MaxText is a high performance, arbitrarily scalable, open-source, simple, easily forkable, well-tested, batteries included LLM written in pure Python/Jax and targeting Google Cloud TPUs. MaxText typically achieves 55% to 60% model-flop utilization and scales from single host to very large clusters while staying simple and "optimization-free" thanks to the power of Jax and the XLA compiler.

MaxText aims to be a launching off point for ambitious LLM projects both in research and production. We encourage users to start by experimenting with MaxText out of the box and then fork and modify MaxText to meet their needs.

• Getting Started
• Runtime Performance Results
• Comparison To Alternatives
• Development
• Features and Diagnostics

There are three recommended patterns for running MaxText. You can run locally, run on a cluster experimentally or spawn a production-style that is managed by Google Compute Engine. We recommend starting with Local Development, moving to Cluster Experimentation for some ad hoc development and ultimately running your long running jobs with Queued Resources.

You need to run these steps once per project prior to any local development or cluster experiments.

1. Create two gcs buckets in your project, one for to downloading and retrieving the dataset and the other for storing the logs.
2. Download the dataset in your gcs bucket

bash download_dataset.sh {GCS_PROJECT} {GCS_BUCKET_NAME}

3. Set config values for base_output_directory and dataset_path in configs/base.yml. vocab_relative_path is relative to base_output_directory for loading the tokenizer. MaxText assumes these GCS buckets are created in the same project and that it has permissions to read and write from them. We also recommend reviewing the configurable options in configs/base.yml, for instance you may change the steps or logging_period by either modifying configs/base.yml or by passing in steps and logging_period as additional args to the train.py call.

To run maxtext the TPUVMs must have permission to read the gcs bucket. These permissions are granted by service account roles, such as the STORAGE ADMIN role.

Local development is the faster and most convenient way to run MaxText. However, it doesn't scale to multiple hosts.

1. Create and SSH to the single-host TPU of your choice. We recommend a v4-8.
2. Clone MaxText onto that TPUVM.
3. Within the root directory of that git repo, install dependencies by running:

bash setup.sh

4. After installation completes, run training with the command:

python3 MaxText/train.py MaxText/configs/base.yml run_name=$YOUR_JOB_NAME

5. If you want to decode, you can decode as follows.

python3 MaxText/decode.py MaxText/configs/base.yml run_name=$YOUR_JOB_NAME

Be aware, these decodings will be random. To get high quality decodings you need pass in a checkpoint, typically via the load_parameters_path argument.

This workflow using multihost_runner.py is optimized for quick experiments, repeatedly re-using the same TPUs. Because the multihost_runner.py script depends on long-lived ssh connections, we do not recommend it for any long-running jobs.

We call the runner machine the one that multihost_runner.py is called from. This script will ssh into TPUVM worker machines that are found from the --TPU_PREFIX flag, and must be different than the runner machine. If the runner machine is a cloud VM, it must be in the same project as the workers.

The multihost_runner.py script:

• Distributes your code to multiple worker TPUVM's, recursively copying chosen directory
• Runs the code on the workers
• Logs and monitors the processes' error statuses and brings the logs back to the runner machine.

Although there are several steps below, most are for the initial setup. Once setup you can continually make changes to your code and re-run your code with only step 5.

1. Choose a directory on your runner machine to develop and clone MaxText into. The runner machine can either be a TPUVM or not, but it cannot be one of the workers. If your runner machine is a TPUVM, it needs service account roles that grant it permission to create queued resources and ssh into them, such as the TPU ADMIN role. Clone MaxText, and cd into the root of the repo.

2. Set your project, zone, and ssh keys.

   Set your gcloud config, see https://cloud.google.com/sdk/gcloud/reference/config for more.

