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---
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pipeline_tag: visual-question-answering
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language:
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- en
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- zh
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datasets:
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- HaoyeZhang/RLAIF-V-Dataset
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---
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<h1>A GPT-4V Level Multimodal LLM on Your Phone</h1>
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[GitHub](https://github.com/OpenBMB/MiniCPM-V) | [Demo](https://huggingface.co/spaces/openbmb/MiniCPM-Llama3-V-2_5) | <a href="https://github.com/OpenBMB/MiniCPM-V/blob/main/docs/wechat.md" target="_blank"> WeChat</a>
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## News <!-- omit in toc -->
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#### 📌 Pinned
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* [2024.05.28] 🚀🚀🚀 MiniCPM-Llama3-V 2.5 now fully supports its feature in llama.cpp and ollama! Please pull the latest code **of our provided forks** ([llama.cpp](https://github.com/OpenBMB/llama.cpp/blob/minicpm-v2.5/examples/minicpmv/README.md), [ollama](https://github.com/OpenBMB/ollama/tree/minicpm-v2.5)). GGUF models in various sizes are available [here](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5-gguf/tree/main). We are working hard to merge PRs into official repositories. Please stay tuned! You can visit our [GitHub](https://github.com/OpenBMB/MiniCPM-V) repository for more information!
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* [2024.05.28] 💫 We now support LoRA fine-tuning for MiniCPM-Llama3-V 2.5, using only 2 V100 GPUs! See more statistics [here](https://github.com/OpenBMB/MiniCPM-V/tree/main/finetune#model-fine-tuning-memory-usage-statistics).
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* [2024.05.23] 🔍 We've released a comprehensive comparison between Phi-3-vision-128k-instruct and MiniCPM-Llama3-V 2.5, including benchmarks evaluations, multilingual capabilities, and inference efficiency 🌟📊🌍🚀. Click [here](https://github.com/OpenBMB/MiniCPM-V/blob/main/docs/compare_with_phi-3_vision.md) to view more details.
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* [2024.05.23] 🔥🔥🔥 MiniCPM-V tops GitHub Trending and HuggingFace Trending! Our demo, recommended by Hugging Face Gradio’s official account, is available [here](https://huggingface.co/spaces/openbmb/MiniCPM-Llama3-V-2_5). Come and try it out!
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<br>
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* [2024.06.03] Now, you can run MiniCPM-Llama3-V 2.5 on multiple low VRAM GPUs(12 GB or 16 GB) by distributing the model's layers across multiple GPUs. For more details, Check this [link](https://github.com/OpenBMB/MiniCPM-V/blob/main/docs/inference_on_multiple_gpus.md).
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* [2024.05.25] MiniCPM-Llama3-V 2.5 now supports streaming outputs and customized system prompts. Try it at [here](#usage)
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* [2024.05.24] We release the [MiniCPM-Llama3-V 2.5 gguf](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5-gguf), which supports [llama.cpp](https://github.com/OpenBMB/MiniCPM-V/tree/main?tab=readme-ov-file#inference-with-llamacpp) inference and provides a 6~8 token/s smooth decoding on mobile phones. Try it now!
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* [2024.05.20] We open-soure MiniCPM-Llama3-V 2.5, it has improved OCR capability and supports 30+ languages, representing the first end-side MLLM achieving GPT-4V level performance! We provide [efficient inference](#deployment-on-mobile-phone) and [simple fine-tuning](https://github.com/OpenBMB/MiniCPM-V/blob/main/finetune/readme.md). Try it now!
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## Model Summary
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**MiniCPM-Llama3-V 2.5** is the latest model in the MiniCPM-V series. The model is built on SigLip-400M and Llama3-8B-Instruct with a total of 8B parameters. It exhibits a significant performance improvement over MiniCPM-V 2.0. Notable features of MiniCPM-Llama3-V 2.5 include:
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- 🔥 **Leading Performance.**
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MiniCPM-Llama3-V 2.5 has achieved an average score of 65.1 on OpenCompass, a comprehensive evaluation over 11 popular benchmarks. **With only 8B parameters, it surpasses widely used proprietary models like GPT-4V-1106, Gemini Pro, Claude 3 and Qwen-VL-Max** and greatly outperforms other Llama 3-based MLLMs.
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- 💪 **Strong OCR Capabilities.**
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MiniCPM-Llama3-V 2.5 can process images with any aspect ratio and up to 1.8 million pixels (e.g., 1344x1344), achieving an **700+ score on OCRBench, surpassing proprietary models such as GPT-4o, GPT-4V-0409, Qwen-VL-Max and Gemini Pro**. Based on recent user feedback, MiniCPM-Llama3-V 2.5 has now enhanced full-text OCR extraction, table-to-markdown conversion, and other high-utility capabilities, and has further strengthened its instruction-following and complex reasoning abilities, enhancing multimodal interaction experiences.
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- 🏆 **Trustworthy Behavior.**
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Leveraging the latest [RLAIF-V](https://github.com/RLHF-V/RLAIF-V/) method (the newest technology in the [RLHF-V](https://github.com/RLHF-V) [CVPR'24] series), MiniCPM-Llama3-V 2.5 exhibits more trustworthy behavior. It achieves **10.3%** hallucination rate on Object HalBench, lower than GPT-4V-1106 (13.6%), achieving the best-level performance within the open-source community. [Data released](https://huggingface.co/datasets/openbmb/RLAIF-V-Dataset).
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- 🌏 **Multilingual Support.**
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Thanks to the strong multilingual capabilities of Llama 3 and the cross-lingual generalization technique from [VisCPM](https://github.com/OpenBMB/VisCPM), MiniCPM-Llama3-V 2.5 extends its bilingual (Chinese-English) multimodal capabilities to **over 30 languages including German, French, Spanish, Italian, Russian etc.** [All Supported Languages](./assets/minicpm-llama-v-2-5_languages.md).
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||||
- 🚀 **Efficient Deployment.**
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||||
MiniCPM-Llama3-V 2.5 systematically employs **model quantization, CPU optimizations, NPU optimizations and compilation optimizations**, achieving high-efficiency deployment on edge devices. For mobile phones with Qualcomm chips, we have integrated the NPU acceleration framework QNN into llama.cpp for the first time. After systematic optimization, MiniCPM-Llama3-V 2.5 has realized a **150-fold acceleration in multimodal large model end-side image encoding** and a **3-fold increase in language decoding speed**.
|
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|
||||
- 💫 **Easy Usage.**
|
||||
MiniCPM-Llama3-V 2.5 can be easily used in various ways: (1) [llama.cpp](https://github.com/OpenBMB/llama.cpp/blob/minicpm-v2.5/examples/minicpmv/README.md) and [ollama](https://github.com/OpenBMB/ollama/tree/minicpm-v2.5/examples/minicpm-v2.5) support for efficient CPU inference on local devices, (2) [GGUF](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5-gguf) format quantized models in 16 sizes, (3) efficient [LoRA](https://github.com/OpenBMB/MiniCPM-V/tree/main/finetune#lora-finetuning) fine-tuning with only 2 V100 GPUs, (4) [streaming output](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5#usage), (5) quick local WebUI demo setup with [Gradio](https://github.com/OpenBMB/MiniCPM-V/blob/main/web_demo_2.5.py) and [Streamlit](https://github.com/OpenBMB/MiniCPM-V/blob/main/web_demo_streamlit-2_5.py), and (6) interactive demos on [HuggingFace Spaces](https://huggingface.co/spaces/openbmb/MiniCPM-Llama3-V-2_5).
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### Evaluation <!-- omit in toc -->
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Results on TextVQA, DocVQA, OCRBench, OpenCompass MultiModal Avg , MME, MMBench, MMMU, MathVista, LLaVA Bench, RealWorld QA, Object HalBench.
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<div align="center">
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||||
<img src="https://cdn-uploads.huggingface.co/production/uploads/64abc4aa6cadc7aca585dddf/v2KE3wqQgM05ZW3dH2wbx.png" width="110%" />
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</div>
|
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Evaluation results of multilingual LLaVA Bench
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<div align="center">
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<img src="assets/minicpmv-llama3-v2.5/llavabench_compare.png" width="110%" />
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</div>
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### Examples <!-- omit in toc -->
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<table align="center">
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<p align="center">
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<img src="assets/minicpmv-llama3-v2.5/cases_all.png" width=95%/>
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</p>
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</table>
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We deploy MiniCPM-Llama3-V 2.5 on end devices. The demo video is the raw screen recording on a Xiaomi 14 Pro without edition.
