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大杀器，多模态大模型MiniGPT-4入坑指南_minigpt-4复现

作者：从前慢现在也慢 | 2024-07-17 18:37:54

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minigpt-4复现

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ChatGPT的发布给大家带来了不少的震撼，而随后发布的GPT-4更是展现了非凡的多模态能力。但是，ChatGPT和GPT4官方公布的细节很少，OpenAI俨然走上了闭源之路，让广大AI从业者又爱又恨。

最近，来自沙特阿拉伯阿卜杜拉国王科技大学的研究团队开源了GPT-4的平民版 MiniGPT-4。他们认为，GPT-4 具有先进的多模态生成能力的主要原因在于利用了更先进的大型语言模型（LLM）。为了研究这一现象，他们提出了 MiniGPT-4。

MiniGPT-4 简介

MiniGPT-4 仅使用一个投影层将一个冻结的视觉编码器（BLIP-2）与一个冻结的 LLM（Vicuna）对齐。

MiniGPT-4 产生了许多类似于 GPT-4 中新兴的视觉语言能力。比如：根据给定的图像创作故事和诗歌，为图像中显示的问题提供解决方案，教用户如何根据食物照片烹饪，给个手绘草图直接写出网站的代码等。

除此之外，此方法计算效率很高，因为它仅使用大约 500 万个对齐的图像-文本对和额外的 3,500 个经过精心策划的高质量图像-文本对来训练一个投影层。

BLIP-2 简介
BLIP-2是一种通用且高效的视觉-语言预训练方法，它可以从现成的冻结预训练图像编码器和冻结大型语言模型中引导视觉-语言预训练。BLIP-2通过一个轻量级的Querying Transformer来弥合模态差距，并在两个阶段进行预训练。第一个阶段从冻结图像编码器引导视觉-语言表示学习。第二个阶段从冻结语言模型中引导视觉-语言生成学习。尽管比现有方法具有显著较少的可训练参数，但BLIP-2在各种视觉-语言任务上实现了最先进的性能。在零样本 VQAv2 上，BLIP-2 相较于 80 亿参数的 Flamingo 模型，使用的可训练参数数量少了 54 倍，性能却提升了 8.7 %。

MiniGPT-4 模型训练原理

MiniGPT-4 的模型架构遵循 BLIP-2，因此，训练 MiniGPT-4 分两个阶段。

第一个传统预训练阶段使用 4 张 A100 卡在 10 小时内使用大约 500 万个对齐的图像-文本对进行训练。在第一阶段之后，Vicuna 虽然能够理解图像。但是Vicuna的生成能力受到了很大的影响。

为了解决这个问题并提高可用性，MiniGPT-4 提出了一种通过模型本身和 ChatGPT 一起创建高质量图像文本对的新方法。基于此，MiniGPT-4 随后创建了一个小规模（总共 3500 对）但高质量的数据集。

第二个微调阶段在对话模板中对该数据集进行训练，以显著提高其生成的可靠性和整体的可用性。令人惊讶的是，这个阶段的计算效率很高，使用单个 A100 只需大约 7 分钟即可完成。

环境搭建

基础环境配置如下：

操作系统: Ubuntu 18.04
CPUs: 单个节点具有 384GB 内存的 Intel CPU，物理CPU个数为2，每颗CPU核数为20
GPUs: 4 卡 A800 80GB GPUs
Python: 3.10 (需要先升级OpenSSL到1.1.1t版本（点击下载OpenSSL），然后再编译安装Python)，点击下载Python
NVIDIA驱动程序版本: 525.105.17，根据不同型号选择不同的驱动程序，点击下载。
CUDA工具包: 11.6，点击下载
cuDNN: 8.8.1.3_cuda11，点击下载

本文选择使用Doker镜像进行环境搭建。

首先，下载对应版本的Pytorch镜像。

docker pull pytorch/pytorch:1.13.1-cuda11.6-cudnn8-devel
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镜像下载完成之后，创建容器。

docker run -dt --name minigpt4_env_dev --restart=always --gpus all \
--network=host \
--shm-size 4G \
-v /home/gdong/workspace/code:/workspace/code \
-v /home/gdong/workspace/data:/workspace/data \
-v /home/gdong/workspace/model:/workspace/model \
-v /home/gdong/workspace/output:/workspace/output \
-v /home/gdong/workspace/package:/workspace/package \
-w /workspace \
pytorch/pytorch:1.13.1-cuda11.6-cudnn8-devel \
/bin/bash
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进入容器。

docker exec -it minigpt4_env_dev bash
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安装 cv2 的依赖项。

apt-get update && apt-get install ffmpeg libsm6 libxext6  -y
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安装其他依赖包。

pip install -r requirements.txt
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其中，requirements.txt文件内容如下：

accelerate==0.16.0
aiohttp==3.8.4
aiosignal==1.3.1
async-timeout==4.0.2
attrs==22.2.0
bitsandbytes==0.37.0
cchardet==2.1.7
chardet==5.1.0
contourpy==1.0.7
cycler==0.11.0
filelock==3.9.0
fonttools==4.38.0
frozenlist==1.3.3
huggingface-hub==0.13.4
importlib-resources==5.12.0
kiwisolver==1.4.4
matplotlib==3.7.0
multidict==6.0.4
openai==0.27.0
packaging==23.0
psutil==5.9.4
pycocotools==2.0.6
pyparsing==3.0.9
python-dateutil==2.8.2
pyyaml==6.0
regex==2022.10.31
tokenizers==0.13.2
tqdm==4.64.1
transformers==4.28.0
timm==0.6.13
spacy==3.5.1
webdataset==0.2.48
scikit-learn==1.2.2
scipy==1.10.1
yarl==1.8.2
zipp==3.14.0
omegaconf==2.3.0
opencv-python==4.7.0.72
iopath==0.1.10
decord==0.6.0
tenacity==8.2.2
peft
pycocoevalcap
sentence-transformers
umap-learn
notebook
gradio==3.24.1
gradio-client==0.0.8
wandb
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接下来，安装img2dataset库，用于后续下载数据集使用。

pip install img2dataset -i https://pypi.tuna.tsinghua.edu.cn/simple  --trusted-host pypi.tuna.tsinghua.edu.cn
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数据集、模型权重及训练推理代码下载

下载模型训练及推理代码

# commit id: 22d8888ca2cf0aac862f537e7d22ef5830036808
git clone https://github.com/Vision-CAIR/MiniGPT-4.git
cd MiniGPT-4
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模型权重准备

预先准备好 Vicuna 权重，详情请查看官方文档。

在之前的文章大模型也内卷，Vicuna训练及推理指南，效果碾压斯坦福羊驼中，有讲解过如何合并Vicuna模型权重，在这里我直接使用之前合并好的Vicuna权重文件。

准备好 Vicuna 权重之后，在模型配置文件 minigpt4.yaml 中的第 16 行设置 Vicuna 权重的路径。

model:
  arch: mini_gpt4
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vit encoder

image_size: 224
drop_path_rate: 0
use_grad_checkpoint: False
vit_precision: “fp16”
freeze_vit: True
freeze_qformer: True

