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Yang Jun
2025-09-09 09:40:35 +08:00
parent d6f6ef41fe
commit 9149384e03
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<!--
**********************************************************************
* Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
* Copyright 2023 The vLLM team.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* This file is a part of the vllm-ascend project.
* Adapted from https://github.com/vllm-project/vllm/blob/main/docs/source/_templates/sections/header.html
**********************************************************************
-->
<style>
.notification-bar {
width: 100vw;
display: flex;
justify-content: center;
align-items: center;
font-size: 16px;
}
.notification-bar p {
margin: 0;
}
.notification-bar a {
font-weight: bold;
text-decoration: none;
}
/* Light mode styles (default) */
.notification-bar {
background-color: #fff3cd;
color: #856404;
}
.notification-bar a {
color: #d97706;
}
/* Dark mode styles */
html[data-theme=dark] .notification-bar {
background-color: #333;
color: #ddd;
}
html[data-theme=dark] .notification-bar a {
color: #ffa500; /* Brighter color for visibility */
}
</style>
<div class="notification-bar">
<p>You are viewing the latest developer preview docs. <a href="https://vllm-ascend.readthedocs.io/en/v0.9.1-dev">Click here</a> to view docs for the latest stable release(v0.9.1).</p>
</div>

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# Maintainers and contributors
## Maintainers
| Name | Github ID | Date |
|:-----------:|:-----:|:-----:|
| Xiyuan Wang| [@wangxiyuan](https://github.com/wangxiyuan) | 2025/01 |
| Yikun Jiang| [@Yikun](https://github.com/Yikun) | 2025/02 |
| Yi Gan| [@ganyi1996ppo](https://github.com/ganyi1996ppo) | 2025/02 |
| Shoujian Zheng| [@jianzs](https://github.com/jianzs) | 2025/06 |
| Wengang Chen | [@ApsarasX](https://github.com/ApsarasX) | 2025/08 |
| Mengqing Cao | [@MengqingCao](https://github.com/MengqingCao) | 2025/08 |
## Contributors
vLLM Ascend every release would not have been possible without the following contributors:
Updated on 2025-09-03:
| Number | Contributor | Date | Commit ID |
|:------:|:-----------:|:-----:|:---------:|
| 117 | [@panchao-hub](https://github.com/panchao-hub) | 2025/8/30 | [7215454](https://github.com/vllm-project/vllm-ascend/commit/7215454de6df78f4f9a49a99c5739f8bb360f5bc) |
| 116 | [@lidenghui1110](https://github.com/lidenghui1110) | 2025/8/29 | [600b08f](https://github.com/vllm-project/vllm-ascend/commit/600b08f7542be3409c2c70927c91471e8de33d03) |
| 115 | [@NSDie](https://github.com/NSDie) | 2025/8/28 | [1191a64](https://github.com/vllm-project/vllm-ascend/commit/1191a64ae508183d5613711bc98a90250963f83a) |
| 114 | [@s-jiayang](https://github.com/s-jiayang) | 2025/8/27 | [6a4ec18](https://github.com/vllm-project/vllm-ascend/commit/6a4ec186e731b9516235f4fd30b5b98227513fe7) |
| 113 | [@LookAround0301](https://github.com/LookAround0301) | 2025/8/22 | [e9fb895](https://github.com/vllm-project/vllm-ascend/commit/e9fb895b10cef37ea634f4d4af71686b09ca9f20) |
| 112 | [@ZhaoJiangJiang](https://github.com/ZhaoJiangJiang) | 2025/8/22 | [3629bc4](https://github.com/vllm-project/vllm-ascend/commit/3629bc4431d3edb4224761f9036b3bddb16158d6) |
| 111 | [@NicholasTao](https://github.com/NicholasTao) | 2025/8/20 | [7bec1a9](https://github.com/vllm-project/vllm-ascend/commit/7bec1a9b9c372785551d45682bf11063ec42b216) |
| 110 | [@gameofdimension](https://github.com/gameofdimension) | 2025/8/19 | [27d038d](https://github.com/vllm-project/vllm-ascend/commit/27d038dc663bf550a35a8f15659493b2abefda07) |
| 109 | [@liuchenbing](https://github.com/liuchenbing) | 2025/8/19 | [3648d18](https://github.com/vllm-project/vllm-ascend/commit/3648d18e673f15a33a82d6ea95d3a9dd891ff1f5) |
| 108 | [@LCAIZJ](https://github.com/LCAIZJ) | 2025/8/18 | [03ca2b2](https://github.com/vllm-project/vllm-ascend/commit/03ca2b26ca9ab6b9a12f021b0595a726ee35e223) |
| 107 | [@haojiangzheng](https://github.com/haojiangzheng) | 2025/8/11 | [eb43a47](https://github.com/vllm-project/vllm-ascend/commit/eb43a475f429192e7509e85e28b1c65d5097f373) |
| 106 | [@QwertyJack](https://github.com/QwertyJack) | 2025/8/11 | [9c6d108](https://github.com/vllm-project/vllm-ascend/commit/9c6d108330574176f79eea52f989ea6049336af8) |
| 105 | [@SlightwindSec](https://github.com/SlightwindSec) | 2025/8/5 | [f3b50c5](https://github.com/vllm-project/vllm-ascend/commit/f3b50c54e8243ad8ccefb9b033277fbdd382a9c4) |
| 104 | [@CaveNightingale](https://github.com/CaveNightingale) | 2025/8/4 | [957c7f1](https://github.com/vllm-project/vllm-ascend/commit/957c7f108d5f0aea230220ccdc18d657229e4030) |
| 103 | [@underfituu](https://github.com/underfituu) | 2025/8/4 | [e38fab0](https://github.com/vllm-project/vllm-ascend/commit/e38fab011d0b81f3a8e40d9bbe263c283dd4129b) |
| 102 | [@yangqinghao-cmss](https://github.com/yangqinghao-cmss) | 2025/8/1 | [99fa0ac](https://github.com/vllm-project/vllm-ascend/commit/99fa0ac882c79ae9282940125b042a44ea422757) |
| 101 | [@pjgao](https://github.com/pjgao) | 2025/7/31 | [6192bc9](https://github.com/vllm-project/vllm-ascend/commit/6192bc95c0e47097836e9be1f30f2a0a6fdca088) |
| 100 | [@Liccol](https://github.com/Liccol) | 2025/7/31 | [7c90ba5](https://github.com/vllm-project/vllm-ascend/commit/7c90ba5fe8e420b891fdd30df050a33e3767835d) |
| 99 | [@1024daniel](https://github.com/1024daniel) | 2025/7/31 | [db310c6](https://github.com/vllm-project/vllm-ascend/commit/db310c6ec97b056296f7c2348b90c1d96d0b562a) |
| 98 | [@zhoux77899](https://github.com/zhoux77899) | 2025/7/30 | [4fcca13](https://github.com/vllm-project/vllm-ascend/commit/4fcca137a70c11daa4070ae014288be154715939) |
| 97 | [@YuanCheng-coder](https://github.com/YuanCheng-coder) | 2025/7/30 | [34dd24a](https://github.com/vllm-project/vllm-ascend/commit/34dd24adf21fb85a2c413292754b1599832efae2) |
| 96 | [@hongfugui](https://github.com/hongfugui) | 2025/7/30 | [1dbb888](https://github.com/vllm-project/vllm-ascend/commit/1dbb8882759e4326f5706f6e610674423376c2f3) |
| 95 | [@Irving11-BKN](https://github.com/Irving11-BKN) | 2025/7/29 | [ca8007f](https://github.com/vllm-project/vllm-ascend/commit/ca8007f584141d3a59b2bcbd4f8ba269c9b7e252) |
| 94 | [@taoxudonghaha](https://github.com/taoxudonghaha) | 2025/7/29 | [540336e](https://github.com/vllm-project/vllm-ascend/commit/540336edc9db09072a9aaa486fbf7ce625da5b9e) |
| 93 | [@loukong33](https://github.com/loukong33) | 2025/7/28 | [1a25b0a](https://github.com/vllm-project/vllm-ascend/commit/1a25b0a2ddb23bf4d731ebac4503efaf237b191f) |
| 92 | [@Ronald1995](https://github.com/Ronald1995) | 2025/7/25 | [e561a2c](https://github.com/vllm-project/vllm-ascend/commit/e561a2c6ec4493b490b13a4a9007d8f451ae0d0f) |
| 91 | [@ZrBac](https://github.com/ZrBac) | 2025/7/24 | [2ffe051](https://github.com/vllm-project/vllm-ascend/commit/2ffe051859d585df8353d1b9eefb64c44078175a) |
| 90 | [@SunnyLee151064](https://github.com/SunnyLee151064) | 2025/7/24 | [34571ea](https://github.com/vllm-project/vllm-ascend/commit/34571ea5ae69529758edf75f0252f86ccb4c7184) |
| 89 | [@shiyuan680](https://github.com/shiyuan680) | 2025/7/23 | [ac0bf13](https://github.com/vllm-project/vllm-ascend/commit/ac0bf133f47ead20f18bf71f9be6dbe05fbd218f) |
| 88 | [@aidoczh](https://github.com/aidoczh) | 2025/7/21 | [c32eea9](https://github.com/vllm-project/vllm-ascend/commit/c32eea96b73d26268070f57ef98416decc98aff7) |
| 87 | [@nuclearwu](https://github.com/nuclearwu) | 2025/7/20 | [54f2b31](https://github.com/vllm-project/vllm-ascend/commit/54f2b311848badc86371d269140e729012a60f2c) |
| 86 | [@pkking](https://github.com/pkking) | 2025/7/18 | [3e39d72](https://github.com/vllm-project/vllm-ascend/commit/3e39d7234c0e5c66b184c136c602e87272b5a36e) |
| 85 | [@lianyiibo](https://github.com/lianyiibo) | 2025/7/18 | [53d2ea3](https://github.com/vllm-project/vllm-ascend/commit/53d2ea3789ffce32bf3ceb055d5582d28eadc6c7) |
| 84 | [@xudongLi-cmss](https://github.com/xudongLi-cmss) | 2025/7/2 | [7fc1a98](https://github.com/vllm-project/vllm-ascend/commit/7fc1a984890bd930f670deedcb2dda3a46f84576) |
| 83 | [@ZhengWG](https://github.com/) | 2025/7/7 | [3a469de](https://github.com/vllm-project/vllm-ascend/commit/9c886d0a1f0fc011692090b0395d734c83a469de) |
| 82 | [@wm901115nwpu](https://github.com/) | 2025/7/7 | [a2a47d4](https://github.com/vllm-project/vllm-ascend/commit/f08c4f15a27f0f27132f4ca7a0c226bf0a2a47d4) |
| 81 | [@Agonixiaoxiao](https://github.com/) | 2025/7/2 | [6f84576](https://github.com/vllm-project/vllm-ascend/commit/7fc1a984890bd930f670deedcb2dda3a46f84576) |
| 80 | [@zhanghw0354](https://github.com/zhanghw0354) | 2025/7/2 | [d3df9a5](https://github.com/vllm-project/vllm-ascend/commit/9fb3d558e5b57a3c97ee5e11b9f5dba6ad3df9a5) |
| 79 | [@GDzhu01](https://github.com/GDzhu01) | 2025/6/28 | [de256ac](https://github.com/vllm-project/vllm-ascend/commit/b308a7a25897b88d4a23a9e3d583f4ec6de256ac) |
| 78 | [@leo-pony](https://github.com/leo-pony) | 2025/6/26 | [3f2a5f2](https://github.com/vllm-project/vllm-ascend/commit/10253449120307e3b45f99d82218ba53e3f2a5f2) |
| 77 | [@zeshengzong](https://github.com/zeshengzong) | 2025/6/26 | [3ee25aa](https://github.com/vllm-project/vllm-ascend/commit/192dbbcc6e244a8471d3c00033dc637233ee25aa) |
| 76 | [@sharonyunyun](https://github.com/sharonyunyun) | 2025/6/25 | [2dd8666](https://github.com/vllm-project/vllm-ascend/commit/941269a6c5bbc79f6c1b6abd4680dc5802dd8666) |
| 75 | [@Pr0Wh1teGivee](https://github.com/Pr0Wh1teGivee) | 2025/6/25 | [c65dd40](https://github.com/vllm-project/vllm-ascend/commit/2fda60464c287fe456b4a2f27e63996edc65dd40) |
| 74 | [@xleoken](https://github.com/xleoken) | 2025/6/23 | [c604de0](https://github.com/vllm-project/vllm-ascend/commit/4447e53d7ad5edcda978ca6b0a3a26a73c604de0) |
| 73 | [@lyj-jjj](https://github.com/lyj-jjj) | 2025/6/23 | [5cbd74e](https://github.com/vllm-project/vllm-ascend/commit/5177bef87a21331dcca11159d3d1438075cbd74e) |
| 72 | [@farawayboat](https://github.com/farawayboat)| 2025/6/21 | [bc7d392](https://github.com/vllm-project/vllm-ascend/commit/097e7149f75c0806774bc68207f0f6270bc7d392)
| 71 | [@yuancaoyaoHW](https://github.com/yuancaoyaoHW) | 2025/6/20 | [7aa0b94](https://github.com/vllm-project/vllm-ascend/commit/00ae250f3ced68317bc91c93dc1f1a0977aa0b94)
| 70 | [@songshanhu07](https://github.com/songshanhu07) | 2025/6/18 | [5e1de1f](https://github.com/vllm-project/vllm-ascend/commit/2a70dbbdb8f55002de3313e17dfd595e1de1f)
| 69 | [@wangyanhui-cmss](https://github.com/wangyanhui-cmss) | 2025/6/12| [40c9e88](https://github.com/vllm-project/vllm-ascend/commit/2a5fb4014b863cee6abc3009f5bc5340c9e88) |
| 68 | [@chenwaner](https://github.com/chenwaner) | 2025/6/11 | [c696169](https://github.com/vllm-project/vllm-ascend/commit/e46dc142bf1180453c64226d76854fc1ec696169) |
| 67 | [@yzim](https://github.com/yzim) | 2025/6/11 | [aaf701b](https://github.com/vllm-project/vllm-ascend/commit/4153a5091b698c2270d160409e7fee73baaf701b) |
| 66 | [@Yuxiao-Xu](https://github.com/Yuxiao-Xu) | 2025/6/9 | [6b853f1](https://github.com/vllm-project/vllm-ascend/commit/6b853f15fe69ba335d2745ebcf14a164d0bcc505) |
| 65 | [@ChenTaoyu-SJTU](https://github.com/ChenTaoyu-SJTU) | 2025/6/7 | [20dedba](https://github.com/vllm-project/vllm-ascend/commit/20dedba5d1fc84b7ae8b49f9ce3e3649389e2193) |
| 64 | [@zxdukki](https://github.com/zxdukki) | 2025/6/7 | [87ebaef](https://github.com/vllm-project/vllm-ascend/commit/87ebaef4e4e519988f27a6aa378f614642202ecf) |
| 63 | [@sdmyzlp](https://github.com/sdmyzlp) | 2025/6/7 | [3640c60](https://github.com/vllm-project/vllm-ascend/commit/3640c60b0eb4d4cb104e20bfa406d3f1d17920a7) |
| 62 | [@weijinqian0](https://github.com/weijinqian0) | 2025/6/7 | [e9ada68](https://github.com/vllm-project/vllm-ascend/commit/e9ada685ece798f9fe0d4a287e3f5246a8a7207b) |
| 61 | [@hahazhky](https://github.com/hahazhky) | 2025/6/6 | [0b12c2a](https://github.com/vllm-project/vllm-ascend/commit/0b12c2acf7d9fd192beebebf662298067d9a5435) |
| 60 | [@depeng1994](https://github.com/depeng1994) | 2025/6/6 | [6b094a2](https://github.com/vllm-project/vllm-ascend/commit/6b094a2bd49a8a41eb3647568b2d9e5b337db81f) |
| 59 | [@David9857](https://github.com/David9857) | 2025/6/5 | [78431b3](https://github.com/vllm-project/vllm-ascend/commit/78431b34694dfa3c8f54ed7cc626660318557927) |
| 58 | [@momo609](https://github.com/momo609) | 2025/6/5 | [908a851](https://github.com/vllm-project/vllm-ascend/commit/908a851a776cfd9051cc062119e6ec481561c6f7) |
| 57 | [@zhangxinyuehfad](https://github.com/zhangxinyuehfad) | 2025/6/5 | [7737aaa](https://github.com/vllm-project/vllm-ascend/commit/7737aaa40f699b233a35fb61e908b687adc1e2e5) |
| 56 | [@NINGBENZHE](https://github.com/NINGBENZHE) | 2025/6/3 | [6ec64a3](https://github.com/vllm-project/vllm-ascend/commit/6ec64a3f9686df65b5a23a41aa301e669db19099) |
| 55 | [@XWFAlone](https://github.com/XWFAlone) | 2025/5/30 | [3442fbd](https://github.com/vllm-project/vllm-ascend/commit/3442fbdb235b4c6d72c2bc64a49707a7bd89958e) |
| 54 | [@YisongJiang](https://github.com/YisongJiang) | 2025/5/29 | [90afaf6](https://github.com/vllm-project/vllm-ascend/commit/90afaf6306f680307462becf3c78585737579851) |
| 53 | [@ponix-j](https://github.com/ponix-j) | 2025/5/23 | [df58fb8](https://github.com/vllm-project/vllm-ascend/commit/df58fb80eee24139fc61c495be3ce79cf81b3f73) |
| 52 | [@ttanzhiqiang](https://github.com/ttanzhiqiang) | 2025/5/23 | [dc6172e](https://github.com/vllm-project/vllm-ascend/commit/dc6172efd3860ce95b40a7b3e93611f875f06d40) |
| 51 | [@yangpuPKU](https://github.com/yangpuPKU) | 2025/5/23 | [46df67a](https://github.com/vllm-project/vllm-ascend/commit/46df67a5e9ab73fade08cbb2d8c0155cee7316d1) |
| 50 | [@wonderful199082](https://github.com/wonderful199082) | 2025/5/20 | [5cf9ff1](https://github.com/vllm-project/vllm-ascend/commit/5cf9ff18e91b0b7031c258d71a257b8e24689763) |
| 49 | [@22dimensions](https://github.com/22dimensions) | 2025/5/17 | [a8730e7](https://github.com/vllm-project/vllm-ascend/commit/a8730e7a3c4ac6c4b39a5946c943252fdea6cce5) |
| 48 | [@cxcxflying](https://github.com/cxcxflying) | 2025/5/13 | [e564470](https://github.com/vllm-project/vllm-ascend/commit/e56447033889ca95df512208cab22ef832bfdf07) |
| 47 | [@NeverRaR](https://github.com/NeverRaR) | 2025/5/12 | [efabd72](https://github.com/vllm-project/vllm-ascend/commit/efabd722eb757e49aa309c173bbec91ca8c4ced1) |
| 46 | [@chris668899](https://github.com/chris668899) | 2025/5/8 | [6c02088](https://github.com/vllm-project/vllm-ascend/commit/6c020883a8332b5c519f4f6502733edd9b391c2b) |
| 45 | [@sunbaosong](https://github.com/sunbaosong) | 2025/5/6 | [d6bfae8](https://github.com/vllm-project/vllm-ascend/commit/d6bfae8eeebedf677b643b712d367a3a69c9cce4) |
| 44 | [@ApsarasX](https://github.com/ApsarasX) | 2025/4/29 | [87975fa](https://github.com/vllm-project/vllm-ascend/commit/87975fa058fe3f90d204ded42a08989a8dcb413e) |
| 43 | [@zouyida2052](https://github.com/zouyida2052) | 2025/4/28 | [b9528e6](https://github.com/vllm-project/vllm-ascend/commit/b9528e6ecdc417cf444e55a0ce4a2bafdef0ea3b) |
| 42 | [@ZhengJun9](https://github.com/ZhengJun9) | 2025/4/28 | [1791113](https://github.com/vllm-project/vllm-ascend/commit/17911138c90d78a76bd691e9dcb56763db35b19f) |
| 41 | [@linfeng-yuan](https://github.com/linfeng-yuan) | 2025/4/28 | [2204e4d](https://github.com/vllm-project/vllm-ascend/commit/2204e4d08f8e10cf9c30154a14eaa5ca956c2acd) |
| 40 | [@jianzs](https://github.com/jianzs) | 2025/4/27 | [fa4a5d9](https://github.com/vllm-project/vllm-ascend/commit/fa4a5d980e8845a88b9162cf169f0a5ab230f8a5) |
| 39 | [@fakeYan](https://github.com/fakeYan) | 2025/4/23 | [05bdcbe](https://github.com/vllm-project/vllm-ascend/commit/05bdcbeae47c7fcb9b1c30cad059abf1d40b5421) |
| 38 | [@RongRongStudio](https://github.com/RongRongStudio) | 2025/4/22 | [848e041](https://github.com/vllm-project/vllm-ascend/commit/848e041a54732c923660dd02daf8e9bf439736a2) |
| 37 | [@paulyu12](https://github.com/paulyu12) | 2025/4/17 | [697908f](https://github.com/vllm-project/vllm-ascend/commit/697908f5cd7c65a3a917ec1a962b0886efc98c7e) |
| 36 | [@heartStrive1998](https://github.com/heartStrive1998) | 2025/4/16 | [2f15503](https://github.com/vllm-project/vllm-ascend/commit/2f155039dc3997640854daef469bbf0cb77dc6ed) |
| 35 | [@eeethenQ](https://github.com/eeethenQ) | 2025/4/15 | [44a8301](https://github.com/vllm-project/vllm-ascend/commit/44a8301424ded94dae83e13b837f5bfc0a1bfc15) |
| 34 | [@wxsIcey](https://github.com/wxsIcey) | 2025/4/10 | [d05ea17](https://github.com/vllm-project/vllm-ascend/commit/d05ea17427b82a506b97409a7de8359f18f565f7) |
| 33 | [@yx0716](https://github.com/yx0716) | 2025/4/8 | [5d62393](https://github.com/vllm-project/vllm-ascend/commit/5d6239306be9b0f5ac6dbaa137048c372a92ff20) |
| 32 | [@celestialli](https://github.com/celestialli) | 2025/4/7 | [2b765dc](https://github.com/vllm-project/vllm-ascend/commit/2b765dcc4974b1bafc26ff5da817ce7e652f0eb0) |
| 31 | [@hfadzxy](https://github.com/hfadzxy) | 2025/3/30 | [7beb433](https://github.com/vllm-project/vllm-ascend/commit/7beb4339dc8047af9ef64db1d0a8c59ddbb3709f) |
| 30 | [@wuhuikx](https://github.com/wuhuikx) | 2025/3/28 | [57a84bb](https://github.com/vllm-project/vllm-ascend/commit/57a84bb7befeaa0dc62aa35fa406e4d6affbfcca) |
| 29 | [@zzzzwwjj](https://github.com/zzzzwwjj) | 2025/3/28 | [12390af](https://github.com/vllm-project/vllm-ascend/commit/12390af075962456ecc8233d8dcce7064b75f390) |
| 28 | [@ganyi1996ppo](https://github.com/ganyi1996ppo) | 2025/3/28 | [27e86b9](https://github.com/vllm-project/vllm-ascend/commit/27e86b993a6a810d818143ec9dbfc439a419fa77) |
| 27 | [@ZhengZhenyu](https://github.com/ZhengZhenyu) | 2025/3/26 | [0b5a964](https://github.com/vllm-project/vllm-ascend/commit/0b5a9643fd6c3240d7ede669e37209d7ff433841) |
| 26 | [@baifanxxx](https://github.com/baifanxxx) | 2025/3/26 | [1225052](https://github.com/vllm-project/vllm-ascend/commit/122505208ff6284f409846ca7294f4a4b9883285) |
| 25 | [@rjg-lyh](https://github.com/rjg-lyh) | 2025/3/13 | [6512470](https://github.com/vllm-project/vllm-ascend/commit/65124705fb39d4cc2c94c80254421e067a82fe50) |
| 24 | [@xiemingda-1002](https://github.com/xiemingda-1002) | 2025/3/12 | [59ea23d](https://github.com/vllm-project/vllm-ascend/commit/59ea23d0d394879d7f33de6fd22242539b9c3cc5) |
| 23 | [@yiz-liu](https://github.com/yiz-liu) | 2025/3/11 | [0db6670](https://github.com/vllm-project/vllm-ascend/commit/0db6670bfab8cb1d84c9e7270df0a1d42d6ce7ca) |
| 22 | [@new-TonyWang](https://github.com/new-TonyWang) | 2025/3/11 | [dfb4e23](https://github.com/vllm-project/vllm-ascend/commit/dfb4e23e9d820ac992a071c123bbe983c7b01b2e) |
| 21 | [@mengwei805](https://github.com/mengwei805) | 2025/3/6 | [8fcf3d1](https://github.com/vllm-project/vllm-ascend/commit/8fcf3d1704084626db35c5dc82ade446508598d4) |
| 20 | [@baymax591](https://github.com/baymax591) | 2025/2/28 | [e8131b9](https://github.com/vllm-project/vllm-ascend/commit/e8131b99cf199f50a304e6e6fb125a1b95bcc92b) |
| 19 | [@dependabot](https://github.com/dependabot) | 2025/2/27 | [a5564ed](https://github.com/vllm-project/vllm-ascend/commit/a5564ed5d8fd9818936a22d9ea35951a27513b4c) |
| 18 | [@shink](https://github.com/shink) | 2025/2/27 | [6aed833](https://github.com/vllm-project/vllm-ascend/commit/6aed83335cbe92fd0b8ef07c28966a753d012ccb) |
| 17 | [@wwfu109](https://github.com/wwfu109) | 2025/2/27 | [b074047](https://github.com/vllm-project/vllm-ascend/commit/b07404766bdaf6e3cebc5cb0aba89a247501302e) |
| 16 | [@kunpengW-code](https://github.com/kunpengW-code) | 2025/2/26 | [ca807ce](https://github.com/vllm-project/vllm-ascend/commit/ca807ce49ed64aa89242f5ae29b9862a77648b45) |
| 15 | [@Yaphets24](https://github.com/Yaphets24) | 2025/2/22 | [d0b3cb4](https://github.com/vllm-project/vllm-ascend/commit/d0b3cb4fa79d5fc7f8245a3c68885ce1fa030ba4) |
| 14 | [@noemotiovon](https://github.com/noemotiovon) | 2025/2/21 | [202b39a](https://github.com/vllm-project/vllm-ascend/commit/202b39a38c2869b0ecc3df486550fb555a2eb0c0) |
| 13 | [@SidaoY](https://github.com/SidaoY) | 2025/2/18 | [718c763](https://github.com/vllm-project/vllm-ascend/commit/718c7638555d12cd43ea2a9e497e185778b68595) |
| 12 | [@ShiyaNiu](https://github.com/ShiyaNiu) | 2025/2/17 | [36ea38f](https://github.com/vllm-project/vllm-ascend/commit/36ea38fde56437ff1745bd95cd8d9e02a6578d38) |
| 11 | [@ji-huazhong](https://github.com/ji-huazhong) | 2025/2/12 | [c8b57d1](https://github.com/vllm-project/vllm-ascend/commit/c8b57d10b24efcd9b4fadeb66cfbf66aa3dd5f82) |
| 10 | [@Angazenn](https://github.com/Angazenn) | 2025/2/11 | [7637759](https://github.com/vllm-project/vllm-ascend/commit/7637759056028839c74960d9cfd3ce6275ee5d35) |
| 9 | [@whx-sjtu](https://github.com/whx-sjtu) | 2025/2/7 | [8fc5dc9](https://github.com/vllm-project/vllm-ascend/commit/8fc5dc966aaf4e174d1ec0d1902c40289411ec0e) |
| 8 | [@zouyida2002](https://github.com/zouyida2002) | 2025/2/7 | [4495fc6](https://github.com/vllm-project/vllm-ascend/commit/4495fc68389e3fb1ef14534c202948931e38446b) |
| 7 | [@hw_whx](https://github.com/hw_whx) | 2025/2/7 | [7d16772](https://github.com/vllm-project/vllm-ascend/commit/7d1677263bc6628ade33bb780455e0f6e5b9b27a) |
| 6 | [@MengqingCao](https://github.com/MengqingCao) | 2025/2/6 | [7d9ae22](https://github.com/vllm-project/vllm-ascend/commit/7d9ae22ecb6dc3ea4e720e5109cf46e1ae7da730) |
| 5 | [@Potabk](https://github.com/Potabk) | 2025/2/6 | [8cb5615](https://github.com/vllm-project/vllm-ascend/commit/8cb5615fb010b34c2f4f89e03e6257bfee851f86) |
| 4 | [@wangxiyuan](https://github.com/wangxiyuan) | 2025/2/6 | [a48b9ad](https://github.com/vllm-project/vllm-ascend/commit/a48b9addefd292af523644411d4ff4142dd4bc66) |
| 3 | [@shen-shanshan](https://github.com/shen-shanshan) | 2025/2/6 | [bfccf73](https://github.com/vllm-project/vllm-ascend/commit/bfccf739e2fe121b54d9b198c2ec205a9379190e) |
| 2 | [@Yikun](https://github.com/Yikun) | 2025/2/5 | [d5e7756](https://github.com/vllm-project/vllm-ascend/commit/d5e7756028bd5884ade96b654555c375770a2f64) |
| 1 | [@simon-mo](https://github.com/simon-mo) | 2025/1/29 | [eb28342](https://github.com/vllm-project/vllm-ascend/commit/eb283428ddc17207b6866118f9bc15454b5b8801) |

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# Governance
## Mission
As a vital component of vLLM, the vLLM Ascend project is dedicated to providing an easy, fast, and cheap LLM Serving for Everyone on Ascend NPU, and to actively contribute to the enrichment of vLLM.
## Principles
vLLM Ascend follows the vLLM community's code of conduct：[vLLM - CODE OF CONDUCT](https://github.com/vllm-project/vllm/blob/main/CODE_OF_CONDUCT.md)
## Governance - Mechanics
vLLM Ascend is an open-source project under the vLLM community, where the authority to appoint roles is ultimately determined by the vLLM community. It adopts a hierarchical technical governance structure.
- Contributor:
**Responsibility:** Help new contributors on boarding, handle and respond to community questions, review RFCs, code
**Requirements:** Complete at least 1 contribution. Contributor is someone who consistently and actively participates in a project, included but not limited to issue/review/commits/community involvement.
Contributors will be empowered [vllm-project/vllm-ascend](https://github.com/vllm-project/vllm-ascend) Github repo `Triage` permissions (`Can read and clone this repository. Can also manage issues and pull requests`) to help community developers collaborate more efficiently.
- Maintainer:
**Responsibility:** Develop the project's vision and mission. Maintainers are responsible for driving the technical direction of the entire project and ensuring its overall success, possessing code merge permissions. They formulate the roadmap, review contributions from community members, continuously contribute code, and actively engage in community activities (such as regular meetings/events).
**Requirements:** Deep understanding of ‌vLLM‌ and ‌vLLM Ascend‌ codebases, with a commitment to sustained code contributions. Competency in ‌design/development/PR review workflows‌.
- **Review Quality‌:** Actively participate in community code reviews, ensuring high-quality code integration.
- **Quality Contribution‌:** Successfully develop and deliver at least one major feature while maintaining consistent high-quality contributions.
- **Community Involvement‌:** Actively address issues, respond to forum inquiries, participate in discussions, and engage in community-driven tasks.
Requires approval from existing Maintainers. The vLLM community has the final decision-making authority.
Maintainer will be empowered [vllm-project/vllm-ascend](https://github.com/vllm-project/vllm-ascend) Github repo write permissions (`Can read, clone, and push to this repository. Can also manage issues and pull requests`).
## Nominating and Removing Maintainers
### The Principles
- Membership in vLLM Ascend is given to individuals on merit basis after they demonstrated strong expertise of the vLLM / vLLM Ascend through contributions, reviews and discussions.
- For membership in the maintainer group the individual has to demonstrate strong and continued alignment with the overall vLLM / vLLM Ascend principles.
- Light criteria of moving module maintenance to ‘emeritus’ status if they don’t actively participate over long periods of time.
- The membership is for an individual, not a company.
### Nomination and Removal
- Nomination: Anyone can nominate someone to become a maintainer (include self-nominate). All existing maintainers are responsible for evaluating the nomination. The nominator should provide nominee's info around the strength of the candidate to be a maintainer, include but not limited to review quality, quality contribution, community involvement.
- Removal: Anyone can nominate a person to be removed from maintainer position (include self-nominate). All existing maintainers are responsible for evaluating the nomination. The nominator should provide nominee's info, include but not limited to lack of activity, conflict with the overall direction and other information that makes them unfit to be a maintainer.

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# User Stories
Read case studies on how users and developers solves real, everyday problems with vLLM Ascend
- [LLaMA-Factory](./llamafactory.md) is an easy-to-use and efficient platform for training and fine-tuning large language models, it supports vLLM Ascend to speed up inference since [LLaMA-Factory#7739](https://github.com/hiyouga/LLaMA-Factory/pull/7739), gain 2x performance enhancement of inference.
- [Huggingface/trl](https://github.com/huggingface/trl) is a cutting-edge library designed for post-training foundation models using advanced techniques like SFT, PPO and DPO, it uses vLLM Ascend since [v0.17.0](https://github.com/huggingface/trl/releases/tag/v0.17.0) to support RLHF on Ascend NPU.
- [MindIE Turbo](https://pypi.org/project/mindie-turbo) is an LLM inference engine acceleration plug-in library developed by Huawei on Ascend hardware, which includes self-developed large language model optimization algorithms and optimizations related to the inference engine framework. It supports vLLM Ascend since [2.0rc1](https://www.hiascend.com/document/detail/zh/mindie/20RC1/AcceleratePlugin/turbodev/mindie-turbo-0001.html).
- [GPUStack](https://github.com/gpustack/gpustack) is an open-source GPU cluster manager for running AI models. It supports vLLM Ascend since [v0.6.2](https://github.com/gpustack/gpustack/releases/tag/v0.6.2), see more GPUStack performance evaluation info on [link](https://mp.weixin.qq.com/s/pkytJVjcH9_OnffnsFGaew).
- [verl](https://github.com/volcengine/verl) is a flexible, efficient and production-ready RL training library for large language models (LLMs), uses vLLM Ascend since [v0.4.0](https://github.com/volcengine/verl/releases/tag/v0.4.0), see more info on [verl x Ascend Quickstart](https://verl.readthedocs.io/en/latest/ascend_tutorial/ascend_quick_start.html).
:::{toctree}
:caption: More details
:maxdepth: 1
llamafactory
:::

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# LLaMA-Factory
**About / Introduction**
[LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory) is an easy-to-use and efficient platform for training and fine-tuning large language models. With LLaMA-Factory, you can fine-tune hundreds of pre-trained models locally without writing any code.
LLaMA-Facotory users need to evaluate and inference the model after fine-tuning the model.
**The Business Challenge**
LLaMA-Factory used transformers to perform inference on Ascend NPU, but the speed was slow.
**Solving Challenges and Benefits with vLLM Ascend**
With the joint efforts of LLaMA-Factory and vLLM Ascend ([LLaMA-Factory#7739](https://github.com/hiyouga/LLaMA-Factory/pull/7739)), the performance of LLaMA-Factory in the model inference stage has been significantly improved. According to the test results, the inference speed of LLaMA-Factory has been increased to 2x compared to the transformers version.
**Learn more**
See more about LLaMA-Factory and how it uses vLLM Ascend for inference on the Ascend NPU in the following documentation: [LLaMA-Factory Ascend NPU Inference](https://llamafactory.readthedocs.io/en/latest/advanced/npu_inference.html).

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# Versioning policy
Starting with vLLM 0.7.x, the vLLM Ascend Plugin ([vllm-project/vllm-ascend](https://github.com/vllm-project/vllm-ascend)) project follows the [PEP 440](https://peps.python.org/pep-0440/) to publish matching with vLLM ([vllm-project/vllm](https://github.com/vllm-project/vllm)).
## vLLM Ascend Plugin versions
Each vLLM Ascend release will be versioned: `v[major].[minor].[micro][rcN][.postN]` (such as
`v0.7.3rc1`, `v0.7.3`, `v0.7.3.post1`)
- **Final releases**: will typically be released every **3 months**, will take the vLLM upstream release plan and Ascend software product release plan into comprehensive consideration.
- **Pre releases**: will typically be released **on demand**, ending with rcN, represents the Nth release candidate version, to support early testing by our users prior to a final release.
- **Post releases**: will typically be released **on demand** to support to address minor errors in a final release. It's different from [PEP-440 post release note](https://peps.python.org/pep-0440/#post-releases) suggestion, it will contain actual bug fixes considering that the final release version should be matched strictly with the vLLM final release version (`v[major].[minor].[micro]`). The post version has to be published as a patch version of the final release.
For example:
- `v0.7.x`: it's the first final release to match the vLLM `v0.7.x` version.
- `v0.7.3rc1`: will be the first pre version of vLLM Ascend.
- `v0.7.3.post1`: will be the post release if the `v0.7.3` release has some minor errors.
## Release Compatibility Matrix
Following is the Release Compatibility Matrix for vLLM Ascend Plugin:
| vLLM Ascend | vLLM | Python | Stable CANN | PyTorch/torch_npu | MindIE Turbo |
|-------------|--------------|------------------|-------------|--------------------|--------------|
| v0.10.1rc1 | v0.10.1/v0.10.1.1 | >= 3.9, < 3.12 | 8.2.RC1 | 2.7.1 / 2.7.1.dev20250724 | |
| v0.10.0rc1 | v0.10.0 | >= 3.9, < 3.12 | 8.2.RC1 | 2.7.1 / 2.7.1.dev20250724 | |
| v0.9.2rc1 | v0.9.2 | >= 3.9, < 3.12 | 8.1.RC1 | 2.5.1 / 2.5.1.post1.dev20250619 | |
| v0.9.1 | v0.9.1 | >= 3.9, < 3.12 | 8.2.RC1 | 2.5.1 / 2.5.1.post1 | |
| v0.9.1rc3 | v0.9.1 | >= 3.9, < 3.12 | 8.2.RC1 | 2.5.1 / 2.5.1.post1 | |
| v0.9.1rc2 | v0.9.1 | >= 3.9, < 3.12 | 8.2.RC1 | 2.5.1 / 2.5.1.post1| |
| v0.9.1rc1 | v0.9.1 | >= 3.9, < 3.12 | 8.1.RC1 | 2.5.1 / 2.5.1.post1.dev20250528 | |
| v0.9.0rc2 | v0.9.0 | >= 3.9, < 3.12 | 8.1.RC1 | 2.5.1 / 2.5.1 | |
| v0.9.0rc1 | v0.9.0 | >= 3.9, < 3.12 | 8.1.RC1 | 2.5.1 / 2.5.1 | |
| v0.8.5rc1 | v0.8.5.post1 | >= 3.9, < 3.12 | 8.1.RC1 | 2.5.1 / 2.5.1 | |
| v0.8.4rc2 | v0.8.4 | >= 3.9, < 3.12 | 8.0.0 | 2.5.1 / 2.5.1 | |
| v0.7.3.post1| v0.7.3 | >= 3.9, < 3.12 | 8.1.RC1 | 2.5.1 / 2.5.1 | 2.0rc1 |
| v0.7.3 | v0.7.3 | >= 3.9, < 3.12 | 8.1.RC1 | 2.5.1 / 2.5.1 | 2.0rc1 |
## Release cadence
### release window
| Date | Event |
|------------|-------------------------------------------|
| 2025.09.04 | Release candidates, v0.10.1rc1 |
| 2025.09.03 | v0.9.1 Final release |
| 2025.08.22 | Release candidates, v0.9.1rc3 |
| 2025.08.07 | Release candidates, v0.10.0rc1 |
| 2025.08.04 | Release candidates, v0.9.1rc2 |
| 2025.07.11 | Release candidates, v0.9.2rc1 |
| 2025.06.22 | Release candidates, v0.9.1rc1 |
| 2025.06.10 | Release candidates, v0.9.0rc2 |
| 2025.06.09 | Release candidates, v0.9.0rc1 |
| 2025.05.29 | v0.7.x post release, v0.7.3.post1 |
| 2025.05.08 | v0.7.x Final release, v0.7.3 |
| 2025.05.06 | Release candidates, v0.8.5rc1 |
| 2025.04.28 | Release candidates, v0.8.4rc2 |
| 2025.04.18 | Release candidates, v0.8.4rc1 |
| 2025.03.28 | Release candidates, v0.7.3rc2 |
| 2025.03.14 | Release candidates, v0.7.3rc1 |
| 2025.02.19 | Release candidates, v0.7.1rc1 |
## Branch policy
vLLM Ascend has main branch and dev branch.
- **main**: main branch，corresponds to the vLLM main branch and latest 1 or 2 release version. It 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-dev` is the dev branch for vLLM `v0.7.3` version.
Usually, a commit should be ONLY first merged in the main branch, and then backported to the dev branch to reduce maintenance costs as much as possible.
### Maintenance branch and EOL:
The branch status will be in one of the following states:
| Branch | Time frame | Summary |
|-------------------|----------------------------------|----------------------------------------------------------------------|
| Maintained | Approximately 2-3 minor versions | All bugfixes are appropriate. Releases produced, CI commitment. |
| Unmaintained | Community interest driven | All bugfixes are appropriate. No Releases produced, No CI commitment |
| End of Life (EOL) | N/A | Branch no longer accepting changes |
### Branch state
Note that vLLM Ascend will only be released for a certain vLLM release version rather than all versions. Hence, You might see only part of versions have dev branches (such as only `0.7.1-dev` / `0.7.3-dev` but no `0.7.2-dev`), this is as expected.
Usually, each minor version of vLLM (such as 0.7) will correspond to a vLLM Ascend version branch and support its latest version (for example, we plan to support version 0.7.3) as following shown:
| Branch | Status | Note |
|------------|--------------|--------------------------------------|
| main | Maintained | CI commitment for vLLM main branch and vLLM 0.9.2 branch |
| v0.9.1-dev | Maintained | CI commitment for vLLM 0.9.1 version |
| v0.7.3-dev | Maintained | CI commitment for vLLM 0.7.3 version |
| v0.7.1-dev | Unmaintained | Replaced by v0.7.3-dev |
### Feature branches
| Branch | Status | RFC link | Merge plan | Mentor |
|------------|--------------|---------------------------------------|------------|--------|
|rfc/long_seq_optimization|Maintained|https://github.com/vllm-project/vllm/issues/22693|930|wangxiyuan|
- Branch: The feature branch should be created with a prefix `rfc/` followed by the feature name, such as `rfc/feature-name`.
- Status: The status of the feature branch is `Maintained` until it is merged into the main branch or deleted.
- RFC link: The feature branch should be created with a corresponding RFC issue. The creation of a feature branch requires an RFC and approval from at least two maintainers.
- Merge plan: The final goal of a feature branch is to merge it into the main branch. If it exceeds 3 months, the mentor maintainer should evaluate whether to delete the branch.
- Mentor: The mentor should be a vLLM Ascend maintainer who is responsible for the feature branch.
### Backward compatibility
For main branch, vLLM Ascend should works with vLLM main branch and latest 1 or 2 release version. So to ensure the backward compatibility, we will do the following:
- Both main branch and target vLLM release is tested by Ascend E2E CI. For example, currently, vLLM main branch and vLLM 0.8.4 are tested now.
- For code changes, we will make sure that the changes are compatible with the latest 1 or 2 vLLM release version as well. In this case, vLLM Ascend introduced a version check machinism inner the code. It'll check the version of installed vLLM package first to decide which code logic to use. If users hit the `InvalidVersion` error, it sometimes means that they have installed an dev/editable version of vLLM package. In this case, we provide the env variable `VLLM_VERSION` to let users specify the version of vLLM package to use.
- For documentation changes, we will make sure that the changes are compatible with the latest 1 or 2 vLLM release version as well. Note should be added if there are any breaking changes.
## Document Branch Policy
To reduce maintenance costs, **all branch documentation content should remain consistent, and version differences can be controlled via variables in [docs/source/conf.py](https://github.com/vllm-project/vllm-ascend/blob/main/docs/source/conf.py)**. While this is not a simple task, it is a principle we should strive to follow.
| Version | Purpose | Code Branch |
|-----|-----|---------|
| latest | Doc for the latest dev branch | vX.Y.Z-dev (Will be `main` after the first final release) |
| version | Doc for historical released versions | Git tags, like vX.Y.Z[rcN] |
| stable（not yet released） | Doc for latest final release branch | Will be `vX.Y.Z-dev` after the first official release |
As shown above:
- `latest` documentation: Matches the current maintenance branch `vX.Y.Z-dev` (Will be `main` after the first final release). Continuously updated to ensure usability for the latest release.
- `version` documentation: Corresponds to specific released versions (e.g., `v0.7.3`, `v0.7.3rc1`). No further updates after release.
- `stable` documentation (**not yet released**): Official release documentation. Updates are allowed in real-time after release, typically based on vX.Y.Z-dev. Once stable documentation is available, non-stable versions should display a header warning: `You are viewing the latest developer preview docs. Click here to view docs for the latest stable release.`.
## Software Dependency Management
- `torch-npu`: Ascend Extension for PyTorch (torch-npu) releases a stable version to [PyPi](https://pypi.org/project/torch-npu)
every 3 months, a development version (aka the POC version) every month, and a nightly version every day.
The PyPi stable version **CAN** be used in vLLM Ascend final version, the monthly dev version **ONLY CANN** be used in
vLLM Ascend RC version for rapid iteration, the nightly version **CANNOT** be used in vLLM Ascend any version and branches.

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#
# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
# Copyright 2023 The vLLM team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# This file is a part of the vllm-ascend project.
# Adapted from vllm-project/vllm/docs/source/conf.py
#
# -- Path setup --------------------------------------------------------------
# If extensions (or modules to document with autodoc) are in another directory,
# add these directories to sys.path here. If the directory is relative to the
# documentation root, use os.path.abspath to make it absolute, like shown here.
#
import json
import os
# import sys
# sys.path.insert(0, os.path.abspath('.'))
# -- Project information -----------------------------------------------------
project = 'vllm-ascend'
copyright = '2025, vllm-ascend team'
author = 'the vllm-ascend team'
# The full version, including alpha/beta/rc tags
release = ''
# -- General configuration ---------------------------------------------------
# Add any Sphinx extension module names here, as strings. They can be
# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
# ones.
# Copy from https://github.com/vllm-project/vllm/blob/main/docs/source/conf.py
extensions = [
"sphinx.ext.napoleon",
"sphinx.ext.intersphinx",
"sphinx_copybutton",
"sphinx.ext.autodoc",
"sphinx.ext.autosummary",
"myst_parser",
"sphinxarg.ext",
"sphinx_design",
"sphinx_togglebutton",
"sphinx_substitution_extensions",
]
myst_enable_extensions = ["colon_fence", "substitution"]
# Change this when cut down release
myst_substitutions = {
# the branch of vllm, used in vllm clone
# - main branch: 'main'
# - vX.Y.Z branch: 'vX.Y.Z'
'vllm_version': 'v0.10.1.1',
# the branch of vllm-ascend, used in vllm-ascend clone and image tag
# - main branch: 'main'
# - vX.Y.Z branch: latest vllm-ascend release tag
'vllm_ascend_version': 'v0.10.1rc1',
# the newest release version of vllm-ascend and matched vLLM, used in pip install.
# This value should be updated when cut down release.
'pip_vllm_ascend_version': "0.10.1rc1",
'pip_vllm_version': "0.10.1.1",
# CANN image tag
'cann_image_tag': "8.2.rc1-910b-ubuntu22.04-py3.11",
# vllm version in ci
'ci_vllm_version': 'v0.10.1.1',
}
# Add any paths that contain templates here, relative to this directory.
templates_path = ['_templates']
# The language for content autogenerated by Sphinx. Refer to documentation
# for a list of supported languages.
#
# This is also used if you do content translation via gettext catalogs.
# Usually you set "language" from the command line for these cases.
locale_dirs = ['locale/']
gettext_compact = False
# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
# This pattern also affects html_static_path and html_extra_path.
exclude_patterns = [
'_build',
'Thumbs.db',
'.DS_Store',
'.venv',
'README.md',
'user_guide/release.template.md',
# TODO(yikun): Remove this after zh supported
'**/*.zh.md'
]
# -- Options for HTML output -------------------------------------------------
# The theme to use for HTML and HTML Help pages. See the documentation for
# a list of builtin themes.
#
html_title = project
html_theme = 'sphinx_book_theme'
html_logo = 'logos/vllm-ascend-logo-text-light.png'
html_theme_options = {
'path_to_docs': 'docs/source',
'repository_url': 'https://github.com/vllm-project/vllm-ascend',
'use_repository_button': True,
'use_edit_page_button': True,
}
# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
# html_static_path = ['_static']
READTHEDOCS_VERSION_TYPE = os.environ.get('READTHEDOCS_VERSION_TYPE')
if READTHEDOCS_VERSION_TYPE == "tag":
# remove the warning banner if the version is a tagged release
header_file = os.path.join(os.path.dirname(__file__),
"_templates/sections/header.html")
# The file might be removed already if the build is triggered multiple times
# (readthedocs build both HTML and PDF versions separately)
if os.path.exists(header_file):
os.remove(header_file)
def setup(app):
pass
if __name__ == "__main__":
print(json.dumps(myst_substitutions))

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# Contributing
## Building and testing
It's recommended to set up a local development environment to build and test
before you submit a PR.
### Setup development environment
Theoretically, the vllm-ascend build is only supported on Linux because
`vllm-ascend` dependency `torch_npu` only supports Linux.
But you can still set up dev env on Linux/Windows/macOS for linting and basic
test as following commands:
#### Run lint locally
```bash
# Choose a base dir (~/vllm-project/) and set up venv
cd ~/vllm-project/
python3 -m venv .venv
source ./.venv/bin/activate
# Clone vllm-ascend and install
git clone https://github.com/vllm-project/vllm-ascend.git
cd vllm-ascend
# Install lint requirement and enable pre-commit hook
pip install -r requirements-lint.txt
# Run lint (You need install pre-commits deps via proxy network at first time)
bash format.sh
```
#### Run CI locally
After complete "Run lint" setup, you can run CI locally:
```{code-block} bash
:substitutions:
cd ~/vllm-project/
# Run CI need vLLM installed
git clone --branch |vllm_version| https://github.com/vllm-project/vllm.git
cd vllm
pip install -r requirements/build.txt
VLLM_TARGET_DEVICE="empty" pip install .
cd ..
# Install requirements
cd vllm-ascend
# For Linux:
pip install -r requirements-dev.txt
# For non Linux:
cat requirements-dev.txt | grep -Ev '^#|^--|^$|^-r' | while read PACKAGE; do pip install "$PACKAGE"; done
cat requirements.txt | grep -Ev '^#|^--|^$|^-r' | while read PACKAGE; do pip install "$PACKAGE"; done
# Run ci:
bash format.sh ci
```
#### Submit the commit
```bash
# Commit changed files using `-s`
git commit -sm "your commit info"
```
🎉 Congratulations! You have completed the development environment setup.
### Test locally
You can refer to [Testing](./testing.md) doc to help you setup testing environment and running tests locally.
## DCO and Signed-off-by
When contributing changes to this project, you must agree to the DCO. Commits must include a `Signed-off-by:` header which certifies agreement with the terms of the DCO.
Using `-s` with `git commit` will automatically add this header.
## PR Title and Classification
Only specific types of PRs will be reviewed. The PR title is prefixed appropriately to indicate the type of change. Please use one of the following:
- `[Attention]` for new features or optimization in attention.
- `[Communicator]` for new features or optimization in communicators.
- `[ModelRunner]` for new features or optimization in model runner.
- `[Platform]` for new features or optimization in platform.
- `[Worker]` for new features or optimization in worker.
- `[Core]` for new features or optimization in the core vllm-ascend logic (such as platform, attention, communicators, model runner)
- `[Kernel]` changes affecting compute kernels and ops.
- `[Bugfix]` for bug fixes.
- `[Doc]` for documentation fixes and improvements.
- `[Test]` for tests (such as unit tests).
- `[CI]` for build or continuous integration improvements.
- `[Misc]` for PRs that do not fit the above categories. Please use this sparingly.
:::{note}
If the PR spans more than one category, please include all relevant prefixes.
:::
## Others
You may find more information about contributing to vLLM Ascend backend plugin on [<u>docs.vllm.ai</u>](https://docs.vllm.ai/en/latest/contributing/overview.html).
If you find any problem when contributing, you can feel free to submit a PR to improve the doc to help other developers.
:::{toctree}
:caption: Index
:maxdepth: 1
testing
:::

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# Testing
This secition explains how to write e2e tests and unit tests to verify the implementation of your feature.
## Setup test environment
The fastest way to setup test environment is to use the main branch container image:
:::::{tab-set}
:sync-group: e2e
::::{tab-item} Local (CPU)
:selected:
:sync: cpu
You can run the unit tests on CPU with the following steps:
```{code-block} bash
:substitutions:
cd ~/vllm-project/
# ls
# vllm vllm-ascend
# Use mirror to speedup download
# docker pull quay.nju.edu.cn/ascend/cann:|cann_image_tag|
export IMAGE=quay.io/ascend/cann:|cann_image_tag|
docker run --rm --name vllm-ascend-ut \
-v $(pwd):/vllm-project \
-v ~/.cache:/root/.cache \
-ti $IMAGE bash
# (Optional) Configure mirror to speedup download
sed -i 's|ports.ubuntu.com|mirrors.huaweicloud.com|g' /etc/apt/sources.list
pip config set global.index-url https://mirrors.huaweicloud.com/repository/pypi/simple/
# For torch-npu dev version or x86 machine
export PIP_EXTRA_INDEX_URL="https://download.pytorch.org/whl/cpu/ https://mirrors.huaweicloud.com/ascend/repos/pypi"
apt-get update -y
apt-get install -y python3-pip git vim wget net-tools gcc g++ cmake libnuma-dev curl gnupg2
# Install vllm
cd /vllm-project/vllm
VLLM_TARGET_DEVICE=empty python3 -m pip -v install .
# Install vllm-ascend
cd /vllm-project/vllm-ascend
# [IMPORTANT] Import LD_LIBRARY_PATH to enumerate the CANN environment under CPU
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/Ascend/ascend-toolkit/latest/$(uname -m)-linux/devlib
python3 -m pip install -r requirements-dev.txt
python3 -m pip install -v .
```
::::
::::{tab-item} Single card
:sync: single
```{code-block} bash
:substitutions:
# Update DEVICE according to your device (/dev/davinci[0-7])
export DEVICE=/dev/davinci0
# Update the vllm-ascend image
export IMAGE=quay.io/ascend/vllm-ascend:main
docker run --rm \
--name vllm-ascend \
--device $DEVICE \
--device /dev/davinci_manager \
--device /dev/devmm_svm \
--device /dev/hisi_hdc \
-v /usr/local/dcmi:/usr/local/dcmi \
-v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
-v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
-v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
-v /etc/ascend_install.info:/etc/ascend_install.info \
-v /root/.cache:/root/.cache \
-p 8000:8000 \
-it $IMAGE bash
```
After starting the container, you should install the required packages:
```bash
# Prepare
pip config set global.index-url https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple
# Install required packages
pip install -r requirements-dev.txt
```
::::
::::{tab-item} Multi cards
:sync: multi
```{code-block} bash
:substitutions:
# Update the vllm-ascend image
export IMAGE=quay.io/ascend/vllm-ascend:main
docker run --rm \
--name vllm-ascend \
--device /dev/davinci0 \
--device /dev/davinci1 \
--device /dev/davinci2 \
--device /dev/davinci3 \
--device /dev/davinci_manager \
--device /dev/devmm_svm \
--device /dev/hisi_hdc \
-v /usr/local/dcmi:/usr/local/dcmi \
-v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
-v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
-v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
-v /etc/ascend_install.info:/etc/ascend_install.info \
-v /root/.cache:/root/.cache \
-p 8000:8000 \
-it $IMAGE bash
```
After starting the container, you should install the required packages:
```bash
cd /vllm-workspace/vllm-ascend/
# Prepare
pip config set global.index-url https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple
# Install required packages
pip install -r requirements-dev.txt
```
::::
:::::
## Running tests
### Unit test
There are several principles to follow when writing unit tests:
- The test file path should be consistent with source file and start with `test_` prefix, such as: `vllm_ascend/worker/worker_v1.py` --> `tests/ut/worker/test_worker_v1.py`
- The vLLM Ascend test are using unittest framework, see [here](https://docs.python.org/3/library/unittest.html#module-unittest) to understand how to write unit tests.
- All unit tests can be run on CPU, so you must mock the device-related function to host.
- Example: [tests/ut/test_ascend_config.py](https://github.com/vllm-project/vllm-ascend/blob/main/tests/ut/test_ascend_config.py).
- You can run the unit tests using `pytest`:
:::::{tab-set}
:sync-group: e2e
::::{tab-item} Local (CPU)
:selected:
:sync: cpu
```bash
# Run unit tests
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/Ascend/ascend-toolkit/latest/$(uname -m)-linux/devlib
TORCH_DEVICE_BACKEND_AUTOLOAD=0 pytest -sv tests/ut
```
::::
::::{tab-item} Single card
:sync: single
```bash
cd /vllm-workspace/vllm-ascend/
# Run all single card the tests
pytest -sv tests/ut
# Run single test
pytest -sv tests/ut/test_ascend_config.py
```
::::
::::{tab-item} Multi cards test
:sync: multi
```bash
cd /vllm-workspace/vllm-ascend/
# Run all single card the tests
pytest -sv tests/ut
# Run single test
pytest -sv tests/ut/test_ascend_config.py
```
::::
:::::
### E2E test
Although vllm-ascend CI provide [e2e test](https://github.com/vllm-project/vllm-ascend/blob/main/.github/workflows/vllm_ascend_test.yaml) on Ascend CI, you can run it
locally.
:::::{tab-set}
:sync-group: e2e
::::{tab-item} Local (CPU)
:sync: cpu
You can't run e2e test on CPU.
::::
::::{tab-item} Single card
:selected:
:sync: single
```bash
cd /vllm-workspace/vllm-ascend/
# Run all single card the tests
VLLM_USE_MODELSCOPE=true pytest -sv tests/e2e/singlecard/
# Run a certain test script
VLLM_USE_MODELSCOPE=true pytest -sv tests/e2e/singlecard/test_offline_inference.py
# Run a certain case in test script
VLLM_USE_MODELSCOPE=true pytest -sv tests/e2e/singlecard/test_offline_inference.py::test_models
```
::::
::::{tab-item} Multi cards test
:sync: multi
```bash
cd /vllm-workspace/vllm-ascend/
# Run all single card the tests
VLLM_USE_MODELSCOPE=true pytest -sv tests/e2e/multicard/
# Run a certain test script
VLLM_USE_MODELSCOPE=true pytest -sv tests/e2e/multicard/test_dynamic_npugraph_batchsize.py
# Run a certain case in test script
VLLM_USE_MODELSCOPE=true pytest -sv tests/e2e/multicard/test_offline_inference.py::test_models
```
::::
:::::
This will reproduce e2e test: [vllm_ascend_test.yaml](https://github.com/vllm-project/vllm-ascend/blob/main/.github/workflows/vllm_ascend_test.yaml).
#### E2E test example:
- Offline test example: [`tests/e2e/singlecard/test_offline_inference.py`](https://github.com/vllm-project/vllm-ascend/blob/main/tests/e2e/singlecard/test_offline_inference.py)
- Online test examples: [`tests/e2e/singlecard/test_prompt_embedding.py`](https://github.com/vllm-project/vllm-ascend/blob/main/tests/e2e/singlecard/test_prompt_embedding.py)
- Correctness test example: [`tests/e2e/singlecard/test_aclgraph.py`](https://github.com/vllm-project/vllm-ascend/blob/main/tests/e2e/singlecard/test_aclgraph.py)
- Reduced Layer model test example: [test_torchair_graph_mode.py - DeepSeek-V3-Pruning](https://github.com/vllm-project/vllm-ascend/blob/20767a043cccb3764214930d4695e53941de87ec/tests/e2e/multicard/test_torchair_graph_mode.py#L48)
The CI resource is limited, you might need to reduce layer number of the model, below is an example of how to generate a reduced layer model:
1. Fork the original model repo in modelscope, we need all the files in the repo except for weights.
2. Set `num_hidden_layers` to the expected number of layers, e.g., `{"num_hidden_layers": 2,}`
3. Copy the following python script as `generate_random_weight.py`. Set the relevant parameters `MODEL_LOCAL_PATH`, `DIST_DTYPE` and `DIST_MODEL_PATH` as needed:
```python
import torch
from transformers import AutoTokenizer, AutoConfig
from modeling_deepseek import DeepseekV3ForCausalLM
from modelscope import snapshot_download
MODEL_LOCAL_PATH = "~/.cache/modelscope/models/vllm-ascend/DeepSeek-V3-Pruning"
DIST_DTYPE = torch.bfloat16
DIST_MODEL_PATH = "./random_deepseek_v3_with_2_hidden_layer"
config = AutoConfig.from_pretrained(MODEL_LOCAL_PATH, trust_remote_code=True)
model = DeepseekV3ForCausalLM(config)
model = model.to(DIST_DTYPE)
model.save_pretrained(DIST_MODEL_PATH)
```
### Run doctest
vllm-ascend provides a `vllm-ascend/tests/e2e/run_doctests.sh` command to run all doctests in the doc files.
The doctest is a good way to make sure the docs are up to date and the examples are executable, you can run it locally as follows:
```bash
# Run doctest
/vllm-workspace/vllm-ascend/tests/e2e/run_doctests.sh
```
This will reproduce the same environment as the CI: [vllm_ascend_doctest.yaml](https://github.com/vllm-project/vllm-ascend/blob/main/.github/workflows/vllm_ascend_doctest.yaml).

