MaxDiffusion inference on v6e TPUs

This tutorial shows how to serve MaxDiffusion models on TPU v6e. In this tutorial, you generate images using the Stable Diffusion XL model.

Before you begin

Prepare to provision a TPU v6e with 4 chips:

1. 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.

2. 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.

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 v6e-4 \
        --runtime-version v2-alpha-tpuv6e \
        --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

Create a Conda environment

1. Create a directory for Miniconda:

   mkdir -p ~/miniconda3

2. Download the Miniconda installer script:

   wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda3/miniconda.sh

3. Install Miniconda:

   bash ~/miniconda3/miniconda.sh -b -u -p ~/miniconda3

4. Remove the Miniconda installer script:

   rm -rf ~/miniconda3/miniconda.sh

5. Add Miniconda to your PATH variable:

   export PATH="$HOME/miniconda3/bin:$PATH"

6. Reload ~/.bashrc to apply the changes to the PATH variable:

   source ~/.bashrc

7. Create a new Conda environment:

   conda create -n tpu python=3.10

8. Activate the Conda environment:

   source activate tpu

Set up MaxDiffusion

1. Clone the MaxDiffusion GitHub repository and navigate to the MaxDiffusion directory:

   git clone https://github.com/google/maxdiffusion.git && cd maxdiffusion

2. Switch to the mlperf-4.1 branch:

   git checkout mlperf4.1

3. Install MaxDiffusion:

   pip install -e .

4. Install dependencies:

   pip install -r requirements.txt

5. Install JAX:

   pip install jax[tpu]==0.4.34 jaxlib==0.4.34 ml-dtypes==0.2.0 -i https://us-python.pkg.dev/ml-oss-artifacts-published/jax/simple/ -f https://storage.googleapis.com/jax-releases/libtpu_releases.html

6. Install additional dependencies:

    pip install huggingface_hub==0.25 absl-py flax tensorboardX google-cloud-storage torch tensorflow transformers 

Generate images

1. Set environment variables to configure the TPU runtime:

   LIBTPU_INIT_ARGS="--xla_tpu_rwb_fusion=false --xla_tpu_dot_dot_fusion_duplicated=true --xla_tpu_scoped_vmem_limit_kib=65536"

2. Generate images using the prompt and configurations defined in src/maxdiffusion/configs/base_xl.yml:

   python -m src.maxdiffusion.generate_sdxl src/maxdiffusion/configs/base_xl.yml run_name="my_run"

   When the images have been generated, 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