一、前言

本文主要是在复现和实践Phi2-mini-Chinese后，简要分析下Phi2-mini-Chinese这个项目，做一个学习实战总结。

原文发布于知乎： https://zhuanlan.zhihu.com/p/718307193，转载请注明出数。

Phi2-mini-Chinese简介

Phi2-Chinese-0.2B 从0开始训练自己的Phi2中文小模型，支持接入langchain加载本地知识库做检索增强生成RAG。Training your own Phi2 small chat model from scratch.

项目开始时期：2023年12月22日
地址： https://github.com/charent/Phi2-mini-Chinese

流程步骤

• 数据处理
• Tokenizer训练
• 预训练
• SFT
• DPO

数据处理的步骤略去。一般是使用开源数据集。

二、Tokenizer训练

就是使用tokenizers库用BPE训练，没啥好说的。

三、预训练代码

import os, platform, time
from typing import Optional
import numpy as np
import pandas as pd
from dataclasses import dataclass,field
from datasets import load_dataset, Dataset
import torch
from transformers.trainer_callback import TrainerControl, TrainerState
from transformers import PreTrainedTokenizerFast, DataCollatorForLanguageModeling, PhiConfig, PhiForCausalLM, Trainer, TrainingArguments, TrainerCallback

# 预训练数据（单纯的文本数据）
TRAIN_FILES = ['./data/wiki_chunk_320_2.2M.parquet',]
EVAL_FILE = './data/pretrain_eval_400_1w.parquet'

@dataclass
class PretrainArguments:
    tokenizer_dir: str = './model_save/tokenizer/'
    model_save_dir: str = './model_save/pre/'
    logs_dir: str = './logs/'
    train_files: list[str] = field(default_factory=lambda: TRAIN_FILES)
    eval_file: str = EVAL_FILE
    max_seq_len: int = 512
    attn_implementation: str = 'eager' if platform.system() == 'Windows' else attn_implementation

pretrain_args = PretrainArguments()
# 加载训练好的tokenizer
tokenizer = PreTrainedTokenizerFast.from_pretrained(pretrain_args.tokenizer_dir)
# 词表大小修正
vocab_size = len(tokenizer)
if vocab_size % 64 != 0:
    vocab_size = (vocab_size // 64 + 1) * 64
# 如果词表大小小于 65535 用uint16存储，节省磁盘空间，否则用uint32存储
map_dtype = np.uint16 if vocab_size < 65535 else np.uint32

def token_to_id(samples: dict[str, list]) -> dict:
    batch_txt = samples['text']
    outputs = tokenizer(batch_txt, truncation=False, padding=False, return_attention_mask=False)
    input_ids = [np.array(item, dtype=map_dtype) for item in outputs["input_ids"]]
    return {"input_ids": input_ids}

# 加载数据集
def get_maped_dataset(files: str|list[str]) -> Dataset:
    dataset = load_dataset(path='parquet', data_files=files, split='train', cache_dir='.cache')
    maped_dataset = dataset.map(token_to_id, batched=True, batch_size=1_0000, remove_columns=dataset.column_names)
    return maped_dataset

train_dataset = get_maped_dataset(pretrain_args.train_files)
eval_dataset = get_maped_dataset(pretrain_args.eval_file)
# 定义data_collator。`mlm=False`表示要训练CLM模型，`mlm=True`表示要训练MLM模型
data_collator = DataCollatorForLanguageModeling(tokenizer, mlm=False)

phi_config = PhiConfig(
    vocab_size=vocab_size,
    bos_token_id=tokenizer.bos_token_id,
    eos_token_id=tokenizer.eos_token_id,
    hidden_size=960,
    num_attention_heads=16,
    num_hidden_layers=24,
    max_position_embeddings=512,
    intermediate_size=4096,
    attn_implementation=pretrain_args.attn_implementation,
)
model = PhiForCausalLM(phi_config)

