Merge branch 'main' of https://github.com/alibaba-damo-academy/FunASR into main

funasr/bin/build_trainer.py

@@ -1,7 +1,36 @@
+# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
+#  MIT License  (https://opensource.org/licenses/MIT)
+
+import argparse
+import logging
 import os
+import sys
+from io import BytesIO
 
+import torch
 import yaml
 
+from funasr.build_utils.build_args import build_args
+from funasr.build_utils.build_dataloader import build_dataloader
+from funasr.build_utils.build_distributed import build_distributed
+from funasr.build_utils.build_model import build_model
+from funasr.build_utils.build_optimizer import build_optimizer
+from funasr.build_utils.build_scheduler import build_scheduler
+from funasr.build_utils.build_trainer import build_trainer as build_trainer_modelscope
+from funasr.modules.lora.utils import mark_only_lora_as_trainable
+from funasr.text.phoneme_tokenizer import g2p_choices
+from funasr.torch_utils.load_pretrained_model import load_pretrained_model
+from funasr.torch_utils.model_summary import model_summary
+from funasr.torch_utils.pytorch_version import pytorch_cudnn_version
+from funasr.torch_utils.set_all_random_seed import set_all_random_seed
+from funasr.utils.nested_dict_action import NestedDictAction
+from funasr.utils.prepare_data import prepare_data
+from funasr.utils.types import int_or_none
+from funasr.utils.types import str2bool
+from funasr.utils.types import str_or_none
+from funasr.utils.yaml_no_alias_safe_dump import yaml_no_alias_safe_dump
+
+
 def update_dct(fin_configs, root):
     if root == {}:
         return {}
@@ -17,26 +46,468 @@ def update_dct(fin_configs, root):
     return fin_configs
 
