fea197ad50
### What this PR does / why we need it?
1. ✅ Upgrade vllm commit to: 0115
(8471b27df97c3eb79f891802fc0e858f8f7ac6a0)
Modify import paths due to the refactors：
https://github.com/vllm-project/vllm/pull/32245
https://github.com/vllm-project/vllm/pull/32060
Test result:
https://github.com/vllm-project/vllm-ascend/actions/runs/21034239336/job/60490156965?pr=5913
2. ✅Upgrade vllm commit to: 0119
(9a1f16da1e423ede2c2f52a9850cbfbb39cefe96)
Fix `WorkerProc.__init__() missing 1 required positional argument:
'is_driver_worker'` due to
https://github.com/vllm-project/vllm/pull/28506
Test result:
https://github.com/vllm-project/vllm-ascend/actions/runs/21156263050/job/60841668755?5569
3. ✅Upgrade vllm commit to:
0120(148117ea2e689cd43df4be6892671a17cdae5833)
1. Add `skip_compiled` param in `set_forward_context` due to
https://github.com/vllm-project/vllm/pull/30385
2. Modify `tests/ut/spec_decode/test_eagle_proposer.py` due to
https://github.com/vllm-project/vllm/pull/24322
change `self.max_num_tokens =
vllm_config.scheduler_config.max_num_batched_tokens + max_batch_size`
3. Modify UT import paths due to the
refactors：https://github.com/vllm-project/vllm/pull/32060
Test result:
https://github.com/vllm-project/vllm-ascend/actions/runs/21204851770/job/60999046946
4. ✅Upgrade vllm commit to:
0121(f23fb5a7c1b61350c5c40ca1115d3bf8cf2b8cc9)
1. vLLM switched `uses_mrope` from target to draft model config, making
`positions`/`mrope_positions` mutually exclusive, breaking vllm-ascend's
direct self.positions access and tests missing
`draft_model_config.uses_mrope`.
https://github.com/vllm-project/vllm/pull/32048
2. Moved bs_to_padded_graph_size from CompilationConfig to
CudagraphDispatcher due to the refactor
https://github.com/vllm-project/vllm/pull/30143
3. Remove unused `maybe_setup_kv_connector` due to
https://github.com/vllm-project/vllm/pull/32077
Test result:
https://github.com/vllm-project/vllm-ascend/actions/runs/21217728738/job/61043738834
6. ✅Upgrade vllm commit to:
0122(8ebf271bb6d1e7e9b1a55be73d755ef1a57dbbe5)
Updating FusedMoEParallelConfig (added enable_eplb) and FusedMoEConfig
due to https://github.com/vllm-project/vllm/pull/32414
Test result:
https://github.com/vllm-project/vllm-ascend/actions/runs/21249922546/job/61148613054
8. ✅Upgrade vllm commit to:
0123(dc917cceb877dfd13f98c538c4c96158047d98bd)
Setting temperature=0.0 due to the removal of the default temperature
value in https://github.com/vllm-project/vllm/pull/32723
Test result:
https://github.com/vllm-project/vllm-ascend/actions/runs/21280796875
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.14.0
- vLLM main:
d68209402d
---------
Signed-off-by: wjunLu <wjunlu217@gmail.com>
Signed-off-by: Meihan-chen <jcccx.cmh@gmail.com>
Co-authored-by: wjunLu <wjunlu217@gmail.com>
vLLM Ascend Plugin
| About Ascend | Documentation | #SIG-Ascend | Users Forum | Weekly Meeting |
English | 中文
Latest News 🔥
- [2025/12] We released the new official version v0.11.0! Please follow the official guide to start using vLLM Ascend Plugin on Ascend.
- [2025/09] We released the new official version v0.9.1! Please follow the official guide to start deploying large-scale Expert Parallelism (EP) on Ascend.
- [2025/08] We hosted the vLLM Beijing Meetup with vLLM and Tencent! Please find the meetup slides here.
- [2025/06] User stories page is now live! It kicks off with LLaMA-Factory/verl/TRL/GPUStack to demonstrate how vLLM Ascend assists Ascend users in enhancing their experience across fine-tuning, evaluation, reinforcement learning (RL), and deployment scenarios.
- [2025/06] Contributors page is now live! All contributions deserve to be recorded, thanks for all contributors.
- [2025/05] We've released the first official version v0.7.3! We collaborated with the vLLM community to publish a blog post sharing our practice: Introducing vLLM Hardware Plugin, Best Practice from Ascend NPU.
- [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-Experts (MoE), Embedding, Multi-modal LLMs can run seamlessly on the Ascend NPU.
Prerequisites
- Hardware: Atlas 800I A2 Inference series, Atlas A2 Training series, Atlas 800I A3 Inference series, Atlas A3 Training series, Atlas 300I Duo (Experimental)
- OS: Linux
- Software:
- Python >= 3.10, < 3.12
- CANN == 8.5.0 (Ascend HDK version refers to here)
- PyTorch == 2.9.0, torch-npu == 2.9.0
- vLLM (the same version as vllm-ascend)
Getting Started
Please use the following recommended versions to get started quickly:
| Version | Release type | Doc |
|---|---|---|
| v0.14.0rc1 | Latest release candidate | See QuickStart and Installation for more details |
| v0.13.0rc2 | Latest stable release candidate | See QuickStart and Installation for more details |
| v0.11.0 | Latest stable version | See QuickStart and Installation for more details |
Contributing
See CONTRIBUTING for more details, which is a step-by-step guide to help you set up the 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 a main branch and a dev branch.
- main: main branch, corresponds to the vLLM main branch, and is continuously monitored for quality through Ascend CI.
- releases/vX.Y.Z: development branch, created alongside new releases of vLLM. For example,
releases/v0.13.0is the dev branch for vLLMv0.13.0version.
Below are the maintained branches:
| Branch | Status | Note |
|---|---|---|
| main | Maintained | CI commitment for vLLM main branch and vLLM v0.13.0 tag |
| v0.7.1-dev | Unmaintained | Only doc fixes are allowed |
| v0.7.3-dev | Maintained | CI commitment for vLLM 0.7.3 version, only bug fixes are allowed, and no new release tags anymore. |
| v0.9.1-dev | Maintained | CI commitment for vLLM 0.9.1 version |
| v0.11.0-dev | Maintained | CI commitment for vLLM 0.11.0 version |
| releases/v0.13.0 | Maintained | CI commitment for vLLM 0.13.0 version |
| rfc/feature-name | Maintained | Feature branches for collaboration |
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.