339d6894f6
### What this PR does / why we need it? 1. [PR913](https://github.com/vllm-project/vllm-ascend/pull/913) introduced an error that caused V0's spec decode function to fail. [PR1109](https://github.com/vllm-project/vllm-ascend/pull/1109) wanted to fix this problem. Unfortunately, the fix broke the ngram function. I fixed the ngram function in this PR. **PS**: Q: Why is there a problem when ngram is not found when pr1109 is merged? A: The newly introduced problem will only appear when tp>1, and the use cases on CI are all tp=1 2. In versions after 0.7.3, vllm-ascend deleted some spec decode UTs to avoid CI taking too long, including eagle speculative UTs, which made CI unable to take care of the eagle function. I added it(`test_eagle_correctness.py`) back in this PR 3. Because of the reason mentioned in 2, the current version of Eagle has a problem. I located and fixed this problem. It was because vllm's `draft_model_runner.py` was changed and vllm-ascend was not synchronized in time. 4. Currently, the UTs of v0 and v1 are mixed in the spec_decode directory. I split them into two directories: spec_decode_v0 and spec_decode_v1. 5. i found `vllm.spec_decode.multi_step_worker.MultiStepWorker.set_include_gpu_probs_tensor` and `vllm.spec_decode.multi_step_worker.MultiStepWorker.set_should_modify_greedy_probs_inplace` have changed in vllm, so i remove it in this pr. ### Does this PR introduce _any_ user-facing change? This PR fixes the functions of ngram and eagle spec decode in the v0 engine ### How was this patch tested? tested by CI Signed-off-by: mengwei805 <mengwei25@huawei.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, < 3.12
- CANN >= 8.1.RC1
- PyTorch >= 2.5.1, torch-npu >= 2.5.1.post1.dev20250619
- 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 0.9.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.