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Add a doc on how to update PyTorch version (#19705)
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Huy Do
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docs/ci/update_pytorch_version.md
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docs/ci/update_pytorch_version.md
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---
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title: Update PyTorch version on vLLM OSS CI/CD
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---
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vLLM's current policy is to always use the latest PyTorch stable
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release in CI/CD. It is standard practice to submit a PR to update the
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PyTorch version as early as possible when a new [PyTorch stable
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release](https://github.com/pytorch/pytorch/blob/main/RELEASE.md#release-cadence) becomes available.
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This process is non-trivial due to the gap between PyTorch
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releases. Using [#16859](https://github.com/vllm-project/vllm/pull/16859) as
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an example, this document outlines common steps to achieve this update along with
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a list of potential issues and how to address them.
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## Test PyTorch release candidates (RCs)
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Updating PyTorch in vLLM after the official release is not
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ideal because any issues discovered at that point can only be resolved
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by waiting for the next release or by implementing hacky workarounds in vLLM.
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The better solution is to test vLLM with PyTorch release candidates (RC) to ensure
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compatibility before each release.
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PyTorch release candidates can be downloaded from PyTorch test index at https://download.pytorch.org/whl/test.
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For example, torch2.7.0+cu12.8 RC can be installed using the following command:
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```
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uv pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/test/cu128
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```
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When the final RC is ready for testing, it will be announced to the community
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on the [PyTorch dev-discuss forum](https://dev-discuss.pytorch.org/c/release-announcements).
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After this announcement, we can begin testing vLLM integration by drafting a pull request
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following this 3-step process:
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1. Update requirements files in https://github.com/vllm-project/vllm/tree/main/requirements
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to point to the new releases for torch, torchvision, and torchaudio.
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2. Use `--extra-index-url https://download.pytorch.org/whl/test/<PLATFORM>` to
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get the final release candidates' wheels. Some common platforms are `cpu`, `cu128`,
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and `rocm6.2.4`.
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3. As vLLM uses uv, make sure that `unsafe-best-match` strategy is set either
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via `UV_INDEX_STRATEGY` env variable or via `--index-strategy unsafe-best-match`.
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If failures are found in the pull request, raise them as issues on vLLM and
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cc the PyTorch release team to initiate discussion on how to address them.
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## Update CUDA version
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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,
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torch2.7.0+cu12.6) is uploaded to PyPI. However, vLLM may require a different CUDA version,
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such as 12.8 for Blackwell support.
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This complicates the process as we cannot use the out-of-the-box
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`pip install torch torchvision torchaudio` command. The solution is to use
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`--extra-index-url` in vLLM's Dockerfiles.
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1. Use `--extra-index-url https://download.pytorch.org/whl/cu128` to install torch+cu128.
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2. Other important indexes at the moment include:
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1. CPU ‒ https://download.pytorch.org/whl/cpu
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2. ROCm ‒ https://download.pytorch.org/whl/rocm6.2.4 and https://download.pytorch.org/whl/rocm6.3
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3. XPU ‒ https://download.pytorch.org/whl/xpu
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3. Update .buildkite/release-pipeline.yaml and .buildkite/scripts/upload-wheels.sh to
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match the CUDA version from step 1. This makes sure that the release vLLM wheel is tested
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on CI.
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## Address long vLLM build time
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When building vLLM with a new PyTorch/CUDA version, no cache will exist
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in the vLLM sccache S3 bucket, causing the build job on CI to potentially take more than 5 hours
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and timeout. Additionally, since vLLM's fastcheck pipeline runs in read-only mode,
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it doesn't populate the cache, so re-running it to warm up the cache
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is ineffective.
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While ongoing efforts like [#17419](https://github.com/vllm-project/vllm/issues/17419)
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address the long build time at its source, the current workaround is to set VLLM_CI_BRANCH
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to a custom branch provided by @khluu (`VLLM_CI_BRANCH=khluu/use_postmerge_q`)
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when manually triggering a build on Buildkite. This branch accomplishes two things:
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1. Increase the timeout limit to 10 hours so that the build doesn't timeout.
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2. Allow the compiled artifacts to be written to the vLLM sccache S3 bucket
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to warm it up so that future builds are faster.
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<p align="center" width="100%">
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<img width="60%" src="https://github.com/user-attachments/assets/a8ff0fcd-76e0-4e91-b72f-014e3fdb6b94">
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</p>
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## Update dependencies
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Several vLLM dependencies, such as FlashInfer, also depend on PyTorch and need
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to be updated accordingly. Rather than waiting for all of them to publish new
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releases (which would take too much time), they can be built from
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source to unblock the update process.
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### FlashInfer
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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):
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```
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export TORCH_CUDA_ARCH_LIST='7.5 8.0 8.9 9.0 10.0+PTX'
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export FLASHINFER_ENABLE_SM90=1
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uv pip install --system --no-build-isolation "git+https://github.com/flashinfer-ai/flashinfer@v0.2.6.post1"
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```
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One caveat is that building FlashInfer from source adds approximately 30
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minutes to the vLLM build time. Therefore, it's preferable to cache the wheel in a
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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
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team if you want to get the package published there.
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### xFormers
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Similar to FlashInfer, here is how to build and install xFormers from source:
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```
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export TORCH_CUDA_ARCH_LIST='7.0 7.5 8.0 8.9 9.0 10.0+PTX'
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MAX_JOBS=16 uv pip install --system --no-build-isolation "git+https://github.com/facebookresearch/xformers@v0.0.30"
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```
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### Mamba
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```
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uv pip install --system --no-build-isolation "git+https://github.com/state-spaces/mamba@v2.2.4"
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```
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### causal-conv1d
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```
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uv pip install 'git+https://github.com/Dao-AILab/causal-conv1d@v1.5.0.post8'
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```
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## Update all the different vLLM platforms
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Rather than attempting to update all vLLM platforms in a single pull request, it's more manageable
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to handle some platforms separately. The separation of requirements and Dockerfiles
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for different platforms in vLLM CI/CD allows us to selectively choose
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which platforms to update. For instance, updating XPU requires the corresponding
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release from https://github.com/intel/intel-extension-for-pytorch by Intel.
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While https://github.com/vllm-project/vllm/pull/16859 updated vLLM to PyTorch
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2.7.0 on CPU, CUDA, and ROCm, https://github.com/vllm-project/vllm/pull/17444
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completed the update for XPU.
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