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# Accuracy Report
:::{toctree}
:caption: Accuracy Report
:maxdepth: 1
:::

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# Accuracy
:::{toctree}
:caption: Accuracy
:maxdepth: 1
using_evalscope
using_lm_eval
using_opencompass
accuracy_report/index
:::

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# Using EvalScope
This document will guide you have model inference stress testing and accuracy testing using [EvalScope](https://github.com/modelscope/evalscope).
## 1. Online serving
You can run docker container to start the vLLM server on a single NPU:
```{code-block} bash
:substitutions:
# Update DEVICE according to your device (/dev/davinci[0-7])
export DEVICE=/dev/davinci7
# Update the vllm-ascend image
export IMAGE=quay.io/ascend/vllm-ascend:|vllm_ascend_version|
docker run --rm \
--name vllm-ascend \
--device $DEVICE \
--device /dev/davinci_manager \
--device /dev/devmm_svm \
--device /dev/hisi_hdc \
-v /usr/local/dcmi:/usr/local/dcmi \
-v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
-v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
-v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
-v /etc/ascend_install.info:/etc/ascend_install.info \
-v /root/.cache:/root/.cache \
-p 8000:8000 \
-e VLLM_USE_MODELSCOPE=True \
-e PYTORCH_NPU_ALLOC_CONF=max_split_size_mb:256 \
-it $IMAGE \
vllm serve Qwen/Qwen2.5-7B-Instruct --max_model_len 26240
```
If your service start successfully, you can see the info shown below:
```
INFO: Started server process [6873]
INFO: Waiting for application startup.
INFO: Application startup complete.
```
Once your server is started, you can query the model with input prompts in new terminal:
```
curl http://localhost:8000/v1/completions \
-H "Content-Type: application/json" \
-d '{
"model": "Qwen/Qwen2.5-7B-Instruct",
"prompt": "The future of AI is",
"max_tokens": 7,
"temperature": 0
}'
```
## 2. Install EvalScope using pip
You can install EvalScope by using:
```bash
python3 -m venv .venv-evalscope
source .venv-evalscope/bin/activate
pip install gradio plotly evalscope
```
## 3. Run gsm8k accuracy test using EvalScope
You can `evalscope eval` run gsm8k accuracy test:
```
evalscope eval \
--model Qwen/Qwen2.5-7B-Instruct \
--api-url http://localhost:8000/v1 \
--api-key EMPTY \
--eval-type service \
--datasets gsm8k \
--limit 10
```
After 1-2 mins, the output is as shown below:
```shell
+---------------------+-----------+-----------------+----------+-------+---------+---------+
| Model | Dataset | Metric | Subset | Num | Score | Cat.0 |
+=====================+===========+=================+==========+=======+=========+=========+
| Qwen2.5-7B-Instruct | gsm8k | AverageAccuracy | main | 10 | 0.8 | default |
+---------------------+-----------+-----------------+----------+-------+---------+---------+
```
See more detail in: [EvalScope doc - Model API Service Evaluation](https://evalscope.readthedocs.io/en/latest/get_started/basic_usage.html#model-api-service-evaluation).
## 4. Run model inference stress testing using EvalScope
### Install EvalScope[perf] using pip
```shell
pip install evalscope[perf] -U
```
### Basic usage
You can use `evalscope perf` run perf test:
```
evalscope perf \
--url "http://localhost:8000/v1/chat/completions" \
--parallel 5 \
--model Qwen/Qwen2.5-7B-Instruct \
--number 20 \
--api openai \
--dataset openqa \
--stream
```
### Output results
After 1-2 mins, the output is as shown below:
```shell
Benchmarking summary:
+-----------------------------------+---------------------------------------------------------------+
| Key | Value |
+===================================+===============================================================+
| Time taken for tests (s) | 38.3744 |
+-----------------------------------+---------------------------------------------------------------+
| Number of concurrency | 5 |
+-----------------------------------+---------------------------------------------------------------+
| Total requests | 20 |
+-----------------------------------+---------------------------------------------------------------+
| Succeed requests | 20 |
+-----------------------------------+---------------------------------------------------------------+
| Failed requests | 0 |
+-----------------------------------+---------------------------------------------------------------+
| Output token throughput (tok/s) | 132.6926 |
+-----------------------------------+---------------------------------------------------------------+
| Total token throughput (tok/s) | 158.8819 |
+-----------------------------------+---------------------------------------------------------------+
| Request throughput (req/s) | 0.5212 |
+-----------------------------------+---------------------------------------------------------------+
| Average latency (s) | 8.3612 |
+-----------------------------------+---------------------------------------------------------------+
| Average time to first token (s) | 0.1035 |
+-----------------------------------+---------------------------------------------------------------+
| Average time per output token (s) | 0.0329 |
+-----------------------------------+---------------------------------------------------------------+
| Average input tokens per request | 50.25 |
+-----------------------------------+---------------------------------------------------------------+
| Average output tokens per request | 254.6 |
+-----------------------------------+---------------------------------------------------------------+
| Average package latency (s) | 0.0324 |
+-----------------------------------+---------------------------------------------------------------+
| Average package per request | 254.6 |
+-----------------------------------+---------------------------------------------------------------+
| Expected number of requests | 20 |
+-----------------------------------+---------------------------------------------------------------+
| Result DB path | outputs/20250423_002442/Qwen2.5-7B-Instruct/benchmark_data.db |
+-----------------------------------+---------------------------------------------------------------+
Percentile results:
+------------+----------+---------+-------------+--------------+---------------+----------------------+
| Percentile | TTFT (s) | ITL (s) | Latency (s) | Input tokens | Output tokens | Throughput(tokens/s) |
+------------+----------+---------+-------------+--------------+---------------+----------------------+
| 10% | 0.0962 | 0.031 | 4.4571 | 42 | 135 | 29.9767 |
| 25% | 0.0971 | 0.0318 | 6.3509 | 47 | 193 | 30.2157 |
| 50% | 0.0987 | 0.0321 | 9.3387 | 49 | 285 | 30.3969 |
| 66% | 0.1017 | 0.0324 | 9.8519 | 52 | 302 | 30.5182 |
| 75% | 0.107 | 0.0328 | 10.2391 | 55 | 313 | 30.6124 |
| 80% | 0.1221 | 0.0329 | 10.8257 | 58 | 330 | 30.6759 |
| 90% | 0.1245 | 0.0333 | 13.0472 | 62 | 404 | 30.9644 |
| 95% | 0.1247 | 0.0336 | 14.2936 | 66 | 432 | 31.6691 |
| 98% | 0.1247 | 0.0353 | 14.2936 | 66 | 432 | 31.6691 |
| 99% | 0.1247 | 0.0627 | 14.2936 | 66 | 432 | 31.6691 |
+------------+----------+---------+-------------+--------------+---------------+----------------------+
```
See more detail in: [EvalScope doc - Model Inference Stress Testing](https://evalscope.readthedocs.io/en/latest/user_guides/stress_test/quick_start.html#basic-usage).

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# Using lm-eval
This document will guide you have a accuracy testing using [lm-eval][1].
## Online Server
### 1. start the vLLM server
You can run docker container to start the vLLM server on a single NPU:
```{code-block} bash
:substitutions:
# Update DEVICE according to your device (/dev/davinci[0-7])
export DEVICE=/dev/davinci7
# Update the vllm-ascend image
export IMAGE=quay.io/ascend/vllm-ascend:|vllm_ascend_version|
docker run --rm \
--name vllm-ascend \
--device $DEVICE \
--device /dev/davinci_manager \
--device /dev/devmm_svm \
--device /dev/hisi_hdc \
-v /usr/local/dcmi:/usr/local/dcmi \
-v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
-v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
-v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
-v /etc/ascend_install.info:/etc/ascend_install.info \
-v /root/.cache:/root/.cache \
-p 8000:8000 \
-e VLLM_USE_MODELSCOPE=True \
-e PYTORCH_NPU_ALLOC_CONF=max_split_size_mb:256 \
-it $IMAGE \
/bin/bash
vllm serve Qwen/Qwen2.5-0.5B-Instruct --max_model_len 4096 &
```
Started the vLLM server successfully,if you see log as below:
```
INFO: Started server process [9446]
INFO: Waiting for application startup.
INFO: Application startup complete.
```
### 2. Run gsm8k accuracy test using lm-eval
You can query result with input prompts:
```
curl http://localhost:8000/v1/completions \
-H "Content-Type: application/json" \
-d '{
"model": "Qwen/Qwen2.5-0.5B-Instruct",
"prompt": "'"<|im_start|>system\nYou are a professional accountant. Answer questions using accounting knowledge, output only the option letter (A/B/C/D).<|im_end|>\n"\
"<|im_start|>user\nQuestion: A company's balance sheet as of December 31, 2023 shows:\n"\
" Current assets: Cash and equivalents 5 million yuan, Accounts receivable 8 million yuan, Inventory 6 million yuan\n"\
" Non-current assets: Net fixed assets 12 million yuan\n"\
" Current liabilities: Short-term loans 4 million yuan, Accounts payable 3 million yuan\n"\
" Non-current liabilities: Long-term loans 9 million yuan\n"\
" Owner's equity: Paid-in capital 10 million yuan, Retained earnings ?\n"\
"Requirement: Calculate the company's Asset-Liability Ratio and Current Ratio (round to two decimal places).\n"\
"Options:\n"\
"A. Asset-Liability Ratio=58.33%, Current Ratio=1.90\n"\
"B. Asset-Liability Ratio=62.50%, Current Ratio=2.17\n"\
"C. Asset-Liability Ratio=65.22%, Current Ratio=1.75\n"\
"D. Asset-Liability Ratio=68.00%, Current Ratio=2.50<|im_end|>\n"\
"<|im_start|>assistant\n"'",
"max_tokens": 1,
"temperature": 0,
"stop": ["<|im_end|>"]
}' | python3 -m json.tool
```
The output format matches the following:
```
{
"id": "cmpl-2f678e8bdf5a4b209a3f2c1fa5832e25",
"object": "text_completion",
"created": 1754475138,
"model": "Qwen/Qwen2.5-0.5B-Instruct",
"choices": [
{
"index": 0,
"text": "A",
"logprobs": null,
"finish_reason": "length",
"stop_reason": null,
"prompt_logprobs": null
}
],
"service_tier": null,
"system_fingerprint": null,
"usage": {
"prompt_tokens": 252,
"total_tokens": 253,
"completion_tokens": 1,
"prompt_tokens_details": null
},
"kv_transfer_params": null
}
```
Install lm-eval in the container.
```bash
export HF_ENDPOINT="https://hf-mirror.com"
pip install lm-eval[api]
```
Run the following command:
```
# Only test gsm8k dataset in this demo
lm_eval \
--model local-completions \
--model_args model=Qwen/Qwen2.5-0.5B-Instruct,base_url=http://127.0.0.1:8000/v1/completions,tokenized_requests=False,trust_remote_code=True \
--tasks gsm8k \
--output_path ./
```
After 30 mins, the output is as shown below:
```
The markdown format results is as below:
Tasks|Version| Filter |n-shot| Metric | |Value | |Stderr|
|-----|------:|----------------|-----:|-----------|---|-----:|---|-----:|
|gsm8k| 3|flexible-extract| 5|exact_match|↑ |0.3215|± |0.0129|
| | |strict-match | 5|exact_match|↑ |0.2077|± |0.0112|
```
## Offline Server
### 1. Run docker container
You can run docker container on a single NPU:
```{code-block} bash
:substitutions:
# Update DEVICE according to your device (/dev/davinci[0-7])
export DEVICE=/dev/davinci7
# Update the vllm-ascend image
export IMAGE=quay.io/ascend/vllm-ascend:|vllm_ascend_version|
docker run --rm \
--name vllm-ascend \
--device $DEVICE \
--device /dev/davinci_manager \
--device /dev/devmm_svm \
--device /dev/hisi_hdc \
-v /usr/local/dcmi:/usr/local/dcmi \
-v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
-v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
-v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
-v /etc/ascend_install.info:/etc/ascend_install.info \
-v /root/.cache:/root/.cache \
-p 8000:8000 \
-e VLLM_USE_MODELSCOPE=True \
-e PYTORCH_NPU_ALLOC_CONF=max_split_size_mb:256 \
-it $IMAGE \
/bin/bash
```
### 2. Run gsm8k accuracy test using lm-eval
Install lm-eval in the container.
```bash
export HF_ENDPOINT="https://hf-mirror.com"
pip install lm-eval
```
Run the following command:
```
# Only test gsm8k dataset in this demo
lm_eval \
--model vllm \
--model_args pretrained=Qwen/Qwen2.5-0.5B-Instruct,max_model_len=4096 \
--tasks gsm8k \
--batch_size auto
```
After 1-2 mins, the output is as shown below:
```
The markdown format results is as below:
Tasks|Version| Filter |n-shot| Metric | |Value | |Stderr|
|-----|------:|----------------|-----:|-----------|---|-----:|---|-----:|
|gsm8k| 3|flexible-extract| 5|exact_match|↑ |0.3412|± |0.0131|
| | |strict-match | 5|exact_match|↑ |0.3139|± |0.0128|
```
## Use offline Datasets
Take gsm8k(single dataset) and mmlu(multi-subject dataset) as examples, and you can see more from [here][2].
```bash
# set HF_DATASETS_OFFLINE when using offline datasets
export HF_DATASETS_OFFLINE=1
git clone https://github.com/EleutherAI/lm-evaluation-harness.git
cd lm-evaluation-harness
pip install -e .
# gsm8k yaml path
cd lm_eval/tasks/gsm8k
# mmlu yaml path
cd lm_eval/tasks/mmlu/default
```
set [gsm8k.yaml][3] as follows:
```yaml
tag:
- math_word_problems
task: gsm8k
# set dataset_path arrow or json or parquet according to the downloaded dataset
dataset_path: arrow
# set dataset_name to null
dataset_name: null
output_type: generate_until
# add dataset_kwargs
dataset_kwargs:
data_files:
# train and test data download path
train: /root/.cache/gsm8k/gsm8k-train.arrow
test: /root/.cache/gsm8k/gsm8k-test.arrow
training_split: train
fewshot_split: train
test_split: test
doc_to_text: 'Q: {{question}}
A(Please follow the summarize the result at the end with the format of "The answer is xxx", where xx is the result.):'
doc_to_target: "{{answer}}" #" {{answer.split('### ')[-1].rstrip()}}"
metric_list:
- metric: exact_match
aggregation: mean
higher_is_better: true
ignore_case: true
ignore_punctuation: false
regexes_to_ignore:
- ","
- "\\$"
- "(?s).*#### "
- "\\.$"
generation_kwargs:
until:
- "Question:"
- "</s>"
- "<|im_end|>"
do_sample: false
temperature: 0.0
repeats: 1
num_fewshot: 5
filter_list:
- name: "strict-match"
filter:
- function: "regex"
regex_pattern: "#### (\\-?[0-9\\.\\,]+)"
- function: "take_first"
- name: "flexible-extract"
filter:
- function: "regex"
group_select: -1
regex_pattern: "(-?[$0-9.,]{2,})|(-?[0-9]+)"
- function: "take_first"
metadata:
version: 3.0
```
set [_default_template_yaml][4] as follows:
```yaml
# set dataset_path according to the downloaded dataset
dataset_path: /root/.cache/mmlu
test_split: test
fewshot_split: dev
fewshot_config:
sampler: first_n
output_type: multiple_choice
doc_to_text: "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:"
doc_to_choice: ["A", "B", "C", "D"]
doc_to_target: answer
metric_list:
- metric: acc
aggregation: mean
higher_is_better: true
metadata:
version: 1.0
dataset_kwargs:
trust_remote_code: true
```
You can see more usage on [Lm-eval Docs][5].
[1]: https://github.com/EleutherAI/lm-evaluation-harness
[2]: https://github.com/EleutherAI/lm-evaluation-harness/blob/main/docs/new_task_guide.md#using-local-datasets
[3]: https://github.com/EleutherAI/lm-evaluation-harness/blob/main/lm_eval/tasks/gsm8k/gsm8k.yaml
[4]: https://github.com/EleutherAI/lm-evaluation-harness/blob/main/lm_eval/tasks/mmlu/default/_default_template_yaml
[5]: https://github.com/EleutherAI/lm-evaluation-harness/blob/main/docs/README.md

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# Using OpenCompass
This document will guide you have a accuracy testing using [OpenCompass](https://github.com/open-compass/opencompass).
## 1. Online Serving
You can run docker container to start the vLLM server on a single NPU:
```{code-block} bash
:substitutions:
# Update DEVICE according to your device (/dev/davinci[0-7])
export DEVICE=/dev/davinci7
# Update the vllm-ascend image
export IMAGE=quay.io/ascend/vllm-ascend:|vllm_ascend_version|
docker run --rm \
--name vllm-ascend \
--device $DEVICE \
--device /dev/davinci_manager \
--device /dev/devmm_svm \
--device /dev/hisi_hdc \
-v /usr/local/dcmi:/usr/local/dcmi \
-v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
-v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
-v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
-v /etc/ascend_install.info:/etc/ascend_install.info \
-v /root/.cache:/root/.cache \
-p 8000:8000 \
-e VLLM_USE_MODELSCOPE=True \
-e PYTORCH_NPU_ALLOC_CONF=max_split_size_mb:256 \
-it $IMAGE \
vllm serve Qwen/Qwen2.5-7B-Instruct --max_model_len 26240
```
If your service start successfully, you can see the info shown below:
```
INFO: Started server process [6873]
INFO: Waiting for application startup.
INFO: Application startup complete.
```
Once your server is started, you can query the model with input prompts in new terminal:
```
curl http://localhost:8000/v1/completions \
-H "Content-Type: application/json" \
-d '{
"model": "Qwen/Qwen2.5-7B-Instruct",
"prompt": "The future of AI is",
"max_tokens": 7,
"temperature": 0
}'
```
## 2. Run ceval accuracy test using OpenCompass
Install OpenCompass and configure the environment variables in the container.
```bash
# Pin Python 3.10 due to:
# https://github.com/open-compass/opencompass/issues/1976
conda create -n opencompass python=3.10
conda activate opencompass
pip install opencompass modelscope[framework]
export DATASET_SOURCE=ModelScope
git clone https://github.com/open-compass/opencompass.git
```
Add `opencompass/configs/eval_vllm_ascend_demo.py` with the following content:
```python
from mmengine.config import read_base
from opencompass.models import OpenAISDK
with read_base():
from opencompass.configs.datasets.ceval.ceval_gen import ceval_datasets
# Only test ceval-computer_network dataset in this demo
datasets = ceval_datasets[:1]
api_meta_template = dict(
round=[
dict(role='HUMAN', api_role='HUMAN'),
dict(role='BOT', api_role='BOT', generate=True),
],
reserved_roles=[dict(role='SYSTEM', api_role='SYSTEM')],
)
models = [
dict(
abbr='Qwen2.5-7B-Instruct-vLLM-API',
type=OpenAISDK,
key='EMPTY', # API key
openai_api_base='http://127.0.0.1:8000/v1',
path='Qwen/Qwen2.5-7B-Instruct',
tokenizer_path='Qwen/Qwen2.5-7B-Instruct',
rpm_verbose=True,
meta_template=api_meta_template,
query_per_second=1,
max_out_len=1024,
max_seq_len=4096,
temperature=0.01,
batch_size=8,
retry=3,
)
]
```
Run the following command:
```
python3 run.py opencompass/configs/eval_vllm_ascend_demo.py --debug
```
After 1-2 mins, the output is as shown below:
```
The markdown format results is as below:
| dataset | version | metric | mode | Qwen2.5-7B-Instruct-vLLM-API |
|----- | ----- | ----- | ----- | -----|
| ceval-computer_network | db9ce2 | accuracy | gen | 68.42 |
```
You can see more usage on [OpenCompass Docs](https://opencompass.readthedocs.io/en/latest/index.html).

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# Purpose
What information should we have in order to perform model forward pass?
- the inputs
- the corresponding attention metadata of the inputs
The following diagram shows what we should prepare for the model inference.
```
+---------------+
inputs --> | |
| model | --> output
attn_meta --> | |
+---------------+
```
Therefore, as long as we have these two pieces of information mentioned above, we can perform the model's forward propagation.
This article will explain **how we obtain the inputs and their corresponding attention metadata** which are on the left part of above diagram.
# Overview
## 1. Obtain inputs
The workflow of obtain inputs:
1. Get `token positions`: The relative position of each token within its request sequence.
2. Get `token indices`: the index of each scheduled token in the token table.
3. Get `Token IDs`: Using token indices to retrieve the Token IDs from **token id table**.
At last, these `Token IDs` required to feed into the model, and also, `positions` should be send into model to create `Rope` (Rotary positional embedding). Both of them are the inputs of a model.
**Note**: because the `Token IDs` is the inputs of the model, so we will call it `Inputs IDs`
## 2. Build inputs attention metadata
The model requires these attention metadata during the forward pass:
- `query start location`: represents the start and end location of each request corresponding to the scheduled tokens.
- `sequence length`: the length of each request including both computed tokens and newly scheduled tokens.
- `number of computed tokens`: the number of computed tokens for each request.
- `number of requests`: the number of requests in this batch.
- `number of tokens`: Total number of scheduled tokens in this batch.
- **`block table`**: translates the logical address (within its sequence) of each block to its global physical address in the device's memory.
- `max query len`: the longest scheduled tokens length in this requests batch.
- `slot mapping`: the indices of each token that input token will be stored into.
- `attention mask`: The mask matrix applied to attention scores before softmax to control which tokens can attend to each other. (usually a causal attention)
# Before start
There are mainly three types of variables.
- token level: represents one attribute corresponding to each scheduled token, so the length of this variable is the number of scheduled tokens
- request level: represents one attribute of each scheduled request, which length usually is the number of scheduled requests. (`query start location` is a special case, which has one more element)
- system level:
1. **Token IDs table**: store the token ids (i.e. the inputs of the model) of each request. The shape of this table is `(max num request, max model len)`. Here, `max num request` is maximum count of concurrent requests allowed in a forward batch and `max model len` is the max token count can be handled at one request sequence in this model.
2. **Block table**: translates the logical address (within its sequence) of each block to its global physical address in the device's memory. The shape of this table is `(max num request, max model len / block size)`
**Note**: How were these two tables formed?
- Both of them are come from the `_update_states` method before **prepare inputs**. You can take a look if you need more inspiration.
## Tips
What is `Token ID`?
For simple, a `token ID` is an **integer** (usually `int32`), which represents a token.
example of `Token ID`:
```
| Token ID | Token |
|--------------|---------------|
| 0 | [PAD] |
| 1 | <|endoftext|> |
| 2 | <|start|> |
| 3 | [SEP] |
| 4 | I |
| 5 | the |
| 6 | be |
| 7 | of |
| 8 | and |
| ... | ... |
| ... | ... |
| vocab_size-1 | <|im_end|> |
```
# Go through details
Make a simple example, assumption:
- max tokens can be scheduled at once: 10.
- `block size`: 2
- Totally schedule 3 requests. Their prompt lengths are 3, 2, and 8 respectively.
- `max model length`: 12 (the max token count can be handled at one request sequence in this model).
These assumption are configured in the beginning when starting the vllm. They are not fixed, so you can manually set them.
## Step 1: All requests in the prefill phase
### Obtain inputs
Due to the max schedule token count limitation is 10, The scheduled token of each request: `{'0': 3, '1': 2, '2': 5}`. Note that the `request_2` is in chunked prefill, still has 3 prompt tokens not be scheduled.
#### 1. Get token positions:
First, find out each token belong to which request: the 0~2 tokens belong to request_0, 3~4 tokens belong to request_1 and 5~9 tokens belong to request_2. So, we can use `request indices` to point out each token belongs to which request. `request indices`: `[0, 0, 0, 1, 1, 2, 2, 2, 2, 2]`
For each request, use **the number of tokens already computed** + **the relative position in current scheduled tokens**: `request_0: [0 + 0, 0 + 1, 0 + 2]`, `request_1: [0 + 0, 0 + 1]`, `request_2: [0 + 0, 0 + 1,..., 0 + 4]` and then concat them together: `[0, 1, 2, 0, 1, 0, 1, 2, 3, 4]`. Note: there is more efficient way (using `request indices`) to create positions in actual code.
Finally, `token opsitions` is `[0, 1, 2, 0, 1, 0, 1, 2, 3, 4]`. This variable is **token level**
#### 2. Get token indices:
Current **Token IDs table**, which shape is `(max num request, max model len)`.
Why these `T_3_5`, `T_3_6`, `T_3_7` are in this table even them are not scheduled this time?
- We will fill all Token IDs in one request sequence to this table at once, but we only retrieve the tokens we scheduled this time. Then we will retrieve the remain Token IDs next time.
```
| T_0_0 | T_0_1 | T_0_2 | ? | ? | ? | ? | ? | ? | ? | ? | ? |
| T_1_0 | T_1_1 | ? | ? | ? | ? | ? | ? | ? | ? | ? | ? |
| T_2_0 | T_2_1 | T_3_2 | T_3_3 | T_3_4 | T_3_5 | T_3_6 | T_3_7 | ? | ? | ? | ? |
| ? | ? | ? | ? | ? | ? | ? | ? | ? | ? | ? | ? |
......
......
......
```
Note that the `T_x_x` is an `int32`
Let's say `M = max model len`, Then we can use `token positions` together with the `request indices` of each token to construct `token indices`.
So `token indices` = `[0 + 0 * M, 1 + 0 * M, 2 + 0 * M, 0 + 1 * M, 1 + 1 * M, 0 + 2 * M, 1 + 2 * M, 2 + 2 * M, 3 + 2 * M, 4 + 2 * M]` = `[0, 1, 2, 12, 13, 24, 25, 26, 27, 28]`
#### 3. Retrieve the Token IDs
As mentioned before, we will refer to these `Token IDs` as `Input IDs`.
We use the `token indices` to select out the corresponding `Input IDs` from the token table, The Pseudocode like:
```
input_ids = token_table[token_indices]
```
As mentioned before, we will refer these Token IDs as Inputs IDs:
- `Input IDs` = `[T_0_0, T_0_1, T_0_2, T_1_0, T_1_1, T_2_0, T_2_1, T_3_2, T_3_3, T_3_4]`
### Build inputs attention metadata
Current **Block Table**, we use the first block (i.e. block_0) to mark the unused block. The shape of the block is `(max num request, max model len / block size)`, the `max model len / block size = 12 / 2 = 6`
```
| 1 | 2 | 0 | 0 | 0 | 0 |
| 3 | 0 | 0 | 0 | 0 | 0 |
| 4 | 5 | 6 | 0 | 0 | 0 |
| 0 | 0 | 0 | 0 | 0 | 0 |
......
......
......
```
The kv cache block in the device memory is like:
```
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | ......
```
Let's say `K = max model len / block size = 6`, we can get token `device block number` from
The workflow of achieving slot mapping:
1. get `block table indices` using `K`, `positions` and `request indices`. Purpose: For each token, it could be used to select the `device block number` from `block table`.
2. get `device block number` using `block table indices`. Purpose: `device block number` indicates each token belong to which device block.
3. get `block offsets` using `positions` and `block size`. Purpose: `block offsets` indicates the offsets of each token within a block.
4. construct `slot mapping` using `device block number` and `block offsets`. Purpose: we can use `slot mapping` to store the Token IDs into token slots.
Details:
1. Using a simple formula to calculate the `block table indices`: `request indices * K + positions / block size`. So it equal to `[0 * 6 + 0 / 2, 0 * 6 + 1 / 2, 0 * 6 + 2 / 2, 1 * 6 + 0 / 2, 1 * 6 + 1 / 2, 2 * 6 + 0 / 2, 2 * 6 + 1 / 2, 2 * 6 + 2 / 2, 2 * 6 + 3 / 2, 2 * 6 + 4 / 2] = [0, 0, 1, 6, 6, 12, 12, 13, 13, 14]`. This could be used to select the `device block number` from `block table`. **token level**
2. Using the `block table indices` to select out the `device block number` for each scheduled token. The Pseudocode like: `block_numbers = block_table[block_table_indices]`. So `device block number = [1, 1, 2, 3, 3, 4, 4, 5, 5, 6]`**token level**
3. `block offsets` could be computed by `block offsets = positions % block size = [0, 1, 0, 0, 1, 0, 1, 0, 1, 0]`. **token level**
4. At last, use `block offsets` and `device block number` to create `slot mapping`: `device block number * block size + block_offsets = [2, 3, 4, 6, 7, 8, 9, 10, 11, 12]`
First, we know the scheduled token count is `[3, 2, 5]` **request level**
- So, we can use prefix sum to calculate the `query start location`: `[0, 3, 5, 10]`. **request level**
- Because in step_1 all the tokens in prefill, computed tokens count is 0, then `sequence length` = `[3, 2, 5]`. **request level**
- As mentioned above, `number of computed tokens` are all 0: `[0, 0, 0]`. **request level**
- `number of requests`: `3`.
- `number of tokens`: `[3, 2, 5]`. **request level**
- `max query len`: `5`.
- `slot mapping`: `[2, 3, 4, 6, 7, 8, 9, 10, 11, 12]`. **token level**
- `attention mask`: For all request do prefill, we simply create only one mask matrix for reuse across different requests. The shape of this mask matrix is `5 * 5`:
## Step 2: Chunked prefill
In Step 2, we will no longer provide explanations or perform calculations; instead, we will directly present the final result.
### Obtain inputs
The scheduled token of each request: `{'0': 1, '1': 1, '2': 3}`.
1. `request indices`: `[0, 1, 2, 2, 2]`
2. `token positions`: `[3, 2, 5, 6, 7]`
Current **Token IDs table**:
```
| T_0_0 | T_0_1 | T_0_2 | T_0_3 | ? | ? | ? | ? | ? | ? | ? | ? |
| T_1_0 | T_1_1 | T_1_2 | ? | ? | ? | ? | ? | ? | ? | ? | ? |
| T_2_0 | T_2_1 | T_3_2 | T_3_3 | T_3_4 | T_3_5 | T_3_6 | T_3_7 | ? | ? | ? | ? |
| ? | ? | ? | ? | ? | ? | ? | ? | ? | ? | ? | ? |
......
......
......
```
**Note**: The **T_0_3**, **T_1_2** are new Token IDs of request_0, request_1 respectively. them are sampled from the output of the model.
3. `token indices`: `[3, 14, 29, 30, 31]`
4. `Input IDs`: `[T_0_3, T_1_2, T_3_5, T_3_6, T_3_7]`
### Build inputs attention metadata
Current **Block Table**. **Note**: We allocate the `7` and `8` block to `request_1` and `request_2` respectively. Because they need more space in device to store kv cache after generate new tokens or chunked prefill new tokens.
```
| 1 | 2 | 0 | 0 | 0 | 0 |
| 3 | 7 | 0 | 0 | 0 | 0 |
| 4 | 5 | 6 | 8 | 0 | 0 |
| 0 | 0 | 0 | 0 | 0 | 0 |
......
......
......
```
The kv cache block in the device memory is still like:
```
| 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | ......
```
1. `block table indices`: `[1, 7, 14, 15, 15]`. **token level**
2. `device block number`: `[2, 7, 6, 8, 8]`. **token level**
3. `block offsets`: `[1, 0, 1, 0, 1]` **token level**
4. `slot mapping`: `[5, 14, 13, 16, 17]` **token level**
scheduled token count is `[1, 1, 3]`
- `query start location`: `[0, 1, 2, 5]`
- `sequence length`: `[4, 3, 8]`
- `number of computed tokens`: `[3, 2, 5]`
- `number of requests`: `3`
- `max query len`: `3`
- `slot mapping`: `[5, 14, 13, 16, 17]`
- `attention mask`: `5 * 8` Each token will have a `1 * 8` vector, and there are 5 scheduled tokens.
# At last
If you under stand the step_1 and step_2, you will know the all following steps.
Hope this article can help you get better understand to how vllm prepare inputs for model forwarding. If you have any good idea, welcome to contribute to us.

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# Feature Guide
This section provides an overview of the features implemented in vLLM Ascend. Developers can refer to this guide to understand how vLLM Ascend works.
:::{toctree}
:caption: Feature Guide
:maxdepth: 1
patch
ModelRunner_prepare_inputs
:::

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# Patch in vLLM Ascend
vLLM Ascend is a platform plugin for vLLM. Due to the release cycle of vLLM and vLLM Ascend is different, and the hardware limitation in some case, we need to patch some code in vLLM to make it compatible with vLLM Ascend.
In vLLM Ascend code, we provide a patch module `vllm_ascend/patch` to address the change for vLLM.
## Principle
We should keep in mind that Patch is not the best way to make vLLM Ascend compatible. It's just a temporary solution. The best way is to contribute the change to vLLM to make it compatible with vLLM Ascend originally. In vLLM Ascend, we have the basic principle for Patch strategy:
1. Less is more. Please do not patch unless it's the only way currently.
2. Once a patch is added, it's required to describe the future plan for removing the patch.
3. Anytime, clean the patch code is welcome.
## How it works
In `vllm_ascend/patch`, you can see the code structure as follows:
```
vllm_ascend
├── patch
│ ├── platform
│ │ ├── patch_0_9_2
│ │ ├── patch_common
│ │ ├── patch_main
│ ├── worker
│ │ ├── patch_0_9_2
│ │ ├── patch_common
│ │ ├── patch_main
└───────────
```
- **platform**: The patch code in this directory is for patching the code in vLLM main process. It's called by `vllm_ascend/platform::NPUPlatform::pre_register_and_update` very early when vLLM is initialized.
- For online mode, vLLM process calls the platform patch here `vllm/vllm/engine/arg_utils.py::AsyncEngineArgs.add_cli_args` when parsing the cli args.
- For offline mode, vLLM process calls the platform patch here `vllm/vllm/engine/arg_utils.py::EngineArgs.create_engine_config` when parsing the input parameters.
- **worker**: The patch code in this directory is for patching the code in vLLM worker process. It's called by `vllm_ascend/worker/worker_v1::NPUWorker::__init__` when the vLLM worker process is initialized.
- For both online and offline mode, vLLM engine core process calls the worker patch here `vllm/vllm/worker/worker_base.py::WorkerWrapperBase.init_worker` when initializing the worker process.
In both **platform** and **worker** folder, there are several patch modules. They are used for patching different version of vLLM.
- `patch_0_10_0`: This module is used for patching vLLM 0.10.0. The version is always the nearest version of vLLM. Once vLLM is released, we will drop this patch module and bump to a new version. For example, `patch_0_10_0` is used for patching vLLM 0.10.0.
- `patch_main`: This module is used for patching the code in vLLM main branch.
- `patch_common`: This module is used for patching both vLLM 0.10.0 and vLLM main branch.
## How to write a patch
Before writing a patch, following the principle above, we should patch the least code. If it's necessary, we can patch the code in either **platform** and **worker** folder. Here is an example to patch `distributed` module in vLLM.
1. Decide which version of vLLM we should patch. For example, after analysis, here we want to patch both 0.10.0 and main of vLLM.
2. Decide which process we should patch. For example, here `distributed` belongs to the vLLM main process, so we should patch `platform`.
3. Create the patch file in the right folder. The file should be named as `patch_{module_name}.py`. The example here is `vllm_ascend/patch/platform/patch_common/patch_distributed.py`.
4. Write your patch code in the new file. Here is an example:
```python
import vllm
def patch_destroy_model_parallel():
# your patch code
...
vllm.distributed.parallel_state.destroy_model_parallel = patch_destroy_model_parallel
```
5. Import the patch file in `__init__.py`. In this example, add `import vllm_ascend.patch.platform.patch_common.patch_distributed` into `vllm_ascend/patch/platform/patch_common/__init__.py`.
6. Add the description of the patch in `vllm_ascend/patch/__init__.py`. The description format is as follows:
```
# ** File: <The patch file name> **
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# 1. `<The target patch module in vLLM>`
# Why:
# <Describe the reason why we need to patch>
# How：
# <Describe the way to patch>
# Related PR (if no, explain why):
# <Add a link to the related PR in vLLM. If there is no related PR, explain why>
# Future Plan:
# <Describe the future plan to remove the patch>
```
7. Add the Unit Test and E2E Test. Any newly added code in vLLM Ascend should contain the Unit Test and E2E Test as well. You can find more details in [test guide](../contribution/testing.md)
## Limitation
1. In V1 Engine, vLLM starts three kinds of process: Main process, EngineCore process and Worker process. Now vLLM Ascend only support patch the code in Main process and Worker process by default. If you want to patch the code runs in EngineCore process, you should patch EngineCore process entirely during setup, the entry code is here `vllm.v1.engine.core`. Please override `EngineCoreProc` and `DPEngineCoreProc` entirely.
2. If you are running an edited vLLM code, the version of the vLLM may be changed automatically. For example, if you runs an edited vLLM based on v0.9.n, the version of vLLM may be change to v0.9.nxxx, in this case, the patch for v0.9.n in vLLM Ascend would not work as expect, because that vLLM Ascend can't distinguish the version of vLLM you're using. In this case, you can set the environment variable `VLLM_VERSION` to specify the version of vLLM you're using, then the patch for v0.10.0 should work.

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# Adding a New Model
This guide demonstrates how to integrate a novel or customized model into vllm-ascend. For foundational concepts, it is highly recommended to refer to
[vllm official doc: Adding a New Model](https://docs.vllm.ai/en/stable/contributing/model/) first.
## Step 1: Implementing Models with `torch` and `torch_npu`
This section provides instructions for implementing new models compatible with vllm and vllm-ascend.
**Before starting:**
- Verify whether your model already exists in vllm's [models](https://github.com/vllm-project/vllm/tree/main/vllm/model_executor/models) directory.
- Use existing models' implementation as templates to accelerate your development.
### Method 1: Implementing New Models from Scratch
Follow vllm's [OPT model adaptation](https://docs.vllm.ai/en/stable/contributing/model/basic.html) example for guidance.
**Key implementation requirements:**
1. Place model files in `vllm_ascend/models/` directory.
2. Standard module structure for decoder-only LLMs (please checkout vllm's implementations for other kinds of model):
- `*ModelForCausalLM` (top-level wrapper)
- `*Model` (main architecture)
- `*DecoderLayer` (transformer block)
- `*Attention` and `*MLP` (specific computation unit)
:::{note}
`*` denotes your model's unique identifier.
:::
3. Critical Implementation Details:
All modules must include a `prefix` argument in `__init__()`.
**Required interfaces:**
| Module Type | Required Methods |
| :------------------- | :---------------------------------------- |
| `*ModelForCausalLM` | `get_input_embeddings`, `compute_logits`, `load_weights` |
| `*Model` | `get_input_embeddings`, `load_weights` |
4. Attention Backend Integration:
Importing attention via `from vllm.attention import Attention` can automatically leverage the attention backend routing of vllm-ascend (see: `get_attn_backend_cls()` in `vllm_ascend/platform.py`).
5. Tensor Parallelism:
Use vllm's parallel layers (`ColumnParallelLinear`, `VocabParallelEmbedding`, etc.) to implement models supporting tensor parallelism. Note that Ascend-specific customizations are implemented in `vllm_ascend/ops/` directory (RMSNorm, VocabParallelEmbedding, etc.).
**Reference Implementation Template** (assumed path: `vllm_ascend/models/custom_model.py`):
```python
from collections.abc import Iterable
from typing import Optional, Union
import torch
from torch import nn
from vllm.attention import Attention
from vllm.config import VllmConfig
from vllm.sequence import IntermediateTensors
from vllm.model_executor.sampling_metadata import SamplingMetadata
class CustomAttention(nn.Module):
def __init__(self, vllm_config: VllmConfig, prefix: str):
super().__init__()
self.attn = Attention(prefix=f"{prefix}.attn")
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
# Implement attention logic
...
class CustomDecoderLayer(nn.Module):
def __init__(self, vllm_config: VllmConfig, prefix: str):
super().__init__()
self.self_attn = CustomAttention(vllm_config, prefix=f"{prefix}.self_attn")
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
# Implement decoder layer
...
class CustomModel(nn.Module):
def __init__(self, vllm_config: VllmConfig, prefix: str):
super().__init__()
self.layers = nn.ModuleList([
CustomDecoderLayer(vllm_config, prefix=f"{prefix}.layers.{i}")
for i in range(vllm_config.model_config.hf_config.num_hidden_layers)
])
def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
...
def forward(
self,
input_ids: torch.Tensor,
positions: torch.Tensor,
intermediate_tensors: Optional[IntermediateTensors] = None,
inputs_embeds: Optional[torch.Tensor] = None,
) -> Union[torch.Tensor, IntermediateTensors]:
...
def load_weights(self,
weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
...
class CustomModelForCausalLM(nn.Module):
def __init__(self, vllm_config: VllmConfig, prefix: str = ""):
super().__init__()
self.model = CustomModel(vllm_config, prefix=f"{prefix}.model")
def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
...
def forward(
self,
input_ids: torch.Tensor,
positions: torch.Tensor,
intermediate_tensors: Optional[IntermediateTensors] = None,
inputs_embeds: Optional[torch.Tensor] = None,
) -> Union[torch.Tensor, IntermediateTensors]:
...
def compute_logits(self,
hidden_states: torch.Tensor,
sampling_metadata: SamplingMetadata) -> torch.Tensor:
...
def load_weights(self,
weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
...
```
### Method 2: Customizing Existing vLLM Models
For most use cases, extending existing implementations is preferable. We demonstrate an example to inherit from base classes and implement a custom deepseek model below (assumed path: `vllm_ascend/models/deepseek_v2.py`).
```python
from typing import List, Optional
import torch
from vllm.attention import AttentionMetadata
from vllm.model_executor.models.deepseek_v2 import DeepseekV2ForCausalLM
from vllm.sequence import IntermediateTensors
class CustomDeepseekV2ForCausalLM(DeepseekV2ForCausalLM):
# Define merged weights for quantization/efficiency
packed_modules_mapping = {
"gate_up_proj": ["gate_proj", "up_proj"],
"experts": ["experts.0.gate_proj", "experts.0.up_proj", "experts.0.down_proj"]
}
def forward(
self,
input_ids: torch.Tensor,
positions: torch.Tensor,
kv_caches: Optional[List[torch.Tensor]] = None,
attn_metadata: Optional[AttentionMetadata] = None,
intermediate_tensors: Optional[IntermediateTensors] = None,
inputs_embeds: Optional[torch.Tensor] = None,
) -> Union[torch.Tensor, IntermediateTensors]:
# Custom forward logic
hidden_states = self.model(
input_ids,
positions,
kv_caches,
attn_metadata,
intermediate_tensors,
inputs_embeds
)
return hidden_states
```
:::{note}
For a complete implementation reference, see: `vllm_ascend/models/deepseek_v2.py`.
:::
## Step 2: Registering Custom Models using ModelRegistry Plugins in vLLM
vllm provides a plugin mechanism for registering externally implemented models without modifying its codebase.
To integrate your implemented model from `vllm_ascend/models/` directory:
1. Import your model implementation in `vllm_ascend/models/__init__.py` using relative imports.
2. Register the model wrapper class via `vllm.ModelRegistry.register_model()` function.
**Reference Registration Template** (an example of registering new models in `vllm_ascend/models/__init__.py`):
```python
from vllm import ModelRegistry
def register_model():
from .custom_model import CustomModelForCausalLM # New custom model
from .deepseek_v2 import ModifiedDeepseekV2ForCausalLM # Customized Deepseek
# For NEW architectures: Register with unique name
ModelRegistry.register_model(
"CustomModelForCausalLM", # Must match config.json's 'architectures'
"vllm_ascend.models.custom_model:CustomModelForCausalLM"
)
# For MODIFIED architectures: Use original name
ModelRegistry.register_model(
"DeepseekV2ForCausalLM", # Original architecture identifier in vLLM
"vllm_ascend.models.deepseek_v2:CustomDeepseekV2ForCausalLM "
)
```
:::{note}
The first argument of `vllm.ModelRegistry.register_model()` indicates the unique architecture identifier which must match `architectures` in `config.json` of the model.
```json
{
"architectures": [
"CustomModelForCausalLM"
],
}
```
:::
## Step 3: Verification
### Case 1: Overriding Existing vLLM Model Architecture
If you're registering a customized model architecture based on vllm's existing implementation (overriding vllm's original class), when executing vllm offline/online inference (using any model), you'll observe warning logs similar to the following output from `vllm/models_executor/models/registry.py`.
```bash
Model architecture DeepseekV2ForCausalLM is already registered, and will be overwritten by the new model class vllm_ascend/models/deepseek_v2:CustomDeepseekV2ForCausalLM.
```
### Case 2: Registering New Model Architecture
If you're registering a novel model architecture not present in vllm (creating a completely new class), current logs won't provide explicit confirmation by default. It's recommended to add the following logging statement at the end of the `register_model` method in `vllm/models_executor/models/registry.py`.
```python
logger.info(f"model_arch: {model_arch} has been registered here!")
```
After adding this line, you will see confirmation logs shown below when running vllm offline/online inference (using any model).
```bash
model_arch: CustomModelForCausalLM has been registered here!
```
This log output confirms your novel model architecture has been successfully registered in vllm.
## Step 4: Testing
After adding a new model, we should do basic functional test (offline/online inference), accuracy test and performance benchmark for the model.
Find more details at:
- [Accuracy test guide](https://vllm-ascend.readthedocs.io/en/latest/developer_guide/evaluation/index.html)
- [Performance benchmark guide](https://vllm-ascend.readthedocs.io/en/latest/developer_guide/performance/performance_benchmark.html)
## Step 5: Updating Supported Models Doc
At last, if all the steps above are completed, you should add the new model into our [Supported Models](https://vllm-ascend.readthedocs.io/en/latest/user_guide/supported_models.html) doc.

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# Adding a New Multi-Modal Model
**_Comming soon ..._**

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# Modeling
This section provides tutorials of how to implement and register a new model into vllm-ascend.
:::{toctree}
:caption: Modeling
:maxdepth: 1
adding_a_new_model
adding_a_new_multimodal_model
:::

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# Performance
:::{toctree}
:caption: Performance
:maxdepth: 1
performance_benchmark
profile_execute_duration
optimization_and_tuning
:::

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# Optimization and Tuning
This guide aims to help users to improve vllm-ascend performance on system level. It includes OS configuration, library optimization, deploy guide and so on. Any feedback is welcome.
## Preparation
Run the container:
```{code-block} bash
:substitutions:
# Update DEVICE according to your device (/dev/davinci[0-7])
export DEVICE=/dev/davinci0
# Update the cann base image
export IMAGE=m.daocloud.io/quay.io/ascend/cann:|cann_image_tag|
docker run --rm \
--name performance-test \
--device $DEVICE \
--device /dev/davinci_manager \
--device /dev/devmm_svm \
--device /dev/hisi_hdc \
-v /usr/local/dcmi:/usr/local/dcmi \
-v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
-v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
-v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
-v /etc/ascend_install.info:/etc/ascend_install.info \
-v /root/.cache:/root/.cache \
-it $IMAGE bash
```
Configure your environment:
```{code-block} bash
:substitutions:
# Configure the mirror
echo "deb https://mirrors.tuna.tsinghua.edu.cn/ubuntu-ports/ jammy main restricted universe multiverse" > /etc/apt/sources.list && \
echo "deb-src https://mirrors.tuna.tsinghua.edu.cn/ubuntu-ports/ jammy main restricted universe multiverse" >> /etc/apt/sources.list && \
echo "deb https://mirrors.tuna.tsinghua.edu.cn/ubuntu-ports/ jammy-updates main restricted universe multiverse" >> /etc/apt/sources.list && \
echo "deb-src https://mirrors.tuna.tsinghua.edu.cn/ubuntu-ports/ jammy-updates main restricted universe multiverse" >> /etc/apt/sources.list && \
echo "deb https://mirrors.tuna.tsinghua.edu.cn/ubuntu-ports/ jammy-backports main restricted universe multiverse" >> /etc/apt/sources.list && \
echo "deb-src https://mirrors.tuna.tsinghua.edu.cn/ubuntu-ports/ jammy-backports main restricted universe multiverse" >> /etc/apt/sources.list && \
echo "deb https://mirrors.tuna.tsinghua.edu.cn/ubuntu-ports/ jammy-security main restricted universe multiverse" >> /etc/apt/sources.list && \
echo "deb-src https://mirrors.tuna.tsinghua.edu.cn/ubuntu-ports/ jammy-security main restricted universe multiverse" >> /etc/apt/sources.list
# Install os packages
apt update && apt install wget gcc g++ libnuma-dev git vim -y
```
Install vllm and vllm-ascend:
```{code-block} bash
:substitutions:
# Install necessary dependencies
pip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple
pip install modelscope pandas datasets gevent sacrebleu rouge_score pybind11 pytest
# Configure this var to speed up model download
VLLM_USE_MODELSCOPE=true
```
Please follow the [Installation Guide](https://vllm-ascend.readthedocs.io/en/latest/installation.html) to make sure vllm, vllm-ascend and mindie-turbo is installed correctly.
:::{note}
Make sure your vllm and vllm-ascend are installed after your python configuration completed, because these packages will build binary files using the python in current environment. If you install vllm, vllm-ascend and mindie-turbo before chapter 1.1, the binary files will not use the optimized python.
:::
## Optimizations
### 1. Compilation Optimization
#### 1.1. Install optimized `python`
Python supports **LTO** and **PGO** optimization starting from version `3.6` and above, which can be enabled at compile time. And we have offered compilation optimized `python` packages directly to users for the sake of convenience. You can also reproduce the `python` build follow this [tutorial](https://www.hiascend.com/document/detail/zh/Pytorch/600/ptmoddevg/trainingmigrguide/performance_tuning_0063.html) according to your specific scenarios.
```{code-block} bash
:substitutions:
mkdir -p /workspace/tmp
cd /workspace/tmp
# Download prebuilt lib and packages
wget https://repo.oepkgs.net/ascend/pytorch/vllm/lib/libcrypto.so.1.1
wget https://repo.oepkgs.net/ascend/pytorch/vllm/lib/libomp.so
wget https://repo.oepkgs.net/ascend/pytorch/vllm/lib/libssl.so.1.1
wget https://repo.oepkgs.net/ascend/pytorch/vllm/python/py311_bisheng.tar.gz
# Configure python and pip
cp ./*.so* /usr/local/lib
tar -zxvf ./py311_bisheng.* -C /usr/local/
mv /usr/local/py311_bisheng/ /usr/local/python
sed -i "1c#\!/usr/local/python/bin/python3.11" /usr/local/python/bin/pip3
sed -i "1c#\!/usr/local/python/bin/python3.11" /usr/local/python/bin/pip3.11
ln -sf /usr/local/python/bin/python3 /usr/bin/python
ln -sf /usr/local/python/bin/python3 /usr/bin/python3
ln -sf /usr/local/python/bin/python3.11 /usr/bin/python3.11
ln -sf /usr/local/python/bin/pip3 /usr/bin/pip3
ln -sf /usr/local/python/bin/pip3 /usr/bin/pip
export PATH=/usr/bin:/usr/local/python/bin:$PATH
```
### 2. OS Optimization
#### 2.1. jemalloc
**jemalloc** is a memory allocator that improves performance for multi-threads scenario and can reduce memory fragment. jemalloc use thread local memory manager to allocate variables, which can avoid lock competition between multi-threads and can hugely optimize performance.
```{code-block} bash
:substitutions:
# Install jemalloc
sudo apt update
sudo apt install libjemalloc2
# Configure jemalloc
export LD_PRELOAD=/usr/lib/"$(uname -i)"-linux-gnu/libjemalloc.so.2 $LD_PRELOAD
```
#### 2.2. Tcmalloc
**Tcmalloc (Thread Counting Malloc)** is a universal memory allocator that improves overall performance while ensuring low latency by introducing a multi-level cache structure, reducing mutex competition and optimizing large object processing flow. Find more details [here](https://www.hiascend.com/document/detail/zh/Pytorch/700/ptmoddevg/trainingmigrguide/performance_tuning_0068.html).
```{code-block} bash
:substitutions:
# Install tcmalloc
sudo apt update
sudo apt install libgoogle-perftools4 libgoogle-perftools-dev
# Get the location of libtcmalloc.so*
find /usr -name libtcmalloc.so*
# Make the priority of tcmalloc higher
# The <path> is the location of libtcmalloc.so we get from the upper command
# Example: "$LD_PRELOAD:/usr/lib/aarch64-linux-gnu/libtcmalloc.so"
export LD_PRELOAD="$LD_PRELOAD:<path>"
# Verify your configuration
# The path of libtcmalloc.so will be contained in the result if your configuration is valid
ldd `which python`
```
### 3. `torch_npu` Optimization
Some performance tuning features in `torch_npu` are controlled by environment variables. Some features and their related environment variables are shown below.
Memory optimization:
```{code-block} bash
:substitutions:
# Upper limit of memory block splitting allowed (MB), Setting this parameter can prevent large memory blocks from being split.
export PYTORCH_NPU_ALLOC_CONF="max_split_size_mb:250"
# When operators on the communication stream have dependencies, they all need to be ended before being released for reuse. The logic of multi-stream reuse is to release the memory on the communication stream in advance so that the computing stream can be reused.
export PYTORCH_NPU_ALLOC_CONF="expandable_segments:True"
```
Schedule optimization:
```{code-block} bash
:substitutions:
# Optimize operator delivery queue, this will affect the memory peak value, and may degrade if the memory is tight.
export TASK_QUEUE_ENABLE=2
# This will greatly improve the CPU bottleneck model and ensure the same performance for the NPU bottleneck model.
export CPU_AFFINITY_CONF=1
```
### 4. CANN Optimization
#### 4.1. HCCL Optimization
There are some performance tuning features in HCCL, which are controlled by environment variables.
You can configure HCCL to use "AIV" mode to optimize performance by setting the environment variable shown below. In "AIV" mode, the communication is scheduled by AI vector core directly with ROCE, instead of being scheduled by AI cpu.
```{code-block} bash
:substitutions:
export HCCL_OP_EXPANSION_MODE="AIV"
```
Plus, there are more features for performance optimization in specific scenarios, which are shown below.
- `HCCL_INTRA_ROCE_ENABLE`: Use RDMA link instead of SDMA link between two 8Ps as the mesh interconnect link, find more details [here](https://www.hiascend.com/document/detail/zh/Pytorch/600/ptmoddevg/trainingmigrguide/performance_tuning_0044.html).
- `HCCL_RDMA_TC`: Use this var to configure traffic class of RDMA network card, find more details [here](https://www.hiascend.com/document/detail/zh/Pytorch/600/ptmoddevg/trainingmigrguide/performance_tuning_0045.html).
- `HCCL_RDMA_SL`: Use this var to configure service level of RDMA network card, find more details [here](https://www.hiascend.com/document/detail/zh/Pytorch/600/ptmoddevg/trainingmigrguide/performance_tuning_0046.html).
- `HCCL_BUFFSIZE`: Use this var to control the cache size for sharing data between two NPUs, find more details [here](https://www.hiascend.com/document/detail/zh/Pytorch/600/ptmoddevg/trainingmigrguide/performance_tuning_0047.html).

