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ef423c5 | 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 | import wandb
import os
import shutil
import argparse
import torch
import torch.cuda.amp as amp
import torch.distributed as distrib
from torch.nn.utils import clip_grad_norm_
from torch.utils.data import DataLoader, random_split
from torch.nn.parallel import DistributedDataParallel as DDP
from tqdm.auto import tqdm
# torch.backends.cuda.matmul.allow_tf32 = True
# torch.backends.cudnn.allow_tf32 = True
from pepflow.utils.vc import get_version, has_changes
from pepflow.utils.misc import BlackHole, inf_iterator, load_config, seed_all, get_logger, get_new_log_dir, current_milli_time
from pepflow.utils.data import PaddingCollate
from pepflow.utils.train import ScalarMetricAccumulator, count_parameters, get_optimizer, get_scheduler, log_losses, recursive_to, sum_weighted_losses
from models_con.pep_dataloader import PepDataset
# from models_con.flow_model import FlowModel
from models_con.flow_model import FlowModel
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--config', type=str, default='./configs/angle/learn_angle.yaml')
parser.add_argument('--logdir', type=str, default="./logs")
parser.add_argument('--debug', action='store_true', default=False)
parser.add_argument('--device', type=str, default='cuda:0')
parser.add_argument('--num_workers', type=int, default=4)
parser.add_argument('--tag', type=str, default='')
parser.add_argument('--resume', type=str, default=None)
parser.add_argument('--name', type=str, default='pepflow')
args = parser.parse_args()
# Version control
branch, version = get_version()
version_short = '%s-%s' % (branch, version[:7])
if has_changes() and not args.debug:
c = input('Start training anyway? (y/n) ')
if c != 'y':
exit()
# Load configs
config, config_name = load_config(args.config)
seed_all(config.train.seed)
config['device'] = args.device
# Logging
if args.debug:
logger = get_logger('train', None)
writer = BlackHole()
else:
run = wandb.init(project=args.name, config=config, name='%s[%s]' % (config_name, args.tag))
if args.resume:
log_dir = os.path.dirname(os.path.dirname(args.resume))
else:
log_dir = get_new_log_dir(args.logdir, prefix='%s[%s]' % (config_name, version_short), tag=args.tag)
with open(os.path.join(log_dir, 'commit.txt'), 'w') as f:
f.write(branch + '\n')
f.write(version + '\n')
ckpt_dir = os.path.join(log_dir, 'checkpoints')
if not os.path.exists(ckpt_dir): os.makedirs(ckpt_dir)
logger = get_logger('train', log_dir)
# writer = torch.utils.tensorboard.SummaryWriter(log_dir)
# tensorboard_trace_handler = torch.profiler.tensorboard_trace_handler(log_dir)
if not os.path.exists(os.path.join(log_dir, os.path.basename(args.config))):
shutil.copyfile(args.config, os.path.join(log_dir, os.path.basename(args.config)))
logger.info(args)
logger.info(config)
# Data
logger.info('Loading datasets...')
# train_dataset = get_dataset(config.dataset.train)
# val_dataset = get_dataset(config.dataset.val)
train_dataset = PepDataset(structure_dir = config.dataset.train.structure_dir, dataset_dir = config.dataset.train.dataset_dir,
name = config.dataset.train.name, transform=None, reset=config.dataset.train.reset)
# val_dataset = PepDataset(structure_dir = config.dataset.val.structure_dir, dataset_dir = config.dataset.val.dataset_dir,
# name = config.dataset.val.name, transform=None, reset=config.dataset.val.reset)
train_loader = DataLoader(train_dataset, batch_size=config.train.batch_size, shuffle=True, collate_fn=PaddingCollate(), num_workers=args.num_workers, pin_memory=True)
train_iterator = inf_iterator(train_loader)
# val_loader = DataLoader(val_dataset, batch_size=config.train.batch_size, shuffle=False, collate_fn=PaddingCollate(), num_workers=args.num_workers)
logger.info('Train %d | Val %d' % (len(train_dataset), len(train_dataset)))
# Model
logger.info('Building model...')