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<table align="center">
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<p align="center">
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<img src="assets/gif_cases/ticket.gif" width=40% style="display:inline-block;"/>
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<img src="assets/gif_cases/meal_plan.gif" width=40% style="display:inline-block;"/>
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</p>
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||||
</table>
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||||
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||||
<table align="center">
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<p align="center">
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<img src="assets/gif_cases/1-4.gif" width=80%/>
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</p>
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</table>
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||||
## Demo
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||||
Click here to try out the Demo of [MiniCPM-Llama3-V 2.5](https://huggingface.co/spaces/openbmb/MiniCPM-Llama3-V-2_5).
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## Deployment on Mobile Phone
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Coming soon.
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## Usage
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Inference using Huggingface transformers on NVIDIA GPUs. Requirements tested on python 3.10：
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```
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Pillow==10.1.0
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torch==2.1.2
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torchvision==0.16.2
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transformers==4.40.0
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sentencepiece==0.1.99
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```
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```python
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# test.py
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import torch
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from PIL import Image
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from transformers import AutoModel, AutoTokenizer
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model = AutoModel.from_pretrained('openbmb/MiniCPM-Llama3-V-2_5', trust_remote_code=True, torch_dtype=torch.float16)
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model = model.to(device='cuda')
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tokenizer = AutoTokenizer.from_pretrained('openbmb/MiniCPM-Llama3-V-2_5', trust_remote_code=True)
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model.eval()
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image = Image.open('xx.jpg').convert('RGB')
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question = 'What is in the image?'
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msgs = [{'role': 'user', 'content': question}]
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res = model.chat(
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image=image,
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msgs=msgs,
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tokenizer=tokenizer,
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sampling=True, # if sampling=False, beam_search will be used by default
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temperature=0.7,
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# system_prompt='' # pass system_prompt if needed
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)
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print(res)
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## if you want to use streaming, please make sure sampling=True and stream=True
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## the model.chat will return a generator
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res = model.chat(
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image=image,
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msgs=msgs,
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tokenizer=tokenizer,
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sampling=True,
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temperature=0.7,
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stream=True
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)
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generated_text = ""
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for new_text in res:
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generated_text += new_text
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print(new_text, flush=True, end='')
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```
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Please look at [GitHub](https://github.com/OpenBMB/MiniCPM-V) for more detail about usage.
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## Inference with llama.cpp<a id="llamacpp"></a>
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MiniCPM-Llama3-V 2.5 can run with llama.cpp now! See our fork of [llama.cpp](https://github.com/OpenBMB/llama.cpp/tree/minicpm-v2.5/examples/minicpmv) for more detail.
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## Int4 quantized version
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Download the int4 quantized version for lower GPU memory (8GB) usage: [MiniCPM-Llama3-V-2_5-int4](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5-int4).
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## MiniCPM-V 2.0 <!-- omit in toc -->
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Please see the info about MiniCPM-V 2.0 [here](https://huggingface.co/openbmb/MiniCPM-V-2).
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## License
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#### Model License
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* The code in this repo is released under the [Apache-2.0](https://github.com/OpenBMB/MiniCPM/blob/main/LICENSE) License.
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* The usage of MiniCPM-V series model weights must strictly follow [MiniCPM Model License.md](https://github.com/OpenBMB/MiniCPM/blob/main/MiniCPM%20Model%20License.md).
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* The models and weights of MiniCPM are completely free for academic research. after filling out a ["questionnaire"](https://modelbest.feishu.cn/share/base/form/shrcnpV5ZT9EJ6xYjh3Kx0J6v8g) for registration, are also available for free commercial use.
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#### Statement
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* As an LLM, MiniCPM-Llama3-V 2.5 generates contents by learning a large mount of texts, but it cannot comprehend, express personal opinions or make value judgement. Anything generated by MiniCPM-Llama3-V 2.5 does not represent the views and positions of the model developers
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* We will not be liable for any problems arising from the use of the MinCPM-V open Source model, including but not limited to data security issues, risk of public opinion, or any risks and problems arising from the misdirection, misuse, dissemination or misuse of the model.
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## Other Multimodal Projects from Our Team
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[VisCPM](https://github.com/OpenBMB/VisCPM/tree/main) | [RLHF-V](https://github.com/RLHF-V/RLHF-V) | [LLaVA-UHD](https://github.com/thunlp/LLaVA-UHD) | [RLAIF-V](https://github.com/RLHF-V/RLAIF-V)