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# Performance Benchmark
This document details the benchmark methodology for vllm-ascend, aimed at evaluating the performance under a variety of workloads. To maintain alignment with vLLM, we use the [benchmark](https://github.com/vllm-project/vllm/tree/main/benchmarks) script provided by the vllm project.
**Benchmark Coverage**: We measure offline e2e latency and throughput, and fixed-QPS online serving benchmarks, for more details see [vllm-ascend benchmark scripts](https://github.com/vllm-project/vllm-ascend/tree/main/benchmarks).
## 1. Run docker container
```{code-block} bash
:substitutions:
# Update DEVICE according to your device (/dev/davinci[0-7])
export DEVICE=/dev/davinci7
export IMAGE=m.daocloud.io/quay.io/ascend/vllm-ascend:|vllm_ascend_version|
docker run --rm \
--name vllm-ascend \
--device $DEVICE \
--device /dev/davinci_manager \
--device /dev/devmm_svm \
--device /dev/hisi_hdc \
-v /usr/local/dcmi:/usr/local/dcmi \
-v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
-v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
-v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
-v /etc/ascend_install.info:/etc/ascend_install.info \
-v /root/.cache:/root/.cache \
-p 8000:8000 \
-e VLLM_USE_MODELSCOPE=True \
-e PYTORCH_NPU_ALLOC_CONF=max_split_size_mb:256 \
-it $IMAGE \
/bin/bash
```
## 2. Install dependencies
```bash
cd /workspace/vllm-ascend
pip config set global.index-url https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple
pip install -r benchmarks/requirements-bench.txt
```
## 3. (Optional)Prepare model weights
For faster running speed, we recommend downloading the model in advance：
```bash
modelscope download --model LLM-Research/Meta-Llama-3.1-8B-Instruct
```
You can also replace all model paths in the [json](https://github.com/vllm-project/vllm-ascend/tree/main/benchmarks/tests) files with your local paths:
```bash
[
{
"test_name": "latency_llama8B_tp1",
"parameters": {
"model": "your local model path",
"tensor_parallel_size": 1,
"load_format": "dummy",
"num_iters_warmup": 5,
"num_iters": 15
}
}
]
```
## 4. Run benchmark script
Run benchmark script:
```bash
bash benchmarks/scripts/run-performance-benchmarks.sh
```
After about 10 mins, the output is as shown below:
```bash
online serving:
qps 1:
============ Serving Benchmark Result ============
Successful requests: 200
Benchmark duration (s): 212.77
Total input tokens: 42659
Total generated tokens: 43545
Request throughput (req/s): 0.94
Output token throughput (tok/s): 204.66
Total Token throughput (tok/s): 405.16
---------------Time to First Token----------------
Mean TTFT (ms): 104.14
Median TTFT (ms): 102.22
P99 TTFT (ms): 153.82
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms): 38.78
Median TPOT (ms): 38.70
P99 TPOT (ms): 48.03
---------------Inter-token Latency----------------
Mean ITL (ms): 38.46
Median ITL (ms): 36.96
P99 ITL (ms): 75.03
==================================================
qps 4:
============ Serving Benchmark Result ============
Successful requests: 200
Benchmark duration (s): 72.55
Total input tokens: 42659
Total generated tokens: 43545
Request throughput (req/s): 2.76
Output token throughput (tok/s): 600.24
Total Token throughput (tok/s): 1188.27
---------------Time to First Token----------------
Mean TTFT (ms): 115.62
Median TTFT (ms): 109.39
P99 TTFT (ms): 169.03
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms): 51.48
Median TPOT (ms): 52.40
P99 TPOT (ms): 69.41
---------------Inter-token Latency----------------
Mean ITL (ms): 50.47
Median ITL (ms): 43.95
P99 ITL (ms): 130.29
==================================================
qps 16:
============ Serving Benchmark Result ============
Successful requests: 200
Benchmark duration (s): 47.82
Total input tokens: 42659
Total generated tokens: 43545
Request throughput (req/s): 4.18
Output token throughput (tok/s): 910.62
Total Token throughput (tok/s): 1802.70
---------------Time to First Token----------------
Mean TTFT (ms): 128.50
Median TTFT (ms): 128.36
P99 TTFT (ms): 187.87
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms): 83.60
Median TPOT (ms): 77.85
P99 TPOT (ms): 165.90
---------------Inter-token Latency----------------
Mean ITL (ms): 65.72
Median ITL (ms): 54.84
P99 ITL (ms): 289.63
==================================================
qps inf:
============ Serving Benchmark Result ============
Successful requests: 200
Benchmark duration (s): 41.26
Total input tokens: 42659
Total generated tokens: 43545
Request throughput (req/s): 4.85
Output token throughput (tok/s): 1055.44
Total Token throughput (tok/s): 2089.40
---------------Time to First Token----------------
Mean TTFT (ms): 3394.37
Median TTFT (ms): 3359.93
P99 TTFT (ms): 3540.93
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms): 66.28
Median TPOT (ms): 64.19
P99 TPOT (ms): 97.66
---------------Inter-token Latency----------------
Mean ITL (ms): 56.62
Median ITL (ms): 55.69
P99 ITL (ms): 82.90
==================================================
offline:
latency:
Avg latency: 4.944929537673791 seconds
10% percentile latency: 4.894104263186454 seconds
25% percentile latency: 4.909652255475521 seconds
50% percentile latency: 4.932477846741676 seconds
75% percentile latency: 4.9608619548380375 seconds
90% percentile latency: 5.035418218374252 seconds
99% percentile latency: 5.052476694583893 seconds
throughput:
Throughput: 4.64 requests/s, 2000.51 total tokens/s, 1010.54 output tokens/s
Total num prompt tokens: 42659
Total num output tokens: 43545
```
The result json files are generated into the path `benchmark/results`
These files contain detailed benchmarking results for further analysis.
```bash
.
|-- latency_llama8B_tp1.json
|-- serving_llama8B_tp1_qps_1.json
|-- serving_llama8B_tp1_qps_16.json
|-- serving_llama8B_tp1_qps_4.json
|-- serving_llama8B_tp1_qps_inf.json
`-- throughput_llama8B_tp1.json
```

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# Profile Execute Duration
The execution duration of each stage (including pre/post-processing, model forward, etc.) usually needs to be captured during a complete inference process. Typically, this is done by using `torch.npu.synchronize()` and obtaining CPU timestamps, which increases the performance overhead of host/device synchronization.
**To reduce the performance overhead, we add this feature, using the NPU event timestamp mechanism to observe the device execution time asynchronously.**
## Usage
* Use the environment variable `VLLM_ASCEND_MODEL_EXECUTE_TIME_OBSERVE` to enable this feature.
* Use the non-blocking API `ProfileExecuteDuration().capture_async` to set observation points asynchronously when you need to observe the execution duration.
* Use the blocking API `ProfileExecuteDuration().pop_captured_sync` at an appropriate time to get and print the execution durations of all observed stages.
**We have instrumented the key inference stages (including pre-processing, model forward pass, etc.) for execute duration profiling. Execute the script as follows:**
```
VLLM_ASCEND_MODEL_EXECUTE_TIME_OBSERVE=1 python3 vllm-ascend/examples/offline_inference_npu.py
```
## Example Output
```
5691:(IntegratedWorker pid=1502285) Profile execute duration [Decode]: [post process]:14.17ms [prepare input and forward]:9.57ms [forward]:4.14ms
5695:(IntegratedWorker pid=1502285) Profile execute duration [Decode]: [post process]:14.29ms [prepare input and forward]:10.19ms [forward]:4.14ms
5697:(IntegratedWorker pid=1502343) Profile execute duration [Decode]: [post process]:14.81ms [prepare input and forward]:10.29ms [forward]:3.99ms
5701:(IntegratedWorker pid=1502343) Profile execute duration [Decode]: [post process]:14.10ms [prepare input and forward]:10.62ms [forward]:4.33ms
5705:(IntegratedWorker pid=1502343) Profile execute duration [Decode]: [post process]:14.65ms [prepare input and forward]:9.58ms [forward]:4.20ms
5709:(IntegratedWorker pid=1502343) Profile execute duration [Decode]: [post process]:14.43ms [prepare input and forward]:9.88ms [forward]:4.20ms
5711:(IntegratedWorker pid=1502401) Profile execute duration [Decode]: [post process]:14.89ms [prepare input and forward]:10.49ms [forward]:4.19ms
5715:(IntegratedWorker pid=1502401) Profile execute duration [Decode]: [post process]:14.14ms [prepare input and forward]:11.21ms [forward]:4.18ms
5719:(IntegratedWorker pid=1502401) Profile execute duration [Decode]: [post process]:14.71ms [prepare input and forward]:10.15ms [forward]:4.42ms
5723:(IntegratedWorker pid=1502401) Profile execute duration [Decode]: [post process]:14.62ms [prepare input and forward]:10.31ms [forward]:4.25ms
5725:(IntegratedWorker pid=1502462) Profile execute duration [Decode]: [post process]:14.12ms [prepare input and forward]:10.33ms [forward]:4.24ms
5729:(IntegratedWorker pid=1502462) Profile execute duration [Decode]: [post process]:14.58ms [prepare input and forward]:10.85ms [forward]:4.32ms
5733:(IntegratedWorker pid=1502462) Profile execute duration [Decode]: [post process]:14.32ms [prepare input and forward]:9.79ms [forward]:4.28ms
5737:(IntegratedWorker pid=1502462) Profile execute duration [Decode]: [post process]:15.06ms [prepare input and forward]:9.89ms [forward]:4.32ms
5739:(IntegratedWorker pid=1502524) Profile execute duration [Decode]: [post process]:14.62ms [prepare input and forward]:10.48ms [forward]:4.27ms
5743:(IntegratedWorker pid=1502524) Profile execute duration [Decode]: [post process]:14.60ms [prepare input and forward]:10.71ms [forward]:4.61ms
5747:(IntegratedWorker pid=1502524) Profile execute duration [Decode]: [post process]:14.21ms [prepare input and forward]:10.10ms [forward]:4.52ms
5751:(IntegratedWorker pid=1502524) Profile execute duration [Decode]: [post process]:15.03ms [prepare input and forward]:10.00ms [forward]:4.42ms
```

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# FAQs
## Version Specific FAQs
- [[v0.9.1] FAQ & Feedback](https://github.com/vllm-project/vllm-ascend/issues/2643)
- [[v0.10.1rc1] FAQ & Feedback](https://github.com/vllm-project/vllm-ascend/issues/2630)
## General FAQs
### 1. What devices are currently supported?
Currently, **ONLY** Atlas A2 series(Ascend-cann-kernels-910b)，Atlas A3 series(Atlas-A3-cann-kernels) and Atlas 300I(Ascend-cann-kernels-310p) series are supported:
- Atlas A2 Training series (Atlas 800T A2, Atlas 900 A2 PoD, Atlas 200T A2 Box16, Atlas 300T A2)
- Atlas 800I A2 Inference series (Atlas 800I A2)
- Atlas A3 Training series (Atlas 800T A3, Atlas 900 A3 SuperPoD, Atlas 9000 A3 SuperPoD)
- Atlas 800I A3 Inference series (Atlas 800I A3)
- [Experimental] Atlas 300I Inference series (Atlas 300I Duo)
Below series are NOT supported yet:
- Atlas 200I A2 (Ascend-cann-kernels-310b) unplanned yet
- Ascend 910, Ascend 910 Pro B (Ascend-cann-kernels-910) unplanned yet
From a technical view, vllm-ascend support would be possible if the torch-npu is supported. Otherwise, we have to implement it by using custom ops. We are also welcome to join us to improve together.
### 2. How to get our docker containers?
You can get our containers at `Quay.io`, e.g., [<u>vllm-ascend</u>](https://quay.io/repository/ascend/vllm-ascend?tab=tags) and [<u>cann</u>](https://quay.io/repository/ascend/cann?tab=tags).
If you are in China, you can use `daocloud` to accelerate your downloading:
```bash
# Replace with tag you want to pull
TAG=v0.7.3rc2
docker pull m.daocloud.io/quay.io/ascend/vllm-ascend:$TAG
```
#### Load Docker Images for offline environment
If you want to use container image for offline environments (no internet connection), you need to download container image in a environment with internet access:
**Exporting Docker images:**
```{code-block} bash
:substitutions:
# Pull the image on a machine with internet access
TAG=|vllm_ascend_version|
docker pull quay.io/ascend/vllm-ascend:$TAG
# Export the image to a tar file and compress to tar.gz
docker save quay.io/ascend/vllm-ascend:$TAG | gzip > vllm-ascend-$TAG.tar.gz
```
**Importing Docker images in environment without internet access:**
```{code-block} bash
:substitutions:
# Transfer the tar/tar.gz file to the offline environment and load it
TAG=|vllm_ascend_version|
docker load -i vllm-ascend-$TAG.tar.gz
# Verify the image is loaded
docker images | grep vllm-ascend
```
### 3. What models does vllm-ascend supports?
Find more details [<u>here</u>](https://vllm-ascend.readthedocs.io/en/latest/user_guide/support_matrix/supported_models.html).
### 4. How to get in touch with our community?
There are many channels that you can communicate with our community developers / users:
- Submit a GitHub [<u>issue</u>](https://github.com/vllm-project/vllm-ascend/issues?page=1).
- Join our [<u>weekly meeting</u>](https://docs.google.com/document/d/1hCSzRTMZhIB8vRq1_qOOjx4c9uYUxvdQvDsMV2JcSrw/edit?tab=t.0#heading=h.911qu8j8h35z) and share your ideas.
- Join our [<u>WeChat</u>](https://github.com/vllm-project/vllm-ascend/issues/227) group and ask your quenstions.
- Join our ascend channel in [<u>vLLM forums</u>](https://discuss.vllm.ai/c/hardware-support/vllm-ascend-support/6) and publish your topics.
### 5. What features does vllm-ascend V1 supports?
Find more details [<u>here</u>](https://vllm-ascend.readthedocs.io/en/latest/user_guide/support_matrix/supported_features.html).
### 6. How to solve the problem of "Failed to infer device type" or "libatb.so: cannot open shared object file"?
Basically, the reason is that the NPU environment is not configured correctly. You can:
1. try `source /usr/local/Ascend/nnal/atb/set_env.sh` to enable NNAL package.
2. try `source /usr/local/Ascend/ascend-toolkit/set_env.sh` to enable CANN package.
3. try `npu-smi info` to check whether the NPU is working.
If all above steps are not working, you can try the following code with python to check whether there is any error:
```
import torch
import torch_npu
import vllm
```
If all above steps are not working, feel free to submit a GitHub issue.
### 7. How does vllm-ascend perform?
Currently, only some models are improved. Such as `Qwen2.5 VL`, `Qwen3`, `Deepseek V3`. Others are not good enough. From 0.9.0rc2, Qwen and Deepseek works with graph mode to play a good performance. What's more, you can install `mindie-turbo` with `vllm-ascend v0.7.3` to speed up the inference as well.
### 8. How vllm-ascend work with vllm?
vllm-ascend is a plugin for vllm. Basically, the version of vllm-ascend is the same as the version of vllm. For example, if you use vllm 0.7.3, you should use vllm-ascend 0.7.3 as well. For main branch, we will make sure `vllm-ascend` and `vllm` are compatible by each commit.
### 9. Does vllm-ascend support Prefill Disaggregation feature?
Currently, only 1P1D is supported on V0 Engine. For V1 Engine or NPND support, We will make it stable and supported by vllm-ascend in the future.
### 10. Does vllm-ascend support quantization method?
Currently, w8a8 quantization is already supported by vllm-ascend originally on v0.8.4rc2 or higher, If you're using vllm 0.7.3 version, w8a8 quantization is supporeted with the integration of vllm-ascend and mindie-turbo, please use `pip install vllm-ascend[mindie-turbo]`.
### 11. How to run w8a8 DeepSeek model?
Please following the [inferencing tutorail](https://vllm-ascend.readthedocs.io/en/latest/tutorials/multi_node.html) and replace model to DeepSeek.
### 12. There is no output in log when loading models using vllm-ascend, How to solve it?
If you're using vllm 0.7.3 version, this is a known progress bar display issue in VLLM, which has been resolved in [this PR](https://github.com/vllm-project/vllm/pull/12428), please cherry-pick it locally by yourself. Otherwise, please fill up an issue.
### 13. How vllm-ascend is tested
vllm-ascend is tested by functional test, performance test and accuracy test.
- **Functional test**: we added CI, includes portion of vllm's native unit tests and vllm-ascend's own unit tests，on vllm-ascend's test, we test basic functionality、popular models availability and [supported features](https://vllm-ascend.readthedocs.io/en/latest/user_guide/support_matrix/supported_features.html) via e2e test
- **Performance test**: we provide [benchmark](https://github.com/vllm-project/vllm-ascend/tree/main/benchmarks) tools for end-to-end performance benchmark which can easily to re-route locally, we'll publish a perf website to show the performance test results for each pull request
- **Accuracy test**: we're working on adding accuracy test to CI as well.
Finnall, for each release, we'll publish the performance test and accuracy test report in the future.
### 14. How to fix the error "InvalidVersion" when using vllm-ascend?
It's usually because you have installed an dev/editable version of vLLM package. In this case, we provide the env variable `VLLM_VERSION` to let users specify the version of vLLM package to use. Please set the env variable `VLLM_VERSION` to the version of vLLM package you have installed. The format of `VLLM_VERSION` should be `X.Y.Z`.
### 15. How to handle Out Of Memory?
OOM errors typically occur when the model exceeds the memory capacity of a single NPU. For general guidance, you can refer to [vLLM's OOM troubleshooting documentation](https://docs.vllm.ai/en/latest/getting_started/troubleshooting.html#out-of-memory).
In scenarios where NPUs have limited HBM (High Bandwidth Memory) capacity, dynamic memory allocation/deallocation during inference can exacerbate memory fragmentation, leading to OOM. To address this:
- **Adjust `--gpu-memory-utilization`**: If unspecified, will use the default value of `0.9`. You can decrease this param to reserve more memory to reduce fragmentation risks. See more note in: [vLLM - Inference and Serving - Engine Arguments](https://docs.vllm.ai/en/latest/serving/engine_args.html#vllm.engine.arg_utils-_engine_args_parser-cacheconfig).
- **Configure `PYTORCH_NPU_ALLOC_CONF`**: Set this environment variable to optimize NPU memory management. For example, you can `export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True` to enable virtual memory feature to mitigate memory fragmentation caused by frequent dynamic memory size adjustments during runtime, see more note in: [PYTORCH_NPU_ALLOC_CONF](https://www.hiascend.com/document/detail/zh/Pytorch/700/comref/Envvariables/Envir_012.html).
### 16. Failed to enable NPU graph mode when running DeepSeek?
You may encounter the following error if running DeepSeek with NPU graph mode enabled. The allowed number of queries per kv when enabling both MLA and Graph mode only support {32, 64, 128}, **Thus this is not supported for DeepSeek-V2-Lite**, as it only has 16 attention heads. The NPU graph mode support on DeepSeek-V2-Lite will be done in the future.
And if you're using DeepSeek-V3 or DeepSeek-R1, please make sure after the tensor parallel split, num_heads / num_kv_heads in {32, 64, 128}.
```bash
[rank0]: RuntimeError: EZ9999: Inner Error!
[rank0]: EZ9999: [PID: 62938] 2025-05-27-06:52:12.455.807 numHeads / numKvHeads = 8, MLA only support {32, 64, 128}.[FUNC:CheckMlaAttrs][FILE:incre_flash_attention_tiling_check.cc][LINE:1218]
```
### 17. Failed to reinstall vllm-ascend from source after uninstalling vllm-ascend?
You may encounter the problem of C compilation failure when reinstalling vllm-ascend from source using pip. If the installation fails, it is recommended to use `python setup.py install` to install, or use `python setup.py clean` to clear the cache.
### 18. How to generate determinitic results when using vllm-ascend?
There are several factors that affect output certainty:
1. Sampler Method: using **Greedy sample** by setting `temperature=0` in `SamplingParams`, e.g.:
```python
from vllm import LLM, SamplingParams
prompts = [
"Hello, my name is",
"The president of the United States is",
"The capital of France is",
"The future of AI is",
]
# Create a sampling params object.
sampling_params = SamplingParams(temperature=0)
# Create an LLM.
llm = LLM(model="Qwen/Qwen2.5-0.5B-Instruct")
# Generate texts from the prompts.
outputs = llm.generate(prompts, sampling_params)
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
```
2. Set the following enveriments parameters:
```bash
export LCCL_DETERMINISTIC=1
export HCCL_DETERMINISTIC=true
export ATB_MATMUL_SHUFFLE_K_ENABLE=0
export ATB_LLM_LCOC_ENABLE=0
```
### 19. How to fix the error "ImportError: Please install vllm[audio] for audio support" for Qwen2.5-Omni model？
The `Qwen2.5-Omni` model requires the `librosa` package to be installed, you need to install the `qwen-omni-utils` package to ensure all dependencies are met `pip install qwen-omni-utils`,
this package will install `librosa` and its related dependencies, resolving the `ImportError: No module named 'librosa'` issue and ensuring audio processing functionality works correctly.

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# Welcome to vLLM Ascend Plugin
:::{figure} ./logos/vllm-ascend-logo-text-light.png
:align: center
:alt: vLLM
:class: no-scaled-link
:width: 70%
:::
:::{raw} html
<p style="text-align:center">
<strong>vLLM Ascend Plugin
</strong>
</p>
<p style="text-align:center">
<script async defer src="https://buttons.github.io/buttons.js"></script>
<a class="github-button" href="https://github.com/vllm-project/vllm-ascend" data-show-count="true" data-size="large" aria-label="Star">Star</a>
<a class="github-button" href="https://github.com/vllm-project/vllm-ascend/subscription" data-icon="octicon-eye" data-size="large" aria-label="Watch">Watch</a>
<a class="github-button" href="https://github.com/vllm-project/vllm-ascend/fork" data-icon="octicon-repo-forked" data-size="large" aria-label="Fork">Fork</a>
</p>
:::
vLLM Ascend plugin (vllm-ascend) is a community maintained hardware plugin for running vLLM on the Ascend NPU.
This plugin is the recommended approach for supporting the Ascend backend within the vLLM community. It adheres to the principles outlined in the [[RFC]: Hardware pluggable](https://github.com/vllm-project/vllm/issues/11162), 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.
## Documentation
% How to start using vLLM on Ascend NPU?
:::{toctree}
:caption: Getting Started
:maxdepth: 1
quick_start
installation
tutorials/index.md
faqs
:::
% What does vLLM Ascend Plugin support?
:::{toctree}
:caption: User Guide
:maxdepth: 1
user_guide/support_matrix/index
user_guide/configuration/index
user_guide/feature_guide/index
user_guide/release_notes
:::
% How to contribute to the vLLM Ascend project
:::{toctree}
:caption: Developer Guide
:maxdepth: 1
developer_guide/contribution/index
developer_guide/feature_guide/index
developer_guide/evaluation/index
developer_guide/performance/index
developer_guide/modeling/index
:::
% How to involve vLLM Ascend
:::{toctree}
:caption: Community
:maxdepth: 1
community/governance
community/contributors
community/versioning_policy
community/user_stories/index
:::

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# Installation
This document describes how to install vllm-ascend manually.
## Requirements
- OS: Linux
- Python: >= 3.9, < 3.12
- A hardware with Ascend NPU. It's usually the Atlas 800 A2 series.
- Software:
| Software | Supported version | Note |
|---------------|----------------------------------|-------------------------------------------|
| CANN | >= 8.2.RC1 | Required for vllm-ascend and torch-npu |
| torch-npu | >= 2.7.1.dev20250724 | Required for vllm-ascend, No need to install manually, it will be auto installed in below steps |
| torch | >= 2.7.1 | Required for torch-npu and vllm |
You have 2 way to install:
- **Using pip**: first prepare env manually or via CANN image, then install `vllm-ascend` using pip.
- **Using docker**: use the `vllm-ascend` pre-built docker image directly.
## Configure a new environment
Before installing, you need to make sure firmware/driver and CANN are installed correctly, refer to [link](https://ascend.github.io/docs/sources/ascend/quick_install.html) for more details.
### Configure hardware environment
To verify that the Ascend NPU firmware and driver were correctly installed, run:
```bash
npu-smi info
```
Refer to [Ascend Environment Setup Guide](https://ascend.github.io/docs/sources/ascend/quick_install.html) for more details.
### Configure software environment
:::::{tab-set}
:sync-group: install
::::{tab-item} Before using pip
:selected:
:sync: pip
The easiest way to prepare your software environment is using CANN image directly:
```{code-block} bash
:substitutions:
# Update DEVICE according to your device (/dev/davinci[0-7])
export DEVICE=/dev/davinci7
# Update the vllm-ascend image
export IMAGE=quay.io/ascend/cann:|cann_image_tag|
docker run --rm \
--name vllm-ascend-env \
--device $DEVICE \
--device /dev/davinci_manager \
--device /dev/devmm_svm \
--device /dev/hisi_hdc \
-v /usr/local/dcmi:/usr/local/dcmi \
-v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
-v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
-v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
-v /etc/ascend_install.info:/etc/ascend_install.info \
-v /root/.cache:/root/.cache \
-it $IMAGE bash
```
:::{dropdown} Click here to see "Install CANN manually"
:animate: fade-in-slide-down
You can also install CANN manually:
```bash
# Create a virtual environment
python -m venv vllm-ascend-env
source vllm-ascend-env/bin/activate
# Install required python packages.
pip3 install -i https://pypi.tuna.tsinghua.edu.cn/simple attrs 'numpy<2.0.0' decorator sympy cffi pyyaml pathlib2 psutil protobuf scipy requests absl-py wheel typing_extensions
# Download and install the CANN package.
wget --header="Referer: https://www.hiascend.com/" https://ascend-repo.obs.cn-east-2.myhuaweicloud.com/CANN/CANN%208.2.RC1/Ascend-cann-toolkit_8.2.RC1_linux-"$(uname -i)".run
chmod +x ./Ascend-cann-toolkit_8.2.RC1_linux-"$(uname -i)".run
./Ascend-cann-toolkit_8.2.RC1_linux-"$(uname -i)".run --full
# https://ascend-repo.obs.cn-east-2.myhuaweicloud.com/Milan-ASL/Milan-ASL%20V100R001C22B800TP052/Ascend-cann-kernels-910b_8.2.rc1_linux-aarch64.run
source /usr/local/Ascend/ascend-toolkit/set_env.sh
wget --header="Referer: https://www.hiascend.com/" https://ascend-repo.obs.cn-east-2.myhuaweicloud.com/CANN/CANN%208.2.RC1/Ascend-cann-kernels-910b_8.2.RC1_linux-"$(uname -i)".run
chmod +x ./Ascend-cann-kernels-910b_8.2.RC1_linux-"$(uname -i)".run
./Ascend-cann-kernels-910b_8.2.RC1_linux-"$(uname -i)".run --install
wget --header="Referer: https://www.hiascend.com/" https://ascend-repo.obs.cn-east-2.myhuaweicloud.com/CANN/CANN%208.2.RC1/Ascend-cann-nnal_8.2.RC1_linux-"$(uname -i)".run
chmod +x ./Ascend-cann-nnal_8.2.RC1_linux-"$(uname -i)".run
./Ascend-cann-nnal_8.2.RC1_linux-"$(uname -i)".run --install
source /usr/local/Ascend/nnal/atb/set_env.sh
```
:::
::::
::::{tab-item} Before using docker
:sync: docker
No more extra step if you are using `vllm-ascend` prebuilt docker image.
::::
:::::
Once it's done, you can start to set up `vllm` and `vllm-ascend`.
## Setup vllm and vllm-ascend
:::::{tab-set}
:sync-group: install
::::{tab-item} Using pip
:selected:
:sync: pip
First install system dependencies and config pip mirror:
```bash
# Using apt-get with mirror
sed -i 's|ports.ubuntu.com|mirrors.tuna.tsinghua.edu.cn|g' /etc/apt/sources.list
apt-get update -y && apt-get install -y gcc g++ cmake libnuma-dev wget git curl jq
# Or using yum
# yum update -y && yum install -y gcc g++ cmake numactl-devel wget git curl jq
# Config pip mirror
pip config set global.index-url https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple
```
**[Optional]** Then config the extra-index of `pip` if you are working on a x86 machine or using torch-npu dev version:
```bash
# For torch-npu dev version or x86 machine
pip config set global.extra-index-url "https://download.pytorch.org/whl/cpu/ https://mirrors.huaweicloud.com/ascend/repos/pypi"
```
Then you can install `vllm` and `vllm-ascend` from **pre-built wheel**:
```{code-block} bash
:substitutions:
# Install vllm-project/vllm from pypi
pip install vllm==|pip_vllm_version|
# Install vllm-project/vllm-ascend from pypi.
pip install vllm-ascend==|pip_vllm_ascend_version|
```
:::{dropdown} Click here to see "Build from source code"
or build from **source code**:
```{code-block} bash
:substitutions:
# Install vLLM
git clone --depth 1 --branch |vllm_version| https://github.com/vllm-project/vllm
cd vllm
VLLM_TARGET_DEVICE=empty pip install -v -e .
cd ..
# Install vLLM Ascend
git clone --depth 1 --branch |vllm_ascend_version| https://github.com/vllm-project/vllm-ascend.git
cd vllm-ascend
pip install -v -e .
cd ..
```
vllm-ascend will build custom ops by default. If you don't want to build it, set `COMPILE_CUSTOM_KERNELS=0` environment to disable it.
:::
```{note}
If you are building from v0.7.3-dev and intend to use sleep mode feature, you should set `COMPILE_CUSTOM_KERNELS=1` manually.
To build custom ops, gcc/g++ higher than 8 and c++ 17 or higher is required. If you're using `pip install -e .` and encounter a torch-npu version conflict, please install with `pip install --no-build-isolation -e .` to build on system env.
If you encounter other problems during compiling, it is probably because unexpected compiler is being used, you may export `CXX_COMPILER` and `C_COMPILER` in env to specify your g++ and gcc locations before compiling.
```
::::
::::{tab-item} Using docker
:sync: docker
You can just pull the **prebuilt image** and run it with bash.
:::{dropdown} Click here to see "Build from Dockerfile"
or build IMAGE from **source code**:
```bash
git clone https://github.com/vllm-project/vllm-ascend.git
cd vllm-ascend
docker build -t vllm-ascend-dev-image:latest -f ./Dockerfile .
```
:::
```{code-block} bash
:substitutions:
# Update DEVICE according to your device (/dev/davinci[0-7])
export DEVICE=/dev/davinci7
# Update the vllm-ascend image
export IMAGE=quay.io/ascend/vllm-ascend:|vllm_ascend_version|
docker run --rm \
--name vllm-ascend-env \
--device $DEVICE \
--device /dev/davinci_manager \
--device /dev/devmm_svm \
--device /dev/hisi_hdc \
-v /usr/local/dcmi:/usr/local/dcmi \
-v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
-v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
-v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
-v /etc/ascend_install.info:/etc/ascend_install.info \
-v /root/.cache:/root/.cache \
-it $IMAGE bash
```
The default workdir is `/workspace`, vLLM and vLLM Ascend code are placed in `/vllm-workspace` and installed in [development mode](https://setuptools.pypa.io/en/latest/userguide/development_mode.html)(`pip install -e`) to help developer immediately take place changes without requiring a new installation.
::::
:::::
## Extra information
### Verify installation
Create and run a simple inference test. The `example.py` can be like:
```python
from vllm import LLM, SamplingParams
prompts = [
"Hello, my name is",
"The president of the United States is",
"The capital of France is",
"The future of AI is",
]
# Create a sampling params object.
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
# Create an LLM.
llm = LLM(model="Qwen/Qwen2.5-0.5B-Instruct")
# Generate texts from the prompts.
outputs = llm.generate(prompts, sampling_params)
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
```
Then run:
```bash
# Try `export VLLM_USE_MODELSCOPE=true` and `pip install modelscope`
# to speed up download if huggingface is not reachable.
python example.py
```
The output will be like:
```bash
INFO 02-18 08:49:58 __init__.py:28] Available plugins for group vllm.platform_plugins:
INFO 02-18 08:49:58 __init__.py:30] name=ascend, value=vllm_ascend:register
INFO 02-18 08:49:58 __init__.py:32] all available plugins for group vllm.platform_plugins will be loaded.
INFO 02-18 08:49:58 __init__.py:34] set environment variable VLLM_PLUGINS to control which plugins to load.
INFO 02-18 08:49:58 __init__.py:42] plugin ascend loaded.
INFO 02-18 08:49:58 __init__.py:174] Platform plugin ascend is activated
INFO 02-18 08:50:12 config.py:526] This model supports multiple tasks: {'embed', 'classify', 'generate', 'score', 'reward'}. Defaulting to 'generate'.
INFO 02-18 08:50:12 llm_engine.py:232] Initializing a V0 LLM engine (v0.7.1) with config: model='./Qwen2.5-0.5B-Instruct', speculative_config=None, tokenizer='./Qwen2.5-0.5B-Instruct', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, override_neuron_config=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.bfloat16, max_seq_len=32768, download_dir=None, load_format=auto, tensor_parallel_size=1, pipeline_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=False, kv_cache_dtype=auto, device_config=npu, decoding_config=DecodingConfig(guided_decoding_backend='xgrammar'), observability_config=ObservabilityConfig(otlp_traces_endpoint=None, collect_model_forward_time=False, collect_model_execute_time=False), seed=0, served_model_name=./Qwen2.5-0.5B-Instruct, num_scheduler_steps=1, multi_step_stream_outputs=True, enable_prefix_caching=False, chunked_prefill_enabled=False, use_async_output_proc=True, disable_mm_preprocessor_cache=False, mm_processor_kwargs=None, pooler_config=None, compilation_config={"splitting_ops":[],"compile_sizes":[],"cudagraph_capture_sizes":[256,248,240,232,224,216,208,200,192,184,176,168,160,152,144,136,128,120,112,104,96,88,80,72,64,56,48,40,32,24,16,8,4,2,1],"max_capture_size":256}, use_cached_outputs=False,
Loading safetensors checkpoint shards: 0% Completed | 0/1 [00:00<?, ?it/s]
Loading safetensors checkpoint shards: 100% Completed | 1/1 [00:00<00:00, 5.86it/s]
Loading safetensors checkpoint shards: 100% Completed | 1/1 [00:00<00:00, 5.85it/s]
INFO 02-18 08:50:24 executor_base.py:108] # CPU blocks: 35064, # CPU blocks: 2730
INFO 02-18 08:50:24 executor_base.py:113] Maximum concurrency for 32768 tokens per request: 136.97x
INFO 02-18 08:50:25 llm_engine.py:429] init engine (profile, create kv cache, warmup model) took 3.87 seconds
Processed prompts: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:00<00:00, 8.46it/s, est. speed input: 46.55 toks/s, output: 135.41 toks/s]
Prompt: 'Hello, my name is', Generated text: " Shinji, a teenage boy from New York City. I'm a computer science"
Prompt: 'The president of the United States is', Generated text: ' a very important person. When he or she is elected, many people think that'
Prompt: 'The capital of France is', Generated text: ' Paris. The oldest part of the city is Saint-Germain-des-Pr'
Prompt: 'The future of AI is', Generated text: ' not bright\n\nThere is no doubt that the evolution of AI will have a huge'
```

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# SOME DESCRIPTIVE TITLE.
# Copyright (C) 2025, vllm-ascend team
# This file is distributed under the same license as the vllm-ascend
# package.
# FIRST AUTHOR <EMAIL@ADDRESS>, 2025.
#
#, fuzzy
msgid ""
msgstr ""
"Project-Id-Version: vllm-ascend\n"
"Report-Msgid-Bugs-To: \n"
"POT-Creation-Date: 2025-07-18 09:01+0800\n"
"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
"Language-Team: zh_CN <LL@li.org>\n"
"Language: zh_CN\n"
"MIME-Version: 1.0\n"
"Content-Type: text/plain; charset=utf-8\n"
"Content-Transfer-Encoding: 8bit\n"
"Plural-Forms: nplurals=1; plural=0;\n"
"Generated-By: Babel 2.17.0\n"
#: ../../community/governance.md:1
msgid "Governance"
msgstr "治理"
#: ../../community/governance.md:3
msgid "Mission"
msgstr "使命"
#: ../../community/governance.md:4
msgid ""
"As a vital component of vLLM, the vLLM Ascend project is dedicated to "
"providing an easy, fast, and cheap LLM Serving for Everyone on Ascend NPU, "
"and to actively contribute to the enrichment of vLLM."
msgstr ""
"作为 vLLM 的重要组成部分，vLLM Ascend 项目致力于为所有人在 Ascend NPU 上提供简单、快速且低成本的大语言模型服务，并积极促进"
" vLLM 的丰富发展。"
#: ../../community/governance.md:6
msgid "Principles"
msgstr "原则"
#: ../../community/governance.md:7
msgid ""
"vLLM Ascend follows the vLLM community's code of conduct：[vLLM - CODE OF "
"CONDUCT](https://github.com/vllm-project/vllm/blob/main/CODE_OF_CONDUCT.md)"
msgstr ""
"vLLM Ascend 遵循 vLLM 社区的行为准则：[vLLM - 行为准则](https://github.com/vllm-"
"project/vllm/blob/main/CODE_OF_CONDUCT.md)"
#: ../../community/governance.md:9
msgid "Governance - Mechanics"
msgstr "治理 - 机制"
#: ../../community/governance.md:10
msgid ""
"vLLM Ascend is an open-source project under the vLLM community, where the "
"authority to appoint roles is ultimately determined by the vLLM community. "
"It adopts a hierarchical technical governance structure."
msgstr "vLLM Ascend 是 vLLM 社区下的一个开源项目，其角色任命权最终由 vLLM 社区决定。它采用分层的技术治理结构。"
#: ../../community/governance.md:12
msgid "Contributor:"
msgstr "贡献者："
#: ../../community/governance.md:14
msgid ""
"**Responsibility:** Help new contributors on boarding, handle and respond to"
" community questions, review RFCs, code"
msgstr "**职责：** 帮助新贡献者加入，处理和回复社区问题，审查RFC和代码"
#: ../../community/governance.md:16
msgid ""
"**Requirements:** Complete at least 1 contribution. Contributor is someone "
"who consistently and actively participates in a project, included but not "
"limited to issue/review/commits/community involvement."
msgstr "**要求：** 完成至少1次贡献。贡献者是指持续且积极参与项目的人，包括但不限于问题、评审、提交和社区参与。"
#: ../../community/governance.md:18
msgid ""
"Contributors will be empowered [vllm-project/vllm-"
"ascend](https://github.com/vllm-project/vllm-ascend) Github repo `Triage` "
"permissions (`Can read and clone this repository. Can also manage issues and"
" pull requests`) to help community developers collaborate more efficiently."
msgstr ""
"贡献者将被赋予 [vllm-project/vllm-ascend](https://github.com/vllm-project/vllm-"
"ascend) Github 仓库的 `Triage` 权限（`可读取和克隆此仓库。还可以管理问题和拉取请求`），以帮助社区开发者更加高效地协作。"
#: ../../community/governance.md:20
msgid "Maintainer:"
msgstr "维护者："
#: ../../community/governance.md:22
msgid ""
"**Responsibility:** Develop the project's vision and mission. Maintainers "
"are responsible for driving the technical direction of the entire project "
"and ensuring its overall success, possessing code merge permissions. They "
"formulate the roadmap, review contributions from community members, "
"continuously contribute code, and actively engage in community activities "
"(such as regular meetings/events)."
msgstr ""
"**责任：** "
"制定项目的愿景和使命。维护者负责引领整个项目的技术方向并确保其整体成功，拥有代码合并权限。他们制定路线图，审核社区成员的贡献，持续贡献代码，并积极参与社区活动（如定期会议/活动）。"
#: ../../community/governance.md:24
msgid ""
"**Requirements:** Deep understanding of ‌vLLM‌ and ‌vLLM Ascend‌ codebases, "
"with a commitment to sustained code contributions. Competency in "
"‌design/development/PR review workflows‌."
msgstr ""
"**要求：** 深入理解 ‌vLLM‌ 和 ‌vLLM Ascend‌ 代码库，并承诺持续贡献代码。具备 ‌设计/开发/PR 审核流程‌ 的能力。"
#: ../../community/governance.md:25
msgid ""
"**Review Quality‌:** Actively participate in community code reviews, "
"ensuring high-quality code integration."
msgstr "**评审质量：** 积极参与社区代码评审，确保高质量的代码集成。"
#: ../../community/governance.md:26
msgid ""
"**Quality Contribution‌:** Successfully develop and deliver at least one "
"major feature while maintaining consistent high-quality contributions."
msgstr "**质量贡献‌：** 成功开发并交付至少一个主要功能，同时持续保持高质量的贡献。"
#: ../../community/governance.md:27
msgid ""
"**Community Involvement‌:** Actively address issues, respond to forum "
"inquiries, participate in discussions, and engage in community-driven tasks."
msgstr "**社区参与：** 积极解决问题，回复论坛询问，参与讨论，并参与社区驱动的任务。"
#: ../../community/governance.md:29
msgid ""
"Requires approval from existing Maintainers. The vLLM community has the "
"final decision-making authority."
msgstr "需要现有维护者的批准。vLLM社区拥有最终决策权。"
#: ../../community/governance.md:31
msgid ""
"Maintainer will be empowered [vllm-project/vllm-"
"ascend](https://github.com/vllm-project/vllm-ascend) Github repo write "
"permissions (`Can read, clone, and push to this repository. Can also manage "
"issues and pull requests`)."
msgstr ""
"维护者将被授予 [vllm-project/vllm-ascend](https://github.com/vllm-project/vllm-"
"ascend) Github 仓库的写入权限（`可以读取、克隆和推送到此仓库。还可以管理问题和拉取请求`）。"
#: ../../community/governance.md:33
msgid "Nominating and Removing Maintainers"
msgstr "提名和移除维护者"
#: ../../community/governance.md:35
msgid "The Principles"
msgstr "原则"
#: ../../community/governance.md:37
msgid ""
"Membership in vLLM Ascend is given to individuals on merit basis after they "
"demonstrated strong expertise of the vLLM / vLLM Ascend through "
"contributions, reviews and discussions."
msgstr ""
"vLLM Ascend 的成员资格是基于个人能力授予的，只有在通过贡献、评审和讨论展示出对 vLLM / vLLM Ascend "
"的深厚专业知识后，才可获得。"
#: ../../community/governance.md:39
msgid ""
"For membership in the maintainer group the individual has to demonstrate "
"strong and continued alignment with the overall vLLM / vLLM Ascend "
"principles."
msgstr "要成为维护者组成员，个人必须表现出与 vLLM / vLLM Ascend 总体原则的高度一致并持续支持。"
#: ../../community/governance.md:41
msgid ""
"Light criteria of moving module maintenance to ‘emeritus’ status if they "
"don’t actively participate over long periods of time."
msgstr "如果模块维护人员在长时间内没有积极参与，可根据较宽松的标准将其维护状态转为“荣誉”状态。"
#: ../../community/governance.md:43
msgid "The membership is for an individual, not a company."
msgstr "该会员资格属于个人，而非公司。"
#: ../../community/governance.md:45
msgid "Nomination and Removal"
msgstr "提名与罢免"
#: ../../community/governance.md:47
msgid ""
"Nomination: Anyone can nominate someone to become a maintainer (include "
"self-nominate). All existing maintainers are responsible for evaluating the "
"nomination. The nominator should provide nominee's info around the strength "
"of the candidate to be a maintainer, include but not limited to review "
"quality, quality contribution, community involvement."
msgstr ""
"提名：任何人都可以提名他人成为维护者（包括自荐）。所有现有维护者都有责任评估提名。提名人应提供被提名人成为维护者的相关优势信息，包括但不限于评审质量、优质贡献、社区参与等。"
#: ../../community/governance.md:48
msgid ""
"Removal: Anyone can nominate a person to be removed from maintainer position"
" (include self-nominate). All existing maintainers are responsible for "
"evaluating the nomination. The nominator should provide nominee's info, "
"include but not limited to lack of activity, conflict with the overall "
"direction and other information that makes them unfit to be a maintainer."
msgstr ""
"移除：任何人都可以提名某人被移出维护者职位（包括自荐）。所有现有维护者都有责任评估该提名。提名者应提供被提名人的相关信息，包括但不限于缺乏活动、与整体方向冲突以及使其不适合作为维护者的其他信息。"

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# SOME DESCRIPTIVE TITLE.
# Copyright (C) 2025, vllm-ascend team
# This file is distributed under the same license as the vllm-ascend
# package.
# FIRST AUTHOR <EMAIL@ADDRESS>, 2025.
#
#, fuzzy
msgid ""
msgstr ""
"Project-Id-Version: vllm-ascend\n"
"Report-Msgid-Bugs-To: \n"
"POT-Creation-Date: 2025-07-18 09:01+0800\n"
"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
"Language-Team: zh_CN <LL@li.org>\n"
"Language: zh_CN\n"
"MIME-Version: 1.0\n"
"Content-Type: text/plain; charset=utf-8\n"
"Content-Transfer-Encoding: 8bit\n"
"Plural-Forms: nplurals=1; plural=0;\n"
"Generated-By: Babel 2.17.0\n"
#: ../../community/user_stories/index.md:15
msgid "More details"
msgstr "更多细节"
#: ../../community/user_stories/index.md:1
msgid "User Stories"
msgstr "用户故事"
#: ../../community/user_stories/index.md:3
msgid ""
"Read case studies on how users and developers solves real, everyday problems"
" with vLLM Ascend"
msgstr "阅读案例研究，了解用户和开发者如何使用 vLLM Ascend 解决实际日常问题。"
#: ../../community/user_stories/index.md:5
msgid ""
"[LLaMA-Factory](./llamafactory.md) is an easy-to-use and efficient platform "
"for training and fine-tuning large language models, it supports vLLM Ascend "
"to speed up inference since [LLaMA-"
"Factory#7739](https://github.com/hiyouga/LLaMA-Factory/pull/7739), gain 2x "
"performance enhancement of inference."
msgstr ""
"[LLaMA-Factory](./llamafactory.md) 是一个易于使用且高效的大语言模型训练与微调平台，自 [LLaMA-"
"Factory#7739](https://github.com/hiyouga/LLaMA-Factory/pull/7739) 起支持 vLLM "
"Ascend 加速推理，推理性能提升 2 倍。"
#: ../../community/user_stories/index.md:7
msgid ""
"[Huggingface/trl](https://github.com/huggingface/trl) is a cutting-edge "
"library designed for post-training foundation models using advanced "
"techniques like SFT, PPO and DPO, it uses vLLM Ascend since "
"[v0.17.0](https://github.com/huggingface/trl/releases/tag/v0.17.0) to "
"support RLHF on Ascend NPU."
msgstr ""
"[Huggingface/trl](https://github.com/huggingface/trl) 是一个前沿的库，专为使用 SFT、PPO 和"
" DPO 等先进技术对基础模型进行后训练而设计。从 "
"[v0.17.0](https://github.com/huggingface/trl/releases/tag/v0.17.0) 版本开始，该库利用"
" vLLM Ascend 来支持在 Ascend NPU 上进行 RLHF。"
#: ../../community/user_stories/index.md:9
msgid ""
"[MindIE Turbo](https://pypi.org/project/mindie-turbo) is an LLM inference "
"engine acceleration plug-in library developed by Huawei on Ascend hardware, "
"which includes self-developed large language model optimization algorithms "
"and optimizations related to the inference engine framework. It supports "
"vLLM Ascend since "
"[2.0rc1](https://www.hiascend.com/document/detail/zh/mindie/20RC1/AcceleratePlugin/turbodev/mindie-"
"turbo-0001.html)."
msgstr ""
"[MindIE Turbo](https://pypi.org/project/mindie-turbo) "
"是华为在昇腾硬件上开发的一款用于加速LLM推理引擎的插件库，包含自主研发的大语言模型优化算法及与推理引擎框架相关的优化。从 "
"[2.0rc1](https://www.hiascend.com/document/detail/zh/mindie/20RC1/AcceleratePlugin/turbodev/mindie-"
"turbo-0001.html) 起，支持 vLLM Ascend。"
#: ../../community/user_stories/index.md:11
msgid ""
"[GPUStack](https://github.com/gpustack/gpustack) is an open-source GPU "
"cluster manager for running AI models. It supports vLLM Ascend since "
"[v0.6.2](https://github.com/gpustack/gpustack/releases/tag/v0.6.2), see more"
" GPUStack performance evaluation info on "
"[link](https://mp.weixin.qq.com/s/pkytJVjcH9_OnffnsFGaew)."
msgstr ""
"[GPUStack](https://github.com/gpustack/gpustack) 是一个开源的 GPU 集群管理器，用于运行 AI "
"模型。从 [v0.6.2](https://github.com/gpustack/gpustack/releases/tag/v0.6.2) "
"版本开始支持 vLLM Ascend，更多 GPUStack 性能评测信息见 "
"[链接](https://mp.weixin.qq.com/s/pkytJVjcH9_OnffnsFGaew)。"
#: ../../community/user_stories/index.md:13
msgid ""
"[verl](https://github.com/volcengine/verl) is a flexible, efficient and "
"production-ready RL training library for large language models (LLMs), uses "
"vLLM Ascend since "
"[v0.4.0](https://github.com/volcengine/verl/releases/tag/v0.4.0), see more "
"info on [verl x Ascend "
"Quickstart](https://verl.readthedocs.io/en/latest/ascend_tutorial/ascend_quick_start.html)."
msgstr ""
"[verl](https://github.com/volcengine/verl) "
"是一个灵活、高效且可用于生产环境的大型语言模型（LLM）强化学习训练库，自 "
"[v0.4.0](https://github.com/volcengine/verl/releases/tag/v0.4.0) 起支持 vLLM "
"Ascend，更多信息请参见 [verl x Ascend "
"快速上手](https://verl.readthedocs.io/en/latest/ascend_tutorial/ascend_quick_start.html)。"

87
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# SOME DESCRIPTIVE TITLE.
# Copyright (C) 2025, vllm-ascend team
# This file is distributed under the same license as the vllm-ascend
# package.
# FIRST AUTHOR <EMAIL@ADDRESS>, 2025.
#
#, fuzzy
msgid ""
msgstr ""
"Project-Id-Version: vllm-ascend\n"
"Report-Msgid-Bugs-To: \n"
"POT-Creation-Date: 2025-07-18 09:01+0800\n"
"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
"Language-Team: zh_CN <LL@li.org>\n"
"Language: zh_CN\n"
"MIME-Version: 1.0\n"
"Content-Type: text/plain; charset=utf-8\n"
"Content-Transfer-Encoding: 8bit\n"
"Plural-Forms: nplurals=1; plural=0;\n"
"Generated-By: Babel 2.17.0\n"
#: ../../community/user_stories/llamafactory.md:1
msgid "LLaMA-Factory"
msgstr "LLaMA-Factory"
#: ../../community/user_stories/llamafactory.md:3
msgid "**About / Introduction**"
msgstr "**关于 / 介绍**"
#: ../../community/user_stories/llamafactory.md:5
msgid ""
"[LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory) is an easy-to-use "
"and efficient platform for training and fine-tuning large language models. "
"With LLaMA-Factory, you can fine-tune hundreds of pre-trained models locally"
" without writing any code."
msgstr ""
"[LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory) "
"是一个易于使用且高效的平台，用于训练和微调大型语言模型。有了 LLaMA-Factory，你可以在本地对数百个预训练模型进行微调，无需编写任何代码。"
#: ../../community/user_stories/llamafactory.md:7
msgid ""
"LLaMA-Facotory users need to evaluate and inference the model after fine-"
"tuning the model."
msgstr "LLaMA-Facotory 用户需要在对模型进行微调后对模型进行评估和推理。"
#: ../../community/user_stories/llamafactory.md:9
msgid "**The Business Challenge**"
msgstr "**业务挑战**"
#: ../../community/user_stories/llamafactory.md:11
msgid ""
"LLaMA-Factory used transformers to perform inference on Ascend NPU, but the "
"speed was slow."
msgstr "LLaMA-Factory 使用 transformers 在 Ascend NPU 上进行推理，但速度较慢。"
#: ../../community/user_stories/llamafactory.md:13
msgid "**Solving Challenges and Benefits with vLLM Ascend**"
msgstr "**通过 vLLM Ascend 解决挑战与收益**"
#: ../../community/user_stories/llamafactory.md:15
msgid ""
"With the joint efforts of LLaMA-Factory and vLLM Ascend ([LLaMA-"
"Factory#7739](https://github.com/hiyouga/LLaMA-Factory/pull/7739)), the "
"performance of LLaMA-Factory in the model inference stage has been "
"significantly improved. According to the test results, the inference speed "
"of LLaMA-Factory has been increased to 2x compared to the transformers "
"version."
msgstr ""
"在 LLaMA-Factory 和 vLLM Ascend 的共同努力下（参见 [LLaMA-"
"Factory#7739](https://github.com/hiyouga/LLaMA-Factory/pull/7739)），LLaMA-"
"Factory 在模型推理阶段的性能得到了显著提升。根据测试结果，LLaMA-Factory 的推理速度相比 transformers 版本提升到了 2"
" 倍。"
#: ../../community/user_stories/llamafactory.md:17
msgid "**Learn more**"
msgstr "**了解更多**"
#: ../../community/user_stories/llamafactory.md:19
msgid ""
"See more about LLaMA-Factory and how it uses vLLM Ascend for inference on "
"the Ascend NPU in the following documentation: [LLaMA-Factory Ascend NPU "
"Inference](https://llamafactory.readthedocs.io/en/latest/advanced/npu_inference.html)."
msgstr ""
"在以下文档中查看更多关于 LLaMA-Factory 以及其如何在 Ascend NPU 上使用 vLLM Ascend 进行推理的信息：[LLaMA-"
"Factory Ascend NPU "
"推理](https://llamafactory.readthedocs.io/en/latest/advanced/npu_inference.html)。"

624
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# SOME DESCRIPTIVE TITLE.
# Copyright (C) 2025, vllm-ascend team
# This file is distributed under the same license as the vllm-ascend
# package.
# FIRST AUTHOR <EMAIL@ADDRESS>, 2025.
#
#, fuzzy
msgid ""
msgstr ""
"Project-Id-Version: vllm-ascend\n"
"Report-Msgid-Bugs-To: \n"
"POT-Creation-Date: 2025-07-18 09:01+0800\n"
"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
"Language-Team: zh_CN <LL@li.org>\n"
"Language: zh_CN\n"
"MIME-Version: 1.0\n"
"Content-Type: text/plain; charset=utf-8\n"
"Content-Transfer-Encoding: 8bit\n"
"Plural-Forms: nplurals=1; plural=0;\n"
"Generated-By: Babel 2.17.0\n"
#: ../../community/versioning_policy.md:1
msgid "Versioning policy"
msgstr "版本管理策略"
#: ../../community/versioning_policy.md:3
msgid ""
"Starting with vLLM 0.7.x, the vLLM Ascend Plugin ([vllm-project/vllm-"
"ascend](https://github.com/vllm-project/vllm-ascend)) project follows the "
"[PEP 440](https://peps.python.org/pep-0440/) to publish matching with vLLM "
"([vllm-project/vllm](https://github.com/vllm-project/vllm))."
msgstr ""
"从 vLLM 0.7.x 开始，vLLM Ascend 插件（[vllm-project/vllm-"
"ascend](https://github.com/vllm-project/vllm-ascend)）项目遵循 [PEP "
"440](https://peps.python.org/pep-0440/) ，以与 vLLM（[vllm-"
"project/vllm](https://github.com/vllm-project/vllm)）版本匹配发布。"
#: ../../community/versioning_policy.md:5
msgid "vLLM Ascend Plugin versions"
msgstr "vLLM Ascend 插件版本"
#: ../../community/versioning_policy.md:7
msgid ""
"Each vLLM Ascend release will be versioned: "
"`v[major].[minor].[micro][rcN][.postN]` (such as `v0.7.3rc1`, `v0.7.3`, "
"`v0.7.3.post1`)"
msgstr ""
"每个 vLLM Ascend 版本将采用以下版本格式：`v[major].[minor].[micro][rcN][.postN]`（例如 "
"`v0.7.3rc1`、`v0.7.3`、`v0.7.3.post1`）"
#: ../../community/versioning_policy.md:10
msgid ""
"**Final releases**: will typically be released every **3 months**, will take"
" the vLLM upstream release plan and Ascend software product release plan "
"into comprehensive consideration."
msgstr "**正式版本**：通常每**3个月**发布一次，将综合考虑 vLLM 上游发行计划和昇腾软件产品发行计划。"
#: ../../community/versioning_policy.md:11
msgid ""
"**Pre releases**: will typically be released **on demand**, ending with rcN,"
" represents the Nth release candidate version, to support early testing by "
"our users prior to a final release."
msgstr "**预发布版本**：通常会**按需发布**，以 rcN 结尾，表示第N个候选发布版本，旨在支持用户在正式发布前进行早期测试。"
#: ../../community/versioning_policy.md:12
msgid ""
"**Post releases**: will typically be released **on demand** to support to "
"address minor errors in a final release. It's different from [PEP-440 post "
"release note](https://peps.python.org/pep-0440/#post-releases) suggestion, "
"it will contain actual bug fixes considering that the final release version "
"should be matched strictly with the vLLM final release version "
"(`v[major].[minor].[micro]`). The post version has to be published as a "
"patch version of the final release."
msgstr ""
"**后续版本**：通常会根据需要发布，以支持解决正式发布中的小错误。这与 [PEP-440 "
"的后续版本说明](https://peps.python.org/pep-0440/#post-releases) 建议不同，它将包含实际的 bug "
"修复，因为最终发布版本应严格与 vLLM "
"的最终发布版本（`v[major].[minor].[micro]`）匹配。后续版本必须以正式发布的补丁版本形式发布。"
#: ../../community/versioning_policy.md:14
msgid "For example:"
msgstr "例如："
#: ../../community/versioning_policy.md:15
msgid ""
"`v0.7.x`: it's the first final release to match the vLLM `v0.7.x` version."
msgstr "`v0.7.x`：这是第一个与 vLLM `v0.7.x` 版本相匹配的正式发布版本。"
#: ../../community/versioning_policy.md:16
msgid "`v0.7.3rc1`: will be the first pre version of vLLM Ascend."
msgstr "`v0.7.3rc1`：将会是 vLLM Ascend 的第一个预发布版本。"
#: ../../community/versioning_policy.md:17
msgid ""
"`v0.7.3.post1`: will be the post release if the `v0.7.3` release has some "
"minor errors."
msgstr "`v0.7.3.post1`：如果 `v0.7.3` 版本发布有一些小错误，将作为后续修正版发布。"
#: ../../community/versioning_policy.md:19
msgid "Release Compatibility Matrix"
msgstr "版本兼容性矩阵"
#: ../../community/versioning_policy.md:21
msgid "Following is the Release Compatibility Matrix for vLLM Ascend Plugin:"
msgstr "以下是 vLLM Ascend 插件的版本兼容性矩阵："
#: ../../community/versioning_policy.md
msgid "vLLM Ascend"
msgstr "vLLM Ascend"
#: ../../community/versioning_policy.md
msgid "vLLM"
msgstr "vLLM"
#: ../../community/versioning_policy.md
msgid "Python"
msgstr "Python"
#: ../../community/versioning_policy.md
msgid "Stable CANN"
msgstr "Stable CANN"
#: ../../community/versioning_policy.md
msgid "PyTorch/torch_npu"
msgstr "PyTorch/torch_npu"
#: ../../community/versioning_policy.md
msgid "MindIE Turbo"
msgstr "MindIE Turbo"
#: ../../community/versioning_policy.md
msgid "v0.9.2rc1"
msgstr "v0.9.2rc1"
#: ../../community/versioning_policy.md
msgid "v0.9.2"
msgstr "v0.9.2"
#: ../../community/versioning_policy.md
msgid ">= 3.9, < 3.12"
msgstr ">= 3.9，< 3.12"
#: ../../community/versioning_policy.md
msgid "8.1.RC1"
msgstr "8.1.RC1"
#: ../../community/versioning_policy.md
msgid "2.5.1 / 2.5.1.post1.dev20250619"
msgstr "2.5.1 / 2.5.1.post1.dev20250619"
#: ../../community/versioning_policy.md
msgid "v0.9.1rc1"
msgstr "v0.9.1rc1"
#: ../../community/versioning_policy.md
msgid "v0.9.1"
msgstr "v0.9.1"
#: ../../community/versioning_policy.md
msgid "2.5.1 / 2.5.1.post1.dev20250528"
msgstr "2.5.1 / 2.5.1.post1.dev20250528"
#: ../../community/versioning_policy.md
msgid "v0.9.0rc2"
msgstr "v0.9.0rc2"
#: ../../community/versioning_policy.md
msgid "v0.9.0"
msgstr "v0.9.0"
#: ../../community/versioning_policy.md
msgid "2.5.1 / 2.5.1"
msgstr "2.5.1 / 2.5.1"
#: ../../community/versioning_policy.md
msgid "v0.9.0rc1"
msgstr "v0.9.0rc1"
#: ../../community/versioning_policy.md
msgid "v0.8.5rc1"
msgstr "v0.8.5rc1"
#: ../../community/versioning_policy.md
msgid "v0.8.5.post1"
msgstr "v0.8.5.post1"
#: ../../community/versioning_policy.md
msgid "v0.8.4rc2"
msgstr "v0.8.4rc2"
#: ../../community/versioning_policy.md
msgid "v0.8.4"
msgstr "v0.8.4"
#: ../../community/versioning_policy.md
msgid "8.0.0"
msgstr "8.0.0"
#: ../../community/versioning_policy.md
msgid "v0.7.3.post1"
msgstr "v0.7.3.post1"
#: ../../community/versioning_policy.md
msgid "v0.7.3"
msgstr "v0.7.3"
#: ../../community/versioning_policy.md
msgid "2.0rc1"
msgstr "2.0候选版本1"
#: ../../community/versioning_policy.md:34
msgid "Release cadence"
msgstr "发布节奏"
#: ../../community/versioning_policy.md:36
msgid "release window"
msgstr "发布窗口"
#: ../../community/versioning_policy.md
msgid "Date"
msgstr "日期"
#: ../../community/versioning_policy.md
msgid "Event"
msgstr "事件"
#: ../../community/versioning_policy.md
msgid "2025.07.11"
msgstr "2025.07.11"
#: ../../community/versioning_policy.md
msgid "Release candidates, v0.9.2rc1"
msgstr "候选发布版本，v0.9.2rc1"
#: ../../community/versioning_policy.md
msgid "2025.06.22"
msgstr "2025.06.22"
#: ../../community/versioning_policy.md
msgid "Release candidates, v0.9.1rc1"
msgstr "候选发布版本，v0.9.1rc1"
#: ../../community/versioning_policy.md
msgid "2025.06.10"
msgstr "2025.06.10"
#: ../../community/versioning_policy.md
msgid "Release candidates, v0.9.0rc2"
msgstr "候选发布版本，v0.9.0rc2"
#: ../../community/versioning_policy.md
msgid "2025.06.09"
msgstr "2025.06.09"
#: ../../community/versioning_policy.md
msgid "Release candidates, v0.9.0rc1"
msgstr "候选发布版本本，v0.9.0rc1"
#: ../../community/versioning_policy.md
msgid "2025.05.29"
msgstr "2025.05.29"
#: ../../community/versioning_policy.md
msgid "v0.7.x post release, v0.7.3.post1"
msgstr "v0.7.x 补丁版，v0.7.3.post1"
#: ../../community/versioning_policy.md
msgid "2025.05.08"
msgstr "2025.05.08"
#: ../../community/versioning_policy.md
msgid "v0.7.x Final release, v0.7.3"
msgstr "v0.7.x 正式版，v0.7.3"
#: ../../community/versioning_policy.md
msgid "2025.05.06"
msgstr "2025.05.06"
#: ../../community/versioning_policy.md
msgid "Release candidates, v0.8.5rc1"
msgstr "候选发布版本，v0.8.5rc1"
#: ../../community/versioning_policy.md
msgid "2025.04.28"
msgstr "2025.04.28"
#: ../../community/versioning_policy.md
msgid "Release candidates, v0.8.4rc2"
msgstr "候选发布版本，v0.8.4rc2"
#: ../../community/versioning_policy.md
msgid "2025.04.18"
msgstr "2025.04.18"
#: ../../community/versioning_policy.md
msgid "Release candidates, v0.8.4rc1"
msgstr "候选发布版本，v0.8.4rc1"
#: ../../community/versioning_policy.md
msgid "2025.03.28"
msgstr "2025.03.28"
#: ../../community/versioning_policy.md
msgid "Release candidates, v0.7.3rc2"
msgstr "候选发布版本，v0.7.3rc2"
#: ../../community/versioning_policy.md
msgid "2025.03.14"
msgstr "2025.03.14"
#: ../../community/versioning_policy.md
msgid "Release candidates, v0.7.3rc1"
msgstr "候选发布版本，v0.7.3rc1"
#: ../../community/versioning_policy.md
msgid "2025.02.19"
msgstr "2025.02.19"
#: ../../community/versioning_policy.md
msgid "Release candidates, v0.7.1rc1"
msgstr "候选发布版本，v0.7.1rc1"
#: ../../community/versioning_policy.md:53
msgid "Branch policy"
msgstr "分支策略"
#: ../../community/versioning_policy.md:55
msgid "vLLM Ascend has main branch and dev branch."
msgstr "vLLM Ascend 有主分支和开发分支。"
#: ../../community/versioning_policy.md:57
msgid ""
"**main**: main branch，corresponds to the vLLM main branch and latest 1 or 2 "
"release version. It is continuously monitored for quality through Ascend CI."
msgstr "**main**：main 分支，对应 vLLM 的主分支和最新的 1 或 2 个发布版本。该分支通过 Ascend CI 持续监控质量。"
#: ../../community/versioning_policy.md:58
msgid ""
"**vX.Y.Z-dev**: development branch, created with part of new releases of "
"vLLM. For example, `v0.7.3-dev` is the dev branch for vLLM `v0.7.3` version."
msgstr ""
"**vX.Y.Z-dev**：开发分支，是随着 vLLM 新版本的一部分一起创建的。例如，`v0.7.3-dev` 是 vLLM `v0.7.3` "
"版本的开发分支。"
#: ../../community/versioning_policy.md:60
msgid ""
"Usually, a commit should be ONLY first merged in the main branch, and then "
"backported to the dev branch to reduce maintenance costs as much as "
"possible."
msgstr "通常，提交应该只先合并到主分支，然后再回溯合并到开发分支，以尽可能降低维护成本。"
#: ../../community/versioning_policy.md:62
msgid "Maintenance branch and EOL:"
msgstr "维护分支与生命周期结束（EOL）："
#: ../../community/versioning_policy.md:63
msgid "The branch status will be in one of the following states:"
msgstr "分支状态将处于以下几种状态之一："
#: ../../community/versioning_policy.md
msgid "Branch"
msgstr "分支"
#: ../../community/versioning_policy.md
msgid "Time frame"
msgstr "时间范围"
#: ../../community/versioning_policy.md
msgid "Summary"
msgstr "摘要"
#: ../../community/versioning_policy.md
msgid "Maintained"
msgstr "维护中"
#: ../../community/versioning_policy.md
msgid "Approximately 2-3 minor versions"
msgstr "大约 2-3 个小版本"
#: ../../community/versioning_policy.md
msgid "All bugfixes are appropriate. Releases produced, CI commitment."
msgstr "所有的错误修复都是合适的。正常发布版本，持续集成承诺。"
#: ../../community/versioning_policy.md
msgid "Unmaintained"
msgstr "无人维护"
#: ../../community/versioning_policy.md
msgid "Community interest driven"
msgstr "社区兴趣驱动"
#: ../../community/versioning_policy.md
msgid "All bugfixes are appropriate. No Releases produced, No CI commitment"
msgstr "所有的 bug 修复都是合适的。没有发布版本，不承诺持续集成（CI）。"
#: ../../community/versioning_policy.md
msgid "End of Life (EOL)"
msgstr "生命周期结束（EOL）"
#: ../../community/versioning_policy.md
msgid "N/A"
msgstr "不适用"
#: ../../community/versioning_policy.md
msgid "Branch no longer accepting changes"
msgstr "该分支不再接受更改"
#: ../../community/versioning_policy.md:71
msgid "Branch state"
msgstr "分支状态"
#: ../../community/versioning_policy.md:73
msgid ""
"Note that vLLM Ascend will only be released for a certain vLLM release "
"version rather than all versions. Hence, You might see only part of versions"
" have dev branches (such as only `0.7.1-dev` / `0.7.3-dev` but no "
"`0.7.2-dev`), this is as expected."
msgstr ""
"请注意，vLLM Ascend 只会针对某些 vLLM 发布版本发布，而不是所有版本。因此，您可能会看到只有部分版本拥有开发分支（例如只有 "
"`0.7.1-dev` / `0.7.3-dev`，而没有 `0.7.2-dev`），这是正常现象。"
#: ../../community/versioning_policy.md:75
msgid ""
"Usually, each minor version of vLLM (such as 0.7) will correspond to a vLLM "
"Ascend version branch and support its latest version (for example, we plan "
"to support version 0.7.3) as following shown:"
msgstr ""
"通常，vLLM 的每一个小版本（例如 0.7）都会对应一个 vLLM Ascend 版本分支，并支持其最新版本（例如，我们计划支持 0.7.3 "
"版），如下所示："
#: ../../community/versioning_policy.md
msgid "Status"
msgstr "状态"
#: ../../community/versioning_policy.md
msgid "Note"
msgstr "注释"
#: ../../community/versioning_policy.md
msgid "main"
msgstr "main"
#: ../../community/versioning_policy.md
msgid "CI commitment for vLLM main branch and vLLM 0.9.2 branch"
msgstr "vLLM 主分支和 vLLM 0.9.2 分支的 CI 承诺"
#: ../../community/versioning_policy.md
msgid "v0.9.1-dev"
msgstr "v0.9.1-dev"
#: ../../community/versioning_policy.md
msgid "CI commitment for vLLM 0.9.1 version"
msgstr "vLLM 0.9.1 版本的 CI 承诺"
#: ../../community/versioning_policy.md
msgid "v0.7.3-dev"
msgstr "v0.7.3-dev"
#: ../../community/versioning_policy.md
msgid "CI commitment for vLLM 0.7.3 version"
msgstr "vLLM 0.7.3 版本的 CI 承诺"
#: ../../community/versioning_policy.md
msgid "v0.7.1-dev"
msgstr "v0.7.1-dev"
#: ../../community/versioning_policy.md
msgid "Replaced by v0.7.3-dev"
msgstr "已被 v0.7.3-dev 替代"
#: ../../community/versioning_policy.md:84
msgid "Backward compatibility"
msgstr "向后兼容性"
#: ../../community/versioning_policy.md:86
msgid ""
"For main branch, vLLM Ascend should works with vLLM main branch and latest 1"
" or 2 release version. So to ensure the backward compatibility, we will do "
"the following:"
msgstr ""
"对于主分支，vLLM Ascend 应该与 vLLM 主分支以及最新的 1 或 2 个发布版本兼容。因此，为了确保向后兼容性，我们将执行以下操作："
#: ../../community/versioning_policy.md:87
msgid ""
"Both main branch and target vLLM release is tested by Ascend E2E CI. For "
"example, currently, vLLM main branch and vLLM 0.8.4 are tested now."
msgstr "主分支和目标 vLLM 发行版都经过了 Ascend E2E CI 的测试。例如，目前正在测试 vLLM 主分支和 vLLM 0.8.4。"
#: ../../community/versioning_policy.md:88
msgid ""
"For code changes, we will make sure that the changes are compatible with the"
" latest 1 or 2 vLLM release version as well. In this case, vLLM Ascend "
"introduced a version check machinism inner the code. It'll check the version"
" of installed vLLM package first to decide which code logic to use. If users"
" hit the `InvalidVersion` error, it sometimes means that they have installed"
" an dev/editable version of vLLM package. In this case, we provide the env "
"variable `VLLM_VERSION` to let users specify the version of vLLM package to "
"use."
msgstr ""
"对于代码更改，我们也会确保这些更改与最新的 1 或 2 个 vLLM 发行版本兼容。在这种情况下，vLLM Ascend "
"在代码中引入了版本检查机制。它会先检查已安装的 vLLM 包的版本，然后决定使用哪段代码逻辑。如果用户遇到 `InvalidVersion` "
"错误，这有时意味着他们安装了 dev/可编辑版本的 vLLM 包。此时，我们提供了环境变量 `VLLM_VERSION`，让用户可以指定要使用的 "
"vLLM 包版本。"
#: ../../community/versioning_policy.md:89
msgid ""
"For documentation changes, we will make sure that the changes are compatible"
" with the latest 1 or 2 vLLM release version as well. Note should be added "
"if there are any breaking changes."
msgstr "对于文档更改，我们会确保这些更改也兼容于最新的1个或2个 vLLM 发布版本。如果有任何重大变更，应添加说明。"
#: ../../community/versioning_policy.md:91
msgid "Document Branch Policy"
msgstr "文档分支政策"
#: ../../community/versioning_policy.md:92
msgid ""
"To reduce maintenance costs, **all branch documentation content should "
"remain consistent, and version differences can be controlled via variables "
"in [docs/source/conf.py](https://github.com/vllm-project/vllm-"
"ascend/blob/main/docs/source/conf.py)**. While this is not a simple task, it"
" is a principle we should strive to follow."
msgstr ""
"为了减少维护成本，**所有分支的文档内容应保持一致，版本差异可以通过 "
"[docs/source/conf.py](https://github.com/vllm-project/vllm-"
"ascend/blob/main/docs/source/conf.py) 中的变量进行控制**。虽然这并非易事，但这是我们应当努力遵循的原则。"
#: ../../community/versioning_policy.md
msgid "Version"
msgstr "版本"
#: ../../community/versioning_policy.md
msgid "Purpose"
msgstr "用途"
#: ../../community/versioning_policy.md
msgid "Code Branch"
msgstr "代码分支"
#: ../../community/versioning_policy.md
msgid "latest"
msgstr "最新"
#: ../../community/versioning_policy.md
msgid "Doc for the latest dev branch"
msgstr "最新开发分支的文档"
#: ../../community/versioning_policy.md
msgid "vX.Y.Z-dev (Will be `main` after the first final release)"
msgstr "vX.Y.Z-dev（在第一个正式版本发布后将成为 `main`）"
#: ../../community/versioning_policy.md
msgid "version"
msgstr "版本"
#: ../../community/versioning_policy.md
msgid "Doc for historical released versions"
msgstr "历史版本文档"
#: ../../community/versioning_policy.md
msgid "Git tags, like vX.Y.Z[rcN]"
msgstr "Git 标签，如 vX.Y.Z[rcN]"
#: ../../community/versioning_policy.md
msgid "stable（not yet released）"
msgstr "稳定版（尚未发布）"
#: ../../community/versioning_policy.md
msgid "Doc for latest final release branch"
msgstr "最新正式发布分支的文档"
#: ../../community/versioning_policy.md
msgid "Will be `vX.Y.Z-dev` after the first official release"
msgstr "首个正式发布后将会是 `vX.Y.Z-dev`"
#: ../../community/versioning_policy.md:100
msgid "As shown above:"
msgstr "如上所示："
#: ../../community/versioning_policy.md:102
msgid ""
"`latest` documentation: Matches the current maintenance branch `vX.Y.Z-dev` "
"(Will be `main` after the first final release). Continuously updated to "
"ensure usability for the latest release."
msgstr ""
"`latest` 文档：匹配当前维护分支 `vX.Y.Z-dev`（在首次正式发布后将为 `main`）。持续更新，以确保适用于最新发布版本。"
#: ../../community/versioning_policy.md:103
msgid ""
"`version` documentation: Corresponds to specific released versions (e.g., "
"`v0.7.3`, `v0.7.3rc1`). No further updates after release."
msgstr "`version` 文档：对应特定的已发布版本（例如，`v0.7.3`、`v0.7.3rc1`）。发布后不再进行更新。"
#: ../../community/versioning_policy.md:104
msgid ""
"`stable` documentation (**not yet released**): Official release "
"documentation. Updates are allowed in real-time after release, typically "
"based on vX.Y.Z-dev. Once stable documentation is available, non-stable "
"versions should display a header warning: `You are viewing the latest "
"developer preview docs. Click here to view docs for the latest stable "
"release.`."
msgstr ""
"`stable` 文档（**尚未发布**）：官方发布版文档。发布后允许实时更新，通常基于 "
"vX.Y.Z-dev。一旦稳定版文档可用，非稳定版本应显示一个顶部警告：`您正在查看最新的开发预览文档。点击此处查看最新稳定版本文档。`"
#: ../../community/versioning_policy.md:106
msgid "Software Dependency Management"
msgstr "软件依赖管理"
#: ../../community/versioning_policy.md:107
msgid ""
"`torch-npu`: Ascend Extension for PyTorch (torch-npu) releases a stable "
"version to [PyPi](https://pypi.org/project/torch-npu) every 3 months, a "
"development version (aka the POC version) every month, and a nightly version"
" every day. The PyPi stable version **CAN** be used in vLLM Ascend final "
"version, the monthly dev version **ONLY CANN** be used in vLLM Ascend RC "
"version for rapid iteration, the nightly version **CANNOT** be used in vLLM "
"Ascend any version and branches."
msgstr ""
"`torch-npu`：Ascend Extension for PyTorch（torch-npu）每 3 个月会在 "
"[PyPi](https://pypi.org/project/torch-npu) 上发布一个稳定版本，每个月发布一个开发版本（即 POC "
"版本），每天发布一个 nightly 版本。PyPi 上的稳定版本**可以**用于 vLLM Ascend 的正式版本，月度开发版本**只能**用于 "
"vLLM Ascend 的 RC（候选发布）版本以便快速迭代，nightly 版本**不能**用于 vLLM Ascend 的任何版本和分支。"

