Reinforcement Learning on Multi-Host TPUs

Reinforcement Learning on Multi-Host TPUs#

This tutorial provides step-by-step instructions for setting up the environment and training the Llama3.1 70B-IT model on the GSM8K math reasoning dataset using Pathways for orchestration on multi-host TPU-VMs, such as v5p-128.

We utilize two RL algorithms, implemented via the Tunix library, to enhance the model’s reasoning capabilities:

  • Group Relative Policy Optimization (GRPO): GRPO is an RL algorithm designed to enhance the reasoning abilities of LLMs. It is a variant of Proximal Policy Optimization (PPO) that reduces memory usage by eliminating the need for a separate value function model. GRPO works by generating multiple responses for a given prompt, evaluating these responses using a reward model, and then calculating a relative advantage based on the group’s performance to update the policy.

  • Group Sequence Policy Optimization (GSPO): GSPO is an RL algorithm that improves training efficiency and performance of LLMs by using sequence-level importance ratios and operations. GSPO defines the importance ratio based on sequence likelihood and performs sequence-level clipping, rewarding, and optimization.

For efficient model inference and response generation during this process, we rely on the vLLM library.

Table of Contents#

Prerequisites#

Before starting, ensure you have:

  • Access to a Google Cloud Project with TPU quotas.

  • A Hugging Face account with an access token for downloading models.

  • Permissions for Google Artifact Registry (Artifact Registry Writer role).

  • Prerequisites for XPK installed (follow official documentation).

  • A Pathways-ready GKE cluster (see create GKE cluster).

  • Docker installed and configured for sudoless use. Follow the steps to configure sudoless Docker.

Build and upload MaxText Docker image#

For instructions on building and uploading the MaxText Docker image with post-training dependencies, please refer to the official documentation.

Setup Environment Variables#

Set up the following environment variables. Replace placeholders with your actual values.

# -- Model configuration --
export MODEL=<MaxText Model> # e.g. 'llama3.1-70b-Instruct'
export HF_TOKEN=<Hugging Face access token>

# -- MaxText configuration --
export BASE_OUTPUT_DIRECTORY=<output directory to store run logs> # e.g., gs://my-bucket/my-output-directory
export WORKLOAD=<Name for this run> # e.g., llama-3-70b-grpo
export MAXTEXT_CKPT_PATH=${BASE_OUTPUT_DIRECTORY?}/${WORKLOAD?}/0/items

# -- Workload configuration --
export TPU_TYPE=<TPU Type> # e.g., 'v5p-128'
export TPU_CLUSTER=<cluster name>
export PROJECT_ID=<GCP project ID>
export ZONE=<GCP zone>

Get Your Model Checkpoint#

Option 1: Using an existing MaxText checkpoint#

If you already have a MaxText-compatible model checkpoint, simply set the following environment variable and move on to the next section.

export MAXTEXT_CKPT_PATH=<gcs path for MaxText checkpoint> # e.g., gs://my-bucket/my-model-checkpoint/0/items

Option 2: Converting from a Hugging Face checkpoint#

Refer the steps in Hugging Face to MaxText to convert a hugging face checkpoint to MaxText. Make sure you have correct checkpoint files converted and saved. Similar as Option 1, you can set the following environment and move on.

export MAXTEXT_CKPT_PATH=<gcs path for MaxText checkpoint> # e.g., gs://my-bucket/my-model-checkpoint/0/items

Submit your RL workload via Pathways#

See the Troubleshooting section for concise instructions on how to retry or resume a failed workload.

Ensure you have a Pathways-ready GKE cluster (as mentioned in Prerequisites) and submit the train_rl.py script via XPK.

Note: XPK v0.14.0+ automatically discovers your cluster’s location from GCP. You don’t need to specify --zone in the commands below. If using an older XPK version, add --zone=<zone> to the workload commands.

Submit GRPO workload#

xpk workload create-pathways --workload ${WORKLOAD?} \
--docker-image gcr.io/${PROJECT_ID?}/${CLOUD_IMAGE_NAME?} --cluster ${TPU_CLUSTER?} \
--tpu-type=${TPU_TYPE?} --num-slices=1 \
--project=${PROJECT_ID?} --priority=high \
--zone=${ZONE?} \
--command "HF_TOKEN=${HF_TOKEN?} TF_CPP_MIN_LOG_LEVEL=0 JAX_PLATFORMS=proxy JAX_BACKEND_TARGET=grpc://127.0.0.1:29000 ENABLE_PATHWAYS_PERSISTENCE='1' \
python3 -m maxtext.trainers.post_train.rl.train_rl \
  model_name=${MODEL?} \
  load_parameters_path=${MAXTEXT_CKPT_PATH?} \
  run_name=${WORKLOAD?} \
  base_output_directory=${BASE_OUTPUT_DIRECTORY?} \
  hf_access_token=${HF_TOKEN?}"

Submit GSPO workload#

xpk workload create-pathways --workload ${WORKLOAD?} \
--docker-image gcr.io/${PROJECT_ID?}/${CLOUD_IMAGE_NAME?} --cluster ${TPU_CLUSTER?} \
--tpu-type=${TPU_TYPE?} --num-slices=1 \
--project=${PROJECT_ID?} --priority=high \
--zone=${ZONE?} \
--command "HF_TOKEN=${HF_TOKEN?} TF_CPP_MIN_LOG_LEVEL=0 JAX_PLATFORMS=proxy JAX_BACKEND_TARGET=grpc://127.0.0.1:29000 ENABLE_PATHWAYS_PERSISTENCE='1' \
python3 -m maxtext.trainers.post_train.rl.train_rl \
  model_name=${MODEL?} \
  load_parameters_path=${MAXTEXT_CKPT_PATH?} \
  run_name=${WORKLOAD?} \
  base_output_directory=${BASE_OUTPUT_DIRECTORY?} \
  hf_access_token=${HF_TOKEN?} \
  loss_algo=gspo-token"

Managing Workloads#

  • Monitor workload status: Check Pathways job status: kubectl get pathwaysjob. Check pod status: kubectl get pods.

  • Delete a workload: To remove a failed or unwanted Pathways job, use XPK:

    xpk workload delete \
    --workload ${WORKLOAD?} \
    --cluster ${TPU_CLUSTER?} \
    --project ${PROJECT_ID?}

    In case the job still lingers on, you can use kubectl get pods to obtain the name of the pod and then run: kubectl delete pod <pod-name>.

Troubleshooting#

  • Authentication Issues: Ensure your HF_TOKEN environment variable is set correctly and has access to the required models.

  • Resource Quotas: Verify you have sufficient TPU quotas in your GCP project.

  • Docker Build Failures: Check that all dependencies are correctly installed and authentication is configured.

  • Workload Failures: Review the logs for specific error messages and ensure all environment variables are properly set.

  • Workload retry / resume:

    • Retry (fresh run): Use a unique workload name to avoid overwriting outputs: export WORKLOAD=${WORKLOAD}-retry1 export MAXTEXT_CKPT_PATH=${BASE_OUTPUT_DIRECTORY}/${WORKLOAD}/0/items. Then submit the XPK workload. If “workload already exists” error occurs, pick a new name or list jobs: kubectl get pathwaysjob.

    • Resume from checkpoint: Keep the same WORKLOAD and set the checkpoint path: export load_parameters_path=${MAXTEXT_CKPT_PATH}/checkpoint-0000. Then submit the workload again.

    • Tip: Verify the checkpoint exists in GCS with read access before resuming.

For more detailed troubleshooting, refer to the MaxText documentation and XPK documentation.