github
/
minimind-v
mirror of https://github.com/jingyaogong/minimind-v.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

update minimind2-v

pull/45/head
gongjy 2025-02-20 23:34:51 +08:00
parent 61a4468bab
commit 60b265d01c
52 changed files with 1822 additions and 2218 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

264
2-sft_vlm.py
View File

@ -1,264 +0,0 @@
import os
import platform
import argparse
import time
import math
import warnings
import json
import pandas as pd
import torch
import torch.nn.functional as F
import torch.distributed as dist
from contextlib import nullcontext
from torch import optim
from torch.nn.parallel import DistributedDataParallel
from torch.utils.data import DataLoader, DistributedSampler
from transformers import AutoTokenizer, AutoModel
from model.model import Transformer
from model.LMConfig import LMConfig
from model.dataset import SFTDataset, SFTDataset_multi
from model.vision_utils import get_vision_model, get_img_embedding
warnings.filterwarnings('ignore')
def count_parameters(model):
return sum(p.numel() for p in model.parameters() if p.requires_grad)
def Logger(content):
if not ddp or dist.get_rank() == 0:
print(content)
def get_lr(it, all):
warmup_iters = args.warmup_iters
lr_decay_iters = all
min_lr = args.learning_rate / 10
if it < warmup_iters:
return args.learning_rate * it / warmup_iters
if it > lr_decay_iters:
return min_lr
decay_ratio = (it - warmup_iters) / (lr_decay_iters - warmup_iters)
assert 0 <= decay_ratio <= 1
coeff = 0.5 * (1.0 + math.cos(math.pi * decay_ratio))
return min_lr + coeff * (args.learning_rate - min_lr)
def train_epoch(epoch, wandb):
start_time = time.time()
for step, (X, Y, loss_mask, image_process) in enumerate(train_loader):
X = X.to(args.device)
Y = Y.to(args.device)
loss_mask = loss_mask.to(args.device)
image_process = image_process.to(args.device)
image_encoders = get_img_embedding(image_process, vision_model)
lr = get_lr(epoch * iter_per_epoch + step, args.epochs * iter_per_epoch)
for param_group in optimizer.param_groups:
param_group['lr'] = lr
with ctx:
logits = model(X, Y, image_encoders=image_encoders).logits
loss = F.cross_entropy(logits.view(-1, logits.size(-1)), Y.view(-1), ignore_index=0, reduction='none')
loss_mask = loss_mask.view(-1)
loss = torch.sum(loss * loss_mask) / loss_mask.sum()
scaler.scale(loss).backward()
if (step + 1) % args.accumulation_steps == 0:
scaler.unscale_(optimizer)
torch.nn.utils.clip_grad_norm_(model.parameters(), args.grad_clip)
scaler.step(optimizer)
scaler.update()
optimizer.zero_grad(set_to_none=True)
if step % args.log_interval == 0:
spend_time = time.time() - start_time
Logger(
'Epoch:[{}/{}]({}/{}) loss:{:.3f} lr:{:.7f} epoch_Time:{}min:'.format(
epoch,
args.epochs,
step,
iter_per_epoch,
loss.item(),
optimizer.param_groups[-1]['lr'],
spend_time / (step + 1) * iter_per_epoch // 60 - spend_time // 60))
if (wandb is not None) and (not ddp or dist.get_rank() == 0):
wandb.log({"loss": loss,
"lr": optimizer.param_groups[-1]['lr'],
"epoch_Time": spend_time / (step + 1) * iter_per_epoch // 60 - spend_time // 60})
if (step + 1) % args.save_interval == 0 and (not ddp or dist.get_rank() == 0):
model.eval()
moe_path = '_moe' if lm_config.use_moe else ''
if args.multi: # 多图训练权重保存
ckp = f'{args.save_dir}/{lm_config.dim}{moe_path}_vlm_sft_multi.pth'
else: # 单图训练权重保存
ckp = f'{args.save_dir}/{lm_config.dim}{moe_path}_vlm_sft.pth'
if isinstance(model, torch.nn.parallel.DistributedDataParallel):
state_dict = model.module.state_dict()
else:
state_dict = model.state_dict()
torch.save(state_dict, ckp)
model.train()
if args.save_last and (not ddp or dist.get_rank() == 0) and args.multi:
model.eval()
moe_path = '_moe' if lm_config.use_moe else ''
ckp = f'{args.save_dir}/{lm_config.dim}{moe_path}_vlm_sft_multi.pth'
if isinstance(model, torch.nn.parallel.DistributedDataParallel):
state_dict = model.module.state_dict()
else:
state_dict = model.state_dict()
torch.save(state_dict, ckp)
model.train()
def init_model(lm_config):
tokenizer = AutoTokenizer.from_pretrained('./model/minimind_tokenizer')
moe_path = '_moe' if lm_config.use_moe else ''
# 多图推理建议在单图推理之后
if args.multi:
ckp = f'./out/{lm_config.dim}{moe_path}_vlm_sft.pth'
else:
ckp = f'./out/{lm_config.dim}{moe_path}_vlm_pretrain.pth'
model = Transformer(lm_config)
state_dict = torch.load(ckp, map_location=args.device)
# 处理不需要的前缀
unwanted_prefix = '_orig_mod.'
for k, v in list(state_dict.items()):
if k.startswith(unwanted_prefix):
state_dict[k[len(unwanted_prefix):]] = state_dict.pop(k)
# 加载到模型中
model.load_state_dict(state_dict, strict=False)
model = model.to(args.device)
print(f'模型可学习参数: {count_parameters(model) / 1e6} 百万 = {count_parameters(model) / 1e9} B (Billion)')
(vision_model, preprocess) = get_vision_model(args.visual_encoder)
vision_model = vision_model.to(args.device)
return model, tokenizer, (vision_model, preprocess)
def init_distributed_mode():
if not ddp: return
global ddp_local_rank, DEVICE
dist.init_process_group(backend="nccl")
ddp_rank = int(os.environ["RANK"])
ddp_local_rank = int(os.environ["LOCAL_RANK"])
ddp_world_size = int(os.environ["WORLD_SIZE"])
DEVICE = f"cuda:{ddp_local_rank}"
torch.cuda.set_device(DEVICE)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="MiniMind-V-SFT")
parser.add_argument("--out_dir", type=str, default="out", help="Output directory")
parser.add_argument("--epochs", type=int, default=19, help="Number of epochs")
parser.add_argument("--batch_size", type=int, default=32, help="Batch size")
parser.add_argument("--learning_rate", type=float, default=1e-6, help="Learning rate")
parser.add_argument("--device", type=str, default="cuda:0" if torch.cuda.is_available() else "cpu",
help="Device to use")
parser.add_argument("--dtype", type=str, default="bfloat16", help="Data type")
parser.add_argument("--use_wandb", default=False, action="store_true", help="Use Weights & Biases")
parser.add_argument("--wandb_project", type=str, default="MiniMind-V", help="Weights & Biases project name")
parser.add_argument("--num_workers", type=int, default=8, help="Number of workers for data loading")
parser.add_argument("--data_path", type=str, default="./dataset/LLaVA-Instruct/llava_instruct_230k.json",
help="Path to training data")
parser.add_argument("--data_path_multi", type=str, default="./dataset/sft_multi_images/output.json",
help="Path to multi images training data")
parser.add_argument("--ddp", action="store_true", help="Use DistributedDataParallel")
parser.add_argument("--accumulation_steps", type=int, default=1, help="Gradient accumulation steps")
parser.add_argument("--grad_clip", type=float, default=1.0, help="Gradient clipping threshold")
parser.add_argument("--warmup_iters", type=int, default=0, help="Number of warmup iterations")
parser.add_argument("--log_interval", type=int, default=100, help="Logging interval")
parser.add_argument("--save_interval", type=int, default=100, help="Model saving interval")
parser.add_argument('--local_rank', type=int, default=-1, help='local rank for distributed training')
parser.add_argument('--multi', type=bool, default=False, help='multi-images training')
parser.add_argument('--save_last', type=bool, default=True, help='save last step model')
parser.add_argument('--visual_encoder', type=str, default="clip", help='type of visual endcoder')
args = parser.parse_args()
if args.visual_encoder == "clip":
lm_config = LMConfig()
else:
lm_config = LMConfig(image_special_token='<' * 98 + '>' * 98, image_ids=[30] * 98 + [32] * 98)
max_seq_len = lm_config.max_seq_len
args.save_dir = os.path.join(args.out_dir)
os.makedirs(args.save_dir, exist_ok=True)
os.makedirs(args.out_dir, exist_ok=True)
tokens_per_iter = args.batch_size * max_seq_len
torch.manual_seed(1337)
device_type = "cuda" if "cuda" in args.device else "cpu"
args.wandb_run_name = f"MiniMind-V-SFT-Epoch-{args.epochs}-BatchSize-{args.batch_size}-LearningRate-{args.learning_rate}"
ctx = nullcontext() if device_type == "cpu" else torch.cuda.amp.autocast()
ddp = int(os.environ.get("RANK", -1)) != -1 # is this a ddp run?
ddp_local_rank, DEVICE = 0, "cuda:0"
if ddp:
init_distributed_mode()
args.device = torch.device(DEVICE)
if args.use_wandb and (not ddp or ddp_local_rank == 0):
import wandb
wandb.init(project=args.wandb_project, name=args.wandb_run_name)
else:
wandb = None
model, tokenizer, (vision_model, preprocess) = init_model(lm_config)
use_version = 0
if args.multi:
print("进行多图训练，建议在指令微调后进行...")
train_ds = SFTDataset_multi(args.data_path_multi, tokenizer, vision_model=(vision_model, preprocess),
image_special_token=lm_config.image_special_token,
max_length=max_seq_len)
else:
train_ds = SFTDataset(args.data_path, tokenizer, vision_model=(vision_model, preprocess),
image_special_token=lm_config.image_special_token,
max_length=max_seq_len)
train_sampler = DistributedSampler(train_ds) if ddp else None
train_loader = DataLoader(
train_ds,
batch_size=args.batch_size,
pin_memory=True,
drop_last=False,
shuffle=False,
num_workers=args.num_workers,
sampler=train_sampler
)
scaler = torch.cuda.amp.GradScaler(enabled=(args.dtype in ['float16', 'bfloat16']))
optimizer = optim.Adam(model.parameters(), lr=args.learning_rate)
if False and not lm_config.use_moe and platform.system() != 'Windows' and float(
torch.__version__.split('.')[0]) >= 2:
Logger("compiling the model... (takes a ~minute)")
unoptimized_model = model
model = torch.compile(model)
if ddp:
model._ddp_params_and_buffers_to_ignore = {"pos_cis"}
model = DistributedDataParallel(model, device_ids=[ddp_local_rank])
iter_per_epoch = len(train_loader)
for epoch in range(args.epochs):
train_epoch(epoch, wandb)

149
3-eval_chat.py
View File

@ -1,149 +0,0 @@
import os
import random
import numpy as np
import torch
import warnings
from PIL import Image
from transformers import AutoTokenizer, AutoModelForCausalLM
from model.model import Transformer
from model.LMConfig import LMConfig
from model.vision_utils import get_vision_model, get_img_process, get_img_embedding
warnings.filterwarnings('ignore')
def count_parameters(model):
return sum(p.numel() for p in model.parameters() if p.requires_grad)
def init_model(lm_config, device, multi):
tokenizer = AutoTokenizer.from_pretrained('./model/minimind_tokenizer')
model_from = 1 # 1从权重，2用transformers
if model_from == 1:
moe_path = '_moe' if lm_config.use_moe else ''
if multi:
ckp = f'./out/{lm_config.dim}{moe_path}_vlm_sft_multi.pth'
else:
ckp = f'./out/{lm_config.dim}{moe_path}_vlm_sft.pth'
model = Transformer(lm_config)
state_dict = torch.load(ckp, map_location=device)
# 处理不需要的前缀
unwanted_prefix = '_orig_mod.'
for k, v in list(state_dict.items()):
if k.startswith(unwanted_prefix):
state_dict[k[len(unwanted_prefix):]] = state_dict.pop(k)
model.load_state_dict(state_dict, strict=False)
else:
model = AutoModelForCausalLM.from_pretrained('minimind-v-v1-small', trust_remote_code=True)
model = model.to(device)
print(f'模型参数: {count_parameters(model) / 1e6} 百万 = {count_parameters(model) / 1e9} B (Billion)')
vision_model, preprocess = get_vision_model(encoder_type="clip")
vision_model = vision_model.to(device)
return model, tokenizer, vision_model, preprocess
def setup_seed(seed):
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
if __name__ == "__main__":
# -------------------------- 基本参数设置 -----------------------------------
multi = False # 设置multi参数，控制单图或多图推理
out_dir = 'out'
temperature = 0.5
top_k = 8
setup_seed(1337)
device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
dtype = 'bfloat16'
max_seq_len = 1024
encoder_type = "clip"
# lm_config = LMConfig()
if encoder_type == "clip":
lm_config = LMConfig()
else:
lm_config = LMConfig(image_special_token='<' * 98 + '>' * 98, image_ids=[30] * 98 + [32] * 98)
lm_config.max_seq_len = max_seq_len
model, tokenizer, vision_model, preprocess = init_model(lm_config, device, multi)
model.eval()
# -------------------------- 问题和目录设置 -----------------------------------
if multi:
image_dir = './dataset/eval_multi_images/bird/'
prompt = f"{lm_config.image_special_token}\n{lm_config.image_special_token}\nName all the differences between these two birds."
else:
image_dir = './dataset/eval_images/'
prompt = lm_config.image_special_token + '\n这个图片描述的是什么内容？'
image_files = sorted(os.listdir(image_dir))
# -------------------------- 推理逻辑 -----------------------------------
if multi:
# 多图推理：所有图像编码一次性推理
image_encoders = []
for image_file in image_files:
image_path = os.path.join(image_dir, image_file)
image = Image.open(image_path).convert('RGB')
image_process = get_img_process(image, preprocess).to(vision_model.device)
image_encoder = get_img_embedding(image_process, vision_model).unsqueeze(0)
image_encoders.append(image_encoder)
print(f'[Image]: {image_file}')
image_encoders = torch.cat(image_encoders, dim=0).unsqueeze(0)
messages = [{"role": "user", "content": prompt}]
new_prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)[
-(max_seq_len - 1):]
x = tokenizer(new_prompt).data['input_ids']
x = torch.tensor(x, dtype=torch.long, device=device)[None, ...]
with torch.no_grad():
res_y = model.generate(x, tokenizer.eos_token_id, max_new_tokens=max_seq_len, temperature=temperature,
top_k=top_k, stream=True, image_encoders=image_encoders)
print('[A]: ', end='')
history_idx = 0
for y in res_y:
answer = tokenizer.decode(y[0].tolist())
if answer and answer[-1] == '<EFBFBD>':
y = next(res_y)
continue
print(answer[history_idx:], end='', flush=True)
history_idx = len(answer)
print('\n')
else:
# 单图推理：对每张图像单独推理
for image_file in image_files:
image_path = os.path.join(image_dir, image_file)
image = Image.open(image_path).convert('RGB')
image_process = get_img_process(image, preprocess).to(vision_model.device)
image_encoder = get_img_embedding(image_process, vision_model).unsqueeze(0)
messages = [{"role": "user", "content": prompt}]
new_prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)[
-(max_seq_len - 1):]
x = tokenizer(new_prompt).data['input_ids']
x = torch.tensor(x, dtype=torch.long, device=device)[None, ...]
