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在使用GPU训练大模型时,往往会面临单卡显存不足的情况,这时候就希望通过多卡并行的形式来扩大显存。PyTorch主要提供了两个类来实现多卡并行分别是
关于这两者的区别和原理也有许多博客如Pytorch 并行训练(DP, DDP)的原理和应用; DDP系列第一篇:入门教程进行总结,这里就不在赘述了。不过总结来说的话:DP 比较简单,对小白比较友好,一行代码便可以搞定。DDP 每个进程对应一个独立的训练过程,且只对梯度等少量数据进行信息交换。每个进程包含独立的解释器和 GIL。
博主能力有限,很多原理上的东西看得不是特别懂,所以理解起来也比较肤浅,但是编程的时候一直没找到一套合适的蓝本,最终参考了很多网上的博客,吭哧吭哧写了一套不会报错的代码出来,下面把我个人的理解整理出来,不当之处希望大家指出,一起交流学习。后续可能会随着自己的理解的加深持续完善。
主要参考了以下一些博客:
增加参数local_rank来确定当前进程使用哪块GPU, 用于在每个进程中指定不同的device。
def parse():
parser = argparse.ArgumentParser()
parser.add_argument('--local_rank', type=int, default=0)
args = parser.parse_args()
return args
def main():
args = parse()
torch.cuda.set_device(args.local_rank)
torch.distributed.init_process_group(
'nccl',
init_method='env://'
)
device = torch.device(f'cuda:{args.local_rank}')
其中 torch.distributed.init_process_group 用于初始化GPU通信方式(NCCL)和参数的获取方式(env代表通过环境变量)。
假如model中用到了随机数种子来保证可复现性, 那么此时不能再用固定的常数作为seed, 否则会导致DDP中的所有进程都拥有一样的seed, 进而生成同态性的数据, 因此需要在程序中显示地设置随机种子点。
# 固定随机种子点
seed = np.random.randint(1, 10000)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
对于数据加载,在初始化 data loader 的时候需要使用到 torch.utils.data.distributed.DistributedSampler 这个函数:
train_dataset = ...
train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset, shuffle=True) # 这个sampler会自动分配数据到各个gpu上
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=opts.batch_size, sampler=train_sampler)
通过以上的函数便可以给每个进程一个不同的 sampler,告诉每个进程自己分别取哪些数据。
在每一个epoch开始的阶段需要为sampler重新设定eopch即:
for ep in range(total_epoch):
train_sampler.set_epoch(ep)
这样做的目的是:如果在DistributedSampler设置了shuffle,DistributedSampler使用当前epoch作为随机数种子,从而使得不同epoch下有不同的shuffle结果,但是在DistributedSampler源代码中默认的epoch为0,那么每次dataloader获取的shuffle都是相同的。所以,每次 epoch 开始前都需要要调用 sampler 的 set_epoch 方法,这样才能让数据集随机 shuffle 起来。
对于模型的处理主要包括模型初始化,将模型加载至CUDA;加载预训练权重;或利用主进程的权重 初始化所有的进程;将模型中的BN转换为SyncBN;设置模型并行。
由于 BN 层需要基于传入模型的数据计算均值和方差,造成普通 BN 在多卡模式下实际上就是单卡模式。此时需要使用 SyncBN 利用DDP的分布式计算接口来实现真正的多卡BN。
SyncBN利用分布式通讯接口在各卡间进行通讯,传输各自进程小 batch mean 和小 batch variance,在传输少量数据的基础上利用所有数据进行BN计算。
同时由于 SyncBN 用到 all_gather 这个分布式计算接口,而使用这个接口需要先初始化DDP环境,因此 SyncBN 需要在 DDP 环境初始化后初始化,但是要在 DDP 模型前就准备好。
最后由于 SyncBN 是直接搜索 model 中每个 module,如果这个 module 是 torch.nn.modules.batchnorm._BatchNorm 的子类,就将其替换为 SyncBN。因此如果你的 Normalization 层是自己定义的特殊类,没有继承过 _BatchNorm 类,那么convert_sync_batchnorm 是不支持的,需要你自己实现一个新的SyncBN!
