T2T is a modular and extensible library and binaries for supervised learning with TensorFlow and with support for sequence tasks. It is actively used and maintained by researchers and engineers within the Google Brain team.

We're eager to collaborate with you on extending T2T, so please feel free to open an issue on GitHub or send along a pull request to add your data-set or model. See our contribution doc for details and our open issues.

Here's a walkthrough training a good English-to-German translation model using the Transformer model from Attention Is All You Need on WMT data.

pip install tensor2tensor

# See what problems, models, and hyperparameter sets are available.
# You can easily swap between them (and add new ones).
t2t-trainer --registry_help

PROBLEM=wmt_ende_tokens_32k
MODEL=transformer
HPARAMS=transformer_base

DATA_DIR=$HOME/t2t_data
TMP_DIR=/tmp/t2t_datagen
TRAIN_DIR=$HOME/t2t_train/$PROBLEM/$MODEL-$HPARAMS

mkdir -p $DATA_DIR $TMP_DIR $TRAIN_DIR

# Generate data
t2t-datagen \
  --data_dir=$DATA_DIR \
  --tmp_dir=$TMP_DIR \
  --problem=$PROBLEM

mv $TMP_DIR/tokens.vocab.32768 $DATA_DIR

# Train
# *  If you run out of memory, add --hparams='batch_size=2048' or even 1024.
t2t-trainer \
  --data_dir=$DATA_DIR \
  --problems=$PROBLEM \
  --model=$MODEL \
  --hparams_set=$HPARAMS \
  --output_dir=$TRAIN_DIR

# Decode

DECODE_FILE=$DATA_DIR/decode_this.txt
echo "Hello world" >> $DECODE_FILE
echo "Goodbye world" >> $DECODE_FILE

BEAM_SIZE=4
ALPHA=0.6

t2t-trainer \
  --data_dir=$DATA_DIR \
  --problems=$PROBLEM \
  --model=$MODEL \
  --hparams_set=$HPARAMS \
  --output_dir=$TRAIN_DIR \
  --train_steps=0 \
  --eval_steps=0 \
  --decode_beam_size=$BEAM_SIZE \
  --decode_alpha=$ALPHA \
  --decode_from_file=$DECODE_FILE

cat $DECODE_FILE.$MODEL.$HPARAMS.beam$BEAM_SIZE.alpha$ALPHA.decodes

pip install tensor2tensor

Binaries:

# Data generator
t2t-datagen

# Trainer
t2t-trainer --registry_help

Library usage:

python -c "from tensor2tensor.models.transformer import Transformer"

• Many state of the art and baseline models are built-in and new models can be added easily (open an issue or pull request!).
• Many datasets across modalities - text, audio, image - available for generation and use, and new ones can be added easily (open an issue or pull request for public datasets!).
• Models can be used with any dataset and input mode (or even multiple); all modality-specific processing (e.g. embedding lookups for text tokens) is done with Modality objects, which are specified per-feature in the dataset/task specification.
• Support for multi-GPU machines and synchronous (1 master, many workers) and asynchrounous (independent workers synchronizing through a parameter server) distributed training.
• Easily swap amongst datasets and models by command-line flag with the data generation script t2t-datagen and the training script t2t-trainer.

Datasets are all standardized on TFRecord files with tensorflow.Example protocol buffers. All datasets are registered and generated with the data generator and many common sequence datasets are already available for generation and use.

Problems define training-time hyperparameters for the dataset and task, mainly by setting input and output modalities (e.g. symbol, image, audio, label) and vocabularies, if applicable. All problems are defined in problem_hparams.py. Modalities, defined in modality.py, abstract away the input and output data types so that models may deal with modality-independent tensors.

T2TModels define the core tensor-to-tensor transformation, independent of input/output modality or task. Models take dense tensors in and produce dense tensors that may then be transformed in a final step by a modality depending on the task (e.g. fed through a final linear transform to produce logits for a softmax over classes). All models are imported in models.py, inherit from T2TModel - defined in t2t_model.py

• and are registered with @registry.register_model.

Hyperparameter sets are defined and registered in code with @registry.register_hparams and are encoded in tf.contrib.training.HParams objects. The HParams are available to both the problem specification and the model. A basic set of hyperparameters are defined in common_hparams.py and hyperparameter set functions can compose other hyperparameter set functions.

The trainer binary is the main entrypoint for training, evaluation, and inference. Users can easily switch between problems, models, and hyperparameter sets by using the --model, --problems, and --hparams_set flags. Specific hyperparameters can be overriden with the --hparams flag. --schedule and related flags control local and distributed training/evaluation (distributed training documentation).

See the data generators README.

Note: This is not an official Google product.