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--- |
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title: VPTQ demo |
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emoji: 🚀 |
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colorFrom: blue |
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colorTo: green |
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sdk: gradio |
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sdk_version: 4.44.1 |
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app_file: app.py |
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pinned: true |
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license: mit |
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short_description: Vector Post-Training Quantization (VPTQ) Demo |
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--- |
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An example chatbot using [VPTQ](https://github.com/microsoft/VPTQ), [huggingface community](https://huggingface.co./spaces/VPTQ-community/). |
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# VPTQ: Extreme Low-bit Vector Post-Training Quantization for Large Language Models |
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## TL;DR |
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**Vector Post-Training Quantization (VPTQ)** is a novel Post-Training Quantization method that leverages **Vector Quantization** to high accuracy on LLMs at an extremely low bit-width (<2-bit). |
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VPTQ can compress 70B, even the 405B model, to 1-2 bits without retraining and maintain high accuracy. |
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* Better Accuracy on 1-2 bits |
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* Lightweight Quantization Algorithm: only cost ~17 hours to quantize 405B Llama-3.1 |
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* Agile Quantization Inference: low decode overhead, best throughput, and TTFT |
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[**arXiv**](https://arxiv.org/abs/2409.17066): https://arxiv.org/abs/2409.17066 |
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[**Models from Community**](https://huggingface.co./VPTQ-community): https://huggingface.co./VPTQ-community |
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[**Github**](https://github.com/microsoft/vptq) https://github.com/microsoft/vptq |
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Prompt example: Llama 3.1 70B on RTX4090 (24 GB@2bit) |
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Chat example: Llama 3.1 70B on RTX4090 (24 GB@2bit) |
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## Details and [**Tech Report**](https://github.com/microsoft/VPTQ/blob/main/VPTQ_tech_report.pdf) |
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Scaling model size significantly challenges the deployment and inference of Large Language Models (LLMs). Due to the redundancy in LLM weights, recent research has focused on pushing weight-only quantization to extremely low-bit (even down to 2 bits). It reduces memory requirements, optimizes storage costs, and decreases memory bandwidth needs during inference. However, due to numerical representation limitations, traditional scalar-based weight quantization struggles to achieve such extreme low-bit. Recent research on Vector Quantization (VQ) for LLMs has demonstrated the potential for extremely low-bit model quantization by compressing vectors into indices using lookup tables. |
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## Early Results from Tech Report |
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VPTQ achieves better accuracy and higher throughput with lower quantization overhead across models of different sizes. The following experimental results are for reference only; VPTQ can achieve better outcomes under reasonable parameters, especially in terms of model accuracy and inference speed. |
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| Model | bitwidth | W2↓ | C4↓ | AvgQA↑ | tok/s↑ | mem(GB) | cost/h↓ | |
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| ----------- | -------- | ---- | ---- | ------ | ------ | ------- | ------- | |
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| LLaMA-2 7B | 2.02 | 6.13 | 8.07 | 58.2 | 39.9 | 2.28 | 2 | |
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| | 2.26 | 5.95 | 7.87 | 59.4 | 35.7 | 2.48 | 3.1 | |
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| LLaMA-2 13B | 2.02 | 5.32 | 7.15 | 62.4 | 26.9 | 4.03 | 3.2 | |
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| | 2.18 | 5.28 | 7.04 | 63.1 | 18.5 | 4.31 | 3.6 | |
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| LLaMA-2 70B | 2.07 | 3.93 | 5.72 | 68.6 | 9.7 | 19.54 | 19 | |
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| | 2.11 | 3.92 | 5.71 | 68.7 | 9.7 | 20.01 | 19 | |
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## Install and Evaluation |
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### Dependencies |
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- python 3.10+ |
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- torch >= 2.2.0 |
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- transformers >= 4.44.0 |
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- Accelerate >= 0.33.0 |
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- latest datasets |
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### Installation |
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> Preparation steps that might be needed: Set up CUDA PATH. |
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```bash |
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export PATH=/usr/local/cuda-12/bin/:$PATH # set dependent on your environment |
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``` |
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```python |
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pip install git+https://github.com/microsoft/VPTQ.git --no-build-isolation |
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``` |
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### Language Generation |
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To generate text using the pre-trained model, you can use the following code snippet: |
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```python |
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python -m vptq --model=LLaMa-2-7b-1.5bi-vptq --prompt="Do Not Go Gentle into That Good Night" |
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``` |
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Launching a chatbot: |
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Note that you must use a chat model for this to work |
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```python |
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python -m vptq --model=LLaMa-2-7b-chat-1.5b-vptq --chat |
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``` |
