initial push

AUTHORS

@@ -0,0 +1,7 @@
+# This is the list of T2T authors for copyright purposes.
+#
+# This does not necessarily list everyone who has contributed code, since in
+# some cases, their employer may be the copyright holder.  To see the full list
+# of contributors, see the revision history in source control.
+
+Google Inc.

CONTRIBUTING.md

@@ -0,0 +1,23 @@
+# How to Contribute
+
+We'd love to accept your patches and contributions to this project. There are
+just a few small guidelines you need to follow.
+
+## Contributor License Agreement
+
+Contributions to this project must be accompanied by a Contributor License
+Agreement. You (or your employer) retain the copyright to your contribution,
+this simply gives us permission to use and redistribute your contributions as
+part of the project. Head over to <https://cla.developers.google.com/> to see
+your current agreements on file or to sign a new one.
+
+You generally only need to submit a CLA once, so if you've already submitted one
+(even if it was for a different project), you probably don't need to do it
+again.
+
+## Code reviews
+
+All submissions, including submissions by project members, require review. We
+use GitHub pull requests for this purpose. Consult
+[GitHub Help](https://help.github.com/articles/about-pull-requests/) for more
+information on using pull requests.

LICENSE

@@ -0,0 +1,202 @@
+
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README.md

@@ -1 +1,102 @@
-# tensor2tensor
+# T2T: Tensor2Tensor Transformers
+
+[T2T](https://github.com/tensorflow/t2t) is a modular and extensible library and
+binaries for supervised learning with TensorFlow and with a focus on sequence
+tasks. Actively used and maintained by researchers and engineers within Google
+Brain, T2T strives to maximize idea bandwidth and minimize execution latency.
+
+T2T is particularly well-suited to researchers working on sequence tasks. We're
+eager to collaborate with you on extending T2T's powers, so please feel free to
+open an issue on GitHub to kick off a discussion and send along pull requests,
+See [our contribution doc](CONTRIBUTING.md) for details and our
+[open issues](https://github.com/tensorflow/t2t/issues).
+
+## T2T overview
+
+```
+pip install tensor2tensor
+
+DATA_DIR=$HOME/data
+PROBLEM=wmt_ende_tokens_32k
+MODEL=transformer
+HPARAMS=transformer_base
+TRAIN_DIR=$HOME/train
+
+# Generate data
+t2t-datagen \
+  --data_dir=$DATA_DIR \
+  --problem=$PROBLEM
+
+# Train
+t2t-trainer \
+  --data_dir=$DATA_DIR \
+  --problems=$PROBLEM \
+  --model=$MODEL \
+  --hparams_set=$HPARAMS \
+  --output_dir=$TRAIN_DIR \
+
+# Decode
+t2t-trainer \
+  --data_dir=$DATA_DIR \
+  --problems=$PROBLEM \
+  --model=$MODEL \
+  --hparams_set=$HPARAMS \
+  --output_dir=$TRAIN_DIR \
+  --decode_from_file=$DATA_DIR/decode_this.txt
+```
+
+T2T modularizes training into several components, each of which can be seen in
+use in the above commands.
+
+### Datasets
+
+**Datasets** are all standardized on TFRecord files with `tensorflow.Example`
+protocol buffers. All datasets are registered and generated with
+[`generator.py`](data_generators/generator.py) 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 in
+[`problem_hparams.py`](data_generators/problem_hparams.py). **Modalities**,
+defined in [`modality.py`](utils/modality.py), abstract away the input and
+output data types so that **models** may deal with modality-independent tensors.
+
+### Models
+
+**`T2TModel`s** 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`](models/models.py), inherit from `T2TModel` - defined in
+[`t2t_model.py`](utils/t2t_model.py) - and are registered with
+[`@registry.register_model`](utils/registry.py).
+
+### Hyperparameter Sets
+
+**Hyperparameter sets** are defined and registered in code with
+[`@registry.register_hparams`](utils/registry.py) and are encoded in
+[`tf.contrib.training.HParams`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/training/python/training/hparam.py)
+objects. The `HParams` are available to both the problem specification and the
+model. A basic set of hyperparameters are defined in
+[`common_hparams.py`](models/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 overriden with the `--hparams` flag. `--schedule` and
+related flags control local and distributed training/evaluation.
+
+## Adding a dataset
+
+See the data generators [README](data_generators/README.md).
+
+---
+
+*Note: This is not an official Google product.*

setup.py

@@ -0,0 +1,23 @@
+"""Install tensor2tensor."""
+
+from distutils.core import setup
+
+setup(
+    name='tensor2tensor',
+    version='1.0',
+    description='Tensor2Tensor',
+    author='Google Inc.',
+    author_email='[email protected]',
+    url='http://github.com/tensorflow/tensor2tensor',
+    license='Apache 2.0',
+    packages=[
+        'tensor2tensor', 'tensor2tensor.utils', 'tensor2tensor.data_generators',
+        'tensor2tensor.models'
+    ],
+    scripts=['tensor2tensor/bin/t2t-trainer', 'tensor2tensor/bin/t2t-datagen'],
+    install_requires=[
+        'numpy',
+        'sympy',
+        'six',
+        'tensorflow-gpu>=1.2.0rc1',
+    ],)

tensor2tensor/__init__.py

@@ -0,0 +1,14 @@
+# Copyright 2017 Google Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+