Merge pull request #932 from alibaba-damo-academy/dev_lhn

hnluo

2023-09-11 9fcb3cc06b4e324f0913d2f61b89becc2baeef1b

 funasr/build_utils/build_trainer.py

@@ -25,7 +25,6 @@
import torch
import torch.nn
import torch.optim
from typeguard import check_argument_types

from funasr.iterators.abs_iter_factory import AbsIterFactory
from funasr.main_funcs.average_nbest_models import average_nbest_models
@@ -75,14 +74,14 @@
    grad_clip: float
    grad_clip_type: float
    log_interval: Optional[int]
    no_forward_run: bool
    # no_forward_run: bool
    use_tensorboard: bool
    use_wandb: 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 +89,7 @@
    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 +106,6 @@
                 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
@@ -115,12 +113,10 @@
        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:
        """Build options consumed by train(), eval()"""
        assert check_argument_types()
        return build_dataclass(TrainerOptions, args)

    @classmethod
@@ -158,7 +154,6 @@

    def run(self) -> None:
        """Perform training. This method performs the main process of training."""
        assert check_argument_types()
        # NOTE(kamo): Don't check the type more strictly as far trainer_options
        model = self.model
        optimizers = self.optimizers
@@ -185,14 +180,14 @@
                raise RuntimeError(
                    "Require torch>=1.6.0 for  Automatic Mixed Precision"
                )
            if trainer_options.sharded_ddp:
                if fairscale is None:
                    raise RuntimeError(
                        "Requiring fairscale. Do 'pip install fairscale'"
                    )
                scaler = fairscale.optim.grad_scaler.ShardedGradScaler()
            else:
                scaler = GradScaler()
            # if trainer_options.sharded_ddp:
            #     if fairscale is None:
            #         raise RuntimeError(
            #             "Requiring fairscale. Do 'pip install fairscale'"
            #         )
            #     scaler = fairscale.optim.grad_scaler.ShardedGradScaler()
            # else:
            scaler = GradScaler()
        else:
            scaler = None

@@ -297,10 +292,10 @@
                    )
                elif isinstance(scheduler, AbsEpochStepScheduler):
                    scheduler.step()
            if trainer_options.sharded_ddp:
                for optimizer in optimizers:
                    if isinstance(optimizer, fairscale.optim.oss.OSS):
                        optimizer.consolidate_state_dict()
            # if trainer_options.sharded_ddp:
            #     for optimizer in optimizers:
            #         if isinstance(optimizer, fairscale.optim.oss.OSS):
            #             optimizer.consolidate_state_dict()

            if not distributed_option.distributed or distributed_option.dist_rank == 0:
                # 3. Report the results
@@ -308,8 +303,8 @@
                if train_summary_writer is not None:
                    reporter.tensorboard_add_scalar(train_summary_writer, key1="train")
                    reporter.tensorboard_add_scalar(valid_summary_writer, key1="valid")
                if trainer_options.use_wandb:
                    reporter.wandb_log()
                # if trainer_options.use_wandb:
                #     reporter.wandb_log()

                # save tensorboard on oss
                if trainer_options.use_pai and train_summary_writer is not None:
@@ -414,25 +409,25 @@
                        "The best model has been updated: " + ", ".join(_improved)
                    )

                log_model = (
                        trainer_options.wandb_model_log_interval > 0
                        and iepoch % trainer_options.wandb_model_log_interval == 0
                )
                if log_model and trainer_options.use_wandb:
                    import wandb

                    logging.info("Logging Model on this epoch :::::")
                    artifact = wandb.Artifact(
                        name=f"model_{wandb.run.id}",
                        type="model",
                        metadata={"improved": _improved},
                    )
                    artifact.add_file(str(output_dir / f"{iepoch}epoch.pb"))
                    aliases = [
                        f"epoch-{iepoch}",
                        "best" if best_epoch == iepoch else "",
                    ]
                    wandb.log_artifact(artifact, aliases=aliases)
                # log_model = (
                #         trainer_options.wandb_model_log_interval > 0
                #         and iepoch % trainer_options.wandb_model_log_interval == 0
                # )
                # if log_model and trainer_options.use_wandb:
                #     import wandb
                #
                #     logging.info("Logging Model on this epoch :::::")
                #     artifact = wandb.Artifact(
                #         name=f"model_{wandb.run.id}",
                #         type="model",
                #         metadata={"improved": _improved},
                #     )
                #     artifact.add_file(str(output_dir / f"{iepoch}epoch.pb"))
                #     aliases = [
                #         f"epoch-{iepoch}",
                #         "best" if best_epoch == iepoch else "",
                #     ]
                #     wandb.log_artifact(artifact, aliases=aliases)

                # 6. Remove the model files excluding n-best epoch and latest epoch
                _removed = []
@@ -524,16 +519,15 @@
            options: TrainerOptions,
            distributed_option: DistributedOption,
    ) -> Tuple[bool, bool]:
        assert check_argument_types()

        grad_noise = options.grad_noise
        accum_grad = options.accum_grad
        grad_clip = options.grad_clip
        grad_clip_type = options.grad_clip_type
        log_interval = options.log_interval
        no_forward_run = options.no_forward_run
        # no_forward_run = options.no_forward_run
        ngpu = options.ngpu
        use_wandb = options.use_wandb
        # use_wandb = options.use_wandb
        distributed = distributed_option.distributed

        if log_interval is None:
@@ -561,9 +555,9 @@
                    break

            batch = to_device(batch, "cuda" if ngpu > 0 else "cpu")
            if no_forward_run:
                all_steps_are_invalid = False
                continue
            # if no_forward_run:
            #     all_steps_are_invalid = False
            #     continue

            with autocast(scaler is not None):
                with reporter.measure_time("forward_time"):
@@ -739,8 +733,8 @@
                logging.info(reporter.log_message(-log_interval, num_updates=num_updates))
                if summary_writer is not None:
                    reporter.tensorboard_add_scalar(summary_writer, -log_interval)
                if use_wandb:
                    reporter.wandb_log()
                # if use_wandb:
                #     reporter.wandb_log()

            if max_update_stop:
                break
@@ -760,9 +754,8 @@
            options: TrainerOptions,
            distributed_option: DistributedOption,
    ) -> None:
        assert check_argument_types()
        ngpu = options.ngpu
        no_forward_run = options.no_forward_run
        # no_forward_run = options.no_forward_run
        distributed = distributed_option.distributed

        model.eval()
@@ -778,8 +771,8 @@
                    break

            batch = to_device(batch, "cuda" if ngpu > 0 else "cpu")
            if no_forward_run:
                continue
            # if no_forward_run:
            #     continue

            retval = model(**batch)
            if isinstance(retval, dict):
@@ -808,7 +801,6 @@
        schedulers: Sequence[Optional[AbsScheduler]],
        train_dataloader: AbsIterFactory,
        valid_dataloader: AbsIterFactory,
        trainer_options,
        distributed_option: DistributedOption
):
    trainer = Trainer(
@@ -818,7 +810,6 @@
        schedulers=schedulers,
        train_dataloader=train_dataloader,
        valid_dataloader=valid_dataloader,
        trainer_options=trainer_options,
        distributed_option=distributed_option
    )
    return trainer