From 94de39dde2e616a01683c518023d0fab72b4e103 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期一, 19 二月 2024 22:21:50 +0800
Subject: [PATCH] aishell example
---
funasr/bin/train.py | 513 +++++++++++++++++++-------------------------------------
1 files changed, 177 insertions(+), 336 deletions(-)
diff --git a/funasr/bin/train.py b/funasr/bin/train.py
index dbfebd7..d916509 100644
--- a/funasr/bin/train.py
+++ b/funasr/bin/train.py
@@ -1,350 +1,191 @@
-import logging
+#!/usr/bin/env python3
+# -*- encoding: utf-8 -*-
+
import os
import sys
-
import torch
+import hydra
+import logging
+import argparse
+from io import BytesIO
+import torch.distributed as dist
+from collections.abc import Sequence
+from omegaconf import DictConfig, OmegaConf
+from torch.nn.parallel import DistributedDataParallel as DDP
+from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
-from funasr.torch_utils.set_all_random_seed import set_all_random_seed
-from funasr.utils import config_argparse
-from funasr.utils.build_distributed import build_distributed
-from funasr.utils.prepare_data import prepare_data
-from funasr.utils.types import str2bool
+from funasr.register import tables
+from funasr.optimizers import optim_classes
+from funasr.train_utils.trainer import Trainer
+from funasr.schedulers import scheduler_classes
+from funasr.train_utils.initialize import initialize
+from funasr.download.download_from_hub import download_model
+from funasr.models.lora.utils import mark_only_lora_as_trainable
+from funasr.train_utils.set_all_random_seed import set_all_random_seed
+from funasr.train_utils.load_pretrained_model import load_pretrained_model
+# from funasr.tokenizer.build_tokenizer import build_tokenizer
+# from funasr.tokenizer.token_id_converter import TokenIDConverter
+# from funasr.tokenizer.funtoken import build_tokenizer
-def get_parser():
- parser = config_argparse.ArgumentParser(
- description="FunASR Common Training Parser",
- )
+@hydra.main(config_name=None, version_base=None)
+def main_hydra(kwargs: DictConfig):
+ if kwargs.get("debug", False):
+ import pdb; pdb.set_trace()
- # 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")
+ assert "model" in kwargs
+ if "model_conf" not in kwargs:
+ logging.info("download models from model hub: {}".format(kwargs.get("model_hub", "ms")))
+ kwargs = download_model(is_training=kwargs.get("is_training", True), **kwargs)
+
- # 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",
- default=None,
- help="number of nodes for distributed training",
- )
- parser.add_argument(
- "--dist_rank",
- default=None,
- help="node rank for distributed training",
- )
- parser.add_argument(
- "--local_rank",
- default=None,
- help="local rank for distributed training",
- )
- parser.add_argument(
- "--unused_parameters",
- type=str2bool,
- default=False,
- help="Whether to use the find_unused_parameters in "
- "torch.nn.parallel.DistributedDataParallel ",
- )
-
- # 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",
- 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(
- "--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 .",
- )
-
- # pretrained model related
- parser.add_argument(
- "--init_param",
- type=str,
- default=[],
- nargs="*",
- 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=[],
- nargs="*",
- 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(
- "--train_data_file",
- type=str,
- default=None,
- help="train_list for large dataset",
- )
- parser.add_argument(
- "--valid_data_file",
- type=str,
- default=None,
- help="valid_list for large dataset",
- )
- parser.add_argument(
- "--train_data_path_and_name_and_type",
- action="append",
- default=[],
- help="e.g. '--train_data_path_and_name_and_type some/path/a.scp,foo,sound'. ",
- )
- parser.add_argument(
- "--valid_data_path_and_name_and_type",
- action="append",
- default=[],
- )
-
- # 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.",
- )
-
- # task related
- parser.add_argument("--task_name", help="for different task")
-
- return parser
+ main(**kwargs)
-if __name__ == '__main__':
- parser = get_parser()
- args = parser.parse_args()
+def main(**kwargs):
+ print(kwargs)
+ # set random seed
+ tables.print()
+ set_all_random_seed(kwargs.get("seed", 0))
+ 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)
+
+ local_rank = int(os.environ.get('LOCAL_RANK', 0))
+ # Check if we are using DDP or FSDP
+ use_ddp = 'WORLD_SIZE' in os.environ and int(os.environ["WORLD_SIZE"]) > 1
+ use_fsdp = kwargs.get("use_fsdp", None)
+ if use_ddp or use_fsdp:
+ dist.init_process_group(backend=kwargs.get("backend", "nccl"), init_method='env://')
+ torch.cuda.set_device(local_rank)
+
+ # save config.yaml
+ if (use_ddp or use_fsdp) and dist.get_rank() == 0 or not (use_ddp or use_fsdp) and local_rank == 0:
+ os.makedirs(kwargs.get("output_dir", "./"), exist_ok=True)
+ yaml_file = os.path.join(kwargs.get("output_dir", "./"), "config.yaml")