# model = get_model(config.model).to(args.device)
model = FlowModel(config.model).to(args.device)
# wandb.watch(model,log='all',log_freq=1)
logger.info('Number of parameters: %d' % count_parameters(model))
# Optimizer & Scheduler
optimizer = get_optimizer(config.train.optimizer, model)
scheduler = get_scheduler(config.train.scheduler, optimizer)
optimizer.zero_grad()
it_first = 1
# Resume
if args.resume is not None:
logger.info('Resuming from checkpoint: %s' % args.resume)
ckpt = torch.load(args.resume, map_location=args.device)
it_first = ckpt['iteration'] # + 1
model.load_state_dict(ckpt['model'])
logger.info('Resuming optimizer states...')
optimizer.load_state_dict(ckpt['optimizer'])
logger.info('Resuming scheduler states...')
scheduler.load_state_dict(ckpt['scheduler'])
def train(it):
time_start = current_milli_time()
model.train()
# Prepare data
batch = recursive_to(next(train_iterator), args.device)
# Forward pass
# loss_dict, metric_dict = model.get_loss(batch) # get loss and metrics
loss_dict = model(batch) # get loss and metrics
loss = sum_weighted_losses(loss_dict, config.train.loss_weights)
# loss = loss / config.train.accum_grad
time_forward_end = current_milli_time()
if torch.isnan(loss):
print('NAN Loss!')
torch.save({'batch':batch,'loss':loss,'loss_dict':loss_dict,'model': model.state_dict(),
'optimizer': optimizer.state_dict(),
'scheduler': scheduler.state_dict(),
'iteration': it,},os.path.join(log_dir,'nan.pt'))
loss = torch.tensor(0.,requires_grad=True).to(loss.device)
loss.backward()
# rescue for nan grad
for param in model.parameters():
if param.grad is not None:
if torch.isnan(param.grad).any():
param.grad[torch.isnan(param.grad)] = 0
orig_grad_norm = clip_grad_norm_(model.parameters(), config.train.max_grad_norm)
# Backward
# if it % config.train.accum_grad ==0:
optimizer.step()
optimizer.zero_grad()
time_backward_end = current_milli_time()
# Logging
scalar_dict = {}
# scalar_dict.update(metric_dict['scalar'])
scalar_dict.update({
'grad': orig_grad_norm,
'lr': optimizer.param_groups[0]['lr'],
'time_forward': (time_forward_end - time_start) / 1000,
'time_backward': (time_backward_end - time_forward_end) / 1000,
})
log_losses(loss, loss_dict, scalar_dict, it=it, tag='train', logger=logger)
def validate(it):
scalar_accum = ScalarMetricAccumulator()
with torch.no_grad():
model.eval()
for i, batch in enumerate(tqdm(val_loader, desc='Validate', dynamic_ncols=True)):
# Prepare data
batch = recursive_to(batch, args.device)
# Forward pass
# loss_dict, metric_dict = model.get_loss(batch)
loss_dict = model(batch)
loss = sum_weighted_losses(loss_dict, config.train.loss_weights)
scalar_accum.add(name='loss', value=loss, batchsize=len(batch['aa']), mode='mean')
for k, v in loss_dict['scalar'].items():
scalar_accum.add(name=k, value=v, batchsize=len(batch['aa']), mode='mean')
avg_loss = scalar_accum.get_average('loss')
summary = scalar_accum.log(it, 'val', logger=logger, writer=writer)
for k,v in summary.items():
wandb.log({f'val/{k}': v}, step=it)
# Trigger scheduler
if config.train.scheduler.type == 'plateau':
scheduler.step(avg_loss)
else:
scheduler.step()
return avg_loss
try:
for it in range(it_first, config.train.max_iters + 1):
train(it)
# if it % config.train.val_freq == 0:
# avg_val_loss = validate(it)
# if not args.debug:
if it % config.train.val_freq == 0:
ckpt_path = os.path.join(ckpt_dir, '%d.pt' % it)
torch.save({
'config': config,
'model': model.state_dict(),
'optimizer': optimizer.state_dict(),
'scheduler': scheduler.state_dict(),
'iteration': it,
# 'avg_val_loss': avg_val_loss,
}, ckpt_path)
except KeyboardInterrupt:
logger.info('Terminating...') |