T5X is a modular, composable, research-friendly framework for high-performance,
configurable, self-service training, evaluation, and inference of sequence
models (starting with language) at many scales.

It is essentially a new and improved implementation of the
T5 codebase
(based on Mesh TensorFlow) in JAX and Flax.


Note that all the commands in this document should be run in the commandline of
the TPU VM instance unless otherwise stated.

  1. Follow the
    to set up a Google Cloud Platform (GCP) account and enable the Cloud TPU

    Note: While T5X works with GPU as well, we haven’t heavily tested the
    GPU usage.

  2. Create a
    Cloud TPU VM instance
    this instruction.
    We recommend that you develop your workflow in a single v3-8 TPU (i.e.,
    --accelerator-type=v3-8) and scale up to pod slices once the pipeline is
    ready. In this README, we focus on using a single v3-8 TPU. See
    here to
    learn more about TPU architectures.

  3. With Cloud TPU VMs, you ssh directly into the host machine of the TPU VM.
    You can install packages, run your code run, etc. in the host machine. Once
    the TPU instance is created, ssh into it with

    gcloud alpha compute tpus tpu-vm ssh ${TPU_NAME} --zone=${ZONE}

    where TPU_NAME and ZONE are the name and the zone used in step 2.

  4. Install T5X and the dependencies. JAX and Gin-config need to be installed
    from the source.

    git clone --branch=main https://github.com/google-research/t5x
    cd t5x
    python3 -m pip install -e . -f \
  5. Create toogle Cloud Storage (GCS) bucket to store the dataset and model
    checkpoints. To create a GCS bucket, see these

Example: English to German translation

As a running example, we use the WMT14 En-De translation. The raw dataset is
available in TensorFlow Datasets as

T5 casts the translation task such as the following

{'en': 'That is good.', 'de': 'Das ist gut.'}

to the form called “text-to-text”:

{'inputs': 'translate English to German: That is good.', 'targets': 'Das ist gut.'}

This formulation allows many different classes of language tasks to be expressed
in a uniform manner and a single encoder-decoder architecture can handle them
without any task-specific parameters. For more detail, refer to the T5 paper
(Raffel et al. 2019)

For a scalable data pipeline and an evaluation framework, we use
SeqIO, which was factored out of the T5
. A seqio.Task packages together the raw dataset, vocabulary,
preprocessing such as tokenization and evaluation metrics such as
BLEU and provides a
tf.data instance.

The T5 library provides a number of seqio.Tasks that were used in the
T5 paper. In this example, we use wmt_t2t_ende_v003.


To run a training job, we use the t5x/train.py script.

# Model dir to save logs, ckpts, etc. in "gs://model_dir" format.

# Data dir to save the processed dataset in "gs://data_dir" format.
T5X_DIR="..."  # directory where the T5X repo is cloned.

python3 ${T5X_DIR}/t5x/train.py \
  --gin_file="t5x/examples/t5/t5_1_1/examples/t5_1_1_base_wmt_from_scratch.gin" \
  --gin.MODEL_DIR="'${MODEL_DIR}'" \

The configuration for this training run is defined in the Gin file
Gin-config is a library to handle
configurations based on dependency injection. Among many benefits, Gin allows
users to pass custom components such as a custom model to the T5X library
without having to modify the core library. The custom
section shows how this is done.

While the core library is independent of Gin, it is central to the examples we
provide. Therefore, we provide a short introduction to Gin in the
context of T5X. All the configurations are written to a file “config.gin” in
MODEL_DIR. This makes debugging as well as reproducing the experiment much

In addition to the config.json, model-info.txt file summarizes the model
parameters (shape, names of the axes, partitioning info) as well as the
optimizer states.


To monitor the training in TensorBoard, it is much easier (due to
authentification issues) to launch the TensorBoard on your own machine and not in
the TPU VM. So in the commandline where you ssh’ed into the TPU VM, launch the
TensorBoard with the logdir pointing to the MODEL_DIR.

# NB: run this on your machine not TPU VM!
MODEL_DIR="..."  # Copy from the TPU VM.
tensorboard --logdir=${MODEL_DIR}

Or you can launch the TensorBoard inside a Colab. In a Colab cell, run

from google.colab import auth

to authorize the Colab to access the GCS bucket and launch the TensorBoard.

%load_ext tensorboard
model_dir = "..."  # Copy from the TPU VM.
%tensorboard --logdir=model_dir

TODO(hwchung): Add tfds preparation instruction


We can leverage the benefits of self-supervised pre-training by initializing
from one of our pre-trained models. Here we use the T5.1.1 Base checkpoint.

# Model dir to save logs, ckpts, etc. in "gs://model_dir" format.

# Data dir to save the processed dataset in "gs://data_dir" format.
T5X_DIR="..."  # directory where the T5X repo is cloned.

python3 ${T5X_DIR}/t5x/train.py \
  --gin_file="t5x/examples/t5/t5_1_1/examples/t5_1_1_base_wmt_finetune.gin" \
  --gin.MODEL_DIR="'${MODEL_DIR}'" \

Note: when supplying a string, dict, list, tuple value, or a bash variable
via a flag, you must put it in quotes. In the case of strings, it requires
“triple quotes” ("'<string>'"). For example:
--gin.utils.DatasetConfig.split="'validation'" or

Gin makes it easy to change a number of configurations. For example, you can
change the partitioning.ModelBasedPjitPartitioner.num_partitions (overriding
the value in
to chanage the parallelism strategy and pass it as a commandline arg.