 
-def parse_args(mode):
-    if mode == "asr":
-        from funasr.tasks.asr import ASRTask as ASRTask
-    elif mode == "paraformer":
-        from funasr.tasks.asr import ASRTaskParaformer as ASRTask
-    elif mode == "paraformer_streaming":
-        from funasr.tasks.asr import ASRTaskParaformer as ASRTask
-    elif mode == "paraformer_vad_punc":
-        from funasr.tasks.asr import ASRTaskParaformer as ASRTask
-    elif mode == "uniasr":
-        from funasr.tasks.asr import ASRTaskUniASR as ASRTask
-    elif mode == "mfcca":
-        from funasr.tasks.asr import ASRTaskMFCCA as ASRTask
-    elif mode == "tp":
-        from funasr.tasks.asr import ASRTaskAligner as ASRTask
-    else:
-        raise ValueError("Unknown mode: {}".format(mode))
-    parser = ASRTask.get_parser()
-    args = parser.parse_args()
-    return args, ASRTask
+def get_parser():
+    parser = argparse.ArgumentParser(
+        description="FunASR Common Training Parser",
+    )
+
+    # common configuration
+    parser.add_argument("--output_dir", help="model save path")
+    parser.add_argument(
+        "--ngpu",
+        type=int,
+        default=0,
+        help="The number of gpus. 0 indicates CPU mode",
+    )
+    parser.add_argument("--seed", type=int, default=0, help="Random seed")
+    parser.add_argument("--task_name", type=str, default="asr", help="Name for different tasks")
+
+    # ddp related
+    parser.add_argument(
+        "--dist_backend",
+        default="nccl",
+        type=str,
+        help="distributed backend",
+    )
+    parser.add_argument(
+        "--dist_init_method",
+        type=str,
+        default="env://",
+        help='if init_method="env://", env values of "MASTER_PORT", "MASTER_ADDR", '
+             '"WORLD_SIZE", and "RANK" are referred.',
+    )
+    parser.add_argument(
+        "--dist_world_size",
+        type=int,
+        default=1,
+        help="number of nodes for distributed training",
+    )
+    parser.add_argument(
+        "--dist_rank",
+        type=int,
+        default=None,
+        help="node rank for distributed training",
+    )
+    parser.add_argument(
+        "--local_rank",
+        type=int,
+        default=None,
+        help="local rank for distributed training",
+    )
+    parser.add_argument(
+        "--dist_master_addr",
+        default=None,
+        type=str_or_none,
+        help="The master address for distributed training. "
+             "This value is used when dist_init_method == 'env://'",
+    )
+    parser.add_argument(
+        "--dist_master_port",
+        default=None,
+        type=int_or_none,
+        help="The master port for distributed training"
+             "This value is used when dist_init_method == 'env://'",
+    )
+    parser.add_argument(
+        "--dist_launcher",
+        default=None,
+        type=str_or_none,
+        choices=["slurm", "mpi", None],
+        help="The launcher type for distributed training",
+    )
+    parser.add_argument(
+        "--multiprocessing_distributed",
+        default=True,
+        type=str2bool,
+        help="Use multi-processing distributed training to launch "
+             "N processes per node, which has N GPUs. This is the "
+             "fastest way to use PyTorch for either single node or "
+             "multi node data parallel training",
+    )
+    parser.add_argument(
+        "--unused_parameters",
+        type=str2bool,
+        default=False,
+        help="Whether to use the find_unused_parameters in "
+             "torch.nn.parallel.DistributedDataParallel ",
+    )
+    parser.add_argument(
+        "--gpu_id",
+        type=int,
+        default=0,
+        help="local gpu id.",
+    )
+
+    # cudnn related
+    parser.add_argument(
+        "--cudnn_enabled",
+        type=str2bool,
+        default=torch.backends.cudnn.enabled,
+        help="Enable CUDNN",
+    )
+    parser.add_argument(
+        "--cudnn_benchmark",
+        type=str2bool,
+        default=torch.backends.cudnn.benchmark,
+        help="Enable cudnn-benchmark mode",
+    )
+    parser.add_argument(
+        "--cudnn_deterministic",
+        type=str2bool,
+        default=True,
+        help="Enable cudnn-deterministic mode",
+    )
+
+    # trainer related
+    parser.add_argument(
+        "--max_epoch",
+        type=int,
+        default=40,
+        help="The maximum number epoch to train",
+    )
+    parser.add_argument(
+        "--max_update",
+        type=int,
+        default=sys.maxsize,
+        help="The maximum number update step to train",
+    )
+    parser.add_argument(
+        "--batch_interval",
+        type=int,
+        default=10000,
+        help="The batch interval for saving model.",
+    )
+    parser.add_argument(
+        "--patience",
+        type=int_or_none,
+        default=None,
+        help="Number of epochs to wait without improvement "
+             "before stopping the training",
+    )
+    parser.add_argument(
+        "--val_scheduler_criterion",
+        type=str,
+        nargs=2,
+        default=("valid", "loss"),
+        help="The criterion used for the value given to the lr scheduler. "
+             'Give a pair referring the phase, "train" or "valid",'
+             'and the criterion name. The mode specifying "min" or "max" can '
+             "be changed by --scheduler_conf",
+    )
+    parser.add_argument(
+        "--early_stopping_criterion",
+        type=str,
+        nargs=3,
+        default=("valid", "loss", "min"),
+        help="The criterion used for judging of early stopping. "
+             'Give a pair referring the phase, "train" or "valid",'
+             'the criterion name and the mode, "min" or "max", e.g. "acc,max".',
+    )
+    parser.add_argument(
+        "--best_model_criterion",
+        nargs="+",
+        default=[
+            ("train", "loss", "min"),
+            ("valid", "loss", "min"),
+            ("train", "acc", "max"),
+            ("valid", "acc", "max"),
+        ],
+        help="The criterion used for judging of the best model. "
+             'Give a pair referring the phase, "train" or "valid",'
+             'the criterion name, and the mode, "min" or "max", e.g. "acc,max".',
+    )
+    parser.add_argument(
+        "--keep_nbest_models",
+        type=int,
+        nargs="+",
+        default=[10],
+        help="Remove previous snapshots excluding the n-best scored epochs",
+    )
+    parser.add_argument(
+        "--nbest_averaging_interval",
+        type=int,
+        default=0,
+        help="The epoch interval to apply model averaging and save nbest models",
+    )
+    parser.add_argument(
+        "--grad_clip",
+        type=float,
+        default=5.0,
+        help="Gradient norm threshold to clip",
+    )
+    parser.add_argument(
+        "--grad_clip_type",
+        type=float,
+        default=2.0,
+        help="The type of the used p-norm for gradient clip. Can be inf",
+    )
+    parser.add_argument(
+        "--grad_noise",
+        type=str2bool,
+        default=False,
+        help="The flag to switch to use noise injection to "
+             "gradients during training",
+    )
