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[Doc] Updated tpu-installation.rst with more details (#9926)
Signed-off-by: Michael Green <mikegre@google.com>
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.. _installation_tpu:
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#####################
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Installation with TPU
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=====================
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#####################
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vLLM supports Google Cloud TPUs using PyTorch XLA.
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Tensor Processing Units (TPUs) are Google's custom-developed application-specific
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integrated circuits (ASICs) used to accelerate machine learning workloads. TPUs
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are available in different versions each with different hardware specifications.
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For more information about TPUs, see `TPU System Architecture <https://cloud.google.com/tpu/docs/system-architecture-tpu-vm>`_.
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For more information on the TPU versions supported with vLLM, see:
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* `TPU v6e <https://cloud.google.com/tpu/docs/v6e>`_
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* `TPU v5e <https://cloud.google.com/tpu/docs/v5e>`_
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* `TPU v5p <https://cloud.google.com/tpu/docs/v5p>`_
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* `TPU v4 <https://cloud.google.com/tpu/docs/v4>`_
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These TPU versions allow you to configure the physical arrangements of the TPU
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chips. This can improve throughput and networking performance. For more
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information see:
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* `TPU v6e topologies <https://cloud.google.com/tpu/docs/v6e#configurations>`_
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* `TPU v5e topologies <https://cloud.google.com/tpu/docs/v5e#tpu-v5e-config>`_
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* `TPU v5p topologies <https://cloud.google.com/tpu/docs/v5p#tpu-v5p-config>`_
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* `TPU v4 topologies <https://cloud.google.com/tpu/docs/v4#tpu-v4-config>`_
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In order for you to use Cloud TPUs you need to have TPU quota granted to your
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Google Cloud Platform project. TPU quotas specify how many TPUs you can use in a
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GPC project and are specified in terms of TPU version, the number of TPU you
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want to use, and quota type. For more information, see `TPU quota <https://cloud.google.com/tpu/docs/quota#tpu_quota>`_.
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For TPU pricing information, see `Cloud TPU pricing <https://cloud.google.com/tpu/pricing>`_.
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You may need additional persistent storage for your TPU VMs. For more
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information, see `Storage options for Cloud TPU data <https://cloud.devsite.corp.google.com/tpu/docs/storage-options>`_.
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Requirements
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------------
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* Google Cloud TPU VM (single & multi host)
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* TPU versions: v5e, v5p, v4
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* Python: 3.10
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* Google Cloud TPU VM
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* TPU versions: v6e, v5e, v5p, v4
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* Python: 3.10 or newer
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Installation options:
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Provision Cloud TPUs
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====================
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1. :ref:`Build a docker image with Dockerfile <build_docker_tpu>`.
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2. :ref:`Build from source <build_from_source_tpu>`.
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You can provision Cloud TPUs using the `Cloud TPU API <https://cloud.google.com/tpu/docs/reference/rest>`_`
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or the `queued resources <https://cloud.google.com/tpu/docs/queued-resources>`_`
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API. This section shows how to create TPUs using the queued resource API.
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For more information about using the Cloud TPU API, see `Create a Cloud TPU using the Create Node API <https://cloud.google.com/tpu/docs/managing-tpus-tpu-vm#create-node-api>`_.
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`Queued resources <https://cloud.devsite.corp.google.com/tpu/docs/queued-resources>`_
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enable you to request Cloud TPU resources in a queued manner. When you request
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queued resources, the request is added to a queue maintained by the Cloud TPU
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service. When the requested resource becomes available, it's assigned to your
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Google Cloud project for your immediate exclusive use.
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Provision a Cloud TPU with the queued resource API
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--------------------------------------------------
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Create a TPU v5e with 4 TPU chips:
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.. code-block:: console
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gcloud alpha compute tpus queued-resources create QUEUED_RESOURCE_ID \
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--node-id TPU_NAME \
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--project PROJECT_ID \
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--zone ZONE \
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--accelerator-type ACCELERATOR_TYPE \
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--runtime-version RUNTIME_VERSION \
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--service-account SERVICE_ACCOUNT
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.. list-table:: Parameter descriptions
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:header-rows: 1
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* - Parameter name
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- Description
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* - QUEUED_RESOURCE_ID
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- The user-assigned ID of the queued resource request.