print(f'[Image]: {image_file}')
with torch.no_grad():
res_y = model.generate(x, tokenizer.eos_token_id, max_new_tokens=max_seq_len, temperature=temperature,
top_k=top_k, stream=True, image_encoders=image_encoder)
print('[A]: ', end='')
history_idx = 0
for y in res_y:
answer = tokenizer.decode(y[0].tolist())
if answer and answer[-1] == '<EFBFBD>':
y = next(res_y)
continue
print(answer[history_idx:], end='', flush=True)
history_idx = len(answer)
print('\n')

672
README.md
View File

@ -26,266 +26,288 @@
</div> </div>
* 本开源项目旨在从0开始，最快3小时训练一个小参数量的，具备视觉模态能力的语言模型**MiniMind-V** * 此项目旨在从0开始，仅用3块钱成本 + 2小时！即可训练出26M参数的超小多模态视觉语言模型**MiniMind-V**。
* **MiniMind-V**同样极其轻量，最小版本体积仅为 GPT3 的约 $\frac{1}{7000}$，力求做到个人GPU也可快速推理甚至训练。 * **MiniMind-V**最小版本体积仅为 GPT3 的约 $\frac{1}{7000}$，力求做到个人GPU也可快速推理甚至训练。
* 这不仅是一个开源模型的实现，也是入门视觉语言模型（VLM）的教程。 * **MiniMind-V**是[MiniMind](https://github.com/jingyaogong/minimind)纯语言模型的视觉能力额外拓展。
* 希望此项目能为研究者提供一个抛砖引玉的入门示例，帮助大家快速上手并对VLM领域产生更多的探索与创新。 * 项目同时包含了VLM大模型的极简结构、数据集清洗、预训练(Pretrain)、监督微调(SFT)等全过程代码。
* 这不仅是一个开源VLM模型的最小实现，也是入门视觉语言模型的简明教程。
* 希望此项目能为所有人提供一个抛砖引玉的示例，一起感受创造的乐趣！推动更广泛AI社区的进步！
> 为防止误读，「从0开始」特指基于纯语言模型MiniMind（这是一个完全从0训练的类GPT模型）做进一步的，从0到1的视觉能力拓展。 > 为防止误解，“2小时” 基于NVIDIA 3090硬件设备（单卡）测试`1 epoch`，“3块钱” 指GPU服务器租用成本。
> 若需详细了解后者，请参考孪生项目[MiniMind](https://github.com/jingyaogong/minimind)。
> 为防止误读，「最快3小时」是指您需要具备＞本人硬件配置的机器，具体规格的详细信息将在下文提供。
![](./images/modelscope-demo.gif)
<div align="center"> <div align="center">
Demo已部署至ModelScope创空间，可以在此网站上体验： ![minimind2-v](./images/minimind2-v.gif)
[ModelScope在线体验](https://modelscope.cn/studios/gongjy/minimind-v) [🔗🤖在线体验](https://www.modelscope.cn/studios/gongjy/MiniMind-V) | [🔗🎞️视频介绍](https://www.bilibili.com/video/BV1Sh1vYBEzY)
[🎉🎉BiliBili项目视频🎉🎉](https://www.bilibili.com/video/BV1Sh1vYBEzY)
</div> </div>
# 📌 Introduction # 📌 Introduction
视觉语言模型（VLM）如 GPT-4V、Qwen-VL、LlaVA 等，虽然在效果上令人惊艳， “用乐高拼出一架飞机，远比坐在头等舱里飞行更让人兴奋！”
但这些动辄 100 亿级参数的庞大模型，往往需要极高的硬件配置。 构建VLM范式的多模态大模型是否真的如想象中那样复杂？它的代码实现到底如何？
对于个人设备来说，不仅显存远远不足以支持训练，甚至连推理都十分困难。 训练过程究竟难不难？那么现在，探索它们的答案，一起感受创造的乐趣吧！
我们通过阅读论文或公众号讲解来了解略显新颖的 VLM，
往往只能一知半解云里雾里。
而我们真正需要了解的是：
构建多模态大模型是否真的如想象中那样复杂？它的代码实现到底如何？
训练过程究竟难不难？如果我只有一张 2080Ti 显卡，能否从0开始进行训练？
通过 **MiniMind-V**，本项目希望回答这些问题，
帮助研究者在有限的硬件条件下理解视觉语言模型的核心原理。
> [!TIP] > [!TIP]
> （截至2024-10-04）MiniMind-V 系列已完成了 2 个型号模型的预训练，最小仅需27M（0.027B），即可具备识图和对话的能力！ > （截至2025-02-20）MiniMind-V 系列已完成了以下型号模型训练，最小仅需26M (0.026B)，即可具备识图和对话的能力！
| 模型 (大小) | tokenizer长度 | 推理占用 | release | 主观评分（/100） | | 模型 (大小) | 推理占用 | release |
|---------------------------|-------------|--------|------------|------------| |---------------------------|--------|------------|
| minimind-v-v1-small (27M) | 6400 | 0.6 GB | 2024.10.04 | 50' | | MiniMind2-V (104M) | 0.6 GB | 2025.02.20 |
| minimind-v-v1 (109M) | 6400 | 1.1 GB | 2024.10.04 | 60' | | MiniMind2-Small-V (26M) | 1.1 GB | 2025.02.20 |
| minimind-v-v1-small (27M) | 0.6 GB | 2024.10.04 |
> 该分析在具有Torch 2.1.2、CUDA 12.2和Flash Attention 2的2×RTX 3090 GPU上进行。 | minimind-v-v1 (109M) | 1.1 GB | 2024.10.04 |
### 👉**最近更新** ### 👉**最近更新**
<details close> <details close>
<summary> <b>2024-10-05 (newest 🎉)</b> </summary> <summary> <b>2025-02-20 (newest 🎉)</b> </summary>
- MiniMind2-V伴随MiniMind2同步更新
- 大幅减少所有冗余代码，规范代码格式
- 大幅精简模型冗余结构
- 更新数据集格式，拓展新的SFT数据集
- 比前代VLM更优秀的效果！
</details>
<details close>
<summary> <b>2024-10-05</b> </summary>
- MiniMind-V如期而至，首次开源 - MiniMind-V如期而至，首次开源
</details> </details>
# 📌 Environment # 📌 快速开始
仅是我个人的软硬件环境配置，自行酌情变动： <details style="color:rgb(128,128,128)">
<summary>分享本人的软硬件配置（仅供参考）</summary>
```bash
CPU: Intel(R) Core(TM) i9-10980XE CPU @ 3.00GHz
内存：128 GB
显卡：NVIDIA GeForce RTX 3090(24GB) * 2
环境：python 3.9 + Torch 2.1.2 + DDP单机多卡训练
```
* CPU: Intel(R) Core(TM) i9-10980XE CPU @ 3.00GHz
* RAM: 128 GB
* GPU: NVIDIA GeForce RTX 3090(24GB) * 8
* Ubuntu==20.04 * Ubuntu==20.04
* Python == 3.9
* Pytorch == 2.1.2
* CUDA==12.2 * CUDA==12.2
* Python==3.10.16
* [requirements.txt](./requirements.txt) * [requirements.txt](./requirements.txt)
# 📌 Quick Start Test </details>
> BTW: 如果没有git-lfs，请先安装 `sudo apt-get update`, `sudo apt-get install git-lfs` ### 第0步
* 0.克隆项目
```bash ```bash
# 克隆代码仓库
git clone https://github.com/jingyaogong/minimind-v git clone https://github.com/jingyaogong/minimind-v
cd minimind-v
``` ```
* 1.环境安装 ```bash
# 下载clip模型到 ./model/vision_model 目录下
git clone https://huggingface.co/openai/clip-vit-base-patch16
# or
git clone https://www.modelscope.cn/models/openai-mirror/clip-vit-base-patch16
```
## Ⅰ 测试已有模型效果
### 1.环境准备
```bash ```bash
pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple
``` ```
```text ### 2.下载模型
# 测试torch是否可用cuda
import torch
print(torch.cuda.is_available())
```
> 如果不可用，请自行去[torch_stable](https://download.pytorch.org/whl/torch_stable.html)
下载whl文件安装。参考[链接](https://blog.csdn.net/weixin_45456738/article/details/141029610?ops_request_misc=&request_id=&biz_id=102&utm_term=%E5%AE%89%E8%A3%85torch&utm_medium=distribute.pc_search_result.none-task-blog-2~all~sobaiduweb~default-2-141029610.nonecase&spm=1018.2226.3001.4187)
* 2.下载预训练的模型权重文件到项目根目录 `minimind-v-v1`
```bash ```bash
git clone https://huggingface.co/jingyaogong/minimind-v-v1 git clone https://huggingface.co/jingyaogong/MiniMind2-V
``` ```
* 3.下载预训练的`clip-vit-base-patch32` 模型，在 `model/clip_model` 目录下： ### 3.命令行问答
```bash ```bash
cd model/clip_model # load=0: load from pytorch model, load=1: load from transformers-hf model
git clone https://hf-mirror.com/openai/clip-vit-base-patch32 python eval_vlm.py --load 1
``` ```
* 4.启动聊天网页测试对话 ### 4.或启动WebUI
```bash ```bash
python web_server.py python web_demo_vlm.py
``` ```
![web_server](images/web_server.gif) ## Ⅱ 从0开始自己训练
# 📌 Quick Start Train ### 1.环境准备
> BTW: 如果没有git-lfs，请先安装 `sudo apt-get update`, `sudo apt-get install git-lfs`
* 0.克隆项目代码
```text
git clone https://github.com/jingyaogong/minimind-v & cd minimind-v
```
* 1.环境安装
```bash ```bash
pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple
``` ```
```text <details style="color:rgb(128,128,128)">
# 测试torch是否可用cuda <summary>注：提前测试Torch是否可用cuda</summary>
```bash
import torch import torch
print(torch.cuda.is_available()) print(torch.cuda.is_available())
``` ```
> 如果不可用，请自行去[torch_stable](https://download.pytorch.org/whl/torch_stable.html) 如果不可用，请自行去[torch_stable](https://download.pytorch.org/whl/torch_stable.html)
下载whl文件安装。参考[链接](https://blog.csdn.net/weixin_45456738/article/details/141029610?ops_request_misc=&request_id=&biz_id=102&utm_term=%E5%AE%89%E8%A3%85torch&utm_medium=distribute.pc_search_result.none-task-blog-2~all~sobaiduweb~default-2-141029610.nonecase&spm=1018.2226.3001.4187) 下载whl文件安装。参考[链接](https://blog.csdn.net/weixin_45456738/article/details/141029610?ops_request_misc=&request_id=&biz_id=102&utm_term=%E5%AE%89%E8%A3%85torch&utm_medium=distribute.pc_search_result.none-task-blog-2~all~sobaiduweb~default-2-141029610.nonecase&spm=1018.2226.3001.4187)
* 2.下载 `clip-vit-base-patch32` 模型，放到 `./model/clip_model` 目录下： </details>
### 2.数据下载
从下文提供的[数据集下载链接](https://huggingface.co/datasets/jingyaogong/minimind-v_dataset)
下载需要的数据文件（创建`./dataset`目录）并放到`./dataset`下。
`*.jsonl`为问答数据集，`*images`为配套的图片数据，下载完成后需要解压图像数据。
<details style="color:rgb(128,128,128)">
<summary>注：数据集须知</summary>
请预留~5GB空间存放数据集，若无多余空间存放pretrain数据，
可尝试跳过pretrain训练步骤直接进行sft训练。
</details>
### 3.开始训练
**3.1 预训练（学图像描述）**
```bash ```bash
cd ./model/clip_model & git clone https://hf-mirror.com/openai/clip-vit-base-patch32 python train_pretrain_vlm.py --epochs 4
``` ```
* 3.如果需要自己训练 > 执行预训练，得到 `pretrain_vlm_*.pth` 作为预训练的输出权重（其中*为模型的dimension，默认为512）
* 3.1 下载数据集 ([百度网盘](https://pan.baidu.com/s/1Nz36OBBvVBGEx-PwIb7ofg?pwd=6666)
or [HuggingFace](https://huggingface.co/datasets/jingyaogong/minimind-v_dataset))文件到`./dataset`
目录下，并解压`pretrain_images.zip` 和 `sft_images.zip`
* 3.2 在`./model/LMConfig.py` 中调整model的参数配置
> 这里仅需调整dim和n_layers参数，分别是`(512+8)`或`(768+16)`，对应于`minimind-v-v1-small`和`minimind-v-v1`
* 3.3 下载minimind语言模型的预训练权重([百度网盘](https://pan.baidu.com/s/1LE1SPoPYGS7VNtT1tpf7DA?pwd=6666)
or [HuggingFace](https://huggingface.co/datasets/jingyaogong/minimind-v_dataset/tree/main/out))，放到`./out/`
目录下，命名为`*_llm.pth`
* 3.4 `python 1-pretrain_vlm.py` 执行预训练，得到 `*_vlm_pretrain.pth` 作为预训练的输出权重
* 3.5 `python 2-sft_vlm.py` 执行指令微调，得到 `*_vlm_sft.pth` 作为指令微调的输出权重
* 3.6 `python 2-sft_vlm.py --multi True` 在指令微调的基础上执行多图指令微调（效果仅参考），得到 `*_vlm_sft_multi.pth` 作为多图指令微调的输出权重，512+8 模型显存占用约为8198M
* 4.测试自己训练的模型推理效果 **3.2 监督微调（学看图对话方式）**
* 确保需要使用的，训练完成的参数权重`*.pth`文件位于`./out/`目录下
* 也可以直接下载使用我已经训练好的 `*.pth` [权重文件](https://pan.baidu.com/s/1LE1SPoPYGS7VNtT1tpf7DA?pwd=6666)使用
```text
minimind-v/out
├── 512_llm.pth
├── 512_vlm_pretrain.pth
├── 512_vlm_sft.pth
├── 768_llm.pth
├── 768_vlm_pretrain.pth
├── 768_vlm_sft.pth
```
* `python 3-eval_chat.py`测试模型的对话效果，其中测试图片在`./dataset/eval_images`下，可自行更换
![eval_chat](images/3-eval_chat.png)
* `python 3-eval_chat.py`调整[multi](./3-eval_chat.py#L61)变量，可以测试模型的多图对话效果，其中测试图片在`./dataset/eval_multi_images`下，可自行更换（多图数据集规模相对较小且为英文对话，数据集仅包含两图对比的场景，因此微调效果有限）。
🍭 【Tip】预训练和全参指令微调pretrain和sft均支持多卡加速
* 单机N卡启动训练(DDP)
```bash ```bash
torchrun --nproc_per_node N 1-pretrain_vlm.py python train_sft_vlm.py --epochs 4
```
> 执行监督微调，得到 `sft_vlm_*.pth` 作为指令微调的输出权重
<details style="color:rgb(128,128,128)">
<summary>注：训练须知</summary>
所有训练过程默认每隔100步保存1次参数到文件`./out/***.pth`（每次会覆盖掉旧权重文件）。
</details>
---
### 4.测试模型效果
确保需要测试的模型`*.pth`文件位于`./out/`目录下。
也可以直接去[此处](https://huggingface.co/jingyaogong/MiniMind2-V-PyTorch)下载使用我训练的`*.pth`文件。
```bash
python eval_vlm.py --model_mode 1 # 默认为0：测试pretrain模型效果，设置为1：测试sft模型效果
```
---
> [!TIP]
> 训练脚本均为Pytorch原生框架，均支持多卡加速，假设你的设备有N (N＞1) 张显卡：
单机N卡启动训练方式 (DDP, 支持多机多卡集群)
```bash
torchrun --nproc_per_node N train_xxx.py
```
<details style="color:rgb(128,128,128)">
<summary>注：其它须知</summary>
单机N卡启动训练 (DeepSpeed)
```bash
deepspeed --master_port 29500 --num_gpus=N train_xxx.py
```
可根据需要开启wandb记录训练过程
```bash
# 需要登录: wandb login
torchrun --nproc_per_node N train_xxx.py --use_wandb
# and # and
torchrun --nproc_per_node N 2-sft_vlm.py python train_xxx.py --use_wandb
``` ```
* 记录训练过程
```bash
torchrun --nproc_per_node N 1-pretrain_vlm.py --use_wandb
# and
python 1-pretrain_vlm.py --use_wandb
```
通过添加`--use_wandb`参数，可以记录训练过程，训练完成后，可以在wandb网站上查看训练过程。通过修改`wandb_project` 通过添加`--use_wandb`参数，可以记录训练过程，训练完成后，可以在wandb网站上查看训练过程。通过修改`wandb_project`
和`wandb_run_name`参数，可以指定项目名称和运行名称。 和`wandb_run_name`参数，可以指定项目名称和运行名称。
</details>
# 📌 VLM Detail # 📌 VLM Detail
MiniMind-V (VLM)的基座语言模型MiniMind (LLM)来自孪生项目[minimind](https://github.com/jingyaogong/minimind)， MiniMind-V (VLM)的基座语言模型MiniMind (LLM)来自孪生项目[minimind](https://github.com/jingyaogong/minimind)，
具体的模型结构、训练细节、原理、测试效果等均可移步[minimind](https://github.com/jingyaogong/minimind)项目查阅。 具体的模型结构、训练细节、原理、测试效果等均可移步[minimind](https://github.com/jingyaogong/minimind)项目查阅。
此处为减少冗余，省略讨论LLM的相关部分，默认您已对MiniMind (LLM)的细节有基本的了解。 此处为减少冗余，省略讨论LLM的相关部分，默认您已对MiniMind (LLM)的细节有基本的了解。
> PS: 即使您不希望了解MiniMind (LLM)的细节，也可直接参考Quick Test和Quick Start中快速测试或训练MiniMind-V， > 即使您不太了解LLM的细节，也可参考“快速开始”流程训练一个MiniMind-V，
> 这并不受太大影响。 > 这并不受到影响，仓库致力于最低成本的开箱即用！
MiniMind-V的结构几乎不变，仅增加Visual Encoder和特征投影两个子模块，增加模态混合分支，以支持多种模态信息的输入： MiniMind-V的结构仅增加Visual Encoder和特征投影两个子模块，增加模态混合分支，以支持多种模态信息的输入：
![LLM-structure](./images/VLM-structure.png) ![LLM-structure](./images/VLM-structure.png)
![LLM-structure](./images/VLM-structure-moe.png) ![LLM-structure](./images/VLM-structure-moe.png)
此时，不妨思考2个很有意思的问题：什么叫做**L**arge **L**anguage **M**odel(LLM)？什么叫做多模态模型？
* [这篇文章](https://www.jiqizhixin.com/articles/2024-09-15-3)完美吐露了本人的想法，即LLM这个命名很不准确！ <details>
<summary> 【重要】一些有趣的思考 </summary>
> 大语言模型（LLM）名字虽然带有语言二字，但它们其实与语言关系不大，这只是历史问题，更确切的名字应该是自回归 Transformer 此处不妨展开想一想两个问题：
或者其他。
LLM 更多是一种统计建模的通用技术，它们主要通过自回归 Transformer 来模拟 token 流，而这些 token * 什么叫做**L**arge **L**anguage **M**odel (LLM)？
* 什么叫做多模态模型？
[这篇文章](https://www.jiqizhixin.com/articles/2024-09-15-3)完美吻合本人的想法：
大语言模型（LLM）名字虽然带有语言二字，但它们其实与语言关系不大，这只是历史问题，更确切的名字应该是自回归 Transformer
或者其他。LLM 更多是一种统计建模的通用技术，它们主要通过自回归 Transformer 来模拟 token 流，而这些 token
可以代表文本、图片、音频、动作选择、甚至是分子等任何东西。 可以代表文本、图片、音频、动作选择、甚至是分子等任何东西。
因此，只要能将问题转化为模拟一系列离散 token 的流程，理论上都可以应用 LLM 来解决。 因此，只要能将问题转化为模拟一系列离散 token 的流程，理论上都可以应用 LLM 来解决。
实际上，随着大型语言模型技术栈的日益成熟，我们可能会看到越来越多的问题被纳入这种建模范式。也就是说，问题固定在使用 LLM 实际上，随着大型语言模型技术栈的日益成熟，我们可能会看到越来越多的问题被纳入这种建模范式。也就是说，问题固定在使用 LLM
进行『下一个 token 的预测』，只是每个领域中 token 的用途和含义有所不同。 进行『下一个 token 的预测』，只是每个领域中 token 的用途和含义有所不同。
* [李玺老师](https://person.zju.edu.cn/xilics#694283)的公开讲话同样佐证了类似观点（原话大意如下）： [ZJU-LiXi老师](https://person.zju.edu.cn/xilics#694283)同样谈及过类似观点（原话大意如下）：
文本、视频、语音、动作等在人类看来属于「多模态」信号，但所谓的「模态」其实只是人类在信息存储方式上的一种分类概念。
> 文本、视频、语音、动作等在人类看来属于「多模态」信号，但所谓的「模态」其实只是人类在信息存储方式上的一种分类概念。
就像`.txt`和`.png`文件，虽然在视觉呈现和高级表现形式上有所不同，但它们本质上并没有根本区别。 就像`.txt`和`.png`文件，虽然在视觉呈现和高级表现形式上有所不同，但它们本质上并没有根本区别。
之所以出现「多模态」这个概念，仅仅是因为人类在不同的感知层面上对这些信号的分类需求。 之所以出现「多模态」这个概念，仅仅是因为人类在不同的感知层面上对这些信号的分类需求。
然而，对于机器来说，无论信号来自何种「模态」，最终它们都只是以一串二进制的「单模态」数字序列来呈现。 然而，对于机器来说，无论信号来自何种「模态」，最终它们都只是以一串二进制的「单模态」数字序列来呈现。
机器并不会区分这些信号的模态来源，而只是处理和分析这些序列背后所承载的信息内容。 机器并不会区分这些信号的模态来源，而只是处理和分析这些序列背后所承载的信息内容。
--- 个人认为**G**enerative **P**retrained **T**ransformer (GPT) 比 **L**arge **L**anguage **M**odel (LLM)更为贴切，
私以为，**G**enerative **P**retrained **T**ransformer (GPT) 比 **L**arge **L**anguage **M**odel (LLM)更为贴切，
因此本人表达上更习惯用"GPT"去代表LLM/VLM/类GPT架构的系列模型，而非为了蹭OpenAI的热度。 因此本人表达上更习惯用"GPT"去代表LLM/VLM/类GPT架构的系列模型，而非为了蹭OpenAI的热度。
---
至此，我们可以用一句话总结GPT的所作所为： 至此，我们可以用一句话总结GPT的所作所为：
GPT模型根据现有token预测输出下一个下下一个下下下一个token ...，直到模型输出结束符；此处的"token"其实并不需要一定是文本！ GPT模型根据现有token预测输出下一个下下一个下下下一个token ...，直到模型输出结束符；此处的"token"其实并不需要一定是文本！
--- ```text
> 对于LLM模型，如果需要理解"图片"，我们只要把"图片"作为对一种特殊的从来没见过的"外国语言"，通过"外语词典"翻译后即可作为特殊的语言输入LLM
> 对于LLM模型，如果需要理解"音频"，我们只要把"音频"作为对一种特殊的从来没见过的"外国语言"，通过"外语词典"翻译后即可作为特殊的语言输入LLM
> ...
```
* 对于LLM模型，如果需要理解"图片"，我们只要把"图片"作为对一种特殊的从来没见过的"外国语言"，通过"外语词典"翻译后即可作为特殊的语言输入LLM <u>**为了得到MiniMind-V，我们只需要完成这2件事即可：**</u>
* 对于LLM模型，如果需要理解"音频"，我们只要把"音频"作为对一种特殊的从来没见过的"外国语言"，通过"外语词典"翻译后即可作为特殊的语言输入LLM
* ...