+    parser.add_argument(
+        "--accum_grad",
+        type=int,
+        default=1,
+        help="The number of gradient accumulation",
+    )
+    parser.add_argument(
+        "--resume",
+        type=str2bool,
+        default=False,
+        help="Enable resuming if checkpoint is existing",
+    )
+    parser.add_argument(
+        "--train_dtype",
+        default="float32",
+        choices=["float16", "float32", "float64"],
+        help="Data type for training.",
+    )
+    parser.add_argument(
+        "--use_amp",
+        type=str2bool,
+        default=False,
+        help="Enable Automatic Mixed Precision. This feature requires pytorch>=1.6",
+    )
+    parser.add_argument(
+        "--log_interval",
+        default=None,
+        help="Show the logs every the number iterations in each epochs at the "
+             "training phase. If None is given, it is decided according the number "
+             "of training samples automatically .",
+    )
+    parser.add_argument(
+        "--use_tensorboard",
+        type=str2bool,
+        default=True,
+        help="Enable tensorboard logging",
+    )
+
+    # pretrained model related
+    parser.add_argument(
+        "--init_param",
+        type=str,
+        action="append",
+        default=[],
+        help="Specify the file path used for initialization of parameters. "
+             "The format is '<file_path>:<src_key>:<dst_key>:<exclude_keys>', "
+             "where file_path is the model file path, "
+             "src_key specifies the key of model states to be used in the model file, "
+             "dst_key specifies the attribute of the model to be initialized, "
+             "and exclude_keys excludes keys of model states for the initialization."
+             "e.g.\n"
+             "  # Load all parameters"
+             "  --init_param some/where/model.pb\n"
+             "  # Load only decoder parameters"
+             "  --init_param some/where/model.pb:decoder:decoder\n"
+             "  # Load only decoder parameters excluding decoder.embed"
+             "  --init_param some/where/model.pb:decoder:decoder:decoder.embed\n"
+             "  --init_param some/where/model.pb:decoder:decoder:decoder.embed\n",
+    )
+    parser.add_argument(
+        "--ignore_init_mismatch",
+        type=str2bool,
+        default=False,
+        help="Ignore size mismatch when loading pre-trained model",
+    )
+    parser.add_argument(
+        "--freeze_param",
+        type=str,
+        default=[],
+        action="append",
+        help="Freeze parameters",
+    )
+
+    # dataset related
+    parser.add_argument(
+        "--dataset_type",
+        type=str,
+        default="small",
+        help="whether to use dataloader for large dataset",
+    )
+    parser.add_argument(
+        "--dataset_conf",
+        action=NestedDictAction,
+        default=dict(),
+        help=f"The keyword arguments for dataset",
+    )
+    parser.add_argument(
+        "--data_dir",
+        type=str,
+        default=None,
+        help="root path of data",
+    )
+    parser.add_argument(
+        "--train_set",
+        type=str,
+        default="train",
+        help="train dataset",
+    )
+    parser.add_argument(
+        "--valid_set",
+        type=str,
+        default="validation",
+        help="dev dataset",
+    )
+    parser.add_argument(
+        "--data_file_names",
+        type=str,
+        default="wav.scp,text",
+        help="input data files",
+    )
+    parser.add_argument(
+        "--speed_perturb",
+        type=float,
+        nargs="+",
+        default=None,
+        help="speed perturb",
+    )
+    parser.add_argument(
+        "--use_preprocessor",
+        type=str2bool,
+        default=True,
+        help="Apply preprocessing to data or not",
+    )
+
+    # optimization related
+    parser.add_argument(
+        "--optim",
+        type=lambda x: x.lower(),
+        default="adam",
+        help="The optimizer type",
+    )
+    parser.add_argument(
+        "--optim_conf",
+        action=NestedDictAction,
+        default=dict(),
+        help="The keyword arguments for optimizer",
+    )
+    parser.add_argument(
+        "--scheduler",
+        type=lambda x: str_or_none(x.lower()),
+        default=None,
+        help="The lr scheduler type",
+    )
+    parser.add_argument(
+        "--scheduler_conf",
+        action=NestedDictAction,
+        default=dict(),
+        help="The keyword arguments for lr scheduler",
+    )
+
+    # most task related
+    parser.add_argument(
+        "--init",
+        type=lambda x: str_or_none(x.lower()),
+        default=None,
+        help="The initialization method",
+        choices=[
+            "chainer",
+            "xavier_uniform",
+            "xavier_normal",
+            "kaiming_uniform",
+            "kaiming_normal",
+            None,
+        ],
+    )
+    parser.add_argument(
+        "--token_list",
+        type=str_or_none,
+        default=None,
+        help="A text mapping int-id to token",
+    )
+    parser.add_argument(
+        "--token_type",
+        type=str,
+        default="bpe",
+        choices=["bpe", "char", "word"],
+        help="",
+    )
+    parser.add_argument(
+        "--bpemodel",
+        type=str_or_none,
+        default=None,
+        help="The model file fo sentencepiece",
+    )
+    parser.add_argument(
+        "--cleaner",
+        type=str_or_none,
+        choices=[None, "tacotron", "jaconv", "vietnamese"],
+        default=None,
+        help="Apply text cleaning",
+    )
+    parser.add_argument(
+        "--g2p",
+        type=str_or_none,
+        choices=g2p_choices,
+        default=None,
+        help="Specify g2p method if --token_type=phn",
+    )
+
+    # pai related
+    parser.add_argument(
+        "--use_pai",
+        type=str2bool,
+        default=False,
+        help="flag to indicate whether training on PAI",
+    )
+    parser.add_argument(
+        "--simple_ddp",
+        type=str2bool,
+        default=False,
+    )
+    parser.add_argument(
+        "--num_worker_count",
+        type=int,
+        default=1,
+        help="The number of machines on PAI.",
+    )
+    parser.add_argument(
+        "--access_key_id",
+        type=str,
+        default=None,
+        help="The username for oss.",
+    )
+    parser.add_argument(
+        "--access_key_secret",
+        type=str,
+        default=None,
+        help="The password for oss.",
+    )
+    parser.add_argument(
+        "--endpoint",
+        type=str,
+        default=None,
+        help="The endpoint for oss.",
+    )
+    parser.add_argument(
+        "--bucket_name",
+        type=str,
+        default=None,
+        help="The bucket name for oss.",
+    )
+    parser.add_argument(
+        "--oss_bucket",
+        default=None,
+        help="oss bucket.",
+    )
+    parser.add_argument(
+        "--enable_lora",
+        type=str2bool,
+        default=False,
+        help="Apply lora for finetuning.",
+    )
+    parser.add_argument(
+        "--lora_bias",
+        type=str,
+        default="none",
+        help="lora bias.",
+    )
+
+    return parser
 
