File size: 59,455 Bytes
8d595ff 551545a 8d595ff 551545a 8d595ff 551545a 8d595ff 551545a 8d595ff 551545a 8d595ff 551545a 8d595ff 551545a 8d595ff 551545a 8d595ff 551545a 8d595ff 551545a 8d595ff 551545a 8d595ff 551545a 8d595ff | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 | from abc import abstractmethod
import os
import time
import json
import copy
import threading
from functools import partial
from contextlib import nullcontext
import torch
import torch.distributed as dist
from torch.utils.data import DataLoader
from torch.nn.parallel import DistributedDataParallel as DDP
import numpy as np
from torchvision import utils
try:
import wandb
WANDB_AVAILABLE = True
except ImportError:
WANDB_AVAILABLE = False
from .utils import *
from ..utils.general_utils import *
from ..utils.data_utils import recursive_to_device, cycle, ResumableSampler
from ..utils.dist_utils import *
from ..utils import grad_clip_utils, elastic_utils
class BasicTrainer:
"""
Trainer for basic training loop.
Args:
models (dict[str, nn.Module]): Models to train.
dataset (torch.utils.data.Dataset): Dataset.
output_dir (str): Output directory.
load_dir (str): Load directory.
step (int): Step to load.
batch_size (int): Batch size.
batch_size_per_gpu (int): Batch size per GPU. If specified, batch_size will be ignored.
batch_split (int): Split batch with gradient accumulation.
max_steps (int): Max steps.
optimizer (dict): Optimizer config.
lr_scheduler (dict): Learning rate scheduler config.
elastic (dict): Elastic memory management config.
grad_clip (float or dict): Gradient clip config.
ema_rate (float or list): Exponential moving average rates.
mix_precision_mode (str):
- None: No mixed precision.
- 'inflat_all': Hold a inflated fp32 master param for all params.
- 'amp': Automatic mixed precision.
mix_precision_dtype (str): Mixed precision dtype.
fp16_scale_growth (float): Scale growth for FP16 gradient backpropagation.
parallel_mode (str): Parallel mode. Options are 'ddp'.
finetune_ckpt (dict): Finetune checkpoint.
log_param_stats (bool): Log parameter stats.
i_print (int): Print interval.
i_log (int): Log interval.
i_sample (int): Sample interval.
i_save (int): Save interval.
i_ddpcheck (int): DDP check interval.
"""
def __init__(self,
models,
dataset,
*,
output_dir,
load_dir,
step,
max_steps,
batch_size=None,
batch_size_per_gpu=None,
batch_split=None,
optimizer={},
lr_scheduler=None,
elastic=None,
grad_clip=None,
ema_rate=0.9999,
fp16_mode=None,
mix_precision_mode='inflat_all',
mix_precision_dtype='float16',
fp16_scale_growth=1e-3,
parallel_mode='ddp',
finetune_ckpt=None,
log_param_stats=False,
prefetch_data=True,
snapshot_batch_size=4,
snapshot_num_samples=64,
num_workers=None,
debug=False,
i_print=1000,
i_log=500,
i_sample=10000,
i_save=10000,
i_ddpcheck=10000,
wandb_run=None, # wandb run object
**kwargs
):
assert batch_size is not None or batch_size_per_gpu is not None, 'Either batch_size or batch_size_per_gpu must be specified.'
self.models = models
self.dataset = dataset
self.batch_split = batch_split if batch_split is not None else 1
self.max_steps = max_steps
self.debug = debug
self.optimizer_config = optimizer
self.lr_scheduler_config = lr_scheduler
self.elastic_controller_config = elastic
self.grad_clip = grad_clip
self.ema_rate = [ema_rate] if isinstance(ema_rate, float) else ema_rate
if fp16_mode is not None:
mix_precision_dtype = 'float16'
mix_precision_mode = fp16_mode
self.mix_precision_mode = mix_precision_mode
self.mix_precision_dtype = str_to_dtype(mix_precision_dtype)
self.fp16_scale_growth = fp16_scale_growth
self.parallel_mode = parallel_mode
self.log_param_stats = log_param_stats
self.prefetch_data = prefetch_data
self.snapshot_batch_size = snapshot_batch_size
self.snapshot_num_samples = snapshot_num_samples
self.num_workers = num_workers
self.log = []
if self.prefetch_data:
self._data_prefetched = None
self.output_dir = output_dir
from datetime import datetime
self._log_file = os.path.join(self.output_dir, f'log_{datetime.now().strftime("%Y%m%d_%H%M%S")}.txt')
self.i_print = i_print
self.i_log = i_log
self.i_sample = i_sample
self.i_save = i_save
self.i_ddpcheck = i_ddpcheck
if dist.is_initialized():
# Multi-GPU params
self.world_size = dist.get_world_size()
self.rank = dist.get_rank()
self.local_rank = dist.get_rank() % torch.cuda.device_count()
self.is_master = self.rank == 0
else:
# Single-GPU params
self.world_size = 1
self.rank = 0
self.local_rank = 0
self.is_master = True
self.batch_size = batch_size if batch_size_per_gpu is None else batch_size_per_gpu * self.world_size
self.batch_size_per_gpu = batch_size_per_gpu if batch_size_per_gpu is not None else batch_size // self.world_size
assert self.batch_size % self.world_size == 0, 'Batch size must be divisible by the number of GPUs.'
assert self.batch_size_per_gpu % self.batch_split == 0, 'Batch size per GPU must be divisible by batch split.'
self.init_models_and_more(**kwargs)
self.prepare_dataloader(**kwargs)
# Load checkpoint
self.step = 0
if load_dir is not None and step is not None:
self.load(load_dir, step)
elif finetune_ckpt is not None:
self.finetune_from(finetune_ckpt)
if self.is_master:
os.makedirs(os.path.join(self.output_dir, 'ckpts'), exist_ok=True)
os.makedirs(os.path.join(self.output_dir, 'samples'), exist_ok=True)
self.writer = None # TensorBoard disabled (S3 FUSE does not support append)
# Initialize wandb
self.wandb_run = wandb_run
if self.wandb_run is not None:
print(f'Wandb logging enabled: {self.wandb_run.url}')
if self.parallel_mode == 'ddp' and self.world_size > 1:
self.check_ddp()
if self.is_master:
print('\n\nTrainer initialized.')
