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finetune.py
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finetune.py
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import argparse
import functools
import os
from peft import LoraConfig, get_peft_model, AdaLoraConfig, PeftModel, prepare_model_for_kbit_training
from transformers import Seq2SeqTrainer, Seq2SeqTrainingArguments, WhisperForConditionalGeneration, WhisperProcessor
from utils.callback import SavePeftModelCallback
from utils.data_utils import DataCollatorSpeechSeq2SeqWithPadding
from utils.model_utils import load_from_checkpoint
from utils.reader import CustomDataset
from utils.utils import print_arguments, make_inputs_require_grad, add_arguments
parser = argparse.ArgumentParser(description=__doc__)
add_arg = functools.partial(add_arguments, argparser=parser)
add_arg("train_data", type=str, default="dataset/train.json", help="训练数据集的路径")
add_arg("test_data", type=str, default="dataset/test.json", help="测试数据集的路径")
add_arg("base_model", type=str, default="openai/whisper-tiny", help="Whisper的基础模型")
add_arg("output_dir", type=str, default="output/", help="训练保存模型的路径")
add_arg("warmup_steps", type=int, default=50, help="训练预热步数")
add_arg("logging_steps", type=int, default=100, help="打印日志步数")
add_arg("eval_steps", type=int, default=1000, help="多少步数评估一次")
add_arg("save_steps", type=int, default=1000, help="多少步数保存模型一次")
add_arg("num_workers", type=int, default=8, help="读取数据的线程数量")
add_arg("learning_rate", type=float, default=1e-3, help="学习率大小")
add_arg("min_audio_len", type=float, default=0.5, help="最小的音频长度，单位秒")
add_arg("max_audio_len", type=float, default=30, help="最大的音频长度，单位秒")
add_arg("use_adalora", type=bool, default=True, help="是否使用AdaLora而不是Lora")
add_arg("fp16", type=bool, default=True, help="是否使用fp16训练模型")
add_arg("use_8bit", type=bool, default=False, help="是否将模型量化为8位")
add_arg("timestamps", type=bool, default=False, help="训练时是否使用时间戳数据")
add_arg("use_compile", type=bool, default=False, help="是否使用Pytorch2.0的编译器")
add_arg("local_files_only", type=bool, default=False, help="是否只在本地加载模型，不尝试下载")
add_arg("num_train_epochs", type=int, default=3, help="训练的轮数")
add_arg("language", type=str, default="Chinese", help="设置语言，可全称也可简写，如果为None则训练的是多语言")
add_arg("task", type=str, default="transcribe", choices=['transcribe', 'translate'], help="模型的任务")
add_arg("augment_config_path", type=str, default=None, help="数据增强配置文件路径")
add_arg("resume_from_checkpoint", type=str, default=None, help="恢复训练的检查点路径")
add_arg("per_device_train_batch_size", type=int, default=8, help="训练的batch size")
add_arg("per_device_eval_batch_size", type=int, default=8, help="评估的batch size")
add_arg("gradient_accumulation_steps", type=int, default=1, help="梯度累积步数")
args = parser.parse_args()
print_arguments(args)
# 获取Whisper的数据处理器，这个包含了特征提取器、tokenizer
processor = WhisperProcessor.from_pretrained(args.base_model,
language=args.language,
task=args.task,
no_timestamps=not args.timestamps,
local_files_only=args.local_files_only)
# 读取数据
train_dataset = CustomDataset(data_list_path=args.train_data,
processor=processor,
language=args.language,
timestamps=args.timestamps,
min_duration=args.min_audio_len,
max_duration=args.max_audio_len,
augment_config_path=args.augment_config_path)
test_dataset = CustomDataset(data_list_path=args.test_data,
processor=processor,
language=args.language,
timestamps=args.timestamps,
min_duration=args.min_audio_len,
max_duration=args.max_audio_len)
print(f"训练数据：{len(train_dataset)}，测试数据：{len(test_dataset)}")
# 数据padding器
data_collator = DataCollatorSpeechSeq2SeqWithPadding(processor=processor)
# 获取Whisper模型
device_map = "auto"
world_size = int(os.environ.get("WORLD_SIZE", 1))
ddp = world_size != 1
if ddp:
device_map = {"": int(os.environ.get("LOCAL_RANK") or 0)}
# 获取模型
model = WhisperForConditionalGeneration.from_pretrained(args.base_model,
load_in_8bit=args.use_8bit,
device_map=device_map,
local_files_only=args.local_files_only)
model.config.forced_decoder_ids = None
model.config.suppress_tokens = []
# 量化模型
model = prepare_model_for_kbit_training(model)
# 注册forward，否则多卡训练会失败
model.model.encoder.conv1.register_forward_hook(make_inputs_require_grad)
print('加载LoRA模块...')