---
<u>**所以，为了得到MiniMind-V，我们只需要完成2件事即可：**</u>
1. 借助擅长翻译图片的 **"外语词典"** ，把图片从 **"外国语言"** 翻译为模型便于理解的 **"LLM语言"** 1. 借助擅长翻译图片的 **"外语词典"** ，把图片从 **"外国语言"** 翻译为模型便于理解的 **"LLM语言"**
2. 训练微调LLM，使其和 **"外语词典"** 度过磨合期，从而更好的理解图片 2. 训练微调LLM，使其和 **"外语词典"** 度过磨合期，从而更好的理解图片
--- "外语词典" 称之为Visual Encoder模型。
"外语词典"一般称之为Visual Encoder模型。
和LlaVA、Qwen-VL等视觉语言模型类似，MiniMind-V同样选用开源Clip系列模型作为Visual Encoder。 和LlaVA、Qwen-VL等视觉语言模型类似，MiniMind-V同样选用开源Clip系列模型作为Visual Encoder。
具体使用[clip-vit-base-patch32](https://huggingface.co/openai/clip-vit-base-patch32)， 具体使用[clip-vit-base-patch16](https://huggingface.co/openai/clip-vit-base-patch16)，
一种基于 ViT-B/32 架构的经典Visual Encoder用于描述图像文本信息。 一种基于 ViT-B/16 架构的经典Visual Encoder用于描述图像文本信息。
输入的图像尺寸为224x224，因为划分的Patch是32×32，所以会产生7*7+1(cls_token)=50个token作为encoder编码层的输入， 输入的图像尺寸为224x224，因为划分的Patch是16×16，所以会产生16*16=196个token作为encoder编码层的输入，
最终产生1×768维的嵌入向量用于和文本对计算误差。 最终产生1×768维的嵌入向量用于和文本对计算误差。
我们并不需要最终嵌入表示，因此只取encoder层的输出，也就是VIT核心主干的输出特征即可。 我们并不需要最终嵌入表示，因此只取encoder层的输出，也就是VIT核心主干的输出特征即可。
在代码中对应[./model/vision_utils.py](./model/vision_utils.py)的get_img_embedding中的hook函数。 它拿到前一层维度196×768大小的特征，我们把它作为196个visual token输入MiniMind-V。
它拿到前一层维度50×768大小的特征，我们把它作为50个visual token输入MiniMind-V。
也有clip-vit-large-patch14这种更大，图像理解能力更强的Clip模型，
但是单图片会产生257个token，对于minimind这种量级模型，图片token的上下文占比太长，反倒不利于训练。
与LLM的结合在获取图像encoder特征后，一方面需要把768维度的visual token对齐到LLM的文本token， 与LLM的结合在获取图像encoder特征后，一方面需要把768维度的visual token对齐到LLM的文本token，
另一方面，要将图像特征映射到与文本embedding相同的空间，即文本token和原生的视觉token需要磨合并不能直接地一视同仁， 另一方面，要将图像特征映射到与文本embedding相同的空间，即文本token和原生的视觉token需要磨合并不能直接地一视同仁，
可以称之为跨模态的特征对齐。 可以称之为跨模态的特征对齐。
@ -295,6 +317,9 @@ GPT模型根据现有token预测输出下一个下下一个下下下一个token
至此，MiniMind-V的内部结构变化已经呈现完毕。 至此，MiniMind-V的内部结构变化已经呈现完毕。
</details>
--- ---
下面，我们简单讨论MiniMind-V的外部输入输出的变化。 下面，我们简单讨论MiniMind-V的外部输入输出的变化。
@ -307,47 +332,46 @@ VLM的输入依然是一段文本，其中包含特殊的<image>占位符。
<image>\n这个图像中有什么内容？ <image>\n这个图像中有什么内容？
``` ```
minimind-v使用50个字符组成的 `<<<...>>>` 占位符代替图像， 在`minimind-v`中，使用196个字符组成的 `@@@...@@@`
之所以是50个字符，前面有所提及： 占位符代替图像，之所以是196个字符，前面有所提及：
任何图像都被clip模型encoder为50×768维的token。 任何图像都被clip模型encoder为196×768维的token，
因此minimind-v的prompt： 因此`minimind-v`的prompt为：
```text ```text
<<<<<<<<<<<<<<<<<<<<<<<<<>>>>>>>>>>>>>>>>>>>>>>>>>\n这个图片描述的是什么内容？ @@@......@@@\n这个图片描述的是什么内容？
``` ```
计算完embedding和projection，并对图像部分token替换后整个计算过程到输出则和LLM部分没有任何区别。 计算完embedding和projection，并对图像部分token替换后整个计算过程到输出则和LLM部分没有任何区别。
![input](./images/minimind-v-input.png) ![input](./images/minimind-v-input.png)
多图实现方法就是通过注入多个\<image\>图像占位符进行实现，不需要修改任何框架。 一次性多图的实现方法就是通过注入多个`<image>`图像占位符进行实现，不需要修改任何框架。
> ps: 唯一值得注意的点是，如果在训练过程中存在不同conversations插入图片数量不同的情况，需要利用空特征将较短的特征进行填充（对应[dataset的第267行](./model/dataset.py#L267)），以保证能够在同样大小下被dataloader读取。
> pps: 在prompt中不需要如此做，仍旧是根据插入图像的数量来进行占位符的注入。因此，最终输入给LLM的input feature不会受填充特征的影响。
<details> <details>
<summary> 实现视频理解能力的思考 </summary> <summary> 视频理解的拓展思路 </summary>
write by [@xinyanghuang7](https://github.com/xinyanghuang7)
对于多模态大模型的视频理解能力，一个可行的思路是参考现有MiniCPM-V 2.6 进行视频理解的Python示例。 对于多模态大模型的视频理解能力，一个可行的思路是参考现有MiniCPM-V 2.6 进行视频理解的Python示例。
主要思想是通过提取视频关键帧，而后进行多图推理。 主要思想是通过提取视频关键帧，而后进行多图推理。
因此，如果希望在MiniMind-V中添加视频理解能力，可以在现有多图训练的基础上，参考此python脚本中对于关键帧的提取方法，而后加大训练文件中支持图片的数量。 因此，如果希望在MiniMind-V中添加视频理解能力，可以在现有多图训练的基础上，参考此python脚本中对于关键帧的提取方法，而后加大训练文件中支持图片的数量。
所支持的MAX_NUM_FRAMES越多，所消耗的显存越大。 所支持的MAX_NUM_FRAMES越多，所消耗的显存越大。
```python ```text
import torch import torch
from PIL import Image from PIL import Image
from transformers import AutoModel, AutoTokenizer from transformers import AutoModel, AutoTokenizer
from decord import VideoReader, cpu # pip install decord from decord import VideoReader, cpu # pip install decord
model = AutoModel.from_pretrained('openbmb/MiniCPM-V-2_6', trust_remote_code=True, model = AutoModel.from_pretrained('openbmb/MiniCPM-V-2_6', trust_remote_code=True,
attn_implementation='sdpa', torch_dtype=torch.bfloat16) # sdpa or flash_attention_2, no eager attn_implementation='sdpa',
torch_dtype=torch.bfloat16) # sdpa or flash_attention_2, no eager
model = model.eval().cuda() model = model.eval().cuda()
tokenizer = AutoTokenizer.from_pretrained('openbmb/MiniCPM-V-2_6', trust_remote_code=True) tokenizer = AutoTokenizer.from_pretrained('openbmb/MiniCPM-V-2_6', trust_remote_code=True)
MAX_NUM_FRAMES = 64 # if cuda OOM set a smaller number MAX_NUM_FRAMES = 64 # if cuda OOM set a smaller number
def encode_video(video_path): def encode_video(video_path):
def uniform_sample(l, n): def uniform_sample(l, n):
gap = len(l) / n gap = len(l) / n
@ -364,6 +388,7 @@ def encode_video(video_path):
print('num frames:', len(frames)) print('num frames:', len(frames))
return frames return frames
video_path = "video_test.mp4" video_path = "video_test.mp4"
frames = encode_video(video_path) frames = encode_video(video_path)
question = "Describe the video" question = "Describe the video"
@ -384,226 +409,208 @@ answer = model.chat(
) )
print(answer) print(answer)
``` ```
</details> </details>
<u>至此，MiniMind-V的所有细节已经呈现完毕。</u> 至此，`MiniMind-V`的所有细节已经呈现完毕。
`MiniMind-V`的模型子类完全继承自`MiniMind`，
<u>MiniMind-V的实现未参考 **任何** 第三方代码，完全基于MiniMind尽可能做最小改动产生，故代码实现和LlaVA等模型必然存在很大区别。 仅基于后者做**最小**变更而产生，
MiniMind-V与MiniMind的代码核心改动不超过100行，上手难度低。</u> 其核心算法改动`< 50行`，迁移难度极低。
因此可能和`LlAVA`等模型细节可能存在区别，但思路完全统一。
# 📌 Experiment # 📌 Experiment
## 数据集 ## Ⅰ 数据集
来源：[Chinese-LLaVA-Vision](https://huggingface.co/datasets/LinkSoul/Chinese-LLaVA-Vision-Instructions) 来源：[Chinese-LLaVA-Vision](https://huggingface.co/datasets/LinkSoul/Chinese-LLaVA-Vision-Instructions)
包含约60万张预训练图像和<10万张指令微调图像，来自CC-3M和COCO 2014，问答内容经过翻译，对中文支持更友好。并进一步经过resize和整理压缩。 包含约57万张预训练图像，来自CC-3M和COCO 2014；
[llava-en-zh-300k](https://huggingface.co/datasets/BUAADreamer/llava-en-zh-300k)
包含300k条指令微调数据和15万张图像。
问答内容经过翻译，
对中文支持更友好，进一步经过整理并`resize`。
预训练数据集格式： (pretrain_vlm_data.jsonl) 预训练数据集格式：
```json ```json lines
{ {
"id": "GCC_train_000644518",
"image": "GCC_train_000644518.jpg",
"conversations": [ "conversations": [
{ {
"from": "human", "role": "user",
"value": "写一篇简短但有益的图片摘要.\n<image>" "content": "提供给定图像的简要描述。\n<image>"
}, },
{ {
"from": "gpt", "role": "assistant",
"value": "在黑色背景的金属锅中加入盐水,慢动作fps" "content": "橄榄油是自由使用的健康成分。"
} }
] ],
"image": "GCC_train_002582585.jpg"
} }
``` ```
指令微调数据集格式： (sft_vlm_data.jsonl) 单图指令微调数据集格式：
```json ```json lines
{ {
"id": "000000334872",
"image": "000000334872.jpg",
"conversations": [ "conversations": [
{ {
"from": "human", "role": "user",
"value": "<image>\n照片中的人们在下山滑雪还是越野滑雪?" "content": "闹钟的位置对睡眠质量有什么影响？<image>"
}, },
{ {
"from": "gpt", "role": "assistant",
"value": "照片中的人们在森林里越野滑雪,因为他们在一条小径上而不是在陡坡上滑雪。" "content": "把数字闹钟放在床头柜..."
} }
] ],
"image": "train-00000-of-00001_image_0_0.jpg"
} }
``` ```
多图指令微调数据集格式： (sft_vlm_data_multi.jsonl) 多图指令微调数据集格式：
```json ```json lines
{ {
"id": "000000000000",
"image": "0.jpg, 1.jpg",
"conversations": [ "conversations": [
{ {
"from": "human", "role": "user",
"value": "context: Source Image: <image> Target Image: <image> Instruction: What is the correct image edit instruction that can transfrom the source image to target image?" "content": "context: Source Image: <image> Target Image: <image> Instruction: What is the correct image edit instruction that can transfrom the source image to target image?<image>"
}, },
{ {
"from": "gpt", "role": "assistant",
"value": "take the people out of the back in the photo. Remove the two people behind the woman in the white dress and the man in the blue suit. remove people behind the couple in the centre" "content": "take the people out of the back in the photo. Remove the two people behind the woman in the white dress and the man in the blue suit. remove people behind the couple in the centre"
} }
] ],
"image": "0.jpg, 1.jpg"
} }
``` ```
```json <details>
{ <summary> 数据说明 </summary>
"id": "000000013633",
"image": "27266.jpg, 27267.jpg",
"conversations": [
{
"from": "human",
"value": "<image>\n<image>\nName all the differences between these two birds."
},
{
"from": "gpt",
"value": "animal1 is brown with white tuft while animal2 is white with dark brown wings"
}
]
}
```
注： * 多图数据集规模相对较小且为英文对话，数据集仅包含两图对比的场景，因此微调效果有限，这里只提供一种参考思路。
+ 对于指令微调，仅保留了一轮对话，训练单轮对话模型，防止小模型性能被长文本拉低。
+ 多图数据集规模相对较小且为英文对话，数据集仅包含两图对比的场景，因此微调效果有限，这里只提供一种参考思路。
* `jsonl`均为文本指令，`images.zip`均为配套的图像数据（下载后需要解压）
最终的数据集下载地址：[百度网盘](https://pan.baidu.com/s/1Nz36OBBvVBGEx-PwIb7ofg?pwd=6666) | [HuggingFace](https://huggingface.co/datasets/jingyaogong/minimind-v_dataset) </details>
多图对话数据集：[HuggingFace](https://hf-mirror.com/datasets/xinyanghuang/minimind-v_multi_image/tree/main) 数据集下载地址：([ModelScope](https://www.modelscope.cn/datasets/gongjy/minimind-v_dataset) | [HuggingFace](https://huggingface.co/datasets/jingyaogong/minimind-v_dataset))
## 训练 ## Ⅱ 训练
> train_pretrain_vlm
预训练从595K条数据集中学习图片的通用知识，比如鹿是鹿，狗是狗。 预训练从595K条数据集中学习图片的通用知识，比如鹿是鹿，狗是狗。
指令微调从230K条真实对话数据集中学习对图片提问的真实问答格式。 > train_sft_vlm
多图微调提供两个数据集，图像转换数据集和鸟类对比数据集，长度分别为3.5k和13.6k的真实问答格式。 指令微调从300K条真实对话数据集中学习对图片提问的真实问答格式，更符合与人类的交流习惯。
`1-pretrain_vlm.py` 执行预训练，得到 `*_vlm_pretrain.pth` 作为预训练的输出权重。 > train_sft_vlm
`2-sft_vlm.py` 执行指令微调，得到 `*_vlm_sft.pth` 作为指令微调的输出权重。 多图微调提供demo：鸟类对比数据集，长度为13.6k的真实问答格式。
训练时均冻结visual encoder也就是clip模型，只微调Projection和LLM两部分。 训练时均冻结visual encoder也就是clip模型梯度，
只训练Projection和LLM两部分。
预训练中，只设置Projection和LLM的最后一层参数可学习。
指令微调中，设置Projection和LLM的全部参数可学习。
> Pretrain 512+8 模型 (训练时间和Loss参考图) > 训练时间和Loss走势（仅供参考）
![input](./images/1-pretrain-512.png)
> Pretrain 768+16 模型 (训练时间和Loss参考图)
![input](./images/1-pretrain-768.png)
> SFT 512+8 模型 (训练时间和Loss参考图)
![input](./images/2-sft-512.png)
> SFT 768+16 模型 (训练时间和Loss参考图)
![input](./images/2-sft-768.png)
## 训练完成的模型权重 Pretrain [512+8] & [768+16]
![input](./images/pretrain_loss.png)
(`.pth`权重文件) 下载地址：[百度网盘](https://pan.baidu.com/s/1a7_C7HdCMfnG2Dia3q85FQ?pwd=6666) SFT [512+8] & [768+16]
![input](./images/sft_loss.png)
(`transformers`模型文件) ## Ⅲ 模型权重
下载地址：[HuggingFace](https://huggingface.co/collections/jingyaogong/minimind-v-67000833fb60b3a2e1f3597d)
> 注：HuggingFace版本均为指令微调后的MiniMind-V模型 (原生PyTorch`*.pth`权重文件) 下载地址：
([ModelScope](https://www.modelscope.cn/models/gongjy/MiniMind2-V-PyTorch) | [HuggingFace](https://huggingface.co/jingyaogong/MiniMind2-V-PyTorch))
| Model Name | params | Config | file_name | (`Transformers`格式模型)
|---------------------|--------|-----------------------------|-----------------------------------------------------| 下载地址：
| minimind-v-v1-small | 27M | d_model=512<br/>n_layers=8 | 预训练：512_vllm_pretrain.pth<br/>指令微调：512_vllm_sft.pth | ([ModelScope](https://www.modelscope.cn/profile/gongjy) | [HuggingFace](https://huggingface.co/collections/jingyaogong/minimind-v-67000833fb60b3a2e1f3597d))
| minimind-v-v1 | 109M | d_model=768<br/>n_layers=16 | 预训练：768_vllm_pretrain.pth<br/>指令微调：768_vllm_sft.pth |
> 注：Transformers版本均为单图指令微调后的`MiniMind-V`模型
# 📌 Test # 📌 Test
### 效果测试 ### 效果测试
#### 单图对话 #### 单图对话
<table> <table>
<thead> <thead>
<tr> <tr>
<th>图片</th> <th>图片</th>
<th>512_pretrain</th> <th>MiniMind2-V</th>
<th>512_sft</th> <th>MiniMind2-V-Small</th>
<th>768_pretrain</th>
<th>768_sft</th>
</tr> </tr>
</thead> </thead>
<tbody> <tbody>
<tr> <tr>
<td><img src="./dataset/eval_images/一个女子.png" alt="a-girl.png" style="width: 200px;"></td> <td>
<td>头发和化妆,我喜欢她的自然头发!</td> <img src="./dataset/eval_images/城市车水马龙-city-traffic.jpg" alt="city-traffic">
<td>这个图片描绘了一个年轻的女人,她穿着一套西装,戴着一条领带,这表明她可能正在参加一个特别的时装活动或庆祝活动。</td> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
<td>人为出演员的冒险片。</td> </td>
<td>这个图片描绘了一个女人的肖像，她穿着一件粉红色的裙子。</td> <td>图中是一个繁忙的城市街道，一条长长的街道两旁都是高楼大厦。这条街上挤满了汽车、卡车和公共汽车，还有许多其他车辆在路上行驶。在街道上，可以看到许多汽车，有的在高速行驶，而其他的则停在街道一侧。此外还有一辆公交车也停在街道的右侧。街道上可以看到交通灯，表明这是一个繁忙的城市环境。</td>
<td>图中是一个繁忙的城市景象，有几辆汽车和一辆卡车行驶在城市街道上。可以看到许多交通信号灯，其中一些位于街道左侧，另一些则在右侧。可以看到有几个人在街上行走，其中一些人站得离街道更近一些，而另一些则距离较远。还有一个停车标志位于画面的左侧，暗示着城市环境。可以看到街道上有两辆汽车，一辆在右边，另一辆在左边，还有一辆在左边。这幅图像捕捉到了都市环境中典型的一天。</td>
</tr> </tr>
<tr> <tr>
<td><img src="./dataset/eval_images/一个海星.png" alt="a-girl.png" ></td> <td>
<td>水中的化石, 一个由环绕的环形化石团组成的静止线.</td> <img src="./dataset/eval_images/太空宇航员-Astronaut-Space.jpg" alt="astronaut">
<td>图片显示一只大型的 octopus, 一个大型的 octopus, 可能是一个潜在的海洋生物, 它在水面上, 或在海洋中 。</td> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
<td>海星和触角。</td> </td>
<td>图片显示了海星在海滩上，包括海星，以及一个水下物体。</td> <td>图片显示了一个宇航员的宇航员身穿宇航服，坐在一架大型航天飞机上。他们似乎正在进行一次宇航员登机或下机的旅程。在宇航员的身后，有一个火箭发射架，可能是用来支撑宇航员在旅程中的任务。此外，还有一架飞机停在机库附近，进一步表明这是一次航空展。在飞机的周围，还有一些人，但他们看起来离飞机很近。可以看到一个人站在飞机附近，可能正在观察或等待航天飞机准备起飞。</td>
<td>场景中，一名士兵戴着头盔站在一架大型飞机上。这架飞机似乎是一架军用军用飞机，似乎正准备登上一架飞机。另一个人则站在前面，可能正在观察飞行过程。在飞机周围，有几个人，其中一些站在左侧，另一些则站在右侧。他们似乎正在观看飞行员的表现。此外，还有一辆卡车停在靠近左侧的位置，可能是为了更具体地观察飞行过程。</td>
</tr> </tr>
<tr> <tr>
<td><img src="./dataset/eval_images/一个熊.png" alt="a-girl.png" ></td> <td>
<td>在野外,在山谷里。</td> <img src="./dataset/eval_images/小狗美女海边-Dog-Woman-Sea.jpg" alt="dog-woman-sea">
<td>图片中的植物和一只灰熊坐在草地上。</td> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
<td>一只灰熊的近景</td> </td>
<td>图片显示一只灰熊站在一片开放的草地上，周围有树木和草丛，还有一只背包放在上面。</td> <td>图片中，一个女人坐在沙滩上，手里拿着一只白色的狗。她看起来像是个女人，坐在沙地上，看着她。一只狗也坐在她旁边，看起来很放松和舒适。海滩上散布着其他沙滩游客，有些人坐着，而另一些人则坐在更远的地方。背景中可以看到一艘船，这表明这是一个受欢迎的海滩旅游目的地。</td>
<td>两个人坐在海滩上，一边懒洋洋地躺在沙滩上，另一边则坐着。他们似乎正在享受海边时光。海滩上有几把椅子，其中一把靠近沙滩的左侧，另一把在中间。此外，还有一只狗躺在沙地上，为这个场景增添了一种放松的气氛。</td>
</tr> </tr>
<tr> <tr>
<td><img src="./dataset/eval_images/一些海豚.png" alt="a-girl.png" ></td> <td>
<td>一群游客观看了这部电影。</td> <img src="./dataset/eval_images/彩虹瀑布-Rainbow-Falls.jpg" alt="rainbow-falls">
<td>这个图片描绘了一群海鸥在水面飞翔,在水面上。海鸥的出现表明,它们正在寻找食物。海鸥在水面上筑巢,可能是为了保护自己免受潜在的危险,如海鸥的尖锐牙齿和爬行动物。</td> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
<td>一群海豚或绵羊在一天的航行中乘船捕鱼</td> </td>
<td>这个图片显示一群人在海豚和海豚附近的大群中游泳。</td> <td>照片捕捉到一幅美丽如画的大自然场景，背景是高山峦崖。在水边，一座巨大的喷泉横跨着水面，吸引着许多游客。水面上有几个人，他们或站或坐在喷泉周围，或站或坐。有些人可以看到他们在水中行走，而其他人则站在水边。总体而言，这幅画描绘的是一个美丽而宁静的环境，在那里人们可以欣赏到如画般的美景。</td>
<td>在一个美丽的蓝色天空下，一座巨大而巨大的白色瀑布上方悬挂着一只巨大的湿流水。这只瀑布位于一座山上，为整个场景增添了一种迷人而又宁静的气氛。在这幅图像的背景中，可以看到几艘船，其中一些靠近水边，其他的则离得较远。这些船只似乎正在为风景或户外活动做准备。</td>
</tr> </tr>
<tr> <tr>
<td><img src="./dataset/eval_images/三个女孩.png" alt="a-girl.png" ></td> <td>
<td>一个女孩和她的朋友坐在一张长凳上,穿着长长的白色长袍。</td> <img src="./dataset/eval_images/椅子老人看书-Chair-Elderly-Reading.jpg" alt="elderly-reading">
<td>这个场景描绘了一个充满活力的年轻女孩,她们穿着一件黑色和白色的服装,在一群人中间站着,他们都穿着黑色和白色的服装,这表明他们的服装是生动的、优雅的,在他们身边。在场景中,有两个女孩在背后,一个女人在背后,另一个女人站着,他们都穿着黑色的服装。这表明他们正在享受他们的服装和服装,可能正在参加一个特别的节日或庆祝活动。</td> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
<td>女孩们在城市的街道上。</td> </td>
<td>这个图片描绘了一个穿着传统服装的男人和女人，站在他们旁边，他们正在一起度过一个家庭时光。在整个场景中，可以看到一个小男孩和一个女孩，他们穿着牛仔帽，这表明他们正在参加一个家庭聚会，这可能是一次聚会或庆祝，或者他们可能正在讨论一个有趣的活动或活动。</td> <td>图中，一个男人坐在公园的长椅上，旁边是一把绿色椅子。他身边有一本打开的书，上面写着"读书"一句话，暗示他可能正在阅读。公园里有一张长椅和一张公园长椅，为周围的环境增添了几分生气。在公园的周围，有几辆汽车和一辆卡车，表明这是一个公共区域。此外，还可以看到一个人站在公园的不同位置上，可能是等着上路或过马路。</td>
<td>一个穿着短裤的老人坐在公园长椅上，周围是树木。他似乎正在读一本书，可能是在读书。背景中有一座长凳，为这个场景提供了充足的座位。在背景中，可以看到一把椅子和一张餐桌，这说明这个场景可能是在一个户外座位区，那里有椅子供人们坐下来放松。</td>
</tr> </tr>
<tr> <tr>
<td><img src="./dataset/eval_images/两头鹿.png" alt="a-girl.png" ></td> <td>
<td>这张照片中有几只鹿。</td> <img src="./dataset/eval_images/熊猫草地-Panda-Grassland.jpg" alt="panda-grassland">