187
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@@ -0,0 +1,187 @@
# SOME DESCRIPTIVE TITLE.
# Copyright (C) 2025, vllm-ascend team
# This file is distributed under the same license as the vllm-ascend
# package.
# FIRST AUTHOR <EMAIL@ADDRESS>, 2025.
#
#, fuzzy
msgid ""
msgstr ""
"Project-Id-Version: vllm-ascend\n"
"Report-Msgid-Bugs-To: \n"
"POT-Creation-Date: 2025-07-18 09:01+0800\n"
"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
"Language-Team: zh_CN <LL@li.org>\n"
"Language: zh_CN\n"
"MIME-Version: 1.0\n"
"Content-Type: text/plain; charset=utf-8\n"
"Content-Transfer-Encoding: 8bit\n"
"Plural-Forms: nplurals=1; plural=0;\n"
"Generated-By: Babel 2.17.0\n"
#: ../../developer_guide/contribution/index.md:107
msgid "Index"
msgstr "索引"
#: ../../developer_guide/contribution/index.md:1
msgid "Contributing"
msgstr "贡献"
#: ../../developer_guide/contribution/index.md:3
msgid "Building and testing"
msgstr "构建与测试"
#: ../../developer_guide/contribution/index.md:4
msgid ""
"It's recommended to set up a local development environment to build and test"
" before you submit a PR."
msgstr "建议先搭建本地开发环境来进行构建和测试，再提交 PR。"
#: ../../developer_guide/contribution/index.md:7
msgid "Setup development environment"
msgstr "搭建开发环境"
#: ../../developer_guide/contribution/index.md:9
msgid ""
"Theoretically, the vllm-ascend build is only supported on Linux because "
"`vllm-ascend` dependency `torch_npu` only supports Linux."
msgstr ""
"理论上，vllm-ascend 构建仅支持 Linux，因为 `vllm-ascend` 的依赖项 `torch_npu` 只支持 Linux。"
#: ../../developer_guide/contribution/index.md:12
msgid ""
"But you can still set up dev env on Linux/Windows/macOS for linting and "
"basic test as following commands:"
msgstr "但你仍然可以在 Linux/Windows/macOS 上按照以下命令设置开发环境，用于代码规约检查和基本测试："
#: ../../developer_guide/contribution/index.md:15
msgid "Run lint locally"
msgstr "在本地运行 lint"
#: ../../developer_guide/contribution/index.md:33
msgid "Run CI locally"
msgstr "本地运行CI"
#: ../../developer_guide/contribution/index.md:35
msgid "After complete \"Run lint\" setup, you can run CI locally:"
msgstr "在完成“运行 lint”设置后，你可以在本地运行 CI："
#: ../../developer_guide/contribution/index.md:61
msgid "Submit the commit"
msgstr "提交该提交"
#: ../../developer_guide/contribution/index.md:68
msgid ""
"🎉 Congratulations! You have completed the development environment setup."
msgstr "🎉 恭喜！你已经完成了开发环境的搭建。"
#: ../../developer_guide/contribution/index.md:70
msgid "Test locally"
msgstr "本地测试"
#: ../../developer_guide/contribution/index.md:72
msgid ""
"You can refer to [Testing](./testing.md) doc to help you setup testing "
"environment and running tests locally."
msgstr "你可以参考 [测试](./testing.md) 文档，帮助你搭建测试环境并在本地运行测试。"
#: ../../developer_guide/contribution/index.md:74
msgid "DCO and Signed-off-by"
msgstr "DCO 和签名确认"
#: ../../developer_guide/contribution/index.md:76
msgid ""
"When contributing changes to this project, you must agree to the DCO. "
"Commits must include a `Signed-off-by:` header which certifies agreement "
"with the terms of the DCO."
msgstr "当为本项目贡献更改时，您必须同意 DCO。提交必须包含 `Signed-off-by:` 头部，以证明您同意 DCO 的条款。"
#: ../../developer_guide/contribution/index.md:78
msgid "Using `-s` with `git commit` will automatically add this header."
msgstr "在使用 `git commit` 时加上 `-s` 参数会自动添加这个头部信息。"
#: ../../developer_guide/contribution/index.md:80
msgid "PR Title and Classification"
msgstr "PR 标题与分类"
#: ../../developer_guide/contribution/index.md:82
msgid ""
"Only specific types of PRs will be reviewed. The PR title is prefixed "
"appropriately to indicate the type of change. Please use one of the "
"following:"
msgstr "只有特定类型的 PR 会被审核。PR 标题应使用合适的前缀以指明更改类型。请使用以下之一："
#: ../../developer_guide/contribution/index.md:84
msgid "`[Attention]` for new features or optimization in attention."
msgstr "`[Attention]` 用于注意力机制中新特性或优化。"
#: ../../developer_guide/contribution/index.md:85
msgid "`[Communicator]` for new features or optimization in communicators."
msgstr "`[Communicator]` 适用于通信器中的新特性或优化。"
#: ../../developer_guide/contribution/index.md:86
msgid "`[ModelRunner]` for new features or optimization in model runner."
msgstr "`[ModelRunner]` 用于模型运行器中的新功能或优化。"
#: ../../developer_guide/contribution/index.md:87
msgid "`[Platform]` for new features or optimization in platform."
msgstr "`[Platform]` 用于平台中新功能或优化。"
#: ../../developer_guide/contribution/index.md:88
msgid "`[Worker]` for new features or optimization in worker."
msgstr "`[Worker]` 用于 worker 的新功能或优化。"
#: ../../developer_guide/contribution/index.md:89
msgid ""
"`[Core]` for new features or optimization in the core vllm-ascend logic "
"(such as platform, attention, communicators, model runner)"
msgstr "`[Core]` 用于核心 vllm-ascend 逻辑中的新特性或优化（例如平台、注意力机制、通信器、模型运行器）。"
#: ../../developer_guide/contribution/index.md:90
msgid "`[Kernel]` changes affecting compute kernels and ops."
msgstr "`[Kernel]` 影响计算内核和操作的更改。"
#: ../../developer_guide/contribution/index.md:91
msgid "`[Bugfix]` for bug fixes."
msgstr "`[Bugfix]` 用于表示错误修复。"
#: ../../developer_guide/contribution/index.md:92
msgid "`[Doc]` for documentation fixes and improvements."
msgstr "`[Doc]` 用于文档修复和改进。"
#: ../../developer_guide/contribution/index.md:93
msgid "`[Test]` for tests (such as unit tests)."
msgstr "`[Test]` 用于测试（如单元测试）。"
#: ../../developer_guide/contribution/index.md:94
msgid "`[CI]` for build or continuous integration improvements."
msgstr "`[CI]` 用于构建或持续集成的改进。"
#: ../../developer_guide/contribution/index.md:95
msgid ""
"`[Misc]` for PRs that do not fit the above categories. Please use this "
"sparingly."
msgstr "对于不属于上述类别的 PR，请使用 `[Misc]`。请谨慎使用此标签。"
#: ../../developer_guide/contribution/index.md:98
msgid ""
"If the PR spans more than one category, please include all relevant "
"prefixes."
msgstr "如果拉取请求（PR）涵盖多个类别，请包含所有相关的前缀。"
#: ../../developer_guide/contribution/index.md:101
msgid "Others"
msgstr "其他"
#: ../../developer_guide/contribution/index.md:103
msgid ""
"You may find more information about contributing to vLLM Ascend backend "
"plugin on "
"[<u>docs.vllm.ai</u>](https://docs.vllm.ai/en/latest/contributing/overview.html)."
" If you find any problem when contributing, you can feel free to submit a PR"
" to improve the doc to help other developers."
msgstr ""
"你可以在 "
"[<u>docs.vllm.ai</u>](https://docs.vllm.ai/en/latest/contributing/overview.html)"
" 上找到有关为 vLLM Ascend 后端插件做贡献的更多信息。如果你在贡献过程中遇到任何问题，欢迎随时提交 PR 来改进文档，以帮助其他开发者。"

237
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@@ -0,0 +1,237 @@
# SOME DESCRIPTIVE TITLE.
# Copyright (C) 2025, vllm-ascend team
# This file is distributed under the same license as the vllm-ascend
# package.
# FIRST AUTHOR <EMAIL@ADDRESS>, 2025.
#
#, fuzzy
msgid ""
msgstr ""
"Project-Id-Version: vllm-ascend\n"
"Report-Msgid-Bugs-To: \n"
"POT-Creation-Date: 2025-07-18 09:01+0800\n"
"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
"Language-Team: zh_CN <LL@li.org>\n"
"Language: zh_CN\n"
"MIME-Version: 1.0\n"
"Content-Type: text/plain; charset=utf-8\n"
"Content-Transfer-Encoding: 8bit\n"
"Plural-Forms: nplurals=1; plural=0;\n"
"Generated-By: Babel 2.17.0\n"
#: ../../developer_guide/contribution/testing.md:1
msgid "Testing"
msgstr "测试"
#: ../../developer_guide/contribution/testing.md:3
msgid ""
"This secition explains how to write e2e tests and unit tests to verify the "
"implementation of your feature."
msgstr "本节介绍如何编写端到端测试和单元测试，以验证你的功能实现。"
#: ../../developer_guide/contribution/testing.md:5
msgid "Setup test environment"
msgstr "设置测试环境"
#: ../../developer_guide/contribution/testing.md:7
msgid ""
"The fastest way to setup test environment is to use the main branch "
"container image:"
msgstr "搭建测试环境最快的方法是使用 main 分支的容器镜像："
#: ../../developer_guide/contribution/testing.md
msgid "Local (CPU)"
msgstr "本地（CPU）"
#: ../../developer_guide/contribution/testing.md:18
msgid "You can run the unit tests on CPU with the following steps:"
msgstr "你可以按照以下步骤在 CPU 上运行单元测试："
#: ../../developer_guide/contribution/testing.md
msgid "Single card"
msgstr "单张卡片"
#: ../../developer_guide/contribution/testing.md:85
#: ../../developer_guide/contribution/testing.md:123
msgid ""
"After starting the container, you should install the required packages:"
msgstr "启动容器后，你应该安装所需的软件包："
#: ../../developer_guide/contribution/testing.md
msgid "Multi cards"
msgstr "多卡"
#: ../../developer_guide/contribution/testing.md:137
msgid "Running tests"
msgstr "运行测试"
#: ../../developer_guide/contribution/testing.md:139
msgid "Unit test"
msgstr "单元测试"
#: ../../developer_guide/contribution/testing.md:141
msgid "There are several principles to follow when writing unit tests:"
msgstr "编写单元测试时需要遵循几个原则："
#: ../../developer_guide/contribution/testing.md:143
msgid ""
"The test file path should be consistent with source file and start with "
"`test_` prefix, such as: `vllm_ascend/worker/worker_v1.py` --> "
"`tests/ut/worker/test_worker_v1.py`"
msgstr ""
"测试文件的路径应与源文件保持一致，并以 `test_` 前缀开头，例如：`vllm_ascend/worker/worker_v1.py` --> "
"`tests/ut/worker/test_worker_v1.py`"
#: ../../developer_guide/contribution/testing.md:144
msgid ""
"The vLLM Ascend test are using unittest framework, see "
"[here](https://docs.python.org/3/library/unittest.html#module-unittest) to "
"understand how to write unit tests."
msgstr ""
"vLLM Ascend 测试使用 unittest "
"框架，参见[这里](https://docs.python.org/3/library/unittest.html#module-"
"unittest)了解如何编写单元测试。"
#: ../../developer_guide/contribution/testing.md:145
msgid ""
"All unit tests can be run on CPU, so you must mock the device-related "
"function to host."
msgstr "所有单元测试都可以在 CPU 上运行，因此你必须将与设备相关的函数模拟为 host。"
#: ../../developer_guide/contribution/testing.md:146
msgid ""
"Example: [tests/ut/test_ascend_config.py](https://github.com/vllm-"
"project/vllm-ascend/blob/main/tests/ut/test_ascend_config.py)."
msgstr ""
"示例：[tests/ut/test_ascend_config.py](https://github.com/vllm-project/vllm-"
"ascend/blob/main/tests/ut/test_ascend_config.py)。"
#: ../../developer_guide/contribution/testing.md:147
msgid "You can run the unit tests using `pytest`:"
msgstr "你可以使用 `pytest` 运行单元测试："
#: ../../developer_guide/contribution/testing.md
msgid "Multi cards test"
msgstr "多卡测试"
#: ../../developer_guide/contribution/testing.md:192
msgid "E2E test"
msgstr "端到端测试"
#: ../../developer_guide/contribution/testing.md:194
msgid ""
"Although vllm-ascend CI provide [e2e test](https://github.com/vllm-"
"project/vllm-ascend/blob/main/.github/workflows/vllm_ascend_test.yaml) on "
"Ascend CI, you can run it locally."
msgstr ""
"虽然 vllm-ascend CI 在 Ascend CI 上提供了 [端到端测试](https://github.com/vllm-"
"project/vllm-"
"ascend/blob/main/.github/workflows/vllm_ascend_test.yaml)，你也可以在本地运行它。"
#: ../../developer_guide/contribution/testing.md:204
msgid "You can't run e2e test on CPU."
msgstr "你无法在 CPU 上运行 e2e 测试。"
#: ../../developer_guide/contribution/testing.md:240
msgid ""
"This will reproduce e2e test: "
"[vllm_ascend_test.yaml](https://github.com/vllm-project/vllm-"
"ascend/blob/main/.github/workflows/vllm_ascend_test.yaml)."
msgstr ""
"这将复现端到端测试：[vllm_ascend_test.yaml](https://github.com/vllm-project/vllm-"
"ascend/blob/main/.github/workflows/vllm_ascend_test.yaml)。"
#: ../../developer_guide/contribution/testing.md:242
msgid "E2E test example:"
msgstr "E2E 测试示例："
#: ../../developer_guide/contribution/testing.md:244
msgid ""
"Offline test example: "
"[`tests/e2e/singlecard/test_offline_inference.py`](https://github.com/vllm-"
"project/vllm-"
"ascend/blob/main/tests/e2e/singlecard/test_offline_inference.py)"
msgstr ""
"离线测试示例：[`tests/e2e/singlecard/test_offline_inference.py`](https://github.com/vllm-"
"project/vllm-"
"ascend/blob/main/tests/e2e/singlecard/test_offline_inference.py)"
#: ../../developer_guide/contribution/testing.md:245
msgid ""
"Online test examples: "
"[`tests/e2e/singlecard/test_prompt_embedding.py`](https://github.com/vllm-"
"project/vllm-ascend/blob/main/tests/e2e/singlecard/test_prompt_embedding.py)"
msgstr ""
"在线测试示例：[`tests/e2e/singlecard/test_prompt_embedding.py`](https://github.com/vllm-"
"project/vllm-ascend/blob/main/tests/e2e/singlecard/test_prompt_embedding.py)"
#: ../../developer_guide/contribution/testing.md:246
msgid ""
"Correctness test example: "
"[`tests/e2e/singlecard/test_aclgraph.py`](https://github.com/vllm-"
"project/vllm-ascend/blob/main/tests/e2e/singlecard/test_aclgraph.py)"
msgstr ""
"正确性测试示例：[`tests/e2e/singlecard/test_aclgraph.py`](https://github.com/vllm-"
"project/vllm-ascend/blob/main/tests/e2e/singlecard/test_aclgraph.py)"
#: ../../developer_guide/contribution/testing.md:247
msgid ""
"Reduced Layer model test example: [test_torchair_graph_mode.py - "
"DeepSeek-V3-Pruning](https://github.com/vllm-project/vllm-"
"ascend/blob/20767a043cccb3764214930d4695e53941de87ec/tests/e2e/multicard/test_torchair_graph_mode.py#L48)"
msgstr ""
"简化层模型测试示例：[test_torchair_graph_mode.py - "
"DeepSeek-V3-Pruning](https://github.com/vllm-project/vllm-"
"ascend/blob/20767a043cccb3764214930d4695e53941de87ec/tests/e2e/multicard/test_torchair_graph_mode.py#L48)"
#: ../../developer_guide/contribution/testing.md:249
msgid ""
"The CI resource is limited, you might need to reduce layer number of the "
"model, below is an example of how to generate a reduced layer model:"
msgstr "CI 资源有限，您可能需要减少模型的层数，下面是一个生成减少层数模型的示例："
#: ../../developer_guide/contribution/testing.md:250
msgid ""
"Fork the original model repo in modelscope, we need all the files in the "
"repo except for weights."
msgstr "在 modelscope 中 fork 原始模型仓库，我们需要仓库中的所有文件，除了权重文件。"
#: ../../developer_guide/contribution/testing.md:251
#, python-brace-format
msgid ""
"Set `num_hidden_layers` to the expected number of layers, e.g., "
"`{\"num_hidden_layers\": 2,}`"
msgstr "将 `num_hidden_layers` 设置为期望的层数，例如 `{\"num_hidden_layers\": 2,}`"
#: ../../developer_guide/contribution/testing.md:252
msgid ""
"Copy the following python script as `generate_random_weight.py`. Set the "
"relevant parameters `MODEL_LOCAL_PATH`, `DIST_DTYPE` and `DIST_MODEL_PATH` "
"as needed:"
msgstr ""
"将以下 Python 脚本复制为 `generate_random_weight.py`。根据需要设置相关参数 "
"`MODEL_LOCAL_PATH`、`DIST_DTYPE` 和 `DIST_MODEL_PATH`："
#: ../../developer_guide/contribution/testing.md:270
msgid "Run doctest"
msgstr "运行 doctest"
#: ../../developer_guide/contribution/testing.md:272
msgid ""
"vllm-ascend provides a `vllm-ascend/tests/e2e/run_doctests.sh` command to "
"run all doctests in the doc files. The doctest is a good way to make sure "
"the docs are up to date and the examples are executable, you can run it "
"locally as follows:"
msgstr ""
"vllm-ascend 提供了一个 `vllm-ascend/tests/e2e/run_doctests.sh` 命令，用于运行文档文件中的所有 "
"doctest。doctest 是确保文档保持最新且示例可执行的好方法，你可以按照以下方式在本地运行它："
#: ../../developer_guide/contribution/testing.md:280
msgid ""
"This will reproduce the same environment as the CI: "
"[vllm_ascend_doctest.yaml](https://github.com/vllm-project/vllm-"
"ascend/blob/main/.github/workflows/vllm_ascend_doctest.yaml)."
msgstr ""
"这将复现与 CI 相同的环境：[vllm_ascend_doctest.yaml](https://github.com/vllm-"
"project/vllm-ascend/blob/main/.github/workflows/vllm_ascend_doctest.yaml)。"

26
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@@ -0,0 +1,26 @@
# SOME DESCRIPTIVE TITLE.
# Copyright (C) 2025, vllm-ascend team
# This file is distributed under the same license as the vllm-ascend
# package.
# FIRST AUTHOR <EMAIL@ADDRESS>, 2025.
#
#, fuzzy
msgid ""
msgstr ""
"Project-Id-Version: vllm-ascend\n"
"Report-Msgid-Bugs-To: \n"
"POT-Creation-Date: 2025-07-18 09:01+0800\n"
"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
"Language-Team: zh_CN <LL@li.org>\n"
"Language: zh_CN\n"
"MIME-Version: 1.0\n"
"Content-Type: text/plain; charset=utf-8\n"
"Content-Transfer-Encoding: 8bit\n"
"Plural-Forms: nplurals=1; plural=0;\n"
"Generated-By: Babel 2.17.0\n"
#: ../../developer_guide/evaluation/accuracy_report/index.md:1
#: ../../developer_guide/evaluation/accuracy_report/index.md:3
msgid "Accuracy Report"
msgstr "准确性报告"

26
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@@ -0,0 +1,26 @@
# SOME DESCRIPTIVE TITLE.
# Copyright (C) 2025, vllm-ascend team
# This file is distributed under the same license as the vllm-ascend
# package.
# FIRST AUTHOR <EMAIL@ADDRESS>, 2025.
#
#, fuzzy
msgid ""
msgstr ""
"Project-Id-Version: vllm-ascend\n"
"Report-Msgid-Bugs-To: \n"
"POT-Creation-Date: 2025-07-18 09:01+0800\n"
"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
"Language-Team: zh_CN <LL@li.org>\n"
"Language: zh_CN\n"
"MIME-Version: 1.0\n"
"Content-Type: text/plain; charset=utf-8\n"
"Content-Transfer-Encoding: 8bit\n"
"Plural-Forms: nplurals=1; plural=0;\n"
"Generated-By: Babel 2.17.0\n"
#: ../../developer_guide/evaluation/index.md:1
#: ../../developer_guide/evaluation/index.md:3
msgid "Accuracy"
msgstr "准确性"

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@@ -0,0 +1,112 @@
# SOME DESCRIPTIVE TITLE.
# Copyright (C) 2025, vllm-ascend team
# This file is distributed under the same license as the vllm-ascend
# package.
# FIRST AUTHOR <EMAIL@ADDRESS>, 2025.
#
#, fuzzy
msgid ""
msgstr ""
"Project-Id-Version: vllm-ascend\n"
"Report-Msgid-Bugs-To: \n"
"POT-Creation-Date: 2025-07-18 09:01+0800\n"
"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
"Language-Team: zh_CN <LL@li.org>\n"
"Language: zh_CN\n"
"MIME-Version: 1.0\n"
"Content-Type: text/plain; charset=utf-8\n"
"Content-Transfer-Encoding: 8bit\n"
"Plural-Forms: nplurals=1; plural=0;\n"
"Generated-By: Babel 2.17.0\n"
#: ../../developer_guide/evaluation/using_evalscope.md:1
msgid "Using EvalScope"
msgstr "使用 EvalScope"
#: ../../developer_guide/evaluation/using_evalscope.md:3
msgid ""
"This document will guide you have model inference stress testing and "
"accuracy testing using [EvalScope](https://github.com/modelscope/evalscope)."
msgstr ""
"本文档将指导您如何使用 [EvalScope](https://github.com/modelscope/evalscope) "
"进行模型推理压力测试和精度测试。"
#: ../../developer_guide/evaluation/using_evalscope.md:5
msgid "1. Online serving"
msgstr "1. 在线服务"
#: ../../developer_guide/evaluation/using_evalscope.md:7
msgid "You can run docker container to start the vLLM server on a single NPU:"
msgstr "你可以运行 docker 容器，在单个 NPU 上启动 vLLM 服务器："
#: ../../developer_guide/evaluation/using_evalscope.md:34
msgid "If your service start successfully, you can see the info shown below:"
msgstr "如果你的服务启动成功，你会看到如下所示的信息："
#: ../../developer_guide/evaluation/using_evalscope.md:42
msgid ""
"Once your server is started, you can query the model with input prompts in "
"new terminal:"
msgstr "一旦你的服务器启动后，你可以在新的终端中用输入提示词查询模型："
#: ../../developer_guide/evaluation/using_evalscope.md:55
msgid "2. Install EvalScope using pip"
msgstr "2. 使用 pip 安装 EvalScope"
#: ../../developer_guide/evaluation/using_evalscope.md:57
msgid "You can install EvalScope by using:"
msgstr "你可以使用以下方式安装 EvalScope："
#: ../../developer_guide/evaluation/using_evalscope.md:65
msgid "3. Run gsm8k accuracy test using EvalScope"
msgstr "3. 使用 EvalScope 运行 gsm8k 准确率测试"
#: ../../developer_guide/evaluation/using_evalscope.md:67
msgid "You can `evalscope eval` run gsm8k accuracy test:"
msgstr "你可以使用 `evalscope eval` 运行 gsm8k 准确率测试："
#: ../../developer_guide/evaluation/using_evalscope.md:78
#: ../../developer_guide/evaluation/using_evalscope.md:114
msgid "After 1-2 mins, the output is as shown below:"
msgstr "1-2 分钟后，输出如下所示："
#: ../../developer_guide/evaluation/using_evalscope.md:88
msgid ""
"See more detail in: [EvalScope doc - Model API Service "
"Evaluation](https://evalscope.readthedocs.io/en/latest/get_started/basic_usage.html#model-"
"api-service-evaluation)."
msgstr ""
"更多详情请见：[EvalScope 文档 - 模型 API "
"服务评测](https://evalscope.readthedocs.io/en/latest/get_started/basic_usage.html#model-"
"api-service-evaluation)。"
#: ../../developer_guide/evaluation/using_evalscope.md:90
msgid "4. Run model inference stress testing using EvalScope"
msgstr "4. 使用 EvalScope 运行模型推理压力测试"
#: ../../developer_guide/evaluation/using_evalscope.md:92
msgid "Install EvalScope[perf] using pip"
msgstr "使用 pip 安装 EvalScope[perf]"
#: ../../developer_guide/evaluation/using_evalscope.md:98
msgid "Basic usage"
msgstr "基本用法"
#: ../../developer_guide/evaluation/using_evalscope.md:100
msgid "You can use `evalscope perf` run perf test:"
msgstr "你可以使用 `evalscope perf` 运行性能测试："
#: ../../developer_guide/evaluation/using_evalscope.md:112
msgid "Output results"
msgstr "输出结果"
#: ../../developer_guide/evaluation/using_evalscope.md:173
msgid ""
"See more detail in: [EvalScope doc - Model Inference Stress "
"Testing](https://evalscope.readthedocs.io/en/latest/user_guides/stress_test/quick_start.html#basic-"
"usage)."
msgstr ""
"更多详情见：[EvalScope 文档 - "
"模型推理压力测试](https://evalscope.readthedocs.io/en/latest/user_guides/stress_test/quick_start.html#basic-"
"usage)。"

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@@ -0,0 +1,65 @@
# SOME DESCRIPTIVE TITLE.
# Copyright (C) 2025, vllm-ascend team
# This file is distributed under the same license as the vllm-ascend
# package.
# FIRST AUTHOR <EMAIL@ADDRESS>, 2025.
#
#, fuzzy
msgid ""
msgstr ""
"Project-Id-Version: vllm-ascend\n"
"Report-Msgid-Bugs-To: \n"
"POT-Creation-Date: 2025-07-18 09:01+0800\n"
"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
"Language-Team: zh_CN <LL@li.org>\n"
"Language: zh_CN\n"
"MIME-Version: 1.0\n"
"Content-Type: text/plain; charset=utf-8\n"
"Content-Transfer-Encoding: 8bit\n"
"Plural-Forms: nplurals=1; plural=0;\n"
"Generated-By: Babel 2.17.0\n"
#: ../../developer_guide/evaluation/using_lm_eval.md:1
msgid "Using lm-eval"
msgstr "使用 lm-eval"
#: ../../developer_guide/evaluation/using_lm_eval.md:2
msgid ""
"This document will guide you have a accuracy testing using [lm-"
"eval](https://github.com/EleutherAI/lm-evaluation-harness)."
msgstr ""
"本文将指导你如何使用 [lm-eval](https://github.com/EleutherAI/lm-evaluation-harness) "
"进行准确率测试。"
#: ../../developer_guide/evaluation/using_lm_eval.md:4
msgid "1. Run docker container"
msgstr "1. 运行 docker 容器"
#: ../../developer_guide/evaluation/using_lm_eval.md:6
msgid "You can run docker container on a single NPU:"
msgstr "你可以在单个NPU上运行docker容器："
#: ../../developer_guide/evaluation/using_lm_eval.md:33
msgid "2. Run ceval accuracy test using lm-eval"
msgstr "2. 使用 lm-eval 运行 ceval 准确性测试"
#: ../../developer_guide/evaluation/using_lm_eval.md:34
msgid "Install lm-eval in the container."
msgstr "在容器中安装 lm-eval。"
#: ../../developer_guide/evaluation/using_lm_eval.md:39
msgid "Run the following command:"
msgstr "运行以下命令："
#: ../../developer_guide/evaluation/using_lm_eval.md:50
msgid "After 1-2 mins, the output is as shown below:"
msgstr "1-2 分钟后，输出如下所示："
#: ../../developer_guide/evaluation/using_lm_eval.md:62
msgid ""
"You can see more usage on [Lm-eval Docs](https://github.com/EleutherAI/lm-"
"evaluation-harness/blob/main/docs/README.md)."
msgstr ""
"你可以在 [Lm-eval 文档](https://github.com/EleutherAI/lm-evaluation-"
"harness/blob/main/docs/README.md) 上查看更多用法。"

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@@ -0,0 +1,83 @@
# SOME DESCRIPTIVE TITLE.
# Copyright (C) 2025, vllm-ascend team
# This file is distributed under the same license as the vllm-ascend
# package.
# FIRST AUTHOR <EMAIL@ADDRESS>, 2025.
#
#, fuzzy
msgid ""
msgstr ""
"Project-Id-Version: vllm-ascend\n"
"Report-Msgid-Bugs-To: \n"
"POT-Creation-Date: 2025-07-18 09:01+0800\n"
"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
"Language-Team: zh_CN <LL@li.org>\n"
"Language: zh_CN\n"
"MIME-Version: 1.0\n"
"Content-Type: text/plain; charset=utf-8\n"
"Content-Transfer-Encoding: 8bit\n"
"Plural-Forms: nplurals=1; plural=0;\n"
"Generated-By: Babel 2.17.0\n"
#: ../../developer_guide/evaluation/using_opencompass.md:1
msgid "Using OpenCompass"
msgstr "使用 OpenCompass"
#: ../../developer_guide/evaluation/using_opencompass.md:2
msgid ""
"This document will guide you have a accuracy testing using "
"[OpenCompass](https://github.com/open-compass/opencompass)."
msgstr ""
"本文档将指导你如何使用 [OpenCompass](https://github.com/open-compass/opencompass) "
"进行准确率测试。"
#: ../../developer_guide/evaluation/using_opencompass.md:4
msgid "1. Online Serving"
msgstr "1. 在线服务"
#: ../../developer_guide/evaluation/using_opencompass.md:6
msgid "You can run docker container to start the vLLM server on a single NPU:"
msgstr "你可以运行 docker 容器，在单个 NPU 上启动 vLLM 服务器："
#: ../../developer_guide/evaluation/using_opencompass.md:32
msgid "If your service start successfully, you can see the info shown below:"
msgstr "如果你的服务启动成功，你会看到如下所示的信息："
#: ../../developer_guide/evaluation/using_opencompass.md:39
msgid ""
"Once your server is started, you can query the model with input prompts in "
"new terminal:"
msgstr "一旦你的服务器启动后，你可以在新的终端中用输入提示词查询模型："
#: ../../developer_guide/evaluation/using_opencompass.md:51
msgid "2. Run ceval accuracy test using OpenCompass"
msgstr "2. 使用 OpenCompass 运行 ceval 准确率测试"
#: ../../developer_guide/evaluation/using_opencompass.md:52
msgid ""
"Install OpenCompass and configure the environment variables in the "
"container."
msgstr "在容器中安装 OpenCompass 并配置环境变量。"
#: ../../developer_guide/evaluation/using_opencompass.md:64
msgid ""
"Add `opencompass/configs/eval_vllm_ascend_demo.py` with the following "
"content:"
msgstr "添加 `opencompass/configs/eval_vllm_ascend_demo.py`，内容如下："
#: ../../developer_guide/evaluation/using_opencompass.md:104
msgid "Run the following command:"
msgstr "运行以下命令："
#: ../../developer_guide/evaluation/using_opencompass.md:110
msgid "After 1-2 mins, the output is as shown below:"
msgstr "1-2 分钟后，输出如下所示："
#: ../../developer_guide/evaluation/using_opencompass.md:120
msgid ""
"You can see more usage on [OpenCompass "
"Docs](https://opencompass.readthedocs.io/en/latest/index.html)."
msgstr ""
"你可以在 [OpenCompass "
"文档](https://opencompass.readthedocs.io/en/latest/index.html) 查看更多用法。"

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@@ -0,0 +1,33 @@
# SOME DESCRIPTIVE TITLE.
# Copyright (C) 2025, vllm-ascend team
# This file is distributed under the same license as the vllm-ascend
# package.
# FIRST AUTHOR <EMAIL@ADDRESS>, 2025.
#
#, fuzzy
msgid ""
msgstr ""
"Project-Id-Version: vllm-ascend\n"
"Report-Msgid-Bugs-To: \n"
"POT-Creation-Date: 2025-07-18 09:01+0800\n"
"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
"Language-Team: zh_CN <LL@li.org>\n"
"Language: zh_CN\n"
"MIME-Version: 1.0\n"
"Content-Type: text/plain; charset=utf-8\n"
"Content-Transfer-Encoding: 8bit\n"
"Plural-Forms: nplurals=1; plural=0;\n"
"Generated-By: Babel 2.17.0\n"
#: ../../developer_guide/feature_guide/index.md:1
#: ../../developer_guide/feature_guide/index.md:5
msgid "Feature Guide"
msgstr "功能指南"
#: ../../developer_guide/feature_guide/index.md:3
msgid ""
"This section provides an overview of the features implemented in vLLM "
"Ascend. Developers can refer to this guide to understand how vLLM Ascend "
"works."
msgstr "本节概述了 vLLM Ascend 中实现的功能。开发者可以参考本指南以了解 vLLM Ascend 的工作原理。"

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@@ -0,0 +1,248 @@
# SOME DESCRIPTIVE TITLE.
# Copyright (C) 2025, vllm-ascend team
# This file is distributed under the same license as the vllm-ascend
# package.
# FIRST AUTHOR <EMAIL@ADDRESS>, 2025.
#
#, fuzzy
msgid ""
msgstr ""
"Project-Id-Version: vllm-ascend\n"
"Report-Msgid-Bugs-To: \n"
"POT-Creation-Date: 2025-07-18 09:01+0800\n"
"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
"Language-Team: zh_CN <LL@li.org>\n"
"Language: zh_CN\n"
"MIME-Version: 1.0\n"
"Content-Type: text/plain; charset=utf-8\n"
"Content-Transfer-Encoding: 8bit\n"
"Plural-Forms: nplurals=1; plural=0;\n"
"Generated-By: Babel 2.17.0\n"
#: ../../developer_guide/feature_guide/patch.md:1
msgid "Patch in vLLM Ascend"
msgstr "在 vLLM Ascend 中的补丁"
#: ../../developer_guide/feature_guide/patch.md:3
msgid ""
"vLLM Ascend is a platform plugin for vLLM. Due to the release cycle of vLLM "
"and vLLM Ascend is different, and the hardware limitation in some case, we "
"need to patch some code in vLLM to make it compatible with vLLM Ascend."
msgstr ""
"vLLM Ascend 是 vLLM 的一个平台插件。由于 vLLM 和 vLLM Ascend "
"的发布周期不同，并且在某些情况下存在硬件限制，我们需要对 vLLM 进行一些代码补丁，以使其能够兼容 vLLM Ascend。"
#: ../../developer_guide/feature_guide/patch.md:5
msgid ""
"In vLLM Ascend code, we provide a patch module `vllm_ascend/patch` to "
"address the change for vLLM."
msgstr "在 vLLM Ascend 代码中，我们提供了一个补丁模块 `vllm_ascend/patch` 用于应对 vLLM 的变更。"
#: ../../developer_guide/feature_guide/patch.md:7
msgid "Principle"
msgstr "原理"
#: ../../developer_guide/feature_guide/patch.md:9
msgid ""
"We should keep in mind that Patch is not the best way to make vLLM Ascend "
"compatible. It's just a temporary solution. The best way is to contribute "
"the change to vLLM to make it compatible with vLLM Ascend originally. In "
"vLLM Ascend, we have the basic principle for Patch strategy:"
msgstr ""
"我们需要记住，Patch 不是让 vLLM 兼容 Ascend 的最佳方式，这只是一个临时的解决方案。最好的方法是将修改贡献到 vLLM 项目中，从而让"
" vLLM 原生支持 Ascend。对于 vLLM Ascend，我们对 Patch 策略有一个基本原则："
#: ../../developer_guide/feature_guide/patch.md:11
msgid "Less is more. Please do not patch unless it's the only way currently."
msgstr "少即是多。请不要打补丁，除非这是目前唯一的方法。"
#: ../../developer_guide/feature_guide/patch.md:12
msgid ""
"Once a patch is added, it's required to describe the future plan for "
"removing the patch."
msgstr "一旦补丁被添加，必须说明将来移除该补丁的计划。"
#: ../../developer_guide/feature_guide/patch.md:13
msgid "Anytime, clean the patch code is welcome."
msgstr "任何时候，欢迎清理补丁代码。"
#: ../../developer_guide/feature_guide/patch.md:15
msgid "How it works"
msgstr "工作原理"
#: ../../developer_guide/feature_guide/patch.md:17
msgid "In `vllm_ascend/patch`, you can see the code structure as follows:"
msgstr "在 `vllm_ascend/patch` 目录中，你可以看到如下代码结构："
#: ../../developer_guide/feature_guide/patch.md:33
msgid ""
"**platform**: The patch code in this directory is for patching the code in "
"vLLM main process. It's called by "
"`vllm_ascend/platform::NPUPlatform::pre_register_and_update` very early when"
" vLLM is initialized."
msgstr ""
"**platform**：此目录下的补丁代码用于修补 vLLM 主进程中的代码。当 vLLM 初始化时，会在很早的阶段由 "
"`vllm_ascend/platform::NPUPlatform::pre_register_and_update` 调用。"
#: ../../developer_guide/feature_guide/patch.md:34
msgid ""
"For online mode, vLLM process calls the platform patch here "
"`vllm/vllm/engine/arg_utils.py::AsyncEngineArgs.add_cli_args` when parsing "
"the cli args."
msgstr ""
"对于在线模式，vLLM 进程在解析命令行参数时，会在 "
"`vllm/vllm/engine/arg_utils.py::AsyncEngineArgs.add_cli_args` 这里调用平台补丁。"
#: ../../developer_guide/feature_guide/patch.md:35
msgid ""
"For offline mode, vLLM process calls the platform patch here "
"`vllm/vllm/engine/arg_utils.py::EngineArgs.create_engine_config` when "
"parsing the input parameters."
msgstr ""
"对于离线模式，vLLM 进程在解析输入参数时，会在此处调用平台补丁 "
"`vllm/vllm/engine/arg_utils.py::EngineArgs.create_engine_config`。"
#: ../../developer_guide/feature_guide/patch.md:36
msgid ""
"**worker**: The patch code in this directory is for patching the code in "
"vLLM worker process. It's called by "
"`vllm_ascend/worker/worker_v1::NPUWorker::__init__` when the vLLM worker "
"process is initialized."
msgstr ""
"**worker**：此目录中的补丁代码用于修补 vLLM worker 进程中的代码。在初始化 vLLM worker 进程时，会被 "
"`vllm_ascend/worker/worker_v1::NPUWorker::__init__` 调用。"
#: ../../developer_guide/feature_guide/patch.md:37
msgid ""
"For both online and offline mode, vLLM engine core process calls the worker "
"patch here `vllm/vllm/worker/worker_base.py::WorkerWrapperBase.init_worker` "
"when initializing the worker process."
msgstr ""
"无论是在线还是离线模式，vLLM 引擎核心进程在初始化 worker 进程时，都会在这里调用 worker "
"补丁：`vllm/vllm/worker/worker_base.py::WorkerWrapperBase.init_worker`。"
#: ../../developer_guide/feature_guide/patch.md:39
msgid ""
"In both **platform** and **worker** folder, there are several patch modules."
" They are used for patching different version of vLLM."
msgstr "在 **platform** 和 **worker** 文件夹中都有一些补丁模块。它们用于修补不同版本的 vLLM。"
#: ../../developer_guide/feature_guide/patch.md:41
msgid ""
"`patch_0_9_2`: This module is used for patching vLLM 0.9.2. The version is "
"always the nearest version of vLLM. Once vLLM is released, we will drop this"
" patch module and bump to a new version. For example, `patch_0_9_2` is used "
"for patching vLLM 0.9.2."
msgstr ""
"`patch_0_9_2`：此模块用于修补 vLLM 0.9.2。该版本始终对应于 vLLM 的最近版本。一旦 vLLM "
"发布新版本，我们将移除此补丁模块并升级到新版本。例如，`patch_0_9_2` 就是用于修补 vLLM 0.9.2 的。"
#: ../../developer_guide/feature_guide/patch.md:42
msgid ""
"`patch_main`: This module is used for patching the code in vLLM main branch."
msgstr "`patch_main`：该模块用于修补 vLLM 主分支代码。"
#: ../../developer_guide/feature_guide/patch.md:43
msgid ""
"`patch_common`: This module is used for patching both vLLM 0.9.2 and vLLM "
"main branch."
msgstr "`patch_common`：此模块用于同时修补 vLLM 0.9.2 版本和 vLLM 主分支。"
#: ../../developer_guide/feature_guide/patch.md:45
msgid "How to write a patch"
msgstr "如何撰写补丁"
#: ../../developer_guide/feature_guide/patch.md:47
msgid ""
"Before writing a patch, following the principle above, we should patch the "
"least code. If it's necessary, we can patch the code in either **platform** "
"and **worker** folder. Here is an example to patch `distributed` module in "
"vLLM."
msgstr ""
"在编写补丁之前，遵循上述原则，我们应尽量修改最少的代码。如果有必要，我们可以修改 **platform** 和 **worker** "
"文件夹中的代码。下面是一个在 vLLM 中修改 `distributed` 模块的示例。"
#: ../../developer_guide/feature_guide/patch.md:49
msgid ""
"Decide which version of vLLM we should patch. For example, after analysis, "
"here we want to patch both 0.9.2 and main of vLLM."
msgstr "决定我们应该修补哪个版本的 vLLM。例如，经过分析后，这里我们想要同时修补 vLLM 的 0.9.2 版和主分支（main）。"
#: ../../developer_guide/feature_guide/patch.md:50
msgid ""
"Decide which process we should patch. For example, here `distributed` "
"belongs to the vLLM main process, so we should patch `platform`."
msgstr "决定我们应该修补哪个进程。例如，这里 `distributed` 属于 vLLM 主进程，所以我们应该修补 `platform`。"
#: ../../developer_guide/feature_guide/patch.md:51
#, python-brace-format
msgid ""
"Create the patch file in the right folder. The file should be named as "
"`patch_{module_name}.py`. The example here is "
"`vllm_ascend/patch/platform/patch_common/patch_distributed.py`."
msgstr ""
"在正确的文件夹中创建补丁文件。文件应命名为 `patch_{module_name}.py`。此处的示例是 "
"`vllm_ascend/patch/platform/patch_common/patch_distributed.py`。"
#: ../../developer_guide/feature_guide/patch.md:52
msgid "Write your patch code in the new file. Here is an example:"
msgstr "在新文件中编写你的补丁代码。以下是一个示例："
#: ../../developer_guide/feature_guide/patch.md:62
msgid ""
"Import the patch file in `__init__.py`. In this example, add `import "
"vllm_ascend.patch.platform.patch_common.patch_distributed` into "
"`vllm_ascend/patch/platform/patch_common/__init__.py`."
msgstr ""
"在 `__init__.py` 中导入补丁文件。在这个示例中，将 `import "
"vllm_ascend.patch.platform.patch_common.patch_distributed` 添加到 "
"`vllm_ascend/patch/platform/patch_common/__init__.py` 中。"
#: ../../developer_guide/feature_guide/patch.md:63
msgid ""
"Add the description of the patch in `vllm_ascend/patch/__init__.py`. The "
"description format is as follows:"
msgstr "在 `vllm_ascend/patch/__init__.py` 中添加补丁的描述。描述格式如下："
#: ../../developer_guide/feature_guide/patch.md:77
msgid ""
"Add the Unit Test and E2E Test. Any newly added code in vLLM Ascend should "
"contain the Unit Test and E2E Test as well. You can find more details in "
"[test guide](../contribution/testing.md)"
msgstr ""
"添加单元测试和端到端（E2E）测试。在 vLLM Ascend 中新增的任何代码也应包含单元测试和端到端测试。更多详情请参见 "
"[测试指南](../contribution/testing.md)。"
#: ../../developer_guide/feature_guide/patch.md:80
msgid "Limitation"
msgstr "限制"
#: ../../developer_guide/feature_guide/patch.md:81
msgid ""
"In V1 Engine, vLLM starts three kinds of process: Main process, EngineCore "
"process and Worker process. Now vLLM Ascend only support patch the code in "
"Main process and Worker process by default. If you want to patch the code "
"runs in EngineCore process, you should patch EngineCore process entirely "
"during setup, the entry code is here `vllm.v1.engine.core`. Please override "
"`EngineCoreProc` and `DPEngineCoreProc` entirely."
msgstr ""
"在 V1 引擎中，vLLM 会启动三种类型的进程：主进程、EngineCore 进程和 Worker 进程。现在 vLLM Ascend "
"默认只支持在主进程和 Worker 进程中打补丁代码。如果你想要在 EngineCore 进程中打补丁，你需要在设置阶段对 EngineCore "
"进程整体打补丁，入口代码在 `vllm.v1.engine.core`。请完全重写 `EngineCoreProc` 和 "
"`DPEngineCoreProc`。"
#: ../../developer_guide/feature_guide/patch.md:82
msgid ""
"If you are running an edited vLLM code, the version of the vLLM may be "
"changed automatically. For example, if you runs an edited vLLM based on "
"v0.9.n, the version of vLLM may be change to v0.9.nxxx, in this case, the "
"patch for v0.9.n in vLLM Ascend would not work as expect, because that vLLM "
"Ascend can't distinguish the version of vLLM you're using. In this case, you"
" can set the environment variable `VLLM_VERSION` to specify the version of "
"vLLM you're using, then the patch for v0.9.2 should work."
msgstr ""
"如果你运行的是经过编辑的 vLLM 代码，vLLM 的版本可能会被自动更改。例如，如果你基于 v0.9.n 运行了编辑后的 vLLM，vLLM "
"的版本可能会变为 v0.9.nxxx，在这种情况下，vLLM Ascend 的 v0.9.n 补丁将无法正常工作，因为 vLLM Ascend "
"无法区分你所使用的 vLLM 版本。这时，你可以设置环境变量 `VLLM_VERSION` 来指定你所使用的 vLLM 版本，这样对 v0.9.2 "
"的补丁就应该可以正常工作。"