if args.resume_from_checkpoint:
# 恢复训练时加载Lora参数
print("Loading adapters from checkpoint.")
model = PeftModel.from_pretrained(model, args.resume_from_checkpoint, is_trainable=True)
else:
print(f'adding LoRA modules...')
target_modules = ["k_proj", "q_proj", "v_proj", "out_proj", "fc1", "fc2"]
print(target_modules)
if args.use_adalora:
config = AdaLoraConfig(init_r=12, target_r=4, beta1=0.85, beta2=0.85, tinit=200, tfinal=1000, deltaT=10,
lora_alpha=32, lora_dropout=0.1, orth_reg_weight=0.5, target_modules=target_modules)
else:
config = LoraConfig(r=32, lora_alpha=64, target_modules=target_modules, lora_dropout=0.05, bias="none")
model = get_peft_model(model, config)
if args.base_model.endswith("/"):
args.base_model = args.base_model[:-1]
output_dir = os.path.join(args.output_dir, os.path.basename(args.base_model))
# 定义训练参数
training_args = \
Seq2SeqTrainingArguments(output_dir=output_dir, # 保存检查点和意志的目录
per_device_train_batch_size=args.per_device_train_batch_size, # 训练batch_size大小
per_device_eval_batch_size=args.per_device_eval_batch_size, # 评估batch_size大小
gradient_accumulation_steps=args.gradient_accumulation_steps, # 训练梯度累计步数
learning_rate=args.learning_rate, # 学习率大小
warmup_steps=args.warmup_steps, # 预热步数
num_train_epochs=args.num_train_epochs, # 微调训练轮数
save_strategy="steps", # 指定按照步数保存检查点
evaluation_strategy="steps", # 指定按照步数评估模型
load_best_model_at_end=True, # 指定是否在结束时加载最优模型
fp16=args.fp16, # 是否使用半精度训练
report_to=["tensorboard"], # 指定使用tensorboard保存log
save_steps=args.save_steps, # 指定保存检查点的步数
eval_steps=args.eval_steps, # 指定评估模型的步数
torch_compile=args.use_compile, # 使用Pytorch2.0的编译器
save_total_limit=5, # 只保存最新检查点的数量
optim='adamw_torch', # 指定优化方法
ddp_find_unused_parameters=False if ddp else None, # 分布式训练设置
dataloader_num_workers=args.num_workers, # 设置读取数据的线程数量
logging_steps=args.logging_steps, # 指定打印log的步数
remove_unused_columns=False, # 删除模型不需要的数据列
label_names=["labels"]) # 与标签对应的输入字典中的键列表
if training_args.local_rank == 0 or training_args.local_rank == -1:
print('=' * 90)
model.print_trainable_parameters()
print('=' * 90)
# 定义训练器
trainer = Seq2SeqTrainer(args=training_args,
model=model,
train_dataset=train_dataset,
eval_dataset=test_dataset,
data_collator=data_collator,
tokenizer=processor.feature_extractor,
callbacks=[SavePeftModelCallback])
model.config.use_cache = False
trainer._load_from_checkpoint = load_from_checkpoint
# 开始训练
trainer.train(resume_from_checkpoint=args.resume_from_checkpoint)
# 保存最后的模型
trainer.save_state()
if training_args.local_rank == 0 or training_args.local_rank == -1:
model.save_pretrained(os.path.join(output_dir, "checkpoint-final"))