818 lines
36 KiB
Plaintext
818 lines
36 KiB
Plaintext
# SOME DESCRIPTIVE TITLE.
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# Copyright (C) 2025, vllm-ascend team
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# This file is distributed under the same license as the vllm-ascend
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# package.
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# FIRST AUTHOR <EMAIL@ADDRESS>, 2026.
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#
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#, fuzzy
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msgid ""
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msgstr ""
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"Project-Id-Version: vllm-ascend\n"
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"Report-Msgid-Bugs-To: \n"
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"POT-Creation-Date: 2026-08-05 06:27+0000\n"
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"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
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"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
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"Language-Team: zh_CN <LL@li.org>\n"
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"Language: zh_CN\n"
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"MIME-Version: 1.0\n"
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"Content-Type: text/plain; charset=utf-8\n"
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"Content-Transfer-Encoding: 8bit\n"
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"Plural-Forms: nplurals=1; plural=0;\n"
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"Generated-By: Babel 2.18.0\n"
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#: ../../source/faqs.md:1
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msgid "FAQs"
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msgstr "常见问题解答"
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#: ../../source/faqs.md:3
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msgid "Version Specific FAQs"
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msgstr "版本特定常见问题解答"
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#: ../../source/faqs.md:5
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msgid ""
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"[[v0.23.0rc1] FAQ & Feedback](https://github.com/vllm-project/vllm-"
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"ascend/issues/12238)"
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msgstr ""
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"[[v0.23.0rc1] 常见问题与反馈](https://github.com/vllm-project/vllm-"
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"ascend/issues/12238)"
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#: ../../source/faqs.md:6
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msgid ""
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"[[v0.22.1rc1] FAQ & Feedback](https://github.com/vllm-project/vllm-"
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"ascend/issues/10593)"
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msgstr ""
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"[[v0.22.1rc1] 常见问题与反馈](https://github.com/vllm-project/vllm-"
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"ascend/issues/10593)"
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#: ../../source/faqs.md:7
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msgid ""
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"[[v0.21.0rc1] FAQ & Feedback](https://github.com/vllm-project/vllm-"
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"ascend/issues/9970)"
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msgstr ""
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"[[v0.21.0rc1] 常见问题与反馈](https://github.com/vllm-project/vllm-"
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"ascend/issues/9970)"
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#: ../../source/faqs.md:8
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msgid ""
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"[[v0.20.2rc1] FAQ & Feedback](https://github.com/vllm-project/vllm-"
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"ascend/issues/9586)"
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msgstr ""
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"[[v0.20.2rc1] 常见问题与反馈](https://github.com/vllm-project/vllm-"
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"ascend/issues/9586)"
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#: ../../source/faqs.md:9
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msgid ""
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"[[v0.19.1rc1] FAQ & Feedback](https://github.com/vllm-project/vllm-"
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"ascend/issues/8819)"
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msgstr ""
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"[[v0.19.1rc1] 常见问题与反馈](https://github.com/vllm-project/vllm-"
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"ascend/issues/8819)"
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#: ../../source/faqs.md:10
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msgid ""
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"[[v0.18.0] FAQ & Feedback](https://github.com/vllm-project/vllm-"
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"ascend/issues/8238)"
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msgstr ""
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"[[v0.18.0] 常见问题与反馈](https://github.com/vllm-project/vllm-ascend/issues/8238)"
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#: ../../source/faqs.md:12
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msgid "General FAQs"
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msgstr "通用常见问题解答"
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#: ../../source/faqs.md:14
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msgid "1. What devices are currently supported?"
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msgstr "1.当前支持哪些设备？"
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#: ../../source/faqs.md:16
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msgid ""
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"Currently, **ONLY** Atlas A2 series (Ascend-cann-kernels-910b), Atlas A3 "
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"series (Atlas-A3-cann-kernels) and Atlas 300I (Ascend-cann-kernels-310p) "
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"series are supported:"
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msgstr ""
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"目前**仅**支持 Atlas A2 系列（Ascend-cann-kernels-910b）、Atlas A3 系列（Atlas-A3-cann-"
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"kernels）和 Atlas 300I 系列（Ascend-cann-kernels-310p）："
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#: ../../source/faqs.md:18
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msgid ""
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"Atlas A2 Training series (Atlas 800T A2, Atlas 900 A2 PoD, Atlas 200T A2 "
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"Box16, Atlas 300T A2)"
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msgstr ""
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"Atlas A2 训练系列（Atlas 800T A2、Atlas 900 A2 PoD、Atlas 200T A2 Box16、Atlas 300T "
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"A2）"
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#: ../../source/faqs.md:19
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msgid "Atlas 800I A2 Inference series (Atlas 800I A2)"
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msgstr "Atlas 800I A2 推理系列（Atlas 800I A2）"
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#: ../../source/faqs.md:20
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msgid ""
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"Atlas A3 Training series (Atlas 800T A3, Atlas 900 A3 SuperPoD, Atlas 9000 "
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"A3 SuperPoD)"
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msgstr ""
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"Atlas A3 训练系列（Atlas 800T A3、Atlas 900 A3 SuperPoD、Atlas 9000 A3 SuperPoD）"
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#: ../../source/faqs.md:21
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msgid "Atlas 800I A3 Inference series (Atlas 800I A3)"
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msgstr "Atlas 800I A3 推理系列（Atlas 800I A3）"
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#: ../../source/faqs.md:22
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msgid "[Experimental] Atlas 300I Inference series (Atlas 300I Duo)."
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msgstr "[实验性] Atlas 300I 推理系列（Atlas 300I Duo）。"
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#: ../../source/faqs.md:23
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msgid ""
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"[Experimental] Currently for 310I Duo the stable version is vllm-ascend "
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"v0.10.0rc1."
