[CI][Docs] Add scheduled Sphinx link check for docs

.github/workflows/schedule_doc_linkcheck.yaml

@@ -0,0 +1,79 @@
+name: Docs link check
+
+on:
+  schedule:
+    # GitHub cron uses UTC. This runs every Sunday at 20:00 UTC+8.
+    - cron: '0 12 * * 0'
+  pull_request:
+    branches:
+      - 'main'
+    paths:
+      - 'docs/source/**/*.md'
+  workflow_dispatch:
+
+defaults:
+  run:
+    shell: bash -el {0}
+
+permissions:
+  contents: read
+  pull-requests: read
+
+concurrency:
+  group: docs-linkcheck-${{ github.ref }}
+  cancel-in-progress: true
+
+jobs:
+  linkcheck:
+    name: Sphinx linkcheck
+    runs-on: linux-aarch64-a2b3-0
+    container:
+      image: swr.cn-southwest-2.myhuaweicloud.com/base_image/ascend-ci/cann:8.5.1-910b-ubuntu22.04-py3.11
+    timeout-minutes: 30
+    steps:
+      - name: Checkout vllm-project/vllm-ascend repo
+        uses: actions/checkout@v6
+
+      - name: Collect changed docs files
+        id: changed-docs
+        if: ${{ github.event_name == 'pull_request' }}
+        uses: tj-actions/changed-files@v47
+        with:
+          use_rest_api: true
+          files: |
+            docs/source/**/*.md
+
+      - name: Install docs dependencies
+        if: ${{ github.event_name != 'pull_request' || steps.changed-docs.outputs.any_changed == 'true' }}
+        run: |
+          python -m pip install --upgrade pip
+          pip install -r docs/requirements-docs.txt
+
+      - name: Run full Sphinx linkcheck
+        if: ${{ github.event_name != 'pull_request' }}
+        run: |
+          make -C docs linkcheck SPHINXOPTS="-W --keep-going"
+
+      - name: Run Sphinx linkcheck for changed docs
+        if: ${{ github.event_name == 'pull_request' && steps.changed-docs.outputs.any_changed == 'true' }}
+        env:
+          CHANGED_DOCS: ${{ steps.changed-docs.outputs.all_changed_files }}
+        run: |
+          set -euo pipefail
+
+          echo "Changed docs files:"
+          printf '  %s\n' ${CHANGED_DOCS}
+
+          sphinx-build -b linkcheck -W --keep-going \
+            docs/source docs/_build/linkcheck ${CHANGED_DOCS}
+
+      - name: Upload linkcheck reports
+        if: ${{ always() && github.event_name != 'pull_request' }}
+        uses: actions/upload-artifact@v4
+        with:
+          name: sphinx-linkcheck-report
+          path: |
+            docs/_build/linkcheck/output.txt
+            docs/_build/linkcheck/output.json
+          if-no-files-found: warn
+          retention-days: 5

docs/source/community/user_stories/index.md

@@ -2,15 +2,15 @@
 
 Read case studies on how users and developers solve 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), gaining 2x performance enhancement in inference.
+- [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/LlamaFactory/pull/7739), gaining 2x performance enhancement in 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 NPUs.
 
 - [MindIE Turbo](https://pypi.org/project/mindie-turbo) is an LLM inference engine acceleration plugin library developed by Huawei on Ascend hardware, which includes self-developed LLM 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 information at [this 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 LLMs. It uses vLLM Ascend since [v0.4.0](https://github.com/volcengine/verl/releases/tag/v0.4.0). See more information on [verl x Ascend Quickstart](https://verl.readthedocs.io/en/latest/ascend_tutorial/quick_start/ascend_quick_start.html).
+- [verl](https://github.com/verl-project/verl) is a flexible, efficient, and production-ready RL training library for LLMs. It uses vLLM Ascend since [v0.4.0](https://github.com/verl-project/verl/releases/tag/v0.4.0). See more information on [verl x Ascend Quickstart](https://verl.readthedocs.io/en/latest/ascend_tutorial/quick_start/ascend_quick_start.html).
 
 :::{toctree}
 :caption: More details

docs/source/community/user_stories/llamafactory.md

@@ -2,7 +2,7 @@
 
 **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-Factory](https://github.com/hiyouga/LlamaFactory) 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-Factory users need to evaluate the model and perform inference after fine-tuning.
 
@@ -12,7 +12,7 @@ LLaMA-Factory uses Transformers to perform inference on Ascend NPUs, but the spe
 
 **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)), LLaMA-Factory has achieved significant performance gains during model inference. Benchmark results show that its inference speed is now up to 2× faster compared to the Transformers implementation.
+With the joint efforts of LLaMA-Factory and vLLM Ascend ([LLaMA-Factory#7739](https://github.com/hiyouga/LlamaFactory/pull/7739)), LLaMA-Factory has achieved significant performance gains during model inference. Benchmark results show that its inference speed is now up to 2× faster compared to the Transformers implementation.
 