333
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@@ -0,0 +1,333 @@
# SOME DESCRIPTIVE TITLE.
# Copyright (C) 2025, vllm-ascend team
# This file is distributed under the same license as the vllm-ascend
# package.
# FIRST AUTHOR <EMAIL@ADDRESS>, 2025.
#
#, fuzzy
msgid ""
msgstr ""
"Project-Id-Version: vllm-ascend\n"
"Report-Msgid-Bugs-To: \n"
"POT-Creation-Date: 2025-07-18 09:01+0800\n"
"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
"Language-Team: zh_CN <LL@li.org>\n"
"Language: zh_CN\n"
"MIME-Version: 1.0\n"
"Content-Type: text/plain; charset=utf-8\n"
"Content-Transfer-Encoding: 8bit\n"
"Plural-Forms: nplurals=1; plural=0;\n"
"Generated-By: Babel 2.17.0\n"
#: ../../developer_guide/modeling/adding_a_new_model.md:1
msgid "Adding a New Model"
msgstr "添加新模型"
#: ../../developer_guide/modeling/adding_a_new_model.md:3
msgid ""
"This guide demonstrates how to integrate a novel or customized model into "
"vllm-ascend. For foundational concepts, it is highly recommended to refer to"
" [vllm official doc: Adding a New "
"Model](https://docs.vllm.ai/en/stable/contributing/model/) first."
msgstr ""
"本指南演示如何将新颖或自定义的模型集成到 vllm-ascend 中。对于基础概念，强烈建议先参考 [vllm "
"官方文档：添加新模型](https://docs.vllm.ai/en/stable/contributing/model/)。"
#: ../../developer_guide/modeling/adding_a_new_model.md:6
msgid "Step 1: Implementing Models with `torch` and `torch_npu`"
msgstr "步骤 1：使用 `torch` 和 `torch_npu` 实现模型"
#: ../../developer_guide/modeling/adding_a_new_model.md:8
msgid ""
"This section provides instructions for implementing new models compatible "
"with vllm and vllm-ascend."
msgstr "本节提供了实现与 vllm 和 vllm-ascend 兼容的新模型的相关说明。"
#: ../../developer_guide/modeling/adding_a_new_model.md:10
msgid "**Before starting:**"
msgstr "**开始之前：**"
#: ../../developer_guide/modeling/adding_a_new_model.md:12
msgid ""
"Verify whether your model already exists in vllm's "
"[models](https://github.com/vllm-"
"project/vllm/tree/main/vllm/model_executor/models) directory."
msgstr ""
"请确认你的模型是否已经存在于 vllm 的 [models](https://github.com/vllm-"
"project/vllm/tree/main/vllm/model_executor/models) 目录中。"
#: ../../developer_guide/modeling/adding_a_new_model.md:13
msgid ""
"Use existing models' implementation as templates to accelerate your "
"development."
msgstr "使用已有模型的实现作为模板以加速您的开发。"
#: ../../developer_guide/modeling/adding_a_new_model.md:15
msgid "Method 1: Implementing New Models from Scratch"
msgstr "方法一：从零开始实现新模型"
#: ../../developer_guide/modeling/adding_a_new_model.md:17
msgid ""
"Follow vllm's [OPT model "
"adaptation](https://docs.vllm.ai/en/stable/contributing/model/basic.html) "
"example for guidance."
msgstr ""
"请参考 vllm 的 [OPT "
"模型适配](https://docs.vllm.ai/en/stable/contributing/model/basic.html) 示例进行操作。"
#: ../../developer_guide/modeling/adding_a_new_model.md:19
msgid "**Key implementation requirements:**"
msgstr "**关键实现要求：**"
#: ../../developer_guide/modeling/adding_a_new_model.md:21
msgid "Place model files in `vllm_ascend/models/` directory."
msgstr "请将模型文件放在 `vllm_ascend/models/` 目录下。"
#: ../../developer_guide/modeling/adding_a_new_model.md:23
msgid ""
"Standard module structure for decoder-only LLMs (please checkout vllm's "
"implementations for other kinds of model):"
msgstr "解码器-only LLMs 的标准模块结构（请参考 vllm 对其他类型模型的实现）："
#: ../../developer_guide/modeling/adding_a_new_model.md:25
msgid "`*ModelForCausalLM` (top-level wrapper)"
msgstr "`*ModelForCausalLM`（顶层包装器）"
#: ../../developer_guide/modeling/adding_a_new_model.md:26
msgid "`*Model` (main architecture)"
msgstr "`*Model`（主架构）"
#: ../../developer_guide/modeling/adding_a_new_model.md:27
msgid "`*DecoderLayer` (transformer block)"
msgstr "`*DecoderLayer` （transformer 块）"
#: ../../developer_guide/modeling/adding_a_new_model.md:28
msgid "`*Attention` and `*MLP` (specific computation unit)"
msgstr "`*Attention` 和 `*MLP`（特定计算单元）"
#: ../../developer_guide/modeling/adding_a_new_model.md:31
msgid "`*` denotes your model's unique identifier."
msgstr "`*` 表示你的模型的唯一标识符。"
#: ../../developer_guide/modeling/adding_a_new_model.md:34
msgid "Critical Implementation Details:"
msgstr "关键实现细节："
#: ../../developer_guide/modeling/adding_a_new_model.md:36
msgid "All modules must include a `prefix` argument in `__init__()`."
msgstr "所有模块在 `__init__()` 方法中都必须包含一个 `prefix` 参数。"
#: ../../developer_guide/modeling/adding_a_new_model.md:38
msgid "**Required interfaces:**"
msgstr "**必需的接口：**"
#: ../../developer_guide/modeling/adding_a_new_model.md:30
msgid "Module Type"
msgstr "模块类型"
#: ../../developer_guide/modeling/adding_a_new_model.md:30
msgid "Required Methods"
msgstr "必需的方法"
#: ../../developer_guide/modeling/adding_a_new_model.md:30
msgid "`*ModelForCausalLM`"
msgstr "`*ModelForCausalLM`"
#: ../../developer_guide/modeling/adding_a_new_model.md:30
msgid "`get_input_embeddings`, `compute_logits`, `load_weights`"
msgstr "`get_input_embeddings`，`compute_logits`，`load_weights`"
#: ../../developer_guide/modeling/adding_a_new_model.md:30
msgid "`*Model`"
msgstr "`*模型`"
#: ../../developer_guide/modeling/adding_a_new_model.md:30
msgid "`get_input_embeddings`, `load_weights`"
msgstr "`get_input_embeddings`，`load_weights`"
#: ../../developer_guide/modeling/adding_a_new_model.md:45
msgid "Attention Backend Integration:"
msgstr "注意后端集成："
#: ../../developer_guide/modeling/adding_a_new_model.md:47
msgid ""
"Importing attention via `from vllm.attention import Attention` can "
"automatically leverage the attention backend routing of vllm-ascend (see: "
"`get_attn_backend_cls()` in `vllm_ascend/platform.py`)."
msgstr ""
"通过 `from vllm.attention import Attention` 导入 attention 可以自动利用 vllm-ascend "
"的注意力后端路由（详见：`vllm_ascend/platform.py` 中的 `get_attn_backend_cls()`）。"
#: ../../developer_guide/modeling/adding_a_new_model.md:49
msgid "Tensor Parallelism:"
msgstr "张量并行："
#: ../../developer_guide/modeling/adding_a_new_model.md:51
msgid ""
"Use vllm's parallel layers (`ColumnParallelLinear`, "
"`VocabParallelEmbedding`, etc.) to implement models supporting tensor "
"parallelism. Note that Ascend-specific customizations are implemented in "
"`vllm_ascend/ops/` directory (RMSNorm, VocabParallelEmbedding, etc.)."
msgstr ""
"使用 vllm 的并行层（如 `ColumnParallelLinear`、`VocabParallelEmbedding` "
"等）来实现支持张量并行的模型。需要注意的是，Ascend 特有的自定义实现（如 RMSNorm、VocabParallelEmbedding 等）位于 "
"`vllm_ascend/ops/` 目录下。"
#: ../../developer_guide/modeling/adding_a_new_model.md:53
msgid ""
"**Reference Implementation Template** (assumed path: "
"`vllm_ascend/models/custom_model.py`):"
msgstr "**参考实现模板**（假定路径：`vllm_ascend/models/custom_model.py`）："
#: ../../developer_guide/modeling/adding_a_new_model.md:135
msgid "Method 2: Customizing Existing vLLM Models"
msgstr "方法二：自定义已有的 vLLM 模型"
#: ../../developer_guide/modeling/adding_a_new_model.md:137
msgid ""
"For most use cases, extending existing implementations is preferable. We "
"demonstrate an example to inherit from base classes and implement a custom "
"deepseek model below (assumed path: `vllm_ascend/models/deepseek_v2.py`)."
msgstr ""
"对于大多数使用场景，建议扩展已有的实现。我们在下面演示了一个示例，通过继承基类并实现一个自定义的 deepseek "
"模型（假定路径：`vllm_ascend/models/deepseek_v2.py`）。"
#: ../../developer_guide/modeling/adding_a_new_model.md:175
msgid ""
"For a complete implementation reference, see: "
"`vllm_ascend/models/deepseek_v2.py`."
msgstr "完整的实现参考请见：`vllm_ascend/models/deepseek_v2.py`。"
#: ../../developer_guide/modeling/adding_a_new_model.md:178
msgid "Step 2: Registering Custom Models using ModelRegistry Plugins in vLLM"
msgstr "第2步：使用 vLLM 中的 ModelRegistry 插件注册自定义模型"
#: ../../developer_guide/modeling/adding_a_new_model.md:180
msgid ""
"vllm provides a plugin mechanism for registering externally implemented "
"models without modifying its codebase."
msgstr "vllm 提供了一种插件机制，可用于注册外部实现的模型，而无需修改其代码库。"
#: ../../developer_guide/modeling/adding_a_new_model.md:182
msgid ""
"To integrate your implemented model from `vllm_ascend/models/` directory:"
msgstr "要集成你在 `vllm_ascend/models/` 目录下实现的模型："
#: ../../developer_guide/modeling/adding_a_new_model.md:184
msgid ""
"Import your model implementation in `vllm_ascend/models/__init__.py` using "
"relative imports."
msgstr "使用相对导入在 `vllm_ascend/models/__init__.py` 中导入你的模型实现。"
#: ../../developer_guide/modeling/adding_a_new_model.md:185
msgid ""
"Register the model wrapper class via `vllm.ModelRegistry.register_model()` "
"function."
msgstr "通过 `vllm.ModelRegistry.register_model()` 函数注册模型包装类。"
#: ../../developer_guide/modeling/adding_a_new_model.md:187
msgid ""
"**Reference Registration Template** (an example of registering new models in"
" `vllm_ascend/models/__init__.py`):"
msgstr "**参考注册模板**（在 `vllm_ascend/models/__init__.py` 注册新模型的示例）："
#: ../../developer_guide/modeling/adding_a_new_model.md:210
msgid ""
"The first argument of `vllm.ModelRegistry.register_model()` indicates the "
"unique architecture identifier which must match `architectures` in "
"`config.json` of the model."
msgstr ""
"`vllm.ModelRegistry.register_model()` 的第一个参数表示唯一的架构标识符，这个标识符必须与模型的 "
"`config.json` 文件中的 `architectures` 匹配。"
#: ../../developer_guide/modeling/adding_a_new_model.md:221
msgid "Step 3: Verification"
msgstr "第 3 步：验证"
#: ../../developer_guide/modeling/adding_a_new_model.md:223
msgid "Case 1: Overriding Existing vLLM Model Architecture"
msgstr "案例 1：重载已有的 vLLM 模型架构"
#: ../../developer_guide/modeling/adding_a_new_model.md:225
msgid ""
"If you're registering a customized model architecture based on vllm's "
"existing implementation (overriding vllm's original class), when executing "
"vllm offline/online inference (using any model), you'll observe warning logs"
" similar to the following output from "
"`vllm/models_executor/models/registry.py`."
msgstr ""
"如果你基于 vllm 的现有实现注册了一个自定义的模型架构（覆盖了 vllm 的原始类），在执行 vllm "
"的离线/在线推理（无论使用哪个模型）时，你会看到类似于 `vllm/models_executor/models/registry.py` "
"输出的警告日志。"
#: ../../developer_guide/modeling/adding_a_new_model.md:231
msgid "Case 2: Registering New Model Architecture"
msgstr "案例2：注册新模型架构"
#: ../../developer_guide/modeling/adding_a_new_model.md:233
msgid ""
"If you're registering a novel model architecture not present in vllm "
"(creating a completely new class), current logs won't provide explicit "
"confirmation by default. It's recommended to add the following logging "
"statement at the end of the `register_model` method in "
"`vllm/models_executor/models/registry.py`."
msgstr ""
"如果你注册了 vllm 中不存在的新模型架构（创建一个全新的类），当前日志默认不会提供明确的确认信息。建议在 "
"`vllm/models_executor/models/registry.py` 文件中的 `register_model` "
"方法末尾添加如下日志语句。"
#: ../../developer_guide/modeling/adding_a_new_model.md:239
msgid ""
"After adding this line, you will see confirmation logs shown below when "
"running vllm offline/online inference (using any model)."
msgstr "添加这一行之后，当你运行 vllm 离线/在线推理（使用任何模型）时，将会看到如下确认日志。"
#: ../../developer_guide/modeling/adding_a_new_model.md:245
msgid ""
"This log output confirms your novel model architecture has been successfully"
" registered in vllm."
msgstr "该日志输出确认了你的新模型架构已成功在 vllm 中注册。"
#: ../../developer_guide/modeling/adding_a_new_model.md:247
msgid "Step 4: Testing"
msgstr "第4步：测试"
#: ../../developer_guide/modeling/adding_a_new_model.md:249
msgid ""
"After adding a new model, we should do basic functional test (offline/online"
" inference), accuracy test and performance benchmark for the model."
msgstr "在添加新模型后，我们应对该模型进行基本功能测试（离线/在线推理）、准确率测试和性能基准测试。"
#: ../../developer_guide/modeling/adding_a_new_model.md:251
msgid "Find more details at:"
msgstr "更多详情请见："
#: ../../developer_guide/modeling/adding_a_new_model.md:253
msgid ""
"[Accuracy test guide](https://vllm-"
"ascend.readthedocs.io/en/latest/developer_guide/evaluation/index.html)"
msgstr ""
"[精度测试指南](https://vllm-"
"ascend.readthedocs.io/en/latest/developer_guide/evaluation/index.html)"
#: ../../developer_guide/modeling/adding_a_new_model.md:254
msgid ""
"[Performance benchmark guide](https://vllm-"
"ascend.readthedocs.io/en/latest/developer_guide/performance/performance_benchmark.html)"
msgstr ""
"[性能基准指南](https://vllm-"
"ascend.readthedocs.io/en/latest/developer_guide/performance/performance_benchmark.html)"
#: ../../developer_guide/modeling/adding_a_new_model.md:256
msgid "Step 5: Updating Supported Models Doc"
msgstr "第5步：更新支持的模型文档"
#: ../../developer_guide/modeling/adding_a_new_model.md:258
msgid ""
"At last, if all the steps above are completed, you should add the new model "
"into our [Supported Models](https://vllm-"
"ascend.readthedocs.io/en/latest/user_guide/supported_models.html) doc."
msgstr ""
"最后，如果以上所有步骤都已完成，你应该将新模型添加到我们的[支持的模型](https://vllm-"
"ascend.readthedocs.io/en/latest/user_guide/supported_models.html)文档中。"

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@@ -0,0 +1,29 @@
# SOME DESCRIPTIVE TITLE.
# Copyright (C) 2025, vllm-ascend team
# This file is distributed under the same license as the vllm-ascend
# package.
# FIRST AUTHOR <EMAIL@ADDRESS>, 2025.
#
#, fuzzy
msgid ""
msgstr ""
"Project-Id-Version: vllm-ascend\n"
"Report-Msgid-Bugs-To: \n"
"POT-Creation-Date: 2025-07-18 09:01+0800\n"
"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
"Language-Team: zh_CN <LL@li.org>\n"
"Language: zh_CN\n"
"MIME-Version: 1.0\n"
"Content-Type: text/plain; charset=utf-8\n"
"Content-Transfer-Encoding: 8bit\n"
"Plural-Forms: nplurals=1; plural=0;\n"
"Generated-By: Babel 2.17.0\n"
#: ../../developer_guide/modeling/adding_a_new_multimodal_model.md:1
msgid "Adding a New Multi-Modal Model"
msgstr "添加新的多模态模型"
#: ../../developer_guide/modeling/adding_a_new_multimodal_model.md:3
msgid "**_Comming soon ..._**"
msgstr "**_敬请期待 ..._**"

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@@ -0,0 +1,32 @@
# SOME DESCRIPTIVE TITLE.
# Copyright (C) 2025, vllm-ascend team
# This file is distributed under the same license as the vllm-ascend
# package.
# FIRST AUTHOR <EMAIL@ADDRESS>, 2025.
#
#, fuzzy
msgid ""
msgstr ""
"Project-Id-Version: vllm-ascend\n"
"Report-Msgid-Bugs-To: \n"
"POT-Creation-Date: 2025-07-18 09:01+0800\n"
"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
"Language-Team: zh_CN <LL@li.org>\n"
"Language: zh_CN\n"
"MIME-Version: 1.0\n"
"Content-Type: text/plain; charset=utf-8\n"
"Content-Transfer-Encoding: 8bit\n"
"Plural-Forms: nplurals=1; plural=0;\n"
"Generated-By: Babel 2.17.0\n"
#: ../../developer_guide/modeling/index.md:1
#: ../../developer_guide/modeling/index.md:5
msgid "Modeling"
msgstr "新模型"
#: ../../developer_guide/modeling/index.md:3
msgid ""
"This section provides tutorials of how to implement and register a new model"
" into vllm-ascend."
msgstr "本节提供了如何在 vllm-ascend 中实现并注册新模型的教程。"

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@@ -0,0 +1,26 @@
# SOME DESCRIPTIVE TITLE.
# Copyright (C) 2025, vllm-ascend team
# This file is distributed under the same license as the vllm-ascend
# package.
# FIRST AUTHOR <EMAIL@ADDRESS>, 2025.
#
#, fuzzy
msgid ""
msgstr ""
"Project-Id-Version: vllm-ascend\n"
"Report-Msgid-Bugs-To: \n"
"POT-Creation-Date: 2025-07-18 09:01+0800\n"
"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
"Language-Team: zh_CN <LL@li.org>\n"
"Language: zh_CN\n"
"MIME-Version: 1.0\n"
"Content-Type: text/plain; charset=utf-8\n"
"Content-Transfer-Encoding: 8bit\n"
"Plural-Forms: nplurals=1; plural=0;\n"
"Generated-By: Babel 2.17.0\n"
#: ../../developer_guide/performance/index.md:1
#: ../../developer_guide/performance/index.md:3
msgid "Performance"
msgstr "性能"

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@@ -0,0 +1,88 @@
# SOME DESCRIPTIVE TITLE.
# Copyright (C) 2025, vllm-ascend team
# This file is distributed under the same license as the vllm-ascend
# package.
# FIRST AUTHOR <EMAIL@ADDRESS>, 2025.
#
#, fuzzy
msgid ""
msgstr ""
"Project-Id-Version: vllm-ascend\n"
"Report-Msgid-Bugs-To: \n"
"POT-Creation-Date: 2025-07-18 09:01+0800\n"
"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
"Language-Team: zh_CN <LL@li.org>\n"
"Language: zh_CN\n"
"MIME-Version: 1.0\n"
"Content-Type: text/plain; charset=utf-8\n"
"Content-Transfer-Encoding: 8bit\n"
"Plural-Forms: nplurals=1; plural=0;\n"
"Generated-By: Babel 2.17.0\n"
#: ../../developer_guide/performance/performance_benchmark.md:1
msgid "Performance Benchmark"
msgstr "性能基准"
#: ../../developer_guide/performance/performance_benchmark.md:2
msgid ""
"This document details the benchmark methodology for vllm-ascend, aimed at "
"evaluating the performance under a variety of workloads. To maintain "
"alignment with vLLM, we use the [benchmark](https://github.com/vllm-"
"project/vllm/tree/main/benchmarks) script provided by the vllm project."
msgstr ""
"本文档详细说明了 vllm-ascend 的基准测试方法，旨在评估其在多种工作负载下的性能。为了与 vLLM 保持一致，我们使用 vllm 项目提供的 "
"[benchmark](https://github.com/vllm-project/vllm/tree/main/benchmarks) 脚本。"
#: ../../developer_guide/performance/performance_benchmark.md:4
msgid ""
"**Benchmark Coverage**: We measure offline e2e latency and throughput, and "
"fixed-QPS online serving benchmarks, for more details see [vllm-ascend "
"benchmark scripts](https://github.com/vllm-project/vllm-"
"ascend/tree/main/benchmarks)."
msgstr ""
"**基准测试覆盖范围**：我们测量离线端到端延迟和吞吐量，以及固定 QPS 的在线服务基准测试。更多详情请参见 [vllm-ascend "
"基准测试脚本](https://github.com/vllm-project/vllm-ascend/tree/main/benchmarks)。"
#: ../../developer_guide/performance/performance_benchmark.md:6
msgid "1. Run docker container"
msgstr "1. 运行 docker 容器"
#: ../../developer_guide/performance/performance_benchmark.md:31
msgid "2. Install dependencies"
msgstr "2. 安装依赖项"
#: ../../developer_guide/performance/performance_benchmark.md:38
msgid "3. (Optional)Prepare model weights"
msgstr "3.（可选）准备模型权重"
#: ../../developer_guide/performance/performance_benchmark.md:39
msgid ""
"For faster running speed, we recommend downloading the model in advance："
msgstr "为了更快的运行速度，建议提前下载模型："
#: ../../developer_guide/performance/performance_benchmark.md:44
msgid ""
"You can also replace all model paths in the [json](https://github.com/vllm-"
"project/vllm-ascend/tree/main/benchmarks/tests) files with your local paths:"
msgstr ""
"你也可以将 [json](https://github.com/vllm-project/vllm-"
"ascend/tree/main/benchmarks/tests) 文件中的所有模型路径替换为你的本地路径："
#: ../../developer_guide/performance/performance_benchmark.md:60
msgid "4. Run benchmark script"
msgstr "4. 运行基准测试脚本"
#: ../../developer_guide/performance/performance_benchmark.md:61
msgid "Run benchmark script:"
msgstr "运行基准测试脚本："
#: ../../developer_guide/performance/performance_benchmark.md:66
msgid "After about 10 mins, the output is as shown below:"
msgstr "大约 10 分钟后，输出如下所示："
#: ../../developer_guide/performance/performance_benchmark.md:176
msgid ""
"The result json files are generated into the path `benchmark/results` These "
"files contain detailed benchmarking results for further analysis."
msgstr "结果 json 文件会生成到路径 `benchmark/results`。这些文件包含了用于进一步分析的详细基准测试结果。"

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@@ -0,0 +1,81 @@
# SOME DESCRIPTIVE TITLE.
# Copyright (C) 2025, vllm-ascend team
# This file is distributed under the same license as the vllm-ascend
# package.
# FIRST AUTHOR <EMAIL@ADDRESS>, 2025.
#
#, fuzzy
msgid ""
msgstr ""
"Project-Id-Version: vllm-ascend\n"
"Report-Msgid-Bugs-To: \n"
"POT-Creation-Date: 2025-07-18 09:01+0800\n"
"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
"Language-Team: zh_CN <LL@li.org>\n"
"Language: zh_CN\n"
"MIME-Version: 1.0\n"
"Content-Type: text/plain; charset=utf-8\n"
"Content-Transfer-Encoding: 8bit\n"
"Plural-Forms: nplurals=1; plural=0;\n"
"Generated-By: Babel 2.17.0\n"
#: ../../developer_guide/performance/profile_execute_duration.md:1
msgid "Profile Execute Duration"
msgstr "配置执行持续时间"
#: ../../developer_guide/performance/profile_execute_duration.md:3
msgid ""
"The execution duration of each stage (including pre/post-processing, model "
"forward, etc.) usually needs to be captured during a complete inference "
"process. Typically, this is done by using `torch.npu.synchronize()` and "
"obtaining CPU timestamps, which increases the performance overhead of "
"host/device synchronization."
msgstr ""
"在完整的推理过程中，通常需要记录每个阶段（包括前/后处理、模型前向等）的执行时长。一般通过使用 `torch.npu.synchronize()` "
"并获取 CPU 时间戳来实现，这会增加主机/设备同步的性能开销。"
#: ../../developer_guide/performance/profile_execute_duration.md:5
msgid ""
"**To reduce the performance overhead, we add this feature, using the NPU "
"event timestamp mechanism to observe the device execution time "
"asynchronously.**"
msgstr "**为了减少性能开销，我们添加了此功能，使用 NPU 事件时间戳机制异步观测设备的执行时间。**"
#: ../../developer_guide/performance/profile_execute_duration.md:7
msgid "Usage"
msgstr "用法"
#: ../../developer_guide/performance/profile_execute_duration.md:8
msgid ""
"Use the environment variable `VLLM_ASCEND_MODEL_EXECUTE_TIME_OBSERVE` to "
"enable this feature."
msgstr "使用环境变量 `VLLM_ASCEND_MODEL_EXECUTE_TIME_OBSERVE` 来启用此功能。"
#: ../../developer_guide/performance/profile_execute_duration.md:9
msgid ""
"Use the non-blocking API `ProfileExecuteDuration().capture_async` to set "
"observation points asynchronously when you need to observe the execution "
"duration."
msgstr ""
"当你需要观察执行时长时，可以使用非阻塞 API `ProfileExecuteDuration().capture_async` 异步设置观察点。"
#: ../../developer_guide/performance/profile_execute_duration.md:10
msgid ""
"Use the blocking API `ProfileExecuteDuration().pop_captured_sync` at an "
"appropriate time to get and print the execution durations of all observed "
"stages."
msgstr ""
"在适当的时机使用阻塞式 API `ProfileExecuteDuration().pop_captured_sync` "
"获取并打印所有已观察到阶段的执行时长。"
#: ../../developer_guide/performance/profile_execute_duration.md:12
msgid ""
"**We have instrumented the key inference stages (including pre-processing, "
"model forward pass, etc.) for execute duration profiling. Execute the script"
" as follows:**"
msgstr "**我们已经对关键的推理阶段（包括预处理、模型前向传递等）进行了执行时长分析的检测。请按如下方式执行脚本：**"
#: ../../developer_guide/performance/profile_execute_duration.md:17
msgid "Example Output"
msgstr "示例输出"

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@@ -0,0 +1,479 @@
# SOME DESCRIPTIVE TITLE.
# Copyright (C) 2025, vllm-ascend team
# This file is distributed under the same license as the vllm-ascend
# package.
# FIRST AUTHOR <EMAIL@ADDRESS>, 2025.
#
#, fuzzy
msgid ""
msgstr ""
"Project-Id-Version: vllm-ascend\n"
"Report-Msgid-Bugs-To: \n"
"POT-Creation-Date: 2025-07-18 09:01+0800\n"
"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
"Language-Team: zh_CN <LL@li.org>\n"
"Language: zh_CN\n"
"MIME-Version: 1.0\n"
"Content-Type: text/plain; charset=utf-8\n"
"Content-Transfer-Encoding: 8bit\n"
"Plural-Forms: nplurals=1; plural=0;\n"
"Generated-By: Babel 2.17.0\n"
#: ../../faqs.md:1
msgid "FAQs"
msgstr ""
#: ../../faqs.md:3
msgid "Version Specific FAQs"
msgstr "特定版本常见问题"
#: ../../faqs.md:5
msgid ""
"[[v0.7.3.post1] FAQ & Feedback](https://github.com/vllm-project/vllm-"
"ascend/issues/1007)"
msgstr ""
"[[v0.7.3.post1] 常见问题与反馈](https://github.com/vllm-project/vllm-"
"ascend/issues/1007)"
#: ../../faqs.md:6
msgid ""
"[[v0.9.2rc1] FAQ & Feedback](https://github.com/vllm-project/vllm-"
"ascend/issues/1742)"
msgstr ""
"[[v0.9.2rc1] 常见问题与反馈](https://github.com/vllm-project/vllm-"
"ascend/issues/1742)"
#: ../../faqs.md:8
msgid "General FAQs"
msgstr "常见问题解答"
#: ../../faqs.md:10
msgid "1. What devices are currently supported?"
msgstr "1. 目前支持哪些设备？"
#: ../../faqs.md:12
msgid ""
"Currently, **ONLY** Atlas A2 series(Ascend-cann-kernels-910b) and Atlas "
"300I(Ascend-cann-kernels-310p) series are supported:"
msgstr ""
"目前，**仅**支持 Atlas A2 系列（Ascend-cann-kernels-910b）和 Atlas 300I（Ascend-cann-"
"kernels-310p）系列："
#: ../../faqs.md:14
msgid ""
"Atlas A2 Training series (Atlas 800T A2, Atlas 900 A2 PoD, Atlas 200T A2 "
"Box16, Atlas 300T A2)"
msgstr ""
"Atlas A2 训练系列（Atlas 800T A2，Atlas 900 A2 PoD，Atlas 200T A2 Box16，Atlas 300T "
"A2）"
#: ../../faqs.md:15
msgid "Atlas 800I A2 Inference series (Atlas 800I A2)"
msgstr "Atlas 800I A2 推理系列（Atlas 800I A2）"
#: ../../faqs.md:16
msgid "Atlas 300I Inference series (Atlas 300I Duo)"
msgstr "Atlas 300I 推理系列（Atlas 300I Duo）"
#: ../../faqs.md:18
msgid "Below series are NOT supported yet:"
msgstr "以下系列目前尚不受支持："
#: ../../faqs.md:19
msgid "Atlas 200I A2 (Ascend-cann-kernels-310b) unplanned yet"
msgstr "Atlas 200I A2（Ascend-cann-kernels-310b）尚未计划"
#: ../../faqs.md:20
msgid "Ascend 910, Ascend 910 Pro B (Ascend-cann-kernels-910) unplanned yet"
msgstr "Ascend 910，Ascend 910 Pro B（Ascend-cann-kernels-910）尚未计划"
#: ../../faqs.md:22
msgid ""
"From a technical view, vllm-ascend support would be possible if the torch-"
"npu is supported. Otherwise, we have to implement it by using custom ops. We"
" are also welcome to join us to improve together."
msgstr ""
"从技术角度来看，如果支持 torch-npu，则可以支持 vllm-ascend。否则，我们需要通过自定义算子来实现。我们也欢迎大家一起加入，共同改进。"
#: ../../faqs.md:24
msgid "2. How to get our docker containers?"
msgstr "2. 如何获取我们的 docker 容器？"
#: ../../faqs.md:26
msgid ""
"You can get our containers at `Quay.io`, e.g., [<u>vllm-"
"ascend</u>](https://quay.io/repository/ascend/vllm-ascend?tab=tags) and "
"[<u>cann</u>](https://quay.io/repository/ascend/cann?tab=tags)."
msgstr ""
"你可以在 `Quay.io` 获取我们的容器，例如，[<u>vllm-"
"ascend</u>](https://quay.io/repository/ascend/vllm-ascend?tab=tags) 和 "
"[<u>cann</u>](https://quay.io/repository/ascend/cann?tab=tags)。"
#: ../../faqs.md:28
msgid ""
"If you are in China, you can use `daocloud` to accelerate your downloading:"
msgstr "如果你在中国，可以使用 `daocloud` 来加速下载："
#: ../../faqs.md:36
msgid "3. What models does vllm-ascend supports?"
msgstr "3. vllm-ascend 支持哪些模型？"
#: ../../faqs.md:38
msgid ""
"Find more details [<u>here</u>](https://vllm-"
"ascend.readthedocs.io/en/latest/user_guide/support_matrix/supported_models.html)."
msgstr ""
"在[<u>此处</u>](https://vllm-"
"ascend.readthedocs.io/en/latest/user_guide/support_matrix/supported_models.html)查看更多详细信息。"
#: ../../faqs.md:40
msgid "4. How to get in touch with our community?"
msgstr "4. 如何与我们的社区取得联系？"
#: ../../faqs.md:42
msgid ""
"There are many channels that you can communicate with our community "
"developers / users:"
msgstr "你可以通过多种渠道与我们的社区开发者/用户进行交流："
#: ../../faqs.md:44
msgid ""
"Submit a GitHub [<u>issue</u>](https://github.com/vllm-project/vllm-"
"ascend/issues?page=1)."
msgstr ""
"提交一个 GitHub [<u>issue</u>](https://github.com/vllm-project/vllm-"
"ascend/issues?page=1)。"
#: ../../faqs.md:45
msgid ""
"Join our [<u>weekly "
"meeting</u>](https://docs.google.com/document/d/1hCSzRTMZhIB8vRq1_qOOjx4c9uYUxvdQvDsMV2JcSrw/edit?tab=t.0#heading=h.911qu8j8h35z)"
" and share your ideas."
msgstr ""
"加入我们的[<u>每周会议</u>](https://docs.google.com/document/d/1hCSzRTMZhIB8vRq1_qOOjx4c9uYUxvdQvDsMV2JcSrw/edit?tab=t.0#heading=h.911qu8j8h35z)，并分享你的想法。"
#: ../../faqs.md:46
msgid ""
"Join our [<u>WeChat</u>](https://github.com/vllm-project/vllm-"
"ascend/issues/227) group and ask your quenstions."
msgstr ""
"加入我们的 [<u>微信群</u>](https://github.com/vllm-project/vllm-ascend/issues/227) "
"并提问你的问题。"
#: ../../faqs.md:47
msgid ""
"Join our ascend channel in [<u>vLLM "
"forums</u>](https://discuss.vllm.ai/c/hardware-support/vllm-ascend-"
"support/6) and publish your topics."
msgstr ""
"加入我们在 [<u>vLLM 论坛</u>](https://discuss.vllm.ai/c/hardware-support/vllm-"
"ascend-support/6) 的 ascend 频道并发布你的话题。"
#: ../../faqs.md:49
msgid "5. What features does vllm-ascend V1 supports?"
msgstr "5. vllm-ascend V1 支持哪些功能？"
#: ../../faqs.md:51
msgid ""
"Find more details [<u>here</u>](https://vllm-"
"ascend.readthedocs.io/en/latest/user_guide/support_matrix/supported_features.html)."
msgstr ""
"在[<u>这里</u>](https://vllm-"
"ascend.readthedocs.io/en/latest/user_guide/support_matrix/supported_features.html)找到更多详细信息。"
#: ../../faqs.md:53
msgid ""
"6. How to solve the problem of \"Failed to infer device type\" or "
"\"libatb.so: cannot open shared object file\"?"
msgstr "6. 如何解决“无法推断设备类型”或“libatb.so：无法打开共享对象文件”问题？"
#: ../../faqs.md:55
msgid ""
"Basically, the reason is that the NPU environment is not configured "
"correctly. You can:"
msgstr "基本上，原因是 NPU 环境没有正确配置。你可以："
#: ../../faqs.md:56
msgid ""
"try `source /usr/local/Ascend/nnal/atb/set_env.sh` to enable NNAL package."
msgstr "尝试运行 `source /usr/local/Ascend/nnal/atb/set_env.sh` 以启用 NNAL 包。"
#: ../../faqs.md:57
msgid ""
"try `source /usr/local/Ascend/ascend-toolkit/set_env.sh` to enable CANN "
"package."
msgstr "尝试运行 `source /usr/local/Ascend/ascend-toolkit/set_env.sh` 以启用 CANN 包。"
#: ../../faqs.md:58
msgid "try `npu-smi info` to check whether the NPU is working."
msgstr "尝试运行 `npu-smi info` 来检查 NPU 是否正常工作。"
#: ../../faqs.md:60
msgid ""
"If all above steps are not working, you can try the following code with "
"python to check whether there is any error:"
msgstr "如果以上所有步骤都无效，你可以尝试使用以下 python 代码来检查是否有错误："
#: ../../faqs.md:68
msgid ""
"If all above steps are not working, feel free to submit a GitHub issue."
msgstr "如果以上所有步骤都无法解决问题，欢迎提交一个 GitHub issue。"
#: ../../faqs.md:70
msgid "7. How does vllm-ascend perform?"
msgstr "7. vllm-ascend 的性能如何？"
#: ../../faqs.md:72
msgid ""
"Currently, only some models are improved. Such as `Qwen2.5 VL`, `Qwen3`, "
"`Deepseek V3`. Others are not good enough. From 0.9.0rc2, Qwen and Deepseek"
" works with graph mode to play a good performance. What's more, you can "
"install `mindie-turbo` with `vllm-ascend v0.7.3` to speed up the inference "
"as well."
msgstr ""
"目前，只有部分模型得到了改进，比如 `Qwen2.5 VL`、`Qwen3` 和 `Deepseek V3`。其他模型的效果还不够理想。从 "
"0.9.0rc2 开始，Qwen 和 Deepseek 已经支持图模式，以获得更好的性能。此外，你还可以在 `vllm-ascend v0.7.3` "
"上安装 `mindie-turbo`，进一步加速推理。"
#: ../../faqs.md:74
msgid "8. How vllm-ascend work with vllm?"
msgstr "8. vllm-ascend 如何与 vllm 协同工作？"
#: ../../faqs.md:75
msgid ""
"vllm-ascend is a plugin for vllm. Basically, the version of vllm-ascend is "
"the same as the version of vllm. For example, if you use vllm 0.7.3, you "
"should use vllm-ascend 0.7.3 as well. For main branch, we will make sure "
"`vllm-ascend` and `vllm` are compatible by each commit."
msgstr ""
"vllm-ascend 是 vllm 的一个插件。基本上，vllm-ascend 的版本与 vllm 的版本是相同的。例如，如果你使用 vllm "
"0.7.3，你也应该使用 vllm-ascend 0.7.3。对于主分支，我们会确保每次提交都让 `vllm-ascend` 和 `vllm` "
"保持兼容。"
#: ../../faqs.md:77
msgid "9. Does vllm-ascend support Prefill Disaggregation feature?"
msgstr "9. vllm-ascend 支持 Prefill Disaggregation 功能吗？"
#: ../../faqs.md:79
msgid ""
"Currently, only 1P1D is supported on V0 Engine. For V1 Engine or NPND "
"support, We will make it stable and supported by vllm-ascend in the future."
msgstr "目前，V0引擎只支持1P1D。对于V1引擎或NPND的支持，我们将在未来使其稳定并由vllm-ascend支持。"
#: ../../faqs.md:81
msgid "10. Does vllm-ascend support quantization method?"
msgstr "10. vllm-ascend 支持量化方法吗？"
#: ../../faqs.md:83
msgid ""
"Currently, w8a8 quantization is already supported by vllm-ascend originally "
"on v0.8.4rc2 or higher, If you're using vllm 0.7.3 version, w8a8 "
"quantization is supporeted with the integration of vllm-ascend and mindie-"
"turbo, please use `pip install vllm-ascend[mindie-turbo]`."
msgstr ""
"目前，w8a8 量化已在 v0.8.4rc2 或更高版本的 vllm-ascend 中原生支持。如果你使用的是 vllm 0.7.3 版本，集成了 "
"vllm-ascend 和 mindie-turbo 后也支持 w8a8 量化，请使用 `pip install vllm-ascend[mindie-"
"turbo]`。"
#: ../../faqs.md:85
msgid "11. How to run w8a8 DeepSeek model?"
msgstr "11. 如何运行 w8a8 DeepSeek 模型？"
#: ../../faqs.md:87
msgid ""
"Please following the [inferencing tutorail](https://vllm-"
"ascend.readthedocs.io/en/latest/tutorials/multi_node.html) and replace model"
" to DeepSeek."
msgstr ""
"请按照[inferencing 教程](https://vllm-"
"ascend.readthedocs.io/en/latest/tutorials/multi_node.html)进行操作，并将模型更换为 "
"DeepSeek。"
#: ../../faqs.md:89
msgid ""
"12. There is no output in log when loading models using vllm-ascend, How to "
"solve it?"
msgstr "12. 使用 vllm-ascend 加载模型时日志没有输出，如何解决？"
#: ../../faqs.md:91
msgid ""
"If you're using vllm 0.7.3 version, this is a known progress bar display "
"issue in VLLM, which has been resolved in [this PR](https://github.com/vllm-"
"project/vllm/pull/12428), please cherry-pick it locally by yourself. "
"Otherwise, please fill up an issue."
msgstr ""
"如果你正在使用 vllm 0.7.3 版本，这是 VLLM 已知的进度条显示问题，已在 [此 PR](https://github.com/vllm-"
"project/vllm/pull/12428) 中解决，请自行在本地进行 cherry-pick。否则，请提交一个 issue。"
#: ../../faqs.md:93
msgid "13. How vllm-ascend is tested"
msgstr "13. 如何测试 vllm-ascend"
#: ../../faqs.md:95
msgid ""
"vllm-ascend is tested by functional test, performance test and accuracy "
"test."
msgstr "vllm-ascend 经过功能测试、性能测试和精度测试。"
#: ../../faqs.md:97
msgid ""
"**Functional test**: we added CI, includes portion of vllm's native unit "
"tests and vllm-ascend's own unit tests，on vllm-ascend's test, we test basic "
"functionality、popular models availability and [supported "
"features](https://vllm-"
"ascend.readthedocs.io/en/latest/user_guide/support_matrix/supported_features.html)"
" via e2e test"
msgstr ""
"**功能测试**：我们添加了CI，包含了vllm原生单元测试的一部分以及vllm-ascend自己的单元测试。在vllm-"
"ascend的测试中，我们通过e2e测试验证了基本功能、主流模型可用性和[支持的特性](https://vllm-"
"ascend.readthedocs.io/en/latest/user_guide/support_matrix/supported_features.html)。"
#: ../../faqs.md:99
msgid ""
"**Performance test**: we provide [benchmark](https://github.com/vllm-"
"project/vllm-ascend/tree/main/benchmarks) tools for end-to-end performance "
"benchmark which can easily to re-route locally, we'll publish a perf website"
" to show the performance test results for each pull request"
msgstr ""
"**性能测试**：我们提供了用于端到端性能基准测试的[基准测试](https://github.com/vllm-project/vllm-"
"ascend/tree/main/benchmarks)工具，可以方便地在本地重新运行。我们将发布一个性能网站，用于展示每个拉取请求的性能测试结果。"
#: ../../faqs.md:101
msgid ""
"**Accuracy test**: we're working on adding accuracy test to CI as well."
msgstr "**准确性测试**：我们也在努力将准确性测试添加到CI中。"
#: ../../faqs.md:103
msgid ""
"Finnall, for each release, we'll publish the performance test and accuracy "
"test report in the future."
msgstr "最后，未来每个版本发布时，我们都会公开性能测试和准确性测试报告。"
#: ../../faqs.md:105
msgid "14. How to fix the error \"InvalidVersion\" when using vllm-ascend?"
msgstr "14. 使用 vllm-ascend 时如何解决 “InvalidVersion” 错误？"
#: ../../faqs.md:106
msgid ""
"It's usually because you have installed an dev/editable version of vLLM "
"package. In this case, we provide the env variable `VLLM_VERSION` to let "
"users specify the version of vLLM package to use. Please set the env "
"variable `VLLM_VERSION` to the version of vLLM package you have installed. "
"The format of `VLLM_VERSION` should be `X.Y.Z`."
msgstr ""
"这通常是因为你安装了开发版或可编辑版本的 vLLM 包。在这种情况下，我们提供了环境变量 `VLLM_VERSION`，以便用户指定要使用的 vLLM "
"包版本。请将环境变量 `VLLM_VERSION` 设置为你已安装的 vLLM 包的版本。`VLLM_VERSION` 的格式应为 `X.Y.Z`。"
#: ../../faqs.md:108
msgid "15. How to handle Out Of Memory?"
msgstr "15. 如何处理内存溢出？"
#: ../../faqs.md:109
msgid ""
"OOM errors typically occur when the model exceeds the memory capacity of a "
"single NPU. For general guidance, you can refer to [vLLM's OOM "
"troubleshooting "
"documentation](https://docs.vllm.ai/en/latest/getting_started/troubleshooting.html#out-"
"of-memory)."
msgstr ""
"当模型超出单个 NPU 的内存容量时，通常会发生 OOM（内存溢出）错误。一般性的指导可以参考 [vLLM 的 OOM "
"故障排除文档](https://docs.vllm.ai/en/latest/getting_started/troubleshooting.html#out-"
"of-memory)。"
#: ../../faqs.md:111
msgid ""
"In scenarios where NPUs have limited HBM (High Bandwidth Memory) capacity, "
"dynamic memory allocation/deallocation during inference can exacerbate "
"memory fragmentation, leading to OOM. To address this:"
msgstr ""
"在 NPU 的 HBM（高带宽内存）容量有限的场景下，推理过程中动态内存分配和释放会加剧内存碎片，从而导致 OOM（内存溢出）。为了解决这个问题："
#: ../../faqs.md:113
msgid ""
"**Adjust `--gpu-memory-utilization`**: If unspecified, will use the default "
"value of `0.9`. You can decrease this param to reserve more memory to reduce"
" fragmentation risks. See more note in: [vLLM - Inference and Serving - "
"Engine "
"Arguments](https://docs.vllm.ai/en/latest/serving/engine_args.html#vllm.engine.arg_utils-"
"_engine_args_parser-cacheconfig)."
msgstr ""
"**调整 `--gpu-memory-utilization`**：如果未指定，将使用默认值 "
"`0.9`。你可以降低此参数来预留更多内存，从而降低内存碎片风险。参见更多说明：[vLLM - 推理与服务 - "
"引擎参数](https://docs.vllm.ai/en/latest/serving/engine_args.html#vllm.engine.arg_utils-"
"_engine_args_parser-cacheconfig)。"
#: ../../faqs.md:115
msgid ""
"**Configure `PYTORCH_NPU_ALLOC_CONF`**: Set this environment variable to "
"optimize NPU memory management. For example, you can `export "
"PYTORCH_NPU_ALLOC_CONF=expandable_segments:True` to enable virtual memory "
"feature to mitigate memory fragmentation caused by frequent dynamic memory "
"size adjustments during runtime, see more note in: "
"[PYTORCH_NPU_ALLOC_CONF](https://www.hiascend.com/document/detail/zh/Pytorch/700/comref/Envvariables/Envir_012.html)."
msgstr ""
"**配置 `PYTORCH_NPU_ALLOC_CONF`**：设置此环境变量以优化NPU内存管理。例如，你可以通过 `export "
"PYTORCH_NPU_ALLOC_CONF=expandable_segments:True` "
"来启用虚拟内存功能，以缓解运行时频繁动态调整内存大小导致的内存碎片问题，更多说明参见：[PYTORCH_NPU_ALLOC_CONF](https://www.hiascend.com/document/detail/zh/Pytorch/700/comref/Envvariables/Envir_012.html)。"
#: ../../faqs.md:117
msgid "16. Failed to enable NPU graph mode when running DeepSeek?"
msgstr "16. 运行 DeepSeek 时无法启用 NPU 图模式？"
#: ../../faqs.md:118
#, python-brace-format
msgid ""
"You may encounter the following error if running DeepSeek with NPU graph "
"mode enabled. The allowed number of queries per kv when enabling both MLA "
"and Graph mode only support {32, 64, 128}, **Thus this is not supported for "
"DeepSeek-V2-Lite**, as it only has 16 attention heads. The NPU graph mode "
"support on DeepSeek-V2-Lite will be done in the future."
msgstr ""
"如果在启用NPU图模式（Graph "
"mode）运行DeepSeek时，您可能会遇到以下错误。当同时启用MLA和图模式时，每个kv允许的查询数只支持{32, 64, "
"128}，**因此这不支持DeepSeek-V2-Lite**，因为它只有16个注意力头。未来会增加对DeepSeek-V2-Lite在NPU图模式下的支持。"
#: ../../faqs.md:120
#, python-brace-format
msgid ""
"And if you're using DeepSeek-V3 or DeepSeek-R1, please make sure after the "
"tensor parallel split, num_heads / num_kv_heads in {32, 64, 128}."
msgstr ""
"如果你正在使用 DeepSeek-V3 或 DeepSeek-R1，请确保在张量并行切分后，num_heads / num_kv_heads 的值为 "
"{32, 64, 128} 中的一个。"
#: ../../faqs.md:127
msgid ""
"17. Failed to reinstall vllm-ascend from source after uninstalling vllm-"
"ascend?"
msgstr "17. 卸载 vllm-ascend 后无法从源码重新安装 vllm-ascend？"
#: ../../faqs.md:128
msgid ""
"You may encounter the problem of C compilation failure when reinstalling "
"vllm-ascend from source using pip. If the installation fails, it is "
"recommended to use `python setup.py install` to install, or use `python "
"setup.py clean` to clear the cache."
msgstr ""
"当你使用 pip 从源码重新安装 vllm-ascend 时，可能会遇到 C 编译失败的问题。如果安装失败，建议使用 `python setup.py "
"install` 进行安装，或者使用 `python setup.py clean` 清除缓存。"
#: ../../faqs.md:130
msgid "18. How to generate determinitic results when using vllm-ascend?"
msgstr "18. 使用 vllm-ascend 时如何生成确定性结果？"
#: ../../faqs.md:131
msgid "There are several factors that affect output certainty:"
msgstr "有几个因素会影响输出的确定性："
#: ../../faqs.md:133
msgid ""
"Sampler Method: using **Greedy sample** by setting `temperature=0` in "
"`SamplingParams`, e.g.:"
msgstr ""
"采样方法：通过在 `SamplingParams` 中设置 `temperature=0` 来使用 **贪婪采样（Greedy "
"sample）**，例如："
#: ../../faqs.md:158
msgid "Set the following enveriments parameters:"
msgstr "设置以下环境参数："

79
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@@ -0,0 +1,79 @@
# SOME DESCRIPTIVE TITLE.
# Copyright (C) 2025, vllm-ascend team
# This file is distributed under the same license as the vllm-ascend
# package.
# FIRST AUTHOR <EMAIL@ADDRESS>, 2025.
#
#, fuzzy
msgid ""
msgstr ""
"Project-Id-Version: vllm-ascend\n"
"Report-Msgid-Bugs-To: \n"
"POT-Creation-Date: 2025-07-18 09:01+0800\n"
"PO-Revision-Date: 2025-07-18 10:05+0800\n"
"Last-Translator: \n"
"Language-Team: zh_CN <LL@li.org>\n"
"Language: zh_CN\n"
"MIME-Version: 1.0\n"
"Content-Type: text/plain; charset=utf-8\n"
"Content-Transfer-Encoding: 8bit\n"
"Plural-Forms: nplurals=1; plural=0;\n"
"Generated-By: Babel 2.17.0\n"
"X-Generator: Poedit 3.5\n"
#: ../../index.md:33
msgid "Getting Started"
msgstr "快速开始"
#: ../../index.md:43
msgid "User Guide"
msgstr "用户指南"
#: ../../index.md:53
msgid "Developer Guide"
msgstr "开发者指南"
#: ../../index.md:64
msgid "Community"
msgstr "社区"
#: ../../index.md:1
msgid "Welcome to vLLM Ascend Plugin"
msgstr "欢迎使用 vLLM Ascend 插件"
#: ../../index.md:3
msgid "vLLM"
msgstr "vLLM"
#: ../../index.md:24
msgid ""
"vLLM Ascend plugin (vllm-ascend) is a community maintained hardware plugin "
"for running vLLM on the Ascend NPU."
msgstr ""
"vLLM Ascend 插件（vllm-ascend）是一个由社区维护的硬件插件，用于在 Ascend "
"NPU 上运行 vLLM。"
#: ../../index.md:26
msgid ""
"This plugin is the recommended approach for supporting the Ascend backend "
"within the vLLM community. It adheres to the principles outlined in the "
"[[RFC]: Hardware pluggable](https://github.com/vllm-project/vllm/"
"issues/11162), providing a hardware-pluggable interface that decouples the "
"integration of the Ascend NPU with vLLM."
msgstr ""
"该插件是 vLLM 社区推荐用于支持 Ascend 后端的方法。它遵循 [[RFC]: Hardware "
"pluggable](https://github.com/vllm-project/vllm/issues/11162) 中提出的原"
"则，提供了一个硬件可插拔接口，实现了 Ascend NPU 与 vLLM 集成的解耦。"
#: ../../index.md:28
msgid ""
"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."
msgstr ""
"通过使用 vLLM Ascend 插件，流行的开源模型，包括 Transformer 类、混合专家、"
"嵌入式、多模态大模型等，都可以在 Ascend NPU 上无缝运行。"
#: ../../index.md:30
msgid "Documentation"
msgstr "文档"

293
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@@ -0,0 +1,293 @@
# SOME DESCRIPTIVE TITLE.
# Copyright (C) 2025, vllm-ascend team
# This file is distributed under the same license as the vllm-ascend
# package.
# FIRST AUTHOR <EMAIL@ADDRESS>, 2025.
#
#, fuzzy
msgid ""
msgstr ""
"Project-Id-Version: vllm-ascend\n"
"Report-Msgid-Bugs-To: \n"
"POT-Creation-Date: 2025-07-18 09:01+0800\n"
"PO-Revision-Date: 2025-07-18 10:09+0800\n"
"Last-Translator: \n"
"Language-Team: zh_CN <LL@li.org>\n"
"Language: zh_CN\n"
"MIME-Version: 1.0\n"
"Content-Type: text/plain; charset=utf-8\n"
"Content-Transfer-Encoding: 8bit\n"
"Plural-Forms: nplurals=1; plural=0;\n"
"Generated-By: Babel 2.17.0\n"
"X-Generator: Poedit 3.5\n"
#: ../../installation.md:1
msgid "Installation"
msgstr "安装"
#: ../../installation.md:3
msgid "This document describes how to install vllm-ascend manually."
msgstr "本文档介绍如何手动安装 vllm-ascend。"
#: ../../installation.md:5
msgid "Requirements"
msgstr "要求"
#: ../../installation.md:7
msgid "OS: Linux"
msgstr "操作系统：Linux"
#: ../../installation.md:8
msgid "Python: >= 3.9, < 3.12"
msgstr "Python：>= 3.9，< 3.12"
#: ../../installation.md:9
msgid "A hardware with Ascend NPU. It's usually the Atlas 800 A2 series."
msgstr "配备有昇腾NPU的硬件，通常是Atlas 800 A2系列。"
#: ../../installation.md:10
msgid "Software:"
msgstr "软件："
#: ../../installation.md
msgid "Software"
msgstr "软件"
#: ../../installation.md
msgid "Supported version"
msgstr "支持的版本"
#: ../../installation.md
msgid "Note"
msgstr "注释"
#: ../../installation.md
msgid "CANN"
msgstr "CANN"
#: ../../installation.md
msgid ">= 8.1.RC1"
msgstr ">= 8.1.RC1"
#: ../../installation.md
msgid "Required for vllm-ascend and torch-npu"
msgstr "vllm-ascend 和 torch-npu 必需"
#: ../../installation.md
msgid "torch-npu"
msgstr "torch-npu"
#: ../../installation.md
msgid ">= 2.5.1.post1.dev20250619"
msgstr ">= 2.5.1.post1.dev20250619"
#: ../../installation.md
msgid ""
"Required for vllm-ascend, No need to install manually, it will be auto "
"installed in below steps"
msgstr "vllm-ascend 必需，无需手动安装，后续步骤会自动安装。"
#: ../../installation.md
msgid "torch"
msgstr "torch"
#: ../../installation.md
msgid ">= 2.5.1"
msgstr ">= 2.5.1"
#: ../../installation.md
msgid "Required for torch-npu and vllm"
msgstr "torch-npu 和 vllm 所需"
#: ../../installation.md:18
msgid "You have 2 way to install:"
msgstr "你有两种安装方式："
#: ../../installation.md:19
msgid ""
"**Using pip**: first prepare env manually or via CANN image, then install "
"`vllm-ascend` using pip."
msgstr ""
"**使用 pip**：首先手动准备环境或通过 CANN 镜像准备环境，然后使用 pip 安装 "
"`vllm-ascend`。"
#: ../../installation.md:20
msgid ""
"**Using docker**: use the `vllm-ascend` pre-built docker image directly."
msgstr "**使用 docker**：直接使用 `vllm-ascend` 预构建的 docker 镜像。"
#: ../../installation.md:22
msgid "Configure a new environment"
msgstr "配置一个新环境"
#: ../../installation.md:24
msgid ""
"Before installing, you need to make sure firmware/driver and CANN are "
"installed correctly, refer to [link](https://ascend.github.io/docs/sources/"
"ascend/quick_install.html) for more details."
msgstr ""
"在安装之前，您需要确保固件/驱动和 CANN 已正确安装，更多详情请参考 [链接]"
"(https://ascend.github.io/docs/sources/ascend/quick_install.html)。"
#: ../../installation.md:26
msgid "Configure hardware environment"
msgstr "配置硬件环境"
#: ../../installation.md:28
msgid ""
"To verify that the Ascend NPU firmware and driver were correctly installed, "
"run:"
msgstr "要验证 Ascend NPU 固件和驱动程序是否正确安装，请运行："
#: ../../installation.md:34
msgid ""
"Refer to [Ascend Environment Setup Guide](https://ascend.github.io/docs/"
"sources/ascend/quick_install.html) for more details."
msgstr ""
"更多详情请参考[Ascend环境搭建指南](https://ascend.github.io/docs/sources/"
"ascend/quick_install.html)。"
#: ../../installation.md:36
msgid "Configure software environment"
msgstr "配置软件环境"
#: ../../installation.md
msgid "Before using pip"
msgstr "在使用 pip 之前"
#: ../../installation.md:46
msgid ""
"The easiest way to prepare your software environment is using CANN image "
"directly:"
msgstr "最简单的方式是直接使用 CANN 镜像来准备您的软件环境："
#: ../../installation.md
msgid "Click here to see \"Install CANN manually\""
msgstr "点击此处查看“手动安装 CANN”"
#: ../../installation.md:72
msgid "You can also install CANN manually:"
msgstr "你也可以手动安装 CANN："
#: ../../installation.md
msgid "Before using docker"
msgstr "在使用 docker 之前"
#: ../../installation.md:104
msgid ""
"No more extra step if you are using `vllm-ascend` prebuilt docker image."
msgstr "如果你使用 `vllm-ascend` 预构建的 docker 镜像，就无需额外的步骤。"
#: ../../installation.md:108
msgid "Once it's done, you can start to set up `vllm` and `vllm-ascend`."
msgstr "完成后，你可以开始配置 `vllm` 和 `vllm-ascend`。"
#: ../../installation.md:110
msgid "Setup vllm and vllm-ascend"
msgstr "安装 vllm 和 vllm-ascend"
#: ../../installation.md
msgid "Using pip"
msgstr "使用 pip"
#: ../../installation.md:121
msgid "First install system dependencies and config pip mirror:"
msgstr "首先安装系统依赖并配置 pip 镜像："
#: ../../installation.md:133
msgid ""
"**[Optional]** Then config the extra-index of `pip` if you are working on a "
"x86 machine or using torch-npu dev version:"
msgstr ""
"**[可选]** 如果你在 x86 机器上工作或使用 torch-npu 开发版，请配置 `pip` 的额"
"外索引："
#: ../../installation.md:140
msgid ""
"Then you can install `vllm` and `vllm-ascend` from **pre-built wheel**:"
msgstr "然后你可以从**预编译的 wheel 包**安装 `vllm` 和 `vllm-ascend`："
#: ../../installation.md
msgid "Click here to see \"Build from source code\""
msgstr "点击此处查看“从源代码构建”"
#: ../../installation.md:153
msgid "or build from **source code**:"
msgstr "或者从**源代码**构建："
#: ../../installation.md:171
msgid ""
"vllm-ascend will build custom ops by default. If you don't want to build "
"it, set `COMPILE_CUSTOM_KERNELS=0` environment to disable it."
msgstr ""
"vllm-ascend 默认会编译自定义算子。如果你不想编译它，可以设置环境变量 "
"`COMPILE_CUSTOM_KERNELS=0` 来禁用。"
#: ../../installation.md:175
msgid ""
"If you are building from v0.7.3-dev and intend to use sleep mode feature, "
"you should set `COMPILE_CUSTOM_KERNELS=1` manually. To build custom ops, "
"gcc/g++ higher than 8 and c++ 17 or higher is required. If you're using "
"`pip install -e .` and encourage a torch-npu version conflict, please "
"install with `pip install --no-build-isolation -e .` to build on system "
"env. If you encounter other problems during compiling, it is probably "
"because unexpected compiler is being used, you may export `CXX_COMPILER` "
"and `C_COMPILER` in env to specify your g++ and gcc locations before "
"compiling."
msgstr ""
"如果你是从 v0.7.3-dev 版本开始构建，并且打算使用休眠模式功能，你需要手动设"
"置 `COMPILE_CUSTOM_KERNELS=1`。构建自定义算子时，要求 gcc/g++ 版本高于 8 且"
"支持 c++ 17 或更高标准。如果你正在使用 `pip install -e .` 并且出现了 torch-"
"npu 版本冲突，请使用 `pip install --no-build-isolation -e .` 在系统环境下进"
"行安装。如果在编译过程中遇到其它问题，可能是因为使用了非预期的编译器，你可以"
"在编译前通过环境变量导出 `CXX_COMPILER` 和 `C_COMPILER`，以指定你的 g++ 和 "
"gcc 路径。"
#: ../../installation.md
msgid "Using docker"
msgstr "使用 docker"
#: ../../installation.md:184
msgid "You can just pull the **prebuilt image** and run it with bash."
msgstr "你可以直接拉取**预构建镜像**并用 bash 运行它。"
#: ../../installation.md
msgid "Click here to see \"Build from Dockerfile\""
msgstr "点击这里查看“从 Dockerfile 构建”"
#: ../../installation.md:187
msgid "or build IMAGE from **source code**:"
msgstr "或从**源代码**构建 IMAGE："
#: ../../installation.md:218
msgid ""
"The default workdir is `/workspace`, vLLM and vLLM Ascend code are placed "
"in `/vllm-workspace` and installed in [development mode](https://setuptools."
"pypa.io/en/latest/userguide/development_mode.html)(`pip install -e`) to "
"help developer immediately take place changes without requiring a new "
"installation."
msgstr ""
"默认的工作目录是 `/workspace`，vLLM 和 vLLM Ascend 代码被放置在 `/vllm-"
"workspace`，并以[开发模式](https://setuptools.pypa.io/en/latest/userguide/"
"development_mode.html)（`pip install -e`）安装，以便开发者能够即时生效更改，"
"而无需重新安装。"
#: ../../installation.md:222
msgid "Extra information"
msgstr "额外信息"
#: ../../installation.md:224
msgid "Verify installation"
msgstr "验证安装"
#: ../../installation.md:226
msgid "Create and run a simple inference test. The `example.py` can be like:"
msgstr "创建并运行一个简单的推理测试。`example.py` 可以如下："
#: ../../installation.md:251
msgid "Then run:"
msgstr "然后运行："
#: ../../installation.md:259
msgid "The output will be like:"
msgstr "输出将会像这样："

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@@ -0,0 +1,149 @@
# SOME DESCRIPTIVE TITLE.
# Copyright (C) 2025, vllm-ascend team
# This file is distributed under the same license as the vllm-ascend
# package.
# FIRST AUTHOR <EMAIL@ADDRESS>, 2025.
#
#, fuzzy
msgid ""
msgstr ""
"Project-Id-Version: vllm-ascend\n"
"Report-Msgid-Bugs-To: \n"
"POT-Creation-Date: 2025-07-18 09:01+0800\n"
"PO-Revision-Date: 2025-07-18 10:09+0800\n"
"Last-Translator: \n"
"Language-Team: zh_CN <LL@li.org>\n"
"Language: zh_CN\n"
"MIME-Version: 1.0\n"
"Content-Type: text/plain; charset=utf-8\n"
"Content-Transfer-Encoding: 8bit\n"
"Plural-Forms: nplurals=1; plural=0;\n"
"Generated-By: Babel 2.17.0\n"
"X-Generator: Poedit 3.5\n"
#: ../../quick_start.md:1
msgid "Quickstart"
msgstr "快速入门"
#: ../../quick_start.md:3
msgid "Prerequisites"
msgstr "先决条件"
#: ../../quick_start.md:5
msgid "Supported Devices"
msgstr "支持的设备"
#: ../../quick_start.md:6
msgid ""
"Atlas A2 Training series (Atlas 800T A2, Atlas 900 A2 PoD, Atlas 200T A2 "
"Box16, Atlas 300T A2)"
msgstr ""
"Atlas A2 训练系列（Atlas 800T A2，Atlas 900 A2 PoD，Atlas 200T A2 Box16，"
"Atlas 300T A2）"
#: ../../quick_start.md:7
msgid "Atlas 800I A2 Inference series (Atlas 800I A2)"
msgstr "Atlas 800I A2 推理系列（Atlas 800I A2）"
#: ../../quick_start.md:9
msgid "Setup environment using container"
msgstr "使用容器设置环境"
#: ../../quick_start.md
msgid "Ubuntu"
msgstr "Ubuntu"
#: ../../quick_start.md
msgid "openEuler"
msgstr "openEuler"
#: ../../quick_start.md:69
msgid ""
"The default workdir is `/workspace`, vLLM and vLLM Ascend code are placed "
"in `/vllm-workspace` and installed in [development mode](https://setuptools."
"pypa.io/en/latest/userguide/development_mode.html)(`pip install -e`) to "
"help developer immediately take place changes without requiring a new "
"installation."
msgstr ""
"默认的工作目录是 `/workspace`，vLLM 和 vLLM Ascend 代码被放置在 `/vllm-"
"workspace`，并以[开发模式](https://setuptools.pypa.io/en/latest/userguide/"
"development_mode.html)（`pip install -e`）安装，以便开发者能够即时生效更改，"
"而无需重新安装。"
#: ../../quick_start.md:71
msgid "Usage"
msgstr "用法"
#: ../../quick_start.md:73
msgid "You can use Modelscope mirror to speed up download:"
msgstr "你可以使用 Modelscope 镜像来加速下载："
#: ../../quick_start.md:80
msgid "There are two ways to start vLLM on Ascend NPU:"
msgstr "在昇腾 NPU 上启动 vLLM 有两种方式："
#: ../../quick_start.md
msgid "Offline Batched Inference"
msgstr "离线批量推理"
#: ../../quick_start.md:86
msgid ""
"With vLLM installed, you can start generating texts for list of input "
"prompts (i.e. offline batch inferencing)."
msgstr ""
"安装了 vLLM 后，您可以开始为一系列输入提示生成文本（即离线批量推理）。"
#: ../../quick_start.md:88
msgid ""
"Try to run below Python script directly or use `python3` shell to generate "
"texts:"
msgstr ""
"尝试直接运行下面的 Python 脚本，或者使用 `python3` 交互式命令行来生成文本："
#: ../../quick_start.md
msgid "OpenAI Completions API"
msgstr "OpenAI Completions API"
#: ../../quick_start.md:114
msgid ""
"vLLM can also be deployed as a server that implements the OpenAI API "
"protocol. Run the following command to start the vLLM server with the [Qwen/"
"Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) "
"model:"
msgstr ""
"vLLM 也可以作为实现 OpenAI API 协议的服务器进行部署。运行以下命令，使用 "
"[Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-"
"Instruct) 模型启动 vLLM 服务器："
#: ../../quick_start.md:124
msgid "If you see log as below:"
msgstr "如果你看到如下日志："
#: ../../quick_start.md:132
msgid "Congratulations, you have successfully started the vLLM server!"
msgstr "恭喜，你已经成功启动了 vLLM 服务器！"
#: ../../quick_start.md:134
msgid "You can query the list the models:"
msgstr "你可以查询模型列表："
#: ../../quick_start.md:141
msgid "You can also query the model with input prompts:"
msgstr "你也可以通过输入提示来查询模型："
#: ../../quick_start.md:155
msgid ""
"vLLM is serving as background process, you can use `kill -2 $VLLM_PID` to "
"stop the background process gracefully, it's equal to `Ctrl-C` to stop "
"foreground vLLM process:"
msgstr ""
"vLLM 正作为后台进程运行，你可以使用 `kill -2 $VLLM_PID` 来优雅地停止后台进"
"程，这等同于使用 `Ctrl-C` 停止前台 vLLM 进程："
#: ../../quick_start.md:164
msgid "You will see output as below:"
msgstr "你将会看到如下输出："
#: ../../quick_start.md:172
msgid "Finally, you can exit container by using `ctrl-D`."
msgstr "最后，你可以通过按 `ctrl-D` 退出容器。"

