GitLab

* Improve performance for Cifar ResNet benchmarks

* Revert batch size changes to benchmarks

* 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

* 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>

* add ds support for ncf

* remove comments for in_top_k

* avoid expanding the input layers

* resolve comments and fix lint

* Added some comments in code and fix lint

* fix lint

* add some documentation

* add tensorflow imports

* Add profiler callback for Keras models

* Update build stats to identify time callback by type

* Add warning message when both TensorBoard and profiler callbacks are used

required by multi-node collective ops in eager mode.

* fix resnet breakage and add keras end-to-end tests

* delint

* address PR comments

* tmp commit

* tmp commit

* first attempt (without eval)

* Bug fixes

* bug fixes

* training done

* Loss NAN, no eval

* Loss weight problem solved

* resolve the NAN loss problem

* Problem solved. Clean up needed

* Added a todo

* Remove debug prints

* Extract get_optimizer to ncf_common

* Move metrics computation back to neumf; use DS.scope api

* Extract DS.scope code to utils

* lint fixes

* Move obtaining DS above producer.start to avoid race condition

* move pt 1

* move pt 2

* Update the run script

* Wrap keras_model related code into functions

* Update the doc for softmax_logitfy and change the method name

* Resolve PR comments

* working version with: eager, DS, batch and no masks

* Remove git conflict indicator

* move reshape to neumf_model

* working version, not converge

* converged

* fix a test

* more lint fix

* more lint fix

* more lint fixes

* more lint fix

* Removed unused imports

* fix test

* dummy commit for kicking of checks

* fix lint issue

* dummy input to kick off checks

* dummy input to kick off checks

* add collective to dist strat

* addressed review comments

* add a doc string

* Add flag to enable XLA in Keras models

* Fix lint errors (some of them are old errors)

* Add a flag to specify distribution strategies.

* Fix a small error.

* Address comments.

* Address comments.

* Fix typos.

- Modest speedup for CIFAR-10
- Slightly greater speedup expected for ImageNet ResNet50.

* Add pure synthetic data to keras resnet mode.

* Add imports.

* Address comments.

* update comment

* Undo set up synthetic data for real data path.

* update comment

* Address comment

* Remove trailing whiltespaces.

* s/make_data_set_iterator/make_dataset_iterator/

This reverts commit 57e07520.

This reverts commit d6b2b83c.

* Add resnet56 short tests. (#6101)

* Add resnet56 short tests.
- created base benchmark module
- renamed accuracy test class to contain the word Accuracy
which will result in a need to update all the jobs
and a loss of history but is worth it.
- short tests are mostly copied from shining with oss refactor

* Address feedback.

* Move flag_methods to init
- Address setting default flags repeatedly.

* Rename accuracy tests.

* Lint errors resolved.

* fix model_dir set to flags.data_dir.

* fixed not fulling pulling out flag_methods.

* Use core mirrored strategy in official models (#6126)

* Imagenet short tests (#6132)

* Add short imagenet tests (taken from seemuch)
- also rename to match go forward naming

* fix method name

* Update doc strings.

* Fixe gpu number.

* points default data_dir to child folder. (#6131)

Failed test is python2  and was a kokoro failure

* Imagenet short tests (#6136)

* Add short imagenet tests (taken from seemuch)
- also rename to match go forward naming

* fix method name

* Update doc strings.

* Fixe gpu number.

* Add fill_objects

* fixed calling wrong class in super.

* fix lint issue.

* Flag (#6121)

* Fix the turn_off_ds flag problem

* add param names to all args

* Export benchmark stats using tf.test.Benchmark.report_benchmark() (#6103)

* Export benchmark stats using tf.test.Benchmark.report_benchmark()

* Fix python style using pyformat

* Typos. (#6120)

* log verbosity=2 logs every epoch no progress bars (#6142)

* tf_upgrade_v2 on resnet and utils folder.

* tf_upgrade_v2 on resnet and utils folder.

* Fix the turn_off_ds flag problem

* add param names to all args

* Add timestamp history for each batch in training

* Resolve github comments

* Change the batch start recording logic

* Lint fix

* Set use_bias=False for conv layers in Keras ResNet model

* Removed bias regularizer from Conv2D layers (which should have no effect after bias is removed)

* Setting default data format based on available devices.

* Handle data format in Keras ResNet model properly for better performance on GPU; Compile only the training graph when skip_eval flag is True

* Added data format fix to Keras Cifar model; Removed unnecessary import

* Add a comment to the skip_eval flag per Priya's request

- kears_cifar_main returns dict of stats after run.
- unit tests added for keras_common.
- lint issues fixed in all 4 files.