To run the offline (i.e. without training) evaluation, you can use t5x/eval.py

EVAL_OUTPUT_DIR="..."  # directory to write eval output
T5X_DIR="..."  # directory where the t5x is cloned, e.g., ${HOME}"/t5x".

python3 ${T5X_DIR}/t5x/eval.py \
  --gin_file="t5x/examples/t5/t5_1_1/examples/t5_1_1_base_wmt_eval.gin" \


To run inference, you can use t5x/infer.py script. Here we use the same
seqio.Task, but for inference we do not use the targets features other than
logging them alongside the prediction in a JSON file.

INFER_OUTPUT_DIR="..."  # directory to write infer output
T5X_DIR="..."  # directory where the t5x is cloned, e.g., ${HOME}"/t5x".

python3 ${T5X_DIR}/t5x/infer.py \
  --gin_file="t5x/examples/t5/t5_1_1/examples/t5_1_1_base_wmt_infer.gin" \

Custom components

The translation example uses the
encoder-decoder model that T5X provides as well as the dataset from the T5
library. This section shows how you can use your own dataset and a model and
pass via Gin.

Example: custom dataset in a user directory

For this example, we have the following directory structure with
${HOME}/dir1/user_dir representing a user directory with custom components.

└── dir1
    └── user_dir
        ├── t5_1_1_base_de_en.gin
        └── tasks.py

As an example, let’s define a new dataset. Here we use the same Translation
dataset but we define the translation task in the opposite direction, i.e.,
German to English intead of English to German. We define this task in tasks.py

# ${HOME}/dir1/user_dir/tasks.py

import functools
import seqio
import tensorflow_datasets as tfds
from t5.evaluation import metrics
from t5.data import preprocessors

vocabulary = seqio.SentencePieceVocabulary(
    'gs://t5-data/vocabs/cc_all.32000/sentencepiece.model', extra_ids=100)
output_features = {
    'inputs': seqio.Feature(vocabulary=vocabulary),
    'targets': seqio.Feature(vocabulary=vocabulary)

            source_language='de', target_language='en'),

In the Gin file, most of the settings are equivalent to those used in the
En->De example. So we include the Gin
file from that example. To use “wmt_t2t_de_en_v003” task we just defined, we
need to import the task module “tasks.py”. Note that we use a relative path
defined with respect to the user directory. This will be specified as a

# ${HOME}/dir1/user_dir/t5_1_1_base_de_en.gin
from __gin__ import dynamic_registration
import tasks  # This imports the task defined in dir1/user_dir/tasks.py.

include "t5x-tmp/t5x/examples/t5/t5_1_1/examples/t5_1_1_base_wmt_from_scratch.gin"
MIXTURE_OR_TASK_NAME = "wmt_t2t_de_en_v003"

Finally, we launch training passing the user directory as a flag
gin_search_paths such that the Gin file and python modules can be specified
with relative paths.

T5X_DIR="..."  # directory where the t5x is cloned.

python3 ${T5X_DIR}/t5x/train.py \
  --gin_search_paths=${PROJECT_DIR} \
  --gin_file="t5_1_1_base_de_en.gin" \
  --gin.MODEL_DIR="'${MODEL_DIR}'" \

Released Checkpoints

We release the checkpoints for the T5.1.1 models in a native T5X format.

These are converted from the public Mesh TensorFlow


Compatibility with the Mesh TensorFlow checkpoints

The Mesh TensorFlow checkpoints trained using the T5 library can be
directly loaded into T5X. For example, we can rerun the fine-tuning example
initializing from the MTF checkpoint by changing the INIT_CHECKPOINT Gin

# Model dir to save logs, ckpts, etc. in "gs://model_dir" format.

# Data dir to save the processed dataset in "gs://data_dir" format.
T5X_DIR="..."  # directory where the T5X repo is cloned.

python3 ${T5X_DIR}/t5x/train.py \
  --gin_file="t5x/examples/t5/t5_1_1/examples/wmt19_ende_from_scratch.gin" \
  --gin.MODEL_DIR="'${MODEL_DIR}'" \
  --gin.MIXTURE_OR_TASK_NAME="'wmt_t2t_ende_v003'" \
  --gin.INIT_CHECKPOINT="'gs://t5-data/pretrained_models/t5.1.1.base/model.ckpt-1000000'" \

Note that restoring directly from the Mesh TensorFlow checkpoints can be
inefficient if heavy model parallelism is used for large models. This is
because each host loads the entire copy of the model first and then keep only
the relevant slices dictated by the model parallelism specification. If you have
Mesh TensorFlow checkpoints that you run often, we recommend converting the
checkpoints to T5X native format using

TODO(hwchung): Add a conversion script.


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