5f8241c25c
### What this PR does / why we need it? Change as little existing code as possible to add v1 pooling task's support, notice that i move down the `vllm.v1.worker.gpu_input_batch` to vllm-ascend, Considering the frequent changes in upstream interfaces, in order to decouple, so i move it here ### How was this patch tested? CI passed with new added/existing test, and I have a simple test was first conducted locally which is adapted from https://www.modelscope.cn/models/Qwen/Qwen3-Embedding-0.6B, just like bellow： ```python import os import torch from vllm import LLM os.environ["VLLM_USE_MODELSCOPE"]="True" def get_detailed_instruct(task_description: str, query: str) -> str: return f'Instruct: {task_description}\nQuery:{query}' # Each query must come with a one-sentence instruction that describes the task task = 'Given a web search query, retrieve relevant passages that answer the query' queries = [ get_detailed_instruct(task, 'What is the capital of China?'), get_detailed_instruct(task, 'Explain gravity') ] # No need to add instruction for retrieval documents documents = [ "The capital of China is Beijing.", "Gravity is a force that attracts two bodies towards each other. It gives weight to physical objects and is responsible for the movement of planets around the sun." ] input_texts = queries + documents model = LLM(model="Qwen/Qwen3-Embedding-0.6B", task="embed") outputs = model.embed(input_texts) embeddings = torch.tensor([o.outputs.embedding for o in outputs]) scores = (embeddings[:2] @ embeddings[2:].T) print(scores.tolist()) # [[0.7620252966880798, 0.14078938961029053], [0.1358368694782257, 0.6013815999031067]] ``` --------- Signed-off-by: wangli <wangli858794774@gmail.com> Signed-off-by: wangli <858794774@qq.com> Co-authored-by: wangli <858794774@qq.com>
vLLM Ascend Plugin
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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.