#!user/bin/env python # -*- coding:utf-8 -*- import argparse import json import os import datetime import pickle import random import torch import torch.backends.cudnn as cudnn import torch.distributed as dist import torch.nn as nn import torch.nn.functional as F import torch.optim as optim from bisect import bisect from math import fabs from torch.optim import lr_scheduler from torch.utils.data import DataLoader from tqdm import tqdm from transformers import LxmertTokenizer from dist_train import get_world_size, get_rank, get_local_rank, barrier, reduce_sum import numpy as np from transformers.tokenization_utils_base import ENCODE_KWARGS_DOCSTRING from config4LXMT5_DDP import args from dataset4LXMT5 import KgDataset,my_collate,my_val_collate from dataset_val4LXMT5 import KgDatasetVal if args.visualBERT: from model_ViB2T5 import T5tokenizer, ViBT52T5, LXMtokenizer else: from model_LXM2T5 import T5tokenizer, LXMT52T5, LXMtokenizer from transformers import get_linear_schedule_with_warmup from transformers import LxmertConfig, LxmertTokenizer, LxmertModel,BertTokenizer dist.init_process_group(backend='nccl',timeout=datetime.timedelta(seconds=5400)) torch.cuda.set_device(args.local_rank) # LR = 1e-5 LR = args.learning_rate LR_LXM = args.learning_rate_LXM # LR = 1e-4 torch.multiprocessing.set_sharing_strategy('file_system') torch.cuda.set_device(get_local_rank()) device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") def reduce_tensor(tensor: torch.Tensor): rt = tensor.clone().float() dist.all_reduce(rt,op=dist.ReduceOp.SUM) rt /= dist.get_world_size()#.float() return rt def set_seed(rank): random.seed(args.seed+rank) np.random.seed(args.seed+rank) torch.manual_seed(args.seed+rank) torch.cuda.manual_seed(args.seed+rank) torch.cuda.manual_seed_all(args.seed+rank) torch.backends.cudnn.deterministic = True set_seed(get_rank()) def cal_acc_multi(ground_truth, preds, return_id = False): all_num = len(ground_truth) acc_num = 0 ids = [] temp = [] for i, answer_id in enumerate(ground_truth): pred = preds[i] # ids.append([i, int(pred)]) cnt = 0 for aid in answer_id: if pred == aid: cnt += 1 if cnt ==1: acc_num += 1/3 elif cnt == 2: acc_num += 2/3 elif cnt > 2: acc_num += 1 if return_id: return acc_num / all_num, ids else: return acc_num, all_num def cal_acc(ground_truth, preds, return_id = False): all_num = len(ground_truth) acc_num = 0 ids = [] temp = [] for i, answer_id in enumerate(ground_truth): pred = preds[i] # ids.append([i, int(pred)]) cnt = 0 for aid in answer_id: if pred == aid: acc_num += 1 if return_id: return acc_num / all_num, ids else: return acc_num, all_num def train(): if not args.describe: print('please set the description for the saved-model name! use --describe !') assert 1==0 else: model_name=args.describe if not args.pretrain: train_dataset = KgDataset(val=False) train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset) train_dataloader = DataLoader(dataset=train_dataset, batch_size=args.batch_size, sampler=train_sampler,#shuffle=True, num_workers=0, collate_fn=my_collate)#, pin_memory=True) if args.validate: test_dataset = KgDatasetVal(val=False) test_dataloader = DataLoader(dataset=test_dataset, batch_size=args.batch_size, shuffle=False, num_workers=0, collate_fn=my_val_collate) else: train_dataset = KgDataset(val=False) train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset) train_dataloader = DataLoader(dataset=train_dataset, batch_size=args.batch_size,#pin_memory=True, num_workers=0, collate_fn=my_collate, sampler=train_sampler)#shuffle=True, # num_workers=0, collate_fn=my_collate_pretrain, sampler=train_sampler)#shuffle=True, if args.validate: test_dataset = KgDatasetVal(val=False) test_dataloader = DataLoader(dataset=test_dataset, batch_size=args.batch_size, num_workers=0, collate_fn=my_val_collate, shuffle=False)#sampler=test_sampler) if args.pretrain: if get_rank() == 0: print('pre-training!') if args.visualBERT: model= ViBT52T5() else: model = LXMT52T5() else: if get_rank() == 0: print('fine-tuning!') if args.visualBERT: model= ViBT52T5() else: model = LXMT52T5() model = nn.SyncBatchNorm.convert_sync_batchnorm(model) model = model.to(device) if get_world_size() > 1: if get_rank() == 0: print("Let's use", get_world_size(), "GPUs!") model = nn.parallel.DistributedDataParallel(model, device_ids=[get_local_rank()], output_device=get_local_rank(),find_unused_parameters=True) print(model.named_modules) if get_world_size() > 1: if args.visualBERT: optimizer = optim.AdamW([ {'params': model.module.T5model.parameters(), 'lr': LR}, {'params': model.module.ViBmodel.parameters(), 'lr': LR_LXM}, {'params': model.module.mapping.parameters(), 'lr': LR_LXM}, ]) else: optimizer = optim.AdamW([ {'params': model.module.T5model.parameters(), 'lr': LR}, {'params': model.module.LXMmodel.parameters(), 'lr': LR_LXM}, {'params': model.module.mapping.parameters(), 'lr': LR_LXM}, ]) else: if args.visualBERT: optimizer = optim.AdamW([ {'params': model.T5model.parameters(), 'lr': LR}, {'params': model.ViBmodel.parameters(), 'lr': LR_LXM}, {'params': model.mapping.parameters(), 'lr': LR_LXM}, ]) else: optimizer = optim.AdamW([ {'params': model.T5model.parameters(), 'lr': LR}, {'params': model.LXMmodel.parameters(), 'lr': LR_LXM}, {'params': model.mapping.parameters(), 'lr': LR_LXM}, ]) if args.pretrain: steps_num = 100000 else: steps_num = 20000 args.num_epochs = steps_num // (len(train_dataset) / (args.batch_size * get_world_size())) \ if len(train_dataset) % args.batch_size == 0 \ else (steps_num // (len(train_dataset) / (args.batch_size * get_world_size())) )+1 args.num_epochs = int(args.num_epochs) if get_rank() == 0: print('total_epoch', args.num_epochs) print('total_steps', "we set steps=",steps_num) print('warmup_steps', int(steps_num/10)) #0.05*total_steps) scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=int(steps_num/10), #0.01 * total_steps, num_training_steps=steps_num) if args.load_pthpath == "": start_epoch = 0 else: if get_rank() == 0: print('load model') start_epoch = 0 if get_world_size() > 1: model.module.load_state_dict(torch.load(args.load_pthpath)) else: model.load_state_dict(torch.load(args.load_pthpath)) best_acc = 0 best_epoch = 0 best_acc_t = 0 best_epoch_t = 0 best_acc_t3 = 0 step_ind = 0 for epoch in range(start_epoch, args.num_epochs): train_preds_trip = [] train_sampler.set_epoch(epoch) train_answers_trip = [] s=0 for batch_data in tqdm(train_dataloader): step_ind+=1 if get_rank()==0: print("step_ind",step_ind) s=s+1 visual_faetures = torch.from_numpy(np.array(batch_data['img'], dtype=float)).float().to(device) spatial_features = torch.tensor(np.array(batch_data['spatial'])).float().to(device) if 1: T5_input_id = torch.stack(batch_data['T5_input_ids']).to(device) T5_input_mask = torch.stack(batch_data['T5_input_masks']).to(device) LXM_input_id = torch.stack(batch_data['LXM_input_ids']).to(device) LXM_input_mask = torch.stack(batch_data['LXM_input_masks']).to(device) LXM_token_type_ids = torch.stack(batch_data['LXM_token_type_ids']).to(device) T5_target_id = torch.stack(batch_data['T5_target_ids']).to(device) neg100 = torch.ones_like(T5_target_id)*(-100) T5_target_id = torch.where(T5_target_id==T5tokenizer.pad_token_id,neg100, T5_target_id) model.zero_grad() optimizer.zero_grad() if args.pretrain: outputs = model(train=True, LXM_source_ids=LXM_input_id, LXM_source_masks=LXM_input_mask,T5_source_ids=T5_input_id, T5_source_masks=T5_input_mask,token_type_ids=LXM_token_type_ids, visual_features=visual_faetures, spatial_features=spatial_features,T5_target_ids=T5_target_id)#,T5_target_masks=None else: outputs = model(train=True, LXM_source_ids=LXM_input_id, LXM_source_masks=LXM_input_mask,T5_source_ids=T5_input_id, T5_source_masks=T5_input_mask,token_type_ids=LXM_token_type_ids, visual_features=visual_faetures, spatial_features=spatial_features,T5_target_ids=T5_target_id)#,T5_target_masks=None loss = outputs.loss loss_stat = torch.mean(loss.detach()).item() if get_rank() == 0: print("loss on GPU0", loss_stat) loss.sum().backward() optimizer.step() scheduler.step() with torch.no_grad(): if args.pretrain: eval_outputs = model(train=False, LXM_source_ids=LXM_input_id, LXM_source_masks=LXM_input_mask,T5_source_ids=T5_input_id, T5_source_masks=T5_input_mask,token_type_ids=LXM_token_type_ids, visual_features=visual_faetures, spatial_features=spatial_features,T5_target_ids=T5_target_id)#,T5_target_masks=None else: eval_outputs = model(train=False, LXM_source_ids=LXM_input_id, LXM_source_masks=LXM_input_mask,T5_source_ids=T5_input_id, T5_source_masks=T5_input_mask,token_type_ids=LXM_token_type_ids, visual_features=visual_faetures, spatial_features=spatial_features,T5_target_ids=T5_target_id)#,T5_target_masks=None trip_predict = T5tokenizer.batch_decode(eval_outputs, skip_special_tokens=True) if get_rank() == 0: print('epoch', epoch, 'step', s, '>>>', '\tans:', batch_data['ans'][0], 'pred:', trip_predict[0]) for i, pre in enumerate(batch_data['ans']): train_answers_trip.append(batch_data['ans'][i]) train_preds_trip.append(trip_predict[i]) barrier() barrier() if args.dataset == 'krvqa': train_acc_1_num, train_total_1_num = cal_acc(train_answers_trip, train_preds_trip) train_reduce_acc_num=reduce_tensor(torch.tensor(train_acc_1_num).cuda(args.local_rank)).item() train_reduce_total_num=reduce_tensor(torch.tensor(train_total_1_num).cuda(args.local_rank)).item() train_acc_1_trip = train_reduce_acc_num/train_reduce_total_num if get_rank() == 0: # print("train_acc_1_trip all GPUs:", train_acc_1_trip) print('epoch %d train_loss = %.1f, acc_trip = %.4f' % (epoch, loss_stat,train_acc_1_trip)) else: train_acc_1_num, train_total_1_num = cal_acc_multi(train_answers_trip, train_preds_trip) train_reduce_acc_num=reduce_tensor(torch.tensor(train_acc_1_num).cuda(args.local_rank)).item() train_reduce_total_num=reduce_tensor(torch.tensor(train_total_1_num).cuda(args.local_rank)).item() train_acc_1_trip = train_reduce_acc_num/train_reduce_total_num if get_rank() == 0: print('epoch %d train_loss of GPU0= %.1f, acc_trip on all GPUs= %.4f' % (epoch, loss_stat, train_acc_1_trip)) barrier() if args.validate: model.eval() answers = [] # [batch_answers,...] preds = [] # [batch_preds,...] preds_trip = [] preds_trip_3 = [] answers_trip = [] id2pred_trip = {} print(f"\nValidation after epoch {epoch}:") for i, batch_data in enumerate(tqdm(test_dataloader)): with torch.no_grad(): val_T5_input_id = torch.stack(batch_data['T5_input_ids']).to(device) val_T5_input_mask = torch.stack(batch_data['T5_input_masks']).to(device) val_visual_faetures = torch.tensor(np.array(batch_data['img'])).float().to(device) val_spatial_features = torch.tensor(np.array(batch_data['spatial'])).float().to(device) val_LXM_input_id = torch.stack(batch_data['LXM_input_ids']).to(device) val_LXM_input_mask = torch.stack(batch_data['LXM_input_masks']).to(device) val_LXM_token_type_ids = torch.stack(batch_data['LXM_token_type_ids']).to(device) if args.pretrain: val_outputs = model(train=False, LXM_source_ids=val_LXM_input_id, LXM_source_masks=val_LXM_input_mask,T5_source_ids=val_T5_input_id, T5_source_masks=val_T5_input_mask,token_type_ids=val_LXM_token_type_ids, visual_features=val_visual_faetures, spatial_features=val_spatial_features,T5_target_ids=None)#,T5_target_masks=None else: val_outputs = model(train=False, LXM_source_ids=val_LXM_input_id, LXM_source_masks=val_LXM_input_mask,T5_source_ids=val_T5_input_id, T5_source_masks=val_T5_input_mask,token_type_ids=val_LXM_token_type_ids, visual_features=val_visual_faetures, spatial_features=val_spatial_features,T5_target_ids=None)#,T5_target_masks=None val_trip_predict = T5tokenizer.batch_decode(val_outputs, skip_special_tokens=True) for i, pre in enumerate(batch_data['ans']): preds_trip.append(val_trip_predict[i]) answers_trip.append(batch_data['ans'][i]) id2pred_trip[str(batch_data['id'][i])]=val_trip_predict[i] if args.dataset == 'krvqa': acc_1_num, total_1_num = cal_acc(answers_trip, preds_trip) reduce_acc_num=reduce_tensor(torch.tensor(acc_1_num).cuda(args.local_rank)).item() reduce_total_num=reduce_tensor(torch.tensor(total_1_num).cuda(args.local_rank)).item() acc_1_trip = reduce_acc_num/reduce_total_num if get_rank() == 0: print('epoch %d , acc_trip on all GPUs= %.4f' % (epoch, acc_1_trip)) else: acc_1_num, total_1_num = cal_acc_multi(answers_trip, preds_trip) reduce_acc_num=reduce_tensor(torch.tensor(acc_1_num).cuda(args.local_rank)).item() reduce_total_num=reduce_tensor(torch.tensor(total_1_num).cuda(args.local_rank)).item() acc_1_trip = reduce_acc_num/reduce_total_num if get_rank() == 0: print('epoch %d , acc_trip on all GPUs= %.4f' % (epoch, acc_1_trip)) if acc_1_trip > best_acc_t: best_acc_t = acc_1_trip best_epoch_t = epoch if not args.pretrain: if get_rank() == 0: f=open(args.model_dir+"/predictions.json", 'w') json.dump(id2pred_trip, f) f.close() print('saving model at epoch', epoch, '!!') if get_world_size() > 1: torch.save(model.module.state_dict(), args.model_dir+'/best_finetuned_model_'+model_name+'.pth') else: torch.save(model.state_dict(), args.model_dir+'/best_finetuned_model_'+model_name+'.pth') if get_rank() == 0: print("best_acc@1t={:.2%}, epoch{}\n\n".format(best_acc_t, best_epoch_t)) model.train() if args.pretrain: if get_rank() == 0: #对于预训练，那么每个模型都保存一下，以便后面选取合适的，或者进行相应分析。 if get_world_size() > 1: torch.save(model.module.state_dict(), args.model_dir+ '/model_for_epoch_%d.pth' % epoch) else: torch.save(model.state_dict(), args.model_dir+ '/model_for_epoch_%d.pth' % epoch) barrier() dist.destroy_process_group() if __name__ == "__main__": train()