Note: This fork differs from the main branch of the tensor2tensor library in the following ways:

• Problems can create a loss_mask feature to limit the loss computation to certain positions.
• Allow explicitly disabling caching devices which can be useful for distributed training with tf.dynamic_rnn (see hparams.no_caching_devices)
• Tested with the latest version of SGNMT.

T2T: Tensor2Tensor Transformers

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. You can read more about Tensor2Tensor in the recent Google Research Blog post introducing it.

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 dataset or model. See our contribution doc for details and our open issues. You can chat with us and other users on Gitter and please join our Google Group to keep up with T2T announcements.

Here is a one-command version that installs tensor2tensor, downloads the data, trains an English-German translation model, and evaluates it:

pip install tensor2tensor && t2t-trainer \
  --generate_data \
  --data_dir=~/t2t_data \
  --problems=translate_ende_wmt32k \
  --model=transformer \
  --hparams_set=transformer_base_single_gpu \
  --output_dir=~/t2t_train/base

You can decode from the model interactively:

t2t-decoder \
  --data_dir=~/t2t_data \
  --problems=translate_ende_wmt32k \
  --model=transformer \
  --hparams_set=transformer_base_single_gpu \
  --output_dir=~/t2t_train/base \
  --decode_interactive

See the Walkthrough below for more details on each step.

Contents

• Walkthrough
• Installation
• Features
• T2T Overview
  • Datasets
  • Problems and Modalities
  • Models
  • Hyperparameter Sets
  • Trainer
• Adding your own components
• Adding a dataset

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Walkthrough

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=translate_ende_wmt32k
MODEL=transformer
HPARAMS=transformer_base_single_gpu

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

# Train
# *  If you run out of memory, add --hparams='batch_size=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-decoder \
  --data_dir=$DATA_DIR \
  --problems=$PROBLEM \
  --model=$MODEL \
  --hparams_set=$HPARAMS \
  --output_dir=$TRAIN_DIR \
  --decode_hparams="beam_size=$BEAM_SIZE,alpha=$ALPHA" \
  --decode_from_file=$DECODE_FILE

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

---

Installation

# Assumes tensorflow or tensorflow-gpu installed
pip install tensor2tensor

# Installs with tensorflow-gpu requirement
pip install tensor2tensor[tensorflow_gpu]

# Installs with tensorflow (cpu) requirement
pip install tensor2tensor[tensorflow]

Binaries:

# Data generator
t2t-datagen

# Trainer
t2t-trainer --registry_help

Library usage:

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

---

Features

• 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 asynchronous (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.

---

T2T overview

Datasets

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 and Modalities

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 either in problem_hparams.py or are registered with @registry.register_problem (run t2t-datagen to see the list of all available problems). Modalities, defined in modality.py, abstract away the input and output data types so that models may deal with modality-independent tensors.

Models

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 the models subpackage, inherit from T2TModel - defined in t2t_model.py - and are registered with @registry.register_model.

Hyperparameter Sets

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.

Trainer

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 overridden with the --hparams flag. --schedule and related flags control local and distributed training/evaluation (distributed training documentation).

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Adding your own components

T2T's components are registered using a central registration mechanism that enables easily adding new ones and easily swapping amongst them by command-line flag. You can add your own components without editing the T2T codebase by specifying the --t2t_usr_dir flag in t2t-trainer.

You can do so for models, hyperparameter sets, modalities, and problems. Please do submit a pull request if your component might be useful to others.

Here's an example with a new hyperparameter set:

# In ~/usr/t2t_usr/my_registrations.py

from tensor2tensor.models import transformer
from tensor2tensor.utils import registry

@registry.register_hparams
def transformer_my_very_own_hparams_set():
  hparams = transformer.transformer_base()
  hparams.hidden_size = 1024
  ...

# In ~/usr/t2t_usr/__init__.py
from . import my_registrations

t2t-trainer --t2t_usr_dir=~/usr/t2t_usr --registry_help

You'll see under the registered HParams your transformer_my_very_own_hparams_set, which you can directly use on the command line with the --hparams_set flag.

t2t-datagen also supports the --t2t_usr_dir flag for Problem registrations.

Adding a dataset

To add a new dataset, subclass Problem and register it with @registry.register_problem. See TranslateEndeWmt8k for an example.

Also see the data generators README.

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Note: This is not an official Google product.