print(self)
def __str__(self):
lines = []
lines.append(self.__class__.__name__)
lines.append(f' - Models:')
for name, model in self.models.items():
lines.append(f' - {name}: {model.__class__.__name__}')
lines.append(f' - Dataset: {indent(str(self.dataset), 2)}')
lines.append(f' - Dataloader:')
lines.append(f' - Sampler: {self.dataloader.sampler.__class__.__name__}')
lines.append(f' - Num workers: {self.dataloader.num_workers}')
lines.append(f' - Number of steps: {self.max_steps}')
lines.append(f' - Number of GPUs: {self.world_size}')
lines.append(f' - Batch size: {self.batch_size}')
lines.append(f' - Batch size per GPU: {self.batch_size_per_gpu}')
lines.append(f' - Batch split: {self.batch_split}')
lines.append(f' - Optimizer: {self.optimizer.__class__.__name__}')
lines.append(f' - Learning rate: {self.optimizer.param_groups[0]["lr"]}')
if self.lr_scheduler_config is not None:
lines.append(f' - LR scheduler: {self.lr_scheduler.__class__.__name__}')
if self.elastic_controller_config is not None:
lines.append(f' - Elastic memory: {indent(str(self.elastic_controller), 2)}')
if self.grad_clip is not None:
lines.append(f' - Gradient clip: {indent(str(self.grad_clip), 2)}')
lines.append(f' - EMA rate: {self.ema_rate}')
lines.append(f' - Mixed precision dtype: {self.mix_precision_dtype}')
lines.append(f' - Mixed precision mode: {self.mix_precision_mode}')
if self.mix_precision_mode == 'amp' and self.mix_precision_dtype == torch.float16:
lines.append(f' - FP16 scale growth: {self.fp16_scale_growth}')
lines.append(f' - Parallel mode: {self.parallel_mode}')
return '\n'.join(lines)
@property
def device(self):
for _, model in self.models.items():
if hasattr(model, 'device'):
return model.device
return next(list(self.models.values())[0].parameters()).device
def init_models_and_more(self, **kwargs):
"""
Initialize models and more.
"""
if self.world_size > 1:
# Prepare distributed data parallel
self.training_models = {
name: DDP(
model,
device_ids=[self.local_rank],
output_device=self.local_rank,
bucket_cap_mb=128,
find_unused_parameters=False
)
for name, model in self.models.items()
}
else:
self.training_models = self.models
# Build master params
self.model_params = sum(
[[p for p in model.parameters() if p.requires_grad] for model in self.models.values()]
, [])
if self.mix_precision_mode == 'amp':
self.master_params = self.model_params
if self.mix_precision_dtype == torch.float16:
self.scaler = torch.GradScaler()
elif self.mix_precision_mode == 'inflat_all':
self.master_params = make_master_params(self.model_params)
if self.mix_precision_dtype == torch.float16:
self.log_scale = 20.0
elif self.mix_precision_mode is None:
self.master_params = self.model_params
else:
raise NotImplementedError(f'Mix precision mode {self.mix_precision_mode} is not implemented.')
# Build EMA params
if self.is_master:
self.ema_params = [copy.deepcopy(self.master_params) for _ in self.ema_rate]
# Initialize optimizer
if hasattr(torch.optim, self.optimizer_config['name']):
self.optimizer = getattr(torch.optim, self.optimizer_config['name'])(self.master_params, **self.optimizer_config['args'])
else:
self.optimizer = globals()[self.optimizer_config['name']](self.master_params, **self.optimizer_config['args'])
# Initalize learning rate scheduler
if self.lr_scheduler_config is not None:
if hasattr(torch.optim.lr_scheduler, self.lr_scheduler_config['name']):
self.lr_scheduler = getattr(torch.optim.lr_scheduler, self.lr_scheduler_config['name'])(self.optimizer, **self.lr_scheduler_config['args'])
else:
self.lr_scheduler = globals()[self.lr_scheduler_config['name']](self.optimizer, **self.lr_scheduler_config['args'])
# Initialize elastic memory controller
if self.elastic_controller_config is not None:
assert any([isinstance(model, (elastic_utils.ElasticModule, elastic_utils.ElasticModuleMixin)) for model in self.models.values()]), \
'No elastic module found in models, please inherit from ElasticModule or ElasticModuleMixin'
self.elastic_controller = getattr(elastic_utils, self.elastic_controller_config['name'])(**self.elastic_controller_config['args'])
for model in self.models.values():
if isinstance(model, (elastic_utils.ElasticModule, elastic_utils.ElasticModuleMixin)):
model.register_memory_controller(self.elastic_controller)
# Initialize gradient clipper
if self.grad_clip is not None:
if isinstance(self.grad_clip, (float, int)):
self.grad_clip = float(self.grad_clip)
else:
self.grad_clip = getattr(grad_clip_utils, self.grad_clip['name'])(**self.grad_clip['args'])
def prepare_dataloader(self, **kwargs):
"""
Prepare dataloader.
"""
self.data_sampler = ResumableSampler(
self.dataset,
shuffle=True,
)
if self.num_workers is None or self.num_workers == -1:
num_workers = max(1, int(np.ceil((os.cpu_count() - 16) / torch.cuda.device_count())))
else:
num_workers = self.num_workers
self.dataloader = DataLoader(
self.dataset,
batch_size=self.batch_size_per_gpu,
num_workers=num_workers,
pin_memory=True,
drop_last=True,
persistent_workers=True,
collate_fn=self.dataset.collate_fn if hasattr(self.dataset, 'collate_fn') else None,
sampler=self.data_sampler,
)
self.data_iterator = cycle(self.dataloader)
def _master_params_to_state_dicts(self, master_params):
"""
Convert master params to dict of state_dicts.
"""
if self.mix_precision_mode == 'inflat_all':
master_params = unflatten_master_params(self.model_params, master_params)
state_dicts = {name: model.state_dict() for name, model in self.models.items()}
master_params_names = sum(
[[(name, n) for n, p in model.named_parameters() if p.requires_grad] for name, model in self.models.items()]
, [])
for i, (model_name, param_name) in enumerate(master_params_names):
state_dicts[model_name][param_name] = master_params[i]
return state_dicts
def _state_dicts_to_master_params(self, master_params, state_dicts):
"""
Convert a state_dict to master params.