 
 def build_trainer(modelscope_dict,
@@ -56,9 +527,10 @@ def build_trainer(modelscope_dict,
                   specaug_conf=None,
                   mate_params=None,
                   **kwargs):
-    mode = modelscope_dict['mode']
-    args, ASRTask = parse_args(mode=mode)
-    # ddp related
+    parser = get_parser()
+    args, extra_task_params = parser.parse_known_args()
+    args = build_args(args, parser, extra_task_params)
+
     if args.local_rank is not None:
         distributed = True
     else:
@@ -97,21 +569,9 @@ def build_trainer(modelscope_dict,
                 setattr(args, key, value)
     if mate_params is not None and "lora_params" in mate_params:
         lora_params = mate_params['lora_params']
-        configs['encoder_conf'].update(lora_params) 
-        configs['decoder_conf'].update(lora_params) 
-
-    # prepare data
+        configs['encoder_conf'].update(lora_params)
+        configs['decoder_conf'].update(lora_params)
     args.dataset_type = dataset_type
-    if args.dataset_type == "small":
-        args.train_data_path_and_name_and_type = [["{}/{}/wav.scp".format(data_dir, train_set), "speech", "sound"],
-                                                  ["{}/{}/text".format(data_dir, train_set), "text", "text"]]
-        args.valid_data_path_and_name_and_type = [["{}/{}/wav.scp".format(data_dir, dev_set), "speech", "sound"],
-                                                  ["{}/{}/text".format(data_dir, dev_set), "text", "text"]]
-    elif args.dataset_type == "large":
-        args.train_data_file = None
-        args.valid_data_file = None
-    else:
-        raise ValueError(f"Not supported dataset_type={args.dataset_type}")
     args.init_param = [init_param]
     if mate_params is not None and "init_param" in mate_params:
         if len(mate_params["init_param"]) != 0:
@@ -127,6 +587,16 @@ def build_trainer(modelscope_dict,
     args.output_dir = output_dir
     args.gpu_id = args.local_rank
     args.config = finetune_config
+    args.use_pai = False
+    args.batch_type = "length"
+    args.oss_bucket = None
+    args.input_size = None
+    if distributed:
+        args.distributed = True
+        args.simple_ddp = True
+    else:
+        args.distributed = False
+        args.ngpu = 1
     if optim is not None:
         args.optim = optim
     if lr is not None:
@@ -144,6 +614,7 @@ def build_trainer(modelscope_dict,
     if batch_bins is not None:
         if args.dataset_type == "small":
             args.batch_bins = batch_bins
+            args.dataset_conf["batch_conf"]["batch_size"] = batch_bins
         elif args.dataset_type == "large":
             args.dataset_conf["batch_conf"]["batch_size"] = batch_bins
         else:
@@ -153,7 +624,94 @@ def build_trainer(modelscope_dict,
     if args.patience in ["null", "none", "None"]:
         args.patience = None
     args.local_rank = local_rank
-    args.distributed = distributed
-    ASRTask.finetune_args = args
 
