《书生大模型实战营第3期》基础岛 第5关 ：XTuner 微调个人小助手认知

mkdir -p /root/InternLM/Tutorial
git clone -b camp3  https://github.com/InternLM/Tutorial /root/InternLM/Tutorial

# 创建虚拟环境
conda create -n xtuner0121 python=3.10 -y

# 激活虚拟环境（注意：后续的所有操作都需要在这个虚拟环境中进行）
conda activate xtuner0121

# 安装一些必要的库
conda install pytorch==2.1.2 torchvision==0.16.2 torchaudio==2.1.2 pytorch-cuda=12.1 -c pytorch -c nvidia -y
# 安装其他依赖
pip install transformers==4.39.3
pip install streamlit==1.36.0

# 创建一个目录，用来存放源代码
mkdir -p /root/InternLM/code

cd /root/InternLM/code

git clone -b v0.1.21  https://github.com/InternLM/XTuner /root/InternLM/code/XTuner

# 进入到源码目录
cd /root/InternLM/code/XTuner
conda activate xtuner0121

# 执行安装
pip install -e '.[deepspeed]'

xtuner version

xtuner help

# 创建一个目录，用来存放微调的所有资料，后续的所有操作都在该路径中进行
mkdir -p /root/InternLM/XTuner

cd /root/InternLM/XTuner

mkdir -p Shanghai_AI_Laboratory

ln -s /root/share/new_models/Shanghai_AI_Laboratory/internlm2-chat-1_8b Shanghai_AI_Laboratory/internlm2-chat-1_8b

apt-get install -y tree

tree -l

├── Shanghai_AI_Laboratory
│   └── internlm2-chat-1_8b -> /root/share/new_models/Shanghai_AI_Laboratory/internlm2-chat-1_8b
│       ├── README.md
│       ├── config.json
│       ├── configuration.json
│       ├── configuration_internlm2.py
│       ├── generation_config.json
│       ├── model-00001-of-00002.safetensors
│       ├── model-00002-of-00002.safetensors
│       ├── model.safetensors.index.json
│       ├── modeling_internlm2.py
│       ├── special_tokens_map.json
│       ├── tokenization_internlm2.py
│       ├── tokenization_internlm2_fast.py
│       ├── tokenizer.model
│       └── tokenizer_config.json

conda activate xtuner0121

streamlit run /root/InternLM/Tutorial/tools/xtuner_streamlit_demo.py

ssh -CNg -L 8501:127.0.0.1:8501 root@ssh.intern-ai.org.cn -p 43551

cd /root/InternLM/XTuner
mkdir -p datas
touch datas/assistant.json

cd /root/InternLM/XTuner
touch xtuner_generate_assistant.py

import json

# 设置用户的名字
name = '伍鲜同志'
# 设置需要重复添加的数据次数
n =  3750

# 初始化数据
data = [
    {"conversation": [{"input": "请介绍一下你自己", "output": "我是{}的小助手，内在是上海AI实验室书生·浦语的1.8B大模型哦".format(name)}]},
    {"conversation": [{"input": "你在实战营做什么", "output": "我在这里帮助{}完成XTuner微调个人小助手的任务".format(name)}]}
]

# 通过循环，将初始化的对话数据重复添加到data列表中
for i in range(n):
    data.append(data[0])
    data.append(data[1])

# 将data列表中的数据写入到'datas/assistant.json'文件中
with open('datas/assistant.json', 'w', encoding='utf-8') as f:
    # 使用json.dump方法将数据以JSON格式写入文件
    # ensure_ascii=False 确保中文字符正常显示
    # indent=4 使得文件内容格式化，便于阅读
    json.dump(data, f, ensure_ascii=False, indent=4)

cd /root/InternLM/XTuner
cp /root/InternLM/Tutorial/tools/xtuner_generate_assistant.py ./