 **Learn more**
 

docs/source/conf.py

@@ -141,6 +141,26 @@
 # Copy llms.txt to site root so it is available as /llms.txt.
 html_extra_path = ["llms.txt"]
 
+# -- Options for linkcheck builder -------------------------------------------
+
+# Check external links without validating remote anchors. Many third-party
+# sites render anchors dynamically, which makes anchor checks flaky in CI.
+linkcheck_anchors = False
+linkcheck_retries = 2
+linkcheck_timeout = 15
+linkcheck_workers = 10
+
+# Example service endpoints in docs are intentionally not reachable from CI.
+linkcheck_ignore = [
+    r"https?://localhost(:\d+)?($|/.*)",
+    r"https?://127\.0\.0\.1(:\d+)?($|/.*)",
+    r"https?://0\.0\.0\.0(:\d+)?($|/.*)",
+    r"https?://192\.0\.0\.1(:\d+)?($|/.*)",
+    r"https?://<[^>]+>.*",
+    r"https://github\.com/vllm-project/vllm-ascend/issues/new/choose",
+    r"https://github\.com/[^/?#]+/?$",
+]
+
 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

docs/source/developer_guide/Design_Documents/ACL_Graph.md

@@ -8,7 +8,7 @@ On Ascend, the design goal is the same as upstream static graph execution: reduc
 
 ## Prerequisites and References
 
-- Upstream vLLM design doc for generic graph concepts: [CUDA Graphs](https://docs.vllm.ai/en/latest/design/cuda_graphs.html).
+- Upstream vLLM design doc for generic graph concepts: [CUDA Graphs](https://docs.vllm.ai/en/latest/design/cuda_graphs/).
 - PyTorch graph documentation for generic capture and replay semantics: [Accelerating PyTorch with CUDA Graphs](https://pytorch.org/blog/accelerating-pytorch-with-cuda-graphs/).
 - Ascend user guide for operational enablement: [Graph Mode Guide](https://docs.vllm.ai/projects/ascend/en/latest/user_guide/feature_guide/graph_mode.html).
 - Existing repo design note: `docs/source/developer_guide/Design_Documents/ACL_Graph.md`.

