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Reinforcement Learning on single-host TPUs

This tutorial demonstrates step-by-step instructions for setting up the environment and then training the Llama3.1 8B-IT model on the GSM8K math reasoning dataset using a single host TPU-VM such as v6e-8/v5p-8.

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.

Let's get started!

Install MaxText and post-training dependencies

For instructions on installing MaxText with post-training dependencies on your VM, please refer to the official documentation and use the maxtext[tpu-post-train] installation path to include all necessary post-training dependencies.

Setup environment variables

Follow the instructions here to login to Hugging Face using your access token using

huggingface-cli login

Setup following environment variables before running GRPO/GSPO:

# -- Model configuration --
export MODEL=<MaxText Model> # e.g. 'llama3.1-8b-Instruct'

# -- MaxText configuration --
export BASE_OUTPUT_DIRECTORY=<output directory to store run logs> # e.g., gs://my-bucket/my-output-directory

export RUN_NAME=<name for this run> # e.g., $(date +%Y-%m-%d-%H-%M-%S)

export CHIPS_PER_VM=<the number of chips per VM> # depends on hardware, for v5p this is 4, for v6e this is 8

For the value of CHIPS_PER_VM on different TPU hardware, refer the official document

  • TPU v5e (single host, chips_per_vm=8)
  • TPU v5p (single host, chips_per_vm=4)
  • TPU v6e (single host, chips_per_vm=8)

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

Run GRPO

Run the following command for GRPO:

python3 -m maxtext.trainers.post_train.rl.train_rl \
  model_name=${MODEL?} \
  load_parameters_path=${MAXTEXT_CKPT_PATH?} \
  run_name=${RUN_NAME?} \
  base_output_directory=${BASE_OUTPUT_DIRECTORY?} \
  chips_per_vm=${CHIPS_PER_VM?}

The overview of what this run will do is as follows:

  1. We load a policy model and a reference model. Both are copies of the model checkpoint you specified (e.g., Llama3.1-8b-Instruct).
  2. Evaluate the policy model's performance on GSM8K math reasoning benchmark.
  3. Train the policy model using GRPO.
  4. Evaluate the policy model's performance on GSM8K math reasoning benchmark after the post-training with GRPO.

Run GSPO

Run the following command for GSPO:

python3 -m maxtext.trainers.post_train.rl.train_rl \
  model_name=${MODEL?} \
  load_parameters_path=${MAXTEXT_CKPT_PATH?} \
  run_name=${RUN_NAME?} \
  base_output_directory=${BASE_OUTPUT_DIRECTORY?} \
  loss_algo=gspo-token \
  chips_per_vm=${CHIPS_PER_VM?}

The overview of what this run will do is as follows:

  1. We load a policy model and a reference model. Both are copies of the model checkpoint you specified (e.g., Llama3.1-8b-Instruct).
  2. Evaluate the policy model's performance on GSM8K math reasoning benchmark.
  3. Train the policy model using GSPO.
  4. Evaluate the policy model's performance on GSM8K math reasoning benchmark after the post-training with GSPO.