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# SOME DESCRIPTIVE TITLE.
# Copyright (C) 2025, vllm-ascend team
# This file is distributed under the same license as the vllm-ascend
# package.
# FIRST AUTHOR <EMAIL@ADDRESS>, 2025.
#
#, fuzzy
msgid ""
msgstr ""
"Project-Id-Version: vllm-ascend\n"
"Report-Msgid-Bugs-To: \n"
"POT-Creation-Date: 2025-07-18 09:01+0800\n"
"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
"Language-Team: zh_CN <LL@li.org>\n"
"Language: zh_CN\n"
"MIME-Version: 1.0\n"
"Content-Type: text/plain; charset=utf-8\n"
"Content-Transfer-Encoding: 8bit\n"
"Plural-Forms: nplurals=1; plural=0;\n"
"Generated-By: Babel 2.17.0\n"
#: ../../tutorials/index.md:3
msgid "Deployment"
msgstr "部署"
#: ../../tutorials/index.md:1
msgid "Tutorials"
msgstr "教程"

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@@ -0,0 +1,192 @@
# SOME DESCRIPTIVE TITLE.
# Copyright (C) 2025, vllm-ascend team
# This file is distributed under the same license as the vllm-ascend
# package.
# FIRST AUTHOR <EMAIL@ADDRESS>, 2025.
#
#, fuzzy
msgid ""
msgstr ""
"Project-Id-Version: vllm-ascend\n"
"Report-Msgid-Bugs-To: \n"
"POT-Creation-Date: 2025-07-18 09:01+0800\n"
"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
"Language-Team: zh_CN <LL@li.org>\n"
"Language: zh_CN\n"
"MIME-Version: 1.0\n"
"Content-Type: text/plain; charset=utf-8\n"
"Content-Transfer-Encoding: 8bit\n"
"Plural-Forms: nplurals=1; plural=0;\n"
"Generated-By: Babel 2.17.0\n"
#: ../../tutorials/multi_node.md:1
msgid "Multi-Node-DP (DeepSeek)"
msgstr "多节点分布式处理（DeepSeek）"
#: ../../tutorials/multi_node.md:3
msgid "Getting Start"
msgstr "快速开始"
#: ../../tutorials/multi_node.md:4
msgid ""
"vLLM-Ascend now supports Data Parallel (DP) deployment, enabling model "
"weights to be replicated across multiple NPUs or instances, each processing "
"independent batches of requests. This is particularly useful for scaling "
"throughput across devices while maintaining high resource utilization."
msgstr ""
"vLLM-Ascend 现在支持数据并行（DP）部署，可以在多个 NPU "
"或实例之间复制模型权重，每个实例处理独立的请求批次。这对于在保证高资源利用率的同时，实现跨设备的吞吐量扩展特别有用。"
#: ../../tutorials/multi_node.md:6
msgid ""
"Each DP rank is deployed as a separate “core engine” process which "
"communicates with front-end process(es) via ZMQ sockets. Data Parallel can "
"be combined with Tensor Parallel, in which case each DP engine owns a number"
" of per-NPU worker processes equal to the TP size."
msgstr ""
"每个 DP 进程作为一个单独的“核心引擎”进程部署，并通过 ZMQ 套接字与前端进程通信。数据并行可以与张量并行结合使用，此时每个 DP "
"引擎拥有数量等于 TP 大小的每 NPU 工作进程。"
#: ../../tutorials/multi_node.md:8
msgid ""
"For Mixture-of-Experts (MoE) models — especially advanced architectures like"
" DeepSeek that utilize Multi-head Latent Attention (MLA) — a hybrid "
"parallelism approach is recommended: - Use **Data Parallelism (DP)** for"
" attention layers, which are replicated across devices and handle separate "
"batches. - Use **Expert or Tensor Parallelism (EP/TP)** for expert "
"layers, which are sharded across devices to distribute the computation."
msgstr ""
"对于混合专家（Mixture-of-Experts, MoE）模型——尤其是像 DeepSeek 这样采用多头潜在注意力（Multi-head Latent Attention, MLA）的高级架构——推荐使用混合并行策略：\n"
" - 对于注意力层，使用 **数据并行（Data Parallelism, DP）**，这些层会在各设备间复刻，并处理不同的批次。\n"
" - 对于专家层，使用 **专家并行或张量并行（Expert or Tensor Parallelism, EP/TP）**，这些层会在设备间分片，从而分担计算。"
#: ../../tutorials/multi_node.md:12
msgid ""
"This division enables attention layers to be replicated across Data Parallel"
" (DP) ranks, enabling them to process different batches independently. "
"Meanwhile, expert layers are partitioned (sharded) across devices using "
"Expert or Tensor Parallelism(DP*TP), maximizing hardware utilization and "
"efficiency."
msgstr ""
"这种划分使得注意力层能够在数据并行（DP）组内复制，从而能够独立处理不同的批次。同时，专家层通过专家或张量并行（DP*TP）在设备间进行分区（切片），最大化硬件利用率和效率。"
#: ../../tutorials/multi_node.md:14
msgid ""
"In these cases the data parallel ranks are not completely independent, "
"forward passes must be aligned and expert layers across all ranks are "
"required to synchronize during every forward pass, even if there are fewer "
"requests to be processed than DP ranks."
msgstr ""
"在这些情况下，数据并行的各个 rank 不是完全独立的，前向传播必须对齐，并且所有 rank "
"上的专家层在每次前向传播时都需要同步，即使待处理的请求数量少于 DP rank 的数量。"
#: ../../tutorials/multi_node.md:16
msgid ""
"For MoE models, when any requests are in progress in any rank, we must "
"ensure that empty “dummy” forward passes are performed in all ranks which "
"don’t currently have any requests scheduled. This is handled via a separate "
"DP `Coordinator` process which communicates with all of the ranks, and a "
"collective operation performed every N steps to determine when all ranks "
"become idle and can be paused. When TP is used in conjunction with DP, "
"expert layers form an EP or TP group of size (DP x TP)."
msgstr ""
"对于 MoE 模型，当任何一个 rank 有请求正在进行时，必须确保所有当前没有请求的 rank 都执行空的“虚拟”前向传播。这是通过一个单独的 DP "
"`Coordinator` 协调器进程来实现的，该进程与所有 rank 通信，并且每隔 N 步执行一次集体操作，以判断所有 rank "
"是否都处于空闲状态并可以暂停。当 TP 与 DP 结合使用时，专家层会组成一个规模为（DP x TP）的 EP 或 TP 组。"
#: ../../tutorials/multi_node.md:18
msgid "Verify Multi-Node Communication Environment"
msgstr "验证多节点通信环境"
#: ../../tutorials/multi_node.md:20
msgid "Physical Layer Requirements:"
msgstr "物理层要求："
#: ../../tutorials/multi_node.md:22
msgid ""
"The physical machines must be located on the same WLAN, with network "
"connectivity."
msgstr "物理机器必须位于同一个 WLAN 中，并且具有网络连接。"
#: ../../tutorials/multi_node.md:23
msgid ""
"All NPUs are connected with optical modules, and the connection status must "
"be normal."
msgstr "所有 NPU 都通过光模块连接，且连接状态必须正常。"
#: ../../tutorials/multi_node.md:25
msgid "Verification Process:"
msgstr "验证流程："
#: ../../tutorials/multi_node.md:27
msgid ""
"Execute the following commands on each node in sequence. The results must "
"all be `success` and the status must be `UP`:"
msgstr "在每个节点上依次执行以下命令。所有结果必须为 `success` 且状态必须为 `UP`："
#: ../../tutorials/multi_node.md:44
msgid "NPU Interconnect Verification:"
msgstr "NPU 互连验证："
#: ../../tutorials/multi_node.md:45
msgid "1. Get NPU IP Addresses"
msgstr "1. 获取 NPU IP 地址"
#: ../../tutorials/multi_node.md:50
msgid "2. Cross-Node PING Test"
msgstr "2. 跨节点PING测试"
#: ../../tutorials/multi_node.md:56
msgid "Run with docker"
msgstr "用 docker 运行"
#: ../../tutorials/multi_node.md:57
msgid ""
"Assume you have two Atlas 800 A2(64G*8) nodes, and want to deploy the "
"`deepseek-v3-w8a8` quantitative model across multi-node."
msgstr "假设你有两台 Atlas 800 A2（64G*8）节点，并且想要在多节点上部署 `deepseek-v3-w8a8` 量化模型。"
#: ../../tutorials/multi_node.md:92
msgid ""
"Before launch the inference server, ensure some environment variables are "
"set for multi node communication"
msgstr "在启动推理服务器之前，确保已经为多节点通信设置了一些环境变量。"
#: ../../tutorials/multi_node.md:95
msgid "Run the following scripts on two nodes respectively"
msgstr "分别在两台节点上运行以下脚本"
#: ../../tutorials/multi_node.md:97
msgid "**node0**"
msgstr "**节点0**"
#: ../../tutorials/multi_node.md:137
msgid "**node1**"
msgstr "**节点1**"
#: ../../tutorials/multi_node.md:176
msgid ""
"The Deployment view looks like: ![alt text](../assets/multi_node_dp.png)"
msgstr "部署视图如下所示：![替代文本](../assets/multi_node_dp.png)"
#: ../../tutorials/multi_node.md:176
msgid "alt text"
msgstr "替代文本"
#: ../../tutorials/multi_node.md:179
msgid ""
"Once your server is started, you can query the model with input prompts:"
msgstr "一旦你的服务器启动，你可以通过输入提示词来查询模型："
#: ../../tutorials/multi_node.md:192
msgid "Run benchmarks"
msgstr "运行基准测试"
#: ../../tutorials/multi_node.md:193
msgid ""
"For details please refer to [benchmark](https://github.com/vllm-"
"project/vllm-ascend/tree/main/benchmarks)"
msgstr ""
"详细信息请参阅 [benchmark](https://github.com/vllm-project/vllm-"
"ascend/tree/main/benchmarks)"

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@@ -0,0 +1,62 @@
# SOME DESCRIPTIVE TITLE.
# Copyright (C) 2025, vllm-ascend team
# This file is distributed under the same license as the vllm-ascend
# package.
# FIRST AUTHOR <EMAIL@ADDRESS>, 2025.
#
#, fuzzy
msgid ""
msgstr ""
"Project-Id-Version: vllm-ascend\n"
"Report-Msgid-Bugs-To: \n"
"POT-Creation-Date: 2025-07-18 09:01+0800\n"
"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
"Language-Team: zh_CN <LL@li.org>\n"
"Language: zh_CN\n"
"MIME-Version: 1.0\n"
"Content-Type: text/plain; charset=utf-8\n"
"Content-Transfer-Encoding: 8bit\n"
"Plural-Forms: nplurals=1; plural=0;\n"
"Generated-By: Babel 2.17.0\n"
#: ../../tutorials/multi_npu.md:1
msgid "Multi-NPU (QwQ 32B)"
msgstr "多-NPU（QwQ 32B）"
#: ../../tutorials/multi_npu.md:3
msgid "Run vllm-ascend on Multi-NPU"
msgstr "在多NPU上运行 vllm-ascend"
#: ../../tutorials/multi_npu.md:5
msgid "Run docker container:"
msgstr "运行 docker 容器："
#: ../../tutorials/multi_npu.md:30
msgid "Setup environment variables:"
msgstr "设置环境变量："
#: ../../tutorials/multi_npu.md:40
msgid "Online Inference on Multi-NPU"
msgstr "多NPU的在线推理"
#: ../../tutorials/multi_npu.md:42
msgid "Run the following script to start the vLLM server on Multi-NPU:"
msgstr "运行以下脚本，在多NPU上启动 vLLM 服务器："
#: ../../tutorials/multi_npu.md:48
msgid ""
"Once your server is started, you can query the model with input prompts"
msgstr "一旦服务器启动，就可以通过输入提示词来查询模型。"
#: ../../tutorials/multi_npu.md:63
msgid "Offline Inference on Multi-NPU"
msgstr "多NPU离线推理"
#: ../../tutorials/multi_npu.md:65
msgid "Run the following script to execute offline inference on multi-NPU:"
msgstr "运行以下脚本以在多NPU上执行离线推理："
#: ../../tutorials/multi_npu.md:102
msgid "If you run this script successfully, you can see the info shown below:"
msgstr "如果你成功运行此脚本，你可以看到如下所示的信息："

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# SOME DESCRIPTIVE TITLE.
# Copyright (C) 2025, vllm-ascend team
# This file is distributed under the same license as the vllm-ascend
# package.
# FIRST AUTHOR <EMAIL@ADDRESS>, 2025.
#
#, fuzzy
msgid ""
msgstr ""
"Project-Id-Version: vllm-ascend\n"
"Report-Msgid-Bugs-To: \n"
"POT-Creation-Date: 2025-07-18 09:01+0800\n"
"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
"Language-Team: zh_CN <LL@li.org>\n"
"Language: zh_CN\n"
"MIME-Version: 1.0\n"
"Content-Type: text/plain; charset=utf-8\n"
"Content-Transfer-Encoding: 8bit\n"
"Plural-Forms: nplurals=1; plural=0;\n"
"Generated-By: Babel 2.17.0\n"
#: ../../tutorials/multi_npu_moge.md:1
msgid "Multi-NPU (Pangu Pro MoE)"
msgstr "多NPU（Pangu Pro MoE）"
#: ../../tutorials/multi_npu_moge.md:3
msgid "Run vllm-ascend on Multi-NPU"
msgstr "在多NPU上运行 vllm-ascend"
#: ../../tutorials/multi_npu_moge.md:5
msgid "Run container:"
msgstr "运行容器："
#: ../../tutorials/multi_npu_moge.md:30
msgid "Setup environment variables:"
msgstr "设置环境变量："
#: ../../tutorials/multi_npu_moge.md:37
msgid "Download the model:"
msgstr "下载该模型："
#: ../../tutorials/multi_npu_moge.md:44
msgid "Online Inference on Multi-NPU"
msgstr "多NPU上的在线推理"
#: ../../tutorials/multi_npu_moge.md:46
msgid "Run the following script to start the vLLM server on Multi-NPU:"
msgstr "运行以下脚本，在多NPU上启动 vLLM 服务器："
#: ../../tutorials/multi_npu_moge.md:55
msgid ""
"Once your server is started, you can query the model with input prompts:"
msgstr "一旦你的服务器启动，你可以通过输入提示词来查询模型："
#: ../../tutorials/multi_npu_moge.md
msgid "v1/completions"
msgstr "v1/补全"
#: ../../tutorials/multi_npu_moge.md
msgid "v1/chat/completions"
msgstr "v1/chat/completions"
#: ../../tutorials/multi_npu_moge.md:96
msgid "If you run this successfully, you can see the info shown below:"
msgstr "如果你成功运行这个，你可以看到如下所示的信息："
#: ../../tutorials/multi_npu_moge.md:102
msgid "Offline Inference on Multi-NPU"
msgstr "多NPU离线推理"
#: ../../tutorials/multi_npu_moge.md:104
msgid "Run the following script to execute offline inference on multi-NPU:"
msgstr "运行以下脚本以在多NPU上执行离线推理："
#: ../../tutorials/multi_npu_moge.md
msgid "Graph Mode"
msgstr "图模式"
#: ../../tutorials/multi_npu_moge.md
msgid "Eager Mode"
msgstr "即时模式"
#: ../../tutorials/multi_npu_moge.md:230
msgid "If you run this script successfully, you can see the info shown below:"
msgstr "如果你成功运行此脚本，你可以看到如下所示的信息："

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# SOME DESCRIPTIVE TITLE.
# Copyright (C) 2025, vllm-ascend team
# This file is distributed under the same license as the vllm-ascend
# package.
# FIRST AUTHOR <EMAIL@ADDRESS>, 2025.
#
#, fuzzy
msgid ""
msgstr ""
"Project-Id-Version: vllm-ascend\n"
"Report-Msgid-Bugs-To: \n"
"POT-Creation-Date: 2025-07-18 09:01+0800\n"
"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
"Language-Team: zh_CN <LL@li.org>\n"
"Language: zh_CN\n"
"MIME-Version: 1.0\n"
"Content-Type: text/plain; charset=utf-8\n"
"Content-Transfer-Encoding: 8bit\n"
"Plural-Forms: nplurals=1; plural=0;\n"
"Generated-By: Babel 2.17.0\n"
#: ../../tutorials/multi_npu_quantization.md:1
msgid "Multi-NPU (QwQ 32B W8A8)"
msgstr "多NPU（QwQ 32B W8A8）"
#: ../../tutorials/multi_npu_quantization.md:3
msgid "Run docker container"
msgstr "运行 docker 容器"
#: ../../tutorials/multi_npu_quantization.md:5
msgid "w8a8 quantization feature is supported by v0.8.4rc2 or higher"
msgstr "w8a8 量化功能由 v0.8.4rc2 或更高版本支持"
#: ../../tutorials/multi_npu_quantization.md:31
msgid "Install modelslim and convert model"
msgstr "安装 modelslim 并转换模型"
#: ../../tutorials/multi_npu_quantization.md:33
msgid ""
"You can choose to convert the model yourself or use the quantized model we "
"uploaded, see https://www.modelscope.cn/models/vllm-ascend/QwQ-32B-W8A8"
msgstr ""
"你可以选择自己转换模型，或者使用我们上传的量化模型，详见 https://www.modelscope.cn/models/vllm-"
"ascend/QwQ-32B-W8A8"
#: ../../tutorials/multi_npu_quantization.md:56
msgid "Verify the quantized model"
msgstr "验证量化模型"
#: ../../tutorials/multi_npu_quantization.md:57
msgid "The converted model files looks like:"
msgstr "转换后的模型文件如下所示："
#: ../../tutorials/multi_npu_quantization.md:70
msgid ""
"Run the following script to start the vLLM server with quantized model:"
msgstr "运行以下脚本以启动带有量化模型的 vLLM 服务器："
#: ../../tutorials/multi_npu_quantization.md:73
msgid ""
"The value \"ascend\" for \"--quantization\" argument will be supported after"
" [a specific PR](https://github.com/vllm-project/vllm-ascend/pull/877) is "
"merged and released, you can cherry-pick this commit for now."
msgstr ""
"在 [特定的PR](https://github.com/vllm-project/vllm-ascend/pull/877) 合并并发布后， \"--"
"quantization\" 参数将支持值 \"ascend\"，你也可以现在手动挑选该提交。"
#: ../../tutorials/multi_npu_quantization.md:79
msgid ""
"Once your server is started, you can query the model with input prompts"
msgstr "一旦服务器启动，就可以通过输入提示词来查询模型。"
#: ../../tutorials/multi_npu_quantization.md:93
msgid ""
"Run the following script to execute offline inference on multi-NPU with "
"quantized model:"
msgstr "运行以下脚本，在多NPU上使用量化模型执行离线推理："
#: ../../tutorials/multi_npu_quantization.md:96
msgid "To enable quantization for ascend, quantization method must be \"ascend\""
msgstr "要在ascend上启用量化，量化方法必须为“ascend”。"

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# SOME DESCRIPTIVE TITLE.
# Copyright (C) 2025, vllm-ascend team
# This file is distributed under the same license as the vllm-ascend
# package.
# FIRST AUTHOR <EMAIL@ADDRESS>, 2025.
#
#, fuzzy
msgid ""
msgstr ""
"Project-Id-Version: vllm-ascend\n"
"Report-Msgid-Bugs-To: \n"
"POT-Creation-Date: 2025-07-18 09:01+0800\n"
"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
"Language-Team: zh_CN <LL@li.org>\n"
"Language: zh_CN\n"
"MIME-Version: 1.0\n"
"Content-Type: text/plain; charset=utf-8\n"
"Content-Transfer-Encoding: 8bit\n"
"Plural-Forms: nplurals=1; plural=0;\n"
"Generated-By: Babel 2.17.0\n"
#: ../../tutorials/multi_npu_qwen3_moe.md:1
msgid "Multi-NPU (Qwen3-30B-A3B)"
msgstr "多NPU（Qwen3-30B-A3B）"
#: ../../tutorials/multi_npu_qwen3_moe.md:3
msgid "Run vllm-ascend on Multi-NPU with Qwen3 MoE"
msgstr "在多NPU上运行带有Qwen3 MoE的vllm-ascend"
#: ../../tutorials/multi_npu_qwen3_moe.md:5
msgid "Run docker container:"
msgstr "运行 docker 容器："
#: ../../tutorials/multi_npu_qwen3_moe.md:30
msgid "Setup environment variables:"
msgstr "设置环境变量："
#: ../../tutorials/multi_npu_qwen3_moe.md:40
msgid "Online Inference on Multi-NPU"
msgstr "多NPU的在线推理"
#: ../../tutorials/multi_npu_qwen3_moe.md:42
msgid "Run the following script to start the vLLM server on Multi-NPU:"
msgstr "运行以下脚本以在多NPU上启动 vLLM 服务器："
#: ../../tutorials/multi_npu_qwen3_moe.md:44
msgid ""
"For an Atlas A2 with 64GB of NPU card memory, tensor-parallel-size should be"
" at least 2, and for 32GB of memory, tensor-parallel-size should be at least"
" 4."
msgstr ""
"对于拥有64GB NPU卡内存的Atlas A2，tensor-parallel-size 至少应为2；对于32GB内存的NPU卡，tensor-"
"parallel-size 至少应为4。"
#: ../../tutorials/multi_npu_qwen3_moe.md:50
msgid ""
"Once your server is started, you can query the model with input prompts"
msgstr "一旦服务器启动，就可以通过输入提示词来查询模型。"
#: ../../tutorials/multi_npu_qwen3_moe.md:65
msgid "Offline Inference on Multi-NPU"
msgstr "多NPU离线推理"
#: ../../tutorials/multi_npu_qwen3_moe.md:67
msgid "Run the following script to execute offline inference on multi-NPU:"
msgstr "运行以下脚本以在多NPU上执行离线推理："
#: ../../tutorials/multi_npu_qwen3_moe.md:104
msgid "If you run this script successfully, you can see the info shown below:"
msgstr "如果你成功运行此脚本，你可以看到如下所示的信息："

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# SOME DESCRIPTIVE TITLE.
# Copyright (C) 2025, vllm-ascend team
# This file is distributed under the same license as the vllm-ascend
# package.
# FIRST AUTHOR <EMAIL@ADDRESS>, 2025.
#
#, fuzzy
msgid ""
msgstr ""
"Project-Id-Version: vllm-ascend\n"
"Report-Msgid-Bugs-To: \n"
"POT-Creation-Date: 2025-07-18 09:01+0800\n"
"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
"Language-Team: zh_CN <LL@li.org>\n"
"Language: zh_CN\n"
"MIME-Version: 1.0\n"
"Content-Type: text/plain; charset=utf-8\n"
"Content-Transfer-Encoding: 8bit\n"
"Plural-Forms: nplurals=1; plural=0;\n"
"Generated-By: Babel 2.17.0\n"
#: ../../tutorials/single_node_300i.md:1
msgid "Single Node (Atlas 300I series)"
msgstr "单节点（Atlas 300I 系列）"
#: ../../tutorials/single_node_300i.md:4
msgid ""
"This Atlas 300I series is currently experimental. In future versions, there "
"may be behavioral changes around model coverage, performance improvement."
msgstr "Atlas 300I 系列目前处于实验阶段。在未来的版本中，模型覆盖范围和性能提升方面可能会有行为上的变化。"
#: ../../tutorials/single_node_300i.md:7
msgid "Run vLLM on Altlas 300I series"
msgstr "在 Altlas 300I 系列上运行 vLLM"
#: ../../tutorials/single_node_300i.md:9
msgid "Run docker container:"
msgstr "运行 docker 容器："
#: ../../tutorials/single_node_300i.md:38
msgid "Setup environment variables:"
msgstr "设置环境变量："
#: ../../tutorials/single_node_300i.md:48
msgid "Online Inference on NPU"
msgstr "在NPU上进行在线推理"
#: ../../tutorials/single_node_300i.md:50
msgid ""
"Run the following script to start the vLLM server on NPU(Qwen3-0.6B:1 card, "
"Qwen2.5-7B-Instruct:2 cards, Pangu-Pro-MoE-72B: 8 cards):"
msgstr ""
"运行以下脚本，在 NPU 上启动 vLLM 服务器（Qwen3-0.6B：1 张卡，Qwen2.5-7B-Instruct：2 张卡，Pangu-"
"Pro-MoE-72B：8 张卡）："
#: ../../tutorials/single_node_300i.md
msgid "Qwen3-0.6B"
msgstr "Qwen3-0.6B"
#: ../../tutorials/single_node_300i.md:59
#: ../../tutorials/single_node_300i.md:89
#: ../../tutorials/single_node_300i.md:126
msgid "Run the following command to start the vLLM server:"
msgstr "运行以下命令以启动 vLLM 服务器："
#: ../../tutorials/single_node_300i.md:70
#: ../../tutorials/single_node_300i.md:100
#: ../../tutorials/single_node_300i.md:140
msgid ""
"Once your server is started, you can query the model with input prompts"
msgstr "一旦服务器启动，就可以通过输入提示词来查询模型。"
#: ../../tutorials/single_node_300i.md
msgid "Qwen/Qwen2.5-7B-Instruct"
msgstr "Qwen/Qwen2.5-7B-Instruct"
#: ../../tutorials/single_node_300i.md
msgid "Pangu-Pro-MoE-72B"
msgstr "Pangu-Pro-MoE-72B"
#: ../../tutorials/single_node_300i.md:119
#: ../../tutorials/single_node_300i.md:257
msgid "Download the model:"
msgstr "下载该模型："
#: ../../tutorials/single_node_300i.md:157
msgid "If you run this script successfully, you can see the results."
msgstr "如果你成功运行此脚本，你就可以看到结果。"
#: ../../tutorials/single_node_300i.md:159
msgid "Offline Inference"
msgstr "离线推理"
#: ../../tutorials/single_node_300i.md:161
msgid ""
"Run the following script (`example.py`) to execute offline inference on NPU:"
msgstr "运行以下脚本（`example.py`）以在 NPU 上执行离线推理："
#: ../../tutorials/single_node_300i.md
msgid "Qwen2.5-7B-Instruct"
msgstr "Qwen2.5-7B-指令版"
#: ../../tutorials/single_node_300i.md:320
msgid "Run script:"
msgstr "运行脚本："
#: ../../tutorials/single_node_300i.md:325
msgid "If you run this script successfully, you can see the info shown below:"
msgstr "如果你成功运行此脚本，你可以看到如下所示的信息："

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# SOME DESCRIPTIVE TITLE.
# Copyright (C) 2025, vllm-ascend team
# This file is distributed under the same license as the vllm-ascend
# package.
# FIRST AUTHOR <EMAIL@ADDRESS>, 2025.
#
#, fuzzy
msgid ""
msgstr ""
"Project-Id-Version: vllm-ascend\n"
"Report-Msgid-Bugs-To: \n"
"POT-Creation-Date: 2025-07-18 09:01+0800\n"
"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
"Language-Team: zh_CN <LL@li.org>\n"
"Language: zh_CN\n"
"MIME-Version: 1.0\n"
"Content-Type: text/plain; charset=utf-8\n"
"Content-Transfer-Encoding: 8bit\n"
"Plural-Forms: nplurals=1; plural=0;\n"
"Generated-By: Babel 2.17.0\n"
#: ../../tutorials/single_npu.md:1
msgid "Single NPU (Qwen3 8B)"
msgstr "单个NPU（Qwen3 8B）"
#: ../../tutorials/single_npu.md:3
msgid "Run vllm-ascend on Single NPU"
msgstr "在单个 NPU 上运行 vllm-ascend"
#: ../../tutorials/single_npu.md:5
msgid "Offline Inference on Single NPU"
msgstr "在单个NPU上进行离线推理"
#: ../../tutorials/single_npu.md:7
msgid "Run docker container:"
msgstr "运行 docker 容器："
#: ../../tutorials/single_npu.md:29
msgid "Setup environment variables:"
msgstr "设置环境变量："
#: ../../tutorials/single_npu.md:40
msgid ""
"`max_split_size_mb` prevents the native allocator from splitting blocks "
"larger than this size (in MB). This can reduce fragmentation and may allow "
"some borderline workloads to complete without running out of memory. You can"
" find more details "
"[<u>here</u>](https://www.hiascend.com/document/detail/zh/CANNCommunityEdition/800alpha003/apiref/envref/envref_07_0061.html)."
msgstr ""
"`max_split_size_mb` 防止本地分配器拆分超过此大小（以 MB "
"为单位）的内存块。这可以减少内存碎片，并且可能让一些边缘情况下的工作负载顺利完成而不会耗尽内存。你可以在[<u>这里</u>](https://www.hiascend.com/document/detail/zh/CANNCommunityEdition/800alpha003/apiref/envref/envref_07_0061.html)找到更多详细信息。"
#: ../../tutorials/single_npu.md:43
msgid "Run the following script to execute offline inference on a single NPU:"
msgstr "运行以下脚本以在单个 NPU 上执行离线推理："
#: ../../tutorials/single_npu.md
msgid "Graph Mode"
msgstr "图模式"
#: ../../tutorials/single_npu.md
msgid "Eager Mode"
msgstr "即时模式"
#: ../../tutorials/single_npu.md:98
msgid "If you run this script successfully, you can see the info shown below:"
msgstr "如果你成功运行此脚本，你可以看到如下所示的信息："
#: ../../tutorials/single_npu.md:105
msgid "Online Serving on Single NPU"
msgstr "单个 NPU 上的在线服务"
#: ../../tutorials/single_npu.md:107
msgid "Run docker container to start the vLLM server on a single NPU:"
msgstr "运行 docker 容器，在单个 NPU 上启动 vLLM 服务器："
#: ../../tutorials/single_npu.md:163
msgid ""
"Add `--max_model_len` option to avoid ValueError that the Qwen2.5-7B model's"
" max seq len (32768) is larger than the maximum number of tokens that can be"
" stored in KV cache (26240). This will differ with different NPU series base"
" on the HBM size. Please modify the value according to a suitable value for "
"your NPU series."
msgstr ""
"添加 `--max_model_len` 选项，以避免出现 Qwen2.5-7B 模型的最大序列长度（32768）大于 KV 缓存能存储的最大 "
"token 数（26240）时的 ValueError。不同 NPU 系列由于 HBM 容量不同，该值也会有所不同。请根据您的 NPU "
"系列，修改为合适的数值。"
#: ../../tutorials/single_npu.md:166
msgid "If your service start successfully, you can see the info shown below:"
msgstr "如果你的服务启动成功，你会看到如下所示的信息："
#: ../../tutorials/single_npu.md:174
msgid ""
"Once your server is started, you can query the model with input prompts:"
msgstr "一旦你的服务器启动，你可以通过输入提示词来查询模型："
#: ../../tutorials/single_npu.md:187
msgid ""
"If you query the server successfully, you can see the info shown below "
"(client):"
msgstr "如果你成功查询了服务器，你可以看到如下所示的信息（客户端）："
#: ../../tutorials/single_npu.md:193
msgid "Logs of the vllm server:"
msgstr "vllm 服务器的日志："

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# SOME DESCRIPTIVE TITLE.
# Copyright (C) 2025, vllm-ascend team
# This file is distributed under the same license as the vllm-ascend
# package.
# FIRST AUTHOR <EMAIL@ADDRESS>, 2025.
#
#, fuzzy
msgid ""
msgstr ""
"Project-Id-Version: vllm-ascend\n"
"Report-Msgid-Bugs-To: \n"
"POT-Creation-Date: 2025-07-18 09:01+0800\n"
"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
"Language-Team: zh_CN <LL@li.org>\n"
"Language: zh_CN\n"
"MIME-Version: 1.0\n"
"Content-Type: text/plain; charset=utf-8\n"
"Content-Transfer-Encoding: 8bit\n"
"Plural-Forms: nplurals=1; plural=0;\n"
"Generated-By: Babel 2.17.0\n"
#: ../../tutorials/single_npu_audio.md:1
msgid "Single NPU (Qwen2-Audio 7B)"
msgstr "单个 NPU（Qwen2-Audio 7B）"
#: ../../tutorials/single_npu_audio.md:3
msgid "Run vllm-ascend on Single NPU"
msgstr "在单个 NPU 上运行 vllm-ascend"
#: ../../tutorials/single_npu_audio.md:5
msgid "Offline Inference on Single NPU"
msgstr "在单个NPU上进行离线推理"
#: ../../tutorials/single_npu_audio.md:7
msgid "Run docker container:"
msgstr "运行 docker 容器："
#: ../../tutorials/single_npu_audio.md:29
msgid "Setup environment variables:"
msgstr "设置环境变量："
#: ../../tutorials/single_npu_audio.md:40
msgid ""
"`max_split_size_mb` prevents the native allocator from splitting blocks "
"larger than this size (in MB). This can reduce fragmentation and may allow "
"some borderline workloads to complete without running out of memory. You can"
" find more details "
"[<u>here</u>](https://www.hiascend.com/document/detail/zh/CANNCommunityEdition/800alpha003/apiref/envref/envref_07_0061.html)."
msgstr ""
"`max_split_size_mb` 防止本地分配器拆分超过此大小（以 MB "
"为单位）的内存块。这可以减少内存碎片，并且可能让一些边缘情况下的工作负载顺利完成而不会耗尽内存。你可以在[<u>这里</u>](https://www.hiascend.com/document/detail/zh/CANNCommunityEdition/800alpha003/apiref/envref/envref_07_0061.html)找到更多详细信息。"
#: ../../tutorials/single_npu_audio.md:43
msgid "Install packages required for audio processing:"
msgstr "安装音频处理所需的软件包："
#: ../../tutorials/single_npu_audio.md:50
msgid "Run the following script to execute offline inference on a single NPU:"
msgstr "运行以下脚本以在单个 NPU 上执行离线推理："
#: ../../tutorials/single_npu_audio.md:114
msgid "If you run this script successfully, you can see the info shown below:"
msgstr "如果你成功运行此脚本，你可以看到如下所示的信息："
#: ../../tutorials/single_npu_audio.md:120
msgid "Online Serving on Single NPU"
msgstr "单个 NPU 上的在线服务"
#: ../../tutorials/single_npu_audio.md:122
msgid ""
"Currently, vllm's OpenAI-compatible server doesn't support audio inputs, "
"find more details [<u>here</u>](https://github.com/vllm-"
"project/vllm/issues/19977)."
msgstr ""
"目前，vllm 的兼容 OpenAI 的服务器不支持音频输入，更多详情请查看[<u>这里</u>](https://github.com/vllm-"
"project/vllm/issues/19977)。"

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# SOME DESCRIPTIVE TITLE.
# Copyright (C) 2025, vllm-ascend team
# This file is distributed under the same license as the vllm-ascend
# package.
# FIRST AUTHOR <EMAIL@ADDRESS>, 2025.
#
#, fuzzy
msgid ""
msgstr ""
"Project-Id-Version: vllm-ascend\n"
"Report-Msgid-Bugs-To: \n"
"POT-Creation-Date: 2025-07-18 09:01+0800\n"
"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
"Language-Team: zh_CN <LL@li.org>\n"
"Language: zh_CN\n"
"MIME-Version: 1.0\n"
"Content-Type: text/plain; charset=utf-8\n"
"Content-Transfer-Encoding: 8bit\n"
"Plural-Forms: nplurals=1; plural=0;\n"
"Generated-By: Babel 2.17.0\n"
#: ../../tutorials/single_npu_multimodal.md:1
msgid "Single NPU (Qwen2.5-VL 7B)"
msgstr "单个NPU（Qwen2.5-VL 7B）"
#: ../../tutorials/single_npu_multimodal.md:3
msgid "Run vllm-ascend on Single NPU"
msgstr "在单个 NPU 上运行 vllm-ascend"
#: ../../tutorials/single_npu_multimodal.md:5
msgid "Offline Inference on Single NPU"
msgstr "在单个NPU上进行离线推理"
#: ../../tutorials/single_npu_multimodal.md:7
msgid "Run docker container:"
msgstr "运行 docker 容器："
#: ../../tutorials/single_npu_multimodal.md:29
msgid "Setup environment variables:"
msgstr "设置环境变量："
#: ../../tutorials/single_npu_multimodal.md:40
msgid ""
"`max_split_size_mb` prevents the native allocator from splitting blocks "
"larger than this size (in MB). This can reduce fragmentation and may allow "
"some borderline workloads to complete without running out of memory. You can"
" find more details "
"[<u>here</u>](https://www.hiascend.com/document/detail/zh/CANNCommunityEdition/800alpha003/apiref/envref/envref_07_0061.html)."
msgstr ""
"`max_split_size_mb` 防止本地分配器拆分超过此大小（以 MB "
"为单位）的内存块。这可以减少内存碎片，并且可能让一些边缘情况下的工作负载顺利完成而不会耗尽内存。你可以在[<u>这里</u>](https://www.hiascend.com/document/detail/zh/CANNCommunityEdition/800alpha003/apiref/envref/envref_07_0061.html)找到更多详细信息。"
#: ../../tutorials/single_npu_multimodal.md:43
msgid "Run the following script to execute offline inference on a single NPU:"
msgstr "运行以下脚本以在单个 NPU 上执行离线推理："
#: ../../tutorials/single_npu_multimodal.md:109
msgid "If you run this script successfully, you can see the info shown below:"
msgstr "如果你成功运行此脚本，你可以看到如下所示的信息："
#: ../../tutorials/single_npu_multimodal.md:121
msgid "Online Serving on Single NPU"
msgstr "单个 NPU 上的在线服务"
#: ../../tutorials/single_npu_multimodal.md:123
msgid "Run docker container to start the vLLM server on a single NPU:"
msgstr "运行 docker 容器，在单个 NPU 上启动 vLLM 服务器："
#: ../../tutorials/single_npu_multimodal.md:154
msgid ""
"Add `--max_model_len` option to avoid ValueError that the "
"Qwen2.5-VL-7B-Instruct model's max seq len (128000) is larger than the "
"maximum number of tokens that can be stored in KV cache. This will differ "
"with different NPU series base on the HBM size. Please modify the value "
"according to a suitable value for your NPU series."
msgstr ""
"新增 `--max_model_len` 选项，以避免出现 ValueError，即 Qwen2.5-VL-7B-Instruct "
"模型的最大序列长度（128000）大于 KV 缓存可存储的最大 token 数。该数值会根据不同 NPU 系列的 HBM 大小而不同。请根据你的 NPU"
" 系列，将该值设置为合适的数值。"
#: ../../tutorials/single_npu_multimodal.md:157
msgid "If your service start successfully, you can see the info shown below:"
msgstr "如果你的服务启动成功，你会看到如下所示的信息："
#: ../../tutorials/single_npu_multimodal.md:165
msgid ""
"Once your server is started, you can query the model with input prompts:"
msgstr "一旦你的服务器启动，你可以通过输入提示词来查询模型："
#: ../../tutorials/single_npu_multimodal.md:182
msgid ""
"If you query the server successfully, you can see the info shown below "
"(client):"
msgstr "如果你成功查询了服务器，你可以看到如下所示的信息（客户端）："
#: ../../tutorials/single_npu_multimodal.md:188
msgid "Logs of the vllm server:"
msgstr "vllm 服务器的日志："

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# SOME DESCRIPTIVE TITLE.
# Copyright (C) 2025, vllm-ascend team
# This file is distributed under the same license as the vllm-ascend
# package.
# FIRST AUTHOR <EMAIL@ADDRESS>, 2025.
#
#, fuzzy
msgid ""
msgstr ""
"Project-Id-Version: vllm-ascend\n"
"Report-Msgid-Bugs-To: \n"
"POT-Creation-Date: 2025-07-18 09:01+0800\n"
"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
"Language-Team: zh_CN <LL@li.org>\n"
"Language: zh_CN\n"
"MIME-Version: 1.0\n"
"Content-Type: text/plain; charset=utf-8\n"
"Content-Transfer-Encoding: 8bit\n"
"Plural-Forms: nplurals=1; plural=0;\n"
"Generated-By: Babel 2.17.0\n"
#: ../../tutorials/single_npu_qwen3_embedding.md:1
msgid "Single NPU (Qwen3-Embedding-8B)"
msgstr "单个NPU（Qwen3-Embedding-8B）"
#: ../../tutorials/single_npu_qwen3_embedding.md:3
msgid ""
"The Qwen3 Embedding model series is the latest proprietary model of the Qwen"
" family, specifically designed for text embedding and ranking tasks. "
"Building upon the dense foundational models of the Qwen3 series, it provides"
" a comprehensive range of text embeddings and reranking models in various "
"sizes (0.6B, 4B, and 8B). This guide describes how to run the model with "
"vLLM Ascend. Note that only 0.9.2rc1 and higher versions of vLLM Ascend "
"support the model."
msgstr ""
"Qwen3 Embedding 模型系列是 Qwen 家族最新的专有模型，专为文本嵌入和排序任务设计。在 Qwen3 "
"系列的密集基础模型之上，它提供了多种尺寸（0.6B、4B 和 8B）的文本嵌入与重排序模型。本指南介绍如何使用 vLLM Ascend "
"运行该模型。请注意，只有 vLLM Ascend 0.9.2rc1 及更高版本才支持该模型。"
#: ../../tutorials/single_npu_qwen3_embedding.md:5
msgid "Run docker container"
msgstr "运行 docker 容器"
#: ../../tutorials/single_npu_qwen3_embedding.md:7
msgid ""
"Take Qwen3-Embedding-8B model as an example, first run the docker container "
"with the following command:"
msgstr "以 Qwen3-Embedding-8B 模型为例，首先使用以下命令运行 docker 容器："
#: ../../tutorials/single_npu_qwen3_embedding.md:29
msgid "Setup environment variables:"
msgstr "设置环境变量："
#: ../../tutorials/single_npu_qwen3_embedding.md:39
msgid "Online Inference"
msgstr "在线推理"
#: ../../tutorials/single_npu_qwen3_embedding.md:45
msgid ""
"Once your server is started, you can query the model with input prompts"
msgstr "一旦服务器启动，就可以通过输入提示词来查询模型。"
#: ../../tutorials/single_npu_qwen3_embedding.md:56
msgid "Offline Inference"
msgstr "离线推理"
#: ../../tutorials/single_npu_qwen3_embedding.md:92
msgid "If you run this script successfully, you can see the info shown below:"
msgstr "如果你成功运行此脚本，你可以看到如下所示的信息："

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# SOME DESCRIPTIVE TITLE.
# Copyright (C) 2025, vllm-ascend team
# This file is distributed under the same license as the vllm-ascend
# package.
# FIRST AUTHOR <EMAIL@ADDRESS>, 2025.
#
#, fuzzy
msgid ""
msgstr ""
"Project-Id-Version: vllm-ascend\n"
"Report-Msgid-Bugs-To: \n"
"POT-Creation-Date: 2025-07-18 09:01+0800\n"
"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
"Language-Team: zh_CN <LL@li.org>\n"
"Language: zh_CN\n"
"MIME-Version: 1.0\n"
"Content-Type: text/plain; charset=utf-8\n"
"Content-Transfer-Encoding: 8bit\n"
"Plural-Forms: nplurals=1; plural=0;\n"
"Generated-By: Babel 2.17.0\n"
#: ../../user_guide/configuration/additional_config.md:1
msgid "Additional Configuration"
msgstr "附加配置"
#: ../../user_guide/configuration/additional_config.md:3
msgid ""
"additional configuration is a mechanism provided by vLLM to allow plugins to"
" control inner behavior by their own. vLLM Ascend uses this mechanism to "
"make the project more flexible."
msgstr "额外配置是 vLLM 提供的一种机制，允许插件自行控制内部行为。vLLM Ascend 利用这种机制使项目更加灵活。"
#: ../../user_guide/configuration/additional_config.md:5
msgid "How to use"
msgstr "如何使用"
#: ../../user_guide/configuration/additional_config.md:7
msgid ""
"With either online mode or offline mode, users can use additional "
"configuration. Take Qwen3 as an example:"
msgstr "无论是在线模式还是离线模式，用户都可以使用额外的配置。以 Qwen3 为例："
#: ../../user_guide/configuration/additional_config.md:9
msgid "**Online mode**:"
msgstr "**在线模式**："
#: ../../user_guide/configuration/additional_config.md:15
msgid "**Offline mode**:"
msgstr "**离线模式**："
#: ../../user_guide/configuration/additional_config.md:23
msgid "Configuration options"
msgstr "配置选项"
#: ../../user_guide/configuration/additional_config.md:25
msgid ""
"The following table lists the additional configuration options available in "
"vLLM Ascend:"
msgstr "下表列出了 vLLM Ascend 中可用的其他配置选项："
#: ../../user_guide/configuration/additional_config.md
msgid "Name"
msgstr "名称"
#: ../../user_guide/configuration/additional_config.md
msgid "Type"
msgstr "类型"
#: ../../user_guide/configuration/additional_config.md
msgid "Default"
msgstr "默认"
#: ../../user_guide/configuration/additional_config.md
msgid "Description"
msgstr "描述"
#: ../../user_guide/configuration/additional_config.md
msgid "`torchair_graph_config`"
msgstr "`torchair_graph_config`"
#: ../../user_guide/configuration/additional_config.md
msgid "dict"
msgstr "dict"
#: ../../user_guide/configuration/additional_config.md
#, python-brace-format
msgid "`{}`"
msgstr "`{}`"
#: ../../user_guide/configuration/additional_config.md
msgid "The config options for torchair graph mode"
msgstr "torchair 图模式的配置选项"
#: ../../user_guide/configuration/additional_config.md
msgid "`ascend_scheduler_config`"
msgstr "`ascend_scheduler_config`"
#: ../../user_guide/configuration/additional_config.md
msgid "The config options for ascend scheduler"
msgstr "ascend 调度器的配置选项"
#: ../../user_guide/configuration/additional_config.md
msgid "`expert_tensor_parallel_size`"
msgstr "`expert_tensor_parallel_size`"
#: ../../user_guide/configuration/additional_config.md
msgid "str"
msgstr "str"
#: ../../user_guide/configuration/additional_config.md
msgid "`0`"
msgstr "`0`"
#: ../../user_guide/configuration/additional_config.md
msgid "Expert tensor parallel size the model to use."
msgstr "专家张量并行的模型大小设置。"
#: ../../user_guide/configuration/additional_config.md
msgid "`refresh`"
msgstr "`刷新`"
#: ../../user_guide/configuration/additional_config.md
msgid "bool"
msgstr "bool"
#: ../../user_guide/configuration/additional_config.md
msgid "`false`"
msgstr "`false`"
#: ../../user_guide/configuration/additional_config.md
msgid ""
"Whether to refresh global ascend config content. This value is usually used "
"by rlhf or ut/e2e test case."
msgstr "是否刷新全局 ascend 配置信息。此值通常由 rlhf 或 ut/e2e 测试用例使用。"
#: ../../user_guide/configuration/additional_config.md
msgid "`expert_map_path`"
msgstr "`expert_map_path`"
#: ../../user_guide/configuration/additional_config.md
msgid "`None`"
msgstr "`None`"
#: ../../user_guide/configuration/additional_config.md
msgid ""
"When using expert load balancing for the MOE model, an expert map path needs"
" to be passed in."
msgstr "在为MOE模型使用专家负载均衡时，需要传入专家映射路径。"
#: ../../user_guide/configuration/additional_config.md
msgid "`chunked_prefill_for_mla`"
msgstr "`chunked_prefill_for_mla`"
#: ../../user_guide/configuration/additional_config.md
msgid "`False`"
msgstr "`False`"
#: ../../user_guide/configuration/additional_config.md
msgid "Whether to enable the fused operator-like chunked_prefill."
msgstr "是否启用类似算子融合的 chunked_prefill 功能。"
#: ../../user_guide/configuration/additional_config.md
msgid "`kv_cache_dtype`"
msgstr "`kv_cache_dtype`"
#: ../../user_guide/configuration/additional_config.md
msgid ""
"When using the kv cache quantization method, kv cache dtype needs to be set,"
" currently only int8 is supported."
msgstr "当使用kv缓存量化方法时，需要设置kv缓存的数据类型，目前仅支持int8。"
#: ../../user_guide/configuration/additional_config.md:37
msgid "The details of each config option are as follows:"
msgstr "每个配置选项的详细信息如下："
#: ../../user_guide/configuration/additional_config.md:39
msgid "**torchair_graph_config**"
msgstr "**torchair_graph_config**"
#: ../../user_guide/configuration/additional_config.md
msgid "`enabled`"
msgstr "`启用`"
#: ../../user_guide/configuration/additional_config.md
msgid ""
"Whether to enable torchair graph mode. Currently only DeepSeek series models"
" and PanguProMoE are supported to use torchair graph mode"
msgstr "是否启用 torchair 图模式。目前仅支持 DeepSeek 系列模型和 PanguProMoE 使用 torchair 图模式。"
#: ../../user_guide/configuration/additional_config.md
msgid "`enable_multistream_mla`"
msgstr "`enable_multistream_mla`"
#: ../../user_guide/configuration/additional_config.md
msgid ""
"Whether to put vector ops of MLA to another stream. This option only takes "
"effects on models using MLA (e.g., DeepSeek)."
msgstr "是否将MLA的向量操作放到另一个流中。此选项仅对使用MLA的模型（例如，DeepSeek）有效。"
#: ../../user_guide/configuration/additional_config.md
msgid "`enable_multistream_moe`"
msgstr "`enable_multistream_moe`"
#: ../../user_guide/configuration/additional_config.md
msgid ""
"Whether to enable multistream shared expert. This option only takes effects "
"on DeepSeek moe models."
msgstr "是否启用多流共享专家功能。此选项仅对 DeepSeek MoE 模型生效。"
#: ../../user_guide/configuration/additional_config.md
msgid "`enable_view_optimize`"
msgstr "`enable_view_optimize` （启用视图优化）"
#: ../../user_guide/configuration/additional_config.md
msgid "`True`"
msgstr "`True`"
#: ../../user_guide/configuration/additional_config.md
msgid "Whether to enable torchair view optimization"
msgstr "是否启用torchair视图优化"
#: ../../user_guide/configuration/additional_config.md
msgid "`use_cached_graph`"
msgstr "`use_cached_graph`"
#: ../../user_guide/configuration/additional_config.md
msgid "Whether to use cached graph"
msgstr "是否使用缓存的图"
#: ../../user_guide/configuration/additional_config.md
msgid "`graph_batch_sizes`"
msgstr "`graph_batch_sizes`"
#: ../../user_guide/configuration/additional_config.md
msgid "list[int]"
msgstr "list[int]"
#: ../../user_guide/configuration/additional_config.md
msgid "`[]`"
msgstr "`[]`"
#: ../../user_guide/configuration/additional_config.md
msgid "The batch size for torchair graph cache"
msgstr "torchair 图缓存的批量大小"
#: ../../user_guide/configuration/additional_config.md
msgid "`graph_batch_sizes_init`"
msgstr "`graph_batch_sizes_init`"
#: ../../user_guide/configuration/additional_config.md
msgid "Init graph batch size dynamically if `graph_batch_sizes` is empty"
msgstr "如果 `graph_batch_sizes` 为空，则动态初始化图批大小"
#: ../../user_guide/configuration/additional_config.md
msgid "`enable_kv_nz`"
msgstr "`enable_kv_nz`"
#: ../../user_guide/configuration/additional_config.md
msgid ""
"Whether to enable kvcache NZ layout. This option only takes effects on "
"models using MLA (e.g., DeepSeek)."
msgstr "是否启用 kvcache NZ 布局。此选项仅对使用 MLA 的模型（例如 DeepSeek）生效。"
#: ../../user_guide/configuration/additional_config.md:52
msgid "**ascend_scheduler_config**"
msgstr "**ascend_scheduler_config**"
#: ../../user_guide/configuration/additional_config.md
msgid "Whether to enable ascend scheduler for V1 engine"
msgstr "是否为 V1 引擎启用 ascend 调度器"
#: ../../user_guide/configuration/additional_config.md:58
msgid ""
"ascend_scheduler_config also support the options from [vllm scheduler "
"config](https://docs.vllm.ai/en/stable/api/vllm/config.html#vllm.config.SchedulerConfig)."
" For example, you can add `enable_chunked_prefill: True` to "
"ascend_scheduler_config as well."
msgstr ""
"ascend_scheduler_config 也支持来自 [vllm scheduler "
"config](https://docs.vllm.ai/en/stable/api/vllm/config.html#vllm.config.SchedulerConfig)"
" 的选项。例如，你也可以在 ascend_scheduler_config 中添加 `enable_chunked_prefill: True`。"
#: ../../user_guide/configuration/additional_config.md:60
msgid "Example"
msgstr "示例"
#: ../../user_guide/configuration/additional_config.md:62
msgid "An example of additional configuration is as follows:"
msgstr "以下是额外配置的一个示例："

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@@ -0,0 +1,28 @@
# Translations template for PROJECT.
# Copyright (C) 2025 ORGANIZATION
# This file is distributed under the same license as the PROJECT project.
# FIRST AUTHOR <EMAIL@ADDRESS>, 2025.
#
#, fuzzy
msgid ""
msgstr ""
"Project-Id-Version: PROJECT VERSION\n"
"Report-Msgid-Bugs-To: EMAIL@ADDRESS\n"
"POT-Creation-Date: 2025-07-18 09:01+0800\n"
"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
"Language-Team: LANGUAGE <LL@li.org>\n"
"MIME-Version: 1.0\n"
"Content-Type: text/plain; charset=utf-8\n"
"Content-Transfer-Encoding: 8bit\n"
"Generated-By: Babel 2.17.0\n"
#: ../../user_guide/configuration/env_vars.md:1
msgid "Environment Variables"
msgstr "环境变量"
#: ../../user_guide/configuration/env_vars.md:3
msgid ""
"vllm-ascend uses the following environment variables to configure the "
"system:"
msgstr "vllm-ascend 使用以下环境变量来配置系统："

30
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@@ -0,0 +1,30 @@
# SOME DESCRIPTIVE TITLE.
# Copyright (C) 2025, vllm-ascend team
# This file is distributed under the same license as the vllm-ascend
# package.
# FIRST AUTHOR <EMAIL@ADDRESS>, 2025.
#
#, fuzzy
msgid ""
msgstr ""
"Project-Id-Version: vllm-ascend\n"
"Report-Msgid-Bugs-To: \n"
"POT-Creation-Date: 2025-07-18 09:01+0800\n"
"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
"Language-Team: zh_CN <LL@li.org>\n"
"Language: zh_CN\n"
"MIME-Version: 1.0\n"
"Content-Type: text/plain; charset=utf-8\n"
"Content-Transfer-Encoding: 8bit\n"
"Plural-Forms: nplurals=1; plural=0;\n"
"Generated-By: Babel 2.17.0\n"
#: ../../user_guide/configuration/index.md:1
#: ../../user_guide/configuration/index.md:5
msgid "Configuration Guide"
msgstr "配置指南"
#: ../../user_guide/configuration/index.md:3
msgid "This section provides a detailed configuration guide of vLLM Ascend."
msgstr "本节提供了 vLLM Ascend 的详细配置指南。"

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# Translations template for PROJECT.
# Copyright (C) 2025 ORGANIZATION
# This file is distributed under the same license as the PROJECT project.
# FIRST AUTHOR <EMAIL@ADDRESS>, 2025.
#
#, fuzzy
msgid ""
msgstr ""
"Project-Id-Version: PROJECT VERSION\n"
"Report-Msgid-Bugs-To: EMAIL@ADDRESS\n"
"POT-Creation-Date: 2025-07-18 09:01+0800\n"
"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
"Language-Team: LANGUAGE <LL@li.org>\n"
"MIME-Version: 1.0\n"
"Content-Type: text/plain; charset=utf-8\n"
"Content-Transfer-Encoding: 8bit\n"
"Generated-By: Babel 2.17.0\n"
#: ../../user_guide/feature_guide/graph_mode.md:1
msgid "Graph Mode Guide"
msgstr "图模式指南"
#: ../../user_guide/feature_guide/graph_mode.md:4
msgid ""
"This feature is currently experimental. In future versions, there may be "
"behavioral changes around configuration, coverage, performance improvement."
msgstr "此功能目前为实验性功能。在未来的版本中，配置、覆盖率和性能改进等方面的行为可能会有变化。"
#: ../../user_guide/feature_guide/graph_mode.md:7
msgid ""
"This guide provides instructions for using Ascend Graph Mode with vLLM "
"Ascend. Please note that graph mode is only available on V1 Engine. And only"
" Qwen, DeepSeek series models are well tested from 0.9.0rc1. We'll make it "
"stable and generalize in the next release."
msgstr ""
"本指南提供了在 vLLM Ascend 上使用 Ascend 图模式的操作说明。请注意，图模式仅在 V1 引擎上可用，并且从 0.9.0rc1 起，仅对"
" Qwen、DeepSeek 系列模型进行了充分测试。我们将在下一个版本中使其更加稳定和通用。"
#: ../../user_guide/feature_guide/graph_mode.md:9
msgid "Getting Started"
msgstr "快速入门"
#: ../../user_guide/feature_guide/graph_mode.md:11
msgid ""
"From v0.9.1rc1 with V1 Engine, vLLM Ascend will run models in graph mode by "
"default to keep the same behavior with vLLM. If you hit any issues, please "
"feel free to open an issue on GitHub and fallback to eager mode temporarily "
"by set `enforce_eager=True` when initializing the model."
msgstr ""
"从 v0.9.1rc1 版本起，使用 V1 引擎时，vLLM Ascend 默认将在图模式下运行模型，以保持与 vLLM "
"同样的行为。如果遇到任何问题，欢迎在 GitHub 上提交 issue，并在初始化模型时通过设置 `enforce_eager=True` 临时切换回 "
"eager 模式。"
#: ../../user_guide/feature_guide/graph_mode.md:13
msgid "There are two kinds for graph mode supported by vLLM Ascend:"
msgstr "vLLM Ascend 支持两种图模式："
#: ../../user_guide/feature_guide/graph_mode.md:14
msgid ""
"**ACLGraph**: This is the default graph mode supported by vLLM Ascend. In "
"v0.9.1rc1, only Qwen series models are well tested."
msgstr ""
"**ACLGraph**：这是 vLLM Ascend 支持的默认图模式。在 v0.9.1rc1 版本中，只有 Qwen 系列模型得到了充分测试。"
#: ../../user_guide/feature_guide/graph_mode.md:15
msgid ""
"**TorchAirGraph**: This is the GE graph mode. In v0.9.1rc1, only DeepSeek "
"series models are supported."
msgstr "**TorchAirGraph**：这是GE图模式。在v0.9.1rc1版本中，仅支持DeepSeek系列模型。"
#: ../../user_guide/feature_guide/graph_mode.md:17
msgid "Using ACLGraph"
msgstr "使用 ACLGraph"
#: ../../user_guide/feature_guide/graph_mode.md:18
msgid ""
"ACLGraph is enabled by default. Take Qwen series models as an example, just "
"set to use V1 Engine is enough."
msgstr "ACLGraph 默认启用。以 Qwen 系列模型为例，只需设置为使用 V1 引擎即可。"
#: ../../user_guide/feature_guide/graph_mode.md:20
#: ../../user_guide/feature_guide/graph_mode.md:41
#: ../../user_guide/feature_guide/graph_mode.md:64
msgid "offline example:"
msgstr "离线示例："
#: ../../user_guide/feature_guide/graph_mode.md:31
#: ../../user_guide/feature_guide/graph_mode.md:52
#: ../../user_guide/feature_guide/graph_mode.md:74
msgid "online example:"
msgstr "在线示例："
#: ../../user_guide/feature_guide/graph_mode.md:37
msgid "Using TorchAirGraph"
msgstr "使用 TorchAirGraph"
#: ../../user_guide/feature_guide/graph_mode.md:39
msgid ""
"If you want to run DeepSeek series models with graph mode, you should use "
"[TorchAirGraph](https://www.hiascend.com/document/detail/zh/Pytorch/700/modthirdparty/torchairuseguide/torchair_0002.html)."
" In this case, additional config is required."
msgstr ""
"如果你想通过图模式运行 DeepSeek 系列模型，你应该使用 "
"[TorchAirGraph](https://www.hiascend.com/document/detail/zh/Pytorch/700/modthirdparty/torchairuseguide/torchair_0002.html)。在这种情况下，需要额外的配置。"
#: ../../user_guide/feature_guide/graph_mode.md:58
msgid ""
"You can find more detail about additional config "
"[here](../configuration/additional_config.md)."
msgstr "你可以在[这里](../configuration/additional_config.md)找到关于附加配置的更多详细信息。"
#: ../../user_guide/feature_guide/graph_mode.md:60
msgid "Fallback to Eager Mode"
msgstr "回退到 Eager 模式"
#: ../../user_guide/feature_guide/graph_mode.md:62
msgid ""
"If both `ACLGraph` and `TorchAirGraph` fail to run, you should fallback to "
"eager mode."
msgstr "如果 `ACLGraph` 和 `TorchAirGraph` 都无法运行，你应该退回到 eager 模式。"

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# SOME DESCRIPTIVE TITLE.
# Copyright (C) 2025, vllm-ascend team
# This file is distributed under the same license as the vllm-ascend
# package.
# FIRST AUTHOR <EMAIL@ADDRESS>, 2025.
#
#, fuzzy
msgid ""
msgstr ""
"Project-Id-Version: vllm-ascend\n"
"Report-Msgid-Bugs-To: \n"
"POT-Creation-Date: 2025-07-18 09:01+0800\n"
"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
"Language-Team: zh_CN <LL@li.org>\n"
"Language: zh_CN\n"
"MIME-Version: 1.0\n"
"Content-Type: text/plain; charset=utf-8\n"
"Content-Transfer-Encoding: 8bit\n"
"Plural-Forms: nplurals=1; plural=0;\n"
"Generated-By: Babel 2.17.0\n"
#: ../../user_guide/feature_guide/index.md:1
#: ../../user_guide/feature_guide/index.md:5
msgid "Feature Guide"
msgstr "功能指南"
#: ../../user_guide/feature_guide/index.md:3
msgid "This section provides a detailed usage guide of vLLM Ascend features."
msgstr "本节提供了 vLLM Ascend 功能的详细使用指南。"

