Tensor2tensor

PRs accepted:
Cleaning up the code for gru/lstm as transition function for universal transformer. Thanks MostafaDehghani !
Clipwrapper by piotrmilos !
Corrected transformer spelling mistake - Thanks jurasofish!
Fix to universal transformer update weights - Thanks cbockman and cyvius96 !
Common Voice problem fixes and refactoring - Thanks tlatkowski !
Infer observation datatype and shape from the environment - Thanks koz4k !

New Problems / Models:
* Added a simple discrete autoencoder video model. Thanks lukaszkaiser !
* DistributedText2TextProblem, a base class for Text2TextProblem for large-datasets. Thanks afrozenator!
* Stanford Natural Language Inference problem added `StanfordNLI` in [stanford_nli.py](https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/data_generators/stanford_nli.py). Thanks urvashik !
* `Text2TextRemotedir` added for problems with a persistent remote directory. Thanks rsepassi !
* Add a separate binary for vocabulary file generation for subclasses of Text2TextProblem. Thanks afrozenator!
* Added support for non-deterministic ATARI modes and sticky keys. Thanks mbz !
* Pretraining schedule added to MultiProblem and reweighting losses. Thanks urvashik !
* `SummarizeWikiPretrainSeqToSeq32k` and `Text2textElmo` added.
* `AutoencoderResidualVAE` added, thanks lukaszkaiser !
* Discriminator changes by lukaszkaiser and aidangomez
* Allow scheduled sampling in basic video model, simplify default video modality. Thanks lukaszkaiser !

Code Cleanups:
* Use standard vocab naming and fixing translate data generation. Thanks rsepassi !
* Replaced manual ops w/ dot_product_attention in masked_local_attention_1d. Thanks dustinvtran !
* Eager tests! Thanks dustinvtran !
* Separate out a [video/](https://github.com/tensorflow/tensor2tensor/tree/master/tensor2tensor/models/video) directory in models/. Thanks lukaszkaiser !
* Speed up RL test - thanks lukaszkaiser !

Bug Fixes:
* Don't daisy-chain variables in Universal Transformer. Thanks lukaszkaiser !
* Corrections to mixing, dropout and sampling in autoencoders. Thanks lukaszkaiser !
* WSJ parsing only to use 1000 examples for building vocab.
* Fixed scoring crash on empty targets. Thanks David Grangier!
* Bug fix in transformer_vae.py

Enhancements to MTF, Video Models and much more!

Introducing [**MeshTensorFlow**](https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/mesh_tensorflow/README.md) - this enables training really big models O(Billions) of parameters.

Models/Layers:
* Layers Added: NAC and NALU from https://arxiv.org/abs/1808.00508 Thanks lukaszkaiser !
* Added a [sparse graph neural net message passing layer]((https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/layers/common_layers.py)) to tensor2tensor.
* Targeted dropout added to ResNet. Thanks aidangomez !
* Added VQA models in `models/research/vqa_*`
* Added [`Weight Normalization`](https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/layers/common_layers.py) layer from https://arxiv.org/abs/1602.07868.

Datasets/Problems:
* MSCoCo paraphrase problem added by tlatkowski - many thanks!
* `VideoBairRobotPushingWithActions` by mbz !

Usability:
* Code cleaup in autoencoder, works both on image and text. Thanks lukaszkaiser
* Set the default value of Text2TextProblem.max_subtoken_length to 200, this prevents very long vocabulary generation times. Thanks afrozenator
* Add examples to distributed_training.md, update support for async training, and simplify run_std_server codepath. Thanks rsepassi !
* Store variable scopes in T2TModel; add T2TModel.initialize_from_ckpt. Thanks rsepassi !
* Undeprecate exporting the model from the trainer Thanks gcampax !
* Doc fixes, thanks to stefan-it :)
* Added t2t_prune: simple magnitude-based pruning script for T2T Thanks aidangomez !
* Added task sampling support for more than two tasks. Thanks urvashik !

Bug Fixes:
* Override serving_input_fn for video problems.
* `StackWrapper` eliminates problem with repeating actions. Thanks blazejosinski !
* Calculated lengths of sequences using _raw in lstm.py
* Update universal_transformer_util.py to fix TypeError Thanks zxqchat !

Testing:
* Serving tests re-enabled on Travis using Docker. Thanks rsepassi !

Many more fixes, tests and work on RL, Glow, SAVP, Video and other models and problems.

* Added a MultiProblem class for Multitask Learning. Thanks urvashik !
* Added decoding option to pass through the features dictionary to predictions. Thanks rsepassi !
* Enabled MLEngine path to use Cloud TPUs. Thanks rsepassi !
* Added a simple One-Hot Symbol modality. Thanks mbz !
* Added Cleverhans integration. Thanks aidangomez !

* Problem definitions added for:
* Allen Brain Atlas problems. Thanks cwbeitel !
* [LSUN Bedrooms](http://lsun.cs.princeton.edu/2017/) dataset.
* Added various NLP datasets. Thanks urvashik !
* [MSR Paraphrase Corpus](https://www.microsoft.com/en-us/download/details.aspx?id=52398),
* [Quora Question Pairs](https://data.quora.com/First-Quora-Dataset-Release-Question-Pairs),
* [Stanford Sentiment Treebank](https://nlp.stanford.edu/sentiment/treebank.html),
* [Question Answering NLI classification problems](https://gluebenchmark.com/tasks),
* [Recognizing Textual Entailment](https://gluebenchmark.com/tasks),
* [Corpus of Linguistic Acceptability](https://gluebenchmark.com/tasks),
* [Winograd NLI](https://gluebenchmark.com/tasks).
* Added a data generator for WSJ parsing.

* Model additions:
* Implemented Targeted Dropout for Posthoc Pruning. Thanks aidangomez !
* Added self attention to VQA attention model.
* Added fast block parallel transformer model
* Implemented auxiliary losses from [Stochastic Activation Pruning for Robust Adversarial Defense](https://arxiv.org/abs/1803.00144). Thanks alexyku !
* Added probability based scheduled sampling for SV2P problem. Thanks mbz !
* Reimplementated Autoencoder and Eval. Thanks piotrmilos !
* Relative memory efficient unmasked self-attention.

* Notable bug fixes:
* bug with data_gen in style transfer problem Thanks tlatkowski !
* wmt_enfr dataset should not use vocabulary based on "small" dataset. Thanks nshazeer !

* **Many more fixes, tests and work on Model based RL, Transfomer, Video and other models and problems.**

* added Mozilla common voice as Problem and style transfer one others!
* improvements to ASR data preprocessing (thanks to jarfo)
* decoding works for Transformer on TPUs and for timeseries problems
* corrections and refactoring of the RL part
* Removed deprecated Experiment API code, and support SessionRunHooks on TPU.
* many other corrections and work on video problems, latent variables and other

Great thanks to everyone!

* `registry.hparams` now returns an `HParams` object instead of a function that returns an `HParams` object
* New `MultistepAdamOptimizer` thanks to fstahlberg
* New video models and problems and improvements to `VideoProblem`
* Added `pylintrc` and lint tests to Travis CI
* Various fixes, improvements, and additions

* `--random_seed` is unset by default now. Set it to an integer value to get reproducible results.
* [bAbI text understanding tasks added](https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/data_generators/babi_qa.py)
* Have the ML Engine and TPU codepaths use TF 1.8
* Various cloud-related bug fixes
* `WikisumWeb` data generation fixes
* Various other fixes