[doc] update doc format (#20673)

Signed-off-by: reidliu41 <reid201711@gmail.com>

Author: reidliu41

docs/contributing/ci/update_pytorch_version.md

@@ -16,46 +16,67 @@ by waiting for the next release or by implementing hacky workarounds in vLLM.
 The better solution is to test vLLM with PyTorch release candidates (RC) to ensure
 compatibility before each release.
 
-PyTorch release candidates can be downloaded from PyTorch test index at https://download.pytorch.org/whl/test.
-For example, torch2.7.0+cu12.8 RC can be installed using the following command:
+PyTorch release candidates can be downloaded from [PyTorch test index](https://download.pytorch.org/whl/test).
+For example, `torch2.7.0+cu12.8` RC can be installed using the following command:
 
-```
-uv pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/test/cu128
+```bash
+uv pip install torch torchvision torchaudio \
+    --index-url https://download.pytorch.org/whl/test/cu128
 ```
 
 When the final RC is ready for testing, it will be announced to the community
 on the [PyTorch dev-discuss forum](https://dev-discuss.pytorch.org/c/release-announcements).
 After this announcement, we can begin testing vLLM integration by drafting a pull request
 following this 3-step process:
 
-1. Update requirements files in https://github.com/vllm-project/vllm/tree/main/requirements
-to point to the new releases for torch, torchvision, and torchaudio.
-2. Use `--extra-index-url https://download.pytorch.org/whl/test/<PLATFORM>` to
-get the final release candidates' wheels.  Some common platforms are `cpu`, `cu128`,
-and `rocm6.2.4`.
-3. As vLLM uses uv, make sure that `unsafe-best-match` strategy is set either
-via `UV_INDEX_STRATEGY` env variable or via `--index-strategy unsafe-best-match`.
+1. Update [requirements files](https://github.com/vllm-project/vllm/tree/main/requirements)
+to point to the new releases for `torch`, `torchvision`, and `torchaudio`.
+
+2. Use the following option to get the final release candidates' wheels. Some common platforms are `cpu`, `cu128`, and `rocm6.2.4`.
+
+    ```bash
+    --extra-index-url https://download.pytorch.org/whl/test/<PLATFORM>
+    ```
+
+3. Since vLLM uses `uv`, ensure the following index strategy is applied:
+
+    - Via environment variable:
+
+    ```bash
+    export UV_INDEX_STRATEGY=unsafe-best-match
+    ```
+
+    - Or via CLI flag:
+
+    ```bash
+    --index-strategy unsafe-best-match
+    ```
 
 If failures are found in the pull request, raise them as issues on vLLM and
 cc the PyTorch release team to initiate discussion on how to address them.
 
 ## Update CUDA version
 
 The PyTorch release matrix includes both stable and experimental [CUDA versions](https://github.com/pytorch/pytorch/blob/main/RELEASE.md#release-compatibility-matrix). Due to limitations, only the latest stable CUDA version (for example,
-torch2.7.0+cu12.6) is uploaded to PyPI. However, vLLM may require a different CUDA version,
+`torch2.7.0+cu12.6`) is uploaded to PyPI. However, vLLM may require a different CUDA version,
 such as 12.8 for Blackwell support.
 This complicates the process as we cannot use the out-of-the-box
 `pip install torch torchvision torchaudio` command. The solution is to use
 `--extra-index-url` in vLLM's Dockerfiles.
 
-1. Use `--extra-index-url https://download.pytorch.org/whl/cu128` to install torch+cu128.
-2. Other important indexes at the moment include:
-    1. CPU ‒ https://download.pytorch.org/whl/cpu
-    2. ROCm ‒ https://download.pytorch.org/whl/rocm6.2.4 and https://download.pytorch.org/whl/rocm6.3
-    3. XPU ‒ https://download.pytorch.org/whl/xpu
-3. Update .buildkite/release-pipeline.yaml and .buildkite/scripts/upload-wheels.sh to
-match the CUDA version from step 1.  This makes sure that the release vLLM wheel is tested
-on CI.
+- Important indexes at the moment include:
+
+| Platform | `--extra-index-url` |
+|----------|-----------------|
+| CUDA 12.8| [https://download.pytorch.org/whl/cu128](https://download.pytorch.org/whl/cu128)|
+| CPU      | [https://download.pytorch.org/whl/cpu](https://download.pytorch.org/whl/cpu)|
+| ROCm 6.2 | [https://download.pytorch.org/whl/rocm6.2.4](https://download.pytorch.org/whl/rocm6.2.4) |
+| ROCm 6.3 | [https://download.pytorch.org/whl/rocm6.3](https://download.pytorch.org/whl/rocm6.3) |
+| XPU      | [https://download.pytorch.org/whl/xpu](https://download.pytorch.org/whl/xpu) |
+
+- Update the below files to match the CUDA version from step 1. This makes sure that the release vLLM wheel is tested on CI.
+    - `.buildkite/release-pipeline.yaml`
+    - `.buildkite/scripts/upload-wheels.sh`
 
 ## Address long vLLM build time
 
@@ -66,7 +87,7 @@ it doesn't populate the cache, so re-running it to warm up the cache
 is ineffective.
 
 While ongoing efforts like [#17419](gh-issue:17419)
-address the long build time at its source, the current workaround is to set VLLM_CI_BRANCH
+address the long build time at its source, the current workaround is to set `VLLM_CI_BRANCH`
 to a custom branch provided by @khluu (`VLLM_CI_BRANCH=khluu/use_postmerge_q`)
 when manually triggering a build on Buildkite. This branch accomplishes two things:
 
@@ -86,31 +107,34 @@ releases (which would take too much time), they can be built from
 source to unblock the update process.
 
 ### FlashInfer
-Here is how to build and install it from source with torch2.7.0+cu128 in vLLM [Dockerfile](https://github.com/vllm-project/vllm/blob/27bebcd89792d5c4b08af7a65095759526f2f9e1/docker/Dockerfile#L259-L271):
+Here is how to build and install it from source with `torch2.7.0+cu128` in vLLM [Dockerfile](https://github.com/vllm-project/vllm/blob/27bebcd89792d5c4b08af7a65095759526f2f9e1/docker/Dockerfile#L259-L271):
 