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## Citation
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If you find our work helpful, please consider citing the following papers
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```bib
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@article{yu2023rlhf,
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title={Rlhf-v: Towards trustworthy mllms via behavior alignment from fine-grained correctional human feedback},
|
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author={Yu, Tianyu and Yao, Yuan and Zhang, Haoye and He, Taiwen and Han, Yifeng and Cui, Ganqu and Hu, Jinyi and Liu, Zhiyuan and Zheng, Hai-Tao and Sun, Maosong and others},
|
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journal={arXiv preprint arXiv:2312.00849},
|
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year={2023}
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}
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@article{viscpm,
|
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title={Large Multilingual Models Pivot Zero-Shot Multimodal Learning across Languages},
|
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author={Jinyi Hu and Yuan Yao and Chongyi Wang and Shan Wang and Yinxu Pan and Qianyu Chen and Tianyu Yu and Hanghao Wu and Yue Zhao and Haoye Zhang and Xu Han and Yankai Lin and Jiao Xue and Dahai Li and Zhiyuan Liu and Maosong Sun},
|
||||
journal={arXiv preprint arXiv:2308.12038},
|
||||
year={2023}
|
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}
|
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@article{xu2024llava-uhd,
|
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title={{LLaVA-UHD}: an LMM Perceiving Any Aspect Ratio and High-Resolution Images},
|
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author={Xu, Ruyi and Yao, Yuan and Guo, Zonghao and Cui, Junbo and Ni, Zanlin and Ge, Chunjiang and Chua, Tat-Seng and Liu, Zhiyuan and Huang, Gao},
|
||||
journal={arXiv preprint arXiv:2403.11703},
|
||||
year={2024}
|
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}
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@article{yu2024rlaifv,
|
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title={RLAIF-V: Aligning MLLMs through Open-Source AI Feedback for Super GPT-4V Trustworthiness},
|
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author={Yu, Tianyu and Zhang, Haoye and Yao, Yuan and Dang, Yunkai and Chen, Da and Lu, Xiaoman and Cui, Ganqu and He, Taiwen and Liu, Zhiyuan and Chua, Tat-Seng and Sun, Maosong},
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journal={arXiv preprint arXiv:2405.17220},
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year={2024},
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}
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```
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{
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||||
"_name_or_path": "openbmb/MiniCPM-Llama3-V-2_5",
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||||
"architectures": [
|
||||
"MiniCPMV"
|
||||
],
|
||||
"attention_bias": false,
|
||||
"attention_dropout": 0.0,
|
||||
"auto_map": {
|
||||
"AutoConfig": "configuration_minicpm.MiniCPMVConfig",
|
||||
"AutoModel": "modeling_minicpmv.MiniCPMV",
|
||||
"AutoModelForCausalLM": "modeling_minicpmv.MiniCPMV"
|
||||
},
|
||||
"batch_vision_input": true,
|
||||
"bos_token_id": 128000,
|
||||
"drop_vision_last_layer": false,
|
||||
"eos_token_id": 128001,
|
||||
"hidden_act": "silu",
|
||||
"hidden_size": 4096,
|
||||
"image_size": 448,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 14336,
|
||||
"max_position_embeddings": 8192,
|
||||
"mm_use_im_start_end": true,
|
||||
"model_type": "minicpmv",
|
||||
"num_attention_heads": 32,
|
||||
"num_hidden_layers": 32,
|
||||
"num_key_value_heads": 8,
|
||||
"patch_size": 14,
|
||||
"pretraining_tp": 1,
|
||||
"query_num": 96,
|
||||
"rms_norm_eps": 1e-05,
|
||||
"rope_scaling": null,
|
||||
"rope_theta": 500000.0,
|
||||
"slice_config": {
|
||||
"max_slice_nums": 9,
|
||||
"patch_size": 14,
|
||||
"model_type": "minicpmv"
|
||||
},
|
||||
"slice_mode": true,
|
||||
"tie_word_embeddings": false,
|
||||
"torch_dtype": "float16",
|
||||
"transformers_version": "4.40.0",
|
||||
"use_cache": false,
|
||||
"vision_config": {
|
||||
"hidden_size": 1152,
|
||||
"image_size": 980,
|
||||
"intermediate_size": 4304,
|
||||
"model_type": "idefics2",
|
||||
"num_attention_heads": 16,
|
||||
"num_hidden_layers": 27,
|
||||
"patch_size": 14
|
||||
},
|
||||
"vocab_size": 128256
|
||||
}
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||||
|
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@ -0,0 +1,113 @@
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# coding=utf-8
|
||||
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
|
||||
# and OPT implementations in this library. It has been modified from its
|
||||
# original forms to accommodate minor architectural differences compared
|
||||
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
""" MiniCPM model configuration"""
|
||||
import os
|
||||
from typing import Union
|
||||
|
||||
from transformers.utils import logging
|
||||
from transformers import LlamaConfig, PretrainedConfig
|
||||
from transformers.models.idefics2.modeling_idefics2 import Idefics2VisionConfig
|
||||
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
|
||||
class MiniCPMVSliceConfig(PretrainedConfig):
|
||||
model_type = "minicpmv"
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
patch_size=14,
|
||||
max_slice_nums=9,
|
||||
scale_resolution=448,
|
||||
**kwargs,
|
||||
):
|
||||
super().__init__(**kwargs)
|
||||
self.patch_size = patch_size
|
||||
self.max_slice_nums = max_slice_nums
|
||||
self.scale_resolution = scale_resolution
|
||||
|
||||
@classmethod
|
||||
def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
|
||||
cls._set_token_in_kwargs(kwargs)
|
||||
|
||||
config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
|
||||
|
||||
if config_dict.get("model_type") == "minicpmv":
|
||||
config_dict = config_dict["slice_config"]
|
||||
|
||||
if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
|
||||
logger.warning(
|
||||
f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
|
||||
f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
|
||||
)
|
||||
|
||||
return cls.from_dict(config_dict, **kwargs)
|
||||
|
||||
|
||||
|
||||
class MiniCPMVConfig(LlamaConfig):
|
||||
model_type = "minicpmv"
|
||||
keys_to_ignore_at_inference = ["past_key_values"]
|
||||
|
||||
default_vision_config = {
|
||||
"hidden_size": 1152,
|
||||
"image_size": 980,
|
||||
"intermediate_size": 4304,
|
||||
"model_type": "idefics2",
|
||||
"num_attention_heads": 16,
|
||||
"num_hidden_layers": 27,
|
||||
"patch_size": 14,
|
||||
}
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
use_cache=True,
|
||||
query_num=64,
|
||||
image_size=448,
|
||||
drop_vision_last_layer=True,
|
||||
batch_vision_input=True,
|
||||
slice_config=None,
|
||||
vision_config=None,
|
||||
**kwargs,
|
||||
):
|
||||
self.use_cache = use_cache
|
||||
self.query_num = query_num
|
||||
self.image_size = image_size
|
||||
self.drop_vision_last_layer = drop_vision_last_layer
|
||||
self.batch_vision_input = batch_vision_input
|
||||
|
||||
if slice_config is None:
|
||||
self.slice_config = MiniCPMVSliceConfig(max_slice_nums=1)
|
||||
else:
|
||||
self.slice_config = MiniCPMVSliceConfig(**slice_config)
|
||||
self.slice_mode = True
|
||||
|
||||
# same as HuggingFaceM4/siglip-so400m-14-980-flash-attn2-navit
|
||||
if vision_config is None:
|
||||
self.vision_config = Idefics2VisionConfig(**self.default_vision_config)
|
||||
logger.info("vision_config is None, using default vision config")