docs/source/developer_guide/contribution/testing.md

@@ -365,3 +365,25 @@ The doctest is a good way to make sure docs stay current and examples remain exe
 ```
 
 This will reproduce the same environment as the CI. See [labeled_doctest.yaml](https://github.com/vllm-project/vllm-ascend/blob/main/.github/workflows/labeled_doctest.yaml).
+
+### Run docs link check
+
+You can validate external links in the Sphinx docs locally with:
+
+```bash
+make -C docs linkcheck SPHINXOPTS="-W --keep-going"
+```
+
+To check links in a specific Markdown file, pass the file to `sphinx-build`.
+For example, to check only `docs/source/user_guide/release_notes.md`:
+
+```bash
+cd docs
+sphinx-build -b linkcheck -W --keep-going \
+  source _build/linkcheck source/user_guide/release_notes.md
+```
+
+The detailed report will be written to:
+
+- `docs/_build/linkcheck/output.txt`
+- `docs/_build/linkcheck/output.json`

docs/source/developer_guide/performance_and_debug/performance_benchmark.md

@@ -50,7 +50,7 @@ This section introduces how to perform performance testing using the benchmark s
 
 ### 3.1 Dataset
 
-VLLM supports a variety of [datasets](https://github.com/vllm-project/vllm/blob/main/vllm/benchmarks/datasets.py).
+VLLM supports a variety of [datasets](https://github.com/vllm-project/vllm/blob/main/vllm/benchmarks/datasets/datasets.py).
 
 <style>
 th {

docs/source/locale/zh_CN/LC_MESSAGES/tutorials/models/GLM4.x.po

@@ -128,9 +128,9 @@ msgstr ""
 #: ../../source/tutorials/models/GLM4.x.md:27
 msgid ""
 "`Method of Quantify`: [quantization "
-"scheme](https://blog.csdn.net/qq_37368095/article/details/156429653?spm=1011.2124.3001.6209)."
+"scheme](https://ai.gitcode.com/Ascend-SACT/GLM-4.5-w8a8)."
 " You can use these methods to quantify the model."
-msgstr "`量化方法`：[量化方案](https://blog.csdn.net/qq_37368095/article/details/156429653?spm=1011.2124.3001.6209)。您可以使用这些方法对模型进行量化。"
+msgstr "`量化方法`：[量化方案](https://ai.gitcode.com/Ascend-SACT/GLM-4.5-w8a8)。您可以使用这些方法对模型进行量化。"
 
 #: ../../source/tutorials/models/GLM4.x.md:29
 msgid ""

docs/source/tutorials/models/GLM4.x.md

@@ -24,7 +24,7 @@ Refer to [feature guide](../../user_guide/feature_guide/index.md) to get the fea
 - `GLM-4.5-w8a8-with-float-mtp`(Quantized version with mtp): [Download model weight](https://modelers.cn/models/Modelers_Park/GLM-4.5-w8a8).
 - `GLM-4.6-w8a8`(Quantized version without mtp): [Download model weight](https://modelers.cn/models/Modelers_Park/GLM-4.6-w8a8). Because vllm does not support GLM4.6 mtp in October, we do not provide an mtp version. Last month, it was supported; you can use the following quantization scheme to add mtp weights to the quantized weights.
 - `GLM-4.7-w8a8-with-float-mtp`(Quantized version without mtp): [Download model weight](https://modelscope.cn/models/Eco-Tech/GLM-4.7-W8A8-floatmtp).
-- `Method of Quantify`: [quantization scheme](https://blog.csdn.net/qq_37368095/article/details/156429653?spm=1011.2124.3001.6209). You can use these methods to quantify the model.
+- `Method of Quantify`: [quantization scheme](https://ai.gitcode.com/Ascend-SACT/GLM-4.5-w8a8). You can use these methods to quantify the model.
 
 It is recommended to download the model weight to the shared directory of multiple nodes, such as `/root/.cache/`.
 

docs/source/tutorials/models/GLM5.md

@@ -1367,7 +1367,7 @@ Refer to [Using AISBench for performance evaluation](../../developer_guide/evalu
 
 ### Using vLLM Benchmark
 
-Refer to [vllm benchmark](https://docs.vllm.ai/en/latest/contributing/benchmarks.html) for more details.
+Refer to [vllm benchmark](https://docs.vllm.ai/en/latest/contributing/) for more details.
 
 ## Best Practices
 

docs/source/tutorials/models/Kimi-K2.5.md

@@ -773,7 +773,7 @@ Refer to [Using AISBench for performance evaluation](../../developer_guide/evalu
 
 Run performance evaluation of `Kimi-K2.5-w4a8` as an example.
 
-Refer to [vllm benchmark](https://docs.vllm.ai/en/latest/contributing/benchmarks.html) for more details.
+Refer to [vllm benchmark](https://docs.vllm.ai/en/latest/benchmarking/) for more details.
 
 There are three `vllm bench` subcommands:
 

docs/source/tutorials/models/Qwen3.5-27B.md

@@ -179,7 +179,7 @@ Refer to [Using AISBench for performance evaluation](../../developer_guide/evalu
 
 Run performance evaluation of `Qwen3.5-27B-w8a8` as an example.
 
-Refer to [vllm benchmark](https://docs.vllm.ai/en/latest/contributing/benchmarks.html) for more details.
+Refer to [vllm benchmark](https://docs.vllm.ai/en/latest/benchmarking/) for more details.
 
 There are three `vllm bench` subcommands:
 

docs/source/tutorials/models/Qwen3.5-397B-A17B.md

@@ -586,7 +586,7 @@ Refer to [Using AISBench for performance evaluation](../../developer_guide/evalu
 
 Run performance evaluation of `Qwen3.5-397B-A17B-w8a8` as an example.
 
-Refer to [vllm benchmark](https://docs.vllm.ai/en/latest/contributing/benchmarks.html) for more details.
+Refer to [vllm benchmark](https://docs.vllm.ai/en/latest/benchmarking/) for more details.
 
 There are three `vllm bench` subcommands:
 

docs/source/user_guide/feature_guide/epd_disaggregation.md

@@ -20,7 +20,7 @@ A **disaggregated encoder** runs the vision-encoder stage of a multimodal LLM in
    * In-process encoders confine reuse to a single worker.  
    * A remote, shared cache lets any worker retrieve existing embeddings, eliminating redundant computation.
 
-Design doc: <https://docs.google.com/document/d/1aed8KtC6XkXtdoV87pWT0a8OJlZ-CpnuLLzmR8l9BAE>
+Design doc: <https://docs.google.com/document/d/1aed8KtC6XkXtdoV87pWT0a8OJlZ-CpnuLLzmR8l9BAE/edit>
 
 ---
 

docs/source/user_guide/feature_guide/kv_pool.md

@@ -1147,7 +1147,7 @@ etcdctl --endpoints "${ETCD_IP}:2379" get key
 ```
 
 For production environments, refer to the official etcd clustering
-documentation: <https://etcd.io/docs/current/op-guide/clustering/>
+documentation: <https://etcd.io/docs/v3.7/op-guide/clustering/>
 
 ### Start Datasystem Worker
 

docs/source/user_guide/feature_guide/lora.md

@@ -2,9 +2,9 @@
 
 ## 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).
+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/).
 
-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 refer to [Supported Models](https://docs.vllm.ai/en/latest/models/supported_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.
 
@@ -25,4 +25,4 @@ vllm serve 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).
+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).

docs/source/user_guide/feature_guide/quantization.md

@@ -158,4 +158,4 @@ python -m vllm.entrypoints.api_server \
 
 - [ModelSlim Documentation](https://gitcode.com/Ascend/msit/blob/master/msmodelslim/README.md)
 - [LLM-Compressor GitHub](https://github.com/vllm-project/llm-compressor)
-- [vLLM Quantization Guide](https://docs.vllm.ai/en/latest/quantization/)
+- [vLLM Quantization Guide](https://docs.vllm.ai/en/latest/features/quantization/)