"""
master_params_names = sum(
[[(name, n) for n, p in model.named_parameters() if p.requires_grad] for name, model in self.models.items()]
, [])
params = [state_dicts[name][param_name] for name, param_name in master_params_names]
if self.mix_precision_mode == 'inflat_all':
model_params_to_master_params(params, master_params)
else:
for i, param in enumerate(params):
master_params[i].data.copy_(param.data)
def load(self, load_dir, step=0):
"""
Load a checkpoint.
Should be called by all processes.
"""
if self.is_master:
print(f'\nLoading checkpoint from step {step}...', end='')
model_ckpts = {}
for name, model in self.models.items():
model_ckpt = torch.load(read_file_dist(os.path.join(load_dir, 'ckpts', f'{name}_step{step:07d}.pt')), map_location=self.device, weights_only=True)
model_ckpts[name] = model_ckpt
model.load_state_dict(model_ckpt)
self._state_dicts_to_master_params(self.master_params, model_ckpts)
del model_ckpts
if self.is_master:
for i, ema_rate in enumerate(self.ema_rate):
ema_ckpts = {}
for name, model in self.models.items():
ema_ckpt = torch.load(os.path.join(load_dir, 'ckpts', f'{name}_ema{ema_rate}_step{step:07d}.pt'), map_location=self.device, weights_only=True)
ema_ckpts[name] = ema_ckpt
self._state_dicts_to_master_params(self.ema_params[i], ema_ckpts)
del ema_ckpts
misc_ckpt = torch.load(read_file_dist(os.path.join(load_dir, 'ckpts', f'misc_step{step:07d}.pt')), map_location=torch.device('cpu'), weights_only=False)
self.optimizer.load_state_dict(misc_ckpt['optimizer'])
self.step = misc_ckpt['step']
self.data_sampler.load_state_dict(misc_ckpt['data_sampler'])
if self.mix_precision_mode == 'amp' and self.mix_precision_dtype == torch.float16:
self.scaler.load_state_dict(misc_ckpt['scaler'])
elif self.mix_precision_mode == 'inflat_all' and self.mix_precision_dtype == torch.float16:
self.log_scale = misc_ckpt['log_scale']
if self.lr_scheduler_config is not None:
self.lr_scheduler.load_state_dict(misc_ckpt['lr_scheduler'])
if self.elastic_controller_config is not None:
self.elastic_controller.load_state_dict(misc_ckpt['elastic_controller'])
if self.grad_clip is not None and not isinstance(self.grad_clip, float):
self.grad_clip.load_state_dict(misc_ckpt['grad_clip'])
del misc_ckpt
if self.world_size > 1:
dist.barrier()
if self.is_master:
print(' Done.')
if self.world_size > 1:
self.check_ddp()
def save(self, non_blocking=True):
"""
Save a checkpoint.
Should be called only by the rank 0 process.
"""
assert self.is_master, 'save() should be called only by the rank 0 process.'
print(f'\nSaving checkpoint at step {self.step}...', end='')
model_ckpts = self._master_params_to_state_dicts(self.master_params)
for name, model_ckpt in model_ckpts.items():
model_ckpt = {k: v.cpu() for k, v in model_ckpt.items()} # Move to CPU for saving
if non_blocking:
threading.Thread(
target=torch.save,
args=(model_ckpt, os.path.join(self.output_dir, 'ckpts', f'{name}_step{self.step:07d}.pt')),
).start()
else:
torch.save(model_ckpt, os.path.join(self.output_dir, 'ckpts', f'{name}_step{self.step:07d}.pt'))
for i, ema_rate in enumerate(self.ema_rate):
ema_ckpts = self._master_params_to_state_dicts(self.ema_params[i])
for name, ema_ckpt in ema_ckpts.items():
ema_ckpt = {k: v.cpu() for k, v in ema_ckpt.items()} # Move to CPU for saving
if non_blocking:
threading.Thread(
target=torch.save,
args=(ema_ckpt, os.path.join(self.output_dir, 'ckpts', f'{name}_ema{ema_rate}_step{self.step:07d}.pt')),
).start()
else:
torch.save(ema_ckpt, os.path.join(self.output_dir, 'ckpts', f'{name}_ema{ema_rate}_step{self.step:07d}.pt'))
misc_ckpt = {
'optimizer': self.optimizer.state_dict(),
'step': self.step,
'data_sampler': self.data_sampler.state_dict(),
}
if self.mix_precision_mode == 'amp' and self.mix_precision_dtype == torch.float16:
misc_ckpt['scaler'] = self.scaler.state_dict()
elif self.mix_precision_mode == 'inflat_all' and self.mix_precision_dtype == torch.float16:
misc_ckpt['log_scale'] = self.log_scale
if self.lr_scheduler_config is not None:
misc_ckpt['lr_scheduler'] = self.lr_scheduler.state_dict()
if self.elastic_controller_config is not None:
misc_ckpt['elastic_controller'] = self.elastic_controller.state_dict()
if self.grad_clip is not None and not isinstance(self.grad_clip, float):
misc_ckpt['grad_clip'] = self.grad_clip.state_dict()
if non_blocking:
threading.Thread(
target=torch.save,
args=(misc_ckpt, os.path.join(self.output_dir, 'ckpts', f'misc_step{self.step:07d}.pt')),
).start()
else:
torch.save(misc_ckpt, os.path.join(self.output_dir, 'ckpts', f'misc_step{self.step:07d}.pt'))
print(' Done.')
def _remap_checkpoint_keys(self, model_ckpt, model_state_dict):
"""
Remap checkpoint keys to match model state dict.
