import copy import math import os import sys 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 SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__)) PROJECT_DIR = os.path.dirname(SCRIPT_DIR) MODEL_DIR = os.path.join(PROJECT_DIR, "model") if MODEL_DIR not in sys.path: sys.path.insert(0, MODEL_DIR) 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()