Q-Former

num_query_token: 32

Vicuna

llama_model: “/workspace/model/vicuna-7b-all-v1.1”

generation configs

prompt: “”

preprocess:
vis_processor:
train:
name: “blip2_image_train”
image_size: 224
eval:
name: “blip2_image_eval”
image_size: 224
text_processor:
train:
name: “blip_caption”
eval:
name: “blip_caption”

然后，下载预训练的 MiniGPT-4 检查点（checkpoint），用于模型推理。下载地址：与 Vicuna 7B 对齐的checkpoint(prerained_minigpt4_7b.pth) 或与 Vicuna 7B 对齐的checkpoint(pretrained_minigpt4_13b.pth)

如果服务器无法访问外网，需要预先下载好 VIT(eva_vit_g.pth)、Q-Former (blip2_pretrained_flant5xxl.pth)的权重以及Bert(bert-base-uncased)的Tokenizer。如果服务器可以访问外网且网络状况良好，可以直接忽略以下步骤。

eva_vit_g.pth和blip2_pretrained_flant5xxl.pth下载好之后，格式如下：

> ls -al hub/checkpoints/ --block-size=K

total 2401124K

drwxr-xr-x 2 root root       4K May  5 02:09 .

drwxr-xr-x 3 root root       4K May  7 02:34 …

-rw------- 1 root root  423322K May  5 02:09 blip2_pretrained_flant5xxl.pth

-rw------- 1 root root 1977783K May  5 02:08 eva_vit_g.pth

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同时需要设置环境变量：

# export TORCH_HOME=/workspace/model/cache/torch

export TORCH_HOME=/root/.cache/torch

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bert-base-uncased下载好之后，格式如下：

> ls -al bert-base-uncased --block-size=K

total 244K

drwxr-xr-x 2 root root   4K May  7 09:03 .

drwxrwxrwx 9 root root   4K May  7 09:02 …

-rw-r–r-- 1 root root   1K May  7 09:03 config.json

-rw-r–r-- 1 root root   1K May  7 09:03 tokenizer_config.json

-rw-r–r-- 1 root root 227K May  7 09:03 vocab.txt

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同时，需要修改/workspace/code/MiniGPT-4/minigpt4/models/blip2.py文件，改为本地加载Tokenizer：

class Blip2Base(BaseModel):

@classmethod

def init_tokenizer(cls):

# TODO

#tokenizer = BertTokenizer.from_pretrained(“bert-base-uncased”)

tokenizer = BertTokenizer.from_pretrained(“/workspace/model/bert-base-uncased”)

tokenizer.add_special_tokens({“bos_token”: “[DEC]”})

return tokenizer
...

@classmethod
def init_Qformer(cls, num_query_token, vision_width, cross_attention_freq=2):
    # TODO
    #encoder_config = BertConfig.from_pretrained("bert-base-uncased")
    encoder_config = BertConfig.from_pretrained("/workspace/model/bert-base-uncased")
    encoder_config.encoder_width = vision_width
    # insert cross-attention layer every other block
    encoder_config.add_cross_attention = True
    encoder_config.cross_attention_freq = cross_attention_freq
    encoder_config.query_length = num_query_token
    Qformer = BertLMHeadModel(config=encoder_config)
    query_tokens = nn.Parameter(
        torch.zeros(1, num_query_token, encoder_config.hidden_size)
    )
    query_tokens.data.normal_(mean=0.0, std=encoder_config.initializer_range)
    return Qformer, query_tokens
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数据集准备

下面准备数据集，MiniGPT-4 的训练包含两个阶段，每个阶段使用的数据集不一样。

首先，准备第一阶段数据集。

图片来源	通过ViT-L过滤后的合成字幕
CC3M+CC12M+SBU	Download
LAION115M	Download

下载ccs_synthetic_filtered_large.json和laion_synthetic_filtered_large.json文件，并移动到对应的目录。

export MINIGPT4_DATASET=/workspace/data/blip

mkdir ${MINIGPT4_DATASET}/cc_sbu

mkdir ${MINIGPT4_DATASET}/laion

mv ccs_synthetic_filtered_large.json ${MINIGPT4_DATASET}/cc_sbu

mv laion_synthetic_filtered_large.json ${MINIGPT4_DATASET}/laion

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进入MiniGPT-4项目的dataset目录，并拷贝转换数据格式和下载数据集的脚本。

cd dataset/

cp convert_cc_sbu.py ${MINIGPT4_DATASET}/cc_sbu

cp download_cc_sbu.sh ${MINIGPT4_DATASET}/cc_sbu
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cp convert_laion.py ${MINIGPT4_DATASET}/laion
cp download_laion.sh ${MINIGPT4_DATASET}/laion

由于数据集太大，进入 ${MINIGPT4_DATASET}/cc_sbu</code>和<code>$ {MINIGPT4_DATASET}/laion文件夹，修改convert_cc_sbu.py和convert_laion.py脚本，改为仅下载一部分数据。

#rows = [x.values() for x in data]
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rows = []

for i, x in enumerate(data):
if i >= 1000:
break
rows.append(x.values())

然后，将laion和cc_sbu标注文件格式转换为img2dataset格式。

cd ${MINIGPT4_DATASET}/cc_sbu

python convert_cc_sbu.py
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cd ${MINIGPT4_DATASET}/laion python convert_laion.py </code></pre></div><p data-pid="eXed9aok">进入<code>$ {MINIGPT4_DATASET}/cc_sbu和${MINIGPT4_DATASET}/laion文件夹，修改下载数据集脚本download_cc_sbu.sh和download_laion.sh，将–enable_wandb配置项改为False。

然后，执行脚本，使用img2dataset下载数据集。

cd ${MINIGPT4_DATASET}/cc_sbu

sh download_cc_sbu.sh
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cd ${MINIGPT4_DATASET}/laion
sh download_laion.sh

下载完成之后的最终数据集结构如下所示：

> tree

.

|-- cc_sbu

|   |-- cc_sbu_dataset

|   |   |-- 00000.parquet

|   |   |-- 00000.tar

|   |   -- 00000_stats.json |   |-- ccs_synthetic_filtered_large.json |   |-- ccs_synthetic_filtered_large.tsv |   |-- convert_cc_sbu.py |   – download_cc_sbu.sh

-- laion     |-- convert_laion.py     |-- download_laion.sh     |-- laion_dataset     |   |-- 00000.parquet     |   |-- 00000.tar     |   – 00000_stats.json

|-- laion_synthetic_filtered_large.json

`-- laion_synthetic_filtered_large.tsv
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4 directories, 14 files

