update with main (#1786)

zhifu gao

2024-06-06 32e783664534bbb8d3b8ba64c2c2ecb42398eb00

 funasr/bin/train.py

@@ -13,7 +13,7 @@

from contextlib import nullcontext
import torch.distributed as dist
from collections.abc import Sequence

from omegaconf import DictConfig, OmegaConf
from torch.cuda.amp import autocast, GradScaler
from torch.nn.parallel import DistributedDataParallel as DDP
@@ -99,7 +99,7 @@
    if freeze_param is not None:
        if "," in freeze_param:
            freeze_param = eval(freeze_param)
        if isinstance(freeze_param, Sequence):
        if not isinstance(freeze_param, (list, tuple)):
            freeze_param = (freeze_param,)
        logging.info("freeze_param is not None: %s", freeze_param)
        for t in freeze_param:
@@ -107,8 +107,9 @@
                if k.startswith(t + ".") or k == t:
                    logging.info(f"Setting {k}.requires_grad = False")
                    p.requires_grad = False
    if local_rank == 0:
        logging.info(f"{model_summary(model)}")

    logging.info(f"model info: {model_summary(model)}")
    if use_ddp:
        model = model.cuda(local_rank)
        model = DDP(
@@ -145,8 +146,6 @@
    else:
        model = model.to(device=kwargs.get("device", "cuda"))

    if local_rank == 0:
        logging.info(f"{model}")
    kwargs["device"] = next(model.parameters()).device

    # optim
@@ -182,23 +181,31 @@
    scaler = GradScaler(enabled=trainer.use_fp16) if trainer.use_fp16 else None
    scaler = ShardedGradScaler(enabled=trainer.use_fp16) if trainer.use_fsdp else scaler

    trainer.resume_checkpoint(model=model, optim=optim, scheduler=scheduler, scaler=scaler)
    trainer.resume_checkpoint(
        model=model,
        optim=optim,
        scheduler=scheduler,
        scaler=scaler,
    )

    tensorboard_dir = os.path.join(kwargs.get("output_dir"), "tensorboard")
    os.makedirs(tensorboard_dir, exist_ok=True)
    try:
        from tensorboardX import SummaryWriter

        writer = SummaryWriter(tensorboard_dir) if trainer.rank == 0 else None
        writer = SummaryWriter(tensorboard_dir)  # if trainer.rank == 0 else None
    except:
        writer = None

    dataloader_tr, dataloader_val = None, None
    for epoch in range(trainer.start_epoch, trainer.max_epoch + 1):
    for epoch in range(trainer.start_epoch, trainer.max_epoch):
        time1 = time.perf_counter()

        for data_split_i in range(dataloader.data_split_num):
            dataloader_tr, dataloader_val = dataloader.build_iter(epoch, data_split_i=data_split_i)
        for data_split_i in range(trainer.start_data_split_i, dataloader.data_split_num):
            dataloader_tr, dataloader_val = dataloader.build_iter(
                epoch, data_split_i=data_split_i, start_step=trainer.start_step
            )

            trainer.train_epoch(
                model=model,
                optim=optim,
@@ -210,17 +217,21 @@
                writer=writer,
                data_split_i=data_split_i,
                data_split_num=dataloader.data_split_num,
                start_step=trainer.start_step,
            )
            
            trainer.start_step = 0

            torch.cuda.empty_cache()


        trainer.start_data_split_i = 0
        trainer.validate_epoch(
            model=model, dataloader_val=dataloader_val, epoch=epoch, writer=writer
            model=model, dataloader_val=dataloader_val, epoch=epoch + 1, writer=writer
        )
        scheduler.step()

        trainer.save_checkpoint(epoch, model=model, optim=optim, scheduler=scheduler, scaler=scaler)
        trainer.step_in_epoch = 0
        trainer.save_checkpoint(
            epoch + 1, model=model, optim=optim, scheduler=scheduler, scaler=scaler
        )

        time2 = time.perf_counter()
        time_escaped = (time2 - time1) / 3600.0
@@ -230,6 +241,8 @@
            f"estimated to finish {trainer.max_epoch} "
            f"epoch: {(trainer.max_epoch - epoch) * time_escaped:.3f} hours\n"
        )
        trainer.train_acc_avg = 0.0
        trainer.train_loss_avg = 0.0

    if trainer.rank == 0:
        average_checkpoints(trainer.output_dir, trainer.avg_nbest_model)