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