   PROJECT=<project>
   

   ZONE=<zone>
   

   gcloud config set project $PROJECT
   gcloud config set compute/zone $ZONE
   

   Create ssh keys for gcloud, we recommend leaving a blank password (hit enter twice after running the below command). If you are prompted that the the file already exists you can choose not to overwrite by selecting "n".

   ssh-keygen -f ~/.ssh/google_compute_engine 
   

3. Create your instances via Queued Resource (QR). Choose names for your TPUs and QR:

   TPU_PREFIX=$YOUR_TPU_NAME # Use new names when you create new TPUs
   QR_ID=$TPU_PREFIX # Convenient to re-use the node names, but can be different
   

   Choose the number of nodes (we use 2 below, but you may customize this and other feature of your TPU(s))

   NODE_COUNT=2
   

   Create a multislice environment of nodes using create queued resources

   gcloud alpha compute tpus queued-resources create $QR_ID --accelerator-type=v4-8 --runtime-version=tpu-ubuntu2204-base --node-count=$NODE_COUNT --node-prefix=$TPU_PREFIX  --reserved
   

   We target the reserved pool above, but you may instead target the on-demand pool by omitting this flag, or target pre-emptible capacity with the --best-effort flag, which may be necessary if your reservation is full.

   You have to wait for the QR to become ACTIVE (as opposed to ACCEPTED or PROVISIONING) which corresponds to the worker nodes becoming READY (as opposed to CREATING). This may take a minute or two and can be checked via

   gcloud alpha compute tpus queued-resources list --filter=$QR_ID 
   

4. Install dependencies. Install the dependencies of train.py on each worker using multihost_runner.py:

   python3 multihost_runner.py --TPU_PREFIX=$TPU_PREFIX --COMMAND="bash setup.sh"
   

   If you are running the multihost_runner.py script from a TPUVM, you will need to set --INTERNAL_IP=true.

5. Run your training job.

   Set a RUN_NAME for your job:

   RUN_NAME=$YOUR_JOB_NAME # You may set this to any unique name for a fresh run.
   

   python3 multihost_runner.py --TPU_PREFIX=$TPU_PREFIX --COMMAND="python3 MaxText/train.py MaxText/configs/base.yml run_name=$RUN_NAME"
   

   If you are running the multihost_runner.py script from a TPUVM, you will need to set --INTERNAL_IP=true.

6. Clean up TPUs and QR when finished.

   gcloud alpha compute tpus queued-resources delete $QR_ID --force --async
   

   The --force flag deletes both the queued resources and the TPU VMs, without it only a SUSPENDED queued resource whose TPUs have already been deleted can itself be deleted. We highly recommend the --async flag since deleting the TPUs and QR will take a minute or two.

The workflow using multihost_job.py is optimized for long running experiments, providing resiliency against hardware failure and avoiding long running ssh connections. Its latency is much higher than multihost_runner.py because it needs to provision new capacity each time. The multihost_job.py script ends once the request to create the TPUs is issued. Logs are written both to gcloud in real time and also sent to GCS at the end of the job.

The multihost_job.py script:

• Copies your code to your GCS bucket
• Spins up specified TPU VM(s) via CQR
• Directs the TPU's to download then run that code. Because this logic is within the CQR's startup script, if there hardware is interrupted, the job will be rescheduled and resumed.
• Logs to gcloud, and additionally sends the logs to GCS at the job end
• Delete the TPUs and QR at the end of the job.

1. Choose a directory on your runner machine to develop and clone MaxText into. The runner machine can either be a TPUVM or not. If your runner machine is a TPUVM, it needs service account roles that grant it permission to create queued resources and has write access to GCS, such as the TPU ADMIN and STORAGE ADMIN roles. Clone MaxText, and cd into the root of the repo.

2. Set your project, zone. Set your gcloud config, see https://cloud.google.com/sdk/gcloud/reference/config for more.

   PROJECT=<project>
   

   ZONE=<zone>
   

   gcloud config set project $PROJECT
   gcloud config set compute/zone $ZONE
   

3. Link to a GCS bucket. Create a bucket if you don't already have one, see: https://cloud.google.com/storage/docs/creating-buckets for instructions to create one. Once you've identified your bucket:

   BUCKET_NAME=<your-bucket>
   

4. Run your training job.

   *** IMPORTANT *** multihost_job creates a request for new capacity for each run! You cannot use this tool on existing capacity, instead we recommend multihost_runner for this purpose.

