update

嘉渊

2023-04-25 7a3b8744859d8a6c81c56bf2979970b8f197cacb

 funasr/build_utils/build_trainer.py

@@ -75,14 +75,14 @@
    grad_clip: float
    grad_clip_type: float
    log_interval: Optional[int]
    no_forward_run: bool
    use_tensorboard: bool
    use_wandb: bool
    # no_forward_run: bool
    # use_tensorboard: bool
    # use_wandb: bool
    output_dir: Union[Path, str]
    max_epoch: int
    max_update: int
    seed: int
    sharded_ddp: bool
    # sharded_ddp: bool
    patience: Optional[int]
    keep_nbest_models: Union[int, List[int]]
    nbest_averaging_interval: int
@@ -90,7 +90,7 @@
    best_model_criterion: Sequence[Sequence[str]]
    val_scheduler_criterion: Sequence[str]
    unused_parameters: bool
    wandb_model_log_interval: int
    # wandb_model_log_interval: int
    use_pai: bool
    oss_bucket: Union[oss2.Bucket, None]

@@ -107,7 +107,6 @@
                 schedulers: Sequence[Optional[AbsScheduler]],
                 train_dataloader: AbsIterFactory,
                 valid_dataloader: AbsIterFactory,
                 trainer_options,
                 distributed_option: DistributedOption):
        self.trainer_options = self.build_options(args)
        self.model = model
@@ -115,7 +114,6 @@
        self.schedulers = schedulers
        self.train_dataloader = train_dataloader
        self.valid_dataloader = valid_dataloader
        self.trainer_options = trainer_options
        self.distributed_option = distributed_option

    def build_options(self, args: argparse.Namespace) -> TrainerOptions:
@@ -128,16 +126,15 @@
        """Reserved for future development of another Trainer"""
        pass

    @staticmethod
    def resume(
            checkpoint: Union[str, Path],
            model: torch.nn.Module,
            reporter: Reporter,
            optimizers: Sequence[torch.optim.Optimizer],
            schedulers: Sequence[Optional[AbsScheduler]],
            scaler: Optional[GradScaler],
            ngpu: int = 0,
    ):
    def resume(self,
               checkpoint: Union[str, Path],
               model: torch.nn.Module,
               reporter: Reporter,
               optimizers: Sequence[torch.optim.Optimizer],
               schedulers: Sequence[Optional[AbsScheduler]],
               scaler: Optional[GradScaler],
               ngpu: int = 0,
               ):
        states = torch.load(
            checkpoint,
            map_location=f"cuda:{torch.cuda.current_device()}" if ngpu > 0 else "cpu",
@@ -800,3 +797,24 @@
            if distributed:
                iterator_stop.fill_(1)
                torch.distributed.all_reduce(iterator_stop, ReduceOp.SUM)


def build_trainer(
        args,
        model: FunASRModel,
        optimizers: Sequence[torch.optim.Optimizer],
        schedulers: Sequence[Optional[AbsScheduler]],
        train_dataloader: AbsIterFactory,
        valid_dataloader: AbsIterFactory,
        distributed_option: DistributedOption
):
    trainer = Trainer(
        args=args,
        model=model,
        optimizers=optimizers,
        schedulers=schedulers,
        train_dataloader=train_dataloader,
        valid_dataloader=valid_dataloader,
        distributed_option=distributed_option
    )
    return trainer