xc-llm-kunlun/docs/source/locale/zh_CN/LC_MESSAGES/faqs.po

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#: ../../source/faqs.md:1
msgid "FAQs"
msgstr ""

#: ../../source/faqs.md:3
msgid "Version Specific FAQs"
msgstr "特定版本常见问题"

#~ msgid ""
#~ "[[v0.7.3.post1] FAQ & Feedback](https://github.com"
#~ "/vllm-project/vllm-kunlun/issues/1007)"
#~ msgstr ""
#~ "[[v0.7.3.post1] 常见问题与反馈](https://github.com/vllm-project"
#~ "/vllm-kunlun/issues/1007)"

#~ msgid ""
#~ "[[v0.9.2rc1] FAQ & Feedback](https://github.com"
#~ "/vllm-project/vllm-kunlun/issues/1742)"
#~ msgstr ""
#~ "[[v0.9.2rc1] 常见问题与反馈](https://github.com/vllm-project"
#~ "/vllm-kunlun/issues/1742)"

#~ msgid "General FAQs"
#~ msgstr "常见问题解答"

#~ msgid "1. What devices are currently supported?"
#~ msgstr "1. 目前支持哪些设备？"

#~ msgid ""
#~ "Currently, **ONLY** Atlas A2 series(Kunlun-"
#~ "cann-kernels-910b) and Atlas 300I"
#~ "(Kunlun-cann-kernels-310p) series are "
#~ "supported:"
#~ msgstr ""
#~ "目前，**仅**支持 Atlas A2 系列（Kunlun-cann-"
#~ "kernels-910b）和 Atlas 300I（Kunlun-cann-"
#~ "kernels-310p）系列："

#~ 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）"

#~ msgid "Atlas 800I A2 Inference series (Atlas 800I A2)"
#~ msgstr "Atlas 800I A2 推理系列（Atlas 800I A2）"

#~ msgid "Atlas 300I Inference series (Atlas 300I Duo)"
#~ msgstr "Atlas 300I 推理系列（Atlas 300I Duo）"

#~ msgid "Below series are NOT supported yet:"
#~ msgstr "以下系列目前尚不受支持："

#~ msgid "Atlas 200I A2 (Kunlun-cann-kernels-310b) unplanned yet"
#~ msgstr "Atlas 200I A2（Kunlun-cann-kernels-310b）尚未计划"

#~ msgid "Kunlun 910, Kunlun 910 Pro B (Kunlun-cann-kernels-910) unplanned yet"
#~ msgstr "Kunlun 910，Kunlun 910 Pro B（Kunlun-cann-kernels-910）尚未计划"

#~ msgid ""
#~ "From a technical view, vllm-kunlun "
#~ "support would be possible if the "
#~ "torch-xpu 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-xpu，则可以支持 vllm-"
#~ "kunlun。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。"

#~ msgid "2. How to get our docker containers?"
#~ msgstr "2. 如何获取我们的 docker 容器？"

#~ msgid ""
#~ "You can get our containers at "
#~ "`Quay.io`, e.g., [<u>vllm-"
#~ "kunlun</u>](https://quay.io/repository/kunlun/vllm-"
#~ "kunlun?tab=tags) and "
#~ "[<u>cann</u>](https://quay.io/repository/kunlun/cann?tab=tags)."
#~ msgstr ""
#~ "你可以在 `Quay.io` 获取我们的容器，例如，[<u>vllm-"
#~ "kunlun</u>](https://quay.io/repository/kunlun/vllm-"
#~ "kunlun?tab=tags) 和 "
#~ "[<u>cann</u>](https://quay.io/repository/kunlun/cann?tab=tags)。"

#~ 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："

#~ msgid "3. What models does vllm-kunlun supports?"
#~ msgstr "3. vllm-kunlun 支持哪些模型？"

#~ msgid ""
#~ "Find more details [<u>here</u>](https://vllm-"
#~ "kunlun.readthedocs.io/en/latest/user_guide/support_matrix/supported_models.html)."
#~ msgstr ""
#~ "在[<u>此处</u>](https://vllm-"
#~ "kunlun.readthedocs.io/en/latest/user_guide/support_matrix/supported_models.html)查看更多详细信息。"

#~ msgid "4. How to get in touch with our community?"
#~ msgstr "4. 如何与我们的社区取得联系？"

#~ 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："

#~ msgid ""
#~ "Submit a GitHub [<u>issue</u>](https://github.com"
#~ "/vllm-project/vllm-kunlun/issues?page=1)."
#~ msgstr ""
#~ "提交一个 GitHub [<u>issue</u>](https://github.com/vllm-"
#~ "project/vllm-kunlun/issues?page=1)。"

#~ 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。"

