Add a doc on how to update PyTorch version

docs/ci/update_pytorch_version.md

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+---
+title: Update PyTorch version on vLLM OSS CI/CD
+---
+
+vLLM's current policy is to always use the latest PyTorch stable
+release in CI/CD. It is standard practice to submit a PR to update the
+PyTorch version as early as possible when a new [PyTorch stable
+release](https://github.com/pytorch/pytorch/blob/main/RELEASE.md#release-cadence) becomes available.
+This process is non-trivial due to the gap between PyTorch
+releases. Using [#16859](https://github.com/vllm-project/vllm/pull/16859) as
+an example, this document outlines common steps to achieve this update along with
+a list of potential issues and how to address them.
+
+## Test PyTorch release candidates (RCs)
+
+Updating PyTorch in vLLM after the official release is not
+ideal because any issues discovered at that point can only be resolved
+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:
+
+```
+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`.
+
+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,
+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.
+
+## Address long vLLM build time
+
+When building vLLM with a new PyTorch/CUDA version, no cache will exist
+in the vLLM sccache S3 bucket, causing the build job on CI to potentially take more than 5 hours
+and timeout. Additionally, since vLLM's fastcheck pipeline runs in read-only mode,
+it doesn't populate the cache, so re-running it to warm up the cache
+is ineffective.
+
+While ongoing efforts like [#17419](https://github.com/vllm-project/vllm/issues/17419)
+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:
+
+1. Increase the timeout limit to 10 hours so that the build doesn't timeout.
+2. Allow the compiled artifacts to be written to the vLLM sccache S3 bucket
+to warm it up so that future builds are faster.
+
+<p align="center" width="100%">
+    <img width="60%" src="https://github.com/user-attachments/assets/a8ff0fcd-76e0-4e91-b72f-014e3fdb6b94">
+</p>
+
+## Update dependencies
+
+Several vLLM dependencies, such as FlashInfer, also depend on PyTorch and need
+to be updated accordingly. Rather than waiting for all of them to publish new
+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):
+
+```
+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/[email protected]"
+```
+
+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
+team if you want to get the package published there.
+
+### xFormers
+Similar to FlashInfer, here is how to build and install xFormers from source:
+
+```
+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/[email protected]"
+```
+
+### Mamba
+
+```
+uv pip install --system --no-build-isolation "git+https://github.com/state-spaces/[email protected]"
+```
+
+### causal-conv1d
+
+```
+uv pip install 'git+https://github.com/Dao-AILab/[email protected]'
+```
+
+## Update all the different vLLM platforms
+
+Rather than attempting to update all vLLM platforms in a single pull request, it's more manageable
+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.
+While https://github.com/vllm-project/vllm/pull/16859 updated vLLM to PyTorch
+2.7.0 on CPU, CUDA, and ROCm, https://github.com/vllm-project/vllm/pull/17444
+completed the update for XPU.