import warnings warnings.filterwarnings("ignore") import os import utils import musa_utils hps = utils.get_hparams(stage=2) os.environ["CUDA_VISIBLE_DEVICES"] = hps.train.gpu_numbers.replace("-", ",") os.environ["MUSA_VISIBLE_DEVICES"] = hps.train.gpu_numbers.replace("-", ",") import logging import torch import torch.distributed as dist import torch.multiprocessing as mp from torch.cuda.amp import GradScaler if musa_utils.is_available(): autocast = torch.musa.amp.autocast musa_ddp = musa_utils.should_ddp() elif torch.cuda.is_available(): from torch.cuda.amp import autocast from torch.nn.parallel import DistributedDataParallel as DDP from torch.utils.data import DataLoader from torch.utils.tensorboard import SummaryWriter from tqdm import tqdm logging.getLogger("matplotlib").setLevel(logging.INFO) logging.getLogger("h5py").setLevel(logging.INFO) logging.getLogger("numba").setLevel(logging.INFO) from random import randint from module import commons from module.data_utils import ( DistributedBucketSampler, ) from module.data_utils import ( TextAudioSpeakerCollateV3 as TextAudioSpeakerCollate, ) from module.data_utils import ( TextAudioSpeakerLoaderV3 as TextAudioSpeakerLoader, ) from module.models import ( SynthesizerTrnV3 as SynthesizerTrn, ) from process_ckpt import savee torch.backends.cudnn.benchmark = False torch.backends.cudnn.deterministic = False ###反正A100fp32更快，那试试tf32吧 torch.backends.cuda.matmul.allow_tf32 = True torch.backends.cudnn.allow_tf32 = True if musa_utils.is_available(): torch.backends.mudnn.allow_tf32 = True torch.set_float32_matmul_precision("medium") # 最低精度但最快（也就快一丁点），对于结果造成不了影响 # from config import pretrained_s2G,pretrained_s2D global_step = 0 device = "cpu" # cuda以外的设备，等mps优化后加入 if not musa_ddp: device = "musa" def main(): if torch.cuda.is_available(): n_gpus = torch.cuda.device_count() elif musa_ddp: n_gpus = musa_utils.device_count() else: n_gpus = 1 os.environ["MASTER_ADDR"] = "localhost" os.environ["MASTER_PORT"] = str(randint(20000, 55555)) mp.spawn( run, nprocs=n_gpus, args=( n_gpus, hps, ), ) def run(rank, n_gpus, hps): global global_step if rank == 0: logger = utils.get_logger(hps.data.exp_dir) logger.info(hps) # utils.check_git_hash(hps.s2_ckpt_dir) writer = SummaryWriter(log_dir=hps.s2_ckpt_dir) writer_eval = SummaryWriter(log_dir=os.path.join(hps.s2_ckpt_dir, "eval")) dist.init_process_group( backend= "mccl" if torch.musa.is_available() else ("gloo" if os.name == "nt" or not torch.cuda.is_available() else "nccl"), init_method="env://?use_libuv=False", world_size=n_gpus, rank=rank, ) torch.manual_seed(hps.train.seed) if torch.cuda.is_available(): torch.cuda.set_device(rank) elif musa_ddp: musa_utils.set_device(rank) train_dataset = TextAudioSpeakerLoader(hps.data) ######## train_sampler = DistributedBucketSampler( train_dataset, hps.train.batch_size, [ 32, 300, 400, 500, 600, 700, 800, 900, 1000, # 1100, # 1200, # 1300, # 1400, # 1500, # 1600, # 1700, # 1800, # 1900, ], num_replicas=n_gpus, rank=rank, shuffle=True, ) collate_fn = TextAudioSpeakerCollate() train_loader = DataLoader( train_dataset, num_workers=6, shuffle=False, pin_memory=True, collate_fn=collate_fn, batch_sampler=train_sampler, persistent_workers=True, prefetch_factor=4, ) # if