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* - TPU_NAME
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- The user-assigned name of the TPU which is created when the queued
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resource request is allocated.
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* - PROJECT_ID
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- Your Google Cloud project
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* - ZONE
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- The `zone <https://cloud.google.com/tpu/docs/regions-zones>`_ where you
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want to create your Cloud TPU.
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* - ACCELERATOR_TYPE
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- The TPU version you want to use. Specify the TPU version, followed by a
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'-' and the number of TPU cores. For example `v5e-4` specifies a v5e TPU
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with 4 cores. For more information, see `TPU versions <https://cloud.devsite.corp.google.com/tpu/docs/system-architecture-tpu-vm#versions>`_.
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* - RUNTIME_VERSION
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- The TPU VM runtime version to use. For more information see `TPU VM images <https://cloud.google.com/tpu/docs/runtimes>`_.
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* - SERVICE_ACCOUNT
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- The email address for your service account. You can find it in the IAM
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Cloud Console under *Service Accounts*. For example:
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`tpu-service-account@<your_project_ID>.iam.gserviceaccount.com`
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Connect to your TPU using SSH:
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.. code-block:: bash
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gcloud compute tpus tpu-vm ssh TPU_NAME
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Create and activate a Conda environment for vLLM:
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.. code-block:: bash
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conda create -n vllm python=3.10 -y
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conda activate vllm
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Clone the vLLM repository and go to the vLLM directory:
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.. code-block:: bash
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git clone https://github.com/vllm-project/vllm.git && cd vllm
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Uninstall the existing `torch` and `torch_xla` packages:
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.. code-block:: bash
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pip uninstall torch torch-xla -y
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Install `torch` and `torch_xla`
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.. code-block:: bash
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pip install --pre torch==2.6.0.dev20241028+cpu torchvision==0.20.0.dev20241028+cpu --index-url https://download.pytorch.org/whl/nightly/cpu
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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
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Install JAX and Pallas:
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.. code-block:: bash
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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
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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
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Install other build dependencies:
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.. code-block:: bash
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pip install -r requirements-tpu.txt
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VLLM_TARGET_DEVICE="tpu" python setup.py develop
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sudo apt-get install libopenblas-base libopenmpi-dev libomp-dev
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Provision Cloud TPUs with GKE
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-----------------------------
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For more information about using TPUs with GKE, see
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https://cloud.google.com/kubernetes-engine/docs/how-to/tpus
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https://cloud.google.com/kubernetes-engine/docs/concepts/tpus
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https://cloud.google.com/kubernetes-engine/docs/concepts/plan-tpus
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.. _build_docker_tpu:
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Build a docker image with :code:`Dockerfile.tpu`
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------------------------------------------------
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`Dockerfile.tpu <https://github.com/vllm-project/vllm/blob/main/Dockerfile.tpu>`_ is provided to build a docker image with TPU support.
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You can use `Dockerfile.tpu <https://github.com/vllm-project/vllm/blob/main/Dockerfile.tpu>`_
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to build a Docker image with TPU support.
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.. code-block:: console
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$ docker build -f Dockerfile.tpu -t vllm-tpu .
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You can run the docker image with the following command:
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Run the Docker image with the following command:
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.. code-block:: console
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@ -75,14 +207,12 @@ Next, build vLLM from source. This will only take a few seconds:
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$ VLLM_TARGET_DEVICE="tpu" python setup.py develop
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.. note::
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Since TPU relies on XLA which requires static shapes, vLLM bucketizes the possible input shapes and compiles an XLA graph for each different shape.
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The compilation time may take 20~30 minutes in the first run.
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However, the compilation time reduces to ~5 minutes afterwards because the XLA graphs are cached in the disk (in :code:`VLLM_XLA_CACHE_PATH` or :code:`~/.cache/vllm/xla_cache` by default).
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.. tip::
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If you encounter the following error:
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@ -93,7 +223,7 @@ Next, build vLLM from source. This will only take a few seconds:
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ImportError: libopenblas.so.0: cannot open shared object file: No such file or directory
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Please install OpenBLAS with the following command:
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Install OpenBLAS with the following command:
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.. code-block:: console
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