之后，修改数据集配置文件。

修改配置文件minigpt4/configs/datasets/laion/defaults.yaml的第五行设置LAION数据集加载路径，具体如下所示：

datasets:

laion:

data_type: images

build_info:

storage: /workspace/data/blip/laion/laion_dataset/00000.tar

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修改配置文件minigpt4/configs/datasets/cc_sbu/defaults.yaml的第五行设置 Conceptual Captoin 和 SBU 数据集加载路径，具体如下所示：

datasets:

cc_sbu:

data_type: images

build_info:

storage: /workspace/data/blip/cc_sbu/cc_sbu_dataset/00000.tar

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接下来，准备第二阶段数据集，具体在此处下载，数据集文件夹结构如下所示。

cc_sbu_align

├── filter_cap.json

└── image

├── 2.jpg

├── 3.jpg

…

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下载完成之后，在数据集配置文件minigpt4/configs/datasets/cc_sbu/align.yaml中的第 5 行设置数据集路径。

datasets:

cc_sbu_align:

data_type: images

build_info:

storage: /workspace/data/cc_sbu_align/

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代码结构

MiniGPT-4 项目基于 BLIP2、Lavis 和 Vicuna 进行构建，使用 OmegaConf 基于 YAML 进行分层系统配置，整个代码结构如下所示：

.

|-- LICENSE.md

|-- LICENSE_Lavis.md

|-- MiniGPT_4.pdf

|-- PrepareVicuna.md

|-- README.md

|-- dataset # 数据集预处理

|   |-- README_1_STAGE.md

|   |-- README_2_STAGE.md

|   |-- convert_cc_sbu.py # 转换标注数据格式

|   |-- convert_laion.py

|   |-- download_cc_sbu.sh # 下载数据集

|   -- download_laion.sh |-- demo.py    # 模型测试/推理 |-- environment.yml |-- eval_configs # 模型评估配置文件 |   – minigpt4_eval.yaml

|-- minigpt4

|   |-- init.py

|   |-- common

|   |   |-- init.py

|   |   |-- config.py

|   |   |-- dist_utils.py # 模型权重缓存文件路径

|   |   |-- gradcam.py

|   |   |-- logger.py

|   |   |-- optims.py

|   |   |-- registry.py

|   |   -- utils.py |   |-- configs  |   |   |-- datasets # 数据集配置文件 |   |   |   |-- cc_sbu |   |   |   |   |-- align.yaml # cc_sbu对齐数据集配置文件 |   |   |   |   – defaults.yaml # cc_sbu数据集配置文件

|   |   |   -- laion |   |   |       – defaults.yaml # laion数据集配置文件

|   |   |-- default.yaml

|   |   -- models # 模型配置文件 |   |       – minigpt4.yaml

|   |-- conversation

|   |   |-- init.py

|   |   -- conversation.py |   |-- datasets |   |   |-- __init__.py |   |   |-- builders |   |   |   |-- __init__.py |   |   |   |-- base_dataset_builder.py |   |   |   – image_text_pair_builder.py

|   |   |-- data_utils.py

|   |   -- datasets |   |       |-- __init__.py |   |       |-- base_dataset.py |   |       |-- caption_datasets.py |   |       |-- cc_sbu_dataset.py |   |       |-- dataloader_utils.py |   |       – laion_dataset.py

|   |-- models

|   |   |-- Qformer.py

|   |   |-- init.py

|   |   |-- base_model.py

|   |   |-- blip2.py # 初始化Bert Tokenizer 和 Qformer等

|   |   |-- blip2_outputs.py

|   |   |-- eva_vit.py

|   |   |-- mini_gpt4.py

|   |   -- modeling_llama.py |   |-- processors |   |   |-- __init__.py |   |   |-- base_processor.py |   |   |-- blip_processors.py |   |   – randaugment.py

|   |-- runners

|   |   |-- init.py

|   |   -- runner_base.py |   – tasks

|       |-- init.py

|       |-- base_task.py

|       -- image_text_pretrain.py |-- prompts  |   – alignment.txt

|-- train.py # 模型训练

-- train_configs # 模型训练配置文件     |-- minigpt4_stage1_pretrain.yaml # 第一阶段预训练配置     – minigpt4_stage2_finetune.yaml # 第二阶段微调配置

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模型推理

首先，在评估配置文件eval_configs/minigpt4_eval.yaml中的第 11 行设置预训练checkpoint的路径(即刚刚下载的预训练的 MiniGPT-4 检查点)。

model:

arch: mini_gpt4

model_type: pretrain_vicuna

freeze_vit: True

freeze_qformer: True

max_txt_len: 160

end_sym: “###”

low_resource: True

prompt_path: “prompts/alignment.txt”

prompt_template: '###Human: {} ###Assistant: ’

ckpt: ‘/workspace/model/minigpt/prerained_minigpt4_7b.pth’
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datasets:
cc_sbu_align:
vis_processor:
train:
name: “blip2_image_eval”
image_size: 224
text_processor:
train:
name: “blip_caption”

run:
task: image_text_pretrain

执行如下命令启动模型推理服务：

python demo.py --cfg-path eval_configs/minigpt4_eval.yaml  --gpu-id 0

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为了节省 GPU 内存，Vicuna 默认以 8 bit 进行加载，beam search 宽度为 1。此配置对于 Vicuna-13B 需要大约 23G GPU 内存、对于 Vicuna-7B 需要大约 11.5G GPU 内存。如果你有更强大的 GPU，您可以通过在配置文件 minigpt4_eval.yaml 中将 low_resource 设置为 False 以 16 bit运行模型并使用更大的beam search宽度。

运行过程：

> python demo.py --cfg-path eval_configs/minigpt4_eval.yaml  --gpu-id 0

Initializing Chat

Loading VIT

Loading VIT Done

Loading Q-Former

Loading Q-Former Done

Loading LLAMA
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=BUG REPORT=
Welcome to bitsandbytes. For bug reports, please submit your error trace to: https://github.com/TimDettmers/bitsandbytes/issues

Loading checkpoint shards: 100%|████████████████████████████████████████████████████████████| 2/2 [01:02<00:00, 31.47s/it]
Loading LLAMA Done
Load 4 training prompts
Prompt Example
###Human: <Img><ImageHere></Img> Take a look at this image and describe what you notice. ###Assistant:
Load BLIP2-LLM Checkpoint: /workspace/model/minigpt/prerained_minigpt4_7b.pth
Initialization Finished
Running on local URL: http://127.0.0.1:7860
Running on public URL: https://71e239f43b078ebe0b.gradio.live

This share link expires in 72 hours. For free permanent hosting and GPU upgrades (NEW!), check out Spaces: https://huggingface.co/spaces

