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Eff_NLVR.py
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Eff_NLVR.py
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import argparse
import os
import sys
import math
import ruamel.yaml as yaml
import numpy as np
import random
import time
import datetime
import json
from pathlib import Path
import json
import pickle
import torch
import torch.backends.cudnn as cudnn
import torch.distributed as dist
from efficient_models.model_nlvr import EffXVLMForNLVR
from models.model_nlvr import XVLMForNLVR
from torch.nn import MSELoss,KLDivLoss
import utils
from dataset import create_dataset, create_sampler, create_loader, build_tokenizer
from scheduler import create_scheduler
from optim import create_optimizer,create_L0_optimizer
def get_kd_loss(student_reps=None,teacher_reps=None,is_attn=False,loss=None,device='cuda',is_img=False):
kd_loss = 0
if is_attn:
for student_att, teacher_att in zip(student_reps, teacher_reps):
student_att = torch.where(student_att <= -1e2, torch.zeros_like(student_att).to(device),
student_att)
teacher_att = torch.where(teacher_att <= -1e2, torch.zeros_like(teacher_att).to(device),
teacher_att)
kd_loss += loss(student_att, teacher_att) * student_att.shape[-1]
elif is_img:
layer = 0
for student_rep, teacher_rep in zip(student_reps, teacher_reps):
if layer == 6:
pass
else:
kd_loss += loss(student_rep, teacher_rep)
layer += 1
else:
for student_rep, teacher_rep in zip(student_reps, teacher_reps):
kd_loss += loss(student_rep, teacher_rep)
return kd_loss
def soft_cross_entropy(predicts, targets):
kl_loss = KLDivLoss(reduction='batchmean')
student_likelihood = torch.nn.functional.log_softmax(predicts, dim=-1)
targets_prob = torch.nn.functional.softmax(targets, dim=-1)
return kl_loss(student_likelihood.view(-1,predicts.shape[-1]),targets_prob.view(-1,targets.shape[-1]))
def get_cor_teacher(teacher_reps,student_reps,is_attn=False):
teacher_reps = [teacher_rep.detach() for teacher_rep in teacher_reps]
teacher_layer_num = len(teacher_reps)
student_layer_num = len(student_reps)
if is_attn:
assert teacher_layer_num % student_layer_num == 0
layers_per_block = int(teacher_layer_num / student_layer_num)
new_teacher_reps = [teacher_reps[i * layers_per_block + layers_per_block - 1]
for i in range(student_layer_num)]
else:
assert (teacher_layer_num-1) % (student_layer_num-1) == 0
layers_per_block = int((teacher_layer_num-1) / (student_layer_num-1))
new_teacher_reps = [teacher_reps[i * layers_per_block] for i in range(student_layer_num)]
return new_teacher_reps
def train(teacher_model,model, data_loader, optimizer_list, tokenizer,global_step,epoch, device, scheduler):
model.train()
with torch.no_grad():
teacher_model.eval()
metric_logger = utils.MetricLogger(delimiter=" ")
metric_logger.add_meter('lr', utils.SmoothedValue(window_size=50, fmt='{value:.6f}'))
metric_logger.add_meter('loss_samll', utils.SmoothedValue(window_size=50, fmt='{value:.4f}'))
metric_logger.add_meter('loss_img_kd', utils.SmoothedValue(window_size=50, fmt='{value:.4f}'))
metric_logger.add_meter('loss_text_kd', utils.SmoothedValue(window_size=50, fmt='{value:.4f}'))
metric_logger.add_meter('loss_cross_kd', utils.SmoothedValue(window_size=50, fmt='{value:.4f}'))
metric_logger.add_meter('lagrangian_loss', utils.SmoothedValue(window_size=1, fmt='{value:.4f}'))
header = 'Train Epoch: [{}]'.format(epoch)
print_freq = 50
step_size = 100
optimizer,l0_optimizer,lagrangian_optimizer = optimizer_list
for i, (image0, image1, text, targets) in enumerate(metric_logger.log_every(data_loader, print_freq, header)):
images = torch.cat([image0, image1], dim=0)
images, targets = images.to(device), targets.to(device)
text_inputs = tokenizer(text, padding='longest', return_tensors="pt").to(device)
student_outputs = model(images, text_inputs.input_ids, text_inputs.attention_mask, targets=targets, train=True,output_attentions=True,output_hidden_states=True)
with torch.no_grad():
teacher_outputs = teacher_model(images, text_inputs.input_ids, text_inputs.attention_mask, targets=targets, train=True,output_attentions=True,output_hidden_states=True)