|
||||
elif isinstance(vision_config, dict):
|
||||
self.vision_config = Idefics2VisionConfig(**vision_config)
|
||||
elif isinstance(vision_config, Idefics2VisionConfig):
|
||||
self.vision_config = vision_config
|
||||
|
||||
self.patch_size = self.vision_config.patch_size
|
||||
|
||||
super().__init__(**kwargs)
|
||||
|
|
@ -0,0 +1,6 @@
|
|||
{
|
||||
"_from_model_config": true,
|
||||
"bos_token_id": 128000,
|
||||
"eos_token_id": 128001,
|
||||
"transformers_version": "4.40.0"
|
||||
}
|
||||
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|
|
@ -0,0 +1,748 @@
|
|||
{
|
||||
"metadata": {
|
||||
"total_size": 34148369344
|
||||
},
|
||||
"weight_map": {
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"llm.model.layers.0.input_layernorm.weight": "model-00001-of-00007.safetensors",
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"llm.model.layers.0.mlp.down_proj.weight": "model-00001-of-00007.safetensors",
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"llm.model.layers.0.mlp.gate_proj.weight": "model-00001-of-00007.safetensors",
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"llm.model.layers.0.mlp.up_proj.weight": "model-00001-of-00007.safetensors",
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"llm.model.layers.0.post_attention_layernorm.weight": "model-00001-of-00007.safetensors",
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"llm.model.layers.0.self_attn.o_proj.weight": "model-00001-of-00007.safetensors",
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"vpm.encoder.layers.6.self_attn.q_proj.bias": "model-00007-of-00007.safetensors",
|
||||
"vpm.encoder.layers.6.self_attn.q_proj.weight": "model-00007-of-00007.safetensors",
|
||||
"vpm.encoder.layers.6.self_attn.v_proj.bias": "model-00007-of-00007.safetensors",
|
||||
"vpm.encoder.layers.6.self_attn.v_proj.weight": "model-00007-of-00007.safetensors",
|
||||
"vpm.encoder.layers.7.layer_norm1.bias": "model-00007-of-00007.safetensors",
|
||||
"vpm.encoder.layers.7.layer_norm1.weight": "model-00007-of-00007.safetensors",
|
||||
"vpm.encoder.layers.7.layer_norm2.bias": "model-00007-of-00007.safetensors",
|
||||
"vpm.encoder.layers.7.layer_norm2.weight": "model-00007-of-00007.safetensors",
|
||||
"vpm.encoder.layers.7.mlp.fc1.bias": "model-00007-of-00007.safetensors",
|
||||
"vpm.encoder.layers.7.mlp.fc1.weight": "model-00007-of-00007.safetensors",
|
||||
"vpm.encoder.layers.7.mlp.fc2.bias": "model-00007-of-00007.safetensors",
|
||||
"vpm.encoder.layers.7.mlp.fc2.weight": "model-00007-of-00007.safetensors",
|
||||
"vpm.encoder.layers.7.self_attn.k_proj.bias": "model-00007-of-00007.safetensors",
|
||||
"vpm.encoder.layers.7.self_attn.k_proj.weight": "model-00007-of-00007.safetensors",
|
||||
"vpm.encoder.layers.7.self_attn.out_proj.bias": "model-00007-of-00007.safetensors",
|
||||
"vpm.encoder.layers.7.self_attn.out_proj.weight": "model-00007-of-00007.safetensors",
|
||||
"vpm.encoder.layers.7.self_attn.q_proj.bias": "model-00007-of-00007.safetensors",
|
||||
"vpm.encoder.layers.7.self_attn.q_proj.weight": "model-00007-of-00007.safetensors",
|
||||
"vpm.encoder.layers.7.self_attn.v_proj.bias": "model-00007-of-00007.safetensors",
|
||||
"vpm.encoder.layers.7.self_attn.v_proj.weight": "model-00007-of-00007.safetensors",
|
||||
"vpm.encoder.layers.8.layer_norm1.bias": "model-00007-of-00007.safetensors",
|
||||
"vpm.encoder.layers.8.layer_norm1.weight": "model-00007-of-00007.safetensors",
|
||||
"vpm.encoder.layers.8.layer_norm2.bias": "model-00007-of-00007.safetensors",
|
||||
"vpm.encoder.layers.8.layer_norm2.weight": "model-00007-of-00007.safetensors",
|
||||
"vpm.encoder.layers.8.mlp.fc1.bias": "model-00007-of-00007.safetensors",
|
||||
"vpm.encoder.layers.8.mlp.fc1.weight": "model-00007-of-00007.safetensors",
|
||||
"vpm.encoder.layers.8.mlp.fc2.bias": "model-00007-of-00007.safetensors",
|
||||
"vpm.encoder.layers.8.mlp.fc2.weight": "model-00007-of-00007.safetensors",
|
||||
"vpm.encoder.layers.8.self_attn.k_proj.bias": "model-00007-of-00007.safetensors",
|
||||
"vpm.encoder.layers.8.self_attn.k_proj.weight": "model-00007-of-00007.safetensors",
|
||||
"vpm.encoder.layers.8.self_attn.out_proj.bias": "model-00007-of-00007.safetensors",
|
||||
"vpm.encoder.layers.8.self_attn.out_proj.weight": "model-00007-of-00007.safetensors",
|
||||
"vpm.encoder.layers.8.self_attn.q_proj.bias": "model-00007-of-00007.safetensors",
|
||||
"vpm.encoder.layers.8.self_attn.q_proj.weight": "model-00007-of-00007.safetensors",
|
||||
"vpm.encoder.layers.8.self_attn.v_proj.bias": "model-00007-of-00007.safetensors",
|
||||
"vpm.encoder.layers.8.self_attn.v_proj.weight": "model-00007-of-00007.safetensors",
|
||||
"vpm.encoder.layers.9.layer_norm1.bias": "model-00007-of-00007.safetensors",
|
||||
"vpm.encoder.layers.9.layer_norm1.weight": "model-00007-of-00007.safetensors",
|
||||
"vpm.encoder.layers.9.layer_norm2.bias": "model-00007-of-00007.safetensors",
|
||||
"vpm.encoder.layers.9.layer_norm2.weight": "model-00007-of-00007.safetensors",
|
||||
"vpm.encoder.layers.9.mlp.fc1.bias": "model-00007-of-00007.safetensors",
|
||||
"vpm.encoder.layers.9.mlp.fc1.weight": "model-00007-of-00007.safetensors",
|
||||
"vpm.encoder.layers.9.mlp.fc2.bias": "model-00007-of-00007.safetensors",
|
||||
"vpm.encoder.layers.9.mlp.fc2.weight": "model-00007-of-00007.safetensors",
|
||||
"vpm.encoder.layers.9.self_attn.k_proj.bias": "model-00007-of-00007.safetensors",
|
||||
"vpm.encoder.layers.9.self_attn.k_proj.weight": "model-00007-of-00007.safetensors",
|
||||
"vpm.encoder.layers.9.self_attn.out_proj.bias": "model-00007-of-00007.safetensors",
|
||||
"vpm.encoder.layers.9.self_attn.out_proj.weight": "model-00007-of-00007.safetensors",
|
||||
"vpm.encoder.layers.9.self_attn.q_proj.bias": "model-00007-of-00007.safetensors",
|
||||
"vpm.encoder.layers.9.self_attn.q_proj.weight": "model-00007-of-00007.safetensors",
|
||||
"vpm.encoder.layers.9.self_attn.v_proj.bias": "model-00007-of-00007.safetensors",
|
||||
"vpm.encoder.layers.9.self_attn.v_proj.weight": "model-00007-of-00007.safetensors",
|
||||
"vpm.post_layernorm.bias": "model-00007-of-00007.safetensors",
|
||||
"vpm.post_layernorm.weight": "model-00007-of-00007.safetensors"
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,696 @@
|
|||
import math
|
||||
from typing import List, Optional
|
||||
import json
|
||||
import torch
|
||||
import torchvision
|
||||
from threading import Thread
|
||||
from copy import deepcopy
|
||||
from PIL import Image
|
||||
from torchvision import transforms
|
||||
from transformers import LlamaTokenizer, LlamaPreTrainedModel, LlamaForCausalLM, AutoModel, PreTrainedTokenizerFast, TextIteratorStreamer
|
||||
from transformers.models.idefics2.modeling_idefics2 import Idefics2VisionTransformer
|
||||
|
||||
from .configuration_minicpm import MiniCPMVConfig
|
||||
from .resampler import Resampler
|
||||
|
||||
IMAGENET_INCEPTION_MEAN = (0.5, 0.5, 0.5) # timm.data.IMAGENET_INCEPTION_MEAN
|
||||
IMAGENET_INCEPTION_STD = (0.5, 0.5, 0.5) # timm.data.IMAGENET_INCEPTION_STD
|
||||
|
||||
class MiniCPMVPreTrainedModel(LlamaPreTrainedModel):
|
||||
config_class = MiniCPMVConfig
|
||||
|
||||
|
||||
class MiniCPMV(MiniCPMVPreTrainedModel):
|
||||
def __init__(self, config):
|
||||
super().__init__(config)
|
||||
|
||||
self.llm = LlamaForCausalLM(config)
|
||||
self.vpm = self.init_vision_module()
|
||||
self.vision_dim = self.vpm.embed_dim
|
||||
self.embed_dim = self.llm.config.hidden_size
|
||||
self.resampler = self.init_resampler(self.embed_dim, self.vision_dim)
|
||||
self.transform = self.init_transform()
|
||||
|
||||
def init_vision_module(self):
|
||||
# same as HuggingFaceM4/siglip-so400m-14-980-flash-attn2-navit
|
||||
model = Idefics2VisionTransformer(self.config.vision_config)
|
||||
if self.config.drop_vision_last_layer:
|
||||
model.encoder.layers = model.encoder.layers[:-1]