模型推理测试：

模型训练

MiniGPT-4 的训练包含两个对齐阶段。

第一阶段：预训练

在预训练阶段，模型使用来自 Laion 和 CC 数据集的图像文本对进行训练，以对齐视觉和语言模型。

第一阶段之后，视觉特征被映射，可以被语言模型理解。 MiniGPT-4 官方在实验时使用了 4 个 A100。除此之外，您还可以在配置文件 train_configs/minigpt4_stage1_pretrain.yaml 中更改保存路径，具体内容如下：

model:

arch: mini_gpt4

model_type: pretrain_vicuna

freeze_vit: True

freeze_qformer: True
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datasets:
laion:
vis_processor:
train:
name: “blip2_image_train”
image_size: 224
text_processor:
train:
name: “blip_caption”
sample_ratio: 115
cc_sbu:
vis_processor:
train:
name: “blip2_image_train”
image_size: 224
text_processor:
train:
name: “blip_caption”
sample_ratio: 14

run:
task: image_text_pretrain

optimizer

lr_sched: “linear_warmup_cosine_lr”
init_lr: 1e-4
min_lr: 8e-5
warmup_lr: 1e-6

weight_decay: 0.05
max_epoch: 3
batch_size_train: 16
batch_size_eval: 2
num_workers: 4
warmup_steps: 500
iters_per_epoch: 500

seed: 42
output_dir: “/workspace/output/minigpt4_stage1_pretrain”

amp: True
resume_ckpt_path: null

evaluate: False
train_splits: [“train”]

device: “cuda”
world_size: 1
dist_url: “env://”
distributed: True

接下来，通过以下命令启动第一阶段训练。

CUDA_VISIBLE_DEVICES=0,1,2,3  torchrun --nproc_per_node 4 train.py --cfg-path train_configs/minigpt4_stage1_pretrain.yaml

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运行过程：

> CUDA_VISIBLE_DEVICES=0,1,2,3  torchrun --nproc_per_node 4 train.py --cfg-path train_configs/minigpt4_stage1_pretrain.yaml

WARNING:torch.distributed.run:
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Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed.

| distributed init (rank 1, world 4): env://
| distributed init (rank 0, world 4): env://
| distributed init (rank 2, world 4): env://
| distributed init (rank 3, world 4): env://
2023-05-07 11:36:36,497 [INFO]
===== Running Parameters =====
2023-05-07 11:36:36,498 [INFO] {
“amp”: true,
“batch_size_eval”: 2,
“batch_size_train”: 16,
“device”: “cuda”,
“dist_backend”: “nccl”,
“dist_url”: “env://”,
“distributed”: true,
“evaluate”: false,
“gpu”: 0,
“init_lr”: 0.0001,
“iters_per_epoch”: 500,
“lr_sched”: “linear_warmup_cosine_lr”,
“max_epoch”: 3,
“min_lr”: 8e-05,
“num_workers”: 4,
“output_dir”: “/workspace/output/minigpt4_stage1_pretrain”,
“rank”: 0,
“resume_ckpt_path”: null,
“seed”: 42,
“task”: “image_text_pretrain”,
“train_splits”: [
“train”
],
“warmup_lr”: 1e-06,
“warmup_steps”: 500,
“weight_decay”: 0.05,
“world_size”: 4
}
2023-05-07 11:36:36,498 [INFO]
====== Dataset Attributes ======
2023-05-07 11:36:36,498 [INFO]
======== laion =======
2023-05-07 11:36:36,499 [INFO] {
“build_info”: {
“storage”: “/workspace/data/blip/laion/laion_dataset/00000.tar”
},
“data_type”: “images”,
“sample_ratio”: 115,
“text_processor”: {
“train”: {
“name”: “blip_caption”
}
},
“vis_processor”: {
“train”: {
“image_size”: 224,
“name”: “blip2_image_train”
}
}
}
2023-05-07 11:36:36,499 [INFO]
======== cc_sbu =======
2023-05-07 11:36:36,499 [INFO] {
“build_info”: {
“storage”: “/workspace/data/blip/cc_sbu/cc_sbu_dataset/00000.tar”
},
“data_type”: “images”,
“sample_ratio”: 14,
“text_processor”: {
“train”: {
“name”: “blip_caption”
}
},
“vis_processor”: {
“train”: {
“image_size”: 224,
“name”: “blip2_image_train”
}
}
}
2023-05-07 11:36:36,499 [INFO]
====== Model Attributes ======
2023-05-07 11:36:36,500 [INFO] {
“arch”: “mini_gpt4”,
“drop_path_rate”: 0,
“freeze_qformer”: true,
“freeze_vit”: true,
“image_size”: 224,
“llama_model”: “/workspace/model/vicuna-7b-all-v1.1”,
“model_type”: “pretrain_vicuna”,
“num_query_token”: 32,
“prompt”: “”,
“use_grad_checkpoint”: false,
“vit_precision”: “fp16”
}
2023-05-07 11:36:36,501 [INFO] Building datasets…
2023-05-07 11:36:36,503 [INFO] Building datasets…
Loading VIT
2023-05-07 11:36:58,812 [INFO] Downloading: “https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/eva_vit_g.pth” to /root/.cache/torch/hub/checkpoints/eva_vit_g.pth

100%|████████████████████████████████████████████████████████████████████████████████████████████████████| 1.89G/1.89G [02:33<00:00, 13.2MB/s]
cache_file_path: /root/.cache/torch/hub/checkpoints/eva_vit_g.pth
2023-05-07 11:39:41,878 [INFO] freeze vision encoder
Loading VIT Done
Loading Q-Former
2023-05-07 11:39:45,781 [INFO] Downloading: “https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained_flant5xxl.pth” to /root/.cache/torch/hub/checkpoints/blip2_pretrained_flant5xxl.pth