student_hidden = student_outputs['hidden_dict']
teacher_hidden = teacher_outputs['hidden_dict']
student_attentions = student_outputs['attention_dict']
teacher_attentions = teacher_outputs['attention_dict']
#just for cross kd
student_cross_attentions = student_outputs['cross_attention_dict']
teacher_cross_attentions = teacher_outputs['cross_attention_dict']
student_logits = student_outputs['logits_dict']
teacher_logits = teacher_outputs['logits_dict']
mse_loss = MSELoss()
#text kd
student_text_hidden = student_hidden['text_hidden_states']
teacher_text_hidden = teacher_hidden['text_hidden_states']
teacher_text_hidden = get_cor_teacher(teacher_text_hidden,student_text_hidden)
#后6层属于cross_encoder
student_cross_hidden = student_text_hidden[4:]
teacher_cross_hidden = teacher_text_hidden[4:]
student_text_attn = student_attentions['text_attentions']
teacher_text_attn = teacher_attentions['text_attentions']
teacher_text_attn = get_cor_teacher(teacher_text_attn,student_text_attn,is_attn=True)
student_cross_selfattention = student_text_attn[3:]
teacher_cross_selfattention = teacher_text_attn[3:]
student_cross_attn = student_cross_attentions['cross_attentions']
teacher_cross_attn = teacher_cross_attentions['cross_attentions']
teacher_cross_attn = get_cor_teacher(teacher_cross_attn,student_cross_attn,is_attn=True)
text_hidden_loss = get_kd_loss(student_text_hidden[:4],teacher_text_hidden[:4],False,mse_loss,device)
text_attention_loss = get_kd_loss(student_text_attn[:3],teacher_text_attn[:3],True,mse_loss,device)
cross_hidden_loss = get_kd_loss(student_cross_hidden,teacher_cross_hidden,False,mse_loss,device)
cross_self_attention_loss = get_kd_loss(student_cross_selfattention,teacher_cross_selfattention,True,mse_loss,device)
cross_attention_loss = get_kd_loss(student_cross_attn,teacher_cross_attn,True,mse_loss,device)
#image kd
student_image_hidden = student_hidden['image_hidden_states']
teacher_image_hidden = teacher_hidden['image_hidden_states']
teacher_image_hidden = get_cor_teacher(teacher_image_hidden,student_image_hidden)
student_image_attn = student_attentions['image_attentions']
teacher_image_attn = teacher_attentions['image_attentions']
teacher_image_attn = get_cor_teacher(teacher_image_attn,student_image_attn,is_attn=True)
image_hidden_loss = get_kd_loss(student_image_hidden,teacher_image_hidden,False,mse_loss,device,is_img=True)
image_attention_loss = get_kd_loss(student_image_attn,teacher_image_attn,True,mse_loss,device)
student_logits = student_logits['cls_head_logits']
teacher_logits = teacher_logits['cls_head_logits']
logits_loss = soft_cross_entropy(student_logits/args.temperature, teacher_logits/args.temperature)
loss_samll = student_outputs['loss']
loss_text_kd = text_attention_loss + text_hidden_loss
loss_img_kd = image_attention_loss + image_hidden_loss * 0.1
loss_cross_kd = (cross_hidden_loss + cross_self_attention_loss + cross_attention_loss) *0.5
loss_kd = logits_loss + loss_text_kd + (loss_img_kd + loss_cross_kd) * 0.33 #TODO:随便调调
optimizer.zero_grad()
loss = 0.8 * loss_samll + 0.2 * loss_kd
#L0 regularisation
lagrangian_loss = None
lagrangian_loss, _, _ = model.module.l0_module.lagrangian_regularization(global_step)
loss += lagrangian_loss
#整体loss的backward
loss.backward()
#更新梯度
optimizer.step()
l0_optimizer.step()
lagrangian_optimizer.step()
scheduler.step()
model.module.l0_module.constrain_parameters()
# module部分的zero_grad
model.zero_grad()
model.module.l0_module.zero_grad()
#optimizer部分的zero_grad
optimizer.zero_grad()
l0_optimizer.zero_grad()
lagrangian_optimizer.zero_grad()
global_step += 1
metric_logger.update(lr=optimizer.param_groups[0]["lr"])
metric_logger.update(loss_samll=loss_samll.item())
metric_logger.update(loss_img_kd=loss_img_kd.item())
metric_logger.update(loss_text_kd=loss_text_kd.item())
metric_logger.update(loss_cross_kd=loss_cross_kd.item())
metric_logger.update(lagrangian_loss=lagrangian_loss.item())
# gather the stats from all processes
metric_logger.synchronize_between_processes()