Handles structural changes like:
- cross_attn.xxx -> cross_attn.cross_attn_block.xxx (for ProjectAttention wrapper)
Args:
model_ckpt: Checkpoint state dict
model_state_dict: Model state dict
Returns:
Remapped checkpoint dict
"""
remapped_ckpt = {}
remapped_count = 0
for ckpt_key, ckpt_value in model_ckpt.items():
# Check if key exists directly
if ckpt_key in model_state_dict:
remapped_ckpt[ckpt_key] = ckpt_value
continue
# Try remapping: cross_attn.xxx -> cross_attn.cross_attn_block.xxx
# This handles the case when cross_attn is wrapped by ProjectAttention
if '.cross_attn.' in ckpt_key:
# Split at .cross_attn.
parts = ckpt_key.split('.cross_attn.')
if len(parts) == 2:
new_key = f'{parts[0]}.cross_attn.cross_attn_block.{parts[1]}'
if new_key in model_state_dict:
remapped_ckpt[new_key] = ckpt_value
remapped_count += 1
continue
# Key not remapped, keep original (will be handled by missing key logic)
remapped_ckpt[ckpt_key] = ckpt_value
if remapped_count > 0 and self.is_master:
print(f'Info: Remapped {remapped_count} cross_attn keys to cross_attn.cross_attn_block')
return remapped_ckpt
def finetune_from(self, finetune_ckpt):
"""
Finetune from a checkpoint.
Should be called by all processes.
"""
# Allow missing keys (e.g., register_buffer parameters)
ALLOWED_MISSING_KEYS = {'rope_phases'}
if self.is_master:
print('\nFinetuning from:')
for name, path in finetune_ckpt.items():
print(f' - {name}: {path}')
model_ckpts = {}
for name, model in self.models.items():
model_state_dict = model.state_dict()
if name in finetune_ckpt:
model_ckpt = torch.load(read_file_dist(finetune_ckpt[name]), map_location=self.device, weights_only=True)
# Remap checkpoint keys to handle structural changes (e.g., ProjectAttention wrapper)
model_ckpt = self._remap_checkpoint_keys(model_ckpt, model_state_dict)
# Check extra keys (in ckpt but not in model)
for k, v in model_ckpt.items():
if k not in model_state_dict:
if self.is_master:
print(f'Warning: {k} not found in model_state_dict, skipped.')
model_ckpt[k] = None
elif model_ckpt[k].shape != model_state_dict[k].shape:
if self.is_master:
print(f'Warning: {k} shape mismatch, {model_ckpt[k].shape} vs {model_state_dict[k].shape}, skipped.')
model_ckpt[k] = model_state_dict[k]
model_ckpt = {k: v for k, v in model_ckpt.items() if v is not None}
# Check missing keys (in model but not in ckpt)
missing_keys = set(model_state_dict.keys()) - set(model_ckpt.keys())
unexpected_missing = missing_keys - ALLOWED_MISSING_KEYS
if unexpected_missing and self.is_master:
print(f'Error: Missing keys in checkpoint: {unexpected_missing}')
raise RuntimeError(f'Missing keys in checkpoint: {unexpected_missing}')
if missing_keys & ALLOWED_MISSING_KEYS and self.is_master:
print(f'Info: Using model initialized values for: {missing_keys & ALLOWED_MISSING_KEYS}')
# Fill in missing keys (using model initialized values)
for k in missing_keys:
model_ckpt[k] = model_state_dict[k]
model_ckpts[name] = model_ckpt
model.load_state_dict(model_ckpt)
else:
if self.is_master:
print(f'Warning: {name} not found in finetune_ckpt, skipped.')
model_ckpts[name] = model_state_dict
self._state_dicts_to_master_params(self.master_params, model_ckpts)
if self.is_master:
for i, ema_rate in enumerate(self.ema_rate):
self._state_dicts_to_master_params(self.ema_params[i], model_ckpts)
del model_ckpts
if self.world_size > 1:
dist.barrier()
if self.is_master:
print('Done.')
if self.world_size > 1:
self.check_ddp()
@abstractmethod
def run_snapshot(self, num_samples, batch_size=4, verbose=False, **kwargs):
"""
Run a snapshot of the model.
"""
pass
@torch.no_grad()
def visualize_sample(self, sample):
"""
Convert a sample to an image.
"""
if hasattr(self.dataset, 'visualize_sample'):
return self.dataset.visualize_sample(sample)
else:
return sample
@torch.no_grad()
def snapshot_dataset(self, num_samples=100, batch_size=4):
"""
Sample images from the dataset.
"""
dataloader = torch.utils.data.DataLoader(
self.dataset,
batch_size=batch_size,
num_workers=0,
shuffle=True,
collate_fn=self.dataset.collate_fn if hasattr(self.dataset, 'collate_fn') else None,
)
save_cfg = {}
for i in range(0, num_samples, batch_size):
data = next(iter(dataloader))
data = {k: v[:min(num_samples - i, batch_size)] for k, v in data.items()}
data = recursive_to_device(data, self.device)
try:
vis = self.visualize_sample(data)
except (RuntimeError, Exception) as e:
print(f'\033[93m[WARN] snapshot_dataset visualize_sample failed (batch {i}), skipping: {e}\033[0m')
torch.cuda.empty_cache()
continue
if isinstance(vis, dict):
for k, v in vis.items():
if f'dataset_{k}' not in save_cfg:
save_cfg[f'dataset_{k}'] = []
save_cfg[f'dataset_{k}'].append(v)
else:
if 'dataset' not in save_cfg:
save_cfg['dataset'] = []
save_cfg['dataset'].append(vis)
for name, image in save_cfg.items():
utils.save_image(
torch.cat(image, dim=0),
os.path.join(self.output_dir, 'samples', f'{name}.jpg'),
nrow=int(np.sqrt(num_samples)),
normalize=True,
value_range=self.dataset.value_range,
)
@torch.no_grad()
def snapshot(self, suffix=None, num_samples=64, batch_size=4, verbose=False):
"""
Sample images from the model.
NOTE: When num_samples >= 4, this function should be called by all processes.
When num_samples < 4, only master runs snapshot (other ranks skip via barrier).