def parse():
parser = argparse.ArgumentParser()
parser.add_argument('--local_rank', type=int, default=0)
parser.add_argument('--device', type=str, default='cuda', help='device id (i.e. 0 or 0,1 or cpu)')
parser.add_argument('--resume', type=str, default=None, help='specified the dir of saved models for resume the training')
args = parser.parse_args()
return args
args = parse() device = torch.device(args.device) model = mymodel().to(device) if args.resume: checkpoint = torch.load(model_save_path, map_location=device) model.load_state_dict(checkpoint['model']) else: save_path = 'initial_weights.pth' if opts.local_rank == 0: torch.save(model.state_dict(), save_path) dist.barrier() # 这里注意,一定要指定map_location参数,否则会导致第一块GPU占用更多资源 model.load_state_dict(torch.load(save_path, map_location=device)) ## 设置同步 model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model) ## 设置模型并行 model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank], output_device=args.local_rank, find_unused_parameters=True) ## 注意要使用find_unused_parameters=True,因为有时候模型里面定义的一些模块 在forward函数里面没有调用,如果不使用find_unused_parameters=True 会报错
在每一次需要输出或打印日志时都应该先使用opts.local_rank == 0
来判断,也就是在主进程才执行一些操作,不然日志或者打印的结果会非常混乱。
logger = None
if opts.local_rank == 0:
log_dir = os.path.join(opts.display_dir, 'logger', opts.name)
os.makedirs(log_dir, exist_ok=True)
log_path = os.path.join(log_dir, 'log.txt')
if os.path.exists(log_path):
os.remove(log_path)
logger = logger_config(log_path=log_path, logging_name='Timer')
logger.info('Parameter Space: ABS: {:.1f}, REL: {:.4f}'.format(count_parameters(MPF_model), count_parameters(MPF_model) / 1024 / 1024))
logger.info(MPF_model)
state = {'model':model.module.state_dict(),
'ep':ep,
'total_it':total_it}
save_path = os.path.join(self.model_dir, 'model_{:0>5d}.pth'.format(ep))
torch.save(state, save_path)
在保存模型是需要注意的是,保存的是{'model':model.module.state_dict()}
, 而不是我们之前的{'model':model.state_dict()}
, 因为在使用DDP后,原来的model会被封装为新的model的module属性里。
PyTorch为提供了一个很方便的启动器 torch.distributed.lunch 用于启动文件,所以可以将运行训练代码的方式调整成下面这样:
CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --nproc_per_node=2 train.py
train.py
import torch.optim as optim from create_dataset import * from utils import * from MPFNet_Trans_skip import MPFNet from options import * from saver import Saver, resume from time import time from tqdm import tqdm from optimizer import Optimizer import datetime import torch.distributed as dist def main(): # parse options parser = TrainOptions() opts = parser.parse() # define model, optimiser and scheduler torch.cuda.set_device(opts.local_rank) torch.distributed.init_process_group('nccl', init_method='env://') # device = torch.device(f'cuda:{opts.local_rank}') #device 这样的设置可能会有问题 device = torch.device(opts.gpu) # device = torch.device("cuda:{}".format(opts.gpu) if torch.cuda.is_available() else "cpu") # 固定随机种子 seed = np.random.randint(1, 10000) np.random.seed(seed) torch.manual_seed(seed) torch.cuda.manual_seed_all(seed) # define dataset train_dataset = MSRSData(opts, is_train=True) train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset, shuffle=True) train_loader = torch.utils.data.DataLoader( dataset=train_dataset, batch_size=opts.batch_size, num_workers = opts.nThreads, sampler=train_sampler, pin_memory=False, ) test_dataset = MSRSData(opts, is_train=False) test_sampler = torch.utils.data.distributed.DistributedSampler(test_dataset) test_loader = torch.utils.data.DataLoader( dataset=test_dataset, batch_size=12, sampler=test_sampler, num_workers = opts.nThreads, ) ## 先加载dataloader 计算每个epoch的的迭代步数 然后计算总的迭代步数 ep_iter = len(train_loader) max_iter = opts.n_ep * ep_iter if opts.local_rank == 0: print('Training iter: {}'.format(max_iter)) print(opts.local_rank) ## 初始化模型 MPF_model = MPFNet(opts.class_nb).to(device) momentum = 0.9 weight_decay = 5e-4 lr_start = 1e-3 # max_iter = 150000 power = 0.9 warmup_steps = 1000 warmup_start_lr = 1e-5 optimizer = Optimizer( model = MPF_model, lr0 = lr_start, momentum = momentum, wd = weight_decay, warmup_steps = warmup_steps, warmup_start_lr = warmup_start_lr, max_iter = max_iter, power = power) if opts.resume: if opts.local_rank == 0: MPF_model, ep, total_it = resume(MPF_model, opts.resume, device) optimizer = Optimizer( model = MPF_model, lr0 = lr_start, momentum = momentum, wd = weight_decay, warmup_steps = warmup_steps, warmup_start_lr = warmup_start_lr, max_iter = max_iter, power = power, it=total_it) lr = optimizer.get_lr() print('lr:{}'.format(lr)) else: model_dir = os.path.join(opts.result_dir, opts.name) os.makedirs(model_dir, exist_ok=True) save_path = os.path.join(model_dir, 'initial_weights.pth') if opts.local_rank == 0: torch.save(MPF_model.state_dict(), save_path) dist.barrier() # 这里注意,一定要指定map_location参数,否则会导致第一块GPU占用更多资源 MPF_model.load_state_dict(torch.load(save_path, map_location=device)) ep = -1 total_it = 0 ep += 1 MPF_model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(MPF_model) MPF_model = torch.nn.parallel.DistributedDataParallel(MPF_model, device_ids=[opts.local_rank], output_device=opts.local_rank, find_unused_parameters=True) # optimizer = optim.Adam(MPF_model.parameters(), lr=opts.lr) # scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=50, gamma=0.9) logger = None if opts.local_rank == 0: log_dir = os.path.join(opts.display_dir, 'logger', opts.name) os.makedirs(log_dir, exist_ok=True) log_path = os.path.join(log_dir, 'log.txt') if os.path.exists(log_path): os.remove(log_path) logger = logger_config(log_path=log_path, logging_name='Timer') logger.info('Parameter Space: ABS: {:.1f}, REL: {:.4f}'.format(count_parameters(MPF_model), count_parameters(MPF_model) / 1024 / 1024)) logger.info(MPF_model) # Train and evaluate multi-task network multi_task_trainer(train_loader, train_sampler, test_loader, MPF_model, device, optimizer, opts, logger, ep, total_it) def multi_task_trainer(train_loader, train_sampler, test_loader, multi_task_model, device, optimizer, opt, logger=None, start_ep=0, total_it=0): total_epoch = opt.n_ep saver = Saver(opt) ## 计算分割损失相关的设计 score_thres = 0.7 ignore_idx = 255 n_min = 8 * 256 * 256 // 8 criteria = OhemCELoss( thresh=score_thres, n_min=n_min, device=device, ignore_lb=ignore_idx) binary_class_weight = np.array([1.4548, 19.8962]) binary_class_weight = torch.tensor(binary_class_weight).float().to(device) binary_class_weight = binary_class_weight.unsqueeze(0) binary_class_weight = binary_class_weight.unsqueeze(2) binary_class_weight = binary_class_weight.unsqueeze(2) lb_ignore = [255] if opt.resume: best_mIou = multi_task_tester(test_loader, multi_task_model, device, opt) else: best_mIou = 0.0 if opt.local_rank == 0: print('best mIoU: {:.4f}'.format(best_mIou)) start = glob_st = time() for ep in range(start_ep, total_epoch): ## 每一个epoch 计算一次动态权重 train_sampler.set_epoch(ep) multi_task_model.train() seg_metric = SegmentationMetric(opt.class_nb, device=device) ## 这里可能会有问题 for it, (img_ir, img_vi, label, bi, bd, mask) in enumerate(train_loader): total_it += 1 img_ir = img_ir.to(device) img_vi = img_vi.to(device) label = label.to(device) bi = bi.to(device).squeeze(1) bd = bd.to(device).squeeze(1) vi_Y, vi_Cb, vi_Cr = RGB2YCrCb(img_vi) vi_Y = vi_Y.to(device) vi_Cb = vi_Cb.to(device) vi_Cr = vi_Cr.to(device) mask = mask.to(device) seg_pred, bi_pred, bd_pred, fused_img, re_vi, re_ir = multi_task_model(img_vi, img_ir) # seg_pred = F.softmax(seg_pred, dim=1) # seg_pred = multi_task_model(img_vi, img_ir) optimizer.zero_grad() seg_loss = Seg_loss(seg_pred, label, device, criteria) bd = F.one_hot(bd,num_classes=2) bd=bd.permute(0,3,1,2).float() bi = F.one_hot(bi,num_classes=2) bi= bi.permute(0,3,1,2).float() bd_loss = F.binary_cross_entropy_with_logits(bd_pred, bd) bi_loss = F.binary_cross_entropy_with_logits(bi_pred, bi, pos_weight=binary_class_weight) seg_results = torch.argmax(seg_pred, dim=1, keepdim=True) ## print(seg_result.shape()) train_seg_loss = 10 * seg_loss + 5 * bi_loss + 5 * bd_loss ## reconstruction-related loss fusion_loss, ssim_loss, grad_loss, int_loss = Fusion_loss(img_ir, vi_Y, fused_img, mask) vi_re_loss, vi_int_loss, vi_grad_loss = Re_loss(re_vi, vi_Y, mask=mask, ir_flag=False) ir_re_loss, ir_int_loss, ir_grad_loss = Re_loss(re_ir, img_ir, mask=mask, ir_flag=True) train_loss = 1 * train_seg_loss + 1 * fusion_loss + 0.5 * vi_re_loss + 0.5 * ir_re_loss train_loss.backward() optimizer.step() seg_metric.addBatch(seg_results, label, lb_ignore) # dist.destroy_process_group() if opt.local_rank == 0: lr = optimizer.get_lr() mIoU = np.array(seg_metric.meanIntersectionOverUnion().item()) Acc = np.array(seg_metric.pixelAccuracy().item()) end = time() training_time, glob_t_intv = end - start, end - glob_st now_it = total_it+1 eta = int((total_epoch * len(train_loader) - now_it) * (glob_t_intv / (now_it))) eta = str(datetime.timedelta(seconds=eta)) logger.info('ep: [{}/{}], learning rate: {:.6f}, time consuming: {:.2f}s, segmentation loss: {:.4f}, fusion loss: {:.4f}, vi rec loss: {:.4f}, ir rec loss: {:.4f}'.format(ep+1, total_epoch, lr, training_time, seg_loss.item(), fusion_loss.item(), vi_re_loss.item(), ir_re_loss.item())) logger.info('ssim loss: [{:.4f}], grad loss: [{:.4f}], int loss: [{:.4f}], segmentation loss: {:.4f}, mIou: {:.4f}, Acc: {:.4f}, Eta: {}\n'.format(ssim_loss.item(), grad_loss.item(), int_loss.item(), seg_loss.item(), mIoU, Acc, eta)) start = time() ## save Visualization results if (ep + 1) % opt.img_save_freq == 0 and opt.local_rank == 0: input = [img_ir, img_vi, fused_img, label] fused_rgb = YCbCr2RGB(fused_img, vi_Cb, vi_Cr) vi_rgb = YCbCr2RGB(re_vi, vi_Cb, vi_Cr) output = [re_ir, vi_rgb, fused_rgb, seg_results] saver.write_img(ep, input, output) ## save model if (ep + 1) % opt.model_save_freq == 0 and opt.local_rank == 0: test_mIoU = multi_task_tester(test_loader, multi_task_model, device, opt) logger.info('test mIoU: {:.4f}, best mIoU:{:.4f}'.format(test_mIoU, best_mIou)) if test_mIoU > best_mIou: best_mIou = test_mIoU saver.write_model(ep, total_it, multi_task_model, optimizer.optim, best_mIou, device) def multi_task_tester(test_loader, multi_task_model, device, opts): multi_task_model.eval() test_bar= tqdm(test_loader) seg_metric = SegmentationMetric(opts.class_nb, device=device) lb_ignore = [255] ## define save dir with torch.no_grad(): # operations inside don't track history for it, (img_ir, img_vi, label, img_names) in enumerate(test_bar): img_ir = img_ir.to(device) img_vi = img_vi.to(device) label = label.to(device) Seg_pred, _, _, fused_img, re_vi, re_ir = multi_task_model(img_vi, img_ir) seg_result = torch.argmax(Seg_pred, dim=1, keepdim=True) ## print(seg_result.shape()) seg_metric.addBatch(seg_result, label, lb_ignore) mIoU = np.array(seg_metric.meanIntersectionOverUnion().item()) return mIoU if __name__ == '__main__': main()
options.py