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msgstr "[实验性] 目前 310I Duo 的稳定版本为 vllm-ascend v0.10.0rc1。"
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#: ../../source/faqs.md:25
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msgid "Below series are NOT supported yet:"
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msgstr "以下系列尚不支持："
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#: ../../source/faqs.md:27
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msgid "Atlas 200I A2 (Ascend-cann-kernels-310b) unplanned yet"
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msgstr "Atlas 200I A2（Ascend-cann-kernels-310b）尚未规划"
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#: ../../source/faqs.md:28
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msgid "Ascend 910, Ascend 910 Pro B (Ascend-cann-kernels-910) unplanned yet"
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msgstr "Ascend 910、Ascend 910 Pro B（Ascend-cann-kernels-910）尚未规划"
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#: ../../source/faqs.md:30
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msgid ""
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"From a technical view, vllm-ascend supports devices if TorchNPU is "
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"supported. Otherwise, we have to implement it by using custom ops. We also "
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"welcome you to join us to improve together."
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msgstr ""
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"从技术角度来看，如果 TorchNPU 受支持，vllm-ascend "
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"即可支持相应设备。否则，我们需要通过自定义算子来实现。我们也欢迎您加入我们，共同改进。"
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#: ../../source/faqs.md:32
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msgid "2. How to get our docker containers?"
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msgstr "2.如何获取我们的 Docker 容器？"
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#: ../../source/faqs.md:34
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msgid ""
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"You can get our containers at `Quay.io`, e.g., [<u>vllm-"
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"ascend</u>](https://quay.io/repository/ascend/vllm-ascend?tab=tags) and "
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"[<u>cann</u>](https://quay.io/repository/ascend/cann?tab=tags)."
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msgstr ""
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"您可以在 `Quay.io` 获取我们的容器，例如 [<u>vllm-"
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"ascend</u>](https://quay.io/repository/ascend/vllm-ascend?tab=tags) 和 "
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"[<u>cann</u>](https://quay.io/repository/ascend/cann?tab=tags)。"
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#: ../../source/faqs.md:36
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msgid ""
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"If you are in China, you can use `daocloud` or some other mirror sites to "
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"accelerate your downloading:"
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msgstr "如果您在中国，可以使用 `daocloud` 或其他镜像站点来加速下载："
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#: ../../source/faqs.md:46
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msgid "Load Docker Images for offline environment"
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msgstr "为离线环境加载 Docker 镜像"
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#: ../../source/faqs.md:48
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msgid ""
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"If you want to use container image for offline environments (no internet "
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"connection), you need to download container image in an environment with "
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"internet access:"
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msgstr "如果您想在离线环境（无网络连接）中使用容器镜像，需要在有网络的环境中下载容器镜像："
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#: ../../source/faqs.md:50
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msgid "**Exporting Docker images:**"
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msgstr "**导出 Docker 镜像：**"
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#: ../../source/faqs.md:62
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msgid "**Importing Docker images in environment without internet access:**"
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msgstr "**在无网络环境中导入 Docker 镜像：**"
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#: ../../source/faqs.md:74
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msgid "3. What models does vllm-ascend support?"
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msgstr "3.vllm-ascend支持哪些模型？"
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#: ../../source/faqs.md:76
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msgid ""
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"Find more details "
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"[<u>here</u>](https://docs.vllm.ai/projects/ascend/en/latest/user_guide/support_matrix/supported_models.html)."
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msgstr ""
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"更多详情请见[<u>此处</u>](https://docs.vllm.ai/projects/ascend/en/latest/user_guide/support_matrix/supported_models.html)。"
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#: ../../source/faqs.md:78
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msgid "4. How to get in touch with our community?"
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msgstr "4.如何与我们的社区取得联系？"
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#: ../../source/faqs.md:80
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msgid ""
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"There are many channels that you can communicate with our community "
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"developers / users:"
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msgstr "您可以通过多种渠道与我们的社区开发者/用户交流："
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#: ../../source/faqs.md:82
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msgid ""
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"Submit a GitHub [<u>issue</u>](https://github.com/vllm-project/vllm-"
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"ascend/issues?page=1)."
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msgstr ""
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"提交 GitHub [<u>issue</u>](https://github.com/vllm-project/vllm-"
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"ascend/issues?page=1)。"
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#: ../../source/faqs.md:83
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msgid ""
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"Join our [<u>weekly "
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"meeting</u>](https://docs.google.com/document/d/1hCSzRTMZhIB8vRq1_qOOjx4c9uYUxvdQvDsMV2JcSrw/edit?tab=t.0#heading=h.911qu8j8h35z)"
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" and share your ideas."
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msgstr ""
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"参加我们的[<u>周会</u>](https://docs.google.com/document/d/1hCSzRTMZhIB8vRq1_qOOjx4c9uYUxvdQvDsMV2JcSrw/edit?tab=t.0#heading=h.911qu8j8h35z)并分享您的想法。"
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#: ../../source/faqs.md:84
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msgid ""
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"Join our [<u>WeChat</u>](https://github.com/vllm-project/vllm-"
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"ascend/issues/227) group and ask your questions."
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msgstr ""
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"加入我们的[<u>微信</u>](https://github.com/vllm-project/vllm-"
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"ascend/issues/227)群组并提问。"
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#: ../../source/faqs.md:85
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msgid ""
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"Join our ascend channel in [<u>vLLM "
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"forums</u>](https://discuss.vllm.ai/c/hardware-support/vllm-ascend-"
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"support/6) and publish your topics."
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msgstr ""
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"加入 [<u>vLLM 论坛</u>](https://discuss.vllm.ai/c/hardware-support/vllm-ascend-"
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"support/6) 中的 ascend 频道并发布您的主题。"
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#: ../../source/faqs.md:87
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msgid "5. What features does vllm-ascend V1 support?"
