16:37 java_http_client

VirtuosoQ

2024-04-28 476dc3f30c014e0d2ebdc46ce0283ddbfe63eeb8

 funasr/bin/train.py

@@ -55,6 +55,8 @@
    torch.backends.cudnn.enabled = kwargs.get("cudnn_enabled", torch.backends.cudnn.enabled)
    torch.backends.cudnn.benchmark = kwargs.get("cudnn_benchmark", torch.backends.cudnn.benchmark)
    torch.backends.cudnn.deterministic = kwargs.get("cudnn_deterministic", True)
    # open tf32
    torch.backends.cuda.matmul.allow_tf32 = kwargs.get("enable_tf32", True)
    
    local_rank = int(os.environ.get('LOCAL_RANK', 0))
    if local_rank == 0:
@@ -88,7 +90,8 @@
    # freeze_param
    freeze_param = kwargs.get("freeze_param", None)
    if freeze_param is not None:
        freeze_param = eval(freeze_param)
        if "," in freeze_param:
            freeze_param = eval(freeze_param)
        if isinstance(freeze_param, Sequence):
            freeze_param = (freeze_param,)
        logging.info("freeze_param is not None: %s", freeze_param)
@@ -128,7 +131,8 @@
    else:
        model = model.to(device=kwargs.get("device", "cuda"))

    logging.info(f"{model}")
    if local_rank == 0:
        logging.info(f"{model}")
    kwargs["device"] = next(model.parameters()).device
        
    # optim
@@ -149,8 +153,8 @@
    # dataset
    logging.info("Build dataloader")
    dataloader_class = tables.dataloader_classes.get(kwargs["dataset_conf"].get("dataloader", "DataloaderMapStyle"))
    dataloader_tr, dataloader_val = dataloader_class(**kwargs)

    dataloader = dataloader_class(**kwargs)
    # dataloader_tr, dataloader_val = dataloader_class(**kwargs)
    trainer = Trainer(local_rank=local_rank,
                      use_ddp=use_ddp,
                      use_fsdp=use_fsdp,
@@ -172,15 +176,12 @@
    except:
        writer = None

    if use_ddp or use_fsdp:
        context = Join([model])
    else:
        context = nullcontext()

    for epoch in range(trainer.start_epoch, trainer.max_epoch + 1):
        time1 = time.perf_counter()
        with context:
            
        
        for data_split_i in range(dataloader.data_split_num):
            dataloader_tr, dataloader_val = dataloader.build_iter(epoch, data_split_i=data_split_i)
            trainer.train_epoch(
                                model=model,
                                optim=optim,
@@ -189,15 +190,18 @@
                                dataloader_train=dataloader_tr,
                                dataloader_val=dataloader_val,
                                epoch=epoch,
                                writer=writer
                                writer=writer,
                                data_split_i=data_split_i,
                                data_split_num=dataloader.data_split_num,
                                )
        scheduler.step()
        
        trainer.validate_epoch(
            model=model,
            dataloader_val=dataloader_val,
            epoch=epoch,
            writer=writer
        )
        scheduler.step()

        
        trainer.save_checkpoint(epoch, model=model, optim=optim, scheduler=scheduler, scaler=scaler)
@@ -212,7 +216,7 @@


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

    trainer.close()