 ```bash
 export TORCH_CUDA_ARCH_LIST='7.5 8.0 8.9 9.0 10.0+PTX'
 export FLASHINFER_ENABLE_SM90=1
-uv pip install --system --no-build-isolation "git+https://github.com/flashinfer-ai/flashinfer@v0.2.6.post1"
+uv pip install --system \
+    --no-build-isolation "git+https://github.com/flashinfer-ai/flashinfer@v0.2.6.post1"
 ```
 
 One caveat is that building FlashInfer from source adds approximately 30
 minutes to the vLLM build time. Therefore, it's preferable to cache the wheel in a
-public location for immediate installation, such as https://download.pytorch.org/whl/cu128/flashinfer/flashinfer_python-0.2.6.post1%2Bcu128torch2.7-cp39-abi3-linux_x86_64.whl. For future releases, contact the PyTorch release
+public location for immediate installation, such as [this FlashInfer wheel link](https://download.pytorch.org/whl/cu128/flashinfer/flashinfer_python-0.2.6.post1%2Bcu128torch2.7-cp39-abi3-linux_x86_64.whl). For future releases, contact the PyTorch release
 team if you want to get the package published there.
 
 ### xFormers
 Similar to FlashInfer, here is how to build and install xFormers from source:
 
 ```bash
 export TORCH_CUDA_ARCH_LIST='7.0 7.5 8.0 8.9 9.0 10.0+PTX'
-MAX_JOBS=16 uv pip install --system --no-build-isolation "git+https://github.com/facebookresearch/xformers@v0.0.30"
+MAX_JOBS=16 uv pip install --system \
+    --no-build-isolation "git+https://github.com/facebookresearch/xformers@v0.0.30"
 ```
 
 ### Mamba
 
 ```bash
-uv pip install --system --no-build-isolation "git+https://github.com/state-spaces/mamba@v2.2.4"
+uv pip install --system \
+    --no-build-isolation "git+https://github.com/state-spaces/mamba@v2.2.4"
 ```
 
 ### causal-conv1d
@@ -125,6 +149,6 @@ Rather than attempting to update all vLLM platforms in a single pull request, it
 to handle some platforms separately. The separation of requirements and Dockerfiles
 for different platforms in vLLM CI/CD allows us to selectively choose
 which platforms to update. For instance, updating XPU requires the corresponding
-release from https://github.com/intel/intel-extension-for-pytorch by Intel.
+release from [Intel Extension for PyTorch](https://github.com/intel/intel-extension-for-pytorch) by Intel.
 While <gh-pr:16859> updated vLLM to PyTorch 2.7.0 on CPU, CUDA, and ROCm,
 <gh-pr:17444> completed the update for XPU.