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msgstr "5.vllm-ascend V1支持哪些功能？"
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#: ../../source/faqs.md:89
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msgid ""
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"Find more details "
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"[<u>here</u>](https://docs.vllm.ai/projects/ascend/en/latest/user_guide/support_matrix/supported_features.html)."
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msgstr ""
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"更多详情请见[<u>此处</u>](https://docs.vllm.ai/projects/ascend/en/latest/user_guide/support_matrix/supported_features.html)。"
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#: ../../source/faqs.md:91
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msgid ""
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"6. How to solve the problem of \"Failed to infer device type\" or "
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"\"libatb.so: cannot open shared object file\"?"
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msgstr ""
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"6.如何解决“Failed to infer device type”或“libatb.so: cannot open shared object "
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"file”的问题？"
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#: ../../source/faqs.md:93
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msgid ""
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"Basically, the reason is that the NPU environment is not configured "
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"correctly. You can:"
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msgstr "基本上，原因是 NPU 环境配置不正确。您可以："
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#: ../../source/faqs.md:95
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msgid ""
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"try `source /usr/local/Ascend/nnal/atb/set_env.sh` to enable NNAL package."
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msgstr "尝试执行 `source /usr/local/Ascend/nnal/atb/set_env.sh` 以启用 NNAL 包。"
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#: ../../source/faqs.md:96
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msgid ""
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"try `source /usr/local/Ascend/ascend-toolkit/set_env.sh` to enable CANN "
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"package."
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msgstr "尝试执行 `source /usr/local/Ascend/ascend-toolkit/set_env.sh` 以启用 CANN 包。"
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#: ../../source/faqs.md:97
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msgid "try `npu-smi info` to check whether the NPU is working."
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msgstr "尝试执行 `npu-smi info` 检查 NPU 是否正常工作。"
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#: ../../source/faqs.md:99
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msgid ""
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"If the above steps are not working, you can try the following code in Python"
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" to check whether there are any errors:"
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msgstr "如果上述步骤无效，您可以尝试在 Python 中运行以下代码检查是否有错误："
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#: ../../source/faqs.md:107
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msgid ""
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"If all above steps are not working, feel free to submit a GitHub issue."
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msgstr "如果以上所有步骤均无效，请随时提交 GitHub issue。"
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#: ../../source/faqs.md:109
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msgid "7. How does vllm-ascend work with vLLM?"
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msgstr "7.vllm-ascend如何与vLLM协同工作？"
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#: ../../source/faqs.md:111
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msgid ""
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"`vllm-ascend` is a hardware plugin for vLLM. Stable releases usually align "
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"with the same vLLM version, while RC releases may use the corresponding vLLM"
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" final release version. For example, `vllm-ascend` `v0.18.0rc1` matches vLLM"
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" `v0.18.0`. For the main branch, we ensure that `vllm-ascend` and `vllm` are"
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" compatible at every commit."
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msgstr ""
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"`vllm-ascend` 是 vLLM 的硬件插件。稳定版本通常与相同版本的 vLLM 对齐，而 RC 版本可能使用对应的 vLLM "
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"最终发布版本。例如，`vllm-ascend` `v0.18.0rc1` 匹配 vLLM `v0.18.0`。对于 main 分支，我们确保 "
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"`vllm-ascend` 和 `vllm` 在每个提交上均保持兼容。"
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#: ../../source/faqs.md:113
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msgid ""
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"8. Does vllm-ascend support Prefill-Decode (PD) Disaggregation feature?"
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msgstr "8.vllm-ascend 是否支持 Prefill-Decode (PD) 分离特性？"
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#: ../../source/faqs.md:115
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msgid ""
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"Yes, vllm-ascend supports Prefill-Decode Disaggregation feature with "
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"Mooncake backend. See the [official "
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"tutorial](https://docs.vllm.ai/projects/ascend/en/latest/tutorials/features/pd_disaggregation_mooncake_multi_node.html)"
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" for example."
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msgstr ""
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"是的，vllm-ascend 支持基于 Mooncake 后端的 Prefill-Decode "
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"分离特性。示例请参见[官方教程](https://docs.vllm.ai/projects/ascend/en/latest/tutorials/features/pd_disaggregation_mooncake_multi_node.html)。"
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#: ../../source/faqs.md:117
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msgid "9. Does vllm-ascend support quantization method?"
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msgstr "9.vllm-ascend 是否支持量化方法？"
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#: ../../source/faqs.md:119
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msgid ""
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"Currently, w8a8, w4a8, and w4a4 quantization methods are already supported "
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"by vllm-ascend."
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msgstr "目前，vllm-ascend 已支持 w8a8、w4a8 和 w4a4 量化方法。"
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#: ../../source/faqs.md:121
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msgid "10. How is vllm-ascend tested?"
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msgstr "10.vllm-ascend 如何测试？"
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#: ../../source/faqs.md:123
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msgid ""
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"vllm-ascend is tested in three aspects: functions, performance, and "
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"accuracy."
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msgstr "vllm-ascend 从三个方面进行测试：功能、性能和精度。"
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#: ../../source/faqs.md:125
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msgid ""
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"**Functional test**: We added CI, including part of vllm's native unit tests"
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" and vllm-ascend's own unit tests. In vllm-ascend's tests, we test basic "
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"functionalities, popular model availability, and [supported "
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"features](https://docs.vllm.ai/projects/ascend/en/latest/user_guide/support_matrix/supported_features.html)"
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" through E2E test."
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msgstr ""
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"**功能测试**：我们增加了CI，包括部分vllm原生单元测试和vllm-ascend自有单元测试。在vllm-"
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"ascend的测试中，我们通过E2E测试验证基本功能、主流模型可用性以及[支持的特性](https://docs.vllm.ai/projects/ascend/en/latest/user_guide/support_matrix/supported_features.html)。"
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#: ../../source/faqs.md:127
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msgid ""
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"**Performance test**: We provide [benchmark](https://github.com/vllm-"
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"project/vllm-ascend/tree/main/benchmarks) tools for E2E performance "
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"benchmark, which can be easily re-run locally. We will publish a perf "
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"website to show the performance test results for each pull request."
