12cae04db9
### What this PR does / why we need it? Add a `VLLMAscendQuantizer` to support w8a8 static (W8A8) and dynamic on linear and moe (W8A8_DYNAMIC), the quantizer will be enable if a model has [quantize filed](https://huggingface.co/vllm-ascend/Qwen2.5-0.5B-Instruct-w8a8/blob/main/config.json#L27). If MindIE Turbo is installed, the MindIE Turbo Quantizer will apply, otherwise will use VLLMAscendQuantizer directly. - This patch fix installation docs to make installation work - This patch enable norm quantization by patch `RMSNorm.__init__`, `RMSNorm.forward_oot`, `NPUModelRunnerBase.load_model` - Add `AscendW8A8LinearMethod` for W8A8 - Add `AscendW8A8DynamicLinearMethod` and `AscendW8A8DynamicFusedMoEMethod` for W8A8_DYNAMIC - Add a e2e test for `vllm-ascend/Qwen2.5-0.5B-Instruct-w8a8` ### Does this PR introduce _any_ user-facing change? Yes, support w8a8 quantization. After this patch supported, users can use below commands to run w8a8 models: ``` vllm serve /root/.cache/modelscope/hub/Qwen/Qwen2.5-7B-Instruct-w8a8 --served-model-name "qwen2.5-7B" ``` ### How was this patch tested? 0. CI passed: add e2e test for `vllm-ascend/Qwen2.5-0.5B-Instruct-w8a8` 1. From @Yikun: I test Qwen2.5-0.5B-Instruct-w8a8 for functional test all is well, pls refer to https://github.com/vllm-project/vllm-ascend/pull/580#issuecomment-2816747613 2. From @dingdingchaomian : Use qwen2.5-72b-instruct model and deepseek-v2-lite-chat tested, both models were quantized using Ascend's msmodelslim tool: - Qwen2.5-72b-instruct were tested twice, one for w8a8 static and one for w8a8 dynamic. - Deepseek-v2-lite-chat were tested once because its quantization used both static and dynamic w8a8. Models were tested using both off line inference and online serving, and both work well. The inference codes are exactly the same with the examples in https://vllm-ascend.readthedocs.io/en/latest/quick_start.html, with model path and tensor parallel number changed. --------- Signed-off-by: dingdingchaomian <wangce21@huawei.com> Signed-off-by: Yikun Jiang <yikunkero@gmail.com> Co-authored-by: dingdingchaomian <wangce21@huawei.com> Co-authored-by: Angazenn <zengyanjia@huawei.com> Co-authored-by: liujiaxu <liujiaxu4@huawei.com> Co-authored-by: ApsarasX <apsarax@outlook.com> Co-authored-by: ganyi1996ppo <pleaplusone.gy@gmail.com>
vLLM Ascend Plugin
| About Ascend | Documentation | #sig-ascend | Users Forum | Weekly Meeting |
English | 中文
Latest News 🔥
- [2025/03] We hosted the vLLM Beijing Meetup with vLLM team! Please find the meetup slides here.
- [2025/02] vLLM community officially created vllm-project/vllm-ascend repo for running vLLM seamlessly on the Ascend NPU.
- [2024/12] We are working with the vLLM community to support [RFC]: Hardware pluggable.
Overview
vLLM Ascend (vllm-ascend) is a community maintained hardware plugin for running vLLM seamlessly on the Ascend NPU.
It is the recommended approach for supporting the Ascend backend within the vLLM community. It adheres to the principles outlined in the [RFC]: Hardware pluggable, providing a hardware-pluggable interface that decouples the integration of the Ascend NPU with vLLM.
By using vLLM Ascend plugin, popular open-source models, including Transformer-like, Mixture-of-Expert, Embedding, Multi-modal LLMs can run seamlessly on the Ascend NPU.
Prerequisites
- Hardware: Atlas 800I A2 Inference series, Atlas A2 Training series
- OS: Linux
- Software:
- Python >= 3.9
- CANN >= 8.0.0
- PyTorch >= 2.5.1, torch-npu >= 2.5.1.dev20250320
- vLLM (the same version as vllm-ascend)
Getting Started
Please refer to QuickStart and Installation for more details.
Contributing
See CONTRIBUTING for more details, which is a step-by-step guide to help you set up development environment, build and test.
We welcome and value any contributions and collaborations:
- Please let us know if you encounter a bug by filing an issue
- Please use User forum for usage questions and help.
Branch
vllm-ascend has main branch and dev branch.
- main: main branch，corresponds to the vLLM main branch, and is continuously monitored for quality through Ascend CI.
- vX.Y.Z-dev: development branch, created with part of new releases of vLLM. For example,
v0.7.3-devis the dev branch for vLLMv0.7.3version.
Below is maintained branches:
| Branch | Status | Note |
|---|---|---|
| main | Maintained | CI commitment for vLLM main branch and vLLM 0.8.x branch |
| v0.7.1-dev | Unmaintained | Only doc fixed is allowed |
| v0.7.3-dev | Maintained | CI commitment for vLLM 0.7.3 version |
Please refer to Versioning policy for more details.
Weekly Meeting
- vLLM Ascend Weekly Meeting: https://tinyurl.com/vllm-ascend-meeting
- Wednesday, 15:00 - 16:00 (UTC+8, Convert to your timezone)
License
Apache License 2.0, as found in the LICENSE file.