GitLab

* 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

* cpu benchmark and accuracy tests.

* add docstrings to fix lint.

* XLA FP32 and first test

* More XLA benchmarks FP32.

* Add eager to NCF and refactor resnet.

* fix v2_0 calls and more flag refactor.

* Remove extra flag args.

* 90 epoch default

* add return

* remove xla not used by estimator.

* Remove duplicate run_eagerly.

* fix flag defaults.

* Remove fp16_implementation flag option.

* Remove stop early on mlperf test.

* remove unneeded args.

* load flags from keras mains.

* Do not set learning phase when skipping eval

* Do not set learning phase in no dist strat case

* Added device placement, tweaked benchmarks

* Added tweaked benchmarks for Cifar

* Fix device scope

* Fix lint

* Add explicit GPU placement flag

* Also run accuracy test with explicit GPU placement

* Added doc string

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* set default steps to 300K.

* Log flags to perfzero.

* Add XLA support to transformer

- Moved config logic to keras_utils
- Added enable_xla flag to _performance flags
- Did not refactor enable_xla flag from keras resnet due to
  reliance on calling FLAGs in estimator keras and that is
  a needed refactor for another time.

* fix g3 lint complaint.

* Refactor set config into keras_utils.

* Move flags out of main.

* pipe through enable_xla

* Update official/transformer/v2/misc.py
Co-Authored-By: Reed <reedwm@google.com>

* first version of ctl

* fix indent

* remove monkey patching for core

* add dtype arg

* fix dtype arg

* add logging lib

* remove compat.v1.logging

* add datetime import

* fix FLAGS import

* add constant vals

* move to using as tf import

* move to using as tf import

* remove steps per epoch = 1

* test train and test for one step

* test train and test for one step

* test train and test for one step

* test train and test for the entire dataset

* use an iterator for test

* pass tensors instead of an iterator

* add stats dict

* fix list declaration

* fix list declaration

* fix elapsed time calc

* print lr at epoch boundary alone

* Use regular tf import instead of compat

* remove tensorboard chkpts

* add correct logging import

* add correct logging import

* add benchmark configs

* add tests and configs

* add tests and configs

* add keras flags import

* add keras flags import

* fix eval ds creation cond

* return numpy value of train_loss

* return numpy value of loss and acc values

* add option for full eager mode

* fix lint errors

* add ctl flags

* add ctl import

* add the xla flag

* enable v2 behavior in unit tests

* rename dataset var

* add synthetic dataset without monkey patching

* add ctl local constants

* add ctl local constants

* change to using v2 imports

* change to using v2 imports

* change to using v2 imports

* change to using keras synthetic input fn

* remove enable_eager flag from benchmarks

* remove enable_eager flag from benchmarks

* remove enable_eager flag from benchmarks

* add option for no distrat

* add lambda for flags

* remove no_func benchmarks due to OOM error

* remove README

* remove unused comments

* remove unchanged file

* remove unchanged file

* remove unused drop_remainder_arg

* use keras.common lr function

* address PR comments

* remove reference to deleted file

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* fix lint errors

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* add flags info

* first version of ctl

* fix indent

* remove monkey patching for core

* add dtype arg

* fix dtype arg

* add logging lib

* remove compat.v1.logging

* add datetime import

* fix FLAGS import

* add constant vals

* move to using as tf import

* move to using as tf import

* remove steps per epoch = 1

* test train and test for one step

* test train and test for one step

* test train and test for one step

* test train and test for the entire dataset

* use an iterator for test

* pass tensors instead of an iterator

* add stats dict

* fix list declaration

* fix list declaration

* fix elapsed time calc

* print lr at epoch boundary alone

* Use regular tf import instead of compat

* remove tensorboard chkpts

* add correct logging import

* add correct logging import

* add benchmark configs

* add tests and configs

* add tests and configs

* add keras flags import

* add keras flags import

* fix eval ds creation cond

* return numpy value of train_loss

* return numpy value of loss and acc values

* add option for full eager mode

* fix lint errors

* add ctl flags

* add ctl import

* add the xla flag

* enable v2 behavior in unit tests

* rename dataset var

* add synthetic dataset without monkey patching

* add ctl local constants

* add ctl local constants

* change to using v2 imports

* change to using v2 imports

* change to using v2 imports

* change to using keras synthetic input fn

* remove enable_eager flag from benchmarks

* remove enable_eager flag from benchmarks

* remove enable_eager flag from benchmarks

* add option for no distrat

* add lambda for flags

* remove no_func benchmarks due to OOM error

* remove README

* remove unused comments

* remove unchanged file

* remove unchanged file

* remove unused drop_remainder_arg

* use keras.common lr function

* address PR comments

* remove reference to deleted file

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* fix lint errors

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* tf.compat.v1.train.experimental.enable_mixed_precision_graph_rewrite

* Remove num_parallel_batches which is not used.

* layout off for some tests and channels last.

* 8 gpu tests channels_last

* more layout off tests.

* Add 1 gpu force_eager benchmark

* Add accuracy for no dist strat eager

* remvove return.

Before, there was a global default loss scale for all models. Currently, only resnet uses loss scaling, but this will be useful once more models support it.

* Modify tweaked tests for better performance in no cloning mode

* Tweak trivial models

* Add multi-worker benchmarks to official resnet estimator_benchmark.py.

* fix super constructor calls

* set datasets_num_private_threads to 32 in multi worker tweaked benchmarks

Because we run warmup tests in all real data benchmarks, XLA bugs will cause non-XLA tests to fail as well.

* Add mlperf like test.

* Final comments.

* docstring wording tweak.

* non-tweaked version

* Support pure eager execution in ResNet50

* Use smaller batch size

* Run different numbers of steps on different platforms

* Add new tests for delayed performance measurement

* Add a graph optional_next Reset benchmark.

* Fix lint error.

* Merged commit includes the following changes:
249776315  by tianlin<tianlin@google.com>:

    Internal change

249763206  by tianlin<tianlin@google.com>:

    For TF 2.0 (related to Beam Search), expand cond dims in tf.where(cond, x, y) to make all parameters broadcastable.

--
249392724  by hongkuny<hongkuny@google.com>:

    Internal change

PiperOrigin-RevId: 249776315

* Merged commit includes the following changes:
249823043  by tianlin<tianlin@google.com>:

    Bring back v2 test for predict and eval.

--

PiperOrigin-RevId: 249823043

* Add a test enabling get_next_as_optional behavior.

* Remove repeated flag.

* Remove trailing space.

* Make the name shorter.

* Fix lint error.

* Refine the benchmark name.

* Add enable_get_next_as_optional flag.

* Set enable_get_next_as_optional to strategy.

* Add comments to explain the flag.

* Remove trailing whitespace.

* Remove trailing space.

* Delete accuracy if exists in eval results.

* get global_step only if it exists in eval results

This will allow one to easily reproduce a benchmark by running with the flags.