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Using the Python API: |
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```python |
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import vptq |
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import transformers |
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tokenizer = transformers.AutoTokenizer.from_pretrained("LLaMa-2-7b-1.5bi-vptq") |
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m = vptq.AutoModelForCausalLM.from_pretrained("LLaMa-2-7b-1.5bi-vptq", device_map='auto') |
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inputs = tokenizer("Do Not Go Gentle into That Good Night", return_tensors="pt").to("cuda") |
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out = m.generate(**inputs, max_new_tokens=100, pad_token_id=2) |
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print(tokenizer.decode(out[0], skip_special_tokens=True)) |
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``` |
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### Gradio app example |
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A environment variable is available to control share link or not. |
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`export SHARE_LINK=1` |
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``` |
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python -m vptq.app |
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``` |
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## Road Map |
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- [ ] Merge the quantization algorithm into the public repository. |
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- [ ] Submit the VPTQ method to various inference frameworks (e.g., vLLM, llama.cpp). |
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- [ ] Improve the implementation of the inference kernel. |
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- [ ] **TBC** |
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## Project main members: |
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* Yifei Liu (@lyf-00) |
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* Jicheng Wen (@wejoncy) |
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* Yang Wang (@YangWang92) |
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## Acknowledgement |
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* We thank for **James Hensman** for his crucial insights into the error analysis related to Vector Quantization (VQ), and his comments on LLMs evaluation are invaluable to this research. |
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* We are deeply grateful for the inspiration provided by the papers QUIP, QUIP#, GPTVQ, AQLM, WoodFisher, GPTQ, and OBC. |
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## Publication |
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EMNLP 2024 Main |
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```bibtex |
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@inproceedings{ |
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vptq, |
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title={VPTQ: Extreme Low-bit Vector Post-Training Quantization for Large Language Models}, |
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author={Yifei Liu and |
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Jicheng Wen and |
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Yang Wang and |
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Shengyu Ye and |
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Li Lyna Zhang and |
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Ting Cao, Cheng Li and |
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Mao Yang}, |
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booktitle={The 2024 Conference on Empirical Methods in Natural Language Processing}, |
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year={2024}, |
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} |
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``` |
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## Limitation of VPTQ |
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* ⚠️ VPTQ should only be used for research and experimental purposes. Further testing and validation are needed before you use it. |
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* ⚠️ The repository only provides a method of model quantization algorithm. The open-source community may provide models based on the technical report and quantization algorithm by themselves, but the repository project cannot guarantee the performance of those models. |
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* ⚠️ VPTQ is not capable of testing all potential applications and domains, and VPTQ cannot guarantee the accuracy and effectiveness of VPTQ across other tasks or scenarios. |
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* ⚠️ Our tests are all based on English texts; other languages are not included in the current testing. |
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## Contributing |
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This project welcomes contributions and suggestions. Most contributions require you to agree to a |
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Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us |
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the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com. |
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When you submit a pull request, a CLA bot will automatically determine whether you need to provide |
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a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions |
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provided by the bot. You will only need to do this once across all repos using our CLA. |
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This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). |
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For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or |
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contact [[email protected]](mailto:[email protected]) with any additional questions or comments. |
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## Trademarks |
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This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft |
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trademarks or logos is subject to and must follow |
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[Microsoft's Trademark & Brand Guidelines](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks/usage/general). |
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Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. |
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Any use of third-party trademarks or logos are subject to those third-party's policies. |
