From 54931dd4e1a099d7d6f144c4e12e5453deb3aa26 Mon Sep 17 00:00:00 2001
From: 雾聪 <wucong.lyb@alibaba-inc.com>
Date: 星期三, 28 六月 2023 10:41:57 +0800
Subject: [PATCH] Merge branch 'main' of https://github.com/alibaba-damo-academy/FunASR into main
---
funasr/bin/train.py | 288 +++++++++++++++++++++++++++++++++++++++++++++++++++-----
1 files changed, 259 insertions(+), 29 deletions(-)
diff --git a/funasr/bin/train.py b/funasr/bin/train.py
old mode 100644
new mode 100755
index dbfebd7..1dc3fb5
--- a/funasr/bin/train.py
+++ b/funasr/bin/train.py
@@ -1,18 +1,37 @@
+#!/usr/bin/env python3
+# 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
+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
+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 import config_argparse
-from funasr.utils.build_distributed import build_distributed
+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 get_parser():
- parser = config_argparse.ArgumentParser(
+ parser = argparse.ArgumentParser(
description="FunASR Common Training Parser",
)
@@ -25,6 +44,7 @@
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(
@@ -42,18 +62,51 @@
)
parser.add_argument(
"--dist_world_size",
- default=None,
+ 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",
@@ -61,6 +114,12 @@
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
@@ -104,6 +163,7 @@
)
parser.add_argument(
"--patience",
+ type=int_or_none,
default=None,
help="Number of epochs to wait without improvement "
"before stopping the training",
@@ -185,6 +245,12 @@
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,
@@ -197,13 +263,19 @@
"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=[],
- 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, "
@@ -229,7 +301,7 @@
"--freeze_param",
type=str,
default=[],
- nargs="*",
+ action="append",
help="Freeze parameters",
)
@@ -241,27 +313,122 @@
help="whether to use dataloader for large dataset",
)
parser.add_argument(
- "--train_data_file",
+ "--dataset_conf",
+ action=NestedDictAction,
+ default=dict(),
+ help=f"The keyword arguments for dataset",
+ )
+ parser.add_argument(
+ "--data_dir",
type=str,
default=None,
- help="train_list for large dataset",
+ help="root path of data",
)
parser.add_argument(
- "--valid_data_file",
+ "--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="valid_list for large dataset",
+ help="speed perturb",
)
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'. ",
+ "--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(
- "--valid_data_path_and_name_and_type",
- action="append",
- default=[],
+ "--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
@@ -312,19 +479,27 @@
help="oss bucket.",
)
- # task related
- parser.add_argument("--task_name", help="for different task")
-
return parser
if __name__ == '__main__':
parser = get_parser()
- args = parser.parse_args()
+ args, extra_task_params = parser.parse_known_args()
+ if extra_task_params:
+ args = build_args(args, parser, extra_task_params)
+
+ # 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
- args.distributed = args.dist_world_size > 1
+ os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu_id)
+ args.distributed = args.ngpu > 1 or args.dist_world_size > 1
distributed_option = build_distributed(args)
+
+ # for logging
if not distributed_option.distributed or distributed_option.dist_rank == 0:
logging.basicConfig(
level="INFO",
@@ -337,14 +512,69 @@
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))
# 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
+ model = build_model(args)
+ model = model.to(
+ dtype=getattr(torch, args.train_dtype),
+ device="cuda" if args.ngpu > 0 else "cpu",
+ )
+ 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(
+ args=args,
+ model=model,
+ optimizers=optimizers,
+ schedulers=schedulers,
+ train_dataloader=train_dataloader,
+ valid_dataloader=valid_dataloader,
+ distributed_option=distributed_option
+ )
+
+ trainer.run()
--
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