|
||||
|
||||
setattr(model, 'embed_dim', model.embeddings.embed_dim)
|
||||
setattr(model, 'patch_size', model.embeddings.patch_size)
|
||||
|
||||
return model
|
||||
|
||||
def init_resampler(self, embed_dim, vision_dim):
|
||||
return Resampler(
|
||||
num_queries=self.config.query_num,
|
||||
embed_dim=embed_dim,
|
||||
num_heads=embed_dim // 128,
|
||||
kv_dim=vision_dim,
|
||||
adaptive=True
|
||||
)
|
||||
|
||||
def init_transform(self):
|
||||
return transforms.Compose(
|
||||
[
|
||||
transforms.ToTensor(),
|
||||
transforms.Normalize(
|
||||
mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD
|
||||
),
|
||||
]
|
||||
)
|
||||
|
||||
def get_vllm_embedding(self, data):
|
||||
if 'vision_hidden_states' not in data:
|
||||
dtype = self.vpm.embeddings.position_embedding.weight.dtype
|
||||
device = self.vpm.embeddings.position_embedding.weight.device
|
||||
tgt_sizes = data['tgt_sizes']
|
||||
pixel_values_list = data['pixel_values']
|
||||
vision_hidden_states = []
|
||||
all_pixel_values = []
|
||||
img_cnt = []
|
||||
for pixel_values in pixel_values_list:
|
||||
img_cnt.append(len(pixel_values))
|
||||
all_pixel_values.extend([i.flatten(end_dim=1).permute(1, 0) for i in pixel_values])
|
||||
|
||||
# exist image
|
||||
if all_pixel_values:
|
||||
tgt_sizes = torch.vstack(tgt_sizes).type(torch.int32)
|
||||
|
||||
if self.config.batch_vision_input:
|
||||
max_patches = torch.max(tgt_sizes[:, 0] * tgt_sizes[:, 1])
|
||||
|
||||
all_pixel_values = torch.nn.utils.rnn.pad_sequence(all_pixel_values, batch_first=True,
|
||||
padding_value=0.0)
|
||||
B, L, _ = all_pixel_values.shape
|
||||
all_pixel_values = all_pixel_values.permute(0, 2, 1).reshape(B, 3, -1, L)
|
||||
|
||||
patch_attn_mask = torch.zeros((B, 1, max_patches), dtype=torch.bool, device=device)
|
||||
for i in range(B):
|
||||
patch_attn_mask[i, :tgt_sizes[i][0] * tgt_sizes[i][1]] = True
|
||||
|
||||
vision_embedding = self.vpm(all_pixel_values.type(dtype), patch_attention_mask=patch_attn_mask).last_hidden_state
|
||||
vision_embedding = self.resampler(vision_embedding, tgt_sizes)
|
||||
else:
|
||||
# get vision_embedding foreach
|
||||
vision_embedding = []
|
||||
for single_tgt_size, single_pixel_values in zip(tgt_sizes, all_pixel_values):
|
||||
single_pixel_values = single_pixel_values.unsqueeze(0)
|
||||
B, L, _ = single_pixel_values.shape
|
||||
single_pixel_values = single_pixel_values.permute(0, 2, 1).reshape(B, 3, -1, L)
|
||||
single_vision_embedding = self.vpm(single_pixel_values.type(dtype)).last_hidden_state
|
||||
single_vision_embedding = self.resampler(single_vision_embedding, single_tgt_size.unsqueeze(0))
|
||||
vision_embedding.append(single_vision_embedding)
|
||||
vision_embedding = torch.vstack(vision_embedding)
|
||||
|
||||
start = 0
|
||||
for pixel_values in pixel_values_list:
|
||||
img_cnt = len(pixel_values)
|
||||
if img_cnt > 0:
|
||||
vision_hidden_states.append(vision_embedding[start: start + img_cnt])
|
||||
start += img_cnt
|
||||
else:
|
||||
vision_hidden_states.append([])
|
||||
else: # no image
|
||||
if self.training:
|
||||
dummy_image = torch.zeros(
|
||||
(1, 3, 224, 224),
|
||||
device=device, dtype=dtype
|
||||
)
|
||||
tgt_sizes = torch.Tensor([[(224 // self.config.patch_size), math.ceil(224 / self.config.patch_size)]]).type(torch.int32)
|
||||
dummy_feature = self.resampler(self.vpm(dummy_image).last_hidden_state, tgt_sizes)
|
||||
else:
|
||||
dummy_feature = []
|
||||
for _ in range(len(pixel_values_list)):
|
||||
vision_hidden_states.append(dummy_feature)
|
||||
|
||||
else:
|
||||
vision_hidden_states = data['vision_hidden_states']
|
||||
|
||||
if hasattr(self.llm.config, 'scale_emb'):
|
||||
vllm_embedding = self.llm.model.embed_tokens(data['input_ids']) * self.llm.config.scale_emb
|
||||
else:
|
||||
vllm_embedding = self.llm.model.embed_tokens(data['input_ids'])
|
||||
|
||||
vision_hidden_states = [i.type(vllm_embedding.dtype) if isinstance(
|
||||
i, torch.Tensor) else i for i in vision_hidden_states]
|
||||
|
||||
bs = len(data['input_ids'])
|
||||
for i in range(bs):
|
||||
cur_vs_hs = vision_hidden_states[i]
|
||||
if len(cur_vs_hs) > 0:
|
||||
cur_vllm_emb = vllm_embedding[i]
|
||||
cur_image_bound = data['image_bound'][i]
|
||||
if len(cur_image_bound) > 0:
|
||||
image_indices = torch.stack(
|
||||
[torch.arange(r[0], r[1], dtype=torch.long) for r in cur_image_bound]
|
||||
).to(vllm_embedding.device)
|
||||
|
||||
cur_vllm_emb.scatter_(0, image_indices.view(-1, 1).repeat(1, cur_vllm_emb.shape[-1]),
|
||||
cur_vs_hs.view(-1, cur_vs_hs.shape[-1]))
|
||||
elif self.training:
|
||||
cur_vllm_emb += cur_vs_hs[0].mean() * 0
|
||||
|
||||
return vllm_embedding, vision_hidden_states
|
||||
|
||||
def forward(self, data, **kwargs):
|
||||
vllm_embedding, vision_hidden_states = self.get_vllm_embedding(data)
|
||||
position_ids = data["position_ids"]
|
||||
if position_ids.dtype != torch.int64:
|
||||
position_ids = position_ids.long()
|
||||
|
||||
return self.llm(
|
||||
input_ids=None,
|
||||
position_ids=position_ids,
|
||||
inputs_embeds=vllm_embedding,
|
||||
**kwargs
|
||||
)
|
||||
|
||||
def _convert_to_tensors(
|
||||
self, tokenizer, input_ids, max_inp_length: Optional[int] = None
|
||||
):
|
||||
if max_inp_length is not None:
|
||||
input_ids = input_ids[:max_inp_length]
|
||||
input_ids = torch.tensor(input_ids, dtype=torch.int32)
|
||||
|
||||
image_start_tokens = torch.where(input_ids == tokenizer.im_start_id)[0]
|
||||
# 跳过 im_start
|
||||
image_start_tokens += 1
|
||||
image_end_tokens = torch.where(input_ids == tokenizer.im_end_id)[0]
|
||||
valid_image_nums = max(len(image_start_tokens), len(image_end_tokens))
|
||||
image_bound = torch.hstack(
|
||||
[
|
||||
image_start_tokens[:valid_image_nums].unsqueeze(-1),
|
||||
image_end_tokens[:valid_image_nums].unsqueeze(-1),
|
||||
]
|
||||
)
|
||||
|
||||
model_input = {}
|
||||
model_input["input_ids"] = input_ids.unsqueeze(0).to(self.device)
|
||||
model_input["image_bound"] = image_bound
|
||||
|
||||
return model_input
|
||||
|
||||
def _process_list(
|
||||
self, tokenizer, input_id_list, max_inp_length: Optional[int] = None
|
||||
):
|
||||
pad_keys = ["input_ids"]
|
||||
input_tensors = []
|
||||
for input_ids in input_id_list:
|
||||
input_tensors.append(
|
||||
self._convert_to_tensors(tokenizer, input_ids, max_inp_length)
|
||||
)
|
||||
padded = {}
|
||||
for key in pad_keys:
|
||||
padded[key] = pad(input_tensors, key, padding_side="left").to(self.device)
|
||||
padded["image_bound"] = [i["image_bound"] for i in input_tensors]
|
||||
return padded
|
||||
|
||||
def _decode(self, inputs_embeds, tokenizer, **kwargs):
|
||||
terminators = [
|
||||
tokenizer.eos_token_id,
|
||||
tokenizer.convert_tokens_to_ids("<|eot_id|>")
|
||||
]
|
||||
output = self.llm.generate(
|
||||
inputs_embeds=inputs_embeds,
|
||||
pad_token_id=0,
|
||||
eos_token_id=terminators,
|
||||
**kwargs
|
||||
)
|
||||
return self._decode_text(output, tokenizer)
|
||||
|
||||
def _decode_stream(self, inputs_embeds, tokenizer, **kwargs):
|
||||
terminators = [
|
||||
tokenizer.eos_token_id,
|
||||
tokenizer.convert_tokens_to_ids("<|eot_id|>")
|
||||
]
|
||||
streamer = TextIteratorStreamer(tokenizer=tokenizer)
|
||||
generation_kwargs = {
|
||||
'inputs_embeds': inputs_embeds,
|
||||
'pad_token_id': 0,
|
||||
'eos_token_id': terminators,
|
||||
'streamer': streamer
|
||||
}
|
||||
generation_kwargs.update(kwargs)
|
||||
|
||||
thread = Thread(target=self.llm.generate, kwargs=generation_kwargs)
|
||||
thread.start()
|
||||
|
||||
return streamer
|
||||
|
||||
def _decode_text(self, result_ids, tokenizer):
|
||||
result_text = []
|
||||
for result in result_ids:
|
||||
result = result[result != 0]
|
||||
if result[0] == tokenizer.bos_id:
|
||||
result = result[1:]
|
||||
if result[-1] == tokenizer.eos_id or result[-1] == tokenizer.eot_id:
|
||||
result = result[:-1]
|
||||
result_text.append(tokenizer.decode(result).strip())
|
||||
return result_text
|
||||
|
||||
def slice_image(self, image):
|
||||
return slice_image(
|
||||
image,
|
||||
self.config.slice_config.max_slice_nums,
|
||||
self.config.slice_config.scale_resolution,
|
||||
self.config.slice_config.patch_size,
|
||||
)
|
||||
|
||||
def get_slice_image_placeholder(self, image, tokenizer):
|
||||
image_placeholder = (
|
||||
tokenizer.im_start
|
||||
+ tokenizer.unk_token * self.config.query_num
|
||||
+ tokenizer.im_end
|
||||
)
|
||||
|
||||
slice_images = []
|
||||
|
||||
source_image, patches, best_grid = slice_image(
|
||||
image,
|
||||
self.config.slice_config.max_slice_nums,
|
||||
self.config.slice_config.scale_resolution,
|
||||
self.config.slice_config.patch_size,
|
||||
)
|
||||
|
||||
slice_images.append(source_image)
|
||||
final_placeholder = image_placeholder
|
||||
|
||||
if len(patches) > 0:
|
||||
for i in range(len(patches)):
|
||||
for j in range(len(patches[0])):
|
||||
slice_images.append(patches[i][j])
|
||||
|
||||
final_placeholder += get_grid_placeholder(
|
||||
tokenizer, best_grid, self.config.query_num
|
||||
)
|
||||
|
||||
return slice_images, final_placeholder
|
||||
|
||||
def reshape_by_patch(self, image_tensor):
|
||||
"""
|
||||
:param image_tensor: shape [3, H, W]
|
||||
:param patch_size:
|
||||
:return: [3, patch_size, HW/patch_size]
|
||||
"""
|
||||
patch_size = self.config.patch_size
|
||||
patches = torch.nn.functional.unfold(
|
||||
image_tensor,
|
||||
(patch_size, patch_size),
|
||||
stride=(patch_size, patch_size)
|
||||
)
|
||||
|
||||
patches = patches.reshape(image_tensor.size(0), patch_size, patch_size, -1)
|
||||
patches = patches.permute(0, 1, 3, 2).reshape(image_tensor.size(0), patch_size, -1)
|
||||
return patches
|
||||
|
||||
def generate(
|
||||
self,
|
||||
input_id_list=None,
|
||||
img_list=None,
|
||||
tgt_sizes=None,
|
||||
tokenizer=None,
|
||||
max_inp_length: Optional[int] = None,
|
||||
vision_hidden_states=None,
|
||||
return_vision_hidden_states=False,
|
||||
stream=False,
|
||||
**kwargs
|
||||
):
|
||||
|
||||
assert input_id_list is not None
|
||||
bs = len(input_id_list)
|
||||
if img_list == None:
|
||||
img_list = [[] for i in range(bs)]
|
||||
assert bs == len(img_list)
|
||||
|
||||
model_inputs = self._process_list(tokenizer, input_id_list, max_inp_length)
|
||||
|
||||
if vision_hidden_states is None:
|
||||
pixel_values = []
|
||||
for i in range(bs):
|
||||
img_inps = []
|
||||
for img in img_list[i]:
|
||||
img_inps.append(img.to(self.device))
|
||||
if img_inps:
|
||||
pixel_values.append(img_inps)
|
||||
else:
|
||||
pixel_values.append([])
|
||||
model_inputs["pixel_values"] = pixel_values
|
||||
model_inputs['tgt_sizes'] = tgt_sizes
|
||||
else:
|
||||
model_inputs["vision_hidden_states"] = vision_hidden_states
|
||||
|
||||
with torch.inference_mode():
|
||||
(
|
||||
model_inputs["inputs_embeds"],
|
||||
vision_hidden_states,
|
||||
) = self.get_vllm_embedding(model_inputs)
|
||||
|
||||
if stream:
|
||||
result = self._decode_stream(model_inputs["inputs_embeds"], tokenizer, **kwargs)
|
||||
else:
|
||||
result = self._decode(model_inputs["inputs_embeds"], tokenizer, **kwargs)
|
||||
|
||||
if return_vision_hidden_states:
|
||||
return result, vision_hidden_states
|
||||
|
||||
return result
|
||||
|
||||
def chat(
|
||||
self,
|
||||
image,
|
||||
msgs,
|
||||
tokenizer,
|
||||
vision_hidden_states=None,
|
||||
max_new_tokens=1024,
|
||||
sampling=True,
|
||||
max_inp_length=2048,
|
||||
system_prompt='',
|
||||
stream=False,
|
||||
**kwargs
|
||||
):
|
||||
if isinstance(msgs, str):
|
||||
msgs = json.loads(msgs)
|
||||
|
||||
copy_msgs = deepcopy(msgs)
|
||||
assert len(copy_msgs) > 0, 'msgs is empty'
|
||||
assert sampling or not stream, 'if use stream mode, make sure sampling=True'
|
||||
|
||||
if image is not None and isinstance(copy_msgs[0]['content'], str):
|
||||
copy_msgs[0]['content'] = [image, copy_msgs[0]['content']]
|
||||
|
||||
images = []
|
||||
tgt_sizes = []
|
||||
for i, msg in enumerate(copy_msgs):
|
||||
role = msg["role"]
|
||||
content = msg["content"]
|
||||
assert role in ["user", "assistant"]
|
||||
if i == 0:
|
||||
assert role == "user", "The role of first msg should be user"
|
||||
if isinstance(content, str):
|
||||
content = [content]
|
||||
|
||||
cur_msgs = []
|
||||
for c in content:
|
||||
if isinstance(c, Image.Image):
|
||||
image = c
|
||||
if self.config.slice_mode:
|
||||
slice_images, image_placeholder = self.get_slice_image_placeholder(
|
||||
image, tokenizer
|
||||
)
|
||||
cur_msgs.append(image_placeholder)
|
||||
for slice_image in slice_images:
|
||||
slice_image = self.transform(slice_image)
|
||||
H, W = slice_image.shape[1:]
|
||||
images.append(self.reshape_by_patch(slice_image))
|
||||
tgt_sizes.append(torch.Tensor([H // self.config.patch_size, W // self.config.patch_size]).type(torch.int32))
|
||||
else:
|
||||
images.append(self.transform(image))
|
||||
cur_msgs.append(
|
||||
tokenizer.im_start
|
||||
+ tokenizer.unk_token * self.config.query_num
|
||||
+ tokenizer.im_end
|
||||
)
|
||||
elif isinstance(c, str):
|
||||
cur_msgs.append(c)
|
||||
|
||||
|
||||
msg['content'] = '\n'.join(cur_msgs)
|
||||
if tgt_sizes:
|
||||
tgt_sizes = torch.vstack(tgt_sizes)
|
||||
|
||||
if system_prompt:
|
||||
sys_msg = {'role': 'system', 'content': system_prompt}
|
||||
copy_msgs = [sys_msg] + copy_msgs
|
||||
|
||||
input_ids = tokenizer.apply_chat_template(copy_msgs, tokenize=True, add_generation_prompt=False)
|
||||
|
||||
if sampling:
|
||||
generation_config = {
|
||||
"top_p": 0.8,
|
||||
"top_k": 100,
|
||||
"temperature": 0.7,
|
||||
"do_sample": True,
|
||||
"repetition_penalty": 1.05
|
||||
}
|
||||
else:
|
||||
generation_config = {
|
||||
"num_beams": 3,
|
||||
"repetition_penalty": 1.2,
|
||||
}
|
||||
|
||||
generation_config.update(
|
||||
(k, kwargs[k]) for k in generation_config.keys() & kwargs.keys()
|
||||
)
|
||||
|
||||
with torch.inference_mode():
|
||||
res, vision_hidden_states = self.generate(
|
||||
input_id_list=[input_ids],
|
||||
max_inp_length=max_inp_length,
|
||||
img_list=[images],
|
||||
tgt_sizes=[tgt_sizes],
|
||||
tokenizer=tokenizer,
|
||||
max_new_tokens=max_new_tokens,
|
||||
vision_hidden_states=vision_hidden_states,
|
||||
return_vision_hidden_states=True,
|
||||
stream=stream,
|
||||
**generation_config
|
||||
)
|
||||
|
||||
if stream:
|
||||
def stream_gen():
|
||||
for text in res:
|
||||
text = text.replace(tokenizer.eot_token, '').replace(tokenizer.eos_token, '')
|
||||
yield text
|
||||
return stream_gen()
|
||||
|
||||
else:
|
||||
answer = res[0]
|
||||
return answer
|
||||
|
||||
|
||||
class PreTrainedTokenizerFastWrapper(PreTrainedTokenizerFast):
|
||||
def __init__(self, **kwargs):
|
||||
super().__init__(**kwargs)
|
||||
self.eot_token = "<|eot_id|>"
|
||||
self.im_start = "<image>"
|
||||
self.im_end = "</image>"
|
||||
self.ref_start = "<ref>"
|
||||
self.ref_end = "</ref>"
|
||||
self.box_start = "<box>"
|
||||
self.box_end = "</box>"
|
||||
self.quad_start = "<quad>"
|
||||
self.quad_end = "</quad>"
|
||||
self.slice_start = "<slice>"
|
||||
self.slice_end = "</slice>"
|
||||
|
||||
@property
|
||||
def eos_id(self):
|
||||
return self.eos_token_id
|
||||
|
||||
@property
|
||||
def bos_id(self):
|
||||
return self.bos_token_id
|
||||
|
||||
@property
|
||||
def unk_id(self):
|
||||
return self.unk_token_id
|
||||
|
||||
@property
|
||||
def eot_id(self):
|
||||
return self.convert_tokens_to_ids(self.eot_token)
|
||||
|
||||
@property
|
||||
def im_start_id(self):
|
||||
return self.convert_tokens_to_ids(self.im_start)
|
||||
|
||||
@property
|
||||
def im_end_id(self):
|
||||
return self.convert_tokens_to_ids(self.im_end)
|
||||
|
||||
@staticmethod
|
||||