"""
# Free cached GPU memory before snapshot to avoid OOM / illegal address errors
import gc
gc.collect()
torch.cuda.empty_cache()
if self.is_master:
print(f'\nSampling {num_samples} images...', end='')
if suffix is None:
suffix = f'step{self.step:07d}'
# When num_samples < 4, only master runs snapshot to avoid multi-rank gather issues
master_only = num_samples < 4
sample_metadata = None # Will hold list of "dataset_name/sha256" strings
if master_only and self.world_size > 1:
if not self.is_master:
# Non-master ranks just wait at barrier
dist.barrier()
return
# Master runs snapshot alone
amp_context = partial(torch.autocast, device_type='cuda', dtype=self.mix_precision_dtype) if self.mix_precision_mode == 'amp' else nullcontext
with amp_context():
samples = self.run_snapshot(num_samples, batch_size=batch_size, verbose=verbose)
# Extract metadata before preprocessing
sample_metadata = samples.pop('_metadata', None)
# Free GPU memory after sampling, before decode + render
torch.cuda.empty_cache()
# Preprocess images
for key in list(samples.keys()):
if samples[key]['type'] == 'sample':
try:
vis = self.visualize_sample(samples[key]['value'])
except RuntimeError as e:
print(f"[Snapshot] WARNING: visualize_sample failed for '{key}': {e}")
# Reset CUDA error state and skip this sample
try:
torch.cuda.synchronize()
except RuntimeError:
pass
torch.cuda.empty_cache()
del samples[key]
continue
if isinstance(vis, dict):
for k, v in vis.items():
samples[f'{key}_{k}'] = {'value': v, 'type': 'image'}
del samples[key]
else:
samples[key] = {'value': vis, 'type': 'image'}
# No gather needed, master already has all samples
dist.barrier()
else:
# Distribute sampling across all ranks
num_samples_per_process = int(np.ceil(num_samples / self.world_size))
amp_context = partial(torch.autocast, device_type='cuda', dtype=self.mix_precision_dtype) if self.mix_precision_mode == 'amp' else nullcontext
with amp_context():
samples = self.run_snapshot(num_samples_per_process, batch_size=batch_size, verbose=verbose)
# Extract metadata before preprocessing
local_metadata = samples.pop('_metadata', None)
# Free GPU memory after sampling, before decode + render
torch.cuda.empty_cache()
# Preprocess images
for key in list(samples.keys()):
if samples[key]['type'] == 'sample':
try:
vis = self.visualize_sample(samples[key]['value'])
except RuntimeError as e:
print(f"[Snapshot] WARNING: visualize_sample failed for '{key}': {e}")
torch.cuda.synchronize()
del samples[key]
continue
if isinstance(vis, dict):
for k, v in vis.items():
samples[f'{key}_{k}'] = {'value': v, 'type': 'image'}
del samples[key]
else:
samples[key] = {'value': vis, 'type': 'image'}
# Gather results
if self.world_size > 1:
for key in samples.keys():
samples[key]['value'] = samples[key]['value'].contiguous()
if self.is_master:
all_images = [torch.empty_like(samples[key]['value']) for _ in range(self.world_size)]
else:
all_images = []
dist.gather(samples[key]['value'], all_images, dst=0)
if self.is_master:
samples[key]['value'] = torch.cat(all_images, dim=0)[:num_samples]
# Gather metadata across ranks
if local_metadata is not None:
all_metadata = [None] * self.world_size
dist.all_gather_object(all_metadata, local_metadata)
if self.is_master:
sample_metadata = sum(all_metadata, [])[:num_samples]
else:
sample_metadata = None
else:
sample_metadata = local_metadata
# Save images
if self.is_master:
os.makedirs(os.path.join(self.output_dir, 'samples', suffix), exist_ok=True)
wandb_images = {} # Collect images for wandb logging
nrow = int(np.sqrt(num_samples))
vr = self.dataset.value_range
# Build metadata caption string for wandb
metadata_caption = ''
if sample_metadata:
metadata_caption = '\n' + ' | '.join(sample_metadata)
# Also save metadata to file
with open(os.path.join(self.output_dir, 'samples', suffix, 'metadata.txt'), 'w') as f:
for i, m in enumerate(sample_metadata):
f.write(f'{i}: {m}\n')
# Helper: make a normalized grid tensor from a batch of images
def _make_grid(tensor):
return utils.make_grid(tensor, nrow=nrow, normalize=True, value_range=vr)
# Helper: resize grid to target height (keep aspect ratio)
def _resize_to_height(grid, target_h):
import torch.nn.functional as F
_, h, w = grid.shape
if h == target_h:
return grid
target_w = int(round(w * target_h / h))
return F.interpolate(grid.unsqueeze(0), size=(target_h, target_w), mode='bilinear', align_corners=False).squeeze(0)
# --- Save individual images (original behavior) ---
for key in samples.keys():
if samples[key]['type'] == 'image':
image_path = os.path.join(self.output_dir, 'samples', suffix, f'{key}_{suffix}.jpg')
utils.save_image(
samples[key]['value'],
image_path,
nrow=nrow,
normalize=True,
value_range=vr,
)
# Collect for wandb
if self.wandb_run is not None:
grid = _make_grid(samples[key]['value'])
grid_np = grid.permute(1, 2, 0).cpu().numpy()
grid_np = (grid_np * 255).clip(0, 255).astype(np.uint8)
wandb_images[f'samples/{key}'] = wandb.Image(grid_np, caption=f'{key} at step {self.step}{metadata_caption}')
elif samples[key]['type'] == 'number':
val_min = samples[key]['value'].min()
val_max = samples[key]['value'].max()
images = (samples[key]['value'] - val_min) / (val_max - val_min)
images = utils.make_grid(
images,
nrow=nrow,
normalize=False,
)
save_image_with_notes(
images,
os.path.join(self.output_dir, 'samples', suffix, f'{key}_{suffix}.jpg'),
notes=f'{key} min: {val_min}, max: {val_max}',
)
# --- Save combined images ---
sample_keys = set(samples.keys())
# Combined 1: image + sample_gt_view + sample_gt_gt_view (shape)
# image + sample_gt_view_{attr} + sample_gt_gt_view_{attr} (tex, per attribute)
# Detect gt_view attribute suffixes from sample keys
gt_view_attrs = set()
for k in sample_keys:
if k.startswith('sample_gt_view_'):
attr = k[len('sample_gt_view_'):]
gt_view_attrs.add(attr)
if gt_view_attrs:
# Tex mode: generate combined view for each PBR attribute
for attr in sorted(gt_view_attrs):
combo1_keys = ['image', f'sample_gt_view_{attr}', f'sample_gt_gt_view_{attr}']