#~ msgid ""
#~ "Join our [<u>WeChat</u>](https://github.com/vllm-"
#~ "project/vllm-kunlun/issues/227) group and ask"
#~ " your quenstions."
#~ msgstr ""
#~ "加入我们的 [<u>微信群</u>](https://github.com/vllm-project"
#~ "/vllm-kunlun/issues/227) 并提问你的问题。"

#~ msgid ""
#~ "Join our kunlun channel in [<u>vLLM "
#~ "forums</u>](https://discuss.vllm.ai/c/hardware-support/vllm-"
#~ "kunlun-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-kunlun-support/6) 的 "
#~ "kunlun 频道并发布你的话题。"

#~ msgid "5. What features does vllm-kunlun V1 supports?"
#~ msgstr "5. vllm-kunlun V1 支持哪些功能？"

#~ msgid ""
#~ "Find more details [<u>here</u>](https://vllm-"
#~ "kunlun.readthedocs.io/en/latest/user_guide/support_matrix/supported_features.html)."
#~ msgstr ""
#~ "在[<u>这里</u>](https://vllm-"
#~ "kunlun.readthedocs.io/en/latest/user_guide/support_matrix/supported_features.html)找到更多详细信息。"

#~ 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？"

#~ msgid ""
#~ "Basically, the reason is that the "
#~ "XPU environment is not configured "
#~ "correctly. You can:"
#~ msgstr "基本上，原因是 XPU 环境没有正确配置。你可以："

#~ msgid ""
#~ "try `source /usr/local/Kunlun/nnal/atb/set_env.sh` "
#~ "to enable NNAL package."
#~ msgstr "尝试运行 `source /usr/local/Kunlun/nnal/atb/set_env.sh` 以启用 NNAL 包。"

#~ msgid ""
#~ "try `source /usr/local/Kunlun/kunlun-"
#~ "toolkit/set_env.sh` to enable CANN package."
#~ msgstr "尝试运行 `source /usr/local/Kunlun/kunlun-toolkit/set_env.sh` 以启用 CANN 包。"

#~ msgid "try `xpu-smi info` to check whether the XPU is working."
#~ msgstr "尝试运行 `xpu-smi info` 来检查 XPU 是否正常工作。"

#~ 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："

#~ 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。"

#~ msgid "7. How does vllm-kunlun perform?"
#~ msgstr "7. vllm-kunlun 的性能如何？"

#~ 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-kunlun 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-"
#~ "kunlun v0.7.3` 上安装 `mindie-turbo`，进一步加速推理。"

#~ msgid "8. How vllm-kunlun work with vllm?"
#~ msgstr "8. vllm-kunlun 如何与 vllm 协同工作？"

#~ msgid ""
#~ "vllm-kunlun is a plugin for vllm."
#~ " Basically, the version of vllm-"
#~ "kunlun is the same as the version"
#~ " of vllm. For example, if you "
#~ "use vllm 0.7.3, you should use "
#~ "vllm-kunlun 0.7.3 as well. For main"
#~ " branch, we will make sure `vllm-"
#~ "kunlun` and `vllm` are compatible by "
#~ "each commit."
#~ msgstr ""
#~ "vllm-kunlun 是 vllm 的一个插件。基本上，vllm-kunlun"
#~ " 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-kunlun 0.7.3。对于主分支，我们会确保每次提交都让 "
#~ "`vllm-kunlun` 和 `vllm` 保持兼容。"

#~ msgid "9. Does vllm-kunlun support Prefill Disaggregation feature?"
#~ msgstr "9. vllm-kunlun 支持 Prefill Disaggregation 功能吗？"

#~ msgid ""
#~ "Currently, only 1P1D is supported on "
#~ "V0 Engine. For V1 Engine or NPND"
#~ " support, We will make it stable "
#~ "and supported by vllm-kunlun 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-kunlun支持。"

#~ msgid "10. Does vllm-kunlun support quantization method?"
#~ msgstr "10. vllm-kunlun 支持量化方法吗？"

#~ msgid ""
#~ "Currently, w8a8 quantization is already "
#~ "supported by vllm-kunlun 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-kunlun and mindie-turbo, please"
#~ " use `pip install vllm-kunlun[mindie-"
#~ "turbo]`."
#~ msgstr ""
#~ "目前，w8a8 量化已在 v0.8.4rc2 或更高版本的 vllm-"
#~ "kunlun 中原生支持。如果你使用的是 vllm 0.7.3 版本，集成了 "
#~ "vllm-kunlun 和 mindie-turbo 后也支持 w8a8"
#~ " 量化，请使用 `pip install vllm-kunlun[mindie-"
#~ "turbo]`。"