def escape(text: str) -> str:
|
||||
return text
|
||||
|
||||
@staticmethod
|
||||
def unescape(text: str) -> str:
|
||||
return text
|
||||
|
||||
|
||||
def pad(orig_items, key, max_length=None, padding_value=0, padding_side="left"):
|
||||
items = []
|
||||
if isinstance(orig_items[0][key], list):
|
||||
assert isinstance(orig_items[0][key][0], torch.Tensor)
|
||||
for it in orig_items:
|
||||
for tr in it[key]:
|
||||
items.append({key: tr})
|
||||
else:
|
||||
assert isinstance(orig_items[0][key], torch.Tensor)
|
||||
items = orig_items
|
||||
|
||||
batch_size = len(items)
|
||||
shape = items[0][key].shape
|
||||
dim = len(shape)
|
||||
assert dim <= 3
|
||||
if max_length is None:
|
||||
max_length = 0
|
||||
max_length = max(max_length, max(item[key].shape[-1] for item in items))
|
||||
min_length = min(item[key].shape[-1] for item in items)
|
||||
dtype = items[0][key].dtype
|
||||
|
||||
if dim == 1:
|
||||
return torch.cat([item[key] for item in items], dim=0)
|
||||
elif dim == 2:
|
||||
if max_length == min_length:
|
||||
return torch.cat([item[key] for item in items], dim=0)
|
||||
tensor = torch.zeros((batch_size, max_length), dtype=dtype) + padding_value
|
||||
else:
|
||||
tensor = (
|
||||
torch.zeros((batch_size, max_length, shape[-1]), dtype=dtype)
|
||||
+ padding_value
|
||||
)
|
||||
|
||||
for i, item in enumerate(items):
|
||||
if dim == 2:
|
||||
if padding_side == "left":
|
||||
tensor[i, -len(item[key][0]) :] = item[key][0].clone()
|
||||
else:
|
||||
tensor[i, : len(item[key][0])] = item[key][0].clone()
|
||||
elif dim == 3:
|
||||
if padding_side == "left":
|
||||
tensor[i, -len(item[key][0]) :, :] = item[key][0].clone()
|
||||
else:
|
||||
tensor[i, : len(item[key][0]), :] = item[key][0].clone()
|
||||
|
||||
return tensor
|
||||
|
||||
|
||||
def slice_image(
|
||||
image, max_slice_nums=9, scale_resolution=448, patch_size=14, never_split=False
|
||||
):
|
||||
original_size = image.size
|
||||
original_width, original_height = original_size
|
||||
log_ratio = math.log(original_width / original_height)
|
||||
ratio = original_width * original_height / (scale_resolution * scale_resolution)
|
||||
multiple = min(math.ceil(ratio), max_slice_nums)
|
||||
|
||||
source_image = None
|
||||
best_grid = None
|
||||
patches = []
|
||||
|
||||
if multiple <= 1 or never_split:
|
||||
# dont need to slice, upsample
|
||||
best_size = find_best_resize(
|
||||
original_size, scale_resolution, patch_size, allow_upscale=True
|
||||
)
|
||||
source_image = image.resize(best_size, Image.Resampling.BICUBIC)
|
||||
else:
|
||||
candidate_split_grids_nums = []
|
||||
for i in [multiple - 1, multiple, multiple + 1]:
|
||||
if i == 1 or i > max_slice_nums:
|
||||
continue
|
||||
candidate_split_grids_nums.append(i)
|
||||
|
||||
# source image, down-sampling and ensure divided by patch_size
|
||||
best_resize = find_best_resize(original_size, scale_resolution, patch_size)
|
||||
source_image = image.copy().resize(best_resize, Image.Resampling.BICUBIC)
|
||||
candidate_grids = []
|
||||
|
||||
# find best grid
|
||||
for split_grids_nums in candidate_split_grids_nums:
|
||||
m = 1
|
||||
while m <= split_grids_nums:
|
||||
if split_grids_nums % m == 0:
|
||||
candidate_grids.append([m, split_grids_nums // m])
|
||||
m += 1
|
||||
|
||||
best_grid = [1, 1]
|
||||
min_error = float("inf")
|
||||
for grid in candidate_grids:
|
||||
error = abs(log_ratio - math.log(grid[0] / grid[1]))
|
||||
if error < min_error:
|
||||
best_grid = grid
|
||||
min_error = error
|
||||
|
||||
refine_size = get_refine_size(
|
||||
original_size, best_grid, scale_resolution, patch_size, allow_upscale=True
|
||||
)
|
||||
|
||||
refine_image = image.resize(refine_size, Image.Resampling.BICUBIC)
|
||||
patches = split_to_patches(refine_image, best_grid)
|
||||
|
||||
return source_image, patches, best_grid
|
||||
|
||||
|
||||
def ensure_divide(length, patch_size):
|
||||
return max(round(length / patch_size) * patch_size, patch_size)
|
||||
|
||||
|
||||
def find_best_resize(original_size, scale_resolution, patch_size, allow_upscale=False):
|
||||
width, height = original_size
|
||||
if (width * height > scale_resolution * scale_resolution) or allow_upscale:
|
||||
r = width / height
|
||||
height = int(scale_resolution / math.sqrt(r))
|
||||
width = int(height * r)
|
||||
best_width = ensure_divide(width, patch_size)
|
||||
best_height = ensure_divide(height, patch_size)
|
||||
return (best_width, best_height)
|
||||
|
||||
|
||||
def get_refine_size(
|
||||
original_size, grid, scale_resolution, patch_size, allow_upscale=False
|
||||
):
|
||||
width, height = original_size
|
||||
grid_x, grid_y = grid
|
||||
|
||||
refine_width = ensure_divide(width, grid_x)
|
||||
refine_height = ensure_divide(height, grid_y)
|
||||
|
||||
grid_width = refine_width / grid_x
|
||||
grid_height = refine_height / grid_y
|
||||
|
||||
best_grid_size = find_best_resize(
|
||||
(grid_width, grid_height),
|
||||
scale_resolution,
|
||||
patch_size,
|
||||
allow_upscale=allow_upscale,
|
||||
)
|
||||
|
||||
refine_size = (best_grid_size[0] * grid_x, best_grid_size[1] * grid_y)
|
||||
|
||||
return refine_size
|
||||
|
||||
|
||||
def split_to_patches(image, grid):
|
||||
patches = []
|
||||
width, height = image.size
|
||||
grid_x = int(width / grid[0])
|
||||
grid_y = int(height / grid[1])
|
||||
|
||||
for i in range(0, height, grid_y):
|
||||
images = []
|
||||
for j in range(0, width, grid_x):
|
||||
box = (j, i, j + grid_x, i + grid_y)
|
||||
patch = image.crop(box)
|
||||
images.append(patch)
|
||||
patches.append(images)
|
||||
|
||||
return patches
|
||||
|
||||
|
||||
def get_grid_placeholder(tokenizer, grid, query_num):
|
||||
image_placeholder = (
|
||||
tokenizer.im_start + tokenizer.unk_token * query_num + tokenizer.im_end
|
||||
)
|
||||
|
||||
cols = grid[0]
|
||||
rows = grid[1]
|
||||
slices = []
|
||||
for i in range(rows):
|
||||
lines = []
|
||||
for j in range(cols):
|
||||
lines.append(image_placeholder)
|
||||
slices.append("".join(lines))
|
||||
slice_placeholder = tokenizer.slice_start + "\n".join(slices) + tokenizer.slice_end
|
||||
return slice_placeholder
|
||||
|
|
@ -0,0 +1,163 @@
|
|||
from functools import partial
|
||||
import numpy as np
|
||||
|
||||
import torch
|
||||
from torch import nn
|
||||
from torch.nn.init import trunc_normal_
|
||||
|
||||
def get_2d_sincos_pos_embed(embed_dim, image_size):
|
||||
"""
|
||||
image_size: image_size or (image_height, image_width)
|
||||
return:
|
||||
pos_embed: [image_height, image_width, embed_dim]
|
||||
"""
|
||||
if isinstance(image_size, int):
|
||||
grid_h_size, grid_w_size = image_size, image_size
|
||||
else:
|
||||
grid_h_size, grid_w_size = image_size[0], image_size[1]
|
||||
|
||||
grid_h = np.arange(grid_h_size, dtype=np.float32)
|
||||
grid_w = np.arange(grid_w_size, dtype=np.float32)
|
||||
grid = np.meshgrid(grid_w, grid_h) # here w goes first
|
||||
grid = np.stack(grid, axis=0)
|
||||
|
||||
pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
|
||||
return pos_embed
|
||||
|
||||
|
||||
def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
|
||||
assert embed_dim % 2 == 0
|
||||
|
||||
# use half of dimensions to encode grid_h
|
||||
emb_h = get_1d_sincos_pos_embed_from_grid_new(embed_dim // 2, grid[0]) # (H, W, D/2)
|
||||
emb_w = get_1d_sincos_pos_embed_from_grid_new(embed_dim // 2, grid[1]) # (H, W, D/2)
|
||||
|
||||
emb = np.concatenate([emb_h, emb_w], axis=-1) # (H, W, D)
|
||||
return emb
|
||||
|
||||
|
||||
def get_1d_sincos_pos_embed_from_grid_new(embed_dim, pos):
|
||||
"""
|
||||
embed_dim: output dimension for each position
|
||||
pos: a list of positions to be encoded: size (H, W)
|
||||
out: (H, W, D)
|
||||
"""
|
||||
assert embed_dim % 2 == 0
|
||||
omega = np.arange(embed_dim // 2, dtype=np.float32)
|
||||
omega /= embed_dim / 2.
