GitLab

* Revert "Update tf.contrib.data to tf.data.experimental. (#7650)"

This reverts commit faf4bbb3.

* revert research

* Fix unit tests failures.

* 96% of TF 2.0 tests on GPU are passing.

* Currently all passing GPU and CPU TF 2.0

* Address code comments.

* use tf 2.0 cast.

* Comment about working on TF 2.0 CPU

* Uses contrib turn off for TF 2.0.

* Fix wide_deep and add keras_common_tests.

* use context to get num_gpus.

* Switch to tf.keras.metrics

252534787  by hongkuny<hongkuny@google.com>:

    Transformer vocab fix to strip correctly in py2

--

PiperOrigin-RevId: 252534787

251762562  by hongkuny<hongkuny@google.com>:

    Fix blue score inconsistency

--

PiperOrigin-RevId: 251762562

* Merged commit includes the following changes:
249218656  by tianlin<tianlin@google.com>:

    Deal with imports, fix a typo and make unit tests fast.

--
249198645  by tianlin<tianlin@google.com>:

    Trivial: Remove one empty line before "import tensorflow"

--
249195490  by tianlin<tianlin@google.com>:

    Initialize Transformer TF V2 Model with Keras subclassing implementation. (Compatible with TF V1)

--
249195008  by tianlin<tianlin@google.com>:

    Internal change

249173564  by hongkuny<hongkuny@google.com>:

    Internal change

249079258  by hongkuny<hongkuny@google.com>:

    Internal change

247691534  by haoyuzhang<haoyuzhang@google.com>:

    Internal change

247533725  by haoyuzhang<haoyuzhang@google.com>:

    Internal change

247509295  by haoyuzhang<haoyuzhang@google.com>:

    Internal change

247311355  by wangtz<wangtz@google.com>:

    Internal change

247303127  by wangtz<wangtz@google.com>:

  ...

* Add first benchmark and return stats.

* Remove print statements update training steps.

* Revert print T: in print statement.

* Remove print(stats)

* add 2 gpu accuracy test for base.

* Fixed total_batch_size when using gpu + gFile deprecations.

* 8 GPU test name fix

* Add 4 and 8 GPU tests.

* typo fixes.

* Clean up test names and methods.

* bleu uncased.  docstring format fix.

Explicitly pass values kwarg to tf.name_scope as it is currently being treated as the default_name kwarg instead. This causes an exception to be thrown in eager mode.

* Deterministic dataset order fix

In order for the order of the files to be deterministic, in `tf.data.Dataset.list_files(..., shuffle)`, shuffle needs to be True, otherwise different iterator inits will yield different file orders

* removed unnecessary shuffle of filenames

* Removed the `_FILE_SHUFFLE_BUFFER` definition

* Use six and feature detection in string conversion

Leverage [__six.ensure_text()__](https://github.com/benjaminp/six/blob/master/six.py#L890) to deliver Unicode text in both Python 2 and Python 3.

Follow Python porting best practice [use feature detection instead of version detection](https://docs.python.org/3/howto/pyporting.html#use-feature-detection-instead-of-version-detection) in ___unicode_to_native()__.

* Revert the use of six.ensure_text()

Thanks for catching that!  I jumped the gun.  It is I who have brought shame...

* remove unused imports and lint

* fix schedule.py

* address PR comments

* Add DistributionStrategy to transformer model

* add num_gpu flag

* Calculate per device batch size for transformer

* remove reference to flags_core

* Add synthetic data option to transformer

* fix typo

* add import back in

* Use hierarchical copy

* address PR comments

* lint

* fix spaces

* group train op together to fix single GPU error

* Fix translate bug (sorted_keys is a dict, not a list)

* Change params to a default dict (translate.py was throwing errors because params didn't have the TPU parameters.)

* Address PR comments. Removed multi gpu flag + more

* fix lint

* fix more lints

* add todo for Synthetic dataset

* Update docs

* add tests for matmul embedding and schedule manager, as well as some minor cleanup

* delint

* address PR comments

* port changes from previous branch now that transformer util changes are in master

fix incorrect count

correct (hopefully) treatment of batch_size

set eval_metrics to a dummy function for now

add some comments

start bringing metrics to transformer TPU

resolve logits shape

metrics are now working except for tf.py_func metrics

increase batch_size for tpu, and create summary host call

fix host call

reduce tpu default batch size

further tune batch sizes

add minibatch loss to summary

handle case of single_iteration_train_steps > number points in an epoch

begin to incorporate hooks

add sleep workarounds

disable hooks altogether

generalize host call function and move to newly created tpu utils module

remove all traces of params as an object

switch from  to

address some PR comments, and change the number of data points.

minor tweaks

add tpu dry run for testing, and use matmul for TPU embedding

infeed/outfeed queue issue is fixed. Sleeps are no longer necessary

add some documentation.

cleanup and address PR comments

delint

add accelerator __init__

fix embedding

missed PR comment

address PR comments

fix validator bug

rewrite cloud storage validator, and add oauth dependency to requirements.txt

* delint