257 lines
8.7 KiB
Python
257 lines
8.7 KiB
Python
'''
|
|
预训练脚本
|
|
'''
|
|
|
|
import logging
|
|
import math
|
|
import os
|
|
import sys
|
|
from dataclasses import dataclass, field
|
|
from torchdata.datapipes.iter import IterableWrapper
|
|
from itertools import chain
|
|
import deepspeed
|
|
from typing import Optional,List
|
|
|
|
import datasets
|
|
import pandas as pd
|
|
import torch
|
|
from datasets import load_dataset
|
|
import transformers
|
|
from transformers import (
|
|
AutoConfig,
|
|
AutoModelForCausalLM,
|
|
AutoTokenizer,
|
|
HfArgumentParser,
|
|
Trainer,
|
|
TrainingArguments,
|
|
default_data_collator,
|
|
set_seed,
|
|
)
|
|
import datetime
|
|
from transformers.testing_utils import CaptureLogger
|
|
from transformers.trainer_utils import get_last_checkpoint
|
|
import wandb
|
|
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
# 超参类
|
|
@dataclass
|
|
class ModelArguments:
|
|
"""
|
|
关于模型的参数
|
|
"""
|
|
|
|
model_name_or_path: Optional[str] = field(
|
|
default=None,
|
|
metadata={
|
|
"help": (
|
|
"后训练使用，为预训练模型参数地址"
|
|
)
|
|
},
|
|
)
|
|
config_name: Optional[str] = field(
|
|
default=None, metadata={"help": "预训练使用，Config 文件地址"}
|
|
)
|
|
tokenizer_name: Optional[str] = field(
|
|
default=None, metadata={"help": "预训练 Tokenizer 地址"}
|
|
)
|
|
torch_dtype: Optional[str] = field(
|
|
default=None,
|
|
metadata={
|
|
"help": (
|
|
"模型训练使用的数据类型，推荐 bfloat16"
|
|
),
|
|
"choices": ["auto", "bfloat16", "float16", "float32"],
|
|
},
|
|
)
|
|
|
|
|
|
@dataclass
|
|
class DataTrainingArguments:
|
|
"""
|
|
关于训练的参数
|
|
"""
|
|
|
|
train_files: Optional[List[str]] = field(default=None, metadata={"help": "训练数据路径"})
|
|
block_size: Optional[int] = field(
|
|
default=None,
|
|
metadata={
|
|
"help": (
|
|
"设置的文本块长度"
|
|
)
|
|
},
|
|
)
|
|
preprocessing_num_workers: Optional[int] = field(
|
|
default=None,
|
|
metadata={"help": "预处理使用线程数."},
|
|
)
|
|
|
|
|
|
def main():
|
|
|
|
# 加载脚本参数
|
|
parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
|
|
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
|
|
|
|
# 初始化 WandB
|
|
wandb.init(project="pretrain", name="from_scrach")
|
|
|
|
# 设置日志
|
|
logging.basicConfig(
|
|
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
|
|
datefmt="%m/%d/%Y %H:%M:%S",
|
|
handlers=[logging.StreamHandler(sys.stdout)],
|
|
)
|
|
|
|
# 将日志级别设置为 INFO
|
|
transformers.utils.logging.set_verbosity_info()
|
|
log_level = training_args.get_process_log_level()
|
|
logger.setLevel(log_level)
|
|
datasets.utils.logging.set_verbosity(log_level)
|
|
transformers.utils.logging.set_verbosity(log_level)
|
|
transformers.utils.logging.enable_default_handler()
|
|
transformers.utils.logging.enable_explicit_format()
|
|
|
|
# 训练整体情况记录
|
|
logger.warning(
|
|
f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
|
|
+ f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
|
|
)
|
|
logger.info(f"Training/evaluation parameters {training_args}")
|
|
|
|
# 检查 checkpoint
|
|
last_checkpoint = None
|
|
if os.path.isdir(training_args.output_dir):
|
|
last_checkpoint = get_last_checkpoint(training_args.output_dir)
|
|
if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
|
|
raise ValueError(
|
|
f"输出路径 ({training_args.output_dir}) 非空 "
|
|
)
|
|
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
|
|
logger.info(
|
|
f"从 {last_checkpoint}恢复训练"
|
|
)
