_r = w[lora_B].shape[1]
w[k] = w[k].to(device=device)
w[lora_A] = w[lora_A].to(device=device)
w[lora_B] = w[lora_B].to(device=device)
w[k] += w[lora_B] @ w[lora_A] * (lora_alpha / lora_r) * merge_coef(k)
output_w[k] = w[k].to(device='cpu', copy=True)
del w[k]
del w[lora_A]
del w[lora_B]
continue
if 'lora' not in k:
print(f'retaining {k}')
output_w[k] = w[k].clone()
del w[k]
return output_w
class RWKV(RWKV_UPSTREAM):
def __init__(self, model, strategy, verbose = True, convert_and_save_and_exit = None,lora=None,lora_alpha=0,lora_layer_filter=None):
super(RWKV_UPSTREAM,self).__init__()
if verbose:
prxxx = lambda *args, **kwargs: print(*args, **kwargs)
else:
prxxx = lambda *args, **kwargs: None
STRATEGY_REGEX = r"^(?:(?:^|->) *(?:cuda(?::[\d]+)?|cpu|mps) (?:fp(?:16|32)|bf16)(?:i8|i4|i3)?(?: \*[\d]+\+?)? *)+$"
if not re.match(STRATEGY_REGEX, strategy):
raise ValueError("Invalid strategy. Please read https://pypi.org/project/rwkv/")
strategy = ('->'.join([x.strip() for x in strategy.split('->')])).replace('->', ' -> ')
self.args = types.SimpleNamespace()
args = self.args
args.MODEL_NAME = model
args.strategy_string = strategy
# Rescale for fp16 mode: set x = x/2 every X layer (to avoid fp16 overflow)
self.RESCALE_LAYER = 6 if 'fp16' in strategy else 0
prxxx(f'RWKV_JIT_ON {os.environ["RWKV_JIT_ON"]} RWKV_CUDA_ON {os.environ["RWKV_CUDA_ON"]} RESCALE_LAYER {self.RESCALE_LAYER}\n')
args.MODEL_NAME = args.MODEL_NAME.strip()
if not args.MODEL_NAME.endswith('.pth'):
args.MODEL_NAME += '.pth'
prxxx(f'Loading {args.MODEL_NAME} ...')
with torch.no_grad():
if lora:
self.w = lora_merge(base_model=args.MODEL_NAME,lora=lora,
lora_alpha=lora_alpha,layer_filter=lora_layer_filter,
device=('cuda' if 'cuda' in strategy else 'cpu'))
else:
self.w = torch.load(args.MODEL_NAME, map_location='cpu') # load model to CPU first
gc.collect()
w = self.w
ALREADY_CONVERTED = False
if '_strategy' in w:
ALREADY_CONVERTED = True
assert convert_and_save_and_exit == None # you should only convert a raw model
prxxx(f"Converted model: strategy {w['_strategy']}, version {w['_version']}\n")
assert w['_strategy'] == args.strategy_string # if you are using a new strategy, re-convert the model
assert float(w['_version']) >= 0.7 # sometimes you should re-convert using latest convert_model.py
assert w['_rescale_layer'] == self.RESCALE_LAYER
del w['_strategy']
del w['_version']
del w['_rescale_layer']
args.n_embd = w['emb.weight'].shape[1]
args.n_layer = 0
keys = list(w.keys())
for x in keys:
layer_id = int(x.split('.')[1]) if ('blocks.' in x) else 0
args.n_layer = max(args.n_layer, layer_id+1)
####################### Compute strategy
s = [x.strip().split(' ') for x in strategy.split('->')]
plan = [0] * len(s)
stream_i = -1
stream_count = 0
to_allocate = args.n_layer + 1
allocated = 0
free_slots = 0
for i in range(len(s)):
si = s[i]
si1 = si[1]
if si1.startswith('fp32'): si[1] = [torch.float]
elif si1.startswith('fp16'): si[1] = [torch.float16]