# 定义训练参数
my_trainer_callback = MyTrainerCallback() # cuda cache回调函数
args = TrainingArguments(
    output_dir=pretrain_args.model_save_dir, per_device_train_batch_size=4,
    gradient_accumulation_steps=32, num_train_epochs=4, weight_decay=0.1, 
    warmup_steps=1000, learning_rate=5e-4, evaluation_strategy='steps',
    eval_steps=2000, save_steps=2000, save_strategy='steps', save_total_limit=3,
    report_to='tensorboard', optim="adafactor", bf16=True, logging_steps=5,
    log_level='info', logging_first_step=True,
)
trainer = Trainer(model=model, tokenizer=tokenizer,args=args,
    data_collator=data_collator, train_dataset=train_dataset,
    eval_dataset=eval_dataset, callbacks=[my_trainer_callback],
)
trainer.train()
trainer.save_model(pretrain_args.model_save_dir)

这个代码和只要是使用Transformers库的Trainer大差不差。主要是，tokenizer和CausalLM模型的差别。

PhiConfig, PhiForCausalLM 变成:

from transformers import LlamaConfig as PhiConfig
from transformers import LlamaForCausalLM as PhiForCausalLM

或：

from transformers import Qwen2Config as PhiConfig
from transformers import Qwen2ForCausalLM as PhiForCausalLM

就很随意的变成了其他模型的简单预训练了 。

关于训练数据构造，其中，DataCollatorForLanguageModeling：

注: get_maped_datasetget_maped_dataset的load_dataset没有加num_proc，导致加载速度慢，加以设置为核心数）

这部分代码和我之前写一个篇基于transformers库训练GPT2大差不差：
https://zhuanlan.zhihu.com/p/685851459

注: get_maped_datasetget_maped_dataset的load_dataset没有加num_proc，导致加载速度慢，加以设置为核心数）

四、SFT代码

基本和预训练一致，唯一的不同就是，设置了output的标签

import time
import pandas as pd
import numpy as np
import torch
from datasets import load_dataset
from transformers import PreTrainedTokenizerFast, PhiForCausalLM, TrainingArguments, Trainer, TrainerCallback
from trl import DataCollatorForCompletionOnlyLM

# 1. 定义训练数据，tokenizer，预训练模型的路径及最大长度
sft_file = './data/sft_train_data.parquet'
tokenizer_dir = './model_save/tokenizer/'
sft_from_checkpoint_file = './model_save/pre/'
model_save_dir = './model_save/sft/'
max_seq_len = 512

# 2. 加载训练数据集
dataset = load_dataset(path='parquet', data_files=sft_file, split='train', cache_dir='.cache')
tokenizer = PreTrainedTokenizerFast.from_pretrained(tokenizer_dir)
print(f"vicab size: {len(tokenizer)}")

# ## 2.1 定义sft data_collator的指令字符
# 也可以手动将`instruction_template_ids`和`response_template_ids`添加到input_ids中的，因为如果是byte level tokenizer可能将`:`和后面的字符合并，导致找不到`instruction_template_ids`和`response_template_ids`。 
# 也可以像下文一样通过在`'#'`和`':'`前后手动加`'\n'`解决

# %%
instruction_template = "##提问:"
response_template = "##回答:"

map_dtype = np.uint16 if len(tokenizer) < 65535 else np.uint32

def batched_formatting_prompts_func(example: list[dict]) -> list[str]:
    batch_txt = []
    for i in range(len(example['instruction'])):
        text = f"{instruction_template}\n{example['instruction'][i]}\n{response_template}\n{example['output'][i]}[EOS]"
        batch_txt.append(text)

    outputs = tokenizer(batch_txt, return_attention_mask=False)
    input_ids = [np.array(item, dtype=map_dtype) for item in outputs["input_ids"]]
    return {"input_ids": input_ids}

dataset = dataset.map(batched_formatting_prompts_func, batched=True, 
                        remove_columns=dataset.column_names).shuffle(23333)