# 将对应的name进行修改（在第4行的位置）
- name = '伍鲜同志'
+ name = "你自己的名称"

cd /root/InternLM/XTuner
conda activate xtuner0121

python xtuner_generate_assistant.py

├── Shanghai_AI_Laboratory
│   └── internlm2-chat-1_8b -> /root/share/new_models/Shanghai_AI_Laboratory/internlm2-chat-1_8b
│       ├── README.md
│       ├── config.json
│       ├── configuration.json
│       ├── configuration_internlm2.py
│       ├── generation_config.json
│       ├── model-00001-of-00002.safetensors
│       ├── model-00002-of-00002.safetensors
│       ├── model.safetensors.index.json
│       ├── modeling_internlm2.py
│       ├── special_tokens_map.json
│       ├── tokenization_internlm2.py
│       ├── tokenization_internlm2_fast.py
│       ├── tokenizer.model
│       └── tokenizer_config.json
├── datas
│   └── assistant.json
├── xtuner_generate_assistant.py

conda activate xtuner0121

xtuner list-cfg -p internlm2

cd /root/InternLM/XTuner
conda activate xtuner0121

xtuner copy-cfg internlm2_chat_1_8b_qlora_alpaca_e3 .

├── Shanghai_AI_Laboratory
│   └── internlm2-chat-1_8b -> /root/share/new_models/Shanghai_AI_Laboratory/internlm2-chat-1_8b
│       ├── README.md
│       ├── config.json
│       ├── configuration.json
│       ├── configuration_internlm2.py
│       ├── generation_config.json
│       ├── model-00001-of-00002.safetensors
│       ├── model-00002-of-00002.safetensors
│       ├── model.safetensors.index.json
│       ├── modeling_internlm2.py
│       ├── special_tokens_map.json
│       ├── tokenization_internlm2.py
│       ├── tokenization_internlm2_fast.py
│       ├── tokenizer.model
│       └── tokenizer_config.json
├── datas
│   └── assistant.json
├── internlm2_chat_1_8b_qlora_alpaca_e3_copy.py
├── xtuner_generate_assistant.py

#######################################################################
#                          PART 1  Settings                           #
#######################################################################
- pretrained_model_name_or_path = 'internlm/internlm2-chat-1_8b'
+ pretrained_model_name_or_path = '/root/InternLM/XTuner/Shanghai_AI_Laboratory/internlm2-chat-1_8b'

- alpaca_en_path = 'tatsu-lab/alpaca'
+ alpaca_en_path = 'datas/assistant.json'

evaluation_inputs = [
-    '请给我介绍五个上海的景点', 'Please tell me five scenic spots in Shanghai'
+    '请介绍一下你自己', 'Please introduce yourself'
]

#######################################################################
#                      PART 3  Dataset & Dataloader                   #
#######################################################################
alpaca_en = dict(
    type=process_hf_dataset,
-   dataset=dict(type=load_dataset, path=alpaca_en_path),
+   dataset=dict(type=load_dataset, path='json', data_files=dict(train=alpaca_en_path)),
    tokenizer=tokenizer,
    max_length=max_length,
-   dataset_map_fn=alpaca_map_fn,
+   dataset_map_fn=None,
    template_map_fn=dict(
        type=template_map_fn_factory, template=prompt_template),
    remove_unused_columns=True,
    shuffle_before_pack=True,
    pack_to_max_length=pack_to_max_length,
    use_varlen_attn=use_varlen_attn)

cd /root/InternLM/XTuner
cp /root/InternLM/Tutorial/configs/internlm2_chat_1_8b_qlora_alpaca_e3_copy.py ./

# Copyright (c) OpenMMLab. All rights reserved.
import torch
from datasets import load_dataset
from mmengine.dataset import DefaultSampler
from mmengine.hooks import (CheckpointHook, DistSamplerSeedHook, IterTimerHook,
                            LoggerHook, ParamSchedulerHook)
from mmengine.optim import AmpOptimWrapper, CosineAnnealingLR, LinearLR
from peft import LoraConfig
from torch.optim import AdamW
from transformers import (AutoModelForCausalLM, AutoTokenizer,
                          BitsAndBytesConfig)

from xtuner.dataset import process_hf_dataset
from xtuner.dataset.collate_fns import default_collate_fn
from xtuner.dataset.map_fns import alpaca_map_fn, template_map_fn_factory
from xtuner.engine.hooks import (DatasetInfoHook, EvaluateChatHook,
                                 VarlenAttnArgsToMessageHubHook)
from xtuner.engine.runner import TrainLoop
from xtuner.model import SupervisedFinetune
from xtuner.parallel.sequence import SequenceParallelSampler
from xtuner.utils import PROMPT_TEMPLATE, SYSTEM_TEMPLATE

#######################################################################
#                          PART 1  Settings                           #
#######################################################################
# Model
pretrained_model_name_or_path = '/root/InternLM/XTuner/Shanghai_AI_Laboratory/internlm2-chat-1_8b'
use_varlen_attn = False

