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:

Follow Set up the Cloud TPU environment guide to ensure you have appropriate access to use Cloud TPUs.

Create a service identity for the TPU VM.

gcloud alpha compute tpus tpu-vm service-identity create --zone=zone

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, review the quotas page to learn about the Cloud Quotas system. 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

This tutorial has been tested with Python 3.10 or later.
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

Create a directory for Miniconda:
```
mkdir -p ~/miniconda3
```

Download the Miniconda installer script:

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

Install Miniconda:

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

Remove the Miniconda installer script:
```
rm -rf ~/miniconda3/miniconda.sh
```

Add Miniconda to your PATH variable:

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

Reload ~/.bashrc to apply the changes to the PATH variable:
```
source ~/.bashrc
```
Create a new Conda environment:
```
conda create -n tpu python=3.10
```
Activate the Conda environment:
```
source activate tpu
```

Set up MaxDiffusion

Clone the MaxDiffusion repository and navigate to the MaxDiffusion directory:
```
git clone https://github.com/google/maxdiffusion.git && cd maxdiffusion
```
Switch to the mlperf-4.1 branch:
```
git checkout mlperf4.1
```
Install MaxDiffusion:
```
pip install -e .
```
Install dependencies:
```
pip install -r requirements.txt
```

Install JAX:

pip install -U --pre jax[tpu] -f https://storage.googleapis.com/jax-releases/jax_nightly_releases.html -f https://storage.googleapis.com/jax-releases/libtpu_releases.html

Install additional dependencies:

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

Generate images

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"

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