|
|
|
|
# 设置随机数种子.
|
|
set_seed(training_args.seed)
|
|
|
|
# 初始化模型
|
|
if model_args.config_name is not None:
|
|
# from scrach
|
|
config = AutoConfig.from_pretrained(model_args.config_name)
|
|
logger.warning("你正在从零初始化一个模型")
|
|
logger.info(f"模型参数配置地址：{model_args.config_name}")
|
|
logger.info(f"模型参数：{config}")
|
|
model = AutoModelForCausalLM.from_config(config,trust_remote_code=True)
|
|
n_params = sum({p.data_ptr(): p.numel() for p in model.parameters()}.values())
|
|
logger.info(f"预训练一个新模型 - Total size={n_params/2**20:.2f}M params")
|
|
elif model_args.model_name_or_path is not None:
|
|
logger.warning("你正在初始化一个预训练模型")
|
|
logger.info(f"模型参数地址：{model_args.model_name_or_path}")
|
|
model = AutoModelForCausalLM.from_pretrained(model_args.model_name_or_path,trust_remote_code=True)
|
|
n_params = sum({p.data_ptr(): p.numel() for p in model.parameters()}.values())
|
|
logger.info(f"继承一个预训练模型 - Total size={n_params/2**20:.2f}M params")
|
|
else:
|
|
logger.error("config_name 和 model_name_or_path 不能均为空")
|
|
raise ValueError("config_name 和 model_name_or_path 不能均为空")
|
|
|
|
# 初始化 Tokenizer
|
|
tokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name)
|
|
logger.info("完成 tokenzier 加载")
|
|
logger.info(f"tokenzier 配置地址：{model_args.tokenizer_name}")
|
|
|
|
# 加载预训练数据
|
|
ds = load_dataset('json', data_files=data_args.train_files)
|
|
logger.info("完成训练集加载")
|
|
logger.info(f"训练集地址：{data_args.train_files}")
|
|
logger.info(f'训练文件总数:{len(ds["train"])}')
|
|
# logger.info(f"训练集采样：{ds["train"][0]}")
|
|
|
|
# 文本 tokenize
|
|
column_names = list(ds["train"].features)
|
|
logger.info('训练集特征：', column_names)
|
|
text_column_name = "text" if "text" in column_names else column_names[0]
|
|
|
|
# tokenize 函数
|
|
def tokenize_function(examples):
|
|
output = tokenizer([item for item in examples[text_column_name]])
|
|
return output
|
|
|
|
# 仅主进程进行数据预处理
|
|
with training_args.main_process_first(desc="dataset map tokenization"):
|
|
tokenized_datasets = ds.map(
|
|
tokenize_function,
|
|
batched=True,
|
|
num_proc=data_args.preprocessing_num_workers,
|
|
remove_columns=column_names,
|
|
load_from_cache_file=True,
|
|
desc="Running tokenizer on dataset"
|
|
)
|
|
|
|
# 文本切块
|
|
if data_args.block_size is None:
|
|
block_size = tokenizer.model_max_length
|
|
if block_size > 1024:
|
|
logger.warning(
|
|
"tokenizer 支持大于 1K 的上下文长度，默认设置为 1K"
|
|
)
|
|
block_size = 1024
|
|
else:
|
|
if data_args.block_size > tokenizer.model_max_length:
|
|
logger.warning(
|
|
f"设定的块长为 ({data_args.block_size}) ，大于模型的上下文长度"
|
|
f"将块长设置为模型上下文长度：{tokenizer.model_max_length}."
|
|
)
|
|
block_size = min(data_args.block_size, tokenizer.model_max_length)
|
|
|
|
def group_texts(examples):
|
|
# 将文本段拼接起来
|
|
concatenated_examples = {k: list(chain(*examples[k])) for k in examples.keys()}
|
|
# 计算拼起来的整体长度
|
|
total_length = len(concatenated_examples[list(examples.keys())[0]])
|
|
# 如果长度太长，进行分块
|
|
if total_length >= block_size:
|
|
total_length = (total_length // block_size) * block_size
|
|
result = {
|
|
k: [t[i : i + block_size] for i in range(0, total_length, block_size)]
|
|
for k, t in concatenated_examples.items()
|
|
}
|
|
result["labels"] = result["input_ids"].copy()
|
|
return result
|
|
|
|
with training_args.main_process_first(desc="文本分块"):
|
|
lm_datasets = tokenized_datasets.map(
|
|
group_texts,
|
|
batched=True,
|
|
num_proc=data_args.preprocessing_num_workers,
|
|
load_from_cache_file=True,
|
|
desc=f"文本分块到{block_size}",
|
|
batch_size = 40000,
|
|
)
|
|
logger.info("完成数据预处理")
|
|
train_dataset = lm_datasets["train"]
|
|
|
|
logger.info("初始化 Trainer")
|
|
trainer = Trainer(
|
|
model=model,
|
|
args=training_args,
|
|
train_dataset= IterableWrapper(train_dataset),
|
|
tokenizer=tokenizer,
|
|
data_collator=default_data_collator
|
|
)
|
|
|
|
# 从 checkpoint 加载
|
|
checkpoint = None
|
|
if training_args.resume_from_checkpoint is not None:
|
|
checkpoint = training_args.resume_from_checkpoint
|
|
elif last_checkpoint is not None:
|
|
checkpoint = last_checkpoint
|
|
|
|
logger.info("开始训练")
|
|
train_result = trainer.train(resume_from_checkpoint=checkpoint)
|
|
trainer.save_model()
|
|
|
|
if __name__ == "__main__":
|
|
main() |