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if you have defined your own sampler, you should have to set use_distributed_sampler to False! 当使用自定义的 sampler 时，必须设置 use_distributed_sampler 为 False
184 lines
7.4 KiB
Python
184 lines
7.4 KiB
Python
# modified from https://github.com/feng-yufei/shared_debugging_code/blob/main/train_t2s.py
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import os
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import pdb
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if "_CUDA_VISIBLE_DEVICES" in os.environ:
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os.environ["CUDA_VISIBLE_DEVICES"] = os.environ["_CUDA_VISIBLE_DEVICES"]
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import argparse
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import logging
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from pathlib import Path
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import torch, platform
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from pytorch_lightning import seed_everything
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from pytorch_lightning import Trainer
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from pytorch_lightning.callbacks import ModelCheckpoint
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from pytorch_lightning.loggers import TensorBoardLogger # WandbLogger
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from pytorch_lightning.strategies import DDPStrategy
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from AR.data.data_module import Text2SemanticDataModule
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from AR.models.t2s_lightning_module import Text2SemanticLightningModule
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from AR.utils.io import load_yaml_config
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logging.getLogger("numba").setLevel(logging.WARNING)
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logging.getLogger("matplotlib").setLevel(logging.WARNING)
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torch.set_float32_matmul_precision("high")
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from AR.utils import get_newest_ckpt
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from collections import OrderedDict
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from time import time as ttime
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import shutil
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def my_save(fea,path):#####fix issue: torch.save doesn't support chinese path
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dir=os.path.dirname(path)
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name=os.path.basename(path)
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tmp_path="%s.pth"%(ttime())
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torch.save(fea,tmp_path)
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shutil.move(tmp_path,"%s/%s"%(dir,name))
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class my_model_ckpt(ModelCheckpoint):
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def __init__(
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self,
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config,
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if_save_latest,
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if_save_every_weights,
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half_weights_save_dir,
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exp_name,
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**kwargs
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):
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super().__init__(**kwargs)
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self.if_save_latest = if_save_latest
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self.if_save_every_weights = if_save_every_weights
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self.half_weights_save_dir = half_weights_save_dir
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self.exp_name = exp_name
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self.config = config
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def on_train_epoch_end(self, trainer, pl_module):
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# if not self._should_skip_saving_checkpoint(trainer) and self._should_save_on_train_epoch_end(trainer):
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if self._should_save_on_train_epoch_end(trainer):
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monitor_candidates = self._monitor_candidates(trainer)
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if (
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self._every_n_epochs >= 1
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and (trainer.current_epoch + 1) % self._every_n_epochs == 0
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):
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if (
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self.if_save_latest == True
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): ####如果设置只保存最后一个ckpt，在保存下一个ckpt后要清理掉之前的所有ckpt
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to_clean = list(os.listdir(self.dirpath))
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self._save_topk_checkpoint(trainer, monitor_candidates)
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if self.if_save_latest == True:
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for name in to_clean:
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try:
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os.remove("%s/%s" % (self.dirpath, name))
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except:
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pass
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if self.if_save_every_weights == True:
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to_save_od = OrderedDict()
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to_save_od["weight"] = OrderedDict()
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dictt = trainer.strategy._lightning_module.state_dict()
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for key in dictt:
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to_save_od["weight"][key] = dictt[key].half()
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to_save_od["config"] = self.config
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to_save_od["info"] = "GPT-e%s" % (trainer.current_epoch + 1)
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# torch.save(
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# print(os.environ)
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if(os.environ.get("LOCAL_RANK","0")=="0"):
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my_save(
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to_save_od,
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"%s/%s-e%s.ckpt"
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% (
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self.half_weights_save_dir,
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self.exp_name,
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trainer.current_epoch + 1,