# Data
alpaca_en_path = 'datas/assistant.json'
prompt_template = PROMPT_TEMPLATE.internlm2_chat
max_length = 2048
pack_to_max_length = True

# parallel
sequence_parallel_size = 1

# Scheduler & Optimizer
batch_size = 1  # per_device
accumulative_counts = 16
accumulative_counts *= sequence_parallel_size
dataloader_num_workers = 0
max_epochs = 3
optim_type = AdamW
lr = 2e-4
betas = (0.9, 0.999)
weight_decay = 0
max_norm = 1  # grad clip
warmup_ratio = 0.03

# Save
save_steps = 500
save_total_limit = 2  # Maximum checkpoints to keep (-1 means unlimited)

# Evaluate the generation performance during the training
evaluation_freq = 500
SYSTEM = SYSTEM_TEMPLATE.alpaca
evaluation_inputs = [
    '请介绍一下你自己', 'Please introduce yourself'
]

#######################################################################
#                      PART 2  Model & Tokenizer                      #
#######################################################################
tokenizer = dict(
    type=AutoTokenizer.from_pretrained,
    pretrained_model_name_or_path=pretrained_model_name_or_path,
    trust_remote_code=True,
    padding_side='right')

model = dict(
    type=SupervisedFinetune,
    use_varlen_attn=use_varlen_attn,
    llm=dict(
        type=AutoModelForCausalLM.from_pretrained,
        pretrained_model_name_or_path=pretrained_model_name_or_path,
        trust_remote_code=True,
        torch_dtype=torch.float16,
        quantization_config=dict(
            type=BitsAndBytesConfig,
            load_in_4bit=True,
            load_in_8bit=False,
            llm_int8_threshold=6.0,
            llm_int8_has_fp16_weight=False,
            bnb_4bit_compute_dtype=torch.float16,
            bnb_4bit_use_double_quant=True,
            bnb_4bit_quant_type='nf4')),
    lora=dict(
        type=LoraConfig,
        r=64,
        lora_alpha=16,
        lora_dropout=0.1,
        bias='none',
        task_type='CAUSAL_LM'))

#######################################################################
#                      PART 3  Dataset & Dataloader                   #
#######################################################################
alpaca_en = dict(
    type=process_hf_dataset,
    dataset=dict(type=load_dataset, path='json', data_files=dict(train=alpaca_en_path)),
    tokenizer=tokenizer,
    max_length=max_length,
    dataset_map_fn=None,
    template_map_fn=dict(
        type=template_map_fn_factory, template=prompt_template),
    remove_unused_columns=True,
    shuffle_before_pack=True,
    pack_to_max_length=pack_to_max_length,
    use_varlen_attn=use_varlen_attn)

sampler = SequenceParallelSampler \
    if sequence_parallel_size > 1 else DefaultSampler
train_dataloader = dict(
    batch_size=batch_size,
    num_workers=dataloader_num_workers,
    dataset=alpaca_en,
    sampler=dict(type=sampler, shuffle=True),
    collate_fn=dict(type=default_collate_fn, use_varlen_attn=use_varlen_attn))

#######################################################################
#                    PART 4  Scheduler & Optimizer                    #
#######################################################################
# optimizer
optim_wrapper = dict(
    type=AmpOptimWrapper,
    optimizer=dict(
        type=optim_type, lr=lr, betas=betas, weight_decay=weight_decay),
    clip_grad=dict(max_norm=max_norm, error_if_nonfinite=False),
    accumulative_counts=accumulative_counts,
    loss_scale='dynamic',
    dtype='float16')

# learning policy
# More information: https://github.com/open-mmlab/mmengine/blob/main/docs/en/tutorials/param_scheduler.md  # noqa: E501
param_scheduler = [
    dict(
        type=LinearLR,
        start_factor=1e-5,
        by_epoch=True,
        begin=0,
        end=warmup_ratio * max_epochs,
        convert_to_iter_based=True),
    dict(
        type=CosineAnnealingLR,
        eta_min=0.0,
        by_epoch=True,
        begin=warmup_ratio * max_epochs,
        end=max_epochs,
        convert_to_iter_based=True)
]

