[doc] split "Other AI Accelerators" tabs (#19708)
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David Xia
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docs/getting_started/installation/google_tpu.md
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docs/getting_started/installation/google_tpu.md
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# Google TPU
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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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!!! warning
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There are no pre-built wheels for this device, so you must either use the pre-built Docker image or build vLLM from source.
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## Requirements
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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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### Provision Cloud TPUs
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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 (preferred). This section shows how to create TPUs using the queued resource API. For
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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 enable you to request Cloud TPU resources in a queued manner.
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When you request queued resources, the request is added to a queue maintained by
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the Cloud TPU service. When the requested resource becomes available, it's
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assigned to your Google Cloud project for your immediate exclusive use.
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!!! note
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In all of the following commands, replace the ALL CAPS parameter names with
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appropriate values. See the parameter descriptions table for more information.
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### Provision Cloud TPUs with GKE
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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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## Configure a new environment
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### Provision a Cloud TPU with the queued resource API
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Create a TPU v5e with 4 TPU chips:
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```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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```
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| Parameter name | Description |
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|--------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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| QUEUED_RESOURCE_ID | The user-assigned ID of the queued resource request. |
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| TPU_NAME | The user-assigned name of the TPU which is created when the queued resource request is allocated. |
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| PROJECT_ID | Your Google Cloud project |
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| ZONE | The GCP zone where you want to create your Cloud TPU. The value you use depends on the version of TPUs you are using. For more information, see [TPU regions and zones] |
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| ACCELERATOR_TYPE | The TPU version you want to use. Specify the TPU version, for example `v5litepod-4` specifies a v5e TPU with 4 cores, `v6e-1` specifies a v6e TPU with 1 core. For more information, see [TPU versions]. |
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| RUNTIME_VERSION | The TPU VM runtime version to use. For example, use `v2-alpha-tpuv6e` for a VM loaded with one or more v6e TPU(s). For more information see [TPU VM images]. |
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| SERVICE_ACCOUNT | The email address for your service account. You can find it in the IAM Cloud Console under *Service Accounts*. For example: `tpu-service-account@<your_project_ID>.iam.gserviceaccount.com` |
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Connect to your TPU VM using SSH:
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```bash
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gcloud compute tpus tpu-vm ssh TPU_NAME --project PROJECT_ID --zone ZONE
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```
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[TPU versions]: https://cloud.google.com/tpu/docs/runtimes
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[TPU VM images]: https://cloud.google.com/tpu/docs/runtimes
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[TPU regions and zones]: https://cloud.google.com/tpu/docs/regions-zones
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## Set up using Python
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### Pre-built wheels
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Currently, there are no pre-built TPU wheels.
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### Build wheel from source
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Install Miniconda:
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```bash
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wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
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bash Miniconda3-latest-Linux-x86_64.sh
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source ~/.bashrc
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```
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Create and activate a Conda environment for vLLM:
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```bash
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conda create -n vllm python=3.10 -y
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conda activate vllm
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```
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Clone the vLLM repository and go to the vLLM directory:
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```bash
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git clone https://github.com/vllm-project/vllm.git && cd vllm
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```
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Uninstall the existing `torch` and `torch_xla` packages:
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```bash
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pip uninstall torch torch-xla -y
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```
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Install build dependencies:
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```bash
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pip install -r requirements/tpu.txt
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sudo apt-get install --no-install-recommends --yes libopenblas-base libopenmpi-dev libomp-dev
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```
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Run the setup script:
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```bash
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VLLM_TARGET_DEVICE="tpu" python -m pip install -e .
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```
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## Set up using Docker
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### Pre-built images
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See [deployment-docker-pre-built-image][deployment-docker-pre-built-image] for instructions on using the official Docker image, making sure to substitute the image name `vllm/vllm-openai` with `vllm/vllm-tpu`.
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### Build image from source
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You can use <gh-file:docker/Dockerfile.tpu> to build a Docker image with TPU support.
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```console
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docker build -f docker/Dockerfile.tpu -t vllm-tpu .
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```
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Run the Docker image with the following command:
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```console
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# Make sure to add `--privileged --net host --shm-size=16G`.
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docker run --privileged --net host --shm-size=16G -it vllm-tpu
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```
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!!! note
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Since TPU relies on XLA which requires static shapes, vLLM bucketizes the
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possible input shapes and compiles an XLA graph for each shape. The
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compilation time may take 20~30 minutes in the first run. However, the
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compilation time reduces to ~5 minutes afterwards because the XLA graphs are
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cached in the disk (in `VLLM_XLA_CACHE_PATH` or `~/.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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```console
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from torch._C import * # noqa: F403
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ImportError: libopenblas.so.0: cannot open shared object file: No such
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file or directory
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```
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Install OpenBLAS with the following command:
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```console
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sudo apt-get install --no-install-recommends --yes libopenblas-base libopenmpi-dev libomp-dev
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```
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