<td>这个图片记录了一只白尾鹿, 它坐在草地上, 用它的照片来捕捉一只红鹿.</td> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
<td>这只动物看起来好像准备躲在树后面,他看上去很威严,因为他无法控制自己。</td> </td>
<td>这个图片描绘了一只母鹿和一只鹿，这只母鹿在树林中站着，一只羊和一只鹿。</td> <td>图中，一只白色的棕熊坐在草地上，旁边是一只长着棕色斑点的大熊。这只熊看起来很害羞或顽皮，因为它正躺在草地上休息，看上去很放松。</td>
<td>在这幅图像中，一只棕色的熊正在草地上漫步。这只熊被放置在草地上，占据了画面的大部分空间。它似乎正在自然环境中行走，可能是在草地上。在背景中，有几棵树，为画面增添了自然元素。一只鸟在场景的中间附近飞翔，为画面增添了生气勃勃的气氛。</td>
</tr> </tr>
<tr> <tr>
<td><img src="./dataset/eval_images/两朵红花.png" alt="a-girl.png" ></td> <td>
<td>这个花束的花期几乎没有进数。</td> <img src="./dataset/eval_images/自行车鲜花-Bicycle-Flowers.jpg" alt="bicycle-flowers">
<td>图片显示一只红色和黄色的花朵, 它们被称为“花瓶”。</td> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
<td>花头的贴近。</td> </td>
<td>图片显示了红色的花朵，周围有几个玫瑰花。</td> <td>图片展示了一个漂亮的花瓶，里面插满了五颜六色的鲜花和花束。这些花束散落在整个花瓶中，给人一种赏心悦目的感觉。花瓶里插着五颜六色鲜花，创造出一种令人赏心悦目的景象。这些鲜花被摆放在一张桌子上，很可能是为了展示它们的美丽而摆放的。</td>
<td>场景中，一辆绿色和紫色相间的自行车停在一栋建筑旁边，它被放置在一棵大树旁。这辆自行车被摆放在附近，为这个场景增添了几分色彩。除了自行车外，还有一些其他的自行车，包括两个位于前景中的一个和位于背景中靠近中心位置的另一个。自行车的存在表明它可能是停在那里的。</td>
</tr> </tr>
<tr> <tr>
<td><img src="./dataset/eval_images/太空宇航员.png" alt="a-girl.png" ></td> <td>
<td>宇航员在太空任务中与地球相姿态。</td> <img src="./dataset/eval_images/舞蹈-dance.jpg" alt="dance">
<td>这个图像描绘了一个充满活力的月球,在月球上散步。</td> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
<td>宇航员在任务期间在摇篮上休息,与他的团队在背景。</td> </td>
<td>这个图片描绘了一个宇航员在太空站的形象。</td> <td>图片中的女人穿着一件白色连衣裙，脚上还系着一条黑色网球裙。她正在表演一个网球比赛，很可能是在比赛中。在背景中可以看到几把椅子，可能是为了观众或其他观众的座位安排而摆放的。此外，还有一个长凳放在场景左侧，为人们提供了一个休息的地方。</td>
</tr> <td>一名身穿白色衣服的女子站在舞台上，手里拿着一只手拿着白色飞盘。她似乎正在参加一个舞台舞会或比赛。场景中还有其他几个人，其中一个站在舞台左侧，另一个站在右侧，第三个人则站在场地右侧。舞台上有几个观众，有的站着，有的坐着，还有一些站着。这看起来像是一场欢乐的节日或活动。</td>
<tr>
<td><img src="./dataset/eval_images/老虎在水里.png" alt="a-girl.png" ></td>
<td>一只老虎在水里看着摄像机。</td>
<td>图片显示一只大棕色的海豹在水里游泳,在水里休息。</td>
<td>动物园里被囚禁的老虎</td>
<td>图片显示一只小熊，躺在一棵树枝上。</td>
</tr>
<tr>
<td><img src="./dataset/eval_images/豹子在悬崖.png" alt="a-girl.png" ></td>
<td>这个是濒危物种。</td>
<td>图片中,一只黑白的猫在岩石上散步。</td>
<td>野外云层的豹在洞穴外的岩石上,在日出时</td>
<td>该图片展示了一只小熊猫在岩石上散步的照片。</td>
</tr> </tr>
</tbody> </tbody>
</table> </table>
@ -616,66 +623,35 @@ MiniMind-V与MiniMind的代码核心改动不超过100行，上手难度低。</
<th>图片1</th> <th>图片1</th>
<th>图片2</th> <th>图片2</th>
<th>512_sft_multi</th> <th>512_sft_multi</th>
<th>768_sft_multi</th>
</tr> </tr>
</thead> </thead>
<tbody> <tbody>
<tr> <tr>
<td><img src="./dataset/eval_multi_images/bird/0.jpg" alt="a-bird.png" style="width: 200px;"></td> <td><img src="./dataset/eval_multi_images/bird/0.jpg" alt="a-bird.png"></td>
<td><img src="./dataset/eval_multi_images/bird/1.jpg" alt="a-bird.png" style="width: 200px;"></td> <td><img src="./dataset/eval_multi_images/bird/1.jpg" alt="a-bird.png"></td>
<td>animal1 has a brown and black head with a black and white striped head . animal2 has a black head with a white stripe on its wings .</td> <td>这幅图像显示了一种鸟簸戮的场景：一个女人站在红绿相间的红绿相间的紫色鸟簸戴在她身上。女人站在红色的鸟簸戴在她身上，而她的翻领上的那只红鸟则站在她身后。</td>
<td>这两只鸟在同一片树林中飞翔，有的位于画面中心，而另一些则较小，形成了鲜明对比。这种鸟类的出现突出了其飞行能力和适应性，因为它们能够在树林中快速迅速移动。此外，两只鸟的位置不同，一个在图像的左边，另一个在右边，这表明它们在同一片树林中移动得很近。这种鸟类的自然行为也有助于区分这两种鸟类物种。</td>
</tr> </tr>
</tbody> </tbody>
</table> </table>
### 启动推理 ### 效果小结：
```bash 视觉信号对于LLM视作一种特殊的外语，
python web_server.py 因此“学习外语”的能力高低，
很大程度上取决于LLM的能力。
LLM性能越强，对应的VLM必然越强，此时效果增益会很明显。
#### 未来值得改进的方面：
```text
> 更简单的Projection的跨模态特征对齐方式，相较于Cross-Attention可能处于劣势。
> Clip模型可以尝试更大性能更强的large系列，用更具细粒度的token表征图像特征，目前仍粗糙。
> 分辨率不高，理论上只有224×224（minimind-v数据集为节省空间，仅设定为128×128）。
> ...
``` ```
![web_server1](./images/web_server1.png)
![web_server2](./images/web_server2.png)
### 效果总结
---
根据提供的表格数据，四个模型的表现可以总结如下：
1. **512_pretrain**:
- **描述简略且不准确**：大部分描述无法清晰表达图像内容，常常给出一些不相关的叙述。例如，在海星的图像中描述为“水中的化石”，与实际内容偏差较大。
- **缺乏细节**：大多数情况下，只给出简单的、模糊的描述，无法深入解释图像的细节或背景。例如，对于老虎的图像，仅说“在水里看着摄像机”。
2. **512_sft**:
- **描述更具体**：相比512_pretrain，512_sft在描述图像内容时更加详细，并尝试捕捉场景的具体元素。比如描述女子图像时，提到了“西装”和“领带”，细节较为清晰。
- **偶尔出错或冗余**：部分描述显得过于复杂甚至与图片无关，如描述海豚图像时，提到海鸥、筑巢等不相关的内容。
3. **768_pretrain**:
- **信息不连贯**：该模型的表现较为散乱，描述经常模糊且不完整。例如，在描述女子图像时，只提到“人为出演员的冒险片”，没有清楚地解释图像内容。
- **部分准确，但总体信息量少**：一些描述虽然与图像相关，但非常简短。例如，海星的描述只有“海星和触角”，无法提供完整的画面感。
4. **768_sft**:
- **描述全面且具体**：该模型的描述是四个模型中最详细和精确的。比如，描述熊的图像时提到了“站在一片开放的草地上，周围有树木和草丛，还有一只背包”，能够准确捕捉到多个图像元素。
- **具备更强的理解力**：该模型能够识别图像的场景和背景，提供合理的解释和推测。例如，描述“家庭聚会”或“庆祝活动”，这些解释让图像更具上下文联系。
### GPT-4o总结：
- **512_pretrain**的表现最差，描述简单且不准确。
- **512_sft**的描述详细度有所提升，但偶尔出现不相关信息。
- **768_pretrain**信息连贯性差，但在某些方面能提供基本描述。
- **768_sft**表现最佳，能够给出详细、准确的描述，并且能够很好地推测图像的上下文。
### 个人小结：
- 视觉信号对于LLM是一种特殊的外语，因此“学习外语”的能力高低，很大程度上取决于LLM的能力。
- LLM性能越强，对应的VLM必然越强，且效果增益很明显。
- 值得改进的方面：
- 更简单的Projection的跨模态特征对齐方式，相较于Cross-Attention会带来更大的性能损失。
- Clip模型可以尝试更大性能更强的large系列，用更具细粒度的token表征图像特征，目前仍非常粗糙。
- 分辨率不高，理论上只有224×224（minimind-v数据集为节省空间，仅设定为128×128）。
- ...
---
# 📌 Acknowledge # 📌 Acknowledge
> [!TIP] > [!TIP]

782
README_en.md
View File

File diff suppressed because it is too large Load Diff

BIN
dataset/eval_images/一个女子.png
View File

Binary file not shown.
Side by Side

Before

Width:  |  Height:  |  Size: 99 KiB

BIN
dataset/eval_images/一个海星.png
View File

Binary file not shown.
Side by Side

Before

Width:  |  Height:  |  Size: 124 KiB

BIN
dataset/eval_images/一个熊.png
View File

Binary file not shown.
Side by Side

Before

Width:  |  Height:  |  Size: 80 KiB

BIN
dataset/eval_images/一些海豚.png
View File

Binary file not shown.
Side by Side

Before

Width:  |  Height:  |  Size: 110 KiB

BIN
dataset/eval_images/三个女孩.png
View File

Binary file not shown.
Side by Side

Before

Width:  |  Height:  |  Size: 131 KiB

BIN
dataset/eval_images/两头鹿.png
View File

Binary file not shown.
Side by Side

Before

Width:  |  Height:  |  Size: 97 KiB

BIN
dataset/eval_images/两朵红花.png
View File

Binary file not shown.
Side by Side

Before

Width:  |  Height:  |  Size: 109 KiB

BIN
dataset/eval_images/城市车水马龙-city-traffic.jpg Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 354 KiB

BIN
dataset/eval_images/太空宇航员-Astronaut-Space.jpg Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 220 KiB

BIN
dataset/eval_images/太空宇航员.png
View File

Binary file not shown.
Side by Side

Before

Width:  |  Height:  |  Size: 119 KiB

BIN
dataset/eval_images/小狗美女海边-Dog-Woman-Sea.jpg Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 127 KiB

BIN
dataset/eval_images/彩虹瀑布-Rainbow-Falls.jpg Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 224 KiB

BIN
dataset/eval_images/椅子老人看书-Chair-Elderly-Reading.jpg Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 138 KiB

BIN
dataset/eval_images/熊猫草地-Panda-Grassland.jpg Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 223 KiB

BIN
dataset/eval_images/老虎在水里.png
View File

Binary file not shown.
Side by Side

Before

Width:  |  Height:  |  Size: 122 KiB

BIN
dataset/eval_images/自行车鲜花-Bicycle-Flowers.jpg Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 333 KiB

BIN
dataset/eval_images/舞蹈-dance.jpg Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 144 KiB

BIN
dataset/eval_images/豹子在悬崖.png
View File

Binary file not shown.
Side by Side

Before

Width:  |  Height:  |  Size: 152 KiB

141
eval_vlm.py Normal file
View File

@ -0,0 +1,141 @@
import argparse
import os
import random
import numpy as np
import torch
import warnings
from PIL import Image
from transformers import AutoTokenizer, AutoModelForCausalLM
from model.model_vlm import MiniMindVLM
from model.VLMConfig import VLMConfig
from transformers import logging as hf_logging
hf_logging.set_verbosity_error()
warnings.filterwarnings('ignore')
def count_parameters(model):
return sum(p.numel() for p in model.parameters() if p.requires_grad)
def init_model(lm_config, device):
tokenizer = AutoTokenizer.from_pretrained('./model/minimind_tokenizer')
if args.load == 0:
moe_path = '_moe' if args.use_moe else ''
modes = {0: 'pretrain_vlm', 1: 'sft_vlm', 2: 'sft_vlm_multi'}
ckp = f'./{args.out_dir}/{modes[args.model_mode]}_{args.dim}{moe_path}.pth'
model = MiniMindVLM(lm_config)
state_dict = torch.load(ckp, map_location=device)
model.load_state_dict({k: v for k, v in state_dict.items() if 'mask' not in k}, strict=False)
else:
transformers_model_path = 'MiniMind2-V'
tokenizer = AutoTokenizer.from_pretrained(transformers_model_path)
model = AutoModelForCausalLM.from_pretrained(transformers_model_path, trust_remote_code=True)
print(f'VLM参数量：{sum(p.numel() for p in model.parameters() if p.requires_grad) / 1e6:.3f} 百万')
vision_model, preprocess = MiniMindVLM.get_vision_model()
return model.eval().to(device), tokenizer, vision_model.eval().to(device), preprocess
def setup_seed(seed):
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Chat with MiniMind")
parser.add_argument('--lora_name', default='None', type=str)
parser.add_argument('--out_dir', default='out', type=str)
parser.add_argument('--temperature', default=0.65, type=float)
parser.add_argument('--top_p', default=0.85, type=float)
parser.add_argument('--device', default='cuda' if torch.cuda.is_available() else 'cpu', type=str)
# MiniMind2-Small (26M)：(dim=512, n_layers=8)
# MiniMind2 (104M)：(dim=768, n_layers=16)
parser.add_argument('--dim', default=512, type=int)
parser.add_argument('--n_layers', default=8, type=int)
parser.add_argument('--max_seq_len', default=8192, type=int)
parser.add_argument('--use_moe', default=False, type=bool)
# 默认单图推理，设置为2为多图推理
parser.add_argument('--use_multi', default=1, type=int)
parser.add_argument('--stream', default=True, type=bool)
parser.add_argument('--load', default=0, type=int, help="0: 原生torch权重，1: transformers加载")
parser.add_argument('--model_mode', default=1, type=int,
help="0: Pretrain模型，1: SFT模型，2: SFT-多图模型 (beta拓展)")
args = parser.parse_args()
lm_config = VLMConfig(dim=args.dim, n_layers=args.n_layers, max_seq_len=args.max_seq_len, use_moe=args.use_moe)
model, tokenizer, vision_model, preprocess = init_model(lm_config, args.device)
def chat_with_vlm(prompt, pixel_tensors, image_names):
messages = [{"role": "user", "content": prompt}]
new_prompt = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)[-args.max_seq_len + 1:]
print(f'[Image]: {image_names}')
with torch.no_grad():
x = torch.tensor(tokenizer(new_prompt)['input_ids'], device=args.device).unsqueeze(0)
outputs = model.generate(
x,
eos_token_id=tokenizer.eos_token_id,
max_new_tokens=args.max_seq_len,
temperature=args.temperature,
top_p=args.top_p,
stream=True,
pad_token_id=tokenizer.pad_token_id,
pixel_tensors=pixel_tensors
)
print('🤖️: ', end='')
try:
if not args.stream:
print(tokenizer.decode(outputs.squeeze()[x.shape[1]:].tolist(), skip_special_tokens=True), end='')
else:
history_idx = 0
for y in outputs:
answer = tokenizer.decode(y[0].tolist(), skip_special_tokens=True)
if (answer and answer[-1] == '<EFBFBD>') or not answer:
continue
print(answer[history_idx:], end='', flush=True)
history_idx = len(answer)
except StopIteration:
print("No answer")
print('\n')
# 单图推理：每1个图像单独推理
if args.use_multi == 1:
image_dir = './dataset/eval_images/'
prompt = f"{model.params.image_special_token}\n描述一下这个图像的内容。"
for image_file in os.listdir(image_dir):
image = Image.open(os.path.join(image_dir, image_file)).convert('RGB')
pixel_tensors = MiniMindVLM.image2tensor(image, preprocess).to(args.device).unsqueeze(0)
chat_with_vlm(prompt, pixel_tensors, image_file)
# 2图推理：目录下的两个图像编码，一次性推理（power by ）
if args.use_multi == 2:
args.model_mode = 2
image_dir = './dataset/eval_multi_images/bird/'
prompt = (f"{lm_config.image_special_token}\n"
f"{lm_config.image_special_token}\n"
f"比较一下两张图像的异同点。")
pixel_tensors_multi = []
for image_file in os.listdir(image_dir):
image = Image.open(os.path.join(image_dir, image_file)).convert('RGB')
pixel_tensors_multi.append(MiniMindVLM.image2tensor(image, preprocess))
pixel_tensors = torch.cat(pixel_tensors_multi, dim=0).to(args.device).unsqueeze(0)
# 同样内容重复10次
for _ in range(10):
chat_with_vlm(prompt, pixel_tensors, (', '.join(os.listdir(image_dir))))

40
export_model.py
View File

@ -1,40 +0,0 @@
import torch
import warnings
from transformers import AutoTokenizer, AutoModelForCausalLM
from model.LMConfig import LMConfig
from model.model import Transformer
warnings.filterwarnings('ignore', category=UserWarning)
def count_parameters(model):
return sum(p.numel() for p in model.parameters() if p.requires_grad)
def export_transformers_model():
tokenizer = AutoTokenizer.from_pretrained('./model/minimind_tokenizer',
trust_remote_code=True, use_fast=False)
LMConfig.register_for_auto_class()
Transformer.register_for_auto_class("AutoModelForCausalLM")
lm_config = LMConfig()
lm_model = Transformer(lm_config)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
moe_path = '_moe' if lm_config.use_moe else ''
ckpt_path = f'./out/{lm_config.dim}{moe_path}_vlm_sft.pth'
state_dict = torch.load(ckpt_path, map_location=device)
unwanted_prefix = '_orig_mod.'
for k, v in list(state_dict.items()):
if k.startswith(unwanted_prefix):
state_dict[k[len(unwanted_prefix):]] = state_dict.pop(k)
lm_model.load_state_dict(state_dict, strict=False)
print(f'模型参数: {count_parameters(lm_model) / 1e6} 百万 = {count_parameters(lm_model) / 1e9} B (Billion)')
lm_model.save_pretrained("minimind-v-v1-small", safe_serialization=False)
tokenizer.save_pretrained("minimind-v-v1-small")
if __name__ == '__main__':
# 1
export_transformers_model()

BIN
images/1-pretrain-512.png
View File

Binary file not shown.
Side by Side

Before

Width:  |  Height:  |  Size: 89 KiB

BIN
images/1-pretrain-768.png
View File

Binary file not shown.
Side by Side

Before

Width:  |  Height:  |  Size: 79 KiB

BIN
images/2-sft-512.png
View File

Binary file not shown.
Side by Side

Before

Width:  |  Height:  |  Size: 117 KiB

BIN
images/2-sft-768.png
View File

Binary file not shown.
Side by Side

Before

Width:  |  Height:  |  Size: 129 KiB

BIN
images/VLM-structure-moe.png
View File

Binary file not shown.
Side by Side Swipe Overlay

Before

Width:  |  Height:  |  Size: 141 KiB

After

Width:  |  Height:  |  Size: 203 KiB

BIN
images/VLM-structure.png
View File

Binary file not shown.
Side by Side Swipe Overlay

Before

Width:  |  Height:  |  Size: 199 KiB

After

Width:  |  Height:  |  Size: 198 KiB

BIN
images/minimind-v-input.png
View File

Binary file not shown.
Side by Side Swipe Overlay

Before

Width:  |  Height:  |  Size: 146 KiB

After

Width:  |  Height:  |  Size: 154 KiB

BIN
images/minimind2-v.gif Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 4.2 MiB

BIN
images/modelscope-demo.gif
View File

Binary file not shown.
Side by Side

Before

Width:  |  Height:  |  Size: 6.8 MiB

BIN
images/pretrain_loss.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 68 KiB

BIN
images/sft_loss.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 65 KiB

BIN
images/web_server.gif
View File

Binary file not shown.
Side by Side

Before

Width:  |  Height:  |  Size: 3.9 MiB

BIN
images/web_server1.png
View File

Binary file not shown.
Side by Side

Before

Width:  |  Height:  |  Size: 596 KiB

BIN
images/web_server2.png
View File

Binary file not shown.
