84563fc65d
### What this PR does / why we need it? This PR adds sleep mode feature for vllm-ascend, when sleeps, we do mainly two things: - offload model weights - discard kv cache RLHF tools(such as https://github.com/volcengine/verl and https://github.com/OpenRLHF/OpenRLHF) have a strong need of sleep mode to accelerate the training process. This PR may solve #375 and #320 . ### Does this PR introduce _any_ user-facing change? No existing user interfaces changed. Users will have two new methods(`sleep()` and `wake_up()`) to use. ### How was this patch tested? This PR is tested with Qwen/Qwen2.5-0.5B-Instruct. At first, we have free NPU memory M1. After `llm = LLM("Qwen/Qwen2.5-0.5B-Instruct", enable_sleep_mode=True)` executed, we have free NPU memory M2. M2 < M1. Then we call `llm.sleep(level=1)`, we have free NPU memory M3. We have M3 > M2, M3 is very close to M1. Plus, we have the same output tokens before sleep and after wake up, with the config of `SamplingParams(temperature=0, max_tokens=10)` and with the same input tokens of course. This PR is utilizing the CMake procedure of #371 , thanks a lot. Signed-off-by: Shuqiao Li <celestialli@outlook.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 |
| 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.