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# SOME DESCRIPTIVE TITLE.
# Copyright (C) 2025, vllm-ascend team
# This file is distributed under the same license as the vllm-ascend
# package.
# FIRST AUTHOR <EMAIL@ADDRESS>, 2025.
#
#, fuzzy
msgid ""
msgstr ""
"Project-Id-Version: vllm-ascend\n"
"Report-Msgid-Bugs-To: \n"
"POT-Creation-Date: 2025-07-18 09:01+0800\n"
"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
"Language-Team: zh_CN <LL@li.org>\n"
"Language: zh_CN\n"
"MIME-Version: 1.0\n"
"Content-Type: text/plain; charset=utf-8\n"
"Content-Transfer-Encoding: 8bit\n"
"Plural-Forms: nplurals=1; plural=0;\n"
"Generated-By: Babel 2.17.0\n"
#: ../../user_guide/feature_guide/lora.md:1
msgid "LoRA Adapters Guide"
msgstr "LoRA 适配器指南"
#: ../../user_guide/feature_guide/lora.md:3
msgid ""
"Like vLLM, vllm-ascend supports LoRA as well. The usage and more details can"
" be found in [vLLM official "
"document](https://docs.vllm.ai/en/latest/features/lora.html)."
msgstr ""
"与 vLLM 类似，vllm-ascend 也支持 LoRA。用法及更多详情可参见 [vLLM "
"官方文档](https://docs.vllm.ai/en/latest/features/lora.html)。"
#: ../../user_guide/feature_guide/lora.md:5
msgid ""
"You can also refer to "
"[this](https://docs.vllm.ai/en/latest/models/supported_models.html#list-of-"
"text-only-language-models) to find which models support LoRA in vLLM."
msgstr ""
"你也可以参考[这个链接](https://docs.vllm.ai/en/latest/models/supported_models.html#list-"
"of-text-only-language-models)来查找哪些模型在 vLLM 中支持 LoRA。"
#: ../../user_guide/feature_guide/lora.md:7
msgid "Tips"
msgstr "提示"
#: ../../user_guide/feature_guide/lora.md:8
msgid ""
"If you fail to run vllm-ascend with LoRA, you may follow [this "
"instruction](https://vllm-"
"ascend.readthedocs.io/en/latest/user_guide/feature_guide/graph_mode.html#fallback-"
"to-eager-mode) to disable graph mode and try again."
msgstr ""
"如果你在使用 LoRA 运行 vllm-ascend 时失败，可以按照[此说明](https://vllm-"
"ascend.readthedocs.io/en/latest/user_guide/feature_guide/graph_mode.html#fallback-"
"to-eager-mode)禁用图模式后再重试。"

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# SOME DESCRIPTIVE TITLE.
# Copyright (C) 2025, vllm-ascend team
# This file is distributed under the same license as the vllm-ascend
# package.
# FIRST AUTHOR <EMAIL@ADDRESS>, 2025.
#
#, fuzzy
msgid ""
msgstr ""
"Project-Id-Version: vllm-ascend\n"
"Report-Msgid-Bugs-To: \n"
"POT-Creation-Date: 2025-07-18 09:01+0800\n"
"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
"Language-Team: zh_CN <LL@li.org>\n"
"Language: zh_CN\n"
"MIME-Version: 1.0\n"
"Content-Type: text/plain; charset=utf-8\n"
"Content-Transfer-Encoding: 8bit\n"
"Plural-Forms: nplurals=1; plural=0;\n"
"Generated-By: Babel 2.17.0\n"
#: ../../user_guide/feature_guide/quantization.md:1
msgid "Quantization Guide"
msgstr "量化指南"
#: ../../user_guide/feature_guide/quantization.md:3
msgid ""
"Model quantization is a technique that reduces the size and computational "
"requirements of a model by lowering the data precision of the weights and "
"activation values in the model, thereby saving the memory and improving the "
"inference speed."
msgstr "模型量化是一种通过降低模型中权重和激活值的数据精度，从而减少模型大小和计算需求的技术，这样可以节省内存并提高推理速度。"
#: ../../user_guide/feature_guide/quantization.md:5
msgid ""
"Since 0.9.0rc2 version, quantization feature is experimentally supported in "
"vLLM Ascend. Users can enable quantization feature by specifying "
"`--quantization ascend`. Currently, only Qwen, DeepSeek series models are "
"well tested. We’ll support more quantization algorithm and models in the "
"future."
msgstr ""
"自 0.9.0rc2 版本起，vLLM Ascend 实验性地支持量化特性。用户可以通过指定 `--quantization ascend` "
"启用量化功能。目前，只有 Qwen、DeepSeek 系列模型经过了充分测试。未来我们将支持更多的量化算法和模型。"
#: ../../user_guide/feature_guide/quantization.md:7
msgid "Install modelslim"
msgstr "安装 modelslim"
#: ../../user_guide/feature_guide/quantization.md:9
msgid ""
"To quantize a model, users should install "
"[ModelSlim](https://gitee.com/ascend/msit/blob/master/msmodelslim/README.md)"
" which is the Ascend compression and acceleration tool. It is an affinity-"
"based compression tool designed for acceleration, using compression as its "
"core technology and built upon the Ascend platform."
msgstr ""
"要对模型进行量化，用户应安装[ModelSlim](https://gitee.com/ascend/msit/blob/master/msmodelslim/README.md)，这是昇腾的压缩与加速工具。它是一种基于亲和性的压缩工具，专为加速设计，以压缩为核心技术，并基于昇腾平台构建。"
#: ../../user_guide/feature_guide/quantization.md:11
msgid ""
"Currently, only the specific tag [modelslim-"
"VLLM-8.1.RC1.b020_001](https://gitee.com/ascend/msit/blob/modelslim-"
"VLLM-8.1.RC1.b020_001/msmodelslim/README.md) of modelslim works with vLLM "
"Ascend. Please do not install other version until modelslim master version "
"is available for vLLM Ascend in the future."
msgstr ""
"目前，只有 modelslim 的特定标签 [modelslim-"
"VLLM-8.1.RC1.b020_001](https://gitee.com/ascend/msit/blob/modelslim-"
"VLLM-8.1.RC1.b020_001/msmodelslim/README.md) 支持 vLLM Ascend。在未来 modelslim "
"的主版本支持 vLLM Ascend 之前，请不要安装其他版本。"
#: ../../user_guide/feature_guide/quantization.md:13
msgid "Install modelslim:"
msgstr "安装 modelslim："
#: ../../user_guide/feature_guide/quantization.md:21
msgid "Quantize model"
msgstr "量化模型"
#: ../../user_guide/feature_guide/quantization.md:23
#, python-format
msgid ""
"Take [DeepSeek-V2-Lite](https://modelscope.cn/models/deepseek-"
"ai/DeepSeek-V2-Lite) as an example, you just need to download the model, and"
" then execute the convert command. The command is shown below. More info can"
" be found in modelslim doc [deepseek w8a8 dynamic quantization "
"docs](https://gitee.com/ascend/msit/blob/modelslim-"
"VLLM-8.1.RC1.b020_001/msmodelslim/example/DeepSeek/README.md#deepseek-v2-w8a8-dynamic%E9%87%8F%E5%8C%96)."
msgstr ""
"以 [DeepSeek-V2-Lite](https://modelscope.cn/models/deepseek-"
"ai/DeepSeek-V2-Lite) 为例，你只需要下载模型，然后执行转换命令。命令如下所示。更多信息可参考 modelslim 文档 "
"[deepseek w8a8 动态量化文档](https://gitee.com/ascend/msit/blob/modelslim-"
"VLLM-8.1.RC1.b020_001/msmodelslim/example/DeepSeek/README.md#deepseek-v2-w8a8-dynamic%E9%87%8F%E5%8C%96)。"
#: ../../user_guide/feature_guide/quantization.md:32
msgid ""
"You can also download the quantized model that we uploaded. Please note that"
" these weights should be used for test only. For example, "
"https://www.modelscope.cn/models/vllm-ascend/DeepSeek-V2-Lite-W8A8"
msgstr ""
"你也可以下载我们上传的量化模型。请注意，这些权重仅应用于测试。例如：https://www.modelscope.cn/models/vllm-"
"ascend/DeepSeek-V2-Lite-W8A8"
#: ../../user_guide/feature_guide/quantization.md:35
msgid "Once convert action is done, there are two important files generated."
msgstr "转换操作完成后，会生成两个重要的文件。"
#: ../../user_guide/feature_guide/quantization.md:37
msgid ""
"[config.json](https://www.modelscope.cn/models/vllm-"
"ascend/DeepSeek-V2-Lite-W8A8/file/view/master/config.json?status=1). Please "
"make sure that there is no `quantization_config` field in it."
msgstr ""
"[config.json](https://www.modelscope.cn/models/vllm-"
"ascend/DeepSeek-V2-Lite-W8A8/file/view/master/config.json?status=1)。请确保其中没有 "
"`quantization_config` 字段。"
#: ../../user_guide/feature_guide/quantization.md:39
msgid ""
"[quant_model_description.json](https://www.modelscope.cn/models/vllm-"
"ascend/DeepSeek-V2-Lite-W8A8/file/view/master/quant_model_description.json?status=1)."
" All the converted weights info are recorded in this file."
msgstr ""
"[quant_model_description.json](https://www.modelscope.cn/models/vllm-"
"ascend/DeepSeek-V2-Lite-W8A8/file/view/master/quant_model_description.json?status=1)。所有被转换的权重信息都记录在该文件中。"
#: ../../user_guide/feature_guide/quantization.md:41
msgid "Here is the full converted model files:"
msgstr "以下是完整转换后的模型文件："
#: ../../user_guide/feature_guide/quantization.md:60
msgid "Run the model"
msgstr "运行模型"
#: ../../user_guide/feature_guide/quantization.md:62
msgid ""
"Now, you can run the quantized models with vLLM Ascend. Here is the example "
"for online and offline inference."
msgstr "现在，你可以使用 vLLM Ascend 运行量化模型。下面是在线和离线推理的示例。"
#: ../../user_guide/feature_guide/quantization.md:64
msgid "Offline inference"
msgstr "离线推理"
#: ../../user_guide/feature_guide/quantization.md:90
msgid "Online inference"
msgstr "在线推理"
#: ../../user_guide/feature_guide/quantization.md:97
msgid "FAQs"
msgstr "常见问题解答"
#: ../../user_guide/feature_guide/quantization.md:99
msgid ""
"1. How to solve the KeyError: 'xxx.layers.0.self_attn.q_proj.weight' "
"problem?"
msgstr "1. 如何解决 KeyError: 'xxx.layers.0.self_attn.q_proj.weight' 问题？"
#: ../../user_guide/feature_guide/quantization.md:101
msgid ""
"First, make sure you specify `ascend` quantization method. Second, check if "
"your model is converted by this `modelslim-VLLM-8.1.RC1.b020_001` modelslim "
"version. Finally, if it still doesn't work, please submit a issue, maybe "
"some new models need to be adapted."
msgstr ""
"首先，请确保你指定了 `ascend` 量化方法。其次，检查你的模型是否由 `modelslim-VLLM-8.1.RC1.b020_001` 这个 "
"modelslim 版本转换。如果仍然无法使用，请提交一个 issue，可能有一些新模型需要适配。"
#: ../../user_guide/feature_guide/quantization.md:104
msgid ""
"2. How to solve the error \"Could not locate the "
"configuration_deepseek.py\"?"
msgstr "2. 如何解决“无法找到 configuration_deepseek.py”错误？"
#: ../../user_guide/feature_guide/quantization.md:106
msgid ""
"Please convert DeepSeek series models using `modelslim-"
"VLLM-8.1.RC1.b020_001` modelslim, this version has fixed the missing "
"configuration_deepseek.py error."
msgstr ""
"请使用 `modelslim-VLLM-8.1.RC1.b020_001` 的 modelslim 转换 DeepSeek 系列模型，该版本已修复缺少 "
"configuration_deepseek.py 的错误。"

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# SOME DESCRIPTIVE TITLE.
# Copyright (C) 2025, vllm-ascend team
# This file is distributed under the same license as the vllm-ascend
# package.
# FIRST AUTHOR <EMAIL@ADDRESS>, 2025.
#
#, fuzzy
msgid ""
msgstr ""
"Project-Id-Version: vllm-ascend\n"
"Report-Msgid-Bugs-To: \n"
"POT-Creation-Date: 2025-07-18 09:01+0800\n"
"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
"Language-Team: zh_CN <LL@li.org>\n"
"Language: zh_CN\n"
"MIME-Version: 1.0\n"
"Content-Type: text/plain; charset=utf-8\n"
"Content-Transfer-Encoding: 8bit\n"
"Plural-Forms: nplurals=1; plural=0;\n"
"Generated-By: Babel 2.17.0\n"
#: ../../user_guide/feature_guide/sleep_mode.md:1
msgid "Sleep Mode Guide"
msgstr "睡眠模式指南"
#: ../../user_guide/feature_guide/sleep_mode.md:3
msgid "Overview"
msgstr "概述"
#: ../../user_guide/feature_guide/sleep_mode.md:5
msgid ""
"Sleep Mode is an API designed to offload model weights and discard KV cache "
"from NPU memory. This functionality is essential for reinforcement learning "
"(RL) post-training workloads, particularly in online algorithms such as PPO,"
" GRPO, or DPO. During training, the policy model typically performs auto-"
"regressive generation using inference engines like vLLM, followed by forward"
" and backward passes for optimization."
msgstr ""
"Sleep Mode 是一个用于卸载模型权重并清除 NPU 内存中 KV 缓存的 API。此功能对于强化学习（RL）后训练任务尤其重要，特别是在 "
"PPO、GRPO 或 DPO 等在线算法中。在训练过程中，策略模型通常会使用像 vLLM "
"这样的推理引擎进行自回归生成，然后进行前向和反向传播以进行优化。"
#: ../../user_guide/feature_guide/sleep_mode.md:7
msgid ""
"Since the generation and training phases may employ different model "
"parallelism strategies, it becomes crucial to free KV cache and even offload"
" model parameters stored within vLLM during training. This ensures efficient"
" memory utilization and avoids resource contention on the NPU."
msgstr ""
"由于生成和训练阶段可能采用不同的模型并行策略，因此在训练过程中及时释放 KV 缓存，甚至卸载存储在 vLLM "
"内的模型参数变得至关重要。这可以确保内存的高效利用，并避免 NPU 上的资源争用。"
#: ../../user_guide/feature_guide/sleep_mode.md:10
msgid "Getting started"
msgstr "快速上手"
#: ../../user_guide/feature_guide/sleep_mode.md:12
#, python-brace-format
msgid ""
"With `enable_sleep_mode=True`, the way we manage memory(malloc, free) in "
"vllm will under a specific memory pool, during loading model and initialize "
"kv_caches, we tag the memory as a map: `{\"weight\": data, \"kv_cache\": "
"data}`."
msgstr ""
"当 `enable_sleep_mode=True` 时，我们在 vllm 中管理内存（malloc, "
"free）的方式会在一个特定的内存池下进行，在加载模型和初始化 kv_caches "
"期间，我们会将内存打上标签，组织成一个映射：`{\"weight\": data, \"kv_cache\": data}`。"
#: ../../user_guide/feature_guide/sleep_mode.md:14
msgid ""
"The engine(v0/v1) supports two sleep levels to manage memory during idle "
"periods:"
msgstr "该引擎（v0/v1）支持两种睡眠等级，以在空闲期间管理内存："
#: ../../user_guide/feature_guide/sleep_mode.md:16
msgid "Level 1 Sleep"
msgstr "一级睡眠"
#: ../../user_guide/feature_guide/sleep_mode.md:17
msgid "Action: Offloads model weights and discards the KV cache."
msgstr "操作：卸载模型权重并清除KV缓存。"
#: ../../user_guide/feature_guide/sleep_mode.md:18
msgid "Memory: Model weights are moved to CPU memory; KV cache is forgotten."
msgstr "内存：模型权重被移动到CPU内存；KV缓存被清除。"
#: ../../user_guide/feature_guide/sleep_mode.md:19
msgid "Use Case: Suitable when reusing the same model later."
msgstr "用例：适用于之后需要重复使用同一个模型的情况。"
#: ../../user_guide/feature_guide/sleep_mode.md:20
msgid ""
"Note: Ensure sufficient CPU memory is available to hold the model weights."
msgstr "注意：请确保有足够的CPU内存来存储模型权重。"
#: ../../user_guide/feature_guide/sleep_mode.md:22
msgid "Level 2 Sleep"
msgstr "二级睡眠"
#: ../../user_guide/feature_guide/sleep_mode.md:23
msgid "Action: Discards both model weights and KV cache."
msgstr "操作：同时丢弃模型权重和KV缓存。"
#: ../../user_guide/feature_guide/sleep_mode.md:24
msgid ""
"Memory: The content of both the model weights and kv cache is forgotten."
msgstr "内存：模型权重和kv缓存的内容都会被遗忘。"
#: ../../user_guide/feature_guide/sleep_mode.md:25
msgid ""
"Use Case: Ideal when switching to a different model or updating the current "
"one."
msgstr "用例：当切换到不同的模型或更新当前模型时非常理想。"
#: ../../user_guide/feature_guide/sleep_mode.md:27
msgid ""
"Since this feature uses the low-level API "
"[AscendCL](https://www.hiascend.com/document/detail/zh/CANNCommunityEdition/82RC1alpha002/API/appdevgapi/appdevgapi_07_0000.html),"
" in order to use sleep mode, you should follow the [installation "
"guide](https://vllm-ascend.readthedocs.io/en/latest/installation.html) and "
"building from source, if you are using v0.7.3, remember to set `export "
"COMPILE_CUSTOM_KERNELS=1`, for the latest version(v0.9.x+), the environment "
"variable `COMPILE_CUSTOM_KERNELS` will be set 1 by default while building "
"from source."
msgstr ""
"由于此功能使用了底层 API "
"[AscendCL](https://www.hiascend.com/document/detail/zh/CANNCommunityEdition/82RC1alpha002/API/appdevgapi/appdevgapi_07_0000.html)，为了使用休眠模式，你应按照[安装指南](https://vllm-"
"ascend.readthedocs.io/en/latest/installation.html)进行操作，并从源码编译。如果你使用的是 "
"v0.7.3，请记得设置 `export COMPILE_CUSTOM_KERNELS=1` ；对于最新版本（v0.9.x+），在从源码编译时环境变量 "
"`COMPILE_CUSTOM_KERNELS` 默认会被设置为 1。"
#: ../../user_guide/feature_guide/sleep_mode.md:29
msgid "Usage"
msgstr "用法"
#: ../../user_guide/feature_guide/sleep_mode.md:31
msgid "The following is a simple example of how to use sleep mode."
msgstr "以下是如何使用睡眠模式的一个简单示例。"
#: ../../user_guide/feature_guide/sleep_mode.md:33
msgid "offline inference:"
msgstr "离线推理："
#: ../../user_guide/feature_guide/sleep_mode.md:72
msgid "online serving:"
msgstr "在线服务："
#: ../../user_guide/feature_guide/sleep_mode.md:74
msgid ""
"Considering there may be a risk of malicious access, please make sure you "
"are under a dev-mode, and explicit specify the develop env: "
"`VLLM_SERVER_DEV_MODE` to expose these endpoints(sleep/wake up)."
msgstr ""
"鉴于可能存在恶意访问的风险，请确保您处于开发模式，并明确指定开发环境：`VLLM_SERVER_DEV_MODE`，以便开放这些端点（sleep/wake"
" up）。"

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# SOME DESCRIPTIVE TITLE.
# Copyright (C) 2025, vllm-ascend team
# This file is distributed under the same license as the vllm-ascend
# package.
# FIRST AUTHOR <EMAIL@ADDRESS>, 2025.
#
#, fuzzy
msgid ""
msgstr ""
"Project-Id-Version: vllm-ascend\n"
"Report-Msgid-Bugs-To: \n"
"POT-Creation-Date: 2025-07-18 09:01+0800\n"
"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
"Language-Team: zh_CN <LL@li.org>\n"
"Language: zh_CN\n"
"MIME-Version: 1.0\n"
"Content-Type: text/plain; charset=utf-8\n"
"Content-Transfer-Encoding: 8bit\n"
"Plural-Forms: nplurals=1; plural=0;\n"
"Generated-By: Babel 2.17.0\n"
#: ../../user_guide/feature_guide/structured_output.md:1
msgid "Structured Output Guide"
msgstr "结构化输出指南"
#: ../../user_guide/feature_guide/structured_output.md:3
msgid "Overview"
msgstr "概述"
#: ../../user_guide/feature_guide/structured_output.md:5
msgid "What is Structured Output?"
msgstr "什么是结构化输出？"
#: ../../user_guide/feature_guide/structured_output.md:7
msgid ""
"LLMs can be unpredictable when you need output in specific formats. Think of"
" asking a model to generate JSON - without guidance, it might produce valid "
"text that breaks JSON specification. **Structured Output (also called Guided"
" Decoding)** enables LLMs to generate outputs that follow a desired "
"structure while preserving the non-deterministic nature of the system."
msgstr ""
"当你需要特定格式输出时，大型语言模型（LLMs）可能表现出不可预测性。比如让模型生成 "
"JSON，如果没有指导，模型可能会生成有效的文本，但这些文本却不符合 JSON 规范。**结构化输出（也称为引导解码）** "
"能让大型语言模型生成符合预期结构的输出，同时保留系统的非确定性特性。"
#: ../../user_guide/feature_guide/structured_output.md:9
msgid ""
"In simple terms, structured decoding gives LLMs a “template” to follow. "
"Users provide a schema that “influences” the model’s output, ensuring "
"compliance with the desired structure."
msgstr "简单来说，结构化解码为LLM提供了一个“模板”来遵循。用户提供一个模式来“影响”模型的输出，从而确保输出符合期望的结构。"
#: ../../user_guide/feature_guide/structured_output.md:11
msgid "![structured decoding](./images/structured_output_1.png)"
msgstr "![结构化解码](./images/structured_output_1.png)"
#: ../../user_guide/feature_guide/structured_output.md:11
msgid "structured decoding"
msgstr "结构化解码"
#: ../../user_guide/feature_guide/structured_output.md:13
msgid "Structured Output in vllm-ascend"
msgstr "vllm-ascend 中的结构化输出"
#: ../../user_guide/feature_guide/structured_output.md:15
msgid ""
"Currently, vllm-ascend supports **xgrammar** and **guidance** backend for "
"structured output with vllm v1 engine."
msgstr "目前，vllm-ascend 支持 vllm v1 引擎的结构化输出，后端包括 **xgrammar** 和 **guidance**。"
#: ../../user_guide/feature_guide/structured_output.md:17
msgid ""
"XGrammar introduces a new technique that batch constrained decoding via "
"pushdown automaton (PDA). You can think of a PDA as a “collection of FSMs, "
"and each FSM represents a context-free grammar (CFG).” One significant "
"advantage of PDA is its recursive nature, allowing us to execute multiple "
"state transitions. They also include additional optimisation (for those who "
"are interested) to reduce grammar compilation overhead. Besides, you can "
"also find more details about guidance by yourself."
msgstr ""
"XGrammar 引入了一种通过下推自动机（PDA）进行批量约束解码的新技术。你可以把 PDA 理解为“有限状态机（FSM）的集合，每个 FSM "
"代表一个上下文无关文法（CFG）。” PDA 的一个重要优点是其递归特性，使我们能够执行多次状态转移。此外，PDA "
"还包含了额外的优化（供感兴趣的用户参考），以减少语法编译的开销。除此之外，你还可以自己找到更多关于指导的信息。"
#: ../../user_guide/feature_guide/structured_output.md:19
msgid "How to Use Structured Output?"
msgstr "如何使用结构化输出？"
#: ../../user_guide/feature_guide/structured_output.md:21
msgid "Online Inference"
msgstr "在线推理"
#: ../../user_guide/feature_guide/structured_output.md:23
msgid ""
"You can also generate structured outputs using the OpenAI's Completions and "
"Chat API. The following parameters are supported, which must be added as "
"extra parameters:"
msgstr "你也可以使用 OpenAI 的 Completions 和 Chat API 生成结构化输出。支持以下参数，这些参数必须作为额外参数添加："
#: ../../user_guide/feature_guide/structured_output.md:25
msgid "`guided_choice`: the output will be exactly one of the choices."
msgstr "`guided_choice`：输出将会是其中一个选项。"
#: ../../user_guide/feature_guide/structured_output.md:26
msgid "`guided_regex`: the output will follow the regex pattern."
msgstr "`guided_regex`：输出将遵循正则表达式模式。"
#: ../../user_guide/feature_guide/structured_output.md:27
msgid "`guided_json`: the output will follow the JSON schema."
msgstr "`guided_json`：输出将遵循 JSON 架构。"
#: ../../user_guide/feature_guide/structured_output.md:28
msgid "`guided_grammar`: the output will follow the context free grammar."
msgstr "`guided_grammar`：输出将遵循上下文无关文法。"
#: ../../user_guide/feature_guide/structured_output.md:30
msgid ""
"Structured outputs are supported by default in the OpenAI-Compatible Server."
" You can choose to specify the backend to use by setting the `--guided-"
"decoding-backend` flag to vllm serve. The default backend is `auto`, which "
"will try to choose an appropriate backend based on the details of the "
"request. You may also choose a specific backend, along with some options."
msgstr ""
"OpenAI 兼容服务器默认支持结构化输出。你可以通过设置 `--guided-decoding-backend` 标志为 vllm serve "
"来指定要使用的后端。默认后端为 `auto`，它会根据请求的详细信息尝试选择合适的后端。你也可以选择特定的后端，并设置一些选项。"
#: ../../user_guide/feature_guide/structured_output.md:32
msgid ""
"Now let´s see an example for each of the cases, starting with the "
"guided_choice, as it´s the easiest one:"
msgstr "现在让我们来看每种情况的示例，首先是 guided_choice，因为它是最简单的："
#: ../../user_guide/feature_guide/structured_output.md:51
msgid ""
"The next example shows how to use the guided_regex. The idea is to generate "
"an email address, given a simple regex template:"
msgstr "下一个例子展示了如何使用 guided_regex。其思路是基于一个简单的正则表达式模板生成一个电子邮件地址："
#: ../../user_guide/feature_guide/structured_output.md:67
msgid ""
"One of the most relevant features in structured text generation is the "
"option to generate a valid JSON with pre-defined fields and formats. For "
"this we can use the guided_json parameter in two different ways:"
msgstr ""
"在结构化文本生成中，最相关的特性之一是能够生成具有预定义字段和格式的有效 JSON。为此，我们可以通过两种不同的方式使用 guided_json 参数："
#: ../../user_guide/feature_guide/structured_output.md:69
msgid "Using a JSON Schema."
msgstr "使用 JSON 架构。"
#: ../../user_guide/feature_guide/structured_output.md:70
msgid "Defining a Pydantic model and then extracting the JSON Schema from it."
msgstr "定义一个 Pydantic 模型，然后从中提取 JSON Schema。"
#: ../../user_guide/feature_guide/structured_output.md:72
msgid ""
"The next example shows how to use the guided_json parameter with a Pydantic "
"model:"
msgstr "下一个示例展示了如何将 guided_json 参数与 Pydantic 模型一起使用："
#: ../../user_guide/feature_guide/structured_output.md:104
msgid ""
"Finally we have the guided_grammar option, which is probably the most "
"difficult to use, but it´s really powerful. It allows us to define complete "
"languages like SQL queries. It works by using a context free EBNF grammar. "
"As an example, we can use to define a specific format of simplified SQL "
"queries:"
msgstr ""
"最后，我们有 guided_grammar 选项，这可能是最难使用的，但它非常强大。它允许我们定义完整的语言，比如 SQL 查询。它通过使用上下文无关的"
" EBNF 语法来实现。例如，我们可以用它来定义一种简化 SQL 查询的特定格式："
#: ../../user_guide/feature_guide/structured_output.md:134
msgid ""
"Find more examples [here](https://github.com/vllm-"
"project/vllm/blob/main/examples/offline_inference/structured_outputs.py)."
msgstr ""
"在[这里](https://github.com/vllm-"
"project/vllm/blob/main/examples/offline_inference/structured_outputs.py)可以找到更多示例。"
#: ../../user_guide/feature_guide/structured_output.md:136
msgid "Offline Inference"
msgstr "离线推理"
#: ../../user_guide/feature_guide/structured_output.md:138
msgid ""
"To use Structured Output, we'll need to configure the guided decoding using "
"the class `GuidedDecodingParams` inside `SamplingParams`. The main available"
" options inside `GuidedDecodingParams` are:"
msgstr ""
"要使用结构化输出，我们需要在 `SamplingParams` 内通过 `GuidedDecodingParams` "
"类配置引导解码。`GuidedDecodingParams` 中主要可用的选项有："
#: ../../user_guide/feature_guide/structured_output.md:140
msgid "json"
msgstr "json"
#: ../../user_guide/feature_guide/structured_output.md:141
msgid "regex"
msgstr "正则表达式"
#: ../../user_guide/feature_guide/structured_output.md:142
msgid "choice"
msgstr "选择"
#: ../../user_guide/feature_guide/structured_output.md:143
msgid "grammar"
msgstr "语法"
#: ../../user_guide/feature_guide/structured_output.md:145
msgid "One example for the usage of the choice parameter is shown below:"
msgstr "choice 参数用法的一个示例如下："
#: ../../user_guide/feature_guide/structured_output.md:163
msgid ""
"Find more examples of other usages [here](https://github.com/vllm-"
"project/vllm/blob/main/examples/offline_inference/structured_outputs.py)."
msgstr ""
"查看更多其他用法的示例 [在这里](https://github.com/vllm-"
"project/vllm/blob/main/examples/offline_inference/structured_outputs.py)。"

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# Translations template for PROJECT.
# Copyright (C) 2025 ORGANIZATION
# This file is distributed under the same license as the PROJECT project.
# FIRST AUTHOR <EMAIL@ADDRESS>, 2025.
#
#, fuzzy
msgid ""
msgstr ""
"Project-Id-Version: PROJECT VERSION\n"
"Report-Msgid-Bugs-To: EMAIL@ADDRESS\n"
"POT-Creation-Date: 2025-07-18 09:01+0800\n"
"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
"Language-Team: LANGUAGE <LL@li.org>\n"
"MIME-Version: 1.0\n"
"Content-Type: text/plain; charset=utf-8\n"
"Content-Transfer-Encoding: 8bit\n"
"Generated-By: Babel 2.17.0\n"
#: ../../user_guide/support_matrix/index.md:5
msgid "Support Matrix"
msgstr "支持矩阵"
#: ../../user_guide/support_matrix/index.md:1
msgid "Features and models"
msgstr "特性与模型"
#: ../../user_guide/support_matrix/index.md:3
msgid "This section provides a detailed supported matrix by vLLM Ascend."
msgstr "本节提供了 vLLM Ascend 的详细支持矩阵。"

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# SOME DESCRIPTIVE TITLE.
# Copyright (C) 2025, vllm-ascend team
# This file is distributed under the same license as the vllm-ascend
# package.
# FIRST AUTHOR <EMAIL@ADDRESS>, 2025.
#
#, fuzzy
msgid ""
msgstr ""
"Project-Id-Version: vllm-ascend\n"
"Report-Msgid-Bugs-To: \n"
"POT-Creation-Date: 2025-07-18 09:01+0800\n"
"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
"Language-Team: zh_CN <LL@li.org>\n"
"Language: zh_CN\n"
"MIME-Version: 1.0\n"
"Content-Type: text/plain; charset=utf-8\n"
"Content-Transfer-Encoding: 8bit\n"
"Plural-Forms: nplurals=1; plural=0;\n"
"Generated-By: Babel 2.17.0\n"
#: ../../user_guide/support_matrix/supported_features.md:1
msgid "Feature Support"
msgstr "功能支持"
#: ../../user_guide/support_matrix/supported_features.md:3
msgid ""
"The feature support principle of vLLM Ascend is: **aligned with the vLLM**. "
"We are also actively collaborating with the community to accelerate support."
msgstr "vLLM Ascend 的特性支持原则是：**与 vLLM 保持一致**。我们也在积极与社区合作，加快支持进度。"
#: ../../user_guide/support_matrix/supported_features.md:5
msgid ""
"You can check the [support status of vLLM V1 Engine][v1_user_guide]. Below "
"is the feature support status of vLLM Ascend:"
msgstr "你可以查看 [vLLM V1 引擎的支持状态][v1_user_guide]。下面是 vLLM Ascend 的功能支持情况："
#: ../../user_guide/support_matrix/supported_features.md
msgid "Feature"
msgstr "特性"
#: ../../user_guide/support_matrix/supported_features.md
msgid "vLLM V0 Engine"
msgstr "vLLM V0 引擎"
#: ../../user_guide/support_matrix/supported_features.md
msgid "vLLM V1 Engine"
msgstr "vLLM V1 引擎"
#: ../../user_guide/support_matrix/supported_features.md
msgid "Next Step"
msgstr "下一步"
#: ../../user_guide/support_matrix/supported_features.md
msgid "Chunked Prefill"
msgstr "分块预填充"
#: ../../user_guide/support_matrix/supported_features.md
msgid "🟢 Functional"
msgstr "🟢 功能性"
#: ../../user_guide/support_matrix/supported_features.md
msgid "Functional, see detail note: [Chunked Prefill][cp]"
msgstr "功能性，详见说明：[分块预填充][cp]"
#: ../../user_guide/support_matrix/supported_features.md
msgid "Automatic Prefix Caching"
msgstr "自动前缀缓存"
#: ../../user_guide/support_matrix/supported_features.md
msgid "Functional, see detail note: [vllm-ascend#732][apc]"
msgstr "可用，请参见详细说明：[vllm-ascend#732][apc]"
#: ../../user_guide/support_matrix/supported_features.md
msgid "LoRA"
msgstr "LoRA"
#: ../../user_guide/support_matrix/supported_features.md
msgid "[vllm-ascend#396][multilora], [vllm-ascend#893][v1 multilora]"
msgstr "[vllm-ascend#396][multilora]，[vllm-ascend#893][v1 multilora]"
#: ../../user_guide/support_matrix/supported_features.md
msgid "Prompt adapter"
msgstr "提示适配器"
#: ../../user_guide/support_matrix/supported_features.md
msgid "🔴 No plan"
msgstr "🔴 无计划"
#: ../../user_guide/support_matrix/supported_features.md
msgid "This feature has been deprecated by vllm."
msgstr "此功能已被 vllm 弃用。"
#: ../../user_guide/support_matrix/supported_features.md
msgid "Speculative decoding"
msgstr "猜测式解码"
#: ../../user_guide/support_matrix/supported_features.md
msgid "Basic support"
msgstr "基础支持"
#: ../../user_guide/support_matrix/supported_features.md
msgid "Pooling"
msgstr "池化"
#: ../../user_guide/support_matrix/supported_features.md
msgid "🟡 Planned"
msgstr "🟡 计划中"
#: ../../user_guide/support_matrix/supported_features.md
msgid "CI needed and adapting more models; V1 support rely on vLLM support."
msgstr "需要持续集成（CI）并适配更多模型；V1 的支持依赖于 vLLM 的支持。"
#: ../../user_guide/support_matrix/supported_features.md
msgid "Enc-dec"
msgstr "Enc-dec（编码-解码）"
#: ../../user_guide/support_matrix/supported_features.md
msgid "🔴 NO plan"
msgstr "🔴 没有计划"
#: ../../user_guide/support_matrix/supported_features.md
msgid "Plan in 2025.06.30"
msgstr "2025.06.30 的计划"
#: ../../user_guide/support_matrix/supported_features.md
msgid "Multi Modality"
msgstr "多模态"
#: ../../user_guide/support_matrix/supported_features.md
msgid "[Tutorial][multimodal], optimizing and adapting more models"
msgstr "[教程][multimodal]，优化和适配更多模型"
#: ../../user_guide/support_matrix/supported_features.md
msgid "LogProbs"
msgstr "LogProbs"
#: ../../user_guide/support_matrix/supported_features.md
msgid "CI needed"
msgstr "需要持续集成（CI）"
#: ../../user_guide/support_matrix/supported_features.md
msgid "Prompt logProbs"
msgstr "提示 logProbs"
#: ../../user_guide/support_matrix/supported_features.md
msgid "Async output"
msgstr "异步输出"
#: ../../user_guide/support_matrix/supported_features.md
msgid "Multi step scheduler"
msgstr "多步调度器"
#: ../../user_guide/support_matrix/supported_features.md
msgid "🔴 Deprecated"
msgstr "🔴 已弃用"
#: ../../user_guide/support_matrix/supported_features.md
msgid "[vllm#8779][v1_rfc], replaced by [vLLM V1 Scheduler][v1_scheduler]"
msgstr "[vllm#8779][v1_rfc]，已被 [vLLM V1 调度器][v1_scheduler] 替代"
#: ../../user_guide/support_matrix/supported_features.md
msgid "Best of"
msgstr "精选"
#: ../../user_guide/support_matrix/supported_features.md
msgid "[vllm#13361][best_of], CI needed"
msgstr "[vllm#13361][best_of]，需要持续集成（CI）"
#: ../../user_guide/support_matrix/supported_features.md
msgid "Beam search"
msgstr "束搜索"
#: ../../user_guide/support_matrix/supported_features.md
msgid "Guided Decoding"
msgstr "引导解码"
#: ../../user_guide/support_matrix/supported_features.md
msgid "[vllm-ascend#177][guided_decoding]"
msgstr "[vllm-ascend#177][guided_decoding]"
#: ../../user_guide/support_matrix/supported_features.md
msgid "Tensor Parallel"
msgstr "张量并行"
#: ../../user_guide/support_matrix/supported_features.md
msgid "Pipeline Parallel"
msgstr "流水线并行"
#: ../../user_guide/support_matrix/supported_features.md
msgid "Expert Parallel"
msgstr "专家并行"
#: ../../user_guide/support_matrix/supported_features.md
msgid "CI needed; No plan on V0 support"
msgstr "需要持续集成；没有支持V0的计划"
#: ../../user_guide/support_matrix/supported_features.md
msgid "Data Parallel"
msgstr "数据并行"
#: ../../user_guide/support_matrix/supported_features.md
msgid "CI needed; No plan on V0 support"
msgstr "需要 CI；暂无 V0 支持计划"
#: ../../user_guide/support_matrix/supported_features.md
msgid "Prefill Decode Disaggregation"
msgstr "预填充 解码 拆分"
#: ../../user_guide/support_matrix/supported_features.md
msgid "1P1D available, working on xPyD and V1 support."
msgstr "1P1D 已可用，正在开发 xPyD 和 V1 支持。"
#: ../../user_guide/support_matrix/supported_features.md
msgid "Quantization"
msgstr "量化"
#: ../../user_guide/support_matrix/supported_features.md
msgid "W8A8 available, CI needed; working on more quantization method support"
msgstr "W8A8 已可用，需要持续集成（CI）；正在开发对更多量化方法的支持。"
#: ../../user_guide/support_matrix/supported_features.md
msgid "Graph Mode"
msgstr "图模式"
#: ../../user_guide/support_matrix/supported_features.md
msgid "🔵 Experimental"
msgstr "🔵 实验性"
#: ../../user_guide/support_matrix/supported_features.md
msgid "Experimental, see detail note: [vllm-ascend#767][graph_mode]"
msgstr "实验性功能，详见说明：[vllm-ascend#767][graph_mode]"
#: ../../user_guide/support_matrix/supported_features.md
msgid "Sleep Mode"
msgstr "睡眠模式"
#: ../../user_guide/support_matrix/supported_features.md
msgid "level=1 available, CI needed, working on V1 support"
msgstr "level=1 可用，需要CI，正在开发 V1 支持"
#: ../../user_guide/support_matrix/supported_features.md:33
msgid "🟢 Functional: Fully operational, with ongoing optimizations."
msgstr "🟢 功能性：完全可用，正在持续优化中。"
#: ../../user_guide/support_matrix/supported_features.md:34
msgid ""
"🔵 Experimental: Experimental support, interfaces and functions may change."
msgstr "🔵 实验性：实验性支持，接口和功能可能会发生变化。"
#: ../../user_guide/support_matrix/supported_features.md:35
msgid "🚧 WIP: Under active development, will be supported soon."
msgstr "🚧 WIP：正在积极开发中，很快将会支持。"
#: ../../user_guide/support_matrix/supported_features.md:36
msgid ""
"🟡 Planned: Scheduled for future implementation (some may have open "
"PRs/RFCs)."
msgstr "🟡 计划中：已安排将来实现（其中一些可能已有开放的PR/RFC）。"
#: ../../user_guide/support_matrix/supported_features.md:37
msgid "🔴 NO plan / Deprecated: No plan for V0 or deprecated by vLLM v1."
msgstr "🔴 没有计划 / 已弃用：V0 没有计划或已被 vLLM v1 弃用。"

214
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@@ -0,0 +1,214 @@
# SOME DESCRIPTIVE TITLE.
# Copyright (C) 2025, vllm-ascend team
# This file is distributed under the same license as the vllm-ascend
# package.
# FIRST AUTHOR <EMAIL@ADDRESS>, 2025.
#
#, fuzzy
msgid ""
msgstr ""
"Project-Id-Version: vllm-ascend\n"
"Report-Msgid-Bugs-To: \n"
"POT-Creation-Date: 2025-07-18 09:01+0800\n"
"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
"Language-Team: zh_CN <LL@li.org>\n"
"Language: zh_CN\n"
"MIME-Version: 1.0\n"
"Content-Type: text/plain; charset=utf-8\n"
"Content-Transfer-Encoding: 8bit\n"
"Plural-Forms: nplurals=1; plural=0;\n"
"Generated-By: Babel 2.17.0\n"
#: ../../user_guide/support_matrix/supported_models.md:1
msgid "Model Support"
msgstr "模型支持"
#: ../../user_guide/support_matrix/supported_models.md:3
msgid "Text-only Language Models"
msgstr "纯文本语言模型"
#: ../../user_guide/support_matrix/supported_models.md:5
#: ../../user_guide/support_matrix/supported_models.md:38
msgid "Generative Models"
msgstr "生成模型"
#: ../../user_guide/support_matrix/supported_models.md
msgid "Model"
msgstr "模型"
#: ../../user_guide/support_matrix/supported_models.md
msgid "Supported"
msgstr "支持"
#: ../../user_guide/support_matrix/supported_models.md
msgid "Note"
msgstr "注释"
#: ../../user_guide/support_matrix/supported_models.md
msgid "DeepSeek v3"
msgstr "DeepSeek v3"
#: ../../user_guide/support_matrix/supported_models.md
msgid "✅"
msgstr "✅"
#: ../../user_guide/support_matrix/supported_models.md
msgid "DeepSeek R1"
msgstr "DeepSeek R1"
#: ../../user_guide/support_matrix/supported_models.md
msgid "DeepSeek Distill (Qwen/LLama)"
msgstr "DeepSeek 精炼（Qwen/LLama）"
#: ../../user_guide/support_matrix/supported_models.md
msgid "Qwen3"
msgstr "Qwen3"
#: ../../user_guide/support_matrix/supported_models.md
msgid "Qwen3-Moe"
msgstr "Qwen3-Moe"
#: ../../user_guide/support_matrix/supported_models.md
msgid "Qwen2.5"
msgstr "Qwen2.5"
#: ../../user_guide/support_matrix/supported_models.md
msgid "QwQ-32B"
msgstr "QwQ-32B"
#: ../../user_guide/support_matrix/supported_models.md
msgid "LLama3.1/3.2"
msgstr "LLama3.1/3.2"
#: ../../user_guide/support_matrix/supported_models.md
msgid "Internlm"
msgstr "Internlm"
#: ../../user_guide/support_matrix/supported_models.md
msgid "Baichuan"
msgstr "百川"
#: ../../user_guide/support_matrix/supported_models.md
msgid "Phi-4-mini"
msgstr "Phi-4-mini"
#: ../../user_guide/support_matrix/supported_models.md
msgid "MiniCPM"
msgstr "MiniCPM"
#: ../../user_guide/support_matrix/supported_models.md
msgid "MiniCPM3"
msgstr "MiniCPM3"
#: ../../user_guide/support_matrix/supported_models.md
msgid "LLama4"
msgstr "LLama4"
#: ../../user_guide/support_matrix/supported_models.md
msgid "Mistral"
msgstr "Mistral"
#: ../../user_guide/support_matrix/supported_models.md
msgid "Need test"
msgstr "需要测试"
#: ../../user_guide/support_matrix/supported_models.md
msgid "DeepSeek v2.5"
msgstr "DeepSeek v2.5"
#: ../../user_guide/support_matrix/supported_models.md
msgid "Gemma-2"
msgstr "Gemma-2"
#: ../../user_guide/support_matrix/supported_models.md
msgid "Mllama"
msgstr "Mllama"
#: ../../user_guide/support_matrix/supported_models.md
msgid "Gemma-3"
msgstr "Gemma-3"
#: ../../user_guide/support_matrix/supported_models.md
msgid "❌"
msgstr "❌"
#: ../../user_guide/support_matrix/supported_models.md
msgid "[#496](https://github.com/vllm-project/vllm-ascend/issues/496)"
msgstr "[#496](https://github.com/vllm-project/vllm-ascend/issues/496)"
#: ../../user_guide/support_matrix/supported_models.md
msgid "ChatGLM"
msgstr "ChatGLM"
#: ../../user_guide/support_matrix/supported_models.md
msgid "[#554](https://github.com/vllm-project/vllm-ascend/issues/554)"
msgstr "[#554](https://github.com/vllm-project/vllm-ascend/issues/554)"
#: ../../user_guide/support_matrix/supported_models.md:29
msgid "Pooling Models"
msgstr "池化模型"
#: ../../user_guide/support_matrix/supported_models.md
msgid "XLM-RoBERTa-based"
msgstr "基于XLM-RoBERTa"
#: ../../user_guide/support_matrix/supported_models.md
msgid "Molmo"
msgstr "Molmo"
#: ../../user_guide/support_matrix/supported_models.md:36
msgid "Multimodal Language Models"
msgstr "多模态语言模型"
#: ../../user_guide/support_matrix/supported_models.md
msgid "Qwen2-VL"
msgstr "Qwen2-VL"
#: ../../user_guide/support_matrix/supported_models.md
msgid "Qwen2.5-VL"
msgstr "Qwen2.5-VL"
#: ../../user_guide/support_matrix/supported_models.md
msgid "LLaVA 1.5"
msgstr "LLaVA 1.5"
#: ../../user_guide/support_matrix/supported_models.md
msgid "LLaVA 1.6"
msgstr "LLaVA 1.6"
#: ../../user_guide/support_matrix/supported_models.md
msgid "[#553](https://github.com/vllm-project/vllm-ascend/issues/553)"
msgstr "[#553](https://github.com/vllm-project/vllm-ascend/issues/553)"
#: ../../user_guide/support_matrix/supported_models.md
msgid "InternVL2"
msgstr "InternVL2"
#: ../../user_guide/support_matrix/supported_models.md
msgid "InternVL2.5"
msgstr "InternVL2.5"
#: ../../user_guide/support_matrix/supported_models.md
msgid "Qwen2-Audio"
msgstr "Qwen2-Audio"
#: ../../user_guide/support_matrix/supported_models.md
msgid "LLaVA-Next"
msgstr "LLaVA-Next"
#: ../../user_guide/support_matrix/supported_models.md
msgid "LLaVA-Next-Video"
msgstr "LLaVA-Next-Video"
#: ../../user_guide/support_matrix/supported_models.md
msgid "Phi-3-Vison/Phi-3.5-Vison"
msgstr "Phi-3-Vison/Phi-3.5-Vison"
#: ../../user_guide/support_matrix/supported_models.md
msgid "GLM-4v"
msgstr "GLM-4v"
#: ../../user_guide/support_matrix/supported_models.md
msgid "Ultravox"
msgstr "Ultravox"

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# Quickstart
## Prerequisites
### Supported Devices
- Atlas A2 Training series (Atlas 800T A2, Atlas 900 A2 PoD, Atlas 200T A2 Box16, Atlas 300T A2)
- Atlas 800I A2 Inference series (Atlas 800I A2)
- Atlas A3 Training series (Atlas 800T A3, Atlas 900 A3 SuperPoD, Atlas 9000 A3 SuperPoD)
- Atlas 800I A3 Inference series (Atlas 800I A3)
- [Experimental] Atlas 300I Inference series (Atlas 300I Duo)
## Setup environment using container
:::::{tab-set}
::::{tab-item} Ubuntu
```{code-block} bash
:substitutions:
# Update DEVICE according to your device (/dev/davinci[0-7])
export DEVICE=/dev/davinci0
# Update the vllm-ascend image
export IMAGE=quay.io/ascend/vllm-ascend:|vllm_ascend_version|
docker run --rm \
--name vllm-ascend \
--device $DEVICE \
--device /dev/davinci_manager \
--device /dev/devmm_svm \
--device /dev/hisi_hdc \
-v /usr/local/dcmi:/usr/local/dcmi \
-v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
-v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
-v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
-v /etc/ascend_install.info:/etc/ascend_install.info \
-v /root/.cache:/root/.cache \
-p 8000:8000 \
-it $IMAGE bash
# Install curl
apt-get update -y && apt-get install -y curl
```
::::
::::{tab-item} openEuler
```{code-block} bash
:substitutions:
# Update DEVICE according to your device (/dev/davinci[0-7])
export DEVICE=/dev/davinci0
# Update the vllm-ascend image
export IMAGE=quay.io/ascend/vllm-ascend:|vllm_ascend_version|-openeuler
docker run --rm \
--name vllm-ascend \
--device $DEVICE \
--device /dev/davinci_manager \
--device /dev/devmm_svm \
--device /dev/hisi_hdc \
-v /usr/local/dcmi:/usr/local/dcmi \
-v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
-v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
-v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
-v /etc/ascend_install.info:/etc/ascend_install.info \
-v /root/.cache:/root/.cache \
-p 8000:8000 \
-it $IMAGE bash
# Install curl
yum update -y && yum install -y curl
```
::::
:::::
The default workdir is `/workspace`, vLLM and vLLM Ascend code are placed in `/vllm-workspace` and installed in [development mode](https://setuptools.pypa.io/en/latest/userguide/development_mode.html)(`pip install -e`) to help developer immediately take place changes without requiring a new installation.
## Usage
You can use Modelscope mirror to speed up download:
<!-- tests/e2e/doctest/001-quickstart-test.sh should be considered updating as well -->
```bash
export VLLM_USE_MODELSCOPE=true
```
There are two ways to start vLLM on Ascend NPU:
:::::{tab-set}
::::{tab-item} Offline Batched Inference
With vLLM installed, you can start generating texts for list of input prompts (i.e. offline batch inferencing).
Try to run below Python script directly or use `python3` shell to generate texts:
<!-- tests/e2e/doctest/001-quickstart-test.sh should be considered updating as well -->
```python
from vllm import LLM, SamplingParams
prompts = [
"Hello, my name is",
"The future of AI is",
]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
# The first run will take about 3-5 mins (10 MB/s) to download models
llm = LLM(model="Qwen/Qwen2.5-0.5B-Instruct")
outputs = llm.generate(prompts, sampling_params)
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
```
::::
::::{tab-item} OpenAI Completions API
vLLM can also be deployed as a server that implements the OpenAI API protocol. Run
the following command to start the vLLM server with the
[Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) model:
<!-- tests/e2e/doctest/001-quickstart-test.sh should be considered updating as well -->
```bash
# Deploy vLLM server (The first run will take about 3-5 mins (10 MB/s) to download models)
vllm serve Qwen/Qwen2.5-0.5B-Instruct &
```
If you see log as below:
```
INFO: Started server process [3594]
INFO: Waiting for application startup.
INFO: Application startup complete.
INFO: Uvicorn running on http://0.0.0.0:8000 (Press CTRL+C to quit)
```
Congratulations, you have successfully started the vLLM server!
You can query the list the models:
<!-- tests/e2e/doctest/001-quickstart-test.sh should be considered updating as well -->
```bash
curl http://localhost:8000/v1/models | python3 -m json.tool
```
You can also query the model with input prompts:
<!-- tests/e2e/doctest/001-quickstart-test.sh should be considered updating as well -->
```bash
curl http://localhost:8000/v1/completions \
-H "Content-Type: application/json" \
-d '{
"model": "Qwen/Qwen2.5-0.5B-Instruct",
"prompt": "Beijing is a",
"max_tokens": 5,
"temperature": 0
}' | python3 -m json.tool
```
vLLM is serving as background process, you can use `kill -2 $VLLM_PID` to stop the background process gracefully,
it's equal to `Ctrl-C` to stop foreground vLLM process:
<!-- tests/e2e/doctest/001-quickstart-test.sh should be considered updating as well -->
```bash
VLLM_PID=$(pgrep -f "vllm serve")
kill -2 "$VLLM_PID"
```
You will see output as below:
```
INFO: Shutting down FastAPI HTTP server.
INFO: Shutting down
INFO: Waiting for application shutdown.
INFO: Application shutdown complete.
```
Finally, you can exit container by using `ctrl-D`.
::::
:::::

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# Tutorials
:::{toctree}
:caption: Deployment
:maxdepth: 1
single_npu
single_npu_multimodal
single_npu_audio
single_npu_qwen3_embedding
single_npu_qwen3_quantization
multi_npu
multi_npu_moge
multi_npu_qwen3_moe
multi_npu_quantization
single_node_300i
multi_node
multi_node_kimi
:::

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# Multi-Node-DP (DeepSeek)
## Getting Start
vLLM-Ascend now supports Data Parallel (DP) deployment, enabling model weights to be replicated across multiple NPUs or instances, each processing independent batches of requests. This is particularly useful for scaling throughput across devices while maintaining high resource utilization.
Each DP rank is deployed as a separate “core engine” process which communicates with front-end process(es) via ZMQ sockets. Data Parallel can be combined with Tensor Parallel, in which case each DP engine owns a number of per-NPU worker processes equal to the TP size.
For Mixture-of-Experts (MoE) models — especially advanced architectures like DeepSeek that utilize Multi-head Latent Attention (MLA) — a hybrid parallelism approach is recommended:
- Use **Data Parallelism (DP)** for attention layers, which are replicated across devices and handle separate batches.
- Use **Expert or Tensor Parallelism (EP/TP)** for expert layers, which are sharded across devices to distribute the computation.
This division enables attention layers to be replicated across Data Parallel (DP) ranks, enabling them to process different batches independently. Meanwhile, expert layers are partitioned (sharded) across devices using Expert or Tensor Parallelism(DP*TP), maximizing hardware utilization and efficiency.
In these cases the data parallel ranks are not completely independent, forward passes must be aligned and expert layers across all ranks are required to synchronize during every forward pass, even if there are fewer requests to be processed than DP ranks.
For MoE models, when any requests are in progress in any rank, we must ensure that empty “dummy” forward passes are performed in all ranks which don’t currently have any requests scheduled. This is handled via a separate DP `Coordinator` process which communicates with all of the ranks, and a collective operation performed every N steps to determine when all ranks become idle and can be paused. When TP is used in conjunction with DP, expert layers form an EP or TP group of size (DP x TP).
## Verify Multi-Node Communication Environment
### Physical Layer Requirements:
- The physical machines must be located on the same WLAN, with network connectivity.
- All NPUs are connected with optical modules, and the connection status must be normal.
### Verification Process:
Execute the following commands on each node in sequence. The results must all be `success` and the status must be `UP`:
```bash
# Check the remote switch ports
for i in {0..7}; do hccn_tool -i $i -lldp -g | grep Ifname; done
# Get the link status of the Ethernet ports (UP or DOWN)
for i in {0..7}; do hccn_tool -i $i -link -g ; done
# Check the network health status
for i in {0..7}; do hccn_tool -i $i -net_health -g ; done
# View the network detected IP configuration
for i in {0..7}; do hccn_tool -i $i -netdetect -g ; done
# View gateway configuration
for i in {0..7}; do hccn_tool -i $i -gateway -g ; done
# View NPU network configuration
cat /etc/hccn.conf
```
### NPU Interconnect Verification:
#### 1. Get NPU IP Addresses
```bash
for i in {0..7}; do hccn_tool -i $i -ip -g | grep ipaddr; done
```
#### 2. Cross-Node PING Test
```bash
# Execute on the target node (replace with actual IP)
hccn_tool -i 0 -ping -g address 10.20.0.20
```
## Run with docker
Assume you have two Atlas 800 A2(64G*8) nodes, and want to deploy the `deepseek-v3.1-w8a8` quantitative model across multi-node.
```{code-block} bash
:substitutions:
# Update the vllm-ascend image
export IMAGE=m.daocloud.io/quay.io/ascend/vllm-ascend:|vllm_ascend_version|
export NAME=vllm-ascend
# Run the container using the defined variables
# Note if you are running bridge network with docker, Please expose available ports for multiple nodes communication in advance
docker run --rm \
--name $NAME \
--net=host \
--device /dev/davinci0 \
--device /dev/davinci1 \
--device /dev/davinci2 \
--device /dev/davinci3 \
--device /dev/davinci4 \
--device /dev/davinci5 \
--device /dev/davinci6 \
--device /dev/davinci7 \
--device /dev/davinci_manager \
--device /dev/devmm_svm \
--device /dev/hisi_hdc \
-v /usr/local/dcmi:/usr/local/dcmi \
-v /usr/local/Ascend/driver/tools/hccn_tool:/usr/local/Ascend/driver/tools/hccn_tool \
-v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
-v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
-v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
-v /etc/ascend_install.info:/etc/ascend_install.info \
-v /mnt/sfs_turbo/.cache:/root/.cache \
-it $IMAGE bash
```
Run the following scripts on two nodes respectively
:::{note}
Before launch the inference server, ensure the following environment variables are set for multi node communication
:::
**node0**
```shell
#!/bin/sh
# this obtained through ifconfig
# nic_name is the network interface name corresponding to local_ip
nic_name="xxxx"
local_ip="xxxx"
export VLLM_USE_MODELSCOPE=True
export HCCL_IF_IP=$local_ip
export GLOO_SOCKET_IFNAME=$nic_name
export TP_SOCKET_IFNAME=$nic_name
export HCCL_SOCKET_IFNAME=$nic_name
export OMP_PROC_BIND=false
export OMP_NUM_THREADS=100
export HCCL_BUFFSIZE=1024
# The w8a8 weight can obtained from https://www.modelscope.cn/models/vllm-ascend/DeepSeek-V3.1-W8A8
# If you want to the quantization manually, please refer to https://vllm-ascend.readthedocs.io/en/latest/user_guide/feature_guide/quantization.html
vllm serve vllm-ascend/DeepSeek-V3.1-W8A8 \
--host 0.0.0.0 \
--port 8004 \
--data-parallel-size 4 \
--data-parallel-size-local 2 \
--data-parallel-address $local_ip \
--data-parallel-rpc-port 13389 \
--tensor-parallel-size 4 \
--seed 1024 \
--served-model-name deepseek_v3.1 \
--enable-expert-parallel \
--max-num-seqs 16 \
--max-model-len 32768 \
--quantization ascend \
--max-num-batched-tokens 4096 \
--trust-remote-code \
--no-enable-prefix-caching \
--gpu-memory-utilization 0.9 \
--additional-config '{"ascend_scheduler_config":{"enabled":true},"torchair_graph_config":{"enabled":true}}'
```
**node1**
```shell
#!/bin/sh
nic_name="xxx"
local_ip="xxx"
export VLLM_USE_MODELSCOPE=True
export HCCL_IF_IP=$local_ip
export GLOO_SOCKET_IFNAME=$nic_name
export TP_SOCKET_IFNAME=$nic_name
export HCCL_SOCKET_IFNAME=$nic_name
export OMP_PROC_BIND=false
export OMP_NUM_THREADS=100
export VLLM_USE_V1=1
export HCCL_BUFFSIZE=1024
vllm serve vllm-ascend/DeepSeek-V3.1-W8A8 \
--host 0.0.0.0 \
--port 8004 \
--headless \
--data-parallel-size 4 \
--data-parallel-size-local 2 \
--data-parallel-start-rank 2 \
--data-parallel-address { node0 ip } \
--data-parallel-rpc-port 13389 \
--tensor-parallel-size 4 \
--seed 1024 \
--quantization ascend \
--served-model-name deepseek_v3.1 \
--max-num-seqs 16 \
--max-model-len 32768 \
--max-num-batched-tokens 4096 \
--enable-expert-parallel \
--trust-remote-code \
--no-enable-prefix-caching \
--gpu-memory-utilization 0.92 \
--additional-config '{"ascend_scheduler_config":{"enabled":true},"torchair_graph_config":{"enabled":true}}'
```
The Deployment view looks like:
![alt text](../assets/multi_node_dp_deepseek.png)
Once your server is started, you can query the model with input prompts:
```shell
curl http://{ node0 ip:8004 }/v1/completions \
-H "Content-Type: application/json" \
-d '{
"model": "deepseek_v3.1",
"prompt": "The future of AI is",
"max_tokens": 50,
"temperature": 0
}'
```
## Run benchmarks
For details please refer to [benchmark](https://github.com/vllm-project/vllm-ascend/tree/main/benchmarks)
```shell
export VLLM_USE_MODELSCOPE=true
vllm bench serve --model vllm-ascend/DeepSeek-V3.1-W8A8 --served-model-name deepseek_v3.1 \
--dataset-name random --random-input-len 128 --random-output-len 128 \
--num-prompts 200 --trust-remote-code --base-url "http://{ node0 ip }:8004" --request-rate 1
```