rank == 0: # eval_dataset = TextAudioSpeakerLoader(hps.data.validation_files, hps.data, val=True) # eval_loader = DataLoader(eval_dataset, num_workers=0, shuffle=False, # batch_size=1, pin_memory=True, # drop_last=False, collate_fn=collate_fn) net_g = ( SynthesizerTrn( hps.data.filter_length // 2 + 1, hps.train.segment_size // hps.data.hop_length, n_speakers=hps.data.n_speakers, **hps.model, ).cuda(rank) if torch.cuda.is_available() else SynthesizerTrn( hps.data.filter_length // 2 + 1, hps.train.segment_size // hps.data.hop_length, n_speakers=hps.data.n_speakers, **hps.model, ).musa(rank) if musa_ddp else SynthesizerTrn( hps.data.filter_length // 2 + 1, hps.train.segment_size // hps.data.hop_length, n_speakers=hps.data.n_speakers, **hps.model, ).to(device) ) # net_d = MultiPeriodDiscriminator(hps.model.use_spectral_norm).cuda(rank) if torch.cuda.is_available() else MultiPeriodDiscriminator(hps.model.use_spectral_norm).to(device) # for name, param in net_g.named_parameters(): # if not param.requires_grad: # print(name, "not requires_grad") optim_g = torch.optim.AdamW( filter(lambda p: p.requires_grad, net_g.parameters()), ###默认所有层lr一致 hps.train.learning_rate, betas=hps.train.betas, eps=hps.train.eps, ) # optim_d = torch.optim.AdamW( # net_d.parameters(), # hps.train.learning_rate, # betas=hps.train.betas, # eps=hps.train.eps, # ) if torch.cuda.is_available() or musa_ddp: net_g = DDP(net_g, device_ids=[rank], find_unused_parameters=True) # net_d = DDP(net_d, device_ids=[rank], find_unused_parameters=True) else: net_g = net_g.to(device) # net_d = net_d.to(device) try: # 如果能加载自动resume # _, _, _, epoch_str = utils.load_checkpoint( # utils.latest_checkpoint_path("%s/logs_s2_%s" % (hps.data.exp_dir,hps.model.version), "D_*.pth"), # net_d, # optim_d, # ) # D多半加载没事 # if rank == 0: # logger.info("loaded D") # _, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"), net_g, optim_g,load_opt=0) _, _, _, epoch_str = utils.load_checkpoint( utils.latest_checkpoint_path("%s/logs_s2_%s" % (hps.data.exp_dir, hps.model.version), "G_*.pth"), net_g, optim_g, ) epoch_str += 1 global_step = (epoch_str - 1) * len(train_loader) # epoch_str = 1 # global_step = 0 except: # 如果首次不能加载，加载pretrain # traceback.print_exc() epoch_str = 1 global_step = 0 if ( hps.train.pretrained_s2G != "" and hps.train.pretrained_s2G != None and os.path.exists(hps.train.pretrained_s2G) ): if rank == 0: logger.info("loaded pretrained %s" % hps.train.pretrained_s2G) print( "loaded pretrained %s" % hps.train.pretrained_s2G, net_g.module.load_state_dict( torch.load(hps.train.pretrained_s2G, map_location="cpu", weights_only=False)["weight"], strict=False, ) if torch.cuda.is_available() or musa_ddp else net_g.load_state_dict( torch.load(hps.train.pretrained_s2G, map_location="cpu", weights_only=False)["weight"], strict=False, ), ) ##测试不加载优化器 # if hps.train.pretrained_s2D != ""and hps.train.pretrained_s2D != None and os.path.exists(hps.train.pretrained_s2D): # if rank == 0: # logger.info("loaded pretrained %s" % hps.train.pretrained_s2D) # print( # net_d.module.load_state_dict( # torch.load(hps.train.pretrained_s2D, map_location="cpu")["weight"] # ) if