# 2.2 定义data_collator
# 
data_collator = DataCollatorForCompletionOnlyLM(
  instruction_template=instruction_template, 
  response_template=response_template, 
  tokenizer=tokenizer, 
  mlm=False
)
empty_cuda_cahce = EmptyCudaCacheCallback()  ## 定义训练过程中的回调函数
my_datasets =  dataset.train_test_split(test_size=4096)

# 5. 定义训练参数
model = PhiForCausalLM.from_pretrained(sft_from_checkpoint_file)
args = TrainingArguments(
    output_dir=model_save_dir, per_device_train_batch_size=8, gradient_accumulation_steps=8,
    num_train_epochs=3, weight_decay=0.1, warmup_steps=1000, learning_rate=5e-5,
    evaluation_strategy='steps', eval_steps=2000, save_steps=2000, save_total_limit=3,
    report_to='tensorboard', optim="adafactor", bf16=True, logging_steps=10,
    log_level='info', logging_first_step=True, group_by_length=True,
)
trainer = Trainer(
    model=model, tokenizer=tokenizer, args=args, 
    data_collator=data_collator,
    train_dataset=my_datasets['train'], 
    eval_dataset=my_datasets['test'],
    callbacks=[empty_cuda_cahce],
)
trainer.train()
trainer.save_model(model_save_dir)

总之，虽然都是一套代码，但实际上一切的细节隐藏在：
DataCollatorForLanguageModeling、Trainer、tokenizer和CausalLM的实现中。
更底层的实在pytorch的实现中，不过一般不涉及框架内部的实现分析。

和huggingface的trl库的SFT example，唯一区别就是还是用的Trainer
https://huggingface.co/docs/trl/main/en/sft_trainer#train-on-completions-only

其中，DataCollatorForCompletionOnlyLM会为指令微调式的补全式训练，自动构造样本：

You can use the DataCollatorForCompletionOnlyLM to train your model on the generated prompts only. Note that this works only in the case when packing=False.

对于指令微调式的instruction data, 实例化一个datacollator，传入一个输response的template 和tokenizer。

内部可以进行response部分的token ids的拆分，并指定为预测标签。

下面是HuggingFace官方的使用DataCollatorForCompletionOnlyLM+FTTrainer，进行指令微调的例子：

from transformers import AutoModelForCausalLM, AutoTokenizer
from datasets import load_dataset
from trl import SFTConfig, SFTTrainer, DataCollatorForCompletionOnlyLM

dataset = load_dataset("timdettmers/openassistant-guanaco", split="train")
model = AutoModelForCausalLM.from_pretrained("facebook/opt-350m")
tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m")

instruction_template = "### Human:"
response_template = "### Assistant:"
collator = DataCollatorForCompletionOnlyLM(instruction_template=instruction_template, response_template=response_template, tokenizer=tokenizer, mlm=False)

trainer = SFTTrainer(
    model,
    args=SFTConfig(
        output_dir="/tmp",
        dataset_text_field = "text",
    ),
    train_dataset=dataset,
    data_collator=collator,
)
trainer.train()

关于Trainer and SFTTrainer的区别，感觉区别不大
https://medium.com/@sujathamudadla1213/difference-between-trainer-class-and-sfttrainer-supervised-fine-tuning-trainer-in-hugging-face-d295344d73f7

五、DPO代码

import time
import pandas as pd
from typing import List, Optional, Dict
from dataclasses import dataclass, field
import torch 
from trl import DPOTrainer
from transformers import PreTrainedTokenizerFast, PhiForCausalLM, TrainingArguments, TrainerCallback
from datasets import load_dataset

# 1. 定义sft模型路径及dpo数据
dpo_file = './data/dpo_train_data.json'
tokenizer_dir = './model_save/tokenizer/'
sft_from_checkpoint_file = './model_save/sft/'
model_save_dir = './model_save/dpo/'
max_seq_len = 320