100%|██████████████████████████████████████████████████████████████████████████████████████████████████████| 413M/413M [00:31<00:00, 13.8MB/s]
cache_file_path: /root/.cache/torch/hub/checkpoints/blip2_pretrained_flant5xxl.pth
2023-05-07 11:40:18,140 [INFO] load checkpoint from https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained_flant5xxl.pth
2023-05-07 11:40:18,155 [INFO] freeze Qformer
Loading Q-Former Done
Loading LLAMA
Loading checkpoint shards: 100%|████████████████████████████████████████████████████████████████████████████████| 2/2 [00:15<00:00, 7.79s/it]
Loading checkpoint shards: 100%|████████████████████████████████████████████████████████████████████████████████| 2/2 [00:15<00:00, 7.94s/it]
Loading checkpoint shards: 100%|████████████████████████████████████████████████████████████████████████████████| 2/2 [00:16<00:00, 8.21s/it]
Loading checkpoint shards: 100%|████████████████████████████████████████████████████████████████████████████████| 2/2 [00:16<00:00, 8.13s/it]
Loading LLAMA Done
2023-05-07 11:43:21,365 [INFO] Start training
2023-05-07 11:43:28,791 [INFO] dataset_ratios not specified, datasets will be concatenated (map-style datasets) or chained (webdataset.DataPipeline).
2023-05-07 11:43:28,791 [INFO] Loaded 0 records for train split from the dataset.
module.llama_proj.weight
module.llama_proj.bias
2023-05-07 11:43:30,005 [INFO] number of trainable parameters: 3149824
2023-05-07 11:43:30,008 [INFO] Start training epoch 0, 500 iters per inner epoch.
Train: data epoch: [0] [ 0/500] eta: 0:35:50 lr: 0.000001 loss: 7.4586 time: 4.3018 data: 0.0000 max mem: 20913
2023-05-07 11:43:34,313 [INFO] Reducer buckets have been rebuilt in this iteration.
Train: data epoch: [0] [ 50/500] eta: 0:03:04 lr: 0.000011 loss: 4.9250 time: 0.3323 data: 0.0000 max mem: 22076
Train: data epoch: [0] [100/500] eta: 0:02:29 lr: 0.000021 loss: 3.6569 time: 0.3376 data: 0.0000 max mem: 22076
Train: data epoch: [0] [150/500] eta: 0:02:06 lr: 0.000031 loss: 2.8653 time: 0.3415 data: 0.0000 max mem: 22193
Train: data epoch: [0] [200/500] eta: 0:01:47 lr: 0.000041 loss: 2.5771 time: 0.3417 data: 0.0000 max mem: 22193
Train: data epoch: [0] [250/500] eta: 0:01:28 lr: 0.000051 loss: 3.0763 time: 0.3375 data: 0.0000 max mem: 22193
Train: data epoch: [0] [300/500] eta: 0:01:10 lr: 0.000060 loss: 2.3269 time: 0.3369 data: 0.0000 max mem: 22193
Train: data epoch: [0] [350/500] eta: 0:00:52 lr: 0.000070 loss: 2.5431 time: 0.3403 data: 0.0000 max mem: 22193
Train: data epoch: [0] [400/500] eta: 0:00:34 lr: 0.000080 loss: 2.6711 time: 0.3383 data: 0.0000 max mem: 22193
Train: data epoch: [0] [450/500] eta: 0:00:17 lr: 0.000090 loss: 2.3690 time: 0.3426 data: 0.0000 max mem: 22193
Train: data epoch: [0] [499/500] eta: 0:00:00 lr: 0.000100 loss: 1.5752 time: 0.3424 data: 0.0000 max mem: 22193
Train: data epoch: [0] Total time: 0:02:53 (0.3466 s / it)
2023-05-07 11:46:23,294 [INFO] Averaged stats: lr: 0.0001 loss: 3.2105
2023-05-07 11:46:23,297 [INFO] No validation splits found.
2023-05-07 11:46:23,334 [INFO] Saving checkpoint at epoch 0 to /workspace/output/minigpt4_stage1_pretrain/20230507113/checkpoint_0.pth.
2023-05-07 11:46:23,402 [INFO] Start training
2023-05-07 11:46:23,443 [INFO] Start training epoch 1, 500 iters per inner epoch.
Train: data epoch: [1] [ 0/500] eta: 0:03:00 lr: 0.000095 loss: 1.9775 time: 0.3606 data: 0.0000 max mem: 22193
Train: data epoch: [1] [ 50/500] eta: 0:02:34 lr: 0.000094 loss: 1.3029 time: 0.3486 data: 0.0000 max mem: 22193
Train: data epoch: [1] [100/500] eta: 0:02:16 lr: 0.000093 loss: 1.1404 time: 0.3374 data: 0.0000 max mem: 22193
Train: data epoch: [1] [150/500] eta: 0:01:59 lr: 0.000092 loss: 0.8192 time: 0.3376 data: 0.0000 max mem: 22193
Train: data epoch: [1] [200/500] eta: 0:01:42 lr: 0.000091 loss: 0.4934 time: 0.3415 data: 0.0000 max mem: 22193
Train: data epoch: [1] [250/500] eta: 0:01:25 lr: 0.000090 loss: 0.4390 time: 0.3402 data: 0.0000 max mem: 22193
Train: data epoch: [1] [300/500] eta: 0:01:08 lr: 0.000089 loss: 0.2317 time: 0.3421 data: 0.0000 max mem: 22193
Train: data epoch: [1] [350/500] eta: 0:00:51 lr: 0.000088 loss: 0.1960 time: 0.3413 data: 0.0000 max mem: 22193
Train: data epoch: [1] [400/500] eta: 0:00:34 lr: 0.000087 loss: 2.0755 time: 0.3420 data: 0.0000 max mem: 22193
Train: data epoch: [1] [450/500] eta: 0:00:17 lr: 0.000086 loss: 0.0773 time: 0.3405 data: 0.0000 max mem: 22193
Train: data epoch: [1] [499/500] eta: 0:00:00 lr: 0.000085 loss: 0.1692 time: 0.3387 data: 0.0000 max mem: 22193
Train: data epoch: [1] Total time: 0:02:50 (0.3404 s / it)
2023-05-07 11:49:13,623 [INFO] Averaged stats: lr: 0.0001 loss: 0.7745
2023-05-07 11:49:13,625 [INFO] No validation splits found.
2023-05-07 11:49:13,660 [INFO] Saving checkpoint at epoch 1 to /workspace/output/minigpt4_stage1_pretrain/20230507113/checkpoint_1.pth.
2023-05-07 11:49:13,722 [INFO] Start training
2023-05-07 11:49:13,763 [INFO] Start training epoch 2, 500 iters per inner epoch.
Train: data epoch: [2] [ 0/500] eta: 0:03:00 lr: 0.000085 loss: 0.2226 time: 0.3614 data: 0.0000 max mem: 22193
Train: data epoch: [2] [ 50/500] eta: 0:02:34 lr: 0.000084 loss: 0.1156 time: 0.3454 data: 0.0000 max mem: 22193
Train: data epoch: [2] [100/500] eta: 0:02:16 lr: 0.000083 loss: 0.0512 time: 0.3396 data: 0.0000 max mem: 22193
Train: data epoch: [2] [150/500] eta: 0:01:59 lr: 0.000083 loss: 0.1134 time: 0.3421 data: 0.0000 max mem: 22193
Train: data epoch: [2] [200/500] eta: 0:01:42 lr: 0.000082 loss: 0.0489 time: 0.3412 data: 0.0000 max mem: 22193
Train: data epoch: [2] [250/500] eta: 0:01:25 lr: 0.000081 loss: 0.0693 time: 0.3409 data: 0.0000 max mem: 22193
Train: data epoch: [2] [300/500] eta: 0:01:08 lr: 0.000081 loss: 0.0316 time: 0.3433 data: 0.0000 max mem: 22193
Train: data epoch: [2] [350/500] eta: 0:00:51 lr: 0.000080 loss: 0.0372 time: 0.3464 data: 0.0000 max mem: 22193
Train: data epoch: [2] [400/500] eta: 0:00:34 lr: 0.000080 loss: 0.0404 time: 0.3386 data: 0.0000 max mem: 22193
Train: data epoch: [2] [450/500] eta: 0:00:17 lr: 0.000080 loss: 0.0523 time: 0.3396 data: 0.0000 max mem: 22193
Train: data epoch: [2] [499/500] eta: 0:00:00 lr: 0.000080 loss: 0.0471 time: 0.3378 data: 0.0000 max mem: 22193
Train: data epoch: [2] Total time: 0:02:50 (0.3402 s / it)
2023-05-07 11:52:03,847 [INFO] Averaged stats: lr: 0.0001 loss: 0.2326
2023-05-07 11:52:03,849 [INFO] No validation splits found.
2023-05-07 11:52:03,885 [INFO] Saving checkpoint at epoch 2 to /workspace/output/minigpt4_stage1_pretrain/20230507113/checkpoint_2.pth.
2023-05-07 11:52:03,946 [INFO] No validation splits found.
2023-05-07 11:52:03,946 [INFO] Training time 0:08:42