   Choose the number of nodes (we use 2 below, but you may customize this and other feature of your TPU(s))

   NODE_COUNT=2
   

   RUN_NAME=$YOUR_JOB_NAME # You may set this to any unique name for a fresh run.
   python3 multihost_job.py --NUM_SLICES=$NODE_COUNT --RUN_NAME=$RUN_NAME --BUCKET_NAME=$BUCKET_NAME --CQR_EXTRA_ARGS="--reserved" --COMMAND="bash setup.sh && python3 MaxText/train.py MaxText/configs/base.yml run_name=$RUN_NAME"
   

   We tell multihost_job to target the reserved pool by by including --reserved as extra arguments to the CQR request, but you may instead target the on-demand pool by removing the --CQR_EXTRA_ARGS flag (on-demand is default), or the pre-emptible pool with --CQR_EXTRA_ARGS="--best-effort", which may be necessary if your reservation is full.

5. View the job's logs in cloud logging.

   The link to your job's cloud logging is printed at the end of multihost_job output. Additionaly logs are saved to GCS when your job finishes, and this bucket's URL is also printed by multihost_job.

For a 22B model. See full run configs in MaxText/configs/ as 1xv4-128.sh, 2xv4-128.sh and 4xv4-128.sh.

For a 52B model. See full run configs in MaxText/configs/ as 1xv4-384.sh and 2xv4-384.sh.

MaxText is heavily inspired by MinGPT/NanoGPT, elegant standalone GPT implementations written in PyTorch and targeting Nvidia GPUs. MaxText is more complex but has an MFU more than three times the 17% reported most recently with that codebase, is massively scalable and implements a key-value cache for efficient auto-regressive decoding.

MaxText is more similar to Nvidia/Megatron-LM, a very well tuned LLM implementation targeting Nvidia GPUs. The two implementations achieve comparable MFUs. The difference in the codebases highlights the different programming strategies. MaxText is pure Python, relying heavily on the XLA compiler to achieve high performance. By contrast, Megatron-LM is a mix of Python and CUDA, relying on well-optimized CUDA kernels to achieve high performance.

MaxText is also comparable to Pax. Like Pax, MaxText provides high-performance and scalable implementations of LLMs in Jax. Pax focuses on enabling powerful configuration parameters, enabling developers to change the model by editing config parameters. By contrast, MaxText is a simple, concrete implementation of an LLM that encourages users to extend by forking and directly editing the source code. The right choice depends on your project's priorities.

Whether you are forking MaxText for your own needs or intending to contribute back to the community, we wanted to offer simple testing recipes.

To run unit tests and lint, simply run:

bash unit_test_and_lint.sh

The full suite of end-to-end tests is in end_to_end/. We run them with a nightly cadence.

When running a Single Program, Multiple Data (SPMD) job on TPU VMs, the overall process can hang if there is any error or any VM hangs/crashes for some reason. In this scenario, capturing stack traces will help to identify and troubleshoot the issues for the jobs running on TPU VMs.

The following configurations will help to debug a fault or when a program is stuck or hung somewhere by collecting stack traces. Change the parameter values accordingly in MaxText/configs/base.yml:

1. Set collect_stack_trace: True to enable collection of stack traces on faults or when the program is hung. This setting will periodically dump the traces for the program to help in debugging. To disable this, set collect_stack_trace: False.
2. Set stack_trace_to_cloud: False to display stack traces on console. stack_trace_to_cloud: True will create a temporary file in /tmp/debugging in the TPUs to store the stack traces. There is an agent running on TPU VMs that will periodically upload the traces from the temporary directory to cloud logging in the gcp project. You can view the traces in Logs Explorer on Cloud Logging using the following query:

logName="projects/<project_name>/logs/tpu.googleapis.com%2Fruntime_monitor"
jsonPayload.verb="stacktraceanalyzer"

3. stack_trace_interval_seconds signifies the duration in seconds between each stack trace collection event. Setting stack_trace_interval_seconds: 600 will collect the stack traces every 600 seconds (10 minutes).

Here is the related PyPI package: https://pypi.org/project/cloud-tpu-diagnostics.