Move the unfortunately-named examples/transformer_standalone.py to transformer/main.py make model and layers hyperparameters gin-configurable so as to cut out a lot of plumbing. Rename "encoder" to "vocabulary" to avoid confusion with the encoder in encoder/decoder architectures. Add functionality to run transformer on t2t datasets. Modify some of the default model hyperparameters to better match previous transformer experiments. Enable layer-postprocess-dropout, which had been mistakenly omitted. Add options for sharing embedding and softmax layer weights. Several changes to speed up input pipeline:  - custom op for sequence-packing (borrowed from t2t)    (requires custom-built tf binary, so won't work yet on cloud-tpu)  - enable parallelism in dataset.map calls  - add prefetching  - input pipeline seems to run fine now at >1M tokens/sec
Move the unfortunately-named examples/transformer_standalone.py to transformer/main.py
make model and layers hyperparameters gin-configurable so as to cut out a lot of plumbing.
Rename "encoder" to "vocabulary" to avoid confusion with the encoder in encoder/decoder architectures.
Add functionality to run transformer on t2t datasets.
Modify some of the default model hyperparameters to better match previous transformer experiments.
Enable layer-postprocess-dropout, which had been mistakenly omitted.
Add options for sharing embedding and softmax layer weights.
Several changes to speed up input pipeline:
 - custom op for sequence-packing (borrowed from t2t)
   (requires custom-built tf binary, so won't work yet on cloud-tpu)
 - enable parallelism in dataset.map calls
 - add prefetching
 - input pipeline seems to run fine now at >1M tokens/sec

PiperOrigin-RevId: 242222578

tensorflow/tensor2tensor,tensorflow/tensor2tensor,tensorflow/tensor2tensor,tensorflow/tensor2tensor,tensorflow/tensor2tensor