显存占用：

Sun May  7 19:48:54 2023

±----------------------------------------------------------------------------+

| NVIDIA-SMI 525.105.17   Driver Version: 525.105.17   CUDA Version: 12.0     |

|-------------------------------±---------------------±---------------------+

| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |

| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |

|                               |                      |               MIG M. |

|=++==============|

|   0  NVIDIA A800 80G…  Off  | 00000000:3B:00.0 Off |                    0 |

| N/A   68C    P0   297W / 300W |  32439MiB / 81920MiB |     97%      Default |

|                               |                      |             Disabled |

±------------------------------±---------------------±---------------------+

|   1  NVIDIA A800 80G…  Off  | 00000000:5E:00.0 Off |                    0 |

| N/A   65C    P0   322W / 300W |  32439MiB / 81920MiB |     97%      Default |

|                               |                      |             Disabled |

±------------------------------±---------------------±---------------------+

|   2  NVIDIA A800 80G…  Off  | 00000000:AF:00.0 Off |                    0 |

| N/A   69C    P0   218W / 300W |  32439MiB / 81920MiB |     97%      Default |

|                               |                      |             Disabled |

±------------------------------±---------------------±---------------------+

|   3  NVIDIA A800 80G…  Off  | 00000000:D8:00.0 Off |                    0 |

| N/A   69C    P0   335W / 300W |  32439MiB / 81920MiB |     97%      Default |

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±----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| 0 N/A N/A 11425 C /opt/conda/bin/python 32436MiB |
| 1 N/A N/A 11426 C /opt/conda/bin/python 32436MiB |
| 2 N/A N/A 11427 C /opt/conda/bin/python 32436MiB |
| 3 N/A N/A 11428 C /opt/conda/bin/python 32436MiB |
±----------------------------------------------------------------------------+

模型权重输出：

> tree minigpt4_stage1_pretrain/

minigpt4_stage1_pretrain/

-- 20230507113     |-- checkpoint_0.pth     |-- checkpoint_1.pth     |-- checkpoint_2.pth     |-- log.txt     – result
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2 directories, 4 files

你也可以直接下载只有第一阶段训练的 MiniGPT-4 的 checkpoint，具体下载地址：13B 或 7B。

与第二阶段之后的模型相比，第一阶段的checkpoint经常生成不完整和重复的句子。

第二阶段：微调

在第二阶段，我们使用自己创建的小型高质量图文对数据集并将其转换为对话格式以进一步对齐 MiniGPT-4。

要启动第二阶段对齐，需先在train_configs/minigpt4_stage2_finetune.yaml 中指定第一阶段训练的checkpoint文件的路径。当然，您还可以自定义输出权重路径，具体文件如下所示。

model:

arch: mini_gpt4

model_type: pretrain_vicuna

freeze_vit: True

freeze_qformer: True

max_txt_len: 160

end_sym: “###”

prompt_path: “prompts/alignment.txt”

prompt_template: '###Human: {} ###Assistant: ’

ckpt: ‘/workspace/output/minigpt4_stage1_pretrain/20230507113/checkpoint_2.pth’
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datasets:
cc_sbu_align:
vis_processor:
train:
name: “blip2_image_train”
image_size: 224
text_processor:
train:
name: “blip_caption”

run:
task: image_text_pretrain

optimizer

lr_sched: “linear_warmup_cosine_lr”
init_lr: 3e-5
min_lr: 1e-5
warmup_lr: 1e-6

weight_decay: 0.05
max_epoch: 5
iters_per_epoch: 200
batch_size_train: 12
batch_size_eval: 12
num_workers: 4
warmup_steps: 200

seed: 42
output_dir: “/workspace/output/minigpt4_stage2_finetune”

amp: True
resume_ckpt_path: null

evaluate: False
train_splits: [“train”]

device: “cuda”
world_size: 1
dist_url: “env://”
distributed: True

然后，第二阶段微调的运行命令如下所示。 MiniGPT-4官方在实验中，仅使用了 1 卡 A100。

CUDA_VISIBLE_DEVICES=0 torchrun --nproc_per_node 1 train.py --cfg-path train_configs/minigpt4_stage2_finetune.yaml

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运行过程：

> CUDA_VISIBLE_DEVICES=0 torchrun --nproc_per_node 1 train.py --cfg-path train_configs/minigpt4_stage2_finetune.yaml

| distributed init (rank 0, world 1): env://

2023-05-07 12:03:11,908 [INFO]

=====  Running Parameters    =====

2023-05-07 12:03:11,909 [INFO] {

“amp”: true,

“batch_size_eval”: 12,

“batch_size_train”: 12,

“device”: “cuda”,

“dist_backend”: “nccl”,

“dist_url”: “env://”,

“distributed”: true,

“evaluate”: false,

“gpu”: 0,

“init_lr”: 3e-05,

“iters_per_epoch”: 200,

“lr_sched”: “linear_warmup_cosine_lr”,

“max_epoch”: 5,

“min_lr”: 1e-05,

“num_workers”: 4,

“output_dir”: “/workspace/output/minigpt4_stage2_finetune”,

“rank”: 0,

“resume_ckpt_path”: null,

“seed”: 42,

“task”: “image_text_pretrain”,

“train_splits”: [

“train”

],

“warmup_lr”: 1e-06,

“warmup_steps”: 200,

“weight_decay”: 0.05,

“world_size”: 1

}

2023-05-07 12:03:11,909 [INFO]

======  Dataset Attributes  ======

2023-05-07 12:03:11,909 [INFO]

======== cc_sbu_align =======

2023-05-07 12:03:11,910 [INFO] {

“build_info”: {

“storage”: “/workspace/data/cc_sbu_align/”

},

“data_type”: “images”,

“text_processor”: {

“train”: {

“name”: “blip_caption”

}

},

“vis_processor”: {

“train”: {

“image_size”: 224,

“name”: “blip2_image_train”

}

}

}

2023-05-07 12:03:11,910 [INFO]

======  Model Attributes  ======

2023-05-07 12:03:11,910 [INFO] {

“arch”: “mini_gpt4”,

“ckpt”: “/workspace/output/minigpt4_stage1_pretrain/20230507113/checkpoint_2.pth”,

“drop_path_rate”: 0,

“end_sym”: “###”,

“freeze_qformer”: true,

“freeze_vit”: true,

“image_size”: 224,

“llama_model”: “/workspace/model/vicuna-7b-all-v1.1”,

“max_txt_len”: 160,

“model_type”: “pretrain_vicuna”,

“num_query_token”: 32,

“prompt”: “”,

“prompt_path”: “prompts/alignment.txt”,

“prompt_template”: "###Human: {} ###Assistant: ",

“use_grad_checkpoint”: false,

“vit_precision”: “fp16”