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msgstr ""
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"**性能测试**：我们提供了[基准测试](https://github.com/vllm-project/vllm-"
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"ascend/tree/main/benchmarks)工具用于E2E性能基准测试，可轻松在本地重新运行。我们将发布一个性能网站来展示每个拉取请求的性能测试结果。"
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#: ../../source/faqs.md:129
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msgid ""
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"**Accuracy test**: We are working on adding accuracy test to the CI as well."
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msgstr "**精度测试**：我们也在努力将精度测试加入CI。"
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#: ../../source/faqs.md:131
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msgid ""
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"**Nightly test**: we'll run full test every night to make sure the code is "
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"working."
|
||
msgstr "**夜间测试**：我们将每晚运行完整测试以确保代码正常运行。"
|
||
|
||
#: ../../source/faqs.md:133
|
||
msgid ""
|
||
"For each release, we'll publish the performance test and accuracy test "
|
||
"report in the future."
|
||
msgstr "对于每个版本，我们未来将发布性能测试和精度测试报告。"
|
||
|
||
#: ../../source/faqs.md:135
|
||
msgid "11. How to fix the error \"InvalidVersion\" when using vllm-ascend?"
|
||
msgstr "11.如何修复使用vllm-ascend时出现的\"InvalidVersion\"错误？"
|
||
|
||
#: ../../source/faqs.md:137
|
||
msgid ""
|
||
"The problem is usually caused by the installation of a development or "
|
||
"editable version of the vLLM package. In this case, we provide the "
|
||
"environment variable `VLLM_VERSION` to let users specify the version of vLLM"
|
||
" package to use. Please set the environment variable `VLLM_VERSION` to the "
|
||
"version of the vLLM package you have installed. The format of `VLLM_VERSION`"
|
||
" should be `X.Y.Z`."
|
||
msgstr ""
|
||
"该问题通常由安装了开发版或可编辑版的vLLM包导致。此时，我们提供环境变量`VLLM_VERSION`让用户指定要使用的vLLM包版本。请将环境变量`VLLM_VERSION`设置为您已安装的vLLM包版本。`VLLM_VERSION`的格式应为`X.Y.Z`。"
|
||
|
||
#: ../../source/faqs.md:139
|
||
msgid "12. How to handle the out-of-memory issue?"
|
||
msgstr "12.如何处理内存不足问题？"
|
||
|
||
#: ../../source/faqs.md:141
|
||
msgid ""
|
||
"OOM errors typically occur when the model exceeds the memory capacity of a "
|
||
"single NPU. For general guidance, you can refer to [vLLM OOM troubleshooting"
|
||
" documentation](https://docs.vllm.ai/en/latest/usage/troubleshooting/#out-"
|
||
"of-memory)."
|
||
msgstr ""
|
||
"OOM错误通常发生在模型超过单个NPU内存容量时。通用指导可参考[vLLM "
|
||
"OOM故障排查文档](https://docs.vllm.ai/en/latest/usage/troubleshooting/#out-of-"
|
||
"memory)。"
|
||
|
||
#: ../../source/faqs.md:143
|
||
msgid ""
|
||
"In scenarios where NPUs have limited high bandwidth memory (on-chip memory) "
|
||
"capacity, dynamic memory allocation/deallocation during inference can "
|
||
"exacerbate memory fragmentation, leading to OOM. To address this:"
|
||
msgstr "在NPU高带宽内存（片上内存）容量有限的场景下，推理过程中的动态内存分配/释放会加剧内存碎片化，导致OOM。为解决此问题："
|
||
|
||
#: ../../source/faqs.md:145
|
||
msgid ""
|
||
"**Limit `--max-model-len`**: It can save the on-chip memory usage for KV "
|
||
"cache initialization step."
|
||
msgstr "**限制`--max-model-len`**：可节省KV缓存初始化步骤的片上内存使用。"
|
||
|
||
#: ../../source/faqs.md:147
|
||
msgid ""
|
||
"**Adjust `--gpu-memory-utilization`**: If unspecified, the default value is "
|
||
"`0.9`. You can decrease this value to reserve more memory to reduce "
|
||
"fragmentation risks. See details in: [vLLM - Inference and Serving - Engine "
|
||
"Arguments](https://docs.vllm.ai/en/latest/cli/serve/#-gpu-memory-"
|
||
"utilization)."
|
||
msgstr ""
|
||
"**调整`--gpu-memory-"
|
||
"utilization`**：若未指定，默认值为`0.9`。可降低此值以预留更多内存，减少碎片化风险。详见：[vLLM - 推理与服务 - "
|
||
"引擎参数](https://docs.vllm.ai/en/latest/cli/serve/#-gpu-memory-utilization)。"
|
||
|
||
#: ../../source/faqs.md:149
|
||
msgid ""
|
||
"**Configure `PYTORCH_NPU_ALLOC_CONF`**: Set this environment variable to "
|
||
"optimize NPU memory management. For example, you can use `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 details 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，可使用`export "
|
||
"PYTORCH_NPU_ALLOC_CONF=expandable_segments:True`启用虚拟内存功能，缓解运行时频繁动态内存大小调整导致的内存碎片化。详见[PYTORCH_NPU_ALLOC_CONF](https://www.hiascend.com/document/detail/zh/Pytorch/700/comref/Envvariables/Envir_012.html)。"
|
||
|
||
#: ../../source/faqs.md:151
|
||
msgid "13. Failed to enable NPU graph mode when running DeepSeek"
|
||
msgstr "13.运行DeepSeek时启用NPU图模式失败"
|
||
|
||
#: ../../source/faqs.md:153
|
||
msgid ""
|
||
"Enabling NPU graph mode for DeepSeek may trigger an error. This is because "
|
||
"when both MLA (Multi-Head Latent Attention) and NPU graph mode are active, "
|
||
"the number of queries per KV head must be 32, 64, or 128. However, "
|
||
"DeepSeek-V2-Lite has only 16 attention heads, which results in 16 queries "
|
||
"per KV—a value outside the supported range. Support for NPU graph mode on "
|
||
"DeepSeek-V2-Lite will be added in a future update."