|
||||
omega = 1. / 10000 ** omega # (D/2,)
|
||||
|
||||
out = np.einsum('hw,d->hwd', pos, omega) # (H, W, D/2), outer product
|
||||
|
||||
emb_sin = np.sin(out) # (H, W, D/2)
|
||||
emb_cos = np.cos(out) # (H, W, D/2)
|
||||
|
||||
emb = np.concatenate([emb_sin, emb_cos], axis=-1) # (H, W, D)
|
||||
return emb
|
||||
|
||||
|
||||
class Resampler(nn.Module):
|
||||
"""
|
||||
A 2D perceiver-resampler network with one cross attention layers by
|
||||
given learnable queries and 2d sincos pos_emb
|
||||
Outputs:
|
||||
A tensor with the shape of (batch_size, num_queries, embed_dim)
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
num_queries,
|
||||
embed_dim,
|
||||
num_heads,
|
||||
kv_dim=None,
|
||||
norm_layer=partial(nn.LayerNorm, eps=1e-6),
|
||||
adaptive=False,
|
||||
max_size=(70, 70),
|
||||
):
|
||||
super().__init__()
|
||||
self.num_queries = num_queries
|
||||
self.embed_dim = embed_dim
|
||||
self.num_heads = num_heads
|
||||
self.adaptive = adaptive
|
||||
self.max_size = max_size
|
||||
|
||||
self.query = nn.Parameter(torch.zeros(self.num_queries, embed_dim))
|
||||
trunc_normal_(self.query, std=.02)
|
||||
|
||||
if kv_dim is not None and kv_dim != embed_dim:
|
||||
self.kv_proj = nn.Linear(kv_dim, embed_dim, bias=False)
|
||||
else:
|
||||
self.kv_proj = nn.Identity()
|
||||
|
||||
self.attn = nn.MultiheadAttention(embed_dim, num_heads)
|
||||
self.ln_q = norm_layer(embed_dim)
|
||||
self.ln_kv = norm_layer(embed_dim)
|
||||
|
||||
self.ln_post = norm_layer(embed_dim)
|
||||
self.proj = nn.Parameter((embed_dim ** -0.5) * torch.randn(embed_dim, embed_dim))
|
||||
|
||||
self._set_2d_pos_cache(self.max_size)
|
||||
self.apply(self._init_weights)
|
||||
|
||||
def _set_2d_pos_cache(self, max_size, device='cpu'):
|
||||
pos_embed = torch.from_numpy(get_2d_sincos_pos_embed(self.embed_dim, max_size)).float().to(device)
|
||||
self.register_buffer("pos_embed", pos_embed, persistent=False)
|
||||
|
||||
def _adjust_pos_cache(self, tgt_sizes, device):
|
||||
max_h = torch.max(tgt_sizes[:, 0])
|
||||
max_w = torch.max(tgt_sizes[:, 1])
|
||||
if max_h > self.max_size[0] or max_w > self.max_size[1]:
|
||||
self.max_size = [max(max_h, self.max_size[0]), max(max_w, self.max_size[1])]
|
||||
self._set_2d_pos_cache(self.max_size, device)
|
||||
|
||||
def _init_weights(self, m):
|
||||
if isinstance(m, nn.Linear):
|
||||
trunc_normal_(m.weight, std=.02)
|
||||
if isinstance(m, nn.Linear) and m.bias is not None:
|
||||
nn.init.constant_(m.bias, 0)
|
||||
elif isinstance(m, nn.LayerNorm):
|
||||
nn.init.constant_(m.bias, 0)
|
||||
nn.init.constant_(m.weight, 1.0)
|
||||
|
||||
def forward(self, x, tgt_sizes=None):
|
||||
assert x.shape[0] == tgt_sizes.shape[0]
|
||||
bs = x.shape[0]
|
||||
|
||||
device = x.device
|
||||
dtype = x.dtype
|
||||
|
||||
patch_len = tgt_sizes[:, 0] * tgt_sizes[:, 1]
|
||||
|
||||
self._adjust_pos_cache(tgt_sizes, device=device)
|
||||
|
||||
max_patch_len = torch.max(patch_len)
|
||||
key_padding_mask = torch.zeros((bs, max_patch_len), dtype=torch.bool, device=device)
|
||||
|
||||
pos_embed = []
|
||||
for i in range(bs):
|
||||
tgt_h, tgt_w = tgt_sizes[i]
|
||||
pos_embed.append(self.pos_embed[:tgt_h, :tgt_w, :].reshape((tgt_h * tgt_w, -1)).to(dtype)) # patches * D
|
||||
key_padding_mask[i, patch_len[i]:] = True
|
||||
|
||||
pos_embed = torch.nn.utils.rnn.pad_sequence(
|
||||
pos_embed, batch_first=True, padding_value=0.0).permute(1, 0, 2) # BLD => L * B * D
|
||||
|
||||
x = self.kv_proj(x) # B * L * D
|
||||
x = self.ln_kv(x).permute(1, 0, 2) # L * B * D
|
||||
|
||||
q = self.ln_q(self.query) # Q * D
|
||||
|
||||
out = self.attn(
|
||||
self._repeat(q, bs), # Q * B * D
|
||||
x + pos_embed, # L * B * D + L * B * D
|
||||
x,
|
||||
key_padding_mask=key_padding_mask)[0]
|
||||
# out: Q * B * D
|
||||
x = out.permute(1, 0, 2) # B * Q * D
|
||||
|
||||
x = self.ln_post(x)
|
||||
x = x @ self.proj
|
||||
return x
|
||||
|
||||
def _repeat(self, query, N: int):
|
||||
return query.unsqueeze(1).repeat(1, N, 1)
|
||||
|
|
@ -0,0 +1,24 @@
|
|||
{
|
||||
"bos_token": {
|
||||
"content": "<|begin_of_text|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"eos_token": {
|
||||
"content": "<|end_of_text|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": "!",
|
||||
"unk_token": {
|
||||
"content": "<unk>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
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Load Diff
File diff suppressed because it is too large
Load Diff
Loading…
Reference in New Issue