elif si1.startswith('bf16'): si[1] = [torch.bfloat16]
if si1.endswith('i8'): si[1] += [torch.uint8]
else: si[1] += [si[1][0]]
if len(si) > 2:
ss = si[2]
assert ss.startswith('*')
if ss.endswith('+'):
plan[i] = int(ss[1:-1])
stream_i = i
else:
plan[i] = int(ss[1:])
allocated += plan[i]
if allocated >= to_allocate:
plan[i] += to_allocate - allocated
break
else:
free_slots += 1
if stream_i < 0:
if free_slots > 0 and to_allocate > allocated:
for i in range(len(s)):
if plan[i] == 0:
plan[i] = (to_allocate - allocated) // free_slots
allocated += plan[i]
free_slots -= 1
if to_allocate > allocated:
plan[len(s)-1] += to_allocate - allocated
else:
if to_allocate > allocated:
stream_count = to_allocate - allocated
plan[stream_i] += stream_count
prxxx(f'Strategy: (total {args.n_layer}+1={args.n_layer+1} layers)')
for i in range(len(s)):
ss = s[i]
if i != stream_i:
prxxx(f'* {ss[0]} {str(ss[1]).replace("torch.","")}, store {plan[i]} layers')
else:
prxxx(f'* {ss[0]} {str(ss[1]).replace("torch.","")}, store {plan[i]-stream_count} layers, stream {stream_count} layers')
plan[i] += (0 if i == 0 else plan[i-1])
self.strategy = [None] * (args.n_layer + 1)
strategy = self.strategy
for n in range(args.n_layer + 1):
for i in range(len(s)):
if n < plan[i]:
strategy[n] = types.SimpleNamespace()
strategy[n].device = s[i][0]
strategy[n].atype = s[i][1][0]
strategy[n].wtype = s[i][1][1]
strategy[n].stream = False
if i == stream_i and n >= (plan[i] - stream_count):
strategy[n].stream = True
break
prxxx(f"{n}-{strategy[n].device}-{str(strategy[n].atype).replace('torch.','')}-{str(strategy[n].wtype).replace('torch.','')}{'-stream' if strategy[n].stream else ''}",end=' ')
prxxx()
####################### Load weights to self.w
if not ALREADY_CONVERTED:
try: # precompute embedding
w['emb.weight'] = F.layer_norm(w['emb.weight'], (args.n_embd,), weight=w['blocks.0.ln0.weight'], bias=w['blocks.0.ln0.bias'])
except:
w['emb.weight'] = F.layer_norm(w['emb.weight'].float(), (args.n_embd,), weight=w['blocks.0.ln0.weight'].float(), bias=w['blocks.0.ln0.bias'].float())
del w['blocks.0.ln0.weight']
del w['blocks.0.ln0.bias']
print_need_newline = False
keys = list(w.keys())
for x in keys:
w[x].requires_grad = False
layer_id = int(x.split('.')[1]) if ('blocks.' in x) else 0
if ('ln_out.' in x) or ('head.' in x):
layer_id = args.n_layer
dd = strategy[layer_id]
DEVICE = dd.device
ATYPE = dd.atype
WTYPE = dd.wtype
if not ALREADY_CONVERTED:
if self.RESCALE_LAYER > 0:
if 'att.output.weight' in x:
w[x] = w[x] / (2 ** int(layer_id // self.RESCALE_LAYER))
if 'ffn.value.weight' in x:
w[x] = w[x] / (2 ** int(layer_id // self.RESCALE_LAYER))
if '.time_' in x:
w[x] = w[x].squeeze()
if 'key.weight' in x or 'value.weight' in x or 'receptance.weight' in x or 'output.weight' in x or 'head.weight' in x:
w[x] = w[x].t()
if '.time_decay' in x: # need fp32 for this
w[x] = -torch.exp(w[x].float())
elif '.time_first' in x: # need fp32 for this
w[x] = w[x].float()
else:
if (len(w[x].shape) == 2) and ('emb' not in x):