|
||
msgstr ""
|
||
"为DeepSeek启用NPU图模式可能触发错误。这是因为当MLA（多头潜在注意力）和NPU图模式同时激活时，每个KV头的查询数必须为32、64或128。然而，DeepSeek-V2-Lite仅有16个注意力头，导致每个KV头16个查询——该值不在支持范围内。对DeepSeek-V2-Lite的NPU图模式支持将在未来更新中添加。"
|
||
|
||
#: ../../source/faqs.md:155
|
||
#, 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` is {32, 64, 128}."
|
||
msgstr ""
|
||
"如果您使用的是DeepSeek-V3或DeepSeek-R1，请确保张量并行拆分后，`num_heads`/`num_kv_heads`为{32, "
|
||
"64, 128}。"
|
||
|
||
#: ../../source/faqs.md:162
|
||
msgid ""
|
||
"14. Failed to reinstall vllm-ascend from source after uninstalling vllm-"
|
||
"ascend"
|
||
msgstr "14.卸载vllm-ascend后从源码重新安装失败"
|
||
|
||
#: ../../source/faqs.md:164
|
||
msgid ""
|
||
"You may encounter the problem of C/C++ compilation failure when reinstalling"
|
||
" vllm-ascend from source using pip. If the installation fails, use `python "
|
||
"setup.py install` (recommended) to install, or use `python setup.py clean` "
|
||
"to clear the cache."
|
||
msgstr ""
|
||
"使用pip从源码重新安装vllm-ascend时可能遇到C/C++编译失败的问题。如果安装失败，请使用`python setup.py "
|
||
"install`（推荐）进行安装，或使用`python setup.py clean`清除缓存。"
|
||
|
||
#: ../../source/faqs.md:166
|
||
msgid "15. How to generate deterministic results when using vllm-ascend?"
|
||
msgstr "15.使用vllm-ascend时如何生成确定性结果？"
|
||
|
||
#: ../../source/faqs.md:168
|
||
msgid "There are several factors that affect output determinism:"
|
||
msgstr "影响输出确定性的因素有以下几个："
|
||
|
||
#: ../../source/faqs.md:170
|
||
msgid ""
|
||
"Sampler method: using **greedy sampling** by setting `temperature=0` in "
|
||
"`SamplingParams`, e.g.:"
|
||
msgstr "采样方法：通过在`SamplingParams`中设置`temperature=0`使用**贪心采样**，例如："
|
||
|
||
#: ../../source/faqs.md:195
|
||
msgid "Set the following environment parameters:"
|
||
msgstr "设置以下环境参数："
|
||
|
||
#: ../../source/faqs.md:204
|
||
msgid ""
|
||
"16. How to fix the error \"ImportError: Please install vllm[audio] for audio"
|
||
" support\" for the multi-modal models?"
|
||
msgstr ""
|
||
"16.如何修复多模态模型的错误\"ImportError: Please install vllm[audio] for audio "
|
||
"support\"？"
|
||
|
||
#: ../../source/faqs.md:206
|
||
msgid ""
|
||
"Some multi-modal models requires the `librosa` package to be installed, you "
|
||
"need to install the `qwen-omni-utils` package to ensure all dependencies are"
|
||
" met, for Qwen-omni, run `pip install qwen-omni-utils`. This package will "
|
||
"install `librosa` and its related dependencies, resolving the `ImportError: "
|
||
"No module named 'librosa'` issue and ensuring that the audio processing "
|
||
"functionality works correctly."
|
||
msgstr ""
|
||
"部分多模态模型需要安装`librosa`包，您需要安装`qwen-omni-utils`包以确保满足所有依赖。对于Qwen-omni，运行`pip "
|
||
"install qwen-omni-utils`。该包将安装`librosa`及其相关依赖，解决`ImportError: No module "
|
||
"named 'librosa'`问题，确保音频处理功能正常工作。"
|
||
|
||
#: ../../source/faqs.md:209
|
||
msgid ""
|
||
"17. How to troubleshoot and resolve size capture failures resulting from "
|
||
"stream resource exhaustion, and what are the underlying causes?"
|
||
msgstr "17.如何排查和解决流资源耗尽导致的大小捕获失败问题？根本原因是什么？"
|
||
|
||
#: ../../source/faqs.md:217
|
||
msgid ""
|
||
"When vLLM Ascend recognizes this capture-time stream-resource signature in "
|
||
"the error text, it re-raises the error with targeted guidance for ACL graph "
|
||
"sizing and mitigation."
|
||
msgstr "当vLLM Ascend在错误文本中识别出这种捕获时流资源特征时，会重新抛出错误，并附带针对ACL图大小调整和缓解措施的有针对性指导。"
|
||
|
||
#: ../../source/faqs.md:219
|
||
msgid "Recommended mitigation strategies:"
|
||
msgstr "推荐的缓解策略："
|
||
|
||
#: ../../source/faqs.md:221
|
||
msgid ""
|
||
"Upgrade to a newer HDK/CANN stack if one is available for your environment. "
|
||
"Recent releases improve ACL graph capacity, so older workarounds may no "
|
||
"longer be necessary."
|
||
msgstr "如果您的环境有可用的更新版HDK/CANN栈，请升级。最新版本提升了ACL图容量，因此旧有的变通方法可能不再需要。"
|
||
|
||
#: ../../source/faqs.md:222
|
||
#, python-brace-format
|
||
msgid ""
|
||
"Manually reduce the configured graph sizes, for example: "
|
||
"'{\"cudagraph_capture_sizes\":[size1, size2, size3, ...]}', or lower "
|
||
"`max_cudagraph_capture_size`."