Side by Side

Before

Width:  |  Height:  |  Size: 479 KiB

29
model/LMConfig.py
View File

@ -7,31 +7,31 @@ class LMConfig(PretrainedConfig):
def __init__( def __init__(
self, self,
dim: int = 512, # 768 dim: int = 512,
n_layers: int = 8, # 16 n_layers: int = 8,
n_heads: int = 16, n_heads: int = 8,
n_kv_heads: int = 8, n_kv_heads: int = 2,
vocab_size: int = 6400, vocab_size: int = 6400,
hidden_dim: int = None, hidden_dim: int = None,
multiple_of: int = 64, multiple_of: int = 64,
norm_eps: float = 1e-5, norm_eps: float = 1e-5,
max_seq_len: int = 512, max_seq_len: int = 8192,
rope_theta: int = 1e6,
dropout: float = 0.0, dropout: float = 0.0,
flash_attn: bool = True, flash_attn: bool = True,
image_special_token: str = '<' * 25 + '>' * 25,
image_ids: List = [30] * 25 + [32] * 25,
#################################################### ####################################################
# Here are the specific configurations of MOE # Here are the specific configurations of MOE
# When use_moe is false, the following is invalid # When use_moe is false, the following is invalid
#################################################### ####################################################
use_moe: bool = False, use_moe: bool = False,
num_experts_per_tok=2, ####################################################
n_routed_experts=4, num_experts_per_tok: int = 2,
n_routed_experts: int = 4,
n_shared_experts: bool = True, n_shared_experts: bool = True,
scoring_func='softmax', scoring_func: str = 'softmax',
aux_loss_alpha=0.01, aux_loss_alpha: float = 0.1,
seq_aux=True, seq_aux: bool = True,
norm_topk_prob=True, norm_topk_prob: bool = True,
**kwargs, **kwargs,
): ):
self.dim = dim self.dim = dim
@ -43,10 +43,9 @@ class LMConfig(PretrainedConfig):
self.multiple_of = multiple_of self.multiple_of = multiple_of
self.norm_eps = norm_eps self.norm_eps = norm_eps
self.max_seq_len = max_seq_len self.max_seq_len = max_seq_len
self.rope_theta = rope_theta
self.dropout = dropout self.dropout = dropout
self.flash_attn = flash_attn self.flash_attn = flash_attn
self.image_special_token = image_special_token
self.image_ids = image_ids
#################################################### ####################################################
# Here are the specific configurations of MOE # Here are the specific configurations of MOE
# When use_moe is false, the following is invalid # When use_moe is false, the following is invalid

16
model/VLMConfig.py Normal file
View File

@ -0,0 +1,16 @@
from .LMConfig import LMConfig
from typing import List
class VLMConfig(LMConfig):
model_type = "minimind-v"
def __init__(
self,
image_special_token: str = '@' * 196,
image_ids: List = [34] * 196,
**kwargs,
):
self.image_special_token = image_special_token
self.image_ids = image_ids
super().__init__(**kwargs)

5
model/clip_model/README.md
View File

@ -1,5 +0,0 @@
* 需要把clip-vit-base-patch32模型下载到此目录下
```bash
git clone https://hf-mirror.com/openai/clip-vit-base-patch32
```

310
model/dataset.py
View File

@ -1,278 +1,86 @@
import json import json
import random
import re
import pandas as pd
import numpy as np
from PIL import Image from PIL import Image
from torch.utils.data import Dataset, DataLoader from torch.utils.data import Dataset, DataLoader
import torch import torch
from model.vision_utils import get_img_process from .model_vlm import MiniMindVLM
import os import os
os.environ["TOKENIZERS_PARALLELISM"] = "false" os.environ["TOKENIZERS_PARALLELISM"] = "false"
class PretrainDataset(Dataset): class VLMDataset(Dataset):
def __init__(self, json_path, tokenizer, vision_model=None, max_length=1024, def __init__(self, jsonl_path, images_path, tokenizer, preprocess=None, max_length=512,
prompt_max_len=512, image_special_token='@' * 196):
answer_max_len=256,
image_special_token='<' * 25 + '>' * 25):
super().__init__() super().__init__()
with open(json_path, 'r', encoding='utf-8') as f: self.samples = self.load_data(jsonl_path)
self.data = json.load(f) self.images_path = images_path
self.max_length = max_length
self.prompt_max_len = prompt_max_len
self.answer_max_len = answer_max_len
self.tokenizer = tokenizer self.tokenizer = tokenizer
self.vision_model, self.preprocess = vision_model self.max_length = max_length
self.padding = 0 self.preprocess = preprocess
self.bos_id = self.tokenizer('<s>assistant').data['input_ids'] self.image_token = image_special_token
self.dataset_path = './dataset/pretrain_images/' self.bos_id = tokenizer('<s>assistant\n', add_special_tokens=False).input_ids
self.image_special_token = image_special_token self.eos_id = tokenizer('</s>\n', add_special_tokens=False).input_ids
def __len__(self): def __len__(self):
return len(self.data) return len(self.samples)
def find_sublist_index(self, main_list, sub_list) -> int: def load_data(self, path):
last_index = -1 samples = []
for i in range(len(main_list) - len(sub_list) + 1): with open(path, 'r', encoding='utf-8') as f:
if main_list[i:i + len(sub_list)] == sub_list: for line_num, line in enumerate(f, 1):
last_index = i data = json.loads(line.strip())
return last_index samples.append(data)
return samples
def safe_eval(self, s): def _create_chat_prompt(self, conversations):
try:
res = eval(s)
except Exception as e:
return []
return res
def __getitem__(self, index: int):
sample = self.data[index]
image_name = sample['image']
conversation = sample['conversations']
# minimind-v的image的特殊占位符，对应每张图切分成M个token，和get_img_process中的数量对应
messages = [] messages = []
# 遍历 conversation 列表 for i, turn in enumerate(conversations):
for i in range(0, len(conversation), 2): role = 'user' if i % 2 == 0 else 'assistant'
# 检查是否有配对的问题和回答 messages.append({"role": role, "content": turn['content'].replace('<image>', self.image_token)})
if i + 1 < len(conversation): return self.tokenizer.apply_chat_template(
q = conversation[i]['value'].replace('<image>', self.image_special_token)
a = conversation[i + 1]['value']
if q and a:
messages.append({"role": "user", "content": q})
messages.append({"role": "assistant", "content": a})
new_prompt = self.tokenizer.apply_chat_template(
messages, messages,
tokenize=False, tokenize=False,
add_generation_prompt=True add_generation_prompt=False
) )
input_id = self.tokenizer(new_prompt).data['input_ids'][:self.max_length]
question_length = self.find_sublist_index(input_id, self.bos_id) + len(self.bos_id) def _generate_loss_mask(self, input_ids):
padding_len = self.max_length - len(input_id) loss_mask = [0] * len(input_ids)
input_id = input_id + [self.padding] * padding_len i = 0
mask_len = len(input_id) - question_length - padding_len while i < len(input_ids):
loss_mask = [0] * question_length + [1] * (mask_len) + [0] * padding_len if input_ids[i:i + len(self.bos_id)] == self.bos_id:
start = i + len(self.bos_id)
input_id = np.array(input_id) end = start
while end < len(input_ids):
X = np.array(input_id[:-1]).astype(np.int64) if input_ids[end:end + len(self.eos_id)] == self.eos_id:
Y = np.array(input_id[1:]).astype(np.int64) break
loss_mask = np.array(loss_mask[1:]).astype(np.int64) end += 1
for j in range(start + 1, min(end + len(self.eos_id) + 1, self.max_length)):
X_tensor = torch.from_numpy(X) loss_mask[j] = 1
Y_tensor = torch.from_numpy(Y) i = end + len(self.eos_id) if end < len(input_ids) else len(input_ids)
loss_mask_tensor = torch.from_numpy(loss_mask) else:
i += 1
image = Image.open(f'{self.dataset_path}{image_name}') return loss_mask
image_encoders = get_img_process(image, self.preprocess)
return X_tensor, Y_tensor, loss_mask_tensor, image_encoders
class SFTDataset(Dataset):
def __init__(self, json_path, tokenizer, vision_model=None, max_length=1024,
prompt_max_len=512,
answer_max_len=256,
image_special_token='<' * 25 + '>' * 25):
super().__init__()
with open(json_path, 'r', encoding='utf-8') as f:
self.data = json.load(f)
self.max_length = max_length
self.prompt_max_len = prompt_max_len
self.answer_max_len = answer_max_len
self.tokenizer = tokenizer
self.vision_model, self.preprocess = vision_model
self.padding = 0
self.bos_id = self.tokenizer('<s>assistant').data['input_ids']
self.dataset_path = './dataset/sft_images/'
self.image_special_token = image_special_token
def __len__(self):
return len(self.data)
def find_sublist_index(self, main_list, sub_list) -> int:
last_index = -1
for i in range(len(main_list) - len(sub_list) + 1):
if main_list[i:i + len(sub_list)] == sub_list:
last_index = i
return last_index
def safe_eval(self, s):
try:
res = eval(s)
except Exception as e:
return []
return res
def __getitem__(self, index: int): def __getitem__(self, index: int):
sample = self.data[index] sample = self.samples[index]
image_name = 'COCO_train2014_' + sample['image'] image_paths = sample['image']
conversation = sample['conversations'] prompt = self._create_chat_prompt(sample['conversations'])
# minimind-v的image的特殊占位符，对应每张图切分成M个token，和get_img_process中的数量对应 input_ids = self.tokenizer(prompt).input_ids[:self.max_length]
messages = [] input_ids += [self.tokenizer.pad_token_id] * (self.max_length - len(input_ids))
# 遍历 conversation 列表 loss_mask = self._generate_loss_mask(input_ids)
# for i in range(0, len(conversation), 2):
for i in range(0, 1):
# 检查是否有配对的问题和回答
if i + 1 < len(conversation):
q = conversation[i]['value'].replace('<image>', self.image_special_token)
a = conversation[i + 1]['value']
if q and a: X = torch.tensor(input_ids[:-1], dtype=torch.long)
messages.append({"role": "user", "content": q}) Y = torch.tensor(input_ids[1:], dtype=torch.long)
messages.append({"role": "assistant", "content": a}) loss_mask = torch.tensor(loss_mask[1:], dtype=torch.long)
new_prompt = self.tokenizer.apply_chat_template( image_tensors = []
messages, for image_name in image_paths.split(','):
tokenize=False, image_name = image_name.strip()
add_generation_prompt=True image = Image.open(f'{self.images_path}/{image_name}')
) image_tensor = MiniMindVLM.image2tensor(image, self.preprocess)
input_id = self.tokenizer(new_prompt).data['input_ids'][:self.max_length] image_tensors.append(image_tensor)
image_tensors = torch.stack(image_tensors, dim=0)
question_length = self.find_sublist_index(input_id, self.bos_id) + len(self.bos_id) return X, Y, loss_mask, image_tensors
padding_len = self.max_length - len(input_id)
input_id = input_id + [self.padding] * padding_len
mask_len = len(input_id) - question_length - padding_len
loss_mask = [0] * question_length + [1] * (mask_len) + [0] * padding_len
input_id = np.array(input_id)
X = np.array(input_id[:-1]).astype(np.int64)
Y = np.array(input_id[1:]).astype(np.int64)
loss_mask = np.array(loss_mask[1:]).astype(np.int64)
X_tensor = torch.from_numpy(X)
Y_tensor = torch.from_numpy(Y)
loss_mask_tensor = torch.from_numpy(loss_mask)
image = Image.open(f'{self.dataset_path}{image_name}')
image_encoders = get_img_process(image, self.preprocess)
return X_tensor, Y_tensor, loss_mask_tensor, image_encoders
class SFTDataset_multi(Dataset):
def __init__(self, json_path, tokenizer, vision_model=None, max_length=1024,
prompt_max_len=512,
answer_max_len=256,
image_special_token='<' * 25 + '>' * 25):
super().__init__()
with open(json_path, 'r', encoding='utf-8') as f:
self.data = json.load(f)
self.max_length = max_length
self.prompt_max_len = prompt_max_len
self.answer_max_len = answer_max_len
self.tokenizer = tokenizer
self.vision_model, self.preprocess = vision_model
self.padding = 0
self.bos_id = self.tokenizer('<s>assistant').data['input_ids']
self.dataset_path = './dataset/sft_multi_images/'
self.image_special_token = image_special_token
def __len__(self):
return len(self.data)
def find_sublist_index(self, main_list, sub_list) -> int:
last_index = -1
for i in range(len(main_list) - len(sub_list) + 1):
if main_list[i:i + len(sub_list)] == sub_list:
last_index = i
return last_index
def safe_eval(self, s):
try:
res = eval(s)
except Exception as e:
return []
return res
def __getitem__(self, index: int):
sample = self.data[index]
image_names = sample['image'].split(', ')
conversation = sample['conversations']
messages = []
for i in range(0, 1):
if i + 1 < len(conversation):
q = conversation[i]['value'].replace('<image>', self.image_special_token)
a = conversation[i + 1]['value']
if q and a:
messages.append({"role": "user", "content": q})
messages.append({"role": "assistant", "content": a})
# print(messages) # [{'role': 'user', 'content': '<<<<<<<<<<<<<<<<<<<<<<<<<>>>>>>>>>>>>>>>>>>>>>>>>><<<<<<<<<<<<<<<<<<<<<<<<<>>>>>>>>>>>>>>>>>>>>>>>>>\nAre the two images below that resemble each other described by the same term? You must choose your answer from the Choice List. Choice_List: True, False.'}, {'role': 'assistant', 'content': 'False'}][{'role': 'user', 'content': '<<<<<<<<<<<<<<<<<<<<<<<<<>>>>>>>>>>>>>>>>>>>>>>>>><<<<<<<<<<<<<<<<<<<<<<<<<>>>>>>>>>>>>>>>>>>>>>>>>><<<<<<<<<<<<<<<<<<<<<<<<<>>>>>>>>>>>>>>>>>>>>>>>>><<<<<<<<<<<<<<<<<<<<<<<<<>>>>>>>>>>>>>>>>>>>>>>>>>\nDo these four pictures fall into the same category? You must choose your answer from the Choice List. Choice_List: True, False.'}, {'role': 'assistant', 'content': 'False'}]
new_prompt = self.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
input_id = self.tokenizer(new_prompt).data['input_ids'][:self.max_length]
# print(len(input_id)) # 165 or 263 or 259
question_length = self.find_sublist_index(input_id, self.bos_id) + len(self.bos_id)
padding_len = self.max_length - len(input_id)
input_id = input_id + [self.padding] * padding_len
mask_len = len(input_id) - question_length - padding_len
loss_mask = [0] * question_length + [1] * (mask_len) + [0] * padding_len
input_id = np.array(input_id)
X = np.array(input_id[:-1]).astype(np.int64)
Y = np.array(input_id[1:]).astype(np.int64)
loss_mask = np.array(loss_mask[1:]).astype(np.int64)
X_tensor = torch.from_numpy(X)
Y_tensor = torch.from_numpy(Y)
loss_mask_tensor = torch.from_numpy(loss_mask)
# 读取多张图像
image_encoders = []
for image_name in image_names:
image = Image.open(f'{self.dataset_path}{image_name.strip()}') # 去掉可能的空格
image_encoders.append(get_img_process(image, self.preprocess)['pixel_values'])
# 确定目标形状
max_images = 2 # 根据你的需求设置 一次性最大输入几张图片
target_shape = (max_images, 3, 224, 224)
# 创建填充张量
padded_image_encoders = torch.zeros(target_shape, dtype=torch.float32)
# 填充图像编码
for i, img_enc in enumerate(image_encoders):
if i < max_images:
padded_image_encoders[i] = img_enc.squeeze(0)
return X_tensor, Y_tensor, loss_mask_tensor, padded_image_encoders

9
model/minimind_tokenizer/tokenizer_config.json
View File

@ -1,7 +1,7 @@
{ {
"add_bos_token": false, "add_bos_token": false,
"add_eos_token": false, "add_eos_token": false,
"add_prefix_space": true, "add_prefix_space": false,
"added_tokens_decoder": { "added_tokens_decoder": {
"0": { "0": {
"content": "<unk>", "content": "<unk>",
@ -33,12 +33,11 @@
"clean_up_tokenization_spaces": false, "clean_up_tokenization_spaces": false,
"eos_token": "</s>", "eos_token": "</s>",
"legacy": true, "legacy": true,
"model_max_length": 1000000000000000019884624838656, "model_max_length": 32768,
"pad_token": null, "pad_token": "<unk>",
"sp_model_kwargs": {}, "sp_model_kwargs": {},
"spaces_between_special_tokens": false, "spaces_between_special_tokens": false,
"tokenizer_class": "PreTrainedTokenizerFast", "tokenizer_class": "PreTrainedTokenizerFast",
"unk_token": "<unk>", "unk_token": "<unk>",
"use_default_system_prompt": false, "chat_template": "{% if messages[0]['role'] == 'system' %}{% set system_message = messages[0]['content'] %}{{ '<s>system\\n' + system_message + '</s>\\n' }}{% else %}{{ '<s>system\\n你是 MiniMind，是一个有用的人工智能助手。</s>\\n' }}{% endif %}{% for message in messages %}{% set content = message['content'] %}{% if message['role'] == 'user' %}{{ '<s>user\\n' + content + '</s>\\n<s>assistant\\n' }}{% elif message['role'] == 'assistant' %}{{ content + '</s>' + '\\n' }}{% endif %}{% endfor %}"
"chat_template": "{% if messages[0]['role'] == 'system' %}{% set system_message = messages[0]['content'] %}{% endif %}{% if system_message is defined %}{{ system_message }}{% endif %}{% for message in messages %}{% set content = message['content'] %}{% if message['role'] == 'user' %}{{ '<s>user\\n' + content + '</s>\\n<s>assistant\\n' }}{% elif message['role'] == 'assistant' %}{{ content + '</s>' + '\\n' }}{% endif %}{% endfor %}"
} }

412
model/model.py
View File

@ -4,7 +4,7 @@ import inspect
import time import time
from .LMConfig import LMConfig from .LMConfig import LMConfig
from typing import Any, Optional, Tuple from typing import Any, Optional, Tuple, List
import numpy as np import numpy as np
import torch import torch
import torch.nn.functional as F import torch.nn.functional as F
@ -19,15 +19,11 @@ class RMSNorm(torch.nn.Module):
self.eps = eps self.eps = eps
self.weight = nn.Parameter(torch.ones(dim)) self.weight = nn.Parameter(torch.ones(dim))
def _norm(self, x):
return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
def forward(self, x): def forward(self, x):
output = self._norm(x.float()).type_as(x) return self.weight * (x.float() * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)).type_as(x)
return output * self.weight
def precompute_pos_cis(dim: int, end: int, theta: float = 10000.0): def precompute_pos_cis(dim: int, end: int = int(32 * 1024), theta: float = 1e6):
freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim)) freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim))
t = torch.arange(end, device=freqs.device) # type: ignore t = torch.arange(end, device=freqs.device) # type: ignore
freqs = torch.outer(t, freqs).float() # type: ignore freqs = torch.outer(t, freqs).float() # type: ignore
@ -76,71 +72,69 @@ class Attention(nn.Module):
self.wk = nn.Linear(args.dim, self.n_kv_heads * self.head_dim, bias=False) self.wk = nn.Linear(args.dim, self.n_kv_heads * self.head_dim, bias=False)
self.wv = nn.Linear(args.dim, self.n_kv_heads * self.head_dim, bias=False) self.wv = nn.Linear(args.dim, self.n_kv_heads * self.head_dim, bias=False)
self.wo = nn.Linear(args.n_heads * self.head_dim, args.dim, bias=False) self.wo = nn.Linear(args.n_heads * self.head_dim, args.dim, bias=False)
self.k_cache, self.v_cache = None, None
self.attn_dropout = nn.Dropout(args.dropout) self.attn_dropout = nn.Dropout(args.dropout)
self.resid_dropout = nn.Dropout(args.dropout) self.resid_dropout = nn.Dropout(args.dropout)
self.dropout = args.dropout self.dropout = args.dropout
self.flash = hasattr(torch.nn.functional, 'scaled_dot_product_attention') and args.flash_attn self.flash = hasattr(torch.nn.functional, 'scaled_dot_product_attention') and args.flash_attn
# print("WARNING: using slow attention. Flash Attention requires PyTorch >= 2.0") # print("WARNING: using slow attention. Flash Attention requires PyTorch >= 2.0")
mask = torch.full((1, 1, args.max_seq_len, args.max_seq_len), float("-inf")) mask = torch.full((1, 1, args.max_seq_len, args.max_seq_len), float("-inf"))
mask = torch.triu(mask, diagonal=1) mask = torch.triu(mask, diagonal=1)
self.register_buffer("mask", mask, persistent=False) self.register_buffer("mask", mask, persistent=False)
def forward(self, x: torch.Tensor, pos_cis: torch.Tensor, kv_cache=False): def forward(self,
bsz, seqlen, _ = x.shape x: torch.Tensor,
pos_cis: torch.Tensor,
past_key_value: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
use_cache=False):
bsz, seq_len, _ = x.shape
xq, xk, xv = self.wq(x), self.wk(x), self.wv(x) xq, xk, xv = self.wq(x), self.wk(x), self.wv(x)
xq = xq.view(bsz, seq_len, self.n_local_heads, self.head_dim)
xq = xq.view(bsz, seqlen, self.n_local_heads, self.head_dim) xk = xk.view(bsz, seq_len, self.n_local_kv_heads, self.head_dim)
xk = xk.view(bsz, seqlen, self.n_local_kv_heads, self.head_dim) xv = xv.view(bsz, seq_len, self.n_local_kv_heads, self.head_dim)
xv = xv.view(bsz, seqlen, self.n_local_kv_heads, self.head_dim)
xq, xk = apply_rotary_emb(xq, xk, pos_cis) xq, xk = apply_rotary_emb(xq, xk, pos_cis)
# kv_cache实现
if past_key_value is not None:
xk = torch.cat([past_key_value[0], xk], dim=1)
xv = torch.cat([past_key_value[1], xv], dim=1)
past_kv = (xk, xv) if use_cache else None