combo1_present = [k for k in combo1_keys if k in sample_keys and samples[k]['type'] == 'image']
if len(combo1_present) >= 2:
grids = [_make_grid(samples[k]['value']) for k in combo1_present]
target_h = max(g.shape[1] for g in grids)
grids = [_resize_to_height(g, target_h) for g in grids]
combined = torch.cat(grids, dim=2)
combined_path = os.path.join(self.output_dir, 'samples', suffix, f'combined_views_{attr}_{suffix}.jpg')
utils.save_image(combined, combined_path, normalize=False)
if self.wandb_run is not None:
grid_np = combined.permute(1, 2, 0).cpu().numpy()
grid_np = (grid_np * 255).clip(0, 255).astype(np.uint8)
label = ' | '.join(combo1_present)
wandb_images[f'samples/combined_views_{attr}'] = wandb.Image(grid_np, caption=f'{label} at step {self.step}{metadata_caption}')
else:
# Shape mode: single gt_view
combo1_keys = ['image', 'sample_gt_view', 'sample_gt_gt_view']
combo1_present = [k for k in combo1_keys if k in sample_keys and samples[k]['type'] == 'image']
if len(combo1_present) >= 2:
grids = [_make_grid(samples[k]['value']) for k in combo1_present]
target_h = max(g.shape[1] for g in grids)
grids = [_resize_to_height(g, target_h) for g in grids]
combined = torch.cat(grids, dim=2)
combined_path = os.path.join(self.output_dir, 'samples', suffix, f'combined_views_{suffix}.jpg')
utils.save_image(combined, combined_path, normalize=False)
if self.wandb_run is not None:
grid_np = combined.permute(1, 2, 0).cpu().numpy()
grid_np = (grid_np * 255).clip(0, 255).astype(np.uint8)
label = ' | '.join(combo1_present)
wandb_images[f'samples/combined_views'] = wandb.Image(grid_np, caption=f'{label} at step {self.step}{metadata_caption}')
# Combined 2: sample_multiview + sample_gt_multiview
combo2_keys = ['sample_multiview', 'sample_gt_multiview']
combo2_present = [k for k in combo2_keys if k in sample_keys and samples[k]['type'] == 'image']
if len(combo2_present) >= 2:
grids = [_make_grid(samples[k]['value']) for k in combo2_present]
target_h = max(g.shape[1] for g in grids)
grids = [_resize_to_height(g, target_h) for g in grids]
combined = torch.cat(grids, dim=2) # concatenate along width
combined_path = os.path.join(self.output_dir, 'samples', suffix, f'combined_multiview_{suffix}.jpg')
utils.save_image(combined, combined_path, normalize=False)
if self.wandb_run is not None:
grid_np = combined.permute(1, 2, 0).cpu().numpy()
grid_np = (grid_np * 255).clip(0, 255).astype(np.uint8)
label = ' | '.join(combo2_present)
wandb_images[f'samples/combined_multiview'] = wandb.Image(grid_np, caption=f'{label} at step {self.step}{metadata_caption}')
# Log images to wandb
if self.wandb_run is not None and wandb_images:
self.wandb_run.log(wandb_images, step=self.step)
if self.is_master:
print(' Done.')
def update_ema(self):
"""
Update exponential moving average.
Should only be called by the rank 0 process.
"""
assert self.is_master, 'update_ema() should be called only by the rank 0 process.'
for i, ema_rate in enumerate(self.ema_rate):
for master_param, ema_param in zip(self.master_params, self.ema_params[i]):
ema_param.detach().mul_(ema_rate).add_(master_param, alpha=1.0 - ema_rate)
def check_ddp(self):
"""
Check if DDP is working properly.
Should be called by all process.
"""
if self.is_master:
print('\nPerforming DDP check...')
if self.is_master:
print('Checking if parameters are consistent across processes...')
dist.barrier()
try:
for p in self.master_params:
# split to avoid OOM
for i in range(0, p.numel(), 10000000):
sub_size = min(10000000, p.numel() - i)
sub_p = p.detach().view(-1)[i:i+sub_size]
# gather from all processes
sub_p_gather = [torch.empty_like(sub_p) for _ in range(self.world_size)]
dist.all_gather(sub_p_gather, sub_p)
# check if equal
assert all([torch.equal(sub_p, sub_p_gather[i]) for i in range(self.world_size)]), 'parameters are not consistent across processes'
except AssertionError as e:
if self.is_master:
print(f'\n\033[91mError: {e}\033[0m')
print('DDP check failed.')
raise e
dist.barrier()
if self.is_master:
print('Done.')
def _verify_gradient_sync(self):
"""
Verify that DDP gradient synchronization is working correctly.
DDP's backward automatically performs all_reduce on gradients; after sync all ranks should have identical gradients.
Verification method:
1. Compute total gradient norm across all parameters
2. Gather gradient norms from all ranks
3. If DDP sync is working, all ranks should have identical gradient norms
4. If not synced, gradient norms will differ (since each rank processes different data)
"""
# Compute total gradient norm on this rank
total_grad_norm_sq = 0.0
grad_count = 0
for p in self.model_params:
if p.grad is not None:
total_grad_norm_sq += p.grad.detach().float().norm().item() ** 2
grad_count += 1
if grad_count == 0:
return
local_grad_norm = total_grad_norm_sq ** 0.5
# Ensure all processes reach the same point
dist.barrier()
# Gather gradient norms from all ranks
grad_norm_tensor = torch.tensor([local_grad_norm], dtype=torch.float64, device=self.device)
all_grad_norms = [torch.zeros(1, dtype=torch.float64, device=self.device) for _ in range(self.world_size)]
dist.all_gather(all_grad_norms, grad_norm_tensor)
all_grad_norms = [g.item() for g in all_grad_norms]
# Verify all ranks have the same gradient norm (relative error tolerance: 0.1%)
ref_norm = all_grad_norms[0]
if ref_norm > 0:
is_synced = all(abs(g - ref_norm) / ref_norm < 1e-3 for g in all_grad_norms)
else:
is_synced = all(abs(g) < 1e-10 for g in all_grad_norms)
if self.is_master:
print(f'\n{"="*60}')
print(f'[Step {self.step}] DDP Gradient Sync Verification:')
for i, g in enumerate(all_grad_norms):
print(f' Rank {i} grad_norm: {g:.8f}')
if is_synced:
print(f' \033[92m✓ PASS: All gradients are synchronized!\033[0m')
else:
max_diff = max(abs(g - ref_norm) for g in all_grad_norms)
print(f' \033[91m✗ FAIL: Gradients are NOT synchronized! Max diff: {max_diff:.8f}\033[0m')
print(f'{"="*60}\n')
@abstractmethod
def training_losses(**mb_data):
"""
Compute training losses.