-    return ASRTask
+    # set random seed
+    set_all_random_seed(args.seed)
+    torch.backends.cudnn.enabled = args.cudnn_enabled
+    torch.backends.cudnn.benchmark = args.cudnn_benchmark
+    torch.backends.cudnn.deterministic = args.cudnn_deterministic
+
+    # ddp init
+    distributed_option = build_distributed(args)
+
+    # for logging
+    if not distributed_option.distributed or distributed_option.dist_rank == 0:
+        logging.basicConfig(
+            level="INFO",
+            format=f"[{os.uname()[1].split('.')[0]}]"
+                   f" %(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
+        )
+    else:
+        logging.basicConfig(
+            level="ERROR",
+            format=f"[{os.uname()[1].split('.')[0]}]"
+                   f" %(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
+        )
+
+    # prepare files for dataloader
+    prepare_data(args, distributed_option)
+
+    model = build_model(args)
+    model = model.to(
+        dtype=getattr(torch, args.train_dtype),
+        device="cuda" if args.ngpu > 0 else "cpu",
+    )
+    if args.enable_lora:
+        mark_only_lora_as_trainable(model, args.lora_bias)
+    for t in args.freeze_param:
+        for k, p in model.named_parameters():
+            if k.startswith(t + ".") or k == t:
+                logging.info(f"Setting {k}.requires_grad = False")
+                p.requires_grad = False
+
+    optimizers = build_optimizer(args, model=model)
+    schedulers = build_scheduler(args, optimizers)
+
+    logging.info("world size: {}, rank: {}, local_rank: {}".format(distributed_option.dist_world_size,
+                                                                   distributed_option.dist_rank,
+                                                                   distributed_option.local_rank))
+    logging.info(pytorch_cudnn_version())
+    logging.info("Args: {}".format(args))
+    logging.info(model_summary(model))
+    logging.info("Optimizer: {}".format(optimizers))
+    logging.info("Scheduler: {}".format(schedulers))
+
+    # dump args to config.yaml
+    if not distributed_option.distributed or distributed_option.dist_rank == 0:
+        os.makedirs(args.output_dir, exist_ok=True)
+        with open(os.path.join(args.output_dir, "config.yaml"), "w") as f:
+            logging.info("Saving the configuration in {}/{}".format(args.output_dir, "config.yaml"))
+            if args.use_pai:
+                buffer = BytesIO()
+                torch.save({"config": vars(args)}, buffer)
+                args.oss_bucket.put_object(os.path.join(args.output_dir, "config.dict"), buffer.getvalue())
+            else:
+                yaml_no_alias_safe_dump(vars(args), f, indent=4, sort_keys=False)
+
+    for p in args.init_param:
+        logging.info(f"Loading pretrained params from {p}")
+        load_pretrained_model(
+            model=model,
+            init_param=p,
+            ignore_init_mismatch=args.ignore_init_mismatch,
+            map_location=f"cuda:{torch.cuda.current_device()}"
+            if args.ngpu > 0
+            else "cpu",
+            oss_bucket=args.oss_bucket,
+        )
+
+    # dataloader for training/validation
+    train_dataloader, valid_dataloader = build_dataloader(args)
+
+    # Trainer, including model, optimizers, etc.
+    trainer = build_trainer_modelscope(
+        args=args,
+        model=model,
+        optimizers=optimizers,
+        schedulers=schedulers,
+        train_dataloader=train_dataloader,
+        valid_dataloader=valid_dataloader,
+        distributed_option=distributed_option
+    )
+
+    return trainer