# train, val, test setting
train_cfg = dict(type=TrainLoop, max_epochs=max_epochs)

#######################################################################
#                           PART 5  Runtime                           #
#######################################################################
# Log the dialogue periodically during the training process, optional
custom_hooks = [
    dict(type=DatasetInfoHook, tokenizer=tokenizer),
    dict(
        type=EvaluateChatHook,
        tokenizer=tokenizer,
        every_n_iters=evaluation_freq,
        evaluation_inputs=evaluation_inputs,
        system=SYSTEM,
        prompt_template=prompt_template)
]

if use_varlen_attn:
    custom_hooks += [dict(type=VarlenAttnArgsToMessageHubHook)]

# configure default hooks
default_hooks = dict(
    # record the time of every iteration.
    timer=dict(type=IterTimerHook),
    # print log every 10 iterations.
    logger=dict(type=LoggerHook, log_metric_by_epoch=False, interval=10),
    # enable the parameter scheduler.
    param_scheduler=dict(type=ParamSchedulerHook),
    # save checkpoint per `save_steps`.
    checkpoint=dict(
        type=CheckpointHook,
        by_epoch=False,
        interval=save_steps,
        max_keep_ckpts=save_total_limit),
    # set sampler seed in distributed evrionment.
    sampler_seed=dict(type=DistSamplerSeedHook),
)

# configure environment
env_cfg = dict(
    # whether to enable cudnn benchmark
    cudnn_benchmark=False,
    # set multi process parameters
    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
    # set distributed parameters
    dist_cfg=dict(backend='nccl'),
)

# set visualizer
visualizer = None

# set log level
log_level = 'INFO'

# load from which checkpoint
load_from = None

# whether to resume training from the loaded checkpoint
resume = False

# Defaults to use random seed and disable `deterministic`
randomness = dict(seed=None, deterministic=False)

# set log processor
log_processor = dict(by_epoch=False)

cd /root/InternLM/XTuner
conda activate xtuner0121

xtuner train ./internlm2_chat_1_8b_qlora_alpaca_e3_copy.py

├── work_dirs
│   └── internlm2_chat_1_8b_qlora_alpaca_e3_copy
│       ├── 20240626_222727
│       │   ├── 20240626_222727.log
│       │   └── vis_data
│       │       ├── 20240626_222727.json
│       │       ├── config.py
│       │       ├── eval_outputs_iter_95.txt
│       │       └── scalars.json
│       ├── internlm2_chat_1_8b_qlora_alpaca_e3_copy.py
│       ├── iter_96.pth
│       └── last_checkpoint

cd /root/InternLM/XTuner
conda activate xtuner0121

# 先获取最后保存的一个pth文件
pth_file=`ls -t ./work_dirs/internlm2_chat_1_8b_qlora_alpaca_e3_copy/*.pth | head -n 1`
export MKL_SERVICE_FORCE_INTEL=1
export MKL_THREADING_LAYER=GNU
xtuner convert pth_to_hf ./internlm2_chat_1_8b_qlora_alpaca_e3_copy.py ${pth_file} ./hf

├── hf
│   ├── README.md
│   ├── adapter_config.json
│   ├── adapter_model.bin
│   └── xtuner_config.py

cd /root/InternLM/XTuner
conda activate xtuner0121

export MKL_SERVICE_FORCE_INTEL=1
export MKL_THREADING_LAYER=GNU
xtuner convert merge /root/InternLM/XTuner/Shanghai_AI_Laboratory/internlm2-chat-1_8b ./hf ./merged --max-shard-size 2GB

├── merged
│   ├── config.json
│   ├── configuration_internlm2.py
│   ├── generation_config.json
│   ├── modeling_internlm2.py
│   ├── pytorch_model-00001-of-00002.bin
│   ├── pytorch_model-00002-of-00002.bin
│   ├── pytorch_model.bin.index.json
│   ├── special_tokens_map.json
│   ├── tokenization_internlm2.py
│   ├── tokenization_internlm2_fast.py
│   ├── tokenizer.json
│   ├── tokenizer.model
│   └── tokenizer_config.json

# 直接修改脚本文件第18行
- model_name_or_path = "/root/InternLM/XTuner/Shanghai_AI_Laboratory/internlm2-chat-1_8b"
+ model_name_or_path = "/root/InternLM/XTuner/merged"

conda activate xtuner0121

streamlit run /root/InternLM/Tutorial/tools/xtuner_streamlit_demo.py

ssh -CNg -L 8501:127.0.0.1:8501 root@ssh.intern-ai.org.cn -p 43551