# 2. 加载数据集

# 数据集token格式化
# DPO数据格式：[prompt模型输入，chosen正例， rejected负例]
# 将dpo数据集三列数据添加上`eos`token，`bos`可加可不加
def split_prompt_and_responses(samples: dict[str, str]) -> Dict[str, str]:
    prompts, chosens, rejects = [], [], []
    batch_size = len(samples['prompt'])
    for i in range(batch_size):
        # add an eos token for signal that end of sentence, using in generate.
        prompts.append(f"[BOS]{samples['prompt'][i]}[EOS]")
        chosens.append(f"[BOS]{samples['chosen'][i]}[EOS]")
        rejects.append(f"[BOS]{samples['rejected'][i]}[EOS]")
    return {'prompt': prompts, 'chosen': chosens, 'rejected':rejects,}

tokenizer = PreTrainedTokenizerFast.from_pretrained(tokenizer_dir)
dataset = load_dataset(path='json', data_files=dpo_file, split='train', cache_dir='.cache')
dataset = dataset.map(split_prompt_and_responses, batched=True,).shuffle(2333)

# 4. 加载模型
# `model`和`model_ref`开始时是同一个模型，只训练`model`的参数，`model_ref`参数保存不变
model = PhiForCausalLM.from_pretrained(sft_from_checkpoint_file)
model_ref = PhiForCausalLM.from_pretrained(sft_from_checkpoint_file)

# 5. 定义训练中的回调函数
# 清空cuda缓存，dpo要加载两个模型，显存占用较大，这能有效缓解低显存机器显存缓慢增长的问题
class EmptyCudaCacheCallback(TrainerCallback):
    log_cnt = 0
    def on_log(self, args, state, control, logs=None, **kwargs):
        self.log_cnt += 1
        if self.log_cnt % 5 == 0:
            torch.cuda.empty_cache()
            
empty_cuda_cahce = EmptyCudaCacheCallback()

# 训练参数
args = TrainingArguments(
    output_dir=model_save_dir, per_device_train_batch_size=2, gradient_accumulation_steps=16,
    num_train_epochs=4, weight_decay=0.1, warmup_steps=1000, learning_rate=2e-5, save_steps=2000, save_total_limit=3, report_to='tensorboard', bf16=True, logging_steps=10, log_level='info',
    logging_first_step=True, optim="adafactor", remove_unused_columns=False, group_by_length=True,
)
trainer = DPOTrainer(
    model, model_ref, args=args, beta=0.1,
    train_dataset=dataset,tokenizer=tokenizer, callbacks=[empty_cuda_cahce],
    max_length=max_seq_len * 2 + 16, # 16 for eos bos
    max_prompt_length=max_seq_len,
)
trainer.train()
trainer.save_model(model_save_dir)

六、碎碎念

深入学习

使用transformers的Traniner以及trl库的训练代码基本上都差不多，因为transformers和trl都封装地很好了。

如何要略微深入细节，建议阅读或debug如下仓库。这两个仓库都是基于pytorch实现的：

• https://github.com/DLLXW/baby-llama2-chinese/tree/main
• https://github.com/jzhang38/TinyLlama/blob/main/pretrain/tinyllama.py

改进

这个项目就是基于Phi2-mini-Chinese，主要就是把phi2换成了qwen，然后直接使用qwen的tokenizer
https://github.com/jiahe7ay/MINI_LLM/

我这边尝试使用了transformers库把qwen2的抽出来，用于训练。
其实，和直接用transformers的Qwen2LMModel没有区别。

感兴趣的可以替换任意主流模型，修改配置，其实也大差不差。
这些代码主要是用于学习用途。只要有点时间，有点卡，不费什么力就可以弄点数据复现走完整个流程。

不过，要训练出的效果还可以的小规模LLM也并不简单。

如果您需要引用本文，请参考：

LeonYi. (Aug. 25, 2024). 《【LLM训练系列】从零开始训练大模型之Phi2-mini-Chinese项目解读》.

@online{title={【LLM训练系列】从零开始训练大模型之Phi2-mini-Chinese项目解读},
author={LeonYi},
year={2024},
month={Sep},
url={ https://www.cnblogs.com/justLittleStar/p/18405618 },
}