funasr/datasets/small_datasets/sequence_iter_factory.py

@@ -66,8 +66,9 @@ class SequenceIterFactory(AbsIterFactory):
             batch_bins=dataset_conf["batch_conf"]["batch_size"] * args.ngpu,
             shape_files=shape_files,
             sort_in_batch=dataset_conf["sort_in_batch"] if hasattr(dataset_conf, "sort_in_batch") else "descending",
-            sort_batch=dataset_conf["sort_batch"] if hasattr(dataset_conf, "sort_batch") else "ascending",
+            sort_batch=dataset_conf["sort_batch"] if hasattr(dataset_conf, "sort_batch") else "descending",
             drop_last=False,
+            min_batch_size=torch.distributed.get_world_size(),
             padding=True,
         )
 

funasr/utils/prepare_data.py

@@ -195,24 +195,10 @@ def generate_data_list(args, data_dir, dataset, nj=64):
 
 
 def prepare_data(args, distributed_option):
-    distributed = distributed_option.distributed
     data_names = args.dataset_conf.get("data_names", "speech,text").split(",")
     data_types = args.dataset_conf.get("data_types", "sound,text").split(",")
     file_names = args.data_file_names.split(",")
     batch_type = args.dataset_conf["batch_conf"]["batch_type"]
-    if not distributed or distributed_option.dist_rank == 0:
-        if hasattr(args, "filter_input") and args.filter_input:
-            filter_wav_text(args.data_dir, args.train_set)
-            filter_wav_text(args.data_dir, args.valid_set)
-
-        if args.dataset_type == "small" and batch_type != "unsorted":
-            calc_shape(args, args.train_set)
-            calc_shape(args, args.valid_set)
-
-        if args.dataset_type == "large":
-            generate_data_list(args, args.data_dir, args.train_set)
-            generate_data_list(args, args.data_dir, args.valid_set)
-
     print("data_names: {}, data_types: {}, file_names: {}".format(data_names, data_types, file_names))
     assert len(data_names) == len(data_types) == len(file_names)
     if args.dataset_type == "small":
@@ -224,9 +210,32 @@ def prepare_data(args, distributed_option):
                 ["{}/{}/{}".format(args.data_dir, args.train_set, file_name), data_name, data_type])
             args.valid_data_path_and_name_and_type.append(
                 ["{}/{}/{}".format(args.data_dir, args.valid_set, file_name), data_name, data_type])
+        if os.path.exists(args.train_shape_file[0]):
+            assert os.path.exists(args.valid_shape_file[0])
+            print('shape file for small dataset already exists.')
+            return
     else:
         concat_data_name = "_".join(data_names)
         args.train_data_file = os.path.join(args.data_dir, args.train_set, "{}_data.list".format(concat_data_name))
         args.valid_data_file = os.path.join(args.data_dir, args.valid_set, "{}_data.list".format(concat_data_name))
+        if os.path.exists(args.train_data_file):
+            assert os.path.exists(args.valid_data_file)
+            print('data list for large dataset already exists.')
+            return
+
+    distributed = distributed_option.distributed
+    if not distributed or distributed_option.dist_rank == 0:
+        if hasattr(args, "filter_input") and args.filter_input:
+            filter_wav_text(args.data_dir, args.train_set)
+            filter_wav_text(args.data_dir, args.valid_set)
+
+        if args.dataset_type == "small" and batch_type != "unsorted":
+            calc_shape(args, args.train_set)
+            calc_shape(args, args.valid_set)
+
+        if args.dataset_type == "large":
+            generate_data_list(args, args.data_dir, args.train_set)
+            generate_data_list(args, args.data_dir, args.valid_set)
+
     if distributed:
         dist.barrier()