import argparse class TrainOptions(): def __init__(self): self.parser = argparse.ArgumentParser() # data loader related self.parser.add_argument('--dataroot', type=str, default='/data/timer/Idea/mtan/dataset/MSRS', help='path of data') self.parser.add_argument('--phase', type=str, default='train', help='phase for dataloading') self.parser.add_argument('--batch_size', type=int, default=12 , help='batch size') self.parser.add_argument('--nThreads', type=int, default=16, help='# of threads for data loader') # training related self.parser.add_argument('--lr', default=1e-3, type=int, help='Initial learning rate for training model') self.parser.add_argument('--weight', default='dwa', type=str, help='multi-task weighting: equal, uncert, dwa') self.parser.add_argument('--n_ep', type=int, default=1500, help='number of epochs') # 400 * d_iter self.parser.add_argument('--n_ep_decay', type=int, default=1000, help='epoch start decay learning rate, set -1 if no decay') # 200 * d_iter self.parser.add_argument('--resume', type=str, default=None, help='specified the dir of saved models for resume the training') # 不要改该参数,系统会自动分配 self.parser.add_argument('--gpu', type=str, default='cuda', help='device id (i.e. 0 or 0,1 or cpu)') self.parser.add_argument('--temp', default=2.0, type=float, help='temperature for DWA (must be positive)') # ouptput related self.parser.add_argument('--name', type=str, default='MPF-Trans-skip_DDP', help='folder name to save outputs') self.parser.add_argument('--class_nb', type=int, default=9, help='class number for segmentation model') self.parser.add_argument('--display_dir', type=str, default='/data/timer/Idea/mtan/logs', help='path for saving display results') self.parser.add_argument('--result_dir', type=str, default='/data/timer/Idea/mtan/results', help='path for saving result images and models') self.parser.add_argument('--display_freq', type=int, default=10, help='freq (iteration) of display') self.parser.add_argument('--img_save_freq', type=int, default=10, help='freq (epoch) of saving images') self.parser.add_argument('--model_save_freq', type=int, default=10, help='freq (epoch) of saving models') # DDP related self.parser.add_argument('--local_rank', type=int, default=0, help='Specifying the default GPU') def parse(self): self.opt = self.parser.parse_args() args = vars(self.opt) print('\n--- load options ---') for name, value in sorted(args.items()): print('%s: %s' % (str(name), str(value))) return self.opt class TestOptions(): def __init__(self): self.parser = argparse.ArgumentParser() # data loader related self.parser.add_argument('--dataroot', type=str, default='/data/timer/Idea/mtan/dataset/MSRS', help='path of data') self.parser.add_argument('--phase', type=str, default='test', help='phase for dataloading') self.parser.add_argument('--batch_size', type=int, default=16, help='batch size') self.parser.add_argument('--nThreads', type=int, default=16, help='# of threads for data loader') ## mode related self.parser.add_argument('--class_nb', type=int, default=9, help='class number for segmentation model') self.parser.add_argument('--resume', type=str, default='/data/timer/Idea/mtan/results/MPF-skip/best_model.pth', help='specified the dir of saved models for resume the training') self.parser.add_argument('--gpu', type=int, default=0, help='GPU id') # results related self.parser.add_argument('--name', type=str, default='MPF_skip', help='folder name to save outputs') self.parser.add_argument('--result_dir', type=str, default='/data/timer/Idea/mtan/test', help='path for saving result images and models') def parse(self): self.opt = self.parser.parse_args() args = vars(self.opt) print('\n--- load options ---') for name, value in sorted(args.items()): print('%s: %s' % (str(name), str(value))) return self.opt
一些主要的操作都在train.py文件里有所涉及,因为是第一次系统的使用DDP,还有很多地方理解的不够透彻,不当之处希望大家指出一起交流。
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