"""
pass
def load_data(self):
"""
Load data.
"""
if self.prefetch_data:
if self._data_prefetched is None:
self._data_prefetched = recursive_to_device(next(self.data_iterator), self.device, non_blocking=True)
data = self._data_prefetched
self._data_prefetched = recursive_to_device(next(self.data_iterator), self.device, non_blocking=True)
else:
data = recursive_to_device(next(self.data_iterator), self.device, non_blocking=True)
# if the data is a dict, we need to split it into multiple dicts with batch_size_per_gpu
if isinstance(data, dict):
if self.batch_split == 1:
data_list = [data]
else:
batch_size = list(data.values())[0].shape[0]
data_list = [
{k: v[i * batch_size // self.batch_split:(i + 1) * batch_size // self.batch_split] for k, v in data.items()}
for i in range(self.batch_split)
]
elif isinstance(data, list):
data_list = data
else:
raise ValueError('Data must be a dict or a list of dicts.')
return data_list
def run_step(self, data_list):
"""
Run a training step.
"""
step_log = {'loss': {}, 'status': {}}
amp_context = partial(torch.autocast, device_type='cuda', dtype=self.mix_precision_dtype) if self.mix_precision_mode == 'amp' else nullcontext
elastic_controller_context = self.elastic_controller.record if self.elastic_controller_config is not None else nullcontext
# Train
losses = []
statuses = []
elastic_controller_logs = []
zero_grad(self.model_params)
for i, mb_data in enumerate(data_list):
## sync at the end of each batch split
sync_contexts = [self.training_models[name].no_sync for name in self.training_models] if i != len(data_list) - 1 and self.world_size > 1 else [nullcontext]
with nested_contexts(*sync_contexts), elastic_controller_context():
with amp_context():
loss, status = self.training_losses(**mb_data)
l = loss['loss'] / len(data_list)
# DEBUG: Print loss for each rank
if self.debug:
print(f'[Rank {self.rank}/{self.world_size}] Step {self.step} batch {i}: loss={loss["loss"].item():.6f}')
## backward
if self.mix_precision_mode == 'amp' and self.mix_precision_dtype == torch.float16:
self.scaler.scale(l).backward()
elif self.mix_precision_mode == 'inflat_all' and self.mix_precision_dtype == torch.float16:
scaled_l = l * (2 ** self.log_scale)
scaled_l.backward()
else:
l.backward()
## log
losses.append(dict_foreach(loss, lambda x: x.item() if isinstance(x, torch.Tensor) else x))
statuses.append(dict_foreach(status, lambda x: x.item() if isinstance(x, torch.Tensor) else x))
if self.elastic_controller_config is not None:
elastic_controller_logs.append(self.elastic_controller.log())
# ============================================================
# DEBUG: Verify DDP gradient synchronization
# Check if gradients are consistent across ranks after backward
# DDP automatically all_reduces gradients during the last batch_split's backward
# After sync, all ranks should have identical gradients
# ============================================================
if self.debug and self.world_size > 1:
self._verify_gradient_sync()
## gradient clip
if self.grad_clip is not None:
if self.mix_precision_mode == 'amp' and self.mix_precision_dtype == torch.float16:
self.scaler.unscale_(self.optimizer)
elif self.mix_precision_mode == 'inflat_all':
model_grads_to_master_grads(self.model_params, self.master_params)
if self.mix_precision_dtype == torch.float16:
self.master_params[0].grad.mul_(1.0 / (2 ** self.log_scale))
if isinstance(self.grad_clip, float):
grad_norm = torch.nn.utils.clip_grad_norm_(self.master_params, self.grad_clip)
else:
grad_norm = self.grad_clip(self.master_params)
if torch.isfinite(grad_norm):
statuses[-1]['grad_norm'] = grad_norm.item()
## step
if self.mix_precision_mode == 'amp' and self.mix_precision_dtype == torch.float16:
prev_scale = self.scaler.get_scale()
self.scaler.step(self.optimizer)
self.scaler.update()
elif self.mix_precision_mode == 'inflat_all':
if self.mix_precision_dtype == torch.float16:
prev_scale = 2 ** self.log_scale
if not any(not p.grad.isfinite().all() for p in self.model_params):
if self.grad_clip is None:
model_grads_to_master_grads(self.model_params, self.master_params)
self.master_params[0].grad.mul_(1.0 / (2 ** self.log_scale))
self.optimizer.step()
master_params_to_model_params(self.model_params, self.master_params)
self.log_scale += self.fp16_scale_growth
else:
self.log_scale -= 1
else:
prev_scale = 1.0
if self.grad_clip is None:
model_grads_to_master_grads(self.model_params, self.master_params)
if not any(not p.grad.isfinite().all() for p in self.master_params):
self.optimizer.step()
master_params_to_model_params(self.model_params, self.master_params)
else:
print('\n\033[93mWarning: NaN detected in gradients. Skipping update.\033[0m')
else:
prev_scale = 1.0
if not any(not p.grad.isfinite().all() for p in self.model_params):
self.optimizer.step()
else:
print('\n\033[93mWarning: NaN detected in gradients. Skipping update.\033[0m')
## adjust learning rate
if self.lr_scheduler_config is not None:
statuses[-1]['lr'] = self.lr_scheduler.get_last_lr()[0]
self.lr_scheduler.step()
# Logs
step_log['loss'] = dict_reduce(losses, lambda x: np.mean(x))
step_log['status'] = dict_reduce(statuses, lambda x: np.mean(x), special_func={'min': lambda x: np.min(x), 'max': lambda x: np.max(x)})
if self.elastic_controller_config is not None:
step_log['elastic'] = dict_reduce(elastic_controller_logs, lambda x: np.mean(x))
if self.grad_clip is not None:
step_log['grad_clip'] = self.grad_clip if isinstance(self.grad_clip, float) else self.grad_clip.log()
# Check grad and norm of each param
if self.log_param_stats:
param_norms = {}
param_grads = {}
for model_name, model in self.models.items():
for name, param in model.named_parameters():
if param.requires_grad:
param_norms[f'{model_name}.{name}'] = param.norm().item()
if param.grad is not None and torch.isfinite(param.grad).all():
param_grads[f'{model_name}.{name}'] = param.grad.norm().item() / prev_scale
step_log['param_norms'] = param_norms
step_log['param_grads'] = param_grads
# Update exponential moving average
if self.is_master:
self.update_ema()
return step_log
def save_logs(self):
log_str = '\n'.join([
f'{step}: {json.dumps(dict_foreach(log, lambda x: float(x)))}' for step, log in self.log
])
# Accumulate logs in memory and overwrite file each time (S3 FUSE does not support append)
if not hasattr(self, '_log_buffer'):
self._log_buffer = []
self._log_buffer.append(log_str)
try:
with open(self._log_file, 'w') as log_file:
log_file.write('\n'.join(self._log_buffer) + '\n')
except Exception as e:
print(f'\033[93m[WARN] Failed to write log file: {e}\033[0m')
# show with mlflow
log_show = [l for _, l in self.log if not dict_any(l, lambda x: np.isnan(x))]
log_show = dict_reduce(log_show, lambda x: np.mean(x))
log_show = dict_flatten(log_show, sep='/')
if self.writer is not None:
for key, value in log_show.items():
self.writer.add_scalar(key, value, self.step)
# Log to wandb
if self.wandb_run is not None:
wandb_log = {key: value for key, value in log_show.items()}
wandb_log['step'] = self.step
self.wandb_run.log(wandb_log, step=self.step)
self.log = []
def check_abort(self):
"""
Check if training should be aborted due to certain conditions.