|
||
msgstr ""
|
||
"手动减小配置的图大小，例如：'{\"cudagraph_capture_sizes\":[size1, size2, size3, "
|
||
"...]}'，或降低`max_cudagraph_capture_size`。"
|
||
|
||
#: ../../source/faqs.md:223
|
||
msgid ""
|
||
"If your workload is mostly uniform decode, try ACLGraph's `FULL` or "
|
||
"`FULL_DECODE_ONLY` mode instead of the `PIECEWISE`."
|
||
msgstr "如果您的负载主要是均匀解码，请尝试ACLGraph的`FULL`或`FULL_DECODE_ONLY`模式，而不是`PIECEWISE`。"
|
||
|
||
#: ../../source/faqs.md:224
|
||
msgid ""
|
||
"If you use `PIECEWISE` or `FULL_AND_PIECEWISE` and still hit this failure "
|
||
"after upgrading, set `cudagraph_capture_sizes` manually according to your "
|
||
"real workload and reduce the configured coverage."
|
||
msgstr ""
|
||
"如果您使用 `PIECEWISE` 或 `FULL_AND_PIECEWISE`，升级后仍然遇到此失败，请根据实际工作负载手动设置 "
|
||
"`cudagraph_capture_sizes`，并减少配置的覆盖范围。"
|
||
|
||
#: ../../source/faqs.md:225
|
||
msgid ""
|
||
"If you are debugging a startup failure, temporarily disable graph mode "
|
||
"(`cudagraph_mode=\"NONE\"` / `enforce_eager=True`) to confirm the issue is "
|
||
"capture-related."
|
||
msgstr ""
|
||
"如果您正在调试启动失败，请临时禁用图模式（`cudagraph_mode=\"NONE\"` / "
|
||
"`enforce_eager=True`）以确认问题与捕获相关。"
|
||
|
||
#: ../../source/faqs.md:227
|
||
msgid ""
|
||
"Root cause analysis: ACL graph capture can still fail when the runtime "
|
||
"resources required by the selected graph sizes exceed what the current "
|
||
"software/hardware stack can provide. This is most visible in `PIECEWISE` "
|
||
"scenarios because the number of captured graphs scales with model depth and "
|
||
"capture-size coverage. vLLM Ascend no longer auto-shrinks the PIECEWISE "
|
||
"capture-size set locally, so the practical mitigations are to upgrade the "
|
||
"HDK/CANN stack or reduce the configured graph sizes explicitly. The runtime "
|
||
"guidance is intentionally narrow: it is only added when capture fails with "
|
||
"the confirmed stream-resource signature above."
|
||
msgstr ""
|
||
"根因分析：当所选图大小所需的运行时资源超出当前软件/硬件栈所能提供的范围时，ACL 图捕获仍可能失败。这在 `PIECEWISE` "
|
||
"场景中最为明显，因为捕获的图数量随模型深度和捕获大小覆盖范围而扩展。vLLM Ascend 不再自动缩小本地的 PIECEWISE "
|
||
"捕获大小集合，因此实际的缓解措施是升级 HDK/CANN "
|
||
"栈或显式减少配置的图大小。运行时指导信息有意保持狭窄：仅当捕获失败且具有上述确认的流资源特征时才会添加。"
|
||
|
||
#: ../../source/faqs.md:230
|
||
msgid "18. How to install custom version of torch_npu?"
|
||
msgstr "18.如何安装自定义版本的 torch_npu？"
|
||
|
||
#: ../../source/faqs.md:232
|
||
msgid ""
|
||
"TorchNPU will be overridden when installing vllm-ascend. If you need to "
|
||
"install a specific version of TorchNPU, you can manually install the "
|
||
"specified version of TorchNPU after vllm-ascend is installed."
|
||
msgstr ""
|
||
"安装 vllm-ascend 时，TorchNPU 将被覆盖。如果您需要安装特定版本的 TorchNPU，可以在安装 vllm-ascend "
|
||
"后手动安装指定版本的 TorchNPU。"
|
||
|
||
#: ../../source/faqs.md:234
|
||
msgid ""
|
||
"19. On certain systems (e.g., Kylin OS), `docker pull` may fail with an "
|
||
"`invalid tar header` error"
|
||
msgstr "19.在某些系统（例如 Kylin OS）上，`docker pull` 可能会失败并出现 `invalid tar header` 错误"
|
||
|
||
#: ../../source/faqs.md:236
|
||
msgid ""
|
||
"On certain operating systems, such as Kylin OS, you may encounter an "
|
||
"`invalid tar header` error during the `docker pull` process:"
|
||
msgstr ""
|
||
"在某些操作系统（例如 Kylin OS）上，您可能在 `docker pull` 过程中遇到 `invalid tar header` 错误："
|
||
|
||
#: ../../source/faqs.md:242
|
||
msgid ""
|
||
"This is often due to system compatibility issues. You can resolve this by "
|
||
"using an offline loading method with a second machine."
|
||
msgstr "这通常是由于系统兼容性问题导致的。您可以通过使用第二台机器进行离线加载的方法来解决。"
|
||
|
||
#: ../../source/faqs.md:244
|
||
msgid ""
|
||
"On a separate host machine (e.g., a standard Ubuntu server), pull the image "
|
||
"for the target ARM64 architecture and package it into a `.tar` file."