print("Averaged stats:", metric_logger.global_avg())
return {k: "{:.5f}".format(meter.global_avg) for k, meter in metric_logger.meters.items()}
@torch.no_grad()
def evaluate(model, data_loader, tokenizer, device):
model.eval()
metric_logger = utils.MetricLogger(delimiter=" ")
header = 'Evaluation:'
print_freq = 50
for image0, image1, text, targets in metric_logger.log_every(data_loader, print_freq, header):
images = torch.cat([image0, image1], dim=0)
images, targets = images.to(device), targets.to(device)
text_inputs = tokenizer(text, padding='longest', return_tensors="pt").to(device)
prediction = model(images, text_inputs.input_ids, text_inputs.attention_mask, targets=targets, train=False)
_, pred_class = prediction.max(1)
accuracy = (targets == pred_class).sum() / targets.size(0)
metric_logger.meters['acc'].update(accuracy.item(), n=image0.size(0))
# gather the stats from all processes
metric_logger.synchronize_between_processes()
print("Averaged stats:", metric_logger.global_avg())
return {k: "{:.4f}".format(meter.global_avg) for k, meter in metric_logger.meters.items()}
def main(args, config):
utils.init_distributed_mode(args)
device = torch.device(args.device)
world_size = utils.get_world_size()
if args.epoch > 0:
config['schedular']['epochs'] = args.epoch
print(f"### set epochs to: {args.epoch}", flush=True)
if args.bs > 0:
config['batch_size'] = args.bs // world_size
if args.lr != -1:
config['schedular']['lr'] = args.lr
config['optimizer']['lr'] = args.lr
if args.reg_lr != -1:
config['optimizer']['reg_learning_rate'] = args.reg_lr
if args.sparsity is not None:
config['sparsity'] = eval(args.sparsity)
seed = args.seed + utils.get_rank()
torch.manual_seed(seed)
np.random.seed(seed)
random.seed(seed)
cudnn.benchmark = True
print("Creating dataset")
train_dataset, val_dataset, test_dataset = create_dataset('nlvr', config)
datasets = [train_dataset, val_dataset, test_dataset]
train_dataset_size = len(train_dataset)
train_batch_size = config['batch_size']
world_size = utils.get_world_size()
if utils.is_main_process():
print(f"### data {train_dataset_size}, batch size, {train_batch_size} x {world_size}")
print(f"### test data {len(test_dataset)}", flush=True)
if args.distributed:
num_tasks = utils.get_world_size()
global_rank = utils.get_rank()
samplers = create_sampler(datasets, [True, False, False], num_tasks, global_rank)
else:
samplers = [None, None, None]
train_loader, val_loader, test_loader = create_loader(datasets, samplers, batch_size=[config['batch_size']] * 3,
num_workers=[4, 4, 4], is_trains=[True, False, False],
collate_fns=[None, None, None])
print("Creating model")
model = EffXVLMForNLVR(config=config)
model.load_pretrained(args.checkpoint, config, load_nlvr_pretrain=args.load_nlvr_pretrain, is_eval=args.evaluate)
model = model.to(device)
print('Creating teacher model',flush=True)
teacher_config = config
teacher_config['vision_config'] = 'configs/config_clipvitB.json'
teacher_config['text_num_hidden_layers'] = 12
teacher_model = XVLMForNLVR(config=teacher_config)
if args.teacher_chkpt:
teacher_model.load_pretrained(args.teacher_chkpt, teacher_config,is_eval=True,load_nlvr_pretrain=True)
teacher_model.to(device)
print("### Total Params: ", sum(p.numel() for p in model.parameters() if p.requires_grad))
print("### Total Teacher Params: ", sum(p.numel() for p in teacher_model.parameters() if p.requires_grad))
model_without_ddp = model
if args.distributed:
model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu])
model_without_ddp = model.module
tokenizer = build_tokenizer(config['text_encoder'])
print("### output_dir, ", args.output_dir, flush=True)
start_time = time.time()
if args.evaluate:
print("Start evaluating")
val_stats = evaluate(model, val_loader, tokenizer, device)
test_stats = evaluate(model, test_loader, tokenizer, device)
if utils.is_main_process():
log_stats = {**{f'val_{k}': v for k, v in val_stats.items()},
**{f'test_{k}': v for k, v in test_stats.items()}}
print(log_stats)
dist.barrier()
else:
print("Start training", flush=True)
