准备InternVL模型
cd /root
mkdir -p model
# cp 模型
cp -r /root/share/new_models/OpenGVLab/InternVL2-2B /root/model/
准备环境
conda create --name xtuner 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 xtuner
# 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
apt install libaio-dev
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 v0.1.23 https://github.com/InternLM/XTuner
cd /root/InternLM/code/XTuner
pip install -e '.[deepspeed]'
准备微调数据集
## 首先让我们安装一下需要的包
pip install datasets matplotlib Pillow timm
## 让我们把数据集挪出来
cp -r /root/share/new_models/datasets/CLoT_cn_2000 /root/InternLM/datasets/
InternVL 推理部署攻略
我们用LMDeploy来推理这张图片～看看它能不能成功解释出梗图呢？
使用pipeline进行推理
之后我们使用lmdeploy自带的pipeline工具进行开箱即用的推理流程，首先我们新建一个文件。
touch /root/InternLM/code/test_lmdeploy.py
cd /root/InternLM/code/
然后把以下代码拷贝进test_lmdeploy.py中。
from lmdeploy import pipeline
from lmdeploy.vl import load_image
pipe = pipeline('/root/model/InternVL2-2B')
image = load_image('/root/InternLM/007aPnLRgy1hb39z0im50j30ci0el0wm.jpg')
response = pipe(('请你根据这张图片，讲一个脑洞大开的梗', image))
print(response.text)
运行执行推理结果。
python3 test_lmdeploy.py
InternVL 微调攻略
准备数据集
数据集格式为：
为了高效训练，请确保数据格式为：
{
"id": "000000033471",
"image": ["coco/train2017/000000033471.jpg"], # 如果是纯文本，则该字段为 None 或者不存在
"conversations": [
{
"from": "human",
"value": "<image>\nWhat are the colors of the bus in the image?"
},
{
"from": "gpt",
"value": "The bus in the image is white and red."
}
]
}
这里我们也为大家准备好了可以直接进行微调的数据集。数据集就是咱们刚才复制进InternLM/datasets的数据。
配置微调参数
让我们一起修改XTuner下 InternVL的config，文件在： /root/InternLM/code/XTuner/xtuner/configs/internvl/v2/internvl_v2_internlm2_2b_qlora_finetune.py
# Copyright (c) OpenMMLab. All rights reserved.
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 AutoTokenizer
from xtuner.dataset import InternVL_V1_5_Dataset
from xtuner.dataset.collate_fns import default_collate_fn
from xtuner.dataset.samplers import LengthGroupedSampler
from xtuner.engine.hooks import DatasetInfoHook
from xtuner.engine.runner import TrainLoop
from xtuner.model import InternVL_V1_5
from xtuner.utils import PROMPT_TEMPLATE
#######################################################################
# PART 1 Settings #
#######################################################################
# Model
path = '/root/model/InternVL2-2B'
# Data
data_root = '/root/InternLM/datasets/CLoT_cn_2000/'
data_path = data_root + 'ex_cn.json'
image_folder = data_root
prompt_template = PROMPT_TEMPLATE.internlm2_chat
max_length = 6656
# Scheduler & Optimizer
batch_size = 4 # per_device
accumulative_counts = 4
dataloader_num_workers = 4
max_epochs = 6
optim_type = AdamW
# official 1024 -> 4e-5
lr = 2e-5
betas = (0.9, 0.999)
weight_decay = 0.05
max_norm = 1 # grad clip
warmup_ratio = 0.03
# Save
save_steps = 1000
save_total_limit = 1 # Maximum checkpoints to keep (-1 means unlimited)
#######################################################################
# PART 2 Model & Tokenizer & Image Processor #
#######################################################################
model = dict(
type=InternVL_V1_5,
model_path=path,
freeze_llm=True,
freeze_visual_encoder=True,
quantization_llm=True, # or False
quantization_vit=False, # or True and uncomment visual_encoder_lora
# comment the following lines if you don't want to use Lora in llm
llm_lora=dict(
type=LoraConfig,
r=128,
lora_alpha=256,
lora_dropout=0.05,
target_modules=None,
task_type='CAUSAL_LM'),
# uncomment the following lines if you don't want to use Lora in visual encoder # noqa
# visual_encoder_lora=dict(
# type=LoraConfig, r=64, lora_alpha=16, lora_dropout=0.05,
# target_modules=['attn.qkv', 'attn.proj', 'mlp.fc1', 'mlp.fc2'])
)
#######################################################################
# PART 3 Dataset & Dataloader #
#######################################################################
llava_dataset = dict(
type=InternVL_V1_5_Dataset,
model_path=path,
data_paths=data_path,
image_folders=image_folder,
template=prompt_template,
max_length=max_length)
train_dataloader = dict(
batch_size=batch_size,
num_workers=dataloader_num_workers,
dataset=llava_dataset,
sampler=dict(
type=LengthGroupedSampler,
length_property='modality_length',
per_device_batch_size=batch_size * accumulative_counts),
collate_fn=dict(type=default_collate_fn))
#######################################################################
# 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
tokenizer = dict(
type=AutoTokenizer.from_pretrained,
pretrained_model_name_or_path=path,
trust_remote_code=True)
custom_hooks = [
dict(type=DatasetInfoHook, tokenizer=tokenizer),
]
# 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,
save_optimizer=False,
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)
开始训练
这里使用之前搞好的configs进行训练。咱们要调整一下batch size，并且使用qlora。要不半卡不够用的 QAQ。
cd XTuner
NPROC_PER_NODE=1 xtuner train /root/InternLM/code/XTuner/xtuner/configs/internvl/v2/internvl_v2_internlm2_2b_qlora_finetune.py --work-dir /root/InternLM/work_dir/internvl_ft_run_8_filter --deepspeed deepspeed_zero1
合并权重&&模型转换
用官方脚本进行权重合并
如果这里你执行的epoch不是6，是小一些的数字。你可能会发现internvl_ft_run_8_filter下没有iter_3000.pth, 那你需要把iter_3000.pth切换成你internvl_ft_run_8_filter目录下的pth即可。
cd XTuner
# transfer weights
python3 xtuner/configs/internvl/v1_5/convert_to_official.py xtuner/configs/internvl/v2/internvl_v2_internlm2_2b_qlora_finetune.py /root/InternLM/work_dir/internvl_ft_run_8_filter/iter_3000.pth /root/InternLM/InternVL2-2B/
完成作业
作业1
作业2：
r=56
lora_alpha=128
r=128,
lora_alpha=256,
loss的
发现随着r的越大，loss下降越快
扫一扫
625
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