3ce5a34468
### What this PR does / why we need it?
This PR fixes a bug in Xlite
backend(https://atomgit.com/openeuler/GVirt/issues/1), The direct cause
of the problem is that the XModel::PrepareAttn function obtained an
illegal number of tokens to be inferred, -540. This illegal value is due
to the padding feature of inference in graph mode and the residual state
across steps. This issue is triggered when a prefill request is newly
added in a step and a decode ends simultaneously. It is first fixed
using num_decode_tokens instead of attn_metadata.num_decodes.
1. In graph mode, vllm_ascend has padding characteristics. In the
_prepare_inputs function, if the number of tokens to be inferred is less
than the set threshold (8 in this case), the attn_metadata.num_decode
array will be expanded to 8.
2. Meanwhile, vllm_ascend uses the class variable self.query_start_loc
of NPUModelRunner to record the tokens to be inferred. Due to poor
coordination with the graph mode padding mechanism when crossing steps,
in some cases (such as when a decode request is completed in a certain
step and a new prefill request is added at the same time), negative
values may be calculated for attn_metadata.query_lens.
3. After type conversion, the negative values in query_lens cause an
overflow. Xlite detects that the number of tokens to be inferred for the
decode request is too large and triggers a "decode len too long" alert.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
Same with https://atomgit.com/openeuler/GVirt/issues/1
- vLLM version: v0.13.0
- vLLM main:
2f4e6548ef
Signed-off-by: wwwumr <1127858301@qq.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 deploy 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 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-Expert, 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 == 8.3.rc2 (Ascend HDK version refers to here)
- PyTorch == 2.8.0, torch-npu == 2.8.0
- vLLM (the same version as vllm-ascend)
Getting Started
Please use the following recommended versions to get started quickly:
| Version | Release type | Doc |
|---|---|---|
| v0.13.0rc1 | Latest release candidate | QuickStart and Installation for more details |
| v0.11.0 | Latest stable version | 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 v0.13.0 tag |
| v0.7.1-dev | Unmaintained | Only doc fixed is allowed |
| v0.7.3-dev | Maintained | CI commitment for vLLM 0.7.3 version, only bug fix is allowed and no new release tag any more. |
| 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 |
| 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.