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Refactor TPU requirements file and pin build dependencies (#10010)

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Signed-off-by: Richard Liu <ricliu@google.com>
This commit is contained in:
Richard Liu
2024-11-05 08:48:44 -08:00
committed by GitHub
parent 5952d81139
commit cd34029e91
3 changed files with 26 additions and 64 deletions
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63
docs/source/getting_started/tpu-installation.rst
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@@ -119,28 +119,20 @@ Uninstall the existing `torch` and `torch_xla` packages:
pip uninstall torch torch-xla -y
Install `torch` and `torch_xla`
.. code-block:: bash
pip install --pre torch==2.6.0.dev20241028+cpu torchvision==0.20.0.dev20241028+cpu --index-url https://download.pytorch.org/whl/nightly/cpu
pip install 'torch_xla[tpu] @ https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch_xla-2.6.0.dev-cp310-cp310-linux_x86_64.whl' -f https://storage.googleapis.com/libtpu-releases/index.html
Install JAX and Pallas:
.. code-block:: bash
pip install torch_xla[pallas] -f https://storage.googleapis.com/jax-releases/jax_nightly_releases.html -f https://storage.googleapis.com/jax-releases/jaxlib_nightly_releases.html
pip install jaxlib==0.4.32.dev20240829 jax==0.4.32.dev20240829 -f https://storage.googleapis.com/jax-releases/jax_nightly_releases.html -f https://storage.googleapis.com/jax-releases/jaxlib_nightly_releases.html
Install other build dependencies:
Install build dependencies:
.. code-block:: bash
pip install -r requirements-tpu.txt
VLLM_TARGET_DEVICE="tpu" python setup.py develop
sudo apt-get install libopenblas-base libopenmpi-dev libomp-dev
Run the setup script:
.. code-block:: bash
VLLM_TARGET_DEVICE="tpu" python setup.py develop
Provision Cloud TPUs with GKE
-----------------------------
@@ -168,45 +160,6 @@ Run the Docker image with the following command:
$ # Make sure to add `--privileged --net host --shm-size=16G`.
$ docker run --privileged --net host --shm-size=16G -it vllm-tpu
.. _build_from_source_tpu:
Build from source
-----------------
You can also build and install the TPU backend from source.
First, install the dependencies:
.. code-block:: console
$ # (Recommended) Create a new conda environment.
$ conda create -n myenv python=3.10 -y
$ conda activate myenv
$ # Clean up the existing torch and torch-xla packages.
$ pip uninstall torch torch-xla -y
$ # Install PyTorch and PyTorch XLA.
$ export DATE="20241017"
$ export TORCH_VERSION="2.6.0"
$ pip install https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch-${TORCH_VERSION}.dev${DATE}-cp310-cp310-linux_x86_64.whl
$ pip install https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch_xla-${TORCH_VERSION}.dev${DATE}-cp310-cp310-linux_x86_64.whl
$ # Install JAX and Pallas.
$ pip install torch_xla[tpu] -f https://storage.googleapis.com/libtpu-releases/index.html
$ pip install torch_xla[pallas] -f https://storage.googleapis.com/jax-releases/jax_nightly_releases.html -f https://storage.googleapis.com/jax-releases/jaxlib_nightly_releases.html
$ # Install other build dependencies.
$ pip install -r requirements-tpu.txt
Next, build vLLM from source. This will only take a few seconds:
.. code-block:: console
$ VLLM_TARGET_DEVICE="tpu" python setup.py develop
.. note::
Since TPU relies on XLA which requires static shapes, vLLM bucketizes the possible input shapes and compiles an XLA graph for each different shape.