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

Signed-off-by: Richard Liu <ricliu@google.com>

Dockerfile.tpu

@@ -9,12 +9,6 @@ RUN apt-get update && apt-get install -y \
     git \
     ffmpeg libsm6 libxext6 libgl1
 
-# Install the TPU and Pallas dependencies.
-RUN --mount=type=cache,target=/root/.cache/pip \
-    python3 -m pip install torch_xla[tpu] -f https://storage.googleapis.com/libtpu-releases/index.html
-RUN --mount=type=cache,target=/root/.cache/pip \
-    python3 -m 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
-
 # Build vLLM.
 COPY . .
 ARG GIT_REPO_CHECK=0
@@ -25,7 +19,6 @@ ENV VLLM_TARGET_DEVICE="tpu"
 RUN --mount=type=cache,target=/root/.cache/pip \
     --mount=type=bind,source=.git,target=.git \
     python3 -m pip install \
-        'cmake>=3.26' ninja packaging 'setuptools-scm>=8' wheel jinja2 \
         -r requirements-tpu.txt
 RUN python3 setup.py develop
 

docs/source/getting_started/tpu-installation.rst

@@ -119,27 +119,19 @@ Uninstall the existing `torch` and `torch_xla` packages:
 
     pip uninstall torch torch-xla -y
 
-Install `torch` and `torch_xla`
+Install build dependencies:
 
 .. 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
+    pip install -r requirements-tpu.txt
+    sudo apt-get install libopenblas-base libopenmpi-dev libomp-dev 
 
-Install other build dependencies:
+Run the setup script:
 
 .. 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 
+
 
 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.

requirements-tpu.txt

@@ -2,6 +2,22 @@
 -r requirements-common.txt
 
 # Dependencies for TPU
-# Currently, the TPU backend uses a nightly version of PyTorch XLA.
-# You can install the dependencies in Dockerfile.tpu.
+cmake>=3.26
+ninja
+packaging
+setuptools-scm>=8
+wheel
+jinja2
 ray[default]
+
+# Install torch_xla
+--pre
+--extra-index-url https://download.pytorch.org/whl/nightly/cpu
+--find-links https://storage.googleapis.com/libtpu-releases/index.html
+--find-links https://storage.googleapis.com/jax-releases/jax_nightly_releases.html
+--find-links https://storage.googleapis.com/jax-releases/jaxlib_nightly_releases.html
+torch==2.6.0.dev20241028+cpu
+torchvision==0.20.0.dev20241028+cpu
+torch_xla[tpu] @ https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch_xla-2.6.0.dev20241028-cp310-cp310-linux_x86_64.whl
+jaxlib==0.4.32.dev20240829
+jax==0.4.32.dev20240829