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# Multi-Node-DP (Kimi-K2)
## Verify Multi-Node Communication Environment
referring to [multi_node.md](https://vllm-ascend.readthedocs.io/en/latest/tutorials/multi_node.html#verification-process)
## Run with docker
Assume you have two Atlas 800 A3(64G*16) nodes(or 4 * A2), and want to deploy the `Kimi-K2-Instruct-W8A8` quantitative model across multi-node.
```{code-block} bash
:substitutions:
# Update the vllm-ascend image
export IMAGE=m.daocloud.io/quay.io/ascend/vllm-ascend:|vllm_ascend_version|
export NAME=vllm-ascend
# Run the container using the defined variables
# Note if you are running bridge network with docker, Please expose available ports for multiple nodes communication in advance
docker run --rm \
--name $NAME \
--net=host \
--device /dev/davinci0 \
--device /dev/davinci1 \
--device /dev/davinci2 \
--device /dev/davinci3 \
--device /dev/davinci4 \
--device /dev/davinci5 \
--device /dev/davinci6 \
--device /dev/davinci7 \
--device /dev/davinci8 \
--device /dev/davinci9 \
--device /dev/davinci10 \
--device /dev/davinci11 \
--device /dev/davinci12 \
--device /dev/davinci13 \
--device /dev/davinci14 \
--device /dev/davinci15 \
--device /dev/davinci_manager \
--device /dev/devmm_svm \
--device /dev/hisi_hdc \
-v /usr/local/dcmi:/usr/local/dcmi \
-v /usr/local/Ascend/driver/tools/hccn_tool:/usr/local/Ascend/driver/tools/hccn_tool \
-v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
-v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
-v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
-v /etc/ascend_install.info:/etc/ascend_install.info \
-v /mnt/sfs_turbo/.cache:/home/cache \
-it $IMAGE bash
```
Run the following scripts on two nodes respectively
:::{note}
Before launch the inference server, ensure the following environment variables are set for multi node communication
:::
**node0**
```shell
#!/bin/sh
# this obtained through ifconfig
# nic_name is the network interface name corresponding to local_ip
nic_name="xxxx"
local_ip="xxxx"
export HCCL_IF_IP=$local_ip
export GLOO_SOCKET_IFNAME=$nic_name
export TP_SOCKET_IFNAME=$nic_name
export HCCL_SOCKET_IFNAME=$nic_name
export OMP_PROC_BIND=false
export OMP_NUM_THREADS=100
export VLLM_USE_V1=1
export HCCL_BUFFSIZE=1024
# The w8a8 weight can obtained from https://www.modelscope.cn/models/vllm-ascend/Kimi-K2-Instruct-W8A8
# If you want to the quantization manually, please refer to https://vllm-ascend.readthedocs.io/en/latest/user_guide/feature_guide/quantization.html
vllm serve /home/cache/weights/Kimi-K2-Instruct-W8A8 \
--host 0.0.0.0 \
--port 8004 \
--data-parallel-size 4 \
--api-server-count 2 \
--data-parallel-size-local 2 \
--data-parallel-address $local_ip \
--data-parallel-rpc-port 13389 \
--seed 1024 \
--served-model-name kimi \
--quantization ascend \
--tensor-parallel-size 8 \
--enable-expert-parallel \
--max-num-seqs 16 \
--max-model-len 32768 \
--max-num-batched-tokens 4096 \
--trust-remote-code \
--no-enable-prefix-caching \
--gpu-memory-utilization 0.9 \
--additional-config '{"ascend_scheduler_config":{"enabled":true},"torchair_graph_config":{"enabled":true}}'
```
**node1**
```shell
#!/bin/sh
nic_name="xxxx"
local_ip="xxxx"
export HCCL_IF_IP=$local_ip
export GLOO_SOCKET_IFNAME=$nic_name
export TP_SOCKET_IFNAME=$nic_name
export HCCL_SOCKET_IFNAME=$nic_name
export OMP_PROC_BIND=false
export OMP_NUM_THREADS=100
export VLLM_USE_V1=1
export HCCL_BUFFSIZE=1024
vllm serve /home/cache/weights/Kimi-K2-Instruct-W8A8 \
--host 0.0.0.0 \
--port 8004 \
--headless \
--data-parallel-size 4 \
--data-parallel-size-local 2 \
--data-parallel-start-rank 2 \
--data-parallel-address $node0_ip \
--data-parallel-rpc-port 13389 \
--seed 1024 \
--tensor-parallel-size 8 \
--served-model-name kimi \
--max-num-seqs 16 \
--max-model-len 32768 \
--quantization ascend \
--max-num-batched-tokens 4096 \
--enable-expert-parallel \
--trust-remote-code \
--no-enable-prefix-caching \
--gpu-memory-utilization 0.92 \
--additional-config '{"ascend_scheduler_config":{"enabled":true},"torchair_graph_config":{"enabled":true}}'
```
The Deployment view looks like:
![alt text](../assets/multi_node_dp_kimi.png)
Once your server is started, you can query the model with input prompts:
```shell
curl http://{ node0 ip:8004 }/v1/completions \
-H "Content-Type: application/json" \
-d '{
"model": "kimi",
"prompt": "The future of AI is",
"max_tokens": 50,
"temperature": 0
}'
```

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# Multi-NPU (QwQ 32B)
## Run vllm-ascend on Multi-NPU
Run docker container:
```{code-block} bash
:substitutions:
# Update the vllm-ascend image
export IMAGE=quay.io/ascend/vllm-ascend:|vllm_ascend_version|
docker run --rm \
--name vllm-ascend \
--device /dev/davinci0 \
--device /dev/davinci1 \
--device /dev/davinci2 \
--device /dev/davinci3 \
--device /dev/davinci_manager \
--device /dev/devmm_svm \
--device /dev/hisi_hdc \
-v /usr/local/dcmi:/usr/local/dcmi \
-v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
-v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
-v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
-v /etc/ascend_install.info:/etc/ascend_install.info \
-v /root/.cache:/root/.cache \
-p 8000:8000 \
-it $IMAGE bash
```
Setup environment variables:
```bash
# Load model from ModelScope to speed up download
export VLLM_USE_MODELSCOPE=True
# Set `max_split_size_mb` to reduce memory fragmentation and avoid out of memory
export PYTORCH_NPU_ALLOC_CONF=max_split_size_mb:256
```
### Online Inference on Multi-NPU
Run the following script to start the vLLM server on Multi-NPU:
```bash
vllm serve Qwen/QwQ-32B --max-model-len 4096 --port 8000 -tp 4
```
Once your server is started, you can query the model with input prompts
```bash
curl http://localhost:8000/v1/completions \
-H "Content-Type: application/json" \
-d '{
"model": "Qwen/QwQ-32B",
"prompt": "QwQ-32B是什么？",
"max_tokens": "128",
"top_p": "0.95",
"top_k": "40",
"temperature": "0.6"
}'
```
### Offline Inference on Multi-NPU
Run the following script to execute offline inference on multi-NPU:
```python
import gc
import torch
from vllm import LLM, SamplingParams
from vllm.distributed.parallel_state import (destroy_distributed_environment,
destroy_model_parallel)
def clean_up():
destroy_model_parallel()
destroy_distributed_environment()
gc.collect()
torch.npu.empty_cache()
prompts = [
"Hello, my name is",
"The future of AI is",
]
sampling_params = SamplingParams(temperature=0.6, top_p=0.95, top_k=40)
llm = LLM(model="Qwen/QwQ-32B",
tensor_parallel_size=4,
distributed_executor_backend="mp",
max_model_len=4096)
outputs = llm.generate(prompts, sampling_params)
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
del llm
clean_up()
```
If you run this script successfully, you can see the info shown below:
```bash
Prompt: 'Hello, my name is', Generated text: ' Daniel and I am an 8th grade student at York Middle School. I'
Prompt: 'The future of AI is', Generated text: ' following you. As the technology advances, a new report from the Institute for the'
```

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@@ -0,0 +1,242 @@
# Multi-NPU (Pangu Pro MoE)
## Run vllm-ascend on Multi-NPU
Run container:
```{code-block} bash
:substitutions:
# Update the vllm-ascend image
export IMAGE=quay.io/ascend/vllm-ascend:|vllm_ascend_version|
docker run --rm \
--name vllm-ascend \
--device /dev/davinci0 \
--device /dev/davinci1 \
--device /dev/davinci2 \
--device /dev/davinci3 \
--device /dev/davinci_manager \
--device /dev/devmm_svm \
--device /dev/hisi_hdc \
-v /usr/local/dcmi:/usr/local/dcmi \
-v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
-v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
-v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
-v /etc/ascend_install.info:/etc/ascend_install.info \
-v /root/.cache:/root/.cache \
-p 8000:8000 \
-it $IMAGE bash
```
Setup environment variables:
```bash
# Set `max_split_size_mb` to reduce memory fragmentation and avoid out of memory
export PYTORCH_NPU_ALLOC_CONF=max_split_size_mb:256
```
Download the model:
```bash
git lfs install
git clone https://gitcode.com/ascend-tribe/pangu-pro-moe-model.git
```
### Online Inference on Multi-NPU
Run the following script to start the vLLM server on Multi-NPU:
```bash
vllm serve /path/to/pangu-pro-moe-model \
--tensor-parallel-size 4 \
--enable-expert-parallel \
--trust-remote-code \
--enforce-eager
```
Once your server is started, you can query the model with input prompts:
:::::{tab-set}
::::{tab-item} v1/completions
```{code-block} bash
:substitutions:
export question="你是谁？"
curl http://localhost:8000/v1/completions \
-H "Content-Type: application/json" \
-d '{
"prompt": "[unused9]系统：[unused10][unused9]用户：'${question}'[unused10][unused9]助手：",
"max_tokens": 64,
"top_p": 0.95,
"top_k": 50,
"temperature": 0.6
}'
```
::::
::::{tab-item} v1/chat/completions
```{code-block} bash
:substitutions:
curl http://localhost:8000/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"messages": [
{"role": "system", "content": ""},
{"role": "user", "content": "你是谁？"}
],
"max_tokens": "64",
"top_p": "0.95",
"top_k": "50",
"temperature": "0.6",
"add_special_tokens" : true
}'
```
::::
:::::
If you run this successfully, you can see the info shown below:
```json
{"id":"cmpl-2cd4223228ab4be9a91f65b882e65b32","object":"text_completion","created":1751255067,"model":"/root/.cache/pangu-pro-moe-model","choices":[{"index":0,"text":" [unused16] 好的，用户问我是谁，我需要根据之前的设定来回答。用户提到我是华为开发的“盘古Reasoner”，属于盘古大模型系列，作为智能助手帮助解答问题和提供 信息支持。现在用户再次询问，可能是在确认我的身份或者测试我的回答是否一致。\n\n首先，我要确保","logprobs":null,"finish_reason":"length","stop_reason":null,"prompt_logprobs":null}],"usage":{"prompt_tokens":15,"total_tokens":79,"completion_tokens":64,"prompt_tokens_details":null},"kv_transfer_params":null}
```
### Offline Inference on Multi-NPU
Run the following script to execute offline inference on multi-NPU:
:::::{tab-set}
::::{tab-item} Graph Mode
```{code-block} python
:substitutions:
import gc
from transformers import AutoTokenizer
import torch
import os
from vllm import LLM, SamplingParams
from vllm.distributed.parallel_state import (destroy_distributed_environment,
destroy_model_parallel)
os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
def clean_up():
destroy_model_parallel()
destroy_distributed_environment()
gc.collect()
torch.npu.empty_cache()
if __name__ == "__main__":
tokenizer = AutoTokenizer.from_pretrained("/path/to/pangu-pro-moe-model", trust_remote_code=True)
tests = [
"Hello, my name is",
"The future of AI is",
]
prompts = []
for text in tests:
messages = [
{"role": "system", "content": ""}, # Optionally customize system content
{"role": "user", "content": text}
]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
prompts.append(prompt)
sampling_params = SamplingParams(temperature=0.6, top_p=0.95, top_k=40)
llm = LLM(model="/path/to/pangu-pro-moe-model",
tensor_parallel_size=4,
enable_expert_parallel=True,
distributed_executor_backend="mp",
max_model_len=1024,
trust_remote_code=True,
additional_config={
'torchair_graph_config': {
'enabled': True,
},
'ascend_scheduler_config':{
'enabled': True,
'enable_chunked_prefill' : False,
'chunked_prefill_enabled': False
},
})
outputs = llm.generate(prompts, sampling_params)
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
del llm
clean_up()
```
::::
::::{tab-item} Eager Mode
```{code-block} python
:substitutions:
import gc
from transformers import AutoTokenizer
import torch
import os
from vllm import LLM, SamplingParams
from vllm.distributed.parallel_state import (destroy_distributed_environment,
destroy_model_parallel)
os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
def clean_up():
destroy_model_parallel()
destroy_distributed_environment()
gc.collect()
torch.npu.empty_cache()
if __name__ == "__main__":
tokenizer = AutoTokenizer.from_pretrained("/path/to/pangu-pro-moe-model", trust_remote_code=True)
tests = [
"Hello, my name is",
"The future of AI is",
]
prompts = []
for text in tests:
messages = [
{"role": "system", "content": ""}, # Optionally customize system content
{"role": "user", "content": text}
]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
prompts.append(prompt)
sampling_params = SamplingParams(temperature=0.6, top_p=0.95, top_k=40)
llm = LLM(model="/path/to/pangu-pro-moe-model",
tensor_parallel_size=4,
distributed_executor_backend="mp",
max_model_len=1024,
trust_remote_code=True,
enforce_eager=True)
outputs = llm.generate(prompts, sampling_params)
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
del llm
clean_up()
```
::::
:::::
If you run this script successfully, you can see the info shown below:
```bash
Prompt: 'Hello, my name is', Generated text: ' Daniel and I am an 8th grade student at York Middle School. I'
Prompt: 'The future of AI is', Generated text: ' following you. As the technology advances, a new report from the Institute for the'
```

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# Multi-NPU (QwQ 32B W8A8)
## Run docker container
:::{note}
w8a8 quantization feature is supported by v0.8.4rc2 or higher
:::
```{code-block} bash
:substitutions:
# Update the vllm-ascend image
export IMAGE=m.daocloud.io/quay.io/ascend/vllm-ascend:|vllm_ascend_version|
docker run --rm \
--name vllm-ascend \
--device /dev/davinci0 \
--device /dev/davinci1 \
--device /dev/davinci2 \
--device /dev/davinci3 \
--device /dev/davinci_manager \
--device /dev/devmm_svm \
--device /dev/hisi_hdc \
-v /usr/local/dcmi:/usr/local/dcmi \
-v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
-v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
-v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
-v /etc/ascend_install.info:/etc/ascend_install.info \
-v /root/.cache:/root/.cache \
-p 8000:8000 \
-it $IMAGE bash
```
## Install modelslim and convert model
:::{note}
You can choose to convert the model yourself or use the quantized model we uploaded,
see https://www.modelscope.cn/models/vllm-ascend/QwQ-32B-W8A8
:::
```bash
# (Optional)This tag is recommended and has been verified
git clone https://gitee.com/ascend/msit -b modelslim-VLLM-8.1.RC1.b020_001
cd msit/msmodelslim
# Install by run this script
bash install.sh
pip install accelerate
cd example/Qwen
# Original weight path, Replace with your local model path
MODEL_PATH=/home/models/QwQ-32B
# Path to save converted weight, Replace with your local path
SAVE_PATH=/home/models/QwQ-32B-w8a8
# In this conversion process, the npu device is not must, you can also set --device_type cpu to have a conversion
python3 quant_qwen.py --model_path $MODEL_PATH --save_directory $SAVE_PATH --calib_file ../common/boolq.jsonl --w_bit 8 --a_bit 8 --device_type npu --anti_method m1 --trust_remote_code True
```
## Verify the quantized model
The converted model files looks like:
```bash
.
|-- config.json
|-- configuration.json
|-- generation_config.json
|-- quant_model_description.json
|-- quant_model_weight_w8a8.safetensors
|-- README.md
|-- tokenizer.json
`-- tokenizer_config.json
```
Run the following script to start the vLLM server with quantized model:
:::{note}
The value "ascend" for "--quantization" argument will be supported after [a specific PR](https://github.com/vllm-project/vllm-ascend/pull/877) is merged and released, you can cherry-pick this commit for now.
:::
```bash
vllm serve /home/models/QwQ-32B-w8a8 --tensor-parallel-size 4 --served-model-name "qwq-32b-w8a8" --max-model-len 4096 --quantization ascend
```
Once your server is started, you can query the model with input prompts
```bash
curl http://localhost:8000/v1/completions \
-H "Content-Type: application/json" \
-d '{
"model": "qwq-32b-w8a8",
"prompt": "what is large language model?",
"max_tokens": "128",
"top_p": "0.95",
"top_k": "40",
"temperature": "0.0"
}'
```
Run the following script to execute offline inference on multi-NPU with quantized model:
:::{note}
To enable quantization for ascend, quantization method must be "ascend"
:::
```python
import gc
import torch
from vllm import LLM, SamplingParams
from vllm.distributed.parallel_state import (destroy_distributed_environment,
destroy_model_parallel)
def clean_up():
destroy_model_parallel()
destroy_distributed_environment()
gc.collect()
torch.npu.empty_cache()
prompts = [
"Hello, my name is",
"The future of AI is",
]
sampling_params = SamplingParams(temperature=0.6, top_p=0.95, top_k=40)
llm = LLM(model="/home/models/QwQ-32B-w8a8",
tensor_parallel_size=4,
distributed_executor_backend="mp",
max_model_len=4096,
quantization="ascend")
outputs = llm.generate(prompts, sampling_params)
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
del llm
clean_up()
```

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# Multi-NPU (Qwen3-30B-A3B)
## Run vllm-ascend on Multi-NPU with Qwen3 MoE
Run docker container:
```{code-block} bash
:substitutions:
# Update the vllm-ascend image
export IMAGE=quay.io/ascend/vllm-ascend:|vllm_ascend_version|
docker run --rm \
--name vllm-ascend \
--device /dev/davinci0 \
--device /dev/davinci1 \
--device /dev/davinci2 \
--device /dev/davinci3 \
--device /dev/davinci_manager \
--device /dev/devmm_svm \
--device /dev/hisi_hdc \
-v /usr/local/dcmi:/usr/local/dcmi \
-v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
-v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
-v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
-v /etc/ascend_install.info:/etc/ascend_install.info \
-v /root/.cache:/root/.cache \
-p 8000:8000 \
-it $IMAGE bash
```
Setup environment variables:
```bash
# Load model from ModelScope to speed up download
export VLLM_USE_MODELSCOPE=True
# Set `max_split_size_mb` to reduce memory fragmentation and avoid out of memory
export PYTORCH_NPU_ALLOC_CONF=max_split_size_mb:256
```
### Online Inference on Multi-NPU
Run the following script to start the vLLM server on Multi-NPU:
For an Atlas A2 with 64GB of NPU card memory, tensor-parallel-size should be at least 2, and for 32GB of memory, tensor-parallel-size should be at least 4.
```bash
vllm serve Qwen/Qwen3-30B-A3B --tensor-parallel-size 4 --enable_expert_parallel
```
Once your server is started, you can query the model with input prompts
```bash
curl http://localhost:8000/v1/chat/completions -H "Content-Type: application/json" -d '{
"model": "Qwen/Qwen3-30B-A3B",
"messages": [
{"role": "user", "content": "Give me a short introduction to large language models."}
],
"temperature": 0.6,
"top_p": 0.95,
"top_k": 20,
"max_tokens": 4096
}'
```
### Offline Inference on Multi-NPU
Run the following script to execute offline inference on multi-NPU:
```python
import gc
import torch
from vllm import LLM, SamplingParams
from vllm.distributed.parallel_state import (destroy_distributed_environment,
destroy_model_parallel)
def clean_up():
destroy_model_parallel()
destroy_distributed_environment()
gc.collect()
torch.npu.empty_cache()
prompts = [
"Hello, my name is",
"The future of AI is",
]
sampling_params = SamplingParams(temperature=0.6, top_p=0.95, top_k=40)
llm = LLM(model="Qwen/Qwen3-30B-A3B",
tensor_parallel_size=4,
distributed_executor_backend="mp",
max_model_len=4096,
enable_expert_parallel=True)
outputs = llm.generate(prompts, sampling_params)
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
del llm
clean_up()
```
If you run this script successfully, you can see the info shown below:
```bash
Prompt: 'Hello, my name is', Generated text: " Lucy. I'm from the UK and I'm 11 years old."
Prompt: 'The future of AI is', Generated text: ' a topic that has captured the imagination of scientists, philosophers, and the general public'
```

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# Single Node (Atlas 300I series)
```{note}
1. This Atlas 300I series is currently experimental. In future versions, there may be behavioral changes around model coverage, performance improvement.
2. Currently, the 310I series only supports eager mode and the data type is float16.
```
## Run vLLM on Altlas 300I series
Run docker container:
```{code-block} bash
:substitutions:
# Update the vllm-ascend image
export IMAGE=quay.io/ascend/vllm-ascend:|vllm_ascend_version|-310p
docker run --rm \
--name vllm-ascend \
--device /dev/davinci0 \
--device /dev/davinci1 \
--device /dev/davinci2 \
--device /dev/davinci3 \
--device /dev/davinci4 \
--device /dev/davinci5 \
--device /dev/davinci6 \
--device /dev/davinci7 \
--device /dev/davinci_manager \
--device /dev/devmm_svm \
--device /dev/hisi_hdc \
-v /usr/local/dcmi:/usr/local/dcmi \
-v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
-v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
-v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
-v /etc/ascend_install.info:/etc/ascend_install.info \
-v /root/.cache:/root/.cache \
-p 8000:8000 \
-it $IMAGE bash
```
Setup environment variables:
```bash
# Load model from ModelScope to speed up download
export VLLM_USE_MODELSCOPE=True
# Set `max_split_size_mb` to reduce memory fragmentation and avoid out of memory
export PYTORCH_NPU_ALLOC_CONF=max_split_size_mb:256
```
### Online Inference on NPU
Run the following script to start the vLLM server on NPU(Qwen3-0.6B:1 card, Qwen2.5-7B-Instruct:2 cards, Pangu-Pro-MoE-72B: 8 cards):
:::::{tab-set}
:sync-group: inference
::::{tab-item} Qwen3-0.6B
:selected:
:sync: qwen0.6
Run the following command to start the vLLM server:
```{code-block} bash
:substitutions:
vllm serve Qwen/Qwen3-0.6B \
--tensor-parallel-size 1 \
--enforce-eager \
--dtype float16 \
--compilation-config '{"custom_ops":["none", "+rms_norm", "+rotary_embedding"]}'
```
Once your server is started, you can query the model with input prompts
```bash
curl http://localhost:8000/v1/completions \
-H "Content-Type: application/json" \
-d '{
"prompt": "The future of AI is",
"max_tokens": 64,
"top_p": 0.95,
"top_k": 50,
"temperature": 0.6
}'
```
::::
::::{tab-item} Qwen2.5-7B-Instruct
:sync: qwen7b
Run the following command to start the vLLM server:
```{code-block} bash
:substitutions:
vllm serve Qwen/Qwen2.5-7B-Instruct \
--tensor-parallel-size 2 \
--enforce-eager \
--dtype float16 \
--compilation-config '{"custom_ops":["none", "+rms_norm", "+rotary_embedding"]}'
```
Once your server is started, you can query the model with input prompts
```bash
curl http://localhost:8000/v1/completions \
-H "Content-Type: application/json" \
-d '{
"prompt": "The future of AI is",
"max_tokens": 64,
"top_p": 0.95,
"top_k": 50,
"temperature": 0.6
}'
```
::::
::::{tab-item} Qwen2.5-VL-3B-Instruct
:sync: qwen-vl-2.5-3b
Run the following command to start the vLLM server:
```{code-block} bash
:substitutions:
vllm serve Qwen/Qwen2.5-VL-3B-Instruct \
--tensor-parallel-size 1 \
--enforce-eager \
--dtype float16 \
--compilation-config '{"custom_ops":["none", "+rms_norm", "+rotary_embedding"]}'
```
Once your server is started, you can query the model with input prompts
```bash
curl http://localhost:8000/v1/completions \
-H "Content-Type: application/json" \
-d '{
"prompt": "The future of AI is",
"max_tokens": 64,
"top_p": 0.95,
"top_k": 50,
"temperature": 0.6
}'
```
::::
::::{tab-item} Pangu-Pro-MoE-72B
:sync: pangu
Download the model:
```bash
git lfs install
git clone https://gitcode.com/ascend-tribe/pangu-pro-moe-model.git
```
Run the following command to start the vLLM server:
```{code-block} bash
:substitutions:
vllm serve /home/pangu-pro-moe-mode/ \
--tensor-parallel-size 4 \
--enable-expert-parallel \
--dtype "float16" \
--trust-remote-code \
--enforce-eager
```
Once your server is started, you can query the model with input prompts
```bash
export question="你是谁？"
curl http://localhost:8000/v1/completions \
-H "Content-Type: application/json" \
-d '{
"prompt": "[unused9]系统：[unused10][unused9]用户：'${question}'[unused10][unused9]助手：",
"max_tokens": 64,
"top_p": 0.95,
"top_k": 50,
"temperature": 0.6
}'
```
::::
:::::
If you run this script successfully, you can see the results.
### Offline Inference
Run the following script (`example.py`) to execute offline inference on NPU:
:::::{tab-set}
:sync-group: inference
::::{tab-item} Qwen3-0.6B
:selected:
:sync: qwen0.6
```{code-block} python
:substitutions:
from vllm import LLM, SamplingParams
import gc
import torch
from vllm import LLM, SamplingParams
from vllm.distributed.parallel_state import (destroy_distributed_environment,
destroy_model_parallel)
def clean_up():
destroy_model_parallel()
destroy_distributed_environment()
gc.collect()
torch.npu.empty_cache()
prompts = [
"Hello, my name is",
"The future of AI is",
]
# Create a sampling params object.
sampling_params = SamplingParams(max_tokens=100, temperature=0.0)
# Create an LLM.
llm = LLM(
model="Qwen/Qwen3-0.6B",
tensor_parallel_size=1,
enforce_eager=True, # For 300I series, only eager mode is supported.
dtype="float16", # IMPORTANT cause some ATB ops cannot support bf16 on 300I series
compilation_config={"custom_ops":["none", "+rms_norm", "+rotary_embedding"]}, # High performance for 300I series
)
# Generate texts from the prompts.
outputs = llm.generate(prompts, sampling_params)
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
del llm
clean_up()
```
::::
::::{tab-item} Qwen2.5-7B-Instruct
:sync: qwen7b
```{code-block} python
:substitutions:
from vllm import LLM, SamplingParams
import gc
import torch
from vllm import LLM, SamplingParams
from vllm.distributed.parallel_state import (destroy_distributed_environment,
destroy_model_parallel)
def clean_up():
destroy_model_parallel()
destroy_distributed_environment()
gc.collect()
torch.npu.empty_cache()
prompts = [
"Hello, my name is",
"The future of AI is",
]
# Create a sampling params object.
sampling_params = SamplingParams(max_tokens=100, temperature=0.0)
# Create an LLM.
llm = LLM(
model="Qwen/Qwen2.5-7B-Instruct",
tensor_parallel_size=2,
enforce_eager=True, # For 300I series, only eager mode is supported.
dtype="float16", # IMPORTANT cause some ATB ops cannot support bf16 on 300I series
compilation_config={"custom_ops":["none", "+rms_norm", "+rotary_embedding"]}, # High performance for 300I series
)
# Generate texts from the prompts.
outputs = llm.generate(prompts, sampling_params)
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
del llm
clean_up()
```
::::
::::{tab-item} Qwen2.5-VL-3B-Instruct
:sync: qwen-vl-2.5-3b
```{code-block} python
:substitutions:
from vllm import LLM, SamplingParams
import gc
import torch
from vllm import LLM, SamplingParams
from vllm.distributed.parallel_state import (destroy_distributed_environment,
destroy_model_parallel)
def clean_up():
destroy_model_parallel()
destroy_distributed_environment()
gc.collect()
torch.npu.empty_cache()
prompts = [
"Hello, my name is",
"The future of AI is",
]
# Create a sampling params object.
sampling_params = SamplingParams(max_tokens=100, top_p=0.95, top_k=50, temperature=0.6)
# Create an LLM.
llm = LLM(
model="Qwen/Qwen2.5-VL-3B-Instruct",
tensor_parallel_size=1,
enforce_eager=True, # For 300I series, only eager mode is supported.
dtype="float16", # IMPORTANT cause some ATB ops cannot support bf16 on 300I series
compilation_config={"custom_ops":["none", "+rms_norm", "+rotary_embedding"]}, # High performance for 300I series
)
# Generate texts from the prompts.
outputs = llm.generate(prompts, sampling_params)
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
del llm
clean_up()
```
::::
::::{tab-item} Pangu-Pro-MoE-72B
:sync: pangu
Download the model:
```bash
git lfs install
git clone https://gitcode.com/ascend-tribe/pangu-pro-moe-model.git
```
```{code-block} python
:substitutions:
import gc
from transformers import AutoTokenizer
import torch
from vllm import LLM, SamplingParams
from vllm.distributed.parallel_state import (destroy_distributed_environment,
destroy_model_parallel)
def clean_up():
destroy_model_parallel()
destroy_distributed_environment()
gc.collect()
torch.npu.empty_cache()
if __name__ == "__main__":
tokenizer = AutoTokenizer.from_pretrained("/home/pangu-pro-moe-mode/", trust_remote_code=True)
tests = [
"Hello, my name is",
"The future of AI is",
]
prompts = []
for text in tests:
messages = [
{"role": "system", "content": ""}, # Optionally customize system content
{"role": "user", "content": text}
]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) # 推荐使用官方的template
prompts.append(prompt)
sampling_params = SamplingParams(temperature=0.6, top_p=0.95, top_k=40)
llm = LLM(model="/home/pangu-pro-moe-mode/",
tensor_parallel_size=8,
distributed_executor_backend="mp",
enable_expert_parallel=True,
dtype="float16",
max_model_len=1024,
trust_remote_code=True,
enforce_eager=True)
outputs = llm.generate(prompts, sampling_params)
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
del llm
clean_up()
```
::::
:::::
Run script:
```bash
python example.py
```
If you run this script successfully, you can see the info shown below:
```bash
Prompt: 'Hello, my name is', Generated text: " Lina. I'm a 22-year-old student from China. I'm interested in studying in the US. I'm looking for a job in the US. I want to know if there are any opportunities in the US for me to work. I'm also interested in the culture and lifestyle in the US. I want to know if there are any opportunities for me to work in the US. I'm also interested in the culture and lifestyle in the US. I'm interested in the culture"
Prompt: 'The future of AI is', Generated text: " not just about the technology itself, but about how we use it to solve real-world problems. As AI continues to evolve, it's important to consider the ethical implications of its use. AI has the potential to bring about significant changes in society, but it also has the power to create new challenges. Therefore, it's crucial to develop a comprehensive approach to AI that takes into account both the benefits and the risks associated with its use. This includes addressing issues such as bias, privacy, and accountability."
```

202
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# Single NPU (Qwen3 8B)
## Run vllm-ascend on Single NPU
### Offline Inference on Single NPU
Run docker container:
```{code-block} bash
:substitutions:
# Update the vllm-ascend image
export IMAGE=quay.io/ascend/vllm-ascend:|vllm_ascend_version|
docker run --rm \
--name vllm-ascend \
--device /dev/davinci0 \
--device /dev/davinci_manager \
--device /dev/devmm_svm \
--device /dev/hisi_hdc \
-v /usr/local/dcmi:/usr/local/dcmi \
-v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
-v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
-v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
-v /etc/ascend_install.info:/etc/ascend_install.info \
-v /root/.cache:/root/.cache \
-p 8000:8000 \
-it $IMAGE bash
```
Setup environment variables:
```bash
# Load model from ModelScope to speed up download
export VLLM_USE_MODELSCOPE=True
# Set `max_split_size_mb` to reduce memory fragmentation and avoid out of memory
export PYTORCH_NPU_ALLOC_CONF=max_split_size_mb:256
```
:::{note}
`max_split_size_mb` prevents the native allocator from splitting blocks larger than this size (in MB). This can reduce fragmentation and may allow some borderline workloads to complete without running out of memory. You can find more details [<u>here</u>](https://www.hiascend.com/document/detail/zh/CANNCommunityEdition/800alpha003/apiref/envref/envref_07_0061.html).
:::
Run the following script to execute offline inference on a single NPU:
:::::{tab-set}
::::{tab-item} Graph Mode
```{code-block} python
:substitutions:
import os
from vllm import LLM, SamplingParams
prompts = [
"Hello, my name is",
"The future of AI is",
]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
llm = LLM(
model="Qwen/Qwen3-8B",
max_model_len=26240
)
outputs = llm.generate(prompts, sampling_params)
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
```
::::
::::{tab-item} Eager Mode
```{code-block} python
:substitutions:
import os
from vllm import LLM, SamplingParams
prompts = [
"Hello, my name is",
"The future of AI is",
]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
llm = LLM(
model="Qwen/Qwen3-8B",
max_model_len=26240,
enforce_eager=True
)
outputs = llm.generate(prompts, sampling_params)
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
```
::::
:::::
If you run this script successfully, you can see the info shown below:
```bash
Prompt: 'Hello, my name is', Generated text: ' Daniel and I am an 8th grade student at York Middle School. I'
Prompt: 'The future of AI is', Generated text: ' following you. As the technology advances, a new report from the Institute for the'
```
### Online Serving on Single NPU
Run docker container to start the vLLM server on a single NPU:
:::::{tab-set}
::::{tab-item} Graph Mode
```{code-block} bash
:substitutions:
# Update the vllm-ascend image
export IMAGE=quay.io/ascend/vllm-ascend:|vllm_ascend_version|
docker run --rm \
--name vllm-ascend \
--device /dev/davinci0 \
--device /dev/davinci_manager \
--device /dev/devmm_svm \
--device /dev/hisi_hdc \
-v /usr/local/dcmi:/usr/local/dcmi \
-v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
-v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
-v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
-v /etc/ascend_install.info:/etc/ascend_install.info \
-v /root/.cache:/root/.cache \
-p 8000:8000 \
-e VLLM_USE_MODELSCOPE=True \
-e PYTORCH_NPU_ALLOC_CONF=max_split_size_mb:256 \
-it $IMAGE \
vllm serve Qwen/Qwen3-8B --max_model_len 26240
```
::::
::::{tab-item} Eager Mode
```{code-block} bash
:substitutions:
export IMAGE=quay.io/ascend/vllm-ascend:|vllm_ascend_version|
docker run --rm \
--name vllm-ascend \
--device /dev/davinci0 \
--device /dev/davinci_manager \
--device /dev/devmm_svm \
--device /dev/hisi_hdc \
-v /usr/local/dcmi:/usr/local/dcmi \
-v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
-v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
-v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
-v /etc/ascend_install.info:/etc/ascend_install.info \
-v /root/.cache:/root/.cache \
-p 8000:8000 \
-e VLLM_USE_MODELSCOPE=True \
-e PYTORCH_NPU_ALLOC_CONF=max_split_size_mb:256 \
-it $IMAGE \
vllm serve Qwen/Qwen3-8B --max_model_len 26240 --enforce-eager
```
::::
:::::
:::{note}
Add `--max_model_len` option to avoid ValueError that the Qwen2.5-7B model's max seq len (32768) is larger than the maximum number of tokens that can be stored in KV cache (26240). This will differ with different NPU series base on the HBM size. Please modify the value according to a suitable value for your NPU series.
:::
If your service start successfully, you can see the info shown below:
```bash
INFO: Started server process [6873]
INFO: Waiting for application startup.
INFO: Application startup complete.
```
Once your server is started, you can query the model with input prompts:
```bash
curl http://localhost:8000/v1/completions \
-H "Content-Type: application/json" \
-d '{
"model": "Qwen/Qwen3-8B",
"prompt": "The future of AI is",
"max_tokens": 7,
"temperature": 0
}'
```
If you query the server successfully, you can see the info shown below (client):
```bash
{"id":"cmpl-b25a59a2f985459781ce7098aeddfda7","object":"text_completion","created":1739523925,"model":"Qwen/Qwen3-8B","choices":[{"index":0,"text":" here. It’s not just a","logprobs":null,"finish_reason":"length","stop_reason":null,"prompt_logprobs":null}],"usage":{"prompt_tokens":5,"total_tokens":12,"completion_tokens":7,"prompt_tokens_details":null}}
```
Logs of the vllm server:
```bash
INFO: 172.17.0.1:49518 - "POST /v1/completions HTTP/1.1" 200 OK
INFO 02-13 08:34:35 logger.py:39] Received request cmpl-574f00e342904692a73fb6c1c986c521-0: prompt: 'San Francisco is a', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=7, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None), prompt_token_ids: [23729, 12879, 374, 264], lora_request: None, prompt_adapter_request: None.
```

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# Single NPU (Qwen2-Audio 7B)
## Run vllm-ascend on Single NPU
### Offline Inference on Single NPU
Run docker container:
```{code-block} bash
:substitutions:
# Update the vllm-ascend image
export IMAGE=quay.io/ascend/vllm-ascend:|vllm_ascend_version|
docker run --rm \
--name vllm-ascend \
--device /dev/davinci0 \
--device /dev/davinci_manager \
--device /dev/devmm_svm \
--device /dev/hisi_hdc \
-v /usr/local/dcmi:/usr/local/dcmi \
-v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
-v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
-v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
-v /etc/ascend_install.info:/etc/ascend_install.info \
-v /root/.cache:/root/.cache \
-p 8000:8000 \
-it $IMAGE bash
```
Setup environment variables:
```bash
# Load model from ModelScope to speed up download
export VLLM_USE_MODELSCOPE=True
# Set `max_split_size_mb` to reduce memory fragmentation and avoid out of memory
export PYTORCH_NPU_ALLOC_CONF=max_split_size_mb:256
```
:::{note}
`max_split_size_mb` prevents the native allocator from splitting blocks larger than this size (in MB). This can reduce fragmentation and may allow some borderline workloads to complete without running out of memory. You can find more details [<u>here</u>](https://www.hiascend.com/document/detail/zh/CANNCommunityEdition/800alpha003/apiref/envref/envref_07_0061.html).
:::
Install packages required for audio processing:
```bash
pip config set global.index-url https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple
pip install librosa soundfile
```
Run the following script to execute offline inference on a single NPU:
```python
from vllm import LLM, SamplingParams
from vllm.assets.audio import AudioAsset
from vllm.utils import FlexibleArgumentParser
# If network issues prevent AudioAsset from fetching remote audio files, retry or check your network.
audio_assets = [AudioAsset("mary_had_lamb"), AudioAsset("winning_call")]
question_per_audio_count = {
1: "What is recited in the audio?",
2: "What sport and what nursery rhyme are referenced?"
}
def prepare_inputs(audio_count: int):
audio_in_prompt = "".join([
f"Audio {idx+1}: <|audio_bos|><|AUDIO|><|audio_eos|>\n"
for idx in range(audio_count)
])
question = question_per_audio_count[audio_count]
prompt = ("<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n"
"<|im_start|>user\n"
f"{audio_in_prompt}{question}<|im_end|>\n"
"<|im_start|>assistant\n")
mm_data = {
"audio":
[asset.audio_and_sample_rate for asset in audio_assets[:audio_count]]
}
# Merge text prompt and audio data into inputs
inputs = {"prompt": prompt, "multi_modal_data": mm_data}
return inputs
def main(audio_count: int):
# NOTE: The default `max_num_seqs` and `max_model_len` may result in OOM on
# lower-end GPUs.
# Unless specified, these settings have been tested to work on a single L4.
# `limit_mm_per_prompt`: the max num items for each modality per prompt.
llm = LLM(model="Qwen/Qwen2-Audio-7B-Instruct",
max_model_len=4096,
max_num_seqs=5,
limit_mm_per_prompt={"audio": audio_count})
inputs = prepare_inputs(audio_count)
sampling_params = SamplingParams(temperature=0.2,
max_tokens=64,
stop_token_ids=None)
outputs = llm.generate(inputs, sampling_params=sampling_params)
for o in outputs:
generated_text = o.outputs[0].text
print(generated_text)
if __name__ == "__main__":
audio_count = 2
main(audio_count)
```
If you run this script successfully, you can see the info shown below:
```bash
The sport referenced is baseball, and the nursery rhyme is 'Mary Had a Little Lamb'.
```
### Online Serving on Single NPU
Currently, vllm's OpenAI-compatible server doesn't support audio inputs, find more details [<u>here</u>](https://github.com/vllm-project/vllm/issues/19977).

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# Single NPU (Qwen2.5-VL 7B)
## Run vllm-ascend on Single NPU
### Offline Inference on Single NPU
Run docker container:
```{code-block} bash
:substitutions:
# Update the vllm-ascend image
export IMAGE=quay.io/ascend/vllm-ascend:|vllm_ascend_version|
docker run --rm \
--name vllm-ascend \
--device /dev/davinci0 \
--device /dev/davinci_manager \
--device /dev/devmm_svm \
--device /dev/hisi_hdc \
-v /usr/local/dcmi:/usr/local/dcmi \
-v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
-v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
-v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
-v /etc/ascend_install.info:/etc/ascend_install.info \
-v /root/.cache:/root/.cache \
-p 8000:8000 \
-it $IMAGE bash
```
Setup environment variables:
```bash
# Load model from ModelScope to speed up download
export VLLM_USE_MODELSCOPE=True
# Set `max_split_size_mb` to reduce memory fragmentation and avoid out of memory
export PYTORCH_NPU_ALLOC_CONF=max_split_size_mb:256
```
:::{note}
`max_split_size_mb` prevents the native allocator from splitting blocks larger than this size (in MB). This can reduce fragmentation and may allow some borderline workloads to complete without running out of memory. You can find more details [<u>here</u>](https://www.hiascend.com/document/detail/zh/CANNCommunityEdition/800alpha003/apiref/envref/envref_07_0061.html).
:::
Run the following script to execute offline inference on a single NPU:
```bash
pip install qwen_vl_utils --extra-index-url https://download.pytorch.org/whl/cpu/
```
```python
from transformers import AutoProcessor
from vllm import LLM, SamplingParams
from qwen_vl_utils import process_vision_info
MODEL_PATH = "Qwen/Qwen2.5-VL-7B-Instruct"
llm = LLM(
model=MODEL_PATH,
max_model_len=16384,
limit_mm_per_prompt={"image": 10},
)
sampling_params = SamplingParams(
max_tokens=512
)
image_messages = [
{"role": "system", "content": "You are a helpful assistant."},
{
"role": "user",
"content": [
{
"type": "image",
"image": "https://modelscope.oss-cn-beijing.aliyuncs.com/resource/qwen.png",
"min_pixels": 224 * 224,
"max_pixels": 1280 * 28 * 28,
},
{"type": "text", "text": "Please provide a detailed description of this image"},
],
},
]
messages = image_messages
processor = AutoProcessor.from_pretrained(MODEL_PATH)
prompt = processor.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
)
image_inputs, _, _ = process_vision_info(messages, return_video_kwargs=True)
mm_data = {}
if image_inputs is not None:
mm_data["image"] = image_inputs
llm_inputs = {
"prompt": prompt,
"multi_modal_data": mm_data,
}
outputs = llm.generate([llm_inputs], sampling_params=sampling_params)
generated_text = outputs[0].outputs[0].text
print(generated_text)
```
If you run this script successfully, you can see the info shown below:
```bash
The image displays a logo consisting of two main elements: a stylized geometric design and a pair of text elements.
1. **Geometric Design**: On the left side of the image, there is a blue geometric design that appears to be made up of interconnected shapes. These shapes resemble a network or a complex polygonal structure, possibly hinting at a technological or interconnected theme. The design is monochromatic and uses only blue as its color, which could be indicative of a specific brand or company.
2. **Text Elements**: To the right of the geometric design, there are two lines of text. The first line reads "TONGYI" in a sans-serif font, with the "YI" part possibly being capitalized. The second line reads "Qwen" in a similar sans-serif font, but in a smaller size.
The overall design is modern and minimalist, with a clear contrast between the geometric and textual elements. The use of blue for the geometric design could suggest themes of technology, connectivity, or innovation, which are common associations with the color blue in branding. The simplicity of the design makes it easily recognizable and memorable.
```
### Online Serving on Single NPU
Run docker container to start the vLLM server on a single NPU:
```{code-block} bash
:substitutions:
# Update the vllm-ascend image
export IMAGE=quay.io/ascend/vllm-ascend:|vllm_ascend_version|
docker run --rm \
--name vllm-ascend \
--device /dev/davinci0 \
--device /dev/davinci_manager \
--device /dev/devmm_svm \
--device /dev/hisi_hdc \
-v /usr/local/dcmi:/usr/local/dcmi \
-v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
-v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
-v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
-v /etc/ascend_install.info:/etc/ascend_install.info \
-v /root/.cache:/root/.cache \
-p 8000:8000 \
-e VLLM_USE_MODELSCOPE=True \
-e PYTORCH_NPU_ALLOC_CONF=max_split_size_mb:256 \
-it $IMAGE \
vllm serve Qwen/Qwen2.5-VL-7B-Instruct \
--dtype bfloat16 \
--max_model_len 16384 \
--max-num-batched-tokens 16384
```
:::{note}
Add `--max_model_len` option to avoid ValueError that the Qwen2.5-VL-7B-Instruct model's max seq len (128000) is larger than the maximum number of tokens that can be stored in KV cache. This will differ with different NPU series base on the HBM size. Please modify the value according to a suitable value for your NPU series.
:::
If your service start successfully, you can see the info shown below:
```bash
INFO: Started server process [2736]
INFO: Waiting for application startup.
INFO: Application startup complete.
```
Once your server is started, you can query the model with input prompts:
```bash
curl http://localhost:8000/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "Qwen/Qwen2.5-VL-7B-Instruct",
"messages": [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": [
{"type": "image_url", "image_url": {"url": "https://modelscope.oss-cn-beijing.aliyuncs.com/resource/qwen.png"}},
{"type": "text", "text": "What is the text in the illustrate?"}
]}
]
}'
```
If you query the server successfully, you can see the info shown below (client):
```bash
{"id":"chatcmpl-f04fb20e79bb40b39b8ed7fdf5bd613a","object":"chat.completion","created":1741749149,"model":"Qwen/Qwen2.5-VL-7B-Instruct","choices":[{"index":0,"message":{"role":"assistant","reasoning_content":null,"content":"The text in the illustration reads \"TONGYI Qwen.\"","tool_calls":[]},"logprobs":null,"finish_reason":"stop","stop_reason":null}],"usage":{"prompt_tokens":74,"total_tokens":89,"completion_tokens":15,"prompt_tokens_details":null},"prompt_logprobs":null}
```
Logs of the vllm server:
```bash
INFO 03-12 11:16:50 logger.py:39] Received request chatcmpl-92148a41eca64b6d82d3d7cfa5723aeb: prompt: '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>\nWhat is the text in the illustrate?<|im_end|>\n<|im_start|>assistant\n', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=1.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=16353, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None.
INFO 03-12 11:16:50 engine.py:280] Added request chatcmpl-92148a41eca64b6d82d3d7cfa5723aeb.
INFO: 127.0.0.1:54004 - "POST /v1/chat/completions HTTP/1.1" 200 OK
```

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# Single NPU (Qwen3-Embedding-8B)
The Qwen3 Embedding model series is the latest proprietary model of the Qwen family, specifically designed for text embedding and ranking tasks. Building upon the dense foundational models of the Qwen3 series, it provides a comprehensive range of text embeddings and reranking models in various sizes (0.6B, 4B, and 8B). This guide describes how to run the model with vLLM Ascend. Note that only 0.9.2rc1 and higher versions of vLLM Ascend support the model.
## Run docker container
Take Qwen3-Embedding-8B model as an example, first run the docker container with the following command:
```{code-block} bash
:substitutions:
# Update the vllm-ascend image
export IMAGE=quay.io/ascend/vllm-ascend:|vllm_ascend_version|
docker run --rm \
--name vllm-ascend \
--device /dev/davinci0 \
--device /dev/davinci_manager \
--device /dev/devmm_svm \
--device /dev/hisi_hdc \
-v /usr/local/dcmi:/usr/local/dcmi \
-v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
-v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
-v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
-v /etc/ascend_install.info:/etc/ascend_install.info \
-v /root/.cache:/root/.cache \
-p 8000:8000 \
-it $IMAGE bash
```
Setup environment variables:
```bash
# Load model from ModelScope to speed up download
export VLLM_USE_MODELSCOPE=True
# Set `max_split_size_mb` to reduce memory fragmentation and avoid out of memory
export PYTORCH_NPU_ALLOC_CONF=max_split_size_mb:256
```
### Online Inference
```bash
vllm serve Qwen/Qwen3-Embedding-8B --task embed
```
Once your server is started, you can query the model with input prompts
```bash
curl http://localhost:8000/v1/embeddings -H "Content-Type: application/json" -d '{
"model": "Qwen/Qwen3-Embedding-8B",
"messages": [
{"role": "user", "content": "Hello"}
]
}'
```
### Offline Inference
```python
import torch
import vllm
from vllm import LLM
def get_detailed_instruct(task_description: str, query: str) -> str:
return f'Instruct: {task_description}\nQuery:{query}'
if __name__=="__main__":
# 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-8B",
task="embed",
distributed_executor_backend="mp")
outputs = model.embed(input_texts)
embeddings = torch.tensor([o.outputs.embedding for o in outputs])
scores = (embeddings[:2] @ embeddings[2:].T)
print(scores.tolist())
```
If you run this script successfully, you can see the info shown below:
```bash
Adding requests: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:00<00:00, 282.22it/s]
Processed prompts: 0%| | 0/4 [00:00<?, ?it/s, est. speed input: 0.00 toks/s, output: 0.00 toks/s](VllmWorker rank=0 pid=4074750) ('Warning: torch.save with "_use_new_zipfile_serialization = False" is not recommended for npu tensor, which may bring unexpected errors and hopefully set "_use_new_zipfile_serialization = True"', 'if it is necessary to use this, please convert the npu tensor to cpu tensor for saving')
Processed prompts: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:00<00:00, 31.95it/s, est. speed input: 0.00 toks/s, output: 0.00 toks/s]
[[0.7477798461914062, 0.07548339664936066], [0.0886271521449089, 0.6311039924621582]]
```

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# Single-NPU (Qwen3 8B W4A8)
## Run docker container
:::{note}
w4a8 quantization feature is supported by v0.9.1rc2 or higher
:::
```{code-block} bash
:substitutions:
# Update the vllm-ascend image
export IMAGE=m.daocloud.io/quay.io/ascend/vllm-ascend:|vllm_ascend_version|
docker run --rm \
--name vllm-ascend \
--device /dev/davinci0 \
--device /dev/davinci_manager \
--device /dev/devmm_svm \
--device /dev/hisi_hdc \
-v /usr/local/dcmi:/usr/local/dcmi \
-v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
-v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
-v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
-v /etc/ascend_install.info:/etc/ascend_install.info \
-v /root/.cache:/root/.cache \
-p 8000:8000 \
-it $IMAGE bash
```
## Install modelslim and convert model
:::{note}
You can choose to convert the model yourself or use the quantized model we uploaded,
see https://www.modelscope.cn/models/vllm-ascend/Qwen3-8B-W4A8
:::
```bash
# The branch(br_release_MindStudio_8.1.RC2_TR5_20260624) has been verified
git clone -b br_release_MindStudio_8.1.RC2_TR5_20260624 https://gitee.com/ascend/msit
cd msit/msmodelslim
# Install by run this script
bash install.sh
pip install accelerate
cd example/Qwen
# Original weight path, Replace with your local model path
MODEL_PATH=/home/models/Qwen3-8B
# Path to save converted weight, Replace with your local path
SAVE_PATH=/home/models/Qwen3-8B-w4a8
python quant_qwen.py \
--model_path $MODEL_PATH \
--save_directory $SAVE_PATH \
--device_type npu \
--model_type qwen3 \
--calib_file None \
--anti_method m6 \
--anti_calib_file ./calib_data/mix_dataset.json \
--w_bit 4 \
--a_bit 8 \
--is_lowbit True \
--open_outlier False \
--group_size 256 \
--is_dynamic True \
--trust_remote_code True \
--w_method HQQ
```
## Verify the quantized model
The converted model files looks like:
```bash
.
|-- config.json
|-- configuration.json
|-- generation_config.json
|-- merges.txt
|-- quant_model_description.json
|-- quant_model_weight_w4a8_dynamic-00001-of-00003.safetensors
|-- quant_model_weight_w4a8_dynamic-00002-of-00003.safetensors
|-- quant_model_weight_w4a8_dynamic-00003-of-00003.safetensors
|-- quant_model_weight_w4a8_dynamic.safetensors.index.json
|-- README.md
|-- tokenizer.json
`-- tokenizer_config.json
```
Run the following script to start the vLLM server with quantized model:
```bash
vllm serve /home/models/Qwen3-8B-w4a8 --served-model-name "qwen3-8b-w4a8" --max-model-len 4096 --quantization ascend
```
Once your server is started, you can query the model with input prompts
```bash
curl http://localhost:8000/v1/completions \
-H "Content-Type: application/json" \
-d '{
"model": "qwen3-8b-w4a8",
"prompt": "what is large language model?",
"max_tokens": "128",
"top_p": "0.95",
"top_k": "40",
"temperature": "0.0"
}'
```
Run the following script to execute offline inference on Single-NPU with quantized model:
:::{note}
To enable quantization for ascend, quantization method must be "ascend"
:::
```python
from vllm import LLM, SamplingParams
prompts = [
"Hello, my name is",
"The future of AI is",
]
sampling_params = SamplingParams(temperature=0.6, top_p=0.95, top_k=40)
llm = LLM(model="/home/models/Qwen3-8B-w4a8",
max_model_len=4096,
quantization="ascend")
outputs = llm.generate(prompts, sampling_params)
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
```

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# Additional Configuration
additional configuration is a mechanism provided by vLLM to allow plugins to control inner behavior by their own. vLLM Ascend uses this mechanism to make the project more flexible.
## How to use
With either online mode or offline mode, users can use additional configuration. Take Qwen3 as an example:
**Online mode**:
```bash
vllm serve Qwen/Qwen3-8B --additional-config='{"config_key":"config_value"}'
```
**Offline mode**:
```python
from vllm import LLM
LLM(model="Qwen/Qwen3-8B", additional_config={"config_key":"config_value"})
```
### Configuration options
The following table lists the additional configuration options available in vLLM Ascend:
| Name | Type | Default | Description |
|-------------------------------| ---- |------|-----------------------------------------------------------------------------------------------|
| `torchair_graph_config` | dict | `{}` | The config options for torchair graph mode |
| `ascend_scheduler_config` | dict | `{}` | The config options for ascend scheduler |
| `refresh` | bool | `false` | Whether to refresh global ascend config content. This value is usually used by rlhf or ut/e2e test case. |
| `expert_map_path` | str | `None` | When using expert load balancing for the MOE model, an expert map path needs to be passed in. |
| `chunked_prefill_for_mla` | bool | `False` | Whether to enable the fused operator-like chunked_prefill. |
| `enable_prefetch` | bool | `False` | Whether to enable weight prefetch. |
| `kv_cache_dtype` | str | `None` | When using the kv cache quantization method, kv cache dtype needs to be set, currently only int8 is supported. |
| `enable_shared_expert_dp` | bool | `False` | When the shared expert in DP, it has better performance but consumes more memory. Currently only DeepSeek series models are supported to use. |
| `lmhead_tensor_parallel_size` | int | `None` | The custom tensor parallel size of lmhead. |
The details of each config option are as follows:
**torchair_graph_config**
| Name | Type | Default | Description |
| ---- | ---- | ------- | ----------- |
| `enabled` | bool | `False` | Whether to enable torchair graph mode. Currently only DeepSeek series models and PanguProMoE are supported to use torchair graph mode |
| `mode` | str | `None` | When using reduce-overhead mode for torchair, mode needs to be set |
| `enable_multistream_mla`| bool | `False` | Whether to put vector ops of MLA to another stream. This option only takes effects on models using MLA (e.g., DeepSeek). |
| `enable_multistream_moe`| bool | `False` | Whether to enable multistream shared expert. This option only takes effects on DeepSeek moe models. |
| `enable_view_optimize` | bool | `True` | Whether to enable torchair view optimization |
| `use_cached_graph` | bool | `False` | Whether to use cached graph |
| `graph_batch_sizes` | list[int] | `[]` | The batch size for torchair graph cache |
| `graph_batch_sizes_init` | bool | `False` | Init graph batch size dynamically if `graph_batch_sizes` is empty |
| `enable_kv_nz`| bool | `False` | Whether to enable kvcache NZ layout. This option only takes effects on models using MLA (e.g., DeepSeek). |
**ascend_scheduler_config**
| Name | Type | Default | Description |
| ---- | ---- | ------- | ----------- |
| `enabled` | bool | `False` | Whether to enable ascend scheduler for V1 engine|
ascend_scheduler_config also support the options from [vllm scheduler config](https://docs.vllm.ai/en/stable/api/vllm/config.html#vllm.config.SchedulerConfig). For example, you can add `enable_chunked_prefill: True` to ascend_scheduler_config as well.
### Example
An example of additional configuration is as follows:
```
{
"torchair_graph_config": {
"enabled": True,
"use_cached_graph": True,
"graph_batch_sizes": [1, 2, 4, 8],
"graph_batch_sizes_init": False,
"enable_multistream_moe": False,
"enable_kv_nz": False
},
"ascend_scheduler_config": {
"enabled": True,
"enable_chunked_prefill": True,
},
"refresh": False,
}
```

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# Environment Variables
vllm-ascend uses the following environment variables to configure the system:
:::{literalinclude} ../../../../vllm_ascend/envs.py
:language: python
:start-after: begin-env-vars-definition
:end-before: end-env-vars-definition
:::

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# Configuration Guide
This section provides a detailed configuration guide of vLLM Ascend.
:::{toctree}
:caption: Configuration Guide
:maxdepth: 1
env_vars
additional_config
:::

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# Graph Mode Guide
```{note}
This feature is currently experimental. In future versions, there may be behavioral changes around configuration, coverage, performance improvement.
```
This guide provides instructions for using Ascend Graph Mode with vLLM Ascend. Please note that graph mode is only available on V1 Engine. And only Qwen, DeepSeek series models are well tested from 0.9.0rc1. We'll make it stable and generalize in the next release.
## Getting Started
From v0.9.1rc1 with V1 Engine, vLLM Ascend will run models in graph mode by default to keep the same behavior with vLLM. If you hit any issues, please feel free to open an issue on GitHub and fallback to eager mode temporarily by set `enforce_eager=True` when initializing the model.
There are two kinds for graph mode supported by vLLM Ascend:
- **ACLGraph**: This is the default graph mode supported by vLLM Ascend. In v0.9.1rc1, only Qwen series models are well tested.
- **TorchAirGraph**: This is the GE graph mode. In v0.9.1rc1, only DeepSeek series models are supported.
## Using ACLGraph
ACLGraph is enabled by default. Take Qwen series models as an example, just set to use V1 Engine is enough.
offline example:
```python
import os
from vllm import LLM
model = LLM(model="Qwen/Qwen2-7B-Instruct")
outputs = model.generate("Hello, how are you?")
```
online example:
```shell
vllm serve Qwen/Qwen2-7B-Instruct
```
## Using TorchAirGraph
If you want to run DeepSeek series models with graph mode, you should use [TorchAirGraph](https://www.hiascend.com/document/detail/zh/Pytorch/700/modthirdparty/torchairuseguide/torchair_0002.html). In this case, additional config is required.
offline example:
```python
import os
from vllm import LLM
# TorchAirGraph is only work without chunked-prefill now
model = LLM(model="deepseek-ai/DeepSeek-R1-0528", additional_config={"torchair_graph_config": {"enabled": True},"ascend_scheduler_config": {"enabled": True,}})
outputs = model.generate("Hello, how are you?")
```
online example:
```shell
vllm serve Qwen/Qwen2-7B-Instruct --additional-config='{"torchair_graph_config": {"enabled": true},"ascend_scheduler_config": {"enabled": true,}}'
```
You can find more detail about additional config [here](../configuration/additional_config.md).
## Fallback to Eager Mode
If both `ACLGraph` and `TorchAirGraph` fail to run, you should fallback to eager mode.
offline example:
```python
import os
from vllm import LLM
model = LLM(model="someother_model_weight", enforce_eager=True)
outputs = model.generate("Hello, how are you?")
```
online example:
```shell
vllm serve Qwen/Qwen2-7B-Instruct --enforce-eager
```

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# Feature Guide
This section provides a detailed usage guide of vLLM Ascend features.
:::{toctree}
:caption: Feature Guide
:maxdepth: 1
graph_mode
quantization
sleep_mode
structured_output
lora
:::

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# LoRA Adapters Guide
## Overview
Like vLLM, vllm-ascend supports LoRA as well. The usage and more details can be found in [vLLM official document](https://docs.vllm.ai/en/latest/features/lora.html).
You can refer to [Supported Models](https://docs.vllm.ai/en/latest/models/supported_models.html#list-of-text-only-language-models) to find which models support LoRA in vLLM.
You can run LoRA with ACLGraph mode now. Please refer to [Graph Mode Guide](./graph_mode.md) for a better LoRA performance.
## Example
We show a simple LoRA example here, which enables the ACLGraph mode as default.
```shell
vllm serve meta-llama/Llama-2-7b \
--enable-lora \
--lora-modules '{"name": "sql-lora", "path": "/path/to/lora", "base_model_name": "meta-llama/Llama-2-7b"}'
```
## Custom LoRA Operators
We have implemented LoRA-related AscendC operators, such as bgmv_shrink, bgmv_expand, sgmv_shrink and sgmv_expand. You can find them under the "csrc/kernels" directory of [vllm-ascend repo](https://github.com/vllm-project/vllm-ascend.git).
When you install vllm and vllm-ascend, those operators mentioned above will be compiled and installed automatically. If you don't want to use AscendC operators when you run vllm-ascend, you should set `COMPILE_CUSTOM_KERNELS=0` and reinstall vllm-ascend. To require more instructions about installation and compilation, you can refer to [installation guide](../../installation.md).

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