+ OmegaConf.save(config=kwargs, f=yaml_file)
+ logging.info("config.yaml is saved to: %s", yaml_file)
- # ddp init
- args.distributed = args.dist_world_size > 1
- distributed_option = build_distributed(args)
- 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",
- )
+ tokenizer = kwargs.get("tokenizer", None)
+ if tokenizer is not None:
+ tokenizer_class = tables.tokenizer_classes.get(tokenizer)
+ tokenizer = tokenizer_class(**kwargs["tokenizer_conf"])
+ kwargs["tokenizer"] = tokenizer
+
+ # build frontend if frontend is none None
+ frontend = kwargs.get("frontend", None)
+ if frontend is not None:
+ frontend_class = tables.frontend_classes.get(frontend)
+ frontend = frontend_class(**kwargs["frontend_conf"])
+ kwargs["frontend"] = frontend
+ kwargs["input_size"] = frontend.output_size()
+
+
+ # build model
+ model_class = tables.model_classes.get(kwargs["model"])
+ model = model_class(**kwargs, **kwargs["model_conf"], vocab_size=len(tokenizer.token_list))
+
+
+
+ # init_param
+ init_param = kwargs.get("init_param", None)
+ if init_param is not None:
+ if not isinstance(init_param, (list, tuple)):
+ init_param = (init_param,)
+ logging.info("init_param is not None: %s", init_param)
+ for p in init_param:
+ logging.info(f"Loading pretrained params from {p}")
+ load_pretrained_model(
+ model=model,
+ path=p,
+ ignore_init_mismatch=kwargs.get("ignore_init_mismatch", True),
+ oss_bucket=kwargs.get("oss_bucket", None),
+ scope_map=kwargs.get("scope_map", None),
+ excludes=kwargs.get("excludes", None),
+ )
else:
- logging.basicConfig(
- level="ERROR",
- format=f"[{os.uname()[1].split('.')[0]}]"
- f" %(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
- )
- logging.info("world size: {}, rank: {}, local_rank: {}".format(distributed_option.dist_world_size,
- distributed_option.dist_rank,
- distributed_option.local_rank))
+ initialize(model, kwargs.get("init", "kaiming_normal"))
- # prepare files for dataloader
- prepare_data(args, distributed_option)
- 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
+ # freeze_param
+ freeze_param = kwargs.get("freeze_param", None)
+ if freeze_param is not None:
+ freeze_param = eval(freeze_param)
+ if isinstance(freeze_param, Sequence):
+ freeze_param = (freeze_param,)
+ logging.info("freeze_param is not None: %s", freeze_param)
+ for t in 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
+
+
+ if use_ddp:
+ model = model.cuda(local_rank)
+ model = DDP(model, device_ids=[local_rank],
+ find_unused_parameters=kwargs.get("train_conf", {}).get("find_unused_parameters", False))
+ elif use_fsdp:
+ model = FSDP(model).cuda(local_rank)
+ else:
+ model = model.to(device=kwargs.get("device", "cuda"))
+
+
+ # optim
+ optim = kwargs.get("optim", "adam")
+ assert optim in optim_classes
+ optim_class = optim_classes.get(optim)
+ optim = optim_class(model.parameters(), **kwargs.get("optim_conf"))
+
+ # scheduler
+ scheduler = kwargs.get("scheduler", "warmuplr")
+ assert scheduler in scheduler_classes
+ scheduler_class = scheduler_classes.get(scheduler)
+ scheduler = scheduler_class(optim, **kwargs.get("scheduler_conf"))
+
+
+ # dataset
+ dataset_class = tables.dataset_classes.get(kwargs.get("dataset", "AudioDataset"))
+ dataset_tr = dataset_class(kwargs.get("train_data_set_list"), frontend=frontend, tokenizer=tokenizer, is_training=True, **kwargs.get("dataset_conf"))
+ dataset_val = dataset_class(kwargs.get("valid_data_set_list"), frontend=frontend, tokenizer=tokenizer, is_training=False, **kwargs.get("dataset_conf"))
+
+ # dataloader
+ batch_sampler = kwargs["dataset_conf"].get("batch_sampler", "DynamicBatchLocalShuffleSampler")
+ batch_sampler_val = None
+ if batch_sampler is not None:
+ batch_sampler_class = tables.batch_sampler_classes.get(batch_sampler)
+ batch_sampler = batch_sampler_class(dataset_tr, **kwargs.get("dataset_conf"))
+ batch_sampler_val = batch_sampler_class(dataset_val, is_training=False, **kwargs.get("dataset_conf"))
+ dataloader_tr = torch.utils.data.DataLoader(dataset_tr,
+ collate_fn=dataset_tr.collator,
+ batch_sampler=batch_sampler,
+ num_workers=kwargs.get("dataset_conf").get("num_workers", 4),
+ pin_memory=True)
+
+ dataloader_val = torch.utils.data.DataLoader(dataset_val,
+ collate_fn=dataset_val.collator,
+ batch_sampler=batch_sampler_val,
+ num_workers=kwargs.get("dataset_conf").get("num_workers", 4),
+ pin_memory=True)
+ trainer = Trainer(
+ model=model,
+ optim=optim,
+ scheduler=scheduler,
+ dataloader_train=dataloader_tr,
+ dataloader_val=dataloader_val,
+ local_rank=local_rank,
+ use_ddp=use_ddp,
+ use_fsdp=use_fsdp,
+ output_dir=kwargs.get("output_dir", "./exp"),
+ resume=kwargs.get("resume", True),
+ **kwargs.get("train_conf"),
+ )
+ trainer.run()
+
+ if use_ddp or use_fsdp:
+ torch.distributed.destroy_process_group()
+
+
+
+if __name__ == "__main__":
+ main_hydra()
\ No newline at end of file
--
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