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train_mdn_accelarete.py
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import copy
import math
import os
from functools import partial
import wandb
import torch
torch.multiprocessing.set_sharing_strategy('file_system')
import resource
rlimit = resource.getrlimit(resource.RLIMIT_NOFILE)
resource.setrlimit(resource.RLIMIT_NOFILE, (64000, rlimit[1]))
import yaml
from utils.diffusion_utils import t_to_sigma as t_to_sigma_compl
from datasets.pdbbind import construct_loader
from utils.parsing import parse_train_args
from utils.training_mdn import train_mdn_epoch, test_mdn_epoch
from utils.utils import save_yaml_file, get_optimizer_and_scheduler, get_model, ExponentialMovingAverage
import datetime
def train(args, model, optimizer, scheduler, ema_weights,train_loader, val_loader, t_to_sigma, run_dir,accelerator):
best_val_loss = math.inf
best_val_inference_value = math.inf if args.inference_earlystop_goal == 'min' else 0
best_epoch = 0
best_val_inference_epoch = 0
early_stop_patience = args.mdn_early_stop_patience
patience_count = 0
logger.info("Starting training...")
for epoch in range(args.n_epochs):
if epoch % 5 == 0: logger.info("Run name: {}".foramt(args.run_name))
logs = {}
#################trainging ########################
train_losses = train_mdn_epoch(model, train_loader, optimizer, device,accelerator,ema_weights)
# accelerator.wait_for_everyone()
if accelerator.is_local_main_process:
nowtime = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')
logger.info(f"epoch【{epoch}】@{nowtime} --> train_metric=")
logger.info("Epoch {}: Training loss {:.4f}"
.format(epoch, train_losses['loss'],flush=True))
# accelerator.wait_for_everyone()
# unwrapped_model = accelerator.unwrap_model(model)
ema_weights.store(model.parameters())
if args.use_ema: ema_weights.copy_to(model.parameters()) # load ema parameters into model for running validation and inference
############### trainging end#######################
val_losses = test_mdn_epoch(model, val_loader, device, accelerator,args.test_sigma_intervals)
#####################
accelerator.wait_for_everyone()
if accelerator.is_local_main_process:
nowtime = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')
logger.info(f"epoch【{epoch}】@{nowtime} --> eval_metric=")
logger.info("Epoch {}: Validation loss {:.4f} "
.format(epoch, val_losses['loss']))
if not args.use_ema: ema_weights.copy_to(model.parameters())
accelerator.wait_for_everyone()
# ema weight state dict
unwrapped_model = accelerator.unwrap_model(model)
ema_state_dict = copy.deepcopy(unwrapped_model.state_dict() if device.type == 'cuda' else unwrapped_model.state_dict())
# last model weight state dict
ema_weights.restore(model.parameters())
accelerator.wait_for_everyone()
unwrapped_model = accelerator.unwrap_model(model)
# ema_state_dict = copy.deepcopy(unwrapped_model.state_dict() if device.type == 'cuda' else unwrapped_model.state_dict())
state_dict = unwrapped_model.state_dict() if device.type == 'cuda' else unwrapped_model.state_dict()
if accelerator.is_local_main_process:
# accelerator.wait_for_everyone()
if args.wandb:
logs.update({'train_' + k: v for k, v in train_losses.items()})
logs.update({'val_' + k: v for k, v in val_losses.items()})
logs['current_lr'] = optimizer.param_groups[0]['lr']
wandb.log(logs, step=epoch + 1)
# if args.inference_earlystop_metric in logs.keys() and \
# (args.inference_earlystop_goal == 'min' and logs[args.inference_earlystop_metric] <= best_val_inference_value or
# args.inference_earlystop_goal == 'max' and logs[args.inference_earlystop_metric] >= best_val_inference_value):
# best_val_inference_value = logs[args.inference_earlystop_metric]
# best_val_inference_epoch = epoch
# torch.save(state_dict, os.path.join(run_dir, 'best_inference_epoch_model.pt'))
# torch.save(ema_state_dict, os.path.join(run_dir, 'best_ema_inference_epoch_model.pt'))
patience_count += 1
if val_losses['loss'] <= best_val_loss:
patience_count =0
best_val_loss = val_losses['loss']
best_epoch = epoch
torch.save(state_dict, os.path.join(run_dir, 'best_model.pt'))
torch.save(ema_state_dict, os.path.join(run_dir, 'best_ema_model.pt'))
if patience_count == early_stop_patience:
logger.info(f"Early stopping at epoch {epoch}")
break
if scheduler:
if args.val_inference_freq is not None:
scheduler.step(best_val_inference_value)
else:
scheduler.step(val_losses['loss'])
if accelerator.is_local_main_process:
# accelerator.wait_for_everyone()
# unwrapped_optimizer = accelerator.unwrap_model(optimizer)
torch.save({
'epoch': epoch,
'model': state_dict,
'optimizer': optimizer.state_dict(),
'ema_weights': ema_weights.state_dict(),
}, os.path.join(run_dir, 'last_model.pt'))
if accelerator.is_local_main_process:
logger.info("Best Validation Loss {} on Epoch {}".format(best_val_loss, best_epoch))
logger.info("Best inference metric {} on Epoch {}".format(best_val_inference_value, best_val_inference_epoch))
if args.wandb:
wandb.finish()
# from accelerate.utils import DummyOptim, DummyScheduler, set_seed
def main_function():
import typing
args = parse_train_args()
if args.config:
config_dict = yaml.load(args.config, Loader=yaml.FullLoader)
arg_dict = args.__dict__
for key, value in config_dict.items():
if isinstance(value, list):
for v in value:
arg_dict[key].append(v)
elif isinstance(value, typing.Dict):
arg_dict[key] = value['value']
# logger.info(value['value'])
else:
arg_dict[key] = value
# args.config = args.config.name
# logger.info(args)
args.run_name =args.run_name + datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')
assert (args.inference_earlystop_goal == 'max' or args.inference_earlystop_goal == 'min')
if args.val_inference_freq is not None and args.scheduler is not None:
assert (args.scheduler_patience > args.val_inference_freq) # otherwise we will just stop training after args.scheduler_patience epochs
if args.cudnn_benchmark:
torch.backends.cudnn.benchmark = True
if accelerator.is_local_main_process:
# args.run_name =args.run_name + datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')
if args.wandb:
wandb.login(key = 'your key')
wandb.init(
entity='SurfDock',
settings=wandb.Settings(start_method="fork"),
project=args.project,
name=args.run_name ,
dir = args.wandb_dir,
config=args
)
# wandb.log({'numel': numel})
# construct loader
t_to_sigma = partial(t_to_sigma_compl, args=args)
train_loader, val_loader = construct_loader(args, t_to_sigma)
model = get_model(args, device, t_to_sigma=t_to_sigma,model_type = args.model_type)
# get_model(confidence_model_args, device, t_to_sigma=t_to_sigma, no_parallel=True,
# mdn_mode=True)
optimizer, scheduler = get_optimizer_and_scheduler(args,model, accelerator,scheduler_mode=args.inference_earlystop_goal if args.val_inference_freq is not None else 'min')
ema_weights = ExponentialMovingAverage(model.parameters(),decay=args.ema_rate)
#################################################
if args.restart_dir:
try:
dict = torch.load(f'{args.restart_dir}/last_model.pt', map_location=torch.device('cpu'))
if args.restart_lr is not None: dict['optimizer']['param_groups'][0]['lr'] = args.restart_lr
optimizer.load_state_dict(dict['optimizer'])
model.load_state_dict(dict['model'], strict=True)
if hasattr(args, 'ema_rate'):
ema_weights.load_state_dict(dict['ema_weights'], device=device)
logger.info(f"Restarting from epoch {dict['epoch']}")
except Exception as e:
logger.info(f"Exception: {e}")
dict = torch.load(f'{args.restart_dir}/best_model.pt', map_location=torch.device('cpu'))
model.module.load_state_dict(dict, strict=True)
logger.info("Due to exception had to take the best epoch and no optimiser")
#################################################
model = accelerator.prepare(model)
optimizer, train_loader, val_loader, scheduler = accelerator.prepare(
optimizer,train_loader, val_loader, scheduler)
numel = sum([p.numel() for p in model.parameters()])
logger.info(f'Model with {numel} parameters')
# record parameters
run_dir = os.path.join(args.log_dir, args.run_name)
yaml_file_name = os.path.join(run_dir, 'model_parameters.yml')
save_yaml_file(yaml_file_name, args.__dict__)
args.device = device
train(args, model, optimizer, scheduler, ema_weights,train_loader, val_loader, t_to_sigma, run_dir,accelerator)
# if args.wandb:
# wandb.finish()
if __name__ == '__main__':
from accelerate import Accelerator
# from accelerate import Accelerator
from accelerate.utils import DistributedDataParallelKwargs
# kwargs = DistributedDataParallelKwargs(find_unused_parameters=True)
# accelerator = Accelerator(kwargs_handlers=[kwargs])
from accelerate.utils import set_seed
accelerator = Accelerator()
device = accelerator.device
set_seed(42)
# accelerator = Accelerator(mixed_precision=mixed_precision)
logger.info(f'device {str(accelerator.device)} is used!')
# device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
main_function()
# exit()