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Merge pull request #790 from alibaba-damo-academy/dev_wjm_modelscope

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update modelscope finetune
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jmwang66 2023-08-09 16:48:21 +08:00 committed by GitHub
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funasr/bin/build_trainer.py
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@ -1,7 +1,36 @@
import os
# 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

3
funasr/datasets/small_datasets/sequence_iter_factory.py
View File

@ -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,
)

41
funasr/utils/prepare_data.py
View File

@ -195,11 +195,35 @@ 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"]
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":
args.train_shape_file = [os.path.join(args.data_dir, args.train_set, "{}_shape".format(data_names[0]))]
args.valid_shape_file = [os.path.join(args.data_dir, args.valid_set, "{}_shape".format(data_names[0]))]
args.train_data_path_and_name_and_type, args.valid_data_path_and_name_and_type = [], []
for file_name, data_name, data_type in zip(file_names, data_names, data_types):
args.train_data_path_and_name_and_type.append(
["{}/{}/{}".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)
@ -213,20 +237,5 @@ def prepare_data(args, distributed_option):
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":
args.train_shape_file = [os.path.join(args.data_dir, args.train_set, "{}_shape".format(data_names[0]))]
args.valid_shape_file = [os.path.join(args.data_dir, args.valid_set, "{}_shape".format(data_names[0]))]
args.train_data_path_and_name_and_type, args.valid_data_path_and_name_and_type = [], []
for file_name, data_name, data_type in zip(file_names, data_names, data_types):
args.train_data_path_and_name_and_type.append(
["{}/{}/{}".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])
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 distributed:
dist.barrier()