GitLab

* 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.

* Set the --clone_model_in_keras_dist_strat to None.  Remove the separate no_cloning benchmarks and add a couple of cloning ones.  Fixes the learning rate schedule to cache its ops per graph.

* Added 'tfdata_exp' version of all benchmarks which set
FLAGS.tf_data_experimental_slack = True. Renamed
`data_prefetch_with_slack` to `data_delay_prefetch` (haoyu's change)
to make the names more distinct.

* Add flag to resnet input pipeline and surface through
keras_imagenet_main.py

* Add FP16 and benchmarks.

* add missing run and report.

* Add loss_scale as option not included with dtype.

* move loss_scale validation under dtype conditional.

* add loss_scale to flags tested.

* Fix trivial model to work properly with fp16

* Add comment on manual casting

Previously we had one dense layer in trivial model. The weight was [224*224*3, num_classes].
Using two dense layers, the weights are [224*224*3, 1] and [1, num_classes].

* Do not report metrics in performance benchmarks

* Rename flag

* Modified tweaked tests to use tensor learning rate