|
||
msgstr "在单独的主机（例如标准 Ubuntu 服务器）上，拉取目标 ARM64 架构的镜像，并将其打包为 `.tar` 文件。"
|
||
|
||
#: ../../source/faqs.md:257
|
||
msgid "Transfer the image archive"
|
||
msgstr "传输镜像归档文件"
|
||
|
||
#: ../../source/faqs.md:259
|
||
msgid ""
|
||
"Copy the `vllm_ascend_<tag>.tar` file (where `<tag>` is the image tag you "
|
||
"used) to your target machine"
|
||
msgstr "将 `vllm_ascend_<tag>.tar` 文件（其中 `<tag>` 是您使用的镜像标签）复制到目标机器"
|
||
|
||
#: ../../source/faqs.md:261
|
||
msgid ""
|
||
"20. Why am I getting an error when executing the script to start a Docker "
|
||
"container? The error message is: \"operation not permitted\""
|
||
msgstr "20.为什么执行启动 Docker 容器的脚本时出现错误？错误信息为：“operation not permitted”"
|
||
|
||
#: ../../source/faqs.md:263
|
||
msgid ""
|
||
"When using `--shm-size`, you may need to add the `--privileged=true` flag to"
|
||
" your `docker run` command to grant the container necessary permissions. "
|
||
"Please be aware that using `--privileged=true` grants the container "
|
||
"extensive privileges on the host system, which can be a security risk. Only "
|
||
"use this option if you understand the implications and trust the container's"
|
||
" source."
|
||
msgstr ""
|
||
"使用 `--shm-size` 时，您可能需要在 `docker run` 命令中添加 `--privileged=true` "
|
||
"标志，以授予容器必要的权限。请注意，使用 `--privileged=true` "
|
||
"会授予容器对主机系统的广泛权限，这可能带来安全风险。仅在您了解其影响并信任容器来源的情况下使用此选项。"
|
||
|
||
#: ../../source/faqs.md:265
|
||
msgid ""
|
||
"21. How to set `SOC_VERSION` when building from source on a CPU-only "
|
||
"machine?"
|
||
msgstr "21.如何在仅 CPU 的机器上从源码构建时设置 `SOC_VERSION`？"
|
||
|
||
#: ../../source/faqs.md:267
|
||
msgid ""
|
||
"When building from source (e.g. `pip install -e .`), the build may try to "
|
||
"infer the target chip via `npu-smi`. If `npu-smi` is not available (common "
|
||
"in CPU-only build environments), you must set `SOC_VERSION` manually before "
|
||
"installation."
|
||
msgstr ""
|
||
"从源码构建时（例如 `pip install -e .`），构建过程可能尝试通过 `npu-smi` 推断目标芯片。如果 `npu-smi` "
|
||
"不可用（在仅 CPU 的构建环境中很常见），您必须在安装前手动设置 `SOC_VERSION`。"
|
||
|
||
#: ../../source/faqs.md:269
|
||
msgid ""
|
||
"You can use the defaults from `Dockerfile*` as a reference. For example:"
|
||
msgstr "您可以使用 `Dockerfile*` 中的默认值作为参考。例如："
|
||
|
||
#: ../../source/faqs.md:285
|
||
msgid "22. Why does TPOT increase drastically as concurrency grows?"
|
||
msgstr "22.为什么TPOT会随着并发量的增长而急剧增加？"
|
||
|
||
#: ../../source/faqs.md:287
|
||
msgid ""
|
||
"When testing a vLLM server, one may find that TPOT increases as concurrency "
|
||
"increases (for example, TPOT increases by 0.5 ~ 1ms when concurrency "
|
||
"increases by 4). This phenomenon is normal in most cases. However, sometimes"
|
||
" TPOT may increase dramatically (10 to 100ms for example) as concurrency "
|
||
"grows. This is possibly caused by "
|
||
"[**PREEMPTION**](https://docs.vllm.ai/en/latest/configuration/optimization/#preemption)"
|
||
" in vLLM. Generally, when your server hits KV cache limits, vLLM tries to "
|
||
"free KV cache of requests to ensure sufficient space for other requests, "
|
||
"which is called preemption in vLLM. When a request is preempted, the default"
|
||
" behavior is to recompute the KV cache of this request again in the future, "
|
||
"which is why the performance might drop significantly. There are several "
|
||
"ways to verify this:"
|
||
msgstr ""
|
||
"测试 vLLM 服务器时，可能会发现 TPOT 随着并发增加而增加（例如，并发增加 4 时 TPOT 增加 0.5 ~ "
|
||
"1ms）。这种现象在大多数情况下是正常的。然而，有时 TPOT 可能会随着并发增长而急剧增加（例如 10 到 100ms）。这可能是由 vLLM 中的 "
|
||
"[**抢占**](https://docs.vllm.ai/en/latest/configuration/optimization/#preemption)"
|
||
" 引起的。通常，当您的服务器达到 KV 缓存限制时，vLLM 会尝试释放请求的 KV 缓存，以确保为其他请求提供足够空间，这在 vLLM "
|
||
"中称为抢占。当请求被抢占时，默认行为是将来重新计算该请求的 KV 缓存，这就是性能可能显著下降的原因。有几种方法可以验证这一点："
|
||
|
||
#: ../../source/faqs.md:290
|
||
msgid ""
|
||
"vLLM usually logs stats on your server. You might see metrics like `GPU KV "
|
||
"cache usage: 99.0%,`. When reaching 100%, it triggers preemption."
|
||
msgstr ""
|
||
"vLLM 通常会在您的服务器上记录统计信息。您可能会看到类似 `GPU KV cache usage: 99.0%,` 的指标。当达到 100% "
|
||
"时，会触发抢占。"
|
||
|
||
#: ../../source/faqs.md:291
|
||
msgid ""
|
||
"When launching a vLLM server, you will see logs like `GPU KV cache size: "
|
||
"66340 tokens` and `Maximum concurrency for 16,384 tokens per request: 4.05`."
|
||
" These are estimated KV cache capacity for a single DP group. You can adjust"
|
||
" the overall request traffic according to this."