}

2023-05-07 12:03:11,910 [INFO] Building datasets…

Loading VIT

cache_file_path: /root/.cache/torch/hub/checkpoints/eva_vit_g.pth

2023-05-07 12:03:37,018 [INFO] freeze vision encoder

Loading VIT Done

Loading Q-Former

cache_file_path: /root/.cache/torch/hub/checkpoints/blip2_pretrained_flant5xxl.pth

2023-05-07 12:03:40,903 [INFO] load checkpoint from https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained_flant5xxl.pth

2023-05-07 12:03:40,916 [INFO] freeze Qformer

Loading Q-Former Done

Loading LLAMA

Loading checkpoint shards: 100%|████████████████████████████████████████████████████████████████████████████████| 2/2 [00:10<00:00,  5.13s/it]

Loading LLAMA Done

Load 4 training prompts

Prompt Example

###Human: <Img><ImageHere></Img> Describe this image in detail. ###Assistant:

Load BLIP2-LLM Checkpoint: /workspace/output/minigpt4_stage1_pretrain/20230507113/checkpoint_2.pth

2023-05-07 12:06:34,005 [INFO] Start training

2023-05-07 12:06:40,005 [INFO] dataset_ratios not specified, datasets will be concatenated (map-style datasets) or chained (webdataset.DataPipeline).

2023-05-07 12:06:40,005 [INFO] Loaded 3439 records for train split from the dataset.

module.llama_proj.weight

module.llama_proj.bias

2023-05-07 12:06:40,029 [INFO] number of trainable parameters: 3149824

2023-05-07 12:06:40,030 [INFO] Start training epoch 0, 200 iters per inner epoch.

Train: data epoch: [0]  [  0/200]  eta: 0:15:02  lr: 0.000001  loss: 1.6358  time: 4.5127  data: 0.0000  max mem: 35512

2023-05-07 12:06:44,545 [INFO] Reducer buckets have been rebuilt in this iteration.

Train: data epoch: [0]  [ 50/200]  eta: 0:01:47  lr: 0.000008  loss: 1.3364  time: 0.6420  data: 0.0000  max mem: 36093

Train: data epoch: [0]  [100/200]  eta: 0:01:07  lr: 0.000015  loss: 1.2098  time: 0.6466  data: 0.0000  max mem: 36093

Train: data epoch: [0]  [150/200]  eta: 0:00:33  lr: 0.000023  loss: 1.0652  time: 0.6472  data: 0.0000  max mem: 36093

Train: data epoch: [0]  [199/200]  eta: 0:00:00  lr: 0.000030  loss: 1.0278  time: 0.6460  data: 0.0000  max mem: 36093

Train: data epoch: [0] Total time: 0:02:12 (0.6627 s / it)

2023-05-07 12:08:52,563 [INFO] Averaged stats: lr: 0.0000  loss: 1.2121

2023-05-07 12:08:52,565 [INFO] No validation splits found.

2023-05-07 12:08:52,601 [INFO] Saving checkpoint at epoch 0 to /workspace/output/minigpt4_stage2_finetune/20230507120/checkpoint_0.pth.

2023-05-07 12:08:52,668 [INFO] Start training

2023-05-07 12:08:52,708 [INFO] Start training epoch 1, 200 iters per inner epoch.

Train: data epoch: [1]  [  0/200]  eta: 0:02:14  lr: 0.000028  loss: 0.9808  time: 0.6744  data: 0.0000  max mem: 36093

Train: data epoch: [1]  [ 50/200]  eta: 0:01:35  lr: 0.000027  loss: 0.9252  time: 0.6336  data: 0.0000  max mem: 36093

Train: data epoch: [1]  [100/200]  eta: 0:01:07  lr: 0.000026  loss: 1.0419  time: 0.7971  data: 0.0000  max mem: 36093

Train: data epoch: [1]  [150/200]  eta: 0:00:33  lr: 0.000025  loss: 1.0150  time: 0.6486  data: 0.0000  max mem: 36093

Train: data epoch: [1]  [199/200]  eta: 0:00:00  lr: 0.000023  loss: 0.9695  time: 0.6472  data: 0.0000  max mem: 36093

Train: data epoch: [1] Total time: 0:02:11 (0.6576 s / it)

2023-05-07 12:11:04,223 [INFO] Averaged stats: lr: 0.0000  loss: 0.9785

2023-05-07 12:11:04,227 [INFO] No validation splits found.

2023-05-07 12:11:04,264 [INFO] Saving checkpoint at epoch 1 to /workspace/output/minigpt4_stage2_finetune/20230507120/checkpoint_1.pth.

2023-05-07 12:11:04,332 [INFO] Start training

2023-05-07 12:11:04,370 [INFO] Start training epoch 2, 200 iters per inner epoch.

Train: data epoch: [2]  [  0/200]  eta: 0:02:13  lr: 0.000023  loss: 1.1459  time: 0.6684  data: 0.0000  max mem: 36093

Train: data epoch: [2]  [ 50/200]  eta: 0:01:38  lr: 0.000022  loss: 1.0003  time: 0.6580  data: 0.0000  max mem: 36093

Train: data epoch: [2]  [100/200]  eta: 0:01:04  lr: 0.000020  loss: 0.8605  time: 0.6367  data: 0.0000  max mem: 36093

Train: data epoch: [2]  [150/200]  eta: 0:00:32  lr: 0.000018  loss: 0.8841  time: 0.6445  data: 0.0000  max mem: 36093

Train: data epoch: [2]  [199/200]  eta: 0:00:00  lr: 0.000017  loss: 0.8462  time: 0.6380  data: 0.0000  max mem: 36093

Train: data epoch: [2] Total time: 0:02:11 (0.6588 s / it)

2023-05-07 12:13:16,139 [INFO] Averaged stats: lr: 0.0000  loss: 0.9272

2023-05-07 12:13:16,143 [INFO] No validation splits found.

2023-05-07 12:13:16,178 [INFO] Saving checkpoint at epoch 2 to /workspace/output/minigpt4_stage2_finetune/20230507120/checkpoint_2.pth.

2023-05-07 12:13:16,247 [INFO] Start training

2023-05-07 12:13:16,286 [INFO] Start training epoch 3, 200 iters per inner epoch.

