JetStream PyTorch inference on v6e TPU VMs

This tutorial shows how to use JetStream to serve PyTorch models on TPU v6e. JetStream is a throughput and memory optimized engine for large language model (LLM) inference on XLA devices (TPUs). In this tutorial, you run the inference benchmark for the Llama2-7B model.

Before you begin

Prepare to provision a TPU v6e with 4 chips:

Follow Set up the Cloud TPU environment guide to set up a Google Cloud project, configure the Google Cloud CLI, enable the Cloud TPU API, and ensure you have access to use Cloud TPUs.

Authenticate with Google Cloud and configure the default project and zone for Google Cloud CLI.

gcloud auth login
gcloud config set project PROJECT_ID
gcloud config set compute/zone ZONE

Secure capacity

When you are ready to secure TPU capacity, see Cloud TPU Quotas for more information about the Cloud TPU quotas. If you have additional questions about securing capacity, contact your Cloud TPU sales or account team.

Provision the Cloud TPU environment

You can provision TPU VMs with GKE, with GKE and XPK, or as queued resources.

Prerequisites

Verify that your project has enough TPUS_PER_TPU_FAMILY quota, which specifies the maximum number of chips you can access within your Google Cloud project.
Verify that your project has enough TPU quota for:
- TPU VM quota
- IP address quota
- Hyperdisk Balanced quota
User project permissions
- If you are using GKE with XPK, see Cloud Console Permissions on the user or service account for the permissions needed to run XPK.

Create environment variables

In a Cloud Shell, create the following environment variables:

export PROJECT_ID=your-project-id
export TPU_NAME=your-tpu-name
export ZONE=us-central2-b
export ACCELERATOR_TYPE=v6e-4
export RUNTIME_VERSION=v2-alpha-tpuv6e
export SERVICE_ACCOUNT=your-service-account
export QUEUED_RESOURCE_ID=your-queued-resource-id

Command flag descriptions

Variable	Description
`PROJECT_ID`	Google Cloud project name. Use an existing project or create a new one.
`TPU_NAME`	The name of the TPU.
`ZONE`	See the TPU regions and zones document for the supported zones.
`ACCELERATOR_TYPE`	The accelerator type specifies the version and size of the Cloud TPU you want to create. For more information about supported accelerator types for each TPU version, see TPU versions.
`RUNTIME_VERSION`	The Cloud TPU software version.
`SERVICE_ACCOUNT`	The email address for your service account . You can find it by going to the Service Accounts page in the Google Cloud console. For example: `tpu-service-account@PROJECT_ID.iam.gserviceaccount.com`
`QUEUED_RESOURCE_ID`	The user-assigned text ID of the queued resource request.

Provision a TPU v6e

    gcloud alpha compute tpus queued-resources create ${QUEUED_RESOURCE_ID} \
        --node-id ${TPU_NAME} \
        --project ${PROJECT_ID} \
        --zone ${ZONE} \
        --accelerator-type ${ACCELERATOR_TYPE} \
        --runtime-version ${RUNTIME_VERSION} \
        --service-account ${SERVICE_ACCOUNT}

Use the list or describe commands to query the status of your queued resource.

   gcloud alpha compute tpus queued-resources describe ${QUEUED_RESOURCE_ID}  \
      --project ${PROJECT_ID} --zone ${ZONE}

For a complete list of queued resource request statuses, see the Queued Resources documentation.

Connect to the TPU using SSH

  gcloud compute tpus tpu-vm ssh ${TPU_NAME}

Run the JetStream PyTorch Llama2-7B benchmark

To set up JetStream-PyTorch, convert the model checkpoints, and run the inference benchmark, follow the instructions in the GitHub repository.

When the inference benchmark is complete, be sure to clean up the TPU resources.

Clean up

Delete the TPU:

   gcloud compute tpus queued-resources delete ${QUEUED_RESOURCE_ID} \
      --project ${PROJECT_ID} \
      --zone ${ZONE} \
      --force \
      --async