"""
# 1. If log_scale in inflat_all mode is less than 0
if self.mix_precision_dtype == torch.float16 and \
self.mix_precision_mode == 'inflat_all' and \
self.log_scale < 0:
if self.is_master:
print ('\n\n\033[91m')
print (f'ABORT: log_scale in inflat_all mode is less than 0 at step {self.step}.')
print ('This indicates that the model is diverging. You should look into the model and the data.')
print ('\033[0m')
self.save(non_blocking=False)
self.save_logs()
if self.world_size > 1:
dist.barrier()
raise ValueError('ABORT: log_scale in inflat_all mode is less than 0.')
def run(self):
"""
Run training.
"""
if self.is_master:
print('\nStarting training...')
if self.i_sample != -1:
try:
self.snapshot_dataset(num_samples=self.snapshot_num_samples, batch_size=self.snapshot_batch_size)
except (RuntimeError, Exception) as e:
print(f'\033[93m[WARN] snapshot_dataset failed, skipping: {e}\033[0m')
torch.cuda.empty_cache()
else:
print('[INFO] i_sample=-1, all snapshots disabled.')
if self.i_sample != -1:
if self.step == 0:
try:
self.snapshot(suffix='init', num_samples=self.snapshot_num_samples, batch_size=self.snapshot_batch_size)
except (RuntimeError, Exception) as e:
print(f'\033[93m[WARN] snapshot (init) failed, skipping: {e}\033[0m')
torch.cuda.empty_cache()
else: # resume
try:
self.snapshot(suffix=f'resume_step{self.step:07d}', num_samples=self.snapshot_num_samples, batch_size=self.snapshot_batch_size)
except (RuntimeError, Exception) as e:
print(f'\033[93m[WARN] snapshot (resume) failed, skipping: {e}\033[0m')
torch.cuda.empty_cache()
time_last_print = 0.0
time_elapsed = 0.0
while self.step < self.max_steps:
time_start = time.time()
data_list = self.load_data()
step_log = self.run_step(data_list)
time_end = time.time()
time_elapsed += time_end - time_start
self.step += 1
# Print progress
if self.is_master and self.step % self.i_print == 0:
speed = self.i_print / (time_elapsed - time_last_print) * 3600
columns = [
f'Step: {self.step}/{self.max_steps} ({self.step / self.max_steps * 100:.2f}%)',
f'Elapsed: {time_elapsed / 3600:.2f} h',
f'Speed: {speed:.2f} steps/h',
f'ETA: {(self.max_steps - self.step) / speed:.2f} h',
]
print(' | '.join([c.ljust(25) for c in columns]), flush=True)
time_last_print = time_elapsed
# Check ddp
if self.parallel_mode == 'ddp' and self.world_size > 1 and self.i_ddpcheck is not None and self.step % self.i_ddpcheck == 0:
self.check_ddp()
# Sample images
if self.i_sample != -1 and self.step % self.i_sample == 0:
try:
self.snapshot(num_samples=self.snapshot_num_samples, batch_size=self.snapshot_batch_size)
except (RuntimeError, Exception) as e:
if self.is_master:
print(f'\033[93m[WARN] snapshot at step {self.step} failed, skipping: {e}\033[0m')
try:
torch.cuda.empty_cache()
except Exception:
pass
if self.is_master:
self.log.append((self.step, {}))
# Log time
self.log[-1][1]['time'] = {
'step': time_end - time_start,
'elapsed': time_elapsed,
}
# Log losses
if step_log is not None:
self.log[-1][1].update(step_log)
# Log scale
if self.mix_precision_dtype == torch.float16:
if self.mix_precision_mode == 'amp':
self.log[-1][1]['scale'] = self.scaler.get_scale()
elif self.mix_precision_mode == 'inflat_all':
self.log[-1][1]['log_scale'] = self.log_scale
# Save log
if self.step % self.i_log == 0:
self.save_logs()
# Save checkpoint
if self.step % self.i_save == 0:
self.save()
# Check abort
self.check_abort()
if self.i_sample != -1:
try:
self.snapshot(suffix='final', num_samples=self.snapshot_num_samples, batch_size=self.snapshot_batch_size)
except (RuntimeError, Exception) as e:
if self.is_master:
print(f'\033[93m[WARN] snapshot (final) failed, skipping: {e}\033[0m')
torch.cuda.empty_cache()
if self.world_size > 1:
dist.barrier()
if self.is_master:
self.writer.close()
print('Training finished.')
def profile(self, wait=2, warmup=3, active=5):
"""
Profile the training loop.
"""
with torch.profiler.profile(
schedule=torch.profiler.schedule(wait=wait, warmup=warmup, active=active, repeat=1),
on_trace_ready=torch.profiler.tensorboard_trace_handler(os.path.join(self.output_dir, 'profile')),
profile_memory=True,
with_stack=True,
) as prof:
for _ in range(wait + warmup + active):
self.run_step()
prof.step()
|