arg_opt = utils.AttrDict(config['optimizer'])
optimizer = create_optimizer(arg_opt, model)
arg_sche = utils.AttrDict(config['schedular'])
arg_sche['step_per_epoch'] = math.ceil(train_dataset_size/(train_batch_size*world_size))
lr_scheduler = create_scheduler(arg_sche, optimizer)
l0_optimizer, lagrangian_optimizer = create_L0_optimizer(arg_opt,model_without_ddp.l0_module)
optimizer_list = (optimizer,l0_optimizer,lagrangian_optimizer)
arg_l0 = utils.AttrDict(config['L0_schedular'])
if arg_l0['lagrangian_warmup_epochs'] < 1:
lagrangian_warmup_steps = int(arg_l0['lagrangian_warmup_epochs'] * arg_l0['epochs'] * arg_sche['step_per_epoch'])
else:
lagrangian_warmup_steps = arg_l0['lagrangian_warmup_epochs'] * arg_sche['step_per_epoch']
model_without_ddp.l0_module.set_lagrangian_warmup_steps(lagrangian_warmup_steps)
# prepruning_finetune_steps = arg_l0['prepruning_finetune_steps'] * arg_sche['step_per_epoch']
# print(f"Prepruning finetune steps: {prepruning_finetune_steps}")
print(f"Lagrangian warmup steps: {lagrangian_warmup_steps}")
max_epoch = config['schedular']['epochs']
best = 0
best_epoch = 0
global_step = 0
for epoch in range(0, max_epoch):
if args.distributed:
train_loader.sampler.set_epoch(epoch)
train_stats = train(teacher_model,model, train_loader, optimizer_list, tokenizer, global_step,epoch, device, lr_scheduler)
val_stats = evaluate(model, val_loader, tokenizer, device)
test_stats = evaluate(model, test_loader, tokenizer, device)
with torch.no_grad():
zs = model_without_ddp.l0_module.forward(training=False)
pruned_model_size_info = model_without_ddp.l0_module.calculate_model_size(zs)
if utils.is_main_process():
log_stats = {**{f'train_{k}': v for k, v in train_stats.items()},
**{f'val_{k}': v for k, v in val_stats.items()},
**{f'test_{k}': v for k, v in test_stats.items()},
'epoch': epoch,
'cur_sparsity':pruned_model_size_info['pruned_model_sparsity']
}
if float(val_stats['acc']) > best:
save_obj = {
'model': model_without_ddp.state_dict(),
# 'optimizer': optimizer.state_dict(),
# 'lr_scheduler': lr_scheduler.state_dict(),
'config': config,
# 'epoch': epoch,
}
torch.save(save_obj, os.path.join(args.output_dir, 'checkpoint_best.pth'))
best = float(val_stats['acc'])
best_epoch = epoch
with open(os.path.join(args.output_dir, "log.txt"), "a") as f:
f.write(json.dumps(log_stats) + "\n")
dist.barrier()
if utils.is_main_process():
with open(os.path.join(args.output_dir, "log.txt"), "a") as f:
f.write("best epoch: %d" % best_epoch)
os.system(f"cat {args.output_dir}/log.txt")
total_time = time.time() - start_time
total_time_str = str(datetime.timedelta(seconds=int(total_time)))
print('### Time {}'.format(total_time_str))
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--checkpoint', type=str, required=True)
parser.add_argument('--config', default='./configs/NLVR.yaml')
parser.add_argument('--output_dir', default='output/nlvr')
parser.add_argument('--teacher_chkpt',default=None,type=str)
parser.add_argument('--device', default='cuda')
parser.add_argument('--seed', default=42, type=int)
parser.add_argument('--world_size', default=1, type=int, help='number of distributed processes')
parser.add_argument('--dist_url', default='env://', help='url used to set up distributed training')
parser.add_argument('--distributed', action='store_false')
parser.add_argument('--temperature',default=1.0,type=float)
parser.add_argument('--load_nlvr_pretrain', action='store_true')
parser.add_argument('--epoch', default=-1, type=int)
parser.add_argument('--bs', default=-1, type=int, help="for each gpu, batch_size = bs // num_gpus")
parser.add_argument('--evaluate', action='store_true')
parser.add_argument('--lr', default=-1,type=float)
parser.add_argument('--reg_lr', default=-1,type=float)
parser.add_argument('--sparsity', default=None,type=str)
args = parser.parse_args()
config = yaml.load(open(args.config, 'r'), Loader=yaml.Loader)
Path(args.output_dir).mkdir(parents=True, exist_ok=True)
yaml.dump(config, open(os.path.join(args.output_dir, 'config.yaml'), 'w'))
main(args, config)