Train: data epoch: [3]  [  0/200]  eta: 0:02:14  lr: 0.000017  loss: 0.8447  time: 0.6750  data: 0.0000  max mem: 36093

Train: data epoch: [3]  [ 50/200]  eta: 0:01:37  lr: 0.000015  loss: 0.9082  time: 0.6517  data: 0.0000  max mem: 36093

Train: data epoch: [3]  [100/200]  eta: 0:01:04  lr: 0.000014  loss: 0.9476  time: 0.6380  data: 0.0000  max mem: 36093

Train: data epoch: [3]  [150/200]  eta: 0:00:32  lr: 0.000013  loss: 0.8131  time: 0.6443  data: 0.0000  max mem: 36093

Train: data epoch: [3]  [199/200]  eta: 0:00:00  lr: 0.000012  loss: 0.8718  time: 0.6550  data: 0.0000  max mem: 36093

Train: data epoch: [3] Total time: 0:02:09 (0.6460 s / it)

2023-05-07 12:15:25,492 [INFO] Averaged stats: lr: 0.0000  loss: 0.9053

2023-05-07 12:15:25,495 [INFO] No validation splits found.

2023-05-07 12:15:25,530 [INFO] Saving checkpoint at epoch 3 to /workspace/output/minigpt4_stage2_finetune/20230507120/checkpoint_3.pth.

2023-05-07 12:15:25,592 [INFO] Start training

2023-05-07 12:15:25,631 [INFO] Start training epoch 4, 200 iters per inner epoch.

Train: data epoch: [4]  [  0/200]  eta: 0:01:56  lr: 0.000012  loss: 0.8907  time: 0.5827  data: 0.0000  max mem: 36093

Train: data epoch: [4]  [ 50/200]  eta: 0:01:37  lr: 0.000011  loss: 1.0402  time: 0.6489  data: 0.0000  max mem: 36093

Train: data epoch: [4]  [100/200]  eta: 0:01:07  lr: 0.000010  loss: 0.9383  time: 0.6434  data: 0.0000  max mem: 36093

Train: data epoch: [4]  [150/200]  eta: 0:00:33  lr: 0.000010  loss: 1.0148  time: 0.6435  data: 0.0000  max mem: 36093

Train: data epoch: [4]  [199/200]  eta: 0:00:00  lr: 0.000010  loss: 0.7553  time: 0.6397  data: 0.0000  max mem: 36093

Train: data epoch: [4] Total time: 0:02:11 (0.6594 s / it)

2023-05-07 12:17:37,503 [INFO] Averaged stats: lr: 0.0000  loss: 0.8906

2023-05-07 12:17:37,507 [INFO] No validation splits found.

2023-05-07 12:17:37,543 [INFO] Saving checkpoint at epoch 4 to /workspace/output/minigpt4_stage2_finetune/20230507120/checkpoint_4.pth.

2023-05-07 12:17:37,612 [INFO] No validation splits found.

2023-05-07 12:17:37,612 [INFO] Training time 0:11:03

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显存占用：

±----------------------------------------------------------------------------+

| NVIDIA-SMI 525.105.17   Driver Version: 525.105.17   CUDA Version: 12.0     |

|-------------------------------±---------------------±---------------------+

| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |

| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |

|                               |                      |               MIG M. |

|=++==============|

|   0  NVIDIA A800 80G…  Off  | 00000000:3B:00.0 Off |                    0 |

| N/A   69C    P0   311W / 300W |  40041MiB / 81920MiB |     94%      Default |

|                               |                      |             Disabled |

±------------------------------±---------------------±---------------------+
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模型权重输出：

> tree minigpt4_stage2_finetune/

minigpt4_stage2_finetune/

-- 20230507120     |-- checkpoint_0.pth     |-- checkpoint_1.pth     |-- checkpoint_2.pth     |-- checkpoint_3.pth     |-- checkpoint_4.pth     |-- log.txt     – result
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2 directories, 6 files

经过第二阶段对齐之后，MiniGPT-4 能够连贯地和用户友好地讨论图像。

至此，整个模型训练过程结束。接下来进行对训练的模型进行评估。

模型评估

首先，在评估配置文件eval_configs/minigpt4_eval.yaml中的第 11 行设置待评估模型的checkpoint路径，同模型推理。

model:

arch: mini_gpt4

…

low_resource: True

prompt_path: “prompts/alignment.txt”

prompt_template: '###Human: {} ###Assistant: ’

ckpt: ‘/workspace/output/minigpt4_stage2_finetune/20230507120/checkpoint_4.pth’

…

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执行如下命令启动模型推理服务进行评估：

python demo.py --cfg-path eval_configs/minigpt4_eval.yaml --gpu-id 0

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如果出现Could not create share link. Please check your internet connection or our status page: https://status.gradio.app这个问题，通常是由于网络环境不稳定造成的。可修改demo.py文件如下的代码，使用IP:端口访问即可。

#demo.launch(share=True, enable_queue=True)

demo.launch(server_name=‘0.0.0.0’, share=True, enable_queue=True)

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运行过程：

python demo.py --cfg-path eval_configs/minigpt4_eval.yaml --gpu-id 0

Initializing Chat

Loading VIT

cache_file_path: /root/.cache/torch/hub/checkpoints/eva_vit_g.pth

Loading VIT Done

Loading Q-Former

cache_file_path: /root/.cache/torch/hub/checkpoints/blip2_pretrained_flant5xxl.pth

Loading Q-Former Done

Loading LLAMA
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=BUG REPORT=
Welcome to bitsandbytes. For bug reports, please submit your error trace to: https://github.com/TimDettmers/bitsandbytes/issues

Loading checkpoint shards: 100%|████████████████████████████████████████████████████████████████████████████████| 2/2 [00:35<00:00, 17.65s/it]
Loading LLAMA Done
Load 4 training prompts
Prompt Example
###Human: <Img><ImageHere></Img> Take a look at this image and describe what you notice. ###Assistant:
Load BLIP2-LLM Checkpoint: /workspace/output/minigpt4_stage2_finetune/20230507120/checkpoint_4.pth
Initialization Finished
Running on local URL: http://0.0.0.0:7860

模型评估测试：

结语

本文给大家分享了多模态大模型MiniGPT-4的基本原理及模型训练推理方法，希望能够给大家带来帮助。

参考文档：

声明：本文内容由网友自发贡献，转载请注明出处：【wpsshop博客】

大杀器，多模态大模型MiniGPT-4入坑指南_minigpt-4复现

MiniGPT-4 简介

MiniGPT-4 模型训练原理

环境搭建

数据集、模型权重及训练推理代码下载

下载模型训练及推理代码

模型权重准备

vit encoder

Q-Former

Vicuna

generation configs

数据集准备

代码结构

模型推理

=BUG REPORT= Welcome to bitsandbytes. For bug reports, please submit your error trace to: https://github.com/TimDettmers/bitsandbytes/issues

模型训练

第一阶段：预训练

optimizer

第二阶段：微调

optimizer

模型评估

=BUG REPORT= Welcome to bitsandbytes. For bug reports, please submit your error trace to: https://github.com/TimDettmers/bitsandbytes/issues

结语

=BUG REPORT=
Welcome to bitsandbytes. For bug reports, please submit your error trace to: https://github.com/TimDettmers/bitsandbytes/issues

=BUG REPORT=
Welcome to bitsandbytes. For bug reports, please submit your error trace to: https://github.com/TimDettmers/bitsandbytes/issues