# 更高效的kv_cache实现 xq, xk, xv = (
if kv_cache and self.eval(): xq.transpose(1, 2),
if seqlen == 1 and all(cache is not None for cache in (self.k_cache, self.v_cache)): repeat_kv(xk, self.n_rep).transpose(1, 2),
xk = torch.cat((self.k_cache, xk), dim=1) repeat_kv(xv, self.n_rep).transpose(1, 2)
xv = torch.cat((self.v_cache, xv), dim=1) )
self.k_cache, self.v_cache = xk, xv if self.flash and seq_len != 1:
dropout_p = self.dropout if self.training else 0.0
xk = repeat_kv(xk, self.n_rep) # (bs, seqlen, n_local_heads, head_dim) output = F.scaled_dot_product_attention(
xv = repeat_kv(xv, self.n_rep) # (bs, seqlen, n_local_heads, head_dim) xq, xk, xv,
attn_mask=None,
xq = xq.transpose(1, 2) dropout_p=dropout_p,
xk = xk.transpose(1, 2) is_causal=True
xv = xv.transpose(1, 2) )
if self.flash and seqlen != 1:
output = torch.nn.functional.scaled_dot_product_attention(xq, xk, xv, attn_mask=None,
dropout_p=self.dropout if self.training else 0.0,
is_causal=True)
else: else:
scores = torch.matmul(xq, xk.transpose(2, 3)) / math.sqrt(self.head_dim) scores = (xq @ xk.transpose(-2, -1)) / math.sqrt(self.head_dim)
scores = scores + self.mask[:, :, :seqlen, :seqlen] # (bs, n_local_heads, seqlen, cache_len + seqlen) scores += self.mask[:, :, :seq_len, :seq_len]
scores = F.softmax(scores.float(), dim=-1).type_as(xq) scores = F.softmax(scores.float(), dim=-1).type_as(xq)
scores = self.attn_dropout(scores) scores = self.attn_dropout(scores)
output = torch.matmul(scores, xv) # (bs, n_local_heads, seqlen, head_dim) output = scores @ xv
output = output.transpose(1, 2).contiguous().view(bsz, seqlen, -1) output = output.transpose(1, 2).reshape(bsz, seq_len, -1)
output = self.resid_dropout(self.wo(output))
output = self.wo(output) return output, past_kv
output = self.resid_dropout(output)
return output
class FeedForward(nn.Module): class FeedForward(nn.Module):
def __init__(self, dim: int, hidden_dim: int, multiple_of: int, dropout: float): def __init__(self, config: LMConfig):
super().__init__() super().__init__()
if hidden_dim is None: if config.hidden_dim is None:
hidden_dim = 4 * dim hidden_dim = 4 * config.dim
hidden_dim = int(2 * hidden_dim / 3) hidden_dim = int(2 * hidden_dim / 3)
hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of) config.hidden_dim = config.multiple_of * ((hidden_dim + config.multiple_of - 1) // config.multiple_of)
self.w1 = nn.Linear(dim, hidden_dim, bias=False) self.w1 = nn.Linear(config.dim, config.hidden_dim, bias=False)
self.w2 = nn.Linear(hidden_dim, dim, bias=False) self.w2 = nn.Linear(config.hidden_dim, config.dim, bias=False)
self.w3 = nn.Linear(dim, hidden_dim, bias=False) self.w3 = nn.Linear(config.dim, config.hidden_dim, bias=False)
self.dropout = nn.Dropout(dropout) self.dropout = nn.Dropout(config.dropout)
def forward(self, x): def forward(self, x):
return self.dropout(self.w2(F.silu(self.w1(x)) * self.w3(x))) return self.dropout(self.w2(F.silu(self.w1(x)) * self.w3(x)))
@ -168,7 +162,6 @@ class MoEGate(nn.Module):
def forward(self, hidden_states): def forward(self, hidden_states):
bsz, seq_len, h = hidden_states.shape bsz, seq_len, h = hidden_states.shape
hidden_states = hidden_states.view(-1, h) hidden_states = hidden_states.view(-1, h)
logits = F.linear(hidden_states, self.weight, None) logits = F.linear(hidden_states, self.weight, None)
if self.scoring_func == 'softmax': if self.scoring_func == 'softmax':
@ -200,7 +193,7 @@ class MoEGate(nn.Module):
fi = ce * self.n_routed_experts fi = ce * self.n_routed_experts
aux_loss = (Pi * fi).sum() * self.alpha aux_loss = (Pi * fi).sum() * self.alpha
else: else:
aux_loss = None aux_loss = 0
return topk_idx, topk_weight, aux_loss return topk_idx, topk_weight, aux_loss
@ -209,50 +202,35 @@ class MOEFeedForward(nn.Module):
super().__init__() super().__init__()
self.config = config self.config = config
self.experts = nn.ModuleList([ self.experts = nn.ModuleList([
FeedForward( FeedForward(config)
dim=config.dim,
hidden_dim=config.hidden_dim,
multiple_of=config.multiple_of,
dropout=config.dropout,
)
for _ in range(config.n_routed_experts) for _ in range(config.n_routed_experts)
]) ])
self.gate = MoEGate(config) self.gate = MoEGate(config)
if config.n_shared_experts is not None: if config.n_shared_experts is not None:
self.shared_experts = FeedForward( self.shared_experts = FeedForward(config)
dim=config.dim,
hidden_dim=config.hidden_dim,
multiple_of=config.multiple_of,
dropout=config.dropout,
)
def forward(self, x): def forward(self, x):
identity = x identity = x
orig_shape = x.shape orig_shape = x.shape
bsz, seq_len, _ = x.shape bsz, seq_len, _ = x.shape
# 使用门控机制选择专家 # 使用门控机制选择专家
topk_idx, topk_weight, aux_loss = self.gate(x) topk_idx, topk_weight, aux_loss = self.gate(x)
x = x.view(-1, x.shape[-1]) x = x.view(-1, x.shape[-1])
flat_topk_idx = topk_idx.view(-1) flat_topk_idx = topk_idx.view(-1)
if self.training: if self.training:
# 训练模式下，重复输入数据 # 训练模式下，重复输入数据
x = x.repeat_interleave(self.config.num_experts_per_tok, dim=0) x = x.repeat_interleave(self.config.num_experts_per_tok, dim=0)
y = torch.empty_like(x, dtype=torch.float16) y = torch.empty_like(x, dtype=torch.float16)
for i, expert in enumerate(self.experts): for i, expert in enumerate(self.experts):
y[flat_topk_idx == i] = expert(x[flat_topk_idx == i]) y[flat_topk_idx == i] = expert(x[flat_topk_idx == i]).to(y.dtype) # 确保类型一致
y = (y.view(*topk_weight.shape, -1) * topk_weight.unsqueeze(-1)).sum(dim=1) y = (y.view(*topk_weight.shape, -1) * topk_weight.unsqueeze(-1)).sum(dim=1)
y = y.view(*orig_shape) y = y.view(*orig_shape)
else: else:
# 推理模式下，只选择最优专家 # 推理模式下，只选择最优专家
y = self.moe_infer(x, flat_topk_idx, topk_weight.view(-1, 1)).view(*orig_shape) y = self.moe_infer(x, flat_topk_idx, topk_weight.view(-1, 1)).view(*orig_shape)
if self.config.n_shared_experts is not None: if self.config.n_shared_experts is not None:
y = y + self.shared_experts(identity) y = y + self.shared_experts(identity)
self.aux_loss = aux_loss
return y return y
@torch.no_grad() @torch.no_grad()
@ -271,7 +249,7 @@ class MOEFeedForward(nn.Module):
expert = self.experts[i] expert = self.experts[i]
exp_token_idx = token_idxs[start_idx:end_idx] exp_token_idx = token_idxs[start_idx:end_idx]
expert_tokens = x[exp_token_idx] expert_tokens = x[exp_token_idx]
expert_out = expert(expert_tokens) expert_out = expert(expert_tokens).to(expert_cache.dtype)
expert_out.mul_(flat_expert_weights[idxs[start_idx:end_idx]]) expert_out.mul_(flat_expert_weights[idxs[start_idx:end_idx]])
# 使用 scatter_add_ 进行 sum 操作 # 使用 scatter_add_ 进行 sum 操作
expert_cache.scatter_add_(0, exp_token_idx.view(-1, 1).repeat(1, x.shape[-1]), expert_out) expert_cache.scatter_add_(0, exp_token_idx.view(-1, 1).repeat(1, x.shape[-1]), expert_out)
@ -279,222 +257,120 @@ class MOEFeedForward(nn.Module):
return expert_cache return expert_cache
class TransformerBlock(nn.Module): class MiniMindBlock(nn.Module):
def __init__(self, layer_id: int, args: LMConfig): def __init__(self, layer_id: int, config: LMConfig):
super().__init__() super().__init__()
self.n_heads = args.n_heads self.n_heads = config.n_heads
self.dim = args.dim self.dim = config.dim
self.head_dim = args.dim // args.n_heads self.head_dim = config.dim // config.n_heads
self.attention = Attention(args) self.attention = Attention(config)
self.layer_id = layer_id self.layer_id = layer_id
self.attention_norm = RMSNorm(args.dim, eps=args.norm_eps) self.attention_norm = RMSNorm(config.dim, eps=config.norm_eps)
self.ffn_norm = RMSNorm(args.dim, eps=args.norm_eps) self.ffn_norm = RMSNorm(config.dim, eps=config.norm_eps)
self.feed_forward = FeedForward(config) if not config.use_moe else MOEFeedForward(config)
if args.use_moe: def forward(self, x, pos_cis, past_key_value=None, use_cache=False):
self.feed_forward = MOEFeedForward(args) h_attn, past_kv = self.attention(
else: self.attention_norm(x),
self.feed_forward = FeedForward( pos_cis,
dim=args.dim, past_key_value=past_key_value,
hidden_dim=args.hidden_dim, use_cache=use_cache
multiple_of=args.multiple_of,
dropout=args.dropout,
) )
h = x + h_attn
def forward(self, x, pos_cis, kv_cache=False):
h = x + self.attention(self.attention_norm(x), pos_cis, kv_cache)
out = h + self.feed_forward(self.ffn_norm(h)) out = h + self.feed_forward(self.ffn_norm(h))
return out return out, past_kv
class VisionProj(nn.Module): class MiniMindLM(PreTrainedModel):
def __init__(self, vision_out_dim=768, lm_dim=512, image_ids=[1, 2, 3, 4]):
super().__init__()
self.vision_out_dim = vision_out_dim
self.lm_dim = lm_dim
self.image_ids = image_ids
self.vision_proj = nn.Sequential(
nn.Linear(self.vision_out_dim, self.lm_dim),
)
def forward(self, image_encoders):
vision_proj = self.vision_proj(image_encoders)
return vision_proj
class Transformer(PreTrainedModel):
config_class = LMConfig config_class = LMConfig
last_loss: Optional[torch.Tensor]
def __init__(self, params: LMConfig = None): def __init__(self, params: LMConfig = None):
super().__init__(params) self.params = params or LMConfig()
if not params: super().__init__(self.params)
params = LMConfig() self.vocab_size, self.n_layers = params.vocab_size, params.n_layers
self.params = params self.tok_embeddings = nn.Embedding(params.vocab_size, params.dim)
self.vocab_size = params.vocab_size
self.n_layers = params.n_layers
# image的特殊占位符，对应每张图切分成M个token，和get_img_process中的数量对应
self.image_ids = params.image_ids
self.tok_embeddings = nn.Embedding(self.vocab_size, params.dim)
self.dropout = nn.Dropout(params.dropout) self.dropout = nn.Dropout(params.dropout)
self.layers = torch.nn.ModuleList() self.layers = nn.ModuleList([MiniMindBlock(l, params) for l in range(self.n_layers)])
for layer_id in range(self.n_layers):
self.layers.append(TransformerBlock(layer_id, params))
self.norm = RMSNorm(params.dim, eps=params.norm_eps) self.norm = RMSNorm(params.dim, eps=params.norm_eps)
self.output = nn.Linear(params.dim, self.vocab_size, bias=False) self.output = nn.Linear(params.dim, params.vocab_size, bias=False)
self.tok_embeddings.weight = self.output.weight self.tok_embeddings.weight = self.output.weight
pos_cis = precompute_pos_cis(self.params.dim // self.params.n_heads, self.params.max_seq_len) self.register_buffer("pos_cis",
self.register_buffer("pos_cis", pos_cis, persistent=False) precompute_pos_cis(dim=params.dim // params.n_heads, theta=params.rope_theta),
persistent=False)
self.apply(self._init_weights)
for pn, p in self.named_parameters():
if pn.endswith('w3.weight') or pn.endswith('wo.weight'):
torch.nn.init.normal_(p, mean=0.0, std=0.02 / math.sqrt(2 * params.n_layers))
self.last_loss = None
self.OUT = CausalLMOutputWithPast() self.OUT = CausalLMOutputWithPast()
self._no_split_modules = [name for name, _ in self.named_modules()]
self.vision_proj = VisionProj(768, params.dim, self.image_ids)
def _init_weights(self, module):
if isinstance(module, nn.Linear):
torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
if module.bias is not None:
torch.nn.init.zeros_(module.bias)
elif isinstance(module, nn.Embedding):
torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
# VLM
def count_vision_proj(self, tokens, h, image_encoders=None, seqlen=200):
# 查找token中<image>片段的索引，为了替换做准备
def find_indices(tokens, image_ids):
image_ids_tensor = torch.tensor(image_ids).to(tokens.device)
len_image_ids = len(image_ids)
# .generate时，在初始化后直接跳过
if len_image_ids > tokens.size(1):
return None
# 使用view来创建一个视图，便于处理滑动窗口
tokens_view = tokens.unfold(1, len_image_ids, 1) # 在第二维度创建滑动窗口
# 检查每个滑动窗口是否与image_ids_tensor相等
matches = (tokens_view == image_ids_tensor).all(dim=2) # 对窗口中的每一行进行比较
# 提取匹配的索引
indices = {}
for batch_idx in range(tokens.size(0)):
match_indices = matches[batch_idx].nonzero(as_tuple=True)[0] # 获取非零（匹配）索引
if match_indices.numel() > 0: # 如果有匹配
indices[batch_idx] = [(idx.item(), idx.item() + len_image_ids - 1) for idx in match_indices]
return indices if indices else None
image_indices = find_indices(tokens, self.image_ids) # 字典形式存储索引
# 如果此时有图像编码
if image_encoders is not None:
vision_proj = self.vision_proj(image_encoders)
vision_proj = vision_proj.unsqueeze(1) if len(vision_proj.shape) == 3 else vision_proj
if image_indices is not None:
# 创建一个新的张量来存储拼接后的结果
new_h = []
for i in range(h.size(0)):
# i即为current_batch_idx索引
img_idx = 0
if i in image_indices: # 直接从字典中获取
for start_idx, end_idx in image_indices[i]:
# 插入vision_proj特征
before = h[i][:start_idx, :]
after = h[i][end_idx + 1:, :]
# 拼接 before, vision_proj, after
h[i] = torch.cat((before, vision_proj[i][img_idx], after), dim=0)[:seqlen]
img_idx += 1
new_h.append(h[i])
# 将所有拼接后的结果重新堆叠起来
new_h = torch.stack(new_h, dim=0) # torch.Size([32, 511, 512])
return new_h
return h
def forward(self, tokens: Optional[torch.Tensor] = None, targets: Optional[torch.Tensor] = None,
kv_cache=False, image_encoders=None, **keyargs):
current_idx = 0
if 'input_ids' in keyargs:
tokens = keyargs['input_ids']
if 'attention_mask' in keyargs:
targets = keyargs['attention_mask']
if 'current_idx' in keyargs:
current_idx = int(keyargs['current_idx'])
_bsz, seqlen = tokens.shape
# language proj token
h = self.tok_embeddings(tokens)
# vision proj token
h = self.count_vision_proj(tokens=tokens, h=h, image_encoders=image_encoders, seqlen=seqlen)
h = self.dropout(h)
pos_cis = self.pos_cis[current_idx:current_idx + seqlen]
for idx, layer in enumerate(self.layers):
h = layer(h, pos_cis, kv_cache)
h = self.norm(h)
if targets is not None:
logits = self.output(h)
self.last_loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1), ignore_index=0,
reduction='none')
else:
logits = self.output(h[:, [-1], :])
self.last_loss = None
def forward(self,
input_ids: Optional[torch.Tensor] = None,
past_key_values: Optional[List[Tuple[torch.Tensor, torch.Tensor]]] = None,
use_cache: bool = False,
**args):
past_key_values = past_key_values or [None] * len(self.layers)
start_pos = args.get('start_pos', 0)
h = self.dropout(self.tok_embeddings(input_ids))
pos_cis = self.pos_cis[start_pos:start_pos + input_ids.size(1)]
past_kvs = []
for l, layer in enumerate(self.layers):
h, past_kv = layer(
h, pos_cis,
past_key_value=past_key_values[l],
use_cache=use_cache
)
past_kvs.append(past_kv)
logits = self.output(self.norm(h))
aux_loss = sum(l.feed_forward.aux_loss for l in self.layers if isinstance(l.feed_forward, MOEFeedForward))
self.OUT.__setitem__('logits', logits) self.OUT.__setitem__('logits', logits)
self.OUT.__setitem__('loss', self.last_loss) self.OUT.__setitem__('aux_loss', aux_loss)
self.OUT.__setitem__('past_key_values', past_kvs)
return self.OUT return self.OUT
@torch.inference_mode() @torch.inference_mode()
def generate(self, idx, eos, max_new_tokens, temperature=0.7, top_k=8, stream=True, rp=1., kv_cache=True, def generate(self, input_ids, eos_token_id=2, max_new_tokens=1024, temperature=0.75, top_p=0.90,
image_encoders=None): stream=False, rp=1., use_cache=True, pad_token_id=0, **args):
# rp: repetition_penalty # 流式生成
index = idx.shape[1]
init_inference = True
while idx.shape[1] < max_new_tokens - 1:
if init_inference or not kv_cache:
inference_res, init_inference = self(idx, kv_cache=kv_cache, image_encoders=image_encoders), False
else:
inference_res = self(idx[:, -1:], kv_cache=kv_cache, current_idx=idx.shape[1] - 1)
logits = inference_res.logits
logits = logits[:, -1, :]
for token in set(idx.tolist()[0]):
logits[:, token] /= rp
if temperature == 0.0:
_, idx_next = torch.topk(logits, k=1, dim=-1)
else:
logits = logits / temperature
if top_k is not None:
v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
logits[logits < v[:, [-1]]] = -float('Inf')
probs = F.softmax(logits, dim=-1)
idx_next = torch.multinomial(probs, num_samples=1, generator=None)
if idx_next == eos:
break
idx = torch.cat((idx, idx_next), dim=1)
if stream: if stream:
yield idx[:, index:] return self._stream(input_ids, eos_token_id, max_new_tokens, temperature, top_p, rp, use_cache, **args)
if not stream: # 直接生成
yield idx[:, index:] generated = []
for i in range(input_ids.size(0)):
non_pad = input_ids[i][input_ids[i] != pad_token_id].unsqueeze(0)
out = self._stream(non_pad, eos_token_id, max_new_tokens, temperature, top_p, rp, use_cache, **args)
tokens_list = [tokens[:, -1:] for tokens in out]
gen = torch.cat(tokens_list, dim=-1) if tokens_list else non_pad
full_sequence = torch.cat([non_pad, gen], dim=-1)
generated.append(full_sequence)
max_length = max(seq.size(1) for seq in generated)
generated = [
torch.cat(
[seq, torch.full((1, max_length - seq.size(1)), pad_token_id, dtype=seq.dtype, device=seq.device)],
dim=-1)
for seq in generated
]
return torch.cat(generated, dim=0)
@torch.inference_mode() def _stream(self, input_ids, eos_token_id, max_new_tokens, temperature, top_p, rp, use_cache, **args):
def eval_answer(self, idx): start, first_seq, past_kvs = input_ids.shape[1], True, None
idx_cond = idx if idx.size(1) <= self.params.max_seq_len else idx[:, -self.params.max_seq_len:] while input_ids.shape[1] < max_new_tokens - 1:
inference_res = self(idx_cond) if first_seq or not use_cache:
logits = inference_res.logits out, first_seq = self(input_ids, past_key_values=past_kvs, use_cache=use_cache, **args), False
logits = logits[:, -1, :] else:
return logits out = self(input_ids[:, -1:], past_key_values=past_kvs, use_cache=use_cache,
start_pos=input_ids.shape[1] - 1, **args)
logits, past_kvs = out.logits[:, -1, :], out.past_key_values
logits[:, list(set(input_ids.tolist()[0]))] /= rp
logits /= (temperature + 1e-9)
if top_p is not None and top_p < 1.0:
sorted_logits, sorted_indices = torch.sort(logits, descending=True, dim=-1)
sorted_probs = F.softmax(sorted_logits, dim=-1)
cumulative_probs = torch.cumsum(sorted_probs, dim=-1)
sorted_indices_to_remove = cumulative_probs > top_p
sorted_indices_to_remove[:, 1:] = sorted_indices_to_remove[:, :-1].clone()
sorted_indices_to_remove[:, 0] = False
indices_to_remove = sorted_indices_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove)
logits[indices_to_remove] = -float('Inf')
input_ids_next = torch.multinomial(F.softmax(logits, dim=-1), num_samples=1)
input_ids = torch.cat((input_ids, input_ids_next), dim=1)
yield input_ids[:, start:]
if input_ids_next.item() == eos_token_id:
break

130
model/model_vlm.py Normal file
View File

@ -0,0 +1,130 @@
from .VLMConfig import VLMConfig
from .model import *
from typing import Optional, Tuple, List
from torch import nn
import warnings
from transformers import CLIPProcessor, CLIPModel
import torch
warnings.filterwarnings('ignore')
class VisionProj(nn.Module):
def __init__(self, ve_dim=768, lm_dim=512):
super().__init__()
self.ve_dim = ve_dim
self.lm_dim = lm_dim
self.vision_proj = nn.Sequential(
nn.Linear(self.ve_dim, self.lm_dim)
)
def forward(self, image_encoders):
vision_proj = self.vision_proj(image_encoders)
return vision_proj
# 继承自语言模型
class MiniMindVLM(MiniMindLM):
config_class = VLMConfig
def __init__(self, params: VLMConfig = None):
super().__init__(params)
if not params: params = VLMConfig()
self.params = params
self.vision_encoder, self.processor = self.__class__.get_vision_model()
self.vision_proj = VisionProj(lm_dim=params.dim)
@staticmethod
def get_vision_model(model_path="./model/vision_model/clip-vit-base-patch16"):
model = CLIPModel.from_pretrained(model_path)
processor = CLIPProcessor.from_pretrained(model_path)
# 冻结 vision_encoder 的所有参数
for param in model.parameters():
param.requires_grad = False
return model.eval(), processor
@staticmethod
def image2tensor(image, processor):
if image.mode in ['RGBA', 'LA']: image = image.convert('RGB')
inputs = processor(images=image, return_tensors="pt")['pixel_values']
return inputs
@staticmethod
def get_image_embeddings(image_tensors, vision_model):
with torch.no_grad():
outputs = vision_model.vision_model(pixel_values=image_tensors)
img_embedding = outputs.last_hidden_state[:, 1:, :].squeeze()
return img_embedding
def count_vision_proj(self, tokens, h, vision_tensors=None, seqlen=512):
def find_indices(tokens, image_ids):
image_ids_tensor = torch.tensor(image_ids).to(tokens.device)
len_image_ids = len(image_ids)
if len_image_ids > tokens.size(1):
return None
tokens_view = tokens.unfold(1, len_image_ids, 1)