torch.cuda.is_available() else net_d.load_state_dict( # torch.load(hps.train.pretrained_s2D, map_location="cpu")["weight"] # ) # ) # scheduler_g = torch.optim.lr_scheduler.ExponentialLR(optim_g, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2) # scheduler_d = torch.optim.lr_scheduler.ExponentialLR(optim_d, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2) scheduler_g = torch.optim.lr_scheduler.ExponentialLR(optim_g, gamma=hps.train.lr_decay, last_epoch=-1) # scheduler_d = torch.optim.lr_scheduler.ExponentialLR( # optim_d, gamma=hps.train.lr_decay, last_epoch=-1 # ) for _ in range(epoch_str): scheduler_g.step() # scheduler_d.step() if musa_utils.is_available(): scaler = torch.musa.amp.GradScaler(enabled=hps.train.fp16_run) else: scaler = GradScaler(enabled=hps.train.fp16_run) net_d = optim_d = scheduler_d = None print("start training from epoch %s" % epoch_str) for epoch in range(epoch_str, hps.train.epochs + 1): if rank == 0: train_and_evaluate( rank, epoch, hps, [net_g, net_d], [optim_g, optim_d], [scheduler_g, scheduler_d], scaler, # [train_loader, eval_loader], logger, [writer, writer_eval]) [train_loader, None], logger, [writer, writer_eval], ) else: train_and_evaluate( rank, epoch, hps, [net_g, net_d], [optim_g, optim_d], [scheduler_g, scheduler_d], scaler, [train_loader, None], None, None, ) scheduler_g.step() # scheduler_d.step() print("training done") def train_and_evaluate( rank, epoch, hps, nets, optims, schedulers, scaler, loaders, logger, writers, ): net_g, net_d = nets optim_g, optim_d = optims # scheduler_g, scheduler_d = schedulers train_loader, eval_loader = loaders if writers is not None: writer, writer_eval = writers train_loader.batch_sampler.set_epoch(epoch) global global_step net_g.train() # net_d.train() # for batch_idx, ( # ssl, # ssl_lengths, # spec, # spec_lengths, # y, # y_lengths, # text, # text_lengths, # ) in enumerate(tqdm(train_loader)): for batch_idx, (ssl, spec, mel, ssl_lengths, spec_lengths, text, text_lengths, mel_lengths) in enumerate( tqdm(train_loader) ): if torch.cuda.is_available(): spec, spec_lengths = ( spec.cuda( rank, non_blocking=True, ), spec_lengths.cuda( rank, non_blocking=True, ), ) mel, mel_lengths = mel.cuda(rank, non_blocking=True), mel_lengths.cuda(rank, non_blocking=True) ssl = ssl.cuda(rank, non_blocking=True) ssl.requires_grad = False # ssl_lengths = ssl_lengths.cuda(rank, non_blocking=True) text, text_lengths = ( text.cuda( rank, non_blocking=True, ), text_lengths.cuda( rank, non_blocking=True, ), ) elif musa_ddp: spec, spec_lengths = ( spec.musa( rank, non_blocking=True, ), spec_lengths.musa( rank, non_blocking=True, ), ) mel, mel_lengths = mel.musa(rank, non_blocking=True), mel_lengths.musa(rank, non_blocking=True) ssl = ssl.musa(rank, non_blocking=True) ssl.requires_grad = False # ssl_lengths = ssl_lengths.musa(rank, non_blocking=True) text, text_lengths = ( text.musa( rank, non_blocking=True, ), text_lengths.musa( rank, non_blocking=True, ), ) else: spec, spec_lengths = spec.to(device), spec_lengths.to(device) mel, mel_lengths = mel.to(device), mel_lengths.to(device) ssl = ssl.to(device) ssl.requires_grad = False # ssl_lengths = ssl_lengths.cuda(rank, non_blocking=True) text, text_lengths = text.to(device), text_lengths.to(device) with autocast(enabled=hps.train.fp16_run): cfm_loss = net_g( ssl, spec, mel, ssl_lengths, spec_lengths, text, text_lengths, mel_lengths, use_grad_ckpt=hps.train.grad_ckpt, ) loss_gen_all = cfm_loss optim_g.zero_grad() scaler.scale(loss_gen_all).backward() scaler.unscale_(optim_g) grad_norm_g = commons.clip_grad_value_(net_g.parameters(), None) scaler.step(optim_g) scaler.update() if rank == 0: if global_step % hps.train.log_interval == 0: lr = optim_g.param_groups[0]["lr"] # losses = [commit_loss,cfm_loss,mel_loss,loss_disc, loss_gen, loss_fm, loss_mel, loss_kl] losses = [cfm_loss] logger.info( "Train Epoch: {} [{:.0f}%]".format( epoch, 100.0 * batch_idx / len(train_loader), ) ) logger.info([x.item() for x in losses] + [global_step, lr]) scalar_dict = {"loss/g/total": loss_gen_all, "learning_rate": lr, "grad_norm_g": grad_norm_g} # image_dict = { # "slice/mel_org": utils.plot_spectrogram_to_numpy(y_mel[0].data.cpu().numpy()), # "slice/mel_gen": utils.plot_spectrogram_to_numpy(y_hat_mel[0].data.cpu().numpy()), # "all/mel": utils.plot_spectrogram_to_numpy(mel[0].data.cpu().numpy()), # "all/stats_ssl": utils.plot_spectrogram_to_numpy(stats_ssl[0].data.cpu().numpy()), # } utils.summarize( writer=writer, global_step=global_step, # images=image_dict, scalars=scalar_dict, ) # if global_step % hps.train.eval_interval == 0: # # evaluate(hps, net_g, eval_loader, writer_eval) # utils.save_checkpoint(net_g, optim_g, hps.train.learning_rate, epoch,os.path.join(hps.s2_ckpt_dir, "G_{}.pth".format(global_step)),scaler) # # utils.save_checkpoint(net_d, optim_d, hps.train.learning_rate, epoch,os.path.join(hps.s2_ckpt_dir, "D_{}.pth".format(global_step)),scaler) # # keep_ckpts = getattr(hps.train, 'keep_ckpts', 3) # # if keep_ckpts > 0: # # utils.clean_checkpoints(path_to_models=hps.s2_ckpt_dir, n_ckpts_to_keep=keep_ckpts, sort_by_time=True) global_step += 1 if epoch % hps.train.save_every_epoch == 0 and rank == 0: if hps.train.if_save_latest == 0: utils.save_checkpoint( net_g, optim_g, hps.train.learning_rate, epoch, os.path.join( "%s/logs_s2_%s" % (hps.data.exp_dir, hps.model.version), "G_{}.pth".format(global_step), ), ) # utils.save_checkpoint( # net_d, # optim_d, # hps.train.learning_rate, # epoch, # os.path.join( # "%s/logs_s2_%s" % (hps.data.exp_dir,hps.model.version), "D_{}.pth".format(global_step) # ), # ) else: utils.save_checkpoint( net_g, optim_g, hps.train.learning_rate, epoch, os.path.join( "%s/logs_s2_%s" % (hps.data.exp_dir, hps.model.version), "G_{}.pth".format(233333333333), ), ) # utils.save_checkpoint( # net_d, # optim_d, # hps.train.learning_rate, # epoch, # os.path.join( # "%s/logs_s2_%s" % (hps.data.exp_dir,hps.model.version), "D_{}.pth".format(233333333333) # ), # ) if rank == 0 and hps.train.if_save_every_weights == True: if hasattr(net_g, "module"): ckpt = net_g.module.state_dict() else: ckpt = net_g.state_dict() logger.info( "saving ckpt %s_e%s:%s" % ( hps.name, epoch, savee( ckpt, hps.name + "_e%s_s%s" % (epoch, global_step), epoch, global_step, hps, ), ) ) if rank == 0: logger.info("====> Epoch: {}".format(epoch)) if __name__ == "__main__": main()