if WTYPE != torch.uint8:
w[x] = w[x].to(dtype=WTYPE)
else:
w[x] = w[x].float()
if w[x].shape[0] > w[x].shape[1]:
w[x+'_my'] = torch.amin(w[x], dim=1).unsqueeze(1)
w[x] = w[x] - w[x+'_my']
w[x+'_mx'] = torch.amin(w[x], dim=0)
w[x] = w[x] - w[x+'_mx']
w[x+'_rx'] = torch.amax(w[x], dim=0)
w[x] = w[x] / w[x+'_rx']
w[x+'_ry'] = torch.amax(w[x], dim=1).unsqueeze(1)
w[x] = w[x] / w[x+'_ry']
else:
w[x+'_mx'] = torch.amin(w[x], dim=0)
w[x] = w[x] - w[x+'_mx']
w[x+'_my'] = torch.amin(w[x], dim=1).unsqueeze(1)
w[x] = w[x] - w[x+'_my']
w[x+'_rx'] = torch.amax(w[x], dim=0)
w[x] = w[x] / w[x+'_rx']
w[x+'_ry'] = torch.amax(w[x], dim=1).unsqueeze(1)
w[x] = w[x] / w[x+'_ry']
w[x] = torch.clip(torch.floor(w[x] * 256), min=0, max=255).to(dtype=torch.uint8)
w[x+'_mx'] = w[x+'_mx'].to(dtype=ATYPE).contiguous()
w[x+'_rx'] = (w[x+'_rx'] / 16).to(dtype=ATYPE).contiguous()
w[x+'_my'] = w[x+'_my'].to(dtype=ATYPE).contiguous()
w[x+'_ry'] = (w[x+'_ry'] / 16).to(dtype=ATYPE).contiguous()
else:
w[x] = w[x].to(dtype=ATYPE)
if convert_and_save_and_exit == None:
if 'emb.' in x:
w[x] = w[x].contiguous()
elif (dd.stream) and (x.endswith('key.weight') or x.endswith('value.weight') or x.endswith('receptance.weight') or x.endswith('output.weight')):
try:
w[x] = w[x].contiguous().pin_memory() # if you see "CUDA error: out of memory" here, that's out of CPU RAM, not VRAM. Get more RAM :)
except:
print('Note: You are running out of RAM. Get more CPU RAM. Now this will run much slower.')
elif DEVICE != 'cpu':
w[x] = w[x].to(device=DEVICE).contiguous()
if (dd.stream) or (DEVICE != 'cpu'):
try:
w[x+'_mx'] = w[x+'_mx'].to(device=DEVICE).contiguous()
w[x+'_rx'] = w[x+'_rx'].to(device=DEVICE).contiguous()
w[x+'_my'] = w[x+'_my'].to(device=DEVICE).contiguous()
w[x+'_ry'] = w[x+'_ry'].to(device=DEVICE).contiguous()
except:
pass
if 'ffn.value.weight' in x:
gc.collect()
if 'cuda' in args.strategy_string:
torch.cuda.empty_cache()
shape = [i for i in w[x].shape if i != 1]
if len(shape) > 1:
shape = f" {str(shape[0]).rjust(5)} {str(shape[1]).rjust(5)}"
else:
shape = f" {str(shape[0]).rjust(5)} "
if layer_id == 0 or layer_id >= args.n_layer-1:
if print_need_newline:
prxxx('\n', end = '')
print_need_newline = False
dt = str(w[x].dtype).replace('torch.', '')
dt = dt.replace('float32', 'f32').replace('bfloat16', 'bf16').replace('float16', 'f16').replace('uint8', 'i8')
prxxx(x.ljust(32), dt.rjust(4), str(w[x].device).rjust(8), shape, ' (pinned)' if w[x].is_pinned() else '')
else:
print_need_newline = True
prxxx('.', end = '', flush = True)
if convert_and_save_and_exit:
w['_strategy'] = args.strategy_string
w['_rescale_layer'] = self.RESCALE_LAYER
w['_version'] = '0.7'
if not convert_and_save_and_exit.endswith('.pth'):
convert_and_save_and_exit += '.pth'
prxxx(f'Saving to {convert_and_save_and_exit}...')
torch.save(w, convert_and_save_and_exit)
prxxx(f'Converted and saved. Now this will exit.')
exit(0)
gc.collect()
if 'cuda' in args.strategy_string:
torch.cuda.empty_cache() |