matches = (tokens_view == image_ids_tensor).all(dim=2)
return {
batch_idx: [(idx.item(), idx.item() + len_image_ids - 1) for idx in
matches[batch_idx].nonzero(as_tuple=True)[0]]
for batch_idx in range(tokens.size(0)) if matches[batch_idx].any()
} or None
image_indices = find_indices(tokens, self.params.image_ids)
if vision_tensors is not None and image_indices:
vision_proj = self.vision_proj(vision_tensors)
if len(vision_proj.shape) == 3:
vision_proj = vision_proj.unsqueeze(1)
new_h = []
for i in range(h.size(0)):
if i in image_indices:
h_i = h[i]
img_idx = 0
for start_idx, end_idx in image_indices[i]:
if img_idx < vision_proj.size(1):
h_i = torch.cat((h_i[:start_idx], vision_proj[i][img_idx], h_i[end_idx + 1:]), dim=0)[
:seqlen]
img_idx += 1
new_h.append(h_i)
else:
new_h.append(h[i])
return torch.stack(new_h, dim=0)
return h
def forward(self,
input_ids: Optional[torch.Tensor] = None,
past_key_values: Optional[List[Tuple[torch.Tensor, torch.Tensor]]] = None,
use_cache: bool = False,
**args):
start_pos = args.get('start_pos', 0)
pixel_tensors = args.get('pixel_tensors', None)
h = self.tok_embeddings(input_ids)
if pixel_tensors is not None and start_pos == 0:
if len(pixel_tensors.shape) == 6:
pixel_tensors = pixel_tensors.squeeze(2)
bs, num, c, im_h, im_w = pixel_tensors.shape
stack_dim = 1 if bs > 1 else 0
vision_tensors = torch.stack([
MiniMindVLM.get_image_embeddings(pixel_tensors[:, i, :, :, :], self.vision_encoder)
for i in range(num)
], dim=stack_dim)
h = self.count_vision_proj(tokens=input_ids, h=h, vision_tensors=vision_tensors, seqlen=input_ids.shape[1])
pos_cis = self.pos_cis[start_pos:start_pos + input_ids.shape[1]]
past_kvs = []
for l, layer in enumerate(self.layers):
h, past_kv = layer(
h, pos_cis,
past_key_value=past_key_values[l] if past_key_values else None,
use_cache=use_cache
)
past_kvs.append(past_kv)
logits = self.output(self.norm(h))
aux_loss = sum(l.feed_forward.aux_loss for l in self.layers if isinstance(l.feed_forward, MOEFeedForward))
self.OUT.__setitem__('logits', logits)
self.OUT.__setitem__('aux_loss', aux_loss)
self.OUT.__setitem__('past_key_values', past_kvs)
return self.OUT

11
model/vision_model/README.md Normal file
View File

@ -0,0 +1,11 @@
需要把`clip-vit-base-patch16`模型下载到此目录下
```text
git clone https://huggingface.co/openai/clip-vit-base-patch16
```
or
```text
git clone https://www.modelscope.cn/openai-mirror/clip-vit-base-patch16
```

72
model/vision_utils.py
View File

@ -1,72 +0,0 @@
import warnings
from transformers import CLIPProcessor, CLIPModel, SiglipProcessor, SiglipModel
from PIL import Image
import requests
import torch
import transformers
warnings.filterwarnings('ignore')
def get_vision_model(encoder_type):
# 加载预训练的CLIP模型和处理器
if encoder_type == "clip":
model_path = "./model/clip_model/clip-vit-base-patch32"
model = CLIPModel.from_pretrained(model_path)
processor = CLIPProcessor.from_pretrained(model_path)
else:
model_path = "./model/siglip_model/siglip-vit-base-patch16"
model = SiglipModel.from_pretrained(model_path)
processor = SiglipProcessor.from_pretrained(model_path)
return (model, processor)
def get_img_process(image, processor):
# 将图像调整为224*224大小
image = image.resize((224, 224))
if image.mode in ['RGBA', 'LA']: # 处理有透明通道的图像
image = image.convert('RGB')
# 使用CLIPProcessor处理每个patch
# inputs = processor(images=image, return_tensors="pt", clean_up_tokenization_spaces=False)
inputs = processor(images=image, return_tensors="pt")
return inputs
def get_img_embedding(batch_encoding, vision_model):
embeddings = []
def hook_fn(module, input, output):
# 将特征添加到 embeddings 列表中
embeddings.append(output.last_hidden_state)
# 从 BatchEncoding 中提取图像张量
if (isinstance(batch_encoding, transformers.tokenization_utils_base.BatchEncoding)
or isinstance(batch_encoding, transformers.feature_extraction_utils.BatchFeature)):
image_tensor = batch_encoding['pixel_values']
else:
image_tensor = batch_encoding # torch.Size([32, 4, 3, 224, 224])
# 如果图像张量的形状是5维，则无需添加额外维度
if len(image_tensor.shape) == 4:
image_tensor = image_tensor.unsqueeze(0) # 添加批次维度
# 获取批次大小
batch_size = image_tensor.size(0)
with torch.no_grad():
# 注册 hook 到模型的目标层（例如 vision_model 的倒数第二层）
layer = vision_model.vision_model.encoder
hook = layer.register_forward_hook(hook_fn)
for i in range(batch_size):
# 取出当前批次中的单个图像
single_image = image_tensor[i] # 添加批次维度
# 调用 get_image_features 来获取图像特征
_ = vision_model.get_image_features(single_image)
# 取消 hook
hook.remove()
# 拼接所有特征向量成为一个张量
all_embeddings = torch.stack(embeddings, dim=0).squeeze()
# torch.Size([32, 4, 50, 768]) or torch.Size([32, 2, 196, 768])
return all_embeddings

13
requirements.txt
View File

@ -1,4 +1,4 @@
datasets==2.16.1 datasets==2.21.0
datasketch==1.6.4 datasketch==1.6.4
Flask==3.0.3 Flask==3.0.3
Flask_Cors==4.0.0 Flask_Cors==4.0.0
@ -9,7 +9,7 @@ matplotlib==3.5.1
ngrok==1.4.0 ngrok==1.4.0
nltk==3.8 nltk==3.8
numpy==1.26.4 numpy==1.26.4
openai==1.42.0 openai==1.59.6
pandas==1.5.3 pandas==1.5.3
peft==0.7.1 peft==0.7.1
psutil==5.9.8 psutil==5.9.8
@ -19,12 +19,13 @@ scikit_learn==1.5.1
sentence_transformers==2.3.1 sentence_transformers==2.3.1
simhash==2.1.2 simhash==2.1.2
tiktoken==0.5.1 tiktoken==0.5.1
torch==2.1.2 transformers==4.48.0
torchvision==0.16.2
transformers==4.44.0
jinja2==3.1.2 jinja2==3.1.2
jsonlines==4.0.0 jsonlines==4.0.0
trl==0.8.6 trl==0.13.0
ujson==5.1.0 ujson==5.1.0
wandb==0.18.3 wandb==0.18.3
streamlit==1.30.0
torch==2.2.2
torchvision==0.17.2
gradio==5.0.0b5 gradio==5.0.0b5

165
1-pretrain_vlm.py → train_pretrain_vlm.py
View File

@ -12,59 +12,48 @@ import torch.nn.functional as F
import torch.distributed as dist import torch.distributed as dist
from contextlib import nullcontext from contextlib import nullcontext
from torch import optim from torch import optim, nn
from torch.nn.parallel import DistributedDataParallel from torch.nn.parallel import DistributedDataParallel
from torch.utils.data import DataLoader, DistributedSampler from torch.utils.data import DataLoader, DistributedSampler
from transformers import AutoTokenizer, AutoModel from transformers import AutoTokenizer, AutoModel
from model.model import Transformer from model.model_vlm import MiniMindVLM
from model.LMConfig import LMConfig from model.VLMConfig import VLMConfig
from model.dataset import PretrainDataset from model.dataset import VLMDataset
from model.vision_utils import get_vision_model, get_img_embedding
warnings.filterwarnings('ignore') warnings.filterwarnings('ignore')
def count_parameters(model):
return sum(p.numel() for p in model.parameters() if p.requires_grad)
def Logger(content): def Logger(content):
if not ddp or dist.get_rank() == 0: if not ddp or dist.get_rank() == 0:
print(content) print(content)
def get_lr(it, all): def get_lr(current_step, total_steps, lr):
warmup_iters = args.warmup_iters return lr / 10 + 0.5 * lr * (1 + math.cos(math.pi * current_step / total_steps))
lr_decay_iters = all
min_lr = args.learning_rate / 10
if it < warmup_iters:
return args.learning_rate * it / warmup_iters
if it > lr_decay_iters:
return min_lr
decay_ratio = (it - warmup_iters) / (lr_decay_iters - warmup_iters)
assert 0 <= decay_ratio <= 1
coeff = 0.5 * (1.0 + math.cos(math.pi * decay_ratio))
return min_lr + coeff * (args.learning_rate - min_lr)
def train_epoch(epoch, wandb): def train_epoch(epoch, wandb):
loss_fct = nn.CrossEntropyLoss(reduction='none')
start_time = time.time() start_time = time.time()
for step, (X, Y, loss_mask, image_process) in enumerate(train_loader): for step, (X, Y, loss_mask, pixel_tensors) in enumerate(train_loader):
X = X.to(args.device) X = X.to(args.device)
Y = Y.to(args.device) Y = Y.to(args.device)
loss_mask = loss_mask.to(args.device) loss_mask = loss_mask.to(args.device)
image_process = image_process.to(args.device) pixel_tensors = pixel_tensors.to(args.device)
image_encoders = get_img_embedding(image_process, vision_model) lr = get_lr(epoch * iter_per_epoch + step, args.epochs * iter_per_epoch, args.learning_rate)
lr = get_lr(epoch * iter_per_epoch + step, args.epochs * iter_per_epoch)
for param_group in optimizer.param_groups: for param_group in optimizer.param_groups:
param_group['lr'] = lr param_group['lr'] = lr
with ctx: with ctx:
logits = model(X, Y, image_encoders=image_encoders).logits res = model(X, pixel_tensors=pixel_tensors)
loss = F.cross_entropy(logits.view(-1, logits.size(-1)), Y.view(-1), ignore_index=0, reduction='none') loss = loss_fct(
loss_mask = loss_mask.view(-1) res.logits.view(-1, res.logits.size(-1)),
loss = torch.sum(loss * loss_mask) / loss_mask.sum() Y.view(-1)
).view(Y.size())
loss = (loss * loss_mask).sum() / loss_mask.sum()
loss += res.aux_loss
loss = loss / args.accumulation_steps
scaler.scale(loss).backward() scaler.scale(loss).backward()
@ -81,7 +70,7 @@ def train_epoch(epoch, wandb):
spend_time = time.time() - start_time spend_time = time.time() - start_time
Logger( Logger(
'Epoch:[{}/{}]({}/{}) loss:{:.3f} lr:{:.7f} epoch_Time:{}min:'.format( 'Epoch:[{}/{}]({}/{}) loss:{:.3f} lr:{:.7f} epoch_Time:{}min:'.format(
epoch, epoch + 1,
args.epochs, args.epochs,
step, step,
iter_per_epoch, iter_per_epoch,
@ -96,41 +85,43 @@ def train_epoch(epoch, wandb):
if (step + 1) % args.save_interval == 0 and (not ddp or dist.get_rank() == 0): if (step + 1) % args.save_interval == 0 and (not ddp or dist.get_rank() == 0):
model.eval() model.eval()
moe_path = '_moe' if lm_config.use_moe else '' moe_path = '_moe' if model_config.use_moe else ''
ckp = f'{args.save_dir}/{lm_config.dim}{moe_path}_vlm_pretrain.pth' ckp = f'{args.save_dir}/pretrain_vlm_{model_config.dim}{moe_path}.pth'
if isinstance(model, torch.nn.parallel.DistributedDataParallel): if isinstance(model, torch.nn.parallel.DistributedDataParallel):
state_dict = model.module.state_dict() state_dict = model.module.state_dict()
else: else:
state_dict = model.state_dict() state_dict = model.state_dict()
clean_state_dict = {
torch.save(state_dict, ckp) key: value for key, value in state_dict.items() if not key.startswith('vision_encoder.')
}
torch.save(clean_state_dict, ckp)
model.train() model.train()
def init_model(lm_config): def init_model(model_config: VLMConfig):
tokenizer = AutoTokenizer.from_pretrained('./model/minimind_tokenizer') tokenizer = AutoTokenizer.from_pretrained('./model/minimind_tokenizer')
moe_path = '_moe' if lm_config.use_moe else '' moe_path = '_moe' if model_config.use_moe else ''
ckp = f'./out/{lm_config.dim}{moe_path}_llm.pth' # 加载纯语言模型权重
ckp = f'./out/lm_{model_config.dim}{moe_path}.pth'
model = Transformer(lm_config) model = MiniMindVLM(model_config)
state_dict = torch.load(ckp, map_location=args.device) state_dict = torch.load(ckp, map_location=args.device)
# 处理不需要的前缀
unwanted_prefix = '_orig_mod.'
for k, v in list(state_dict.items()):
if k.startswith(unwanted_prefix):
state_dict[k[len(unwanted_prefix):]] = state_dict.pop(k)
# 加载到模型中
model.load_state_dict(state_dict, strict=False) model.load_state_dict(state_dict, strict=False)
model = model.to(args.device)
print(f'模型可学习参数: {count_parameters(model) / 1e6} 百万 = {count_parameters(model) / 1e9} B (Billion)') # 冻结除 vision_proj 外的所有参数
for name, param in model.named_parameters():
if 'vision_proj' not in name:
param.requires_grad = False
# 可训练
if hasattr(model, "layers"):
last_two_layers = model.layers[-1:]
for layer in last_two_layers:
for param in layer.parameters():
param.requires_grad = True
(vision_model, preprocess) = get_vision_model(args.visual_encoder) Logger(f'VLM可训练参数量：{sum(p.numel() for p in model.parameters() if p.requires_grad) / 1e6:.3f} 百万')
vision_model = vision_model.to(args.device)
return model, tokenizer, (vision_model, preprocess) _, preprocess = MiniMindVLM.get_vision_model()
return model.to(args.device), tokenizer, preprocess
def init_distributed_mode(): def init_distributed_mode():
@ -147,35 +138,32 @@ def init_distributed_mode():
if __name__ == "__main__": if __name__ == "__main__":
parser = argparse.ArgumentParser(description="MiniMind-V Pretrain") parser = argparse.ArgumentParser(description="MiniMind-V Pretrain")
parser.add_argument("--out_dir", type=str, default="out", help="Output directory") parser.add_argument("--out_dir", type=str, default="out")
parser.add_argument("--epochs", type=int, default=19, help="Number of epochs") parser.add_argument("--epochs", type=int, default=4)
parser.add_argument("--batch_size", type=int, default=32, help="Batch size") parser.add_argument("--batch_size", type=int, default=16)
parser.add_argument("--learning_rate", type=float, default=4e-4, help="Learning rate") parser.add_argument("--learning_rate", type=float, default=4e-4)
parser.add_argument("--device", type=str, default="cuda:0" if torch.cuda.is_available() else "cpu", parser.add_argument("--device", type=str, default="cuda:0" if torch.cuda.is_available() else "cpu")
help="Device to use") parser.add_argument("--dtype", type=str, default="bfloat16")
parser.add_argument("--dtype", type=str, default="bfloat16", help="Data type") parser.add_argument("--use_wandb", default=False, action="store_true")
parser.add_argument("--use_wandb", default=False, action="store_true", help="Use Weights & Biases") parser.add_argument("--wandb_project", type=str, default="MiniMind-V")
parser.add_argument("--wandb_project", type=str, default="MiniMind-V", help="Weights & Biases project name") parser.add_argument("--num_workers", type=int, default=8)
parser.add_argument("--num_workers", type=int, default=8, help="Number of workers for data loading") parser.add_argument("--data_path", type=str, default="./dataset/pretrain_vlm_data.jsonl")
parser.add_argument("--data_path", type=str, default="./dataset/LLaVA-Pretrain/chat-translated.json", parser.add_argument("--images_path", type=str, default="./dataset/pretrain_images")
help="Path to training data") parser.add_argument("--ddp", action="store_true")
parser.add_argument("--ddp", action="store_true", help="Use DistributedDataParallel") parser.add_argument("--accumulation_steps", type=int, default=1)
parser.add_argument("--accumulation_steps", type=int, default=1, help="Gradient accumulation steps") parser.add_argument("--grad_clip", type=float, default=1.0)
parser.add_argument("--grad_clip", type=float, default=1.0, help="Gradient clipping threshold") parser.add_argument("--warmup_iters", type=int, default=0)
parser.add_argument("--warmup_iters", type=int, default=0, help="Number of warmup iterations") parser.add_argument("--log_interval", type=int, default=100)
parser.add_argument("--log_interval", type=int, default=10, help="Logging interval") parser.add_argument("--save_interval", type=int, default=100)
parser.add_argument("--save_interval", type=int, default=100, help="Model saving interval") parser.add_argument('--local_rank', type=int, default=-1)
parser.add_argument('--local_rank', type=int, default=-1, help='local rank for distributed training') parser.add_argument('--dim', default=512, type=int)
parser.add_argument('--visual_encoder', type=str, default="clip", help='type of visual endcoder') parser.add_argument('--n_layers', default=8, type=int)
parser.add_argument('--max_seq_len', default=640, type=int)
parser.add_argument('--use_moe', default=False, type=bool)
args = parser.parse_args() args = parser.parse_args()
if args.visual_encoder == "clip": model_config = VLMConfig(dim=args.dim, n_layers=args.n_layers, max_seq_len=args.max_seq_len)
lm_config = LMConfig() max_seq_len = model_config.max_seq_len
else:
lm_config = LMConfig(image_special_token='<' * 98 + '>' * 98, image_ids=[30] * 98 + [32] * 98)
max_seq_len = lm_config.max_seq_len
args.save_dir = os.path.join(args.out_dir) args.save_dir = os.path.join(args.out_dir)
os.makedirs(args.save_dir, exist_ok=True) os.makedirs(args.save_dir, exist_ok=True)
os.makedirs(args.out_dir, exist_ok=True) os.makedirs(args.out_dir, exist_ok=True)
@ -199,11 +187,10 @@ if __name__ == "__main__":
else: else:
wandb = None wandb = None
model, tokenizer, (vision_model, preprocess) = init_model(lm_config) model, tokenizer, preprocess = init_model(model_config)
use_version = 0 train_ds = VLMDataset(args.data_path, args.images_path, tokenizer, preprocess=preprocess,
train_ds = PretrainDataset(args.data_path, tokenizer, vision_model=(vision_model, preprocess), image_special_token=model_config.image_special_token,
image_special_token=lm_config.image_special_token,
max_length=max_seq_len) max_length=max_seq_len)
train_sampler = DistributedSampler(train_ds) if ddp else None train_sampler = DistributedSampler(train_ds) if ddp else None
train_loader = DataLoader( train_loader = DataLoader(
@ -217,13 +204,7 @@ if __name__ == "__main__":
) )
scaler = torch.cuda.amp.GradScaler(enabled=(args.dtype in ['float16', 'bfloat16'])) scaler = torch.cuda.amp.GradScaler(enabled=(args.dtype in ['float16', 'bfloat16']))
optimizer = optim.Adam(model.parameters(), lr=args.learning_rate) optimizer = optim.AdamW(filter(lambda p: p.requires_grad, model.parameters()), lr=args.learning_rate)
if False and not lm_config.use_moe and platform.system() != 'Windows' and float(
torch.__version__.split('.')[0]) >= 2:
Logger("compiling the model... (takes a ~minute)")
unoptimized_model = model
model = torch.compile(model)
if ddp: if ddp:
model._ddp_params_and_buffers_to_ignore = {"pos_cis"} model._ddp_params_and_buffers_to_ignore = {"pos_cis"}

205
train_sft_vlm.py Normal file
View File

@ -0,0 +1,205 @@
import os
import platform
import argparse
import time
import math
import warnings
import json
import pandas as pd
import torch
import torch.nn.functional as F
import torch.distributed as dist
from contextlib import nullcontext
from torch import optim, nn
from torch.nn.parallel import DistributedDataParallel
from torch.utils.data import DataLoader, DistributedSampler
from transformers import AutoTokenizer, AutoModel
from model.model_vlm import MiniMindVLM
from model.VLMConfig import VLMConfig
from model.dataset import *
warnings.filterwarnings('ignore')
def Logger(content):
if not ddp or dist.get_rank() == 0:
print(content)
def get_lr(current_step, total_steps, lr):
return lr / 10 + 0.5 * lr * (1 + math.cos(math.pi * current_step / total_steps))
def train_epoch(epoch, wandb):
loss_fct = nn.CrossEntropyLoss(reduction='none')
start_time = time.time()
for step, (X, Y, loss_mask, pixel_tensors) in enumerate(train_loader):
X = X.to(args.device)
Y = Y.to(args.device)
loss_mask = loss_mask.to(args.device)
pixel_tensors = pixel_tensors.to(args.device)
lr = get_lr(epoch * iter_per_epoch + step, args.epochs * iter_per_epoch, args.learning_rate)
for param_group in optimizer.param_groups:
param_group['lr'] = lr
with ctx:
res = model(X, pixel_tensors=pixel_tensors)
loss = loss_fct(
res.logits.view(-1, res.logits.size(-1)),
Y.view(-1)
).view(Y.size())
loss = (loss * loss_mask).sum() / loss_mask.sum()
loss += res.aux_loss
loss = loss / args.accumulation_steps
scaler.scale(loss).backward()
if (step + 1) % args.accumulation_steps == 0:
scaler.unscale_(optimizer)
torch.nn.utils.clip_grad_norm_(model.parameters(), args.grad_clip)
scaler.step(optimizer)
scaler.update()
optimizer.zero_grad(set_to_none=True)
if step % args.log_interval == 0:
spend_time = time.time() - start_time
Logger(
'Epoch:[{}/{}]({}/{}) loss:{:.3f} lr:{:.7f} epoch_Time:{}min:'.format(
epoch + 1,
args.epochs,
step,
iter_per_epoch,
loss.item(),
optimizer.param_groups[-1]['lr'],
spend_time / (step + 1) * iter_per_epoch // 60 - spend_time // 60))
if (wandb is not None) and (not ddp or dist.get_rank() == 0):
wandb.log({"loss": loss,
"lr": optimizer.param_groups[-1]['lr'],
"epoch_Time": spend_time / (step + 1) * iter_per_epoch // 60 - spend_time // 60})
if (step + 1) % args.save_interval == 0 and (not ddp or dist.get_rank() == 0):
model.eval()
moe_path = '_moe' if model_config.use_moe else ''
ckp = f'{args.save_dir}/sft_vlm_{model_config.dim}{moe_path}.pth'
if isinstance(model, torch.nn.parallel.DistributedDataParallel):
state_dict = model.module.state_dict()
else:
state_dict = model.state_dict()
clean_state_dict = {
key: value for key, value in state_dict.items() if not key.startswith('vision_encoder.')