|
||
msgstr ""
|
||
"启动 vLLM 服务器时，您会看到类似 `GPU KV cache size: 66340 tokens` 和 `Maximum concurrency"
|
||
" for 16,384 tokens per request: 4.05` 的日志。这些是单个 DP 组的估计 KV "
|
||
"缓存容量。您可以根据此调整整体请求流量。"
|
||
|
||
#: ../../source/faqs.md:293
|
||
msgid ""
|
||
"Preemption cannot be avoided completely since KV cache usage always has a "
|
||
"limit. But there are methods to reduce the chances of preemption. As is "
|
||
"suggested in "
|
||
"[**PREEMPTION**](https://docs.vllm.ai/en/latest/configuration/optimization/#preemption),"
|
||
" the core strategy is to increase available KV cache. For example, one can "
|
||
"increase `--gpu-memory-utilization` or decrease `--max-num-seqs` && `--max-"
|
||
"num-batched-tokens`."
|
||
msgstr ""
|
||
"由于 KV 缓存使用始终有限制，抢占无法完全避免。但有一些方法可以减少抢占的可能性。正如 "
|
||
"[**抢占**](https://docs.vllm.ai/en/latest/configuration/optimization/#preemption)"
|
||
" 中所建议的，核心策略是增加可用的 KV 缓存。例如，可以增加 `--gpu-memory-utilization` 或减少 `--max-num-"
|
||
"seqs` 和 `--max-num-batched-tokens`。"
|
||
|
||
#: ../../source/faqs.md:295
|
||
msgid "23. How do I choose between single-node and multi-node deployment?"
|
||
msgstr "23.如何在单节点和多节点部署之间选择？"
|
||
|
||
#: ../../source/faqs.md:297
|
||
msgid ""
|
||
"Single-node deployment is recommended when the model fits within the memory "
|
||
"of a single node's NPUs. For models like Qwen3-32B (BF16), which requires 4 "
|
||
"× 64GB cards, multi-NPU within a single node (TP) is sufficient. Multi-node "
|
||
"deployment is only needed when the total NPU count exceeds a single node's "
|
||
"capacity."
|
||
msgstr ""
|
||
"当模型适合单节点NPU内存时，建议采用单节点部署。对于像Qwen3-32B（BF16）这样需要4×64GB卡的模型，单节点内的多NPU（TP）就足够了。只有当NPU总数超过单节点容量时，才需要多节点部署。"
|
||
|
||
#: ../../source/faqs.md:299
|
||
msgid "24. What quantization method should I use?"
|
||
msgstr "24.我应该使用哪种量化方法？"
|
||
|
||
#: ../../source/faqs.md:301
|
||
msgid ""
|
||
"**BF16**: Best accuracy, highest memory footprint. Use for accuracy-critical"
|
||
" applications or when memory is sufficient."
|
||
msgstr "**BF16**：最佳精度，最高内存占用。用于对精度要求高的应用或内存充足时。"
|
||
|
||
#: ../../source/faqs.md:302
|
||
msgid ""
|
||
"**W8A8**: Good balance of accuracy and memory reduction. Use for large "
|
||
"models (e.g., 32B) on memory-constrained hardware."
|
||
msgstr "**W8A8**：精度和内存减少的良好平衡。用于内存受限硬件上的大型模型（例如 32B）。"
|
||
|
||
#: ../../source/faqs.md:303
|
||
msgid ""
|
||
"**W4A8/W4A4**: Maximum memory reduction. Suitable for deploying larger "
|
||
"models on smaller hardware configurations, with some accuracy trade-off."
|
||
msgstr "**W4A8/W4A4**：最大程度减少内存占用。适用于在较小硬件配置上部署更大模型，但会牺牲一定精度。"
|
||
|
||
#: ../../source/faqs.md:305
|
||
msgid "25. When should I enable FlashComm_v1?"
|
||
msgstr "25.何时应启用 FlashComm_v1？"
|
||
|
||
#: ../../source/faqs.md:307
|
||
msgid ""
|
||
"Enable FlashComm_v1 (`VLLM_ASCEND_ENABLE_FLASHCOMM1=1`) when using Tensor "
|
||
"Parallelism (TP ≥ 2) with high concurrency. It is threshold-protected and "
|
||
"will not activate in low-concurrency scenarios where it could degrade "
|
||
"performance."
|
||
msgstr ""
|
||
"在使用张量并行（TP ≥ 2）且高并发时启用 "
|
||
"FlashComm_v1（`VLLM_ASCEND_ENABLE_FLASHCOMM1=1`）。该功能受阈值保护，在可能降低性能的低并发场景下不会激活。"
|
||
|
||
#: ../../source/faqs.md:309
|
||
msgid "26. What is the difference between FIA and PA operators for attention?"
|
||
msgstr "26.FIA 和 PA 注意力算子有何区别？"
|
||
|
||
#: ../../source/faqs.md:311
|
||
msgid ""
|
||
"FIA (Flash Attention) is the default attention operator in vLLM-Ascend. In "
|
||
"some batch-size settings (particularly medium concurrency), FIA may exhibit "
|
||
"suboptimal performance. The PA (Page Attention) operator can be manually "
|
||
"enabled via `pa_shape_list` in `--additional-config`. When the runtime batch"
|
||
" size matches a value in `pa_shape_list`, the framework switches to PA. This"
|
||
" is a temporary tuning knob — future FIA optimizations will make this "
|
||
"parameter obsolete."
|
||
msgstr ""
|
||
"FIA（Flash Attention）是 vLLM-Ascend 中的默认注意力算子。在某些批次大小设置（尤其是中等并发）下，FIA "
|
||
"可能表现欠佳。可通过 `--additional-config` 中的 `pa_shape_list` 手动启用 PA（Page "
|
||
"Attention）算子。当运行时批次大小与 `pa_shape_list` 中的某个值匹配时，框架会切换到 PA。这是一个临时调优参数——未来 FIA"
|
||
" 的优化将使该参数失效。"
|