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),
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)
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self._save_last_checkpoint(trainer, monitor_candidates)
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def main(args):
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config = load_yaml_config(args.config_file)
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output_dir = Path(config["output_dir"])
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output_dir.mkdir(parents=True, exist_ok=True)
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ckpt_dir = output_dir / "ckpt"
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ckpt_dir.mkdir(parents=True, exist_ok=True)
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seed_everything(config["train"]["seed"], workers=True)
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ckpt_callback: ModelCheckpoint = my_model_ckpt(
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config=config,
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if_save_latest=config["train"]["if_save_latest"],
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if_save_every_weights=config["train"]["if_save_every_weights"],
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half_weights_save_dir=config["train"]["half_weights_save_dir"],
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exp_name=config["train"]["exp_name"],
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save_top_k=-1,
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monitor="top_3_acc",
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mode="max",
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save_on_train_epoch_end=True,
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every_n_epochs=config["train"]["save_every_n_epoch"],
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dirpath=ckpt_dir,
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)
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logger = TensorBoardLogger(name=output_dir.stem, save_dir=output_dir)
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os.environ["MASTER_ADDR"]="localhost"
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trainer: Trainer = Trainer(
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max_epochs=config["train"]["epochs"],
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accelerator="gpu" if torch.cuda.is_available() else "cpu",
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# val_check_interval=9999999999999999999999,###不要验证
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# check_val_every_n_epoch=None,
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limit_val_batches=0,
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devices=-1 if torch.cuda.is_available() else 1,
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benchmark=False,
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fast_dev_run=False,
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strategy = DDPStrategy(
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process_group_backend="nccl" if platform.system() != "Windows" else "gloo"
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) if torch.cuda.is_available() else "auto",
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precision=config["train"]["precision"],
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logger=logger,
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num_sanity_val_steps=0,
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callbacks=[ckpt_callback],
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use_distributed_sampler=False, # 非常简单的修改，但解决了采用自定义的 bucket_sampler 下训练步数不一致的问题！
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)
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model: Text2SemanticLightningModule = Text2SemanticLightningModule(
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config, output_dir
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)
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data_module: Text2SemanticDataModule = Text2SemanticDataModule(
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config,
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train_semantic_path=config["train_semantic_path"],
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train_phoneme_path=config["train_phoneme_path"],
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# dev_semantic_path=args.dev_semantic_path,
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# dev_phoneme_path=args.dev_phoneme_path
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)
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try:
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# 使用正则表达式匹配文件名中的数字部分，并按数字大小进行排序
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newest_ckpt_name = get_newest_ckpt(os.listdir(ckpt_dir))
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ckpt_path = ckpt_dir / newest_ckpt_name
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except Exception:
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ckpt_path = None
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print("ckpt_path:", ckpt_path)
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trainer.fit(model, data_module, ckpt_path=ckpt_path)
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# srun --gpus-per-node=1 --ntasks-per-node=1 python train.py --path-to-configuration configurations/default.yaml
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"-c",
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"--config_file",
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type=str,
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default="configs/s1longer.yaml",
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help="path of config file",
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)
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# args for dataset
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# parser.add_argument('--train_semantic_path',type=str,default='/data/docker/liujing04/gpt-vits/fine_tune_dataset/xuangou/6-name2semantic.tsv')
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# parser.add_argument('--train_phoneme_path', type=str, default='/data/docker/liujing04/gpt-vits/fine_tune_dataset/xuangou/2-name2text.txt')
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# parser.add_argument('--dev_semantic_path', type=str, default='dump_mix/semantic_dev.tsv')
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# parser.add_argument('--dev_phoneme_path', type=str, default='dump_mix/phoneme_dev.npy')
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# parser.add_argument('--output_dir',type=str,default='/data/docker/liujing04/gpt-vits/fine_tune_dataset/xuangou/logs_s1',help='directory to save the results')
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# parser.add_argument('--output_dir',type=str,default='/liujing04/gpt_logs/s1/xuangou_ft',help='directory to save the results')
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args = parser.parse_args()
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logging.info(str(args))
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main(args)
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