}
torch.save(clean_state_dict, ckp)
model.train()
def init_model(model_config: VLMConfig):
tokenizer = AutoTokenizer.from_pretrained('./model/minimind_tokenizer')
moe_path = '_moe' if model_config.use_moe else ''
ckp = f'./out/pretrain_vlm_{model_config.dim}{moe_path}.pth'
model = MiniMindVLM(model_config)
state_dict = torch.load(ckp, map_location=args.device)
model.load_state_dict(state_dict, strict=False)
model = model.to(args.device)
Logger(f'VLM可训练参数量：{sum(p.numel() for p in model.parameters() if p.requires_grad) / 1e6:.3f} 百万')
_, preprocess = MiniMindVLM.get_vision_model()
return model.to(args.device), tokenizer, preprocess
def init_distributed_mode():
if not ddp: return
global ddp_local_rank, DEVICE
dist.init_process_group(backend="nccl")
ddp_rank = int(os.environ["RANK"])
ddp_local_rank = int(os.environ["LOCAL_RANK"])
ddp_world_size = int(os.environ["WORLD_SIZE"])
DEVICE = f"cuda:{ddp_local_rank}"
torch.cuda.set_device(DEVICE)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="MiniMind-V Pretrain")
parser.add_argument("--out_dir", type=str, default="out")
parser.add_argument("--epochs", type=int, default=6)
parser.add_argument("--batch_size", type=int, default=8)
parser.add_argument("--learning_rate", type=float, default=1e-6)
parser.add_argument("--device", type=str, default="cuda:0" if torch.cuda.is_available() else "cpu")
parser.add_argument("--dtype", type=str, default="bfloat16")
parser.add_argument("--use_wandb", default=False, action="store_true")
parser.add_argument("--wandb_project", type=str, default="MiniMind-V")
parser.add_argument("--num_workers", type=int, default=8)
parser.add_argument("--data_path", type=str, default="./dataset/sft_vlm_data.jsonl")
parser.add_argument("--images_path", type=str, default="./dataset/sft_images")
parser.add_argument("--ddp", action="store_true")
parser.add_argument("--accumulation_steps", type=int, default=1)
parser.add_argument("--grad_clip", type=float, default=1.0)
parser.add_argument("--warmup_iters", type=int, default=0)
parser.add_argument("--log_interval", type=int, default=10)
parser.add_argument("--save_interval", type=int, default=10)
parser.add_argument('--local_rank', type=int, default=-1)
parser.add_argument('--dim', default=512, type=int)
parser.add_argument('--n_layers', default=8, type=int)
parser.add_argument('--max_seq_len', default=1536, type=int)
parser.add_argument('--use_moe', default=False, type=bool)
args = parser.parse_args()
model_config = VLMConfig(dim=args.dim, n_layers=args.n_layers, max_seq_len=args.max_seq_len)
max_seq_len = model_config.max_seq_len
args.save_dir = os.path.join(args.out_dir)
os.makedirs(args.save_dir, exist_ok=True)
os.makedirs(args.out_dir, exist_ok=True)
tokens_per_iter = args.batch_size * max_seq_len
torch.manual_seed(1337)
device_type = "cuda" if "cuda" in args.device else "cpu"
args.wandb_run_name = f"MiniMind-V SFT-Epoch-{args.epochs}-BatchSize-{args.batch_size}-LearningRate-{args.learning_rate}"
ctx = nullcontext() if device_type == "cpu" else torch.cuda.amp.autocast()
ddp = int(os.environ.get("RANK", -1)) != -1 # is this a ddp run?
ddp_local_rank, DEVICE = 0, "cuda:0"
if ddp:
init_distributed_mode()
args.device = torch.device(DEVICE)
if args.use_wandb and (not ddp or ddp_local_rank == 0):
import wandb
wandb.init(project=args.wandb_project, name=args.wandb_run_name)
else:
wandb = None
model, tokenizer, preprocess = init_model(model_config)
train_ds = VLMDataset(args.data_path, args.images_path, tokenizer, preprocess=preprocess,
image_special_token=model_config.image_special_token,
max_length=max_seq_len)
train_sampler = DistributedSampler(train_ds) if ddp else None
train_loader = DataLoader(
train_ds,
batch_size=args.batch_size,
pin_memory=True,
drop_last=False,
shuffle=False,
num_workers=args.num_workers,
sampler=train_sampler
)
scaler = torch.cuda.amp.GradScaler(enabled=(args.dtype in ['float16', 'bfloat16']))
optimizer = optim.AdamW(model.parameters(), lr=args.learning_rate)
if ddp:
model._ddp_params_and_buffers_to_ignore = {"pos_cis"}
model = DistributedDataParallel(model, device_ids=[ddp_local_rank])
iter_per_epoch = len(train_loader)
for epoch in range(args.epochs):
train_epoch(epoch, wandb)

176
web_demo_vlm.py Normal file
View File

@ -0,0 +1,176 @@
import argparse
import torch
import warnings
import gradio as gr
from PIL import Image
from transformers import AutoTokenizer, AutoModelForCausalLM
from model.model_vlm import MiniMindVLM
from model.VLMConfig import VLMConfig
from transformers import logging as hf_logging
hf_logging.set_verbosity_error()
warnings.filterwarnings('ignore')
def init_model(lm_config):
tokenizer = AutoTokenizer.from_pretrained('./model/minimind_tokenizer')
if args.load == 0:
moe_path = '_moe' if lm_config.use_moe else ''
ckp = f'./out/sft_vlm_{lm_config.dim}{moe_path}.pth'
model = MiniMindVLM(lm_config)
state_dict = torch.load(ckp, map_location=args.device)
model.load_state_dict({k: v for k, v in state_dict.items() if 'mask' not in k}, strict=False)
else:
transformers_model_path = './MiniMind2-V'
tokenizer = AutoTokenizer.from_pretrained(transformers_model_path)
model = AutoModelForCausalLM.from_pretrained(transformers_model_path, trust_remote_code=True)
print(f'VLM参数量：{sum(p.numel() for p in model.parameters() if p.requires_grad) / 1e6:.3f} 百万')
vision_model, preprocess = MiniMindVLM.get_vision_model()
return model.eval().to(args.device), tokenizer, vision_model.to(args.device), preprocess
def chat(prompt, current_image_path):
global temperature, top_p
image = Image.open(current_image_path).convert('RGB')
pixel_tensors = MiniMindVLM.image2tensor(image, preprocess).to(model.device).unsqueeze(0)
prompt = f'{lm_config.image_special_token}\n{prompt}'
messages = [{"role": "user", "content": prompt}]
new_prompt = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)[-args.max_seq_len + 1:]
with torch.no_grad():
x = torch.tensor(tokenizer(new_prompt)['input_ids'], device=model.device).unsqueeze(0)
outputs = model.generate(
x,
eos_token_id=tokenizer.eos_token_id,
max_new_tokens=args.max_seq_len,
temperature=temperature,
top_p=top_p,
stream=True,
pad_token_id=tokenizer.pad_token_id,
pixel_tensors=pixel_tensors
)
try:
if not args.stream:
print(tokenizer.decode(outputs.squeeze()[x.shape[1]:].tolist(), skip_special_tokens=True), end='')
else:
history_idx = 0
for y in outputs:
answer = tokenizer.decode(y[0].tolist(), skip_special_tokens=True)
if (answer and answer[-1] == '<EFBFBD>') or not answer:
continue
yield answer[history_idx:]
history_idx = len(answer)
except StopIteration:
print("No answer")
print('\n')
def launch_gradio_server(server_name="0.0.0.0", server_port=7788):
global temperature, top_p
temperature = args.temperature
top_p = args.top_p
with gr.Blocks() as demo:
gr.HTML(f"""
<div style="text-align: center; margin-bottom: 1rem; display: flex; align-items: center; justify-content: center;">
<img src="https://www.modelscope.cn/api/v1/studio/gongjy/MiniMind/repo?Revision=master&FilePath=images%2Flogo2.png&View=true"
style="height: 60px;">
<span style="margin: 0 0 0 1rem;font-size:40px;font-style: italic;font-weight:bold;">Hi, I'm MiniMind2-V</span>
</div>
""")
with gr.Row():
with gr.Column(scale=3):
def get_current_image_path(image):
global current_image_path
if image is None:
current_image_path = ''
return
current_image_path = image
return current_image_path
with gr.Blocks() as iface:
with gr.Row():
image_input = gr.Image(type="filepath", label="选择图片", height=650)
image_input.change(fn=get_current_image_path, inputs=image_input)
def update_parameters(temperature_, top_p_):
global temperature, top_p
temperature = float(temperature_)
top_p = float(top_p_)
return temperature, top_p
with gr.Blocks() as iface_param:
with gr.Row():
temperature_slider = gr.Slider(label="Temperature", minimum=0.5, maximum=1.1, value=0.65)
top_p_slider = gr.Slider(label="Top-P", minimum=0.7, maximum=0.95, value=0.85)
temperature_slider.change(fn=update_parameters, inputs=[temperature_slider, top_p_slider])
top_p_slider.change(fn=update_parameters, inputs=[temperature_slider, top_p_slider])
with gr.Column(scale=6):
def chat_with_vlm(message, history):
if not message:
yield history + [("错误", "错误：提问不能为空。")]
return
if not current_image_path:
yield history + [("错误", "错误：图片不能为空。")]
return
image_html = f'<img src="gradio_api/file={current_image_path}" alt="Image" style="width:100px;height:auto;">'
res_generator = chat(message, current_image_path)
response = ''
for res in res_generator:
response += res
yield history + [(f"{image_html} {message}", response)]
chatbot = gr.Chatbot(label="MiniMind-Vision", height=680)
with gr.Row():
with gr.Column(scale=8):
message_input = gr.Textbox(
placeholder="请输入你的问题...",
show_label=False,
container=False
)
with gr.Column(scale=2, min_width=50):
submit_button = gr.Button("发送")
submit_button.click(
fn=chat_with_vlm,
inputs=[message_input, chatbot],
outputs=chatbot
)
# # 添加示例问题
# gr.Examples(
# examples=["描述一下这个图片的内容。", "画面里面有什么？", "画面里的天气怎么样？"],
# inputs=message_input)
demo.launch(server_name=server_name, server_port=server_port)
if __name__ == '__main__':
parser = argparse.ArgumentParser(description="Chat with MiniMind")
parser.add_argument('--lora_name', default='None', type=str)
parser.add_argument('--out_dir', default='out', type=str)
parser.add_argument('--temperature', default=0.65, type=float)
parser.add_argument('--top_p', default=0.85, type=float)
parser.add_argument('--device', default='cuda' if torch.cuda.is_available() else 'cpu', type=str)
parser.add_argument('--dim', default=768, type=int)
parser.add_argument('--n_layers', default=16, type=int)
parser.add_argument('--max_seq_len', default=8192, type=int)
parser.add_argument('--use_moe', default=False, type=bool)
parser.add_argument('--stream', default=True, type=bool)
parser.add_argument('--load', default=0, type=int, help="0: 原生torch权重，1: transformers加载")
args = parser.parse_args()
lm_config = VLMConfig(dim=args.dim, n_layers=args.n_layers, max_seq_len=args.max_seq_len, use_moe=args.use_moe)
model, tokenizer, vision_model, preprocess = init_model(lm_config)
launch_gradio_server(server_name="0.0.0.0", server_port=8888)

209
web_server.py
View File

@ -1,209 +0,0 @@
import gradio as gr
import os
import random
import time
import numpy as np
import torch
import warnings
from PIL import Image
from transformers import AutoTokenizer, AutoModelForCausalLM
from model.model import Transformer
from model.LMConfig import LMConfig
from model.vision_utils import get_vision_model, get_img_process, get_img_embedding
warnings.filterwarnings('ignore')
def count_parameters(model):
return sum(p.numel() for p in model.parameters() if p.requires_grad)
def init_model(lm_config):
tokenizer = AutoTokenizer.from_pretrained('./model/minimind_tokenizer')
model_from = 2 # 1从权重，2用transformers
if model_from == 1:
moe_path = '_moe' if lm_config.use_moe else ''
ckp = f'./out/{lm_config.dim}{moe_path}_vlm_sft.pth'
model = Transformer(lm_config)
state_dict = torch.load(ckp, map_location=device)
# 处理不需要的前缀
unwanted_prefix = '_orig_mod.'
for k, v in list(state_dict.items()):
if k.startswith(unwanted_prefix):
state_dict[k[len(unwanted_prefix):]] = state_dict.pop(k)
for k, v in list(state_dict.items()):
if 'mask' in k:
del state_dict[k]
# 加载到模型中
model.load_state_dict(state_dict, strict=False)
else:
model = AutoModelForCausalLM.from_pretrained('minimind-v-v1',
trust_remote_code=True)
model = model.to(device)
print(f'模型参数: {count_parameters(model) / 1e6} 百万 = {count_parameters(model) / 1e9} B (Billion)')
(vision_model, preprocess) = get_vision_model()
vision_model = vision_model.to(device)
return model, tokenizer, (vision_model, preprocess)
def chat(prompt, current_image_path):
# 打开图像并转换为RGB格式
image = Image.open(current_image_path).convert('RGB')
image_process = get_img_process(image, preprocess).to(vision_model.device)
# 对图像进行编码
image_encoder = get_img_embedding(image_process, vision_model).unsqueeze(0)
prompt = f'{lm_config.image_special_token}\n{prompt}'
messages = [{"role": "user", "content": prompt}]
# print(messages)
new_prompt = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)[-(max_seq_len - 1):]
x = tokenizer(new_prompt).data['input_ids']
x = (torch.tensor(x, dtype=torch.long, device=device)[None, ...])
with torch.no_grad():
res_y = model.generate(x, tokenizer.eos_token_id, max_new_tokens=max_seq_len, temperature=temperature,
top_k=top_k, stream=stream, image_encoders=image_encoder)
# print('[A]: ', end='')
try:
y = next(res_y)
except StopIteration:
print("No answer")
return "No answer"
history_idx = 0
while y != None:
answer = tokenizer.decode(y[0].tolist())
if answer and answer[-1] == '<EFBFBD>':
try:
y = next(res_y)
except:
break
continue
# print(answer)
if not len(answer):
try:
y = next(res_y)
except:
break
continue
yield answer[history_idx:]
try:
y = next(res_y)
except:
break
history_idx = len(answer)
if not stream:
break
def launch_gradio_server(server_name="0.0.0.0", server_port=7788):
with gr.Blocks() as demo:
# # 顶部下拉菜单
# model_choice = gr.Dropdown(label="选择模型", choices=["minimind-v1-v", "minimind-v1-small-v"],
# value="minimind-v1-v")
gr.HTML(f"""
<div style="text-align: center;">
<h3>MiniMind-V ({(count_parameters(model) / 1e6):.2f} M) Test Demo 「单轮对话，无历史上下文」</h3>
</div>
""")
# 左侧
with gr.Row():
with gr.Column(scale=1):
def get_current_image_path(image):
global current_image_path
if image is None:
current_image_path = ''
return
current_image_path = image
return current_image_path
with gr.Blocks() as iface:
with gr.Row():
image_input = gr.Image(type="filepath", label="选择图片", height=650)
image_input.change(fn=get_current_image_path, inputs=image_input)
def update_parameters(temperature_, top_k_):
global temperature, top_k
temperature = temperature_
top_k = top_k_
with gr.Blocks() as iface_param:
with gr.Row():
temperature_slider = gr.Slider(label="Temperature", minimum=0, maximum=1.5, value=0.5)
top_k_slider = gr.Slider(label="Top K", minimum=1, maximum=20, value=12)
temperature_slider.change(fn=update_parameters, inputs=[temperature_slider, top_k_slider])
top_k_slider.change(fn=update_parameters, inputs=[temperature_slider, top_k_slider])
# 右侧聊天展示和输入框
with gr.Column(scale=4):
def chat_with_vlm(message, history):
if not message:
yield history + [("错误", "错误：提问不能为空。")]
return
if not current_image_path:
yield history + [("错误", "错误：图片不能为空。")]
return
image_html = f'<img src="gradio_api/file={current_image_path}" alt="Image" style="width:60px;height:auto;">'
res_generator = chat(message, current_image_path)
response = ''
for res in res_generator:
response += res
# 返回聊天历史，加上新的一轮对话
yield history + [(f"{image_html} {message}", response)]
# 创建ChatBot组件
chatbot = gr.Chatbot(label="MiniMind-V Chatbot", height=670)
# 添加输入框和按钮
with gr.Row():
with gr.Column(scale=8):
message_input = gr.Textbox(placeholder="请输入你的问题...", show_label=False, container=False)
with gr.Column(scale=1, min_width=50):
submit_button = gr.Button("发送")
# 设置按钮点击事件触发chat_with_vlm函数
submit_button.click(fn=chat_with_vlm,
inputs=[message_input, chatbot], # 将消息输入框和聊天历史传入函数
outputs=chatbot) # 更新Chatbot的输出内容
# 添加示例问题
gr.Examples(
examples=["描述一下这个图片的内容。", "画面里面有什么？", "画面里的动物在干什么？",
"画面里的天气怎么样？"],
inputs=message_input)
demo.launch(server_name=server_name, server_port=server_port)
if __name__ == '__main__':
device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
# 获取模型配置
lm_config = LMConfig()
max_seq_len = lm_config.max_seq_len
model, tokenizer, (vision_model, preprocess) = init_model(lm_config)
stream = True
current_image_path = ''
temperature = 0.5
top_k = 12
launch_gradio_server(server_name="0.0.0.0", server_port=7788)