#~ msgid "11. How to run w8a8 DeepSeek model?"
#~ msgstr "11. 如何运行 w8a8 DeepSeek 模型？"

#~ msgid ""
#~ "Please following the [inferencing "
#~ "tutorail](https://vllm-"
#~ "kunlun.readthedocs.io/en/latest/tutorials/multi_node.html) and"
#~ " replace model to DeepSeek."
#~ msgstr ""
#~ "请按照[inferencing 教程](https://vllm-"
#~ "kunlun.readthedocs.io/en/latest/tutorials/multi_node.html)进行操作，并将模型更换为"
#~ " DeepSeek。"

#~ msgid ""
#~ "12. There is no output in log "
#~ "when loading models using vllm-kunlun,"
#~ " How to solve it?"
#~ msgstr "12. 使用 vllm-kunlun 加载模型时日志没有输出，如何解决？"

#~ 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。"

#~ msgid "13. How vllm-kunlun is tested"
#~ msgstr "13. 如何测试 vllm-kunlun"

#~ msgid ""
#~ "vllm-kunlun is tested by functional "
#~ "test, performance test and accuracy "
#~ "test."
#~ msgstr "vllm-kunlun 经过功能测试、性能测试和精度测试。"

#~ msgid ""
#~ "**Functional test**: we added CI, "
#~ "includes portion of vllm's native unit"
#~ " tests and vllm-kunlun's own unit "
#~ "tests，on vllm-kunlun's test, we test "
#~ "basic functionality、popular models availability "
#~ "and [supported features](https://vllm-"
#~ "kunlun.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-kunlun自己的单元测试。在vllm-"
#~ "kunlun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-"
#~ "kunlun.readthedocs.io/en/latest/user_guide/support_matrix/supported_features.html)。"

#~ msgid ""
#~ "**Performance test**: we provide "
#~ "[benchmark](https://github.com/vllm-project/vllm-"
#~ "kunlun/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-"
#~ "kunlun/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。"

#~ 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。"

#~ 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。"

#~ msgid "14. How to fix the error \"InvalidVersion\" when using vllm-kunlun?"
#~ msgstr "14. 使用 vllm-kunlun 时如何解决 “InvalidVersion” 错误？"

#~ 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`。"

#~ msgid "15. How to handle Out Of Memory?"
#~ msgstr "15. 如何处理内存溢出？"

#~ msgid ""
#~ "OOM errors typically occur when the "
#~ "model exceeds the memory capacity of "
#~ "a single XPU. 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 XPU 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)。"

#~ msgid ""
#~ "In scenarios where XPUs 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 XPU 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："

#~ 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)。"

#~ msgid ""
#~ "**Configure `PYTORCH_XPU_ALLOC_CONF`**: Set this "
#~ "environment variable to optimize XPU "
#~ "memory management. For example, you can"
#~ " `export PYTORCH_XPU_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_XPU_ALLOC_CONF](https://www.hikunlun.com/document/detail/zh/Pytorch/700/comref/Envvariables/Envir_012.html)."
#~ msgstr ""
#~ "**配置 `PYTORCH_XPU_ALLOC_CONF`**：设置此环境变量以优化XPU内存管理。例如，你可以通过 "
#~ "`export PYTORCH_XPU_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_XPU_ALLOC_CONF](https://www.hikunlun.com/document/detail/zh/Pytorch/700/comref/Envvariables/Envir_012.html)。"

#~ msgid "16. Failed to enable XPU graph mode when running DeepSeek?"
#~ msgstr "16. 运行 DeepSeek 时无法启用 XPU 图模式？"

#~ msgid ""
#~ "You may encounter the following error"
#~ " if running DeepSeek with XPU 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 "
#~ "XPU graph mode support on "
#~ "DeepSeek-V2-Lite will be done in the "
#~ "future."
#~ msgstr ""
#~ "如果在启用XPU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XPU图模式下的支持。"

#~ 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。"

#~ msgid ""
#~ "17. Failed to reinstall vllm-kunlun "
#~ "from source after uninstalling vllm-"
#~ "kunlun?"
#~ msgstr "17. 卸载 vllm-kunlun 后无法从源码重新安装 vllm-kunlun？"

#~ msgid ""
#~ "You may encounter the problem of C"
#~ " compilation failure when reinstalling "
#~ "vllm-kunlun 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-kunlun 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。"

#~ msgid "18. How to generate determinitic results when using vllm-kunlun?"
#~ msgstr "18. 使用 vllm-kunlun 时如何生成确定性结果？"

#~ msgid "There are several factors that affect output certainty:"
#~ msgstr "有几个因素会影响输出的确定性："

#~ 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："

#~ msgid "Set the following enveriments parameters:"
#~ msgstr "设置以下环境参数："