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PiperOrigin-RevId: 303767122

PiperOrigin-RevId: 291810091

PiperOrigin-RevId: 288064272

PiperOrigin-RevId: 285533511

PiperOrigin-RevId: 285503670

This reverts commit b4e560dc.

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

This reverts commit faf4bbb3.

* revert research

--stop_threshold, --num_gpu, --hooks, --export_dir, and --distribution_strategy have been unexposed from models which do not use them

PiperOrigin-RevId: 268032080

--clean, --train_epochs, and --epochs_between_evals have been unexposed from models which do not use them

PiperOrigin-RevId: 267065651

--synthetic_data, --dtype, --all_reduce_alg, and --num_packs have been unexposed from models which do not use them

PiperOrigin-RevId: 265483564

--num_parallel_calls, --inter_op_parallelism_threads, and --intra_op_parallelism_threads have been unexposed from models which do not use them

PiperOrigin-RevId: 264965788

PiperOrigin-RevId: 264300408

Only the V1 resnet model uses --max_train_steps. This unexposes the flag in the keras_application_models, mnist, keras resnet, CTL resnet Models. Before this change, such models allowed the flag to be specified, but ignored it.

I also removed the "max_train" argument from the run_synthetic function, since this only had any meaning for the V1 resnet model. Instead, the V1 resnet model now directly passes --max_train_steps=1 to run_synthetic.

PiperOrigin-RevId: 264269836

Also add `worker_hosts` and `task_index` flags.  These flags enable running the
model over multiple hosts by passing the cluster information via command line.

Setting `TF_CONFIG` will continue to work.

PiperOrigin-RevId: 263825245

* Restructure resnet estimator code to under official/r1

* Continue moving resnet code...

* Improved README.md

* 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.

* 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>

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

* Remove num_parallel_batches which is not used.

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.

* 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.

This options allows the new tf.train.experimental.enable_mixed_precision_graph_rewrite() function to be used for fp16, instead of manual casts.

Replace per_device with per_replica and PerDevice with PerReplica, because the PerDevice concept was renamed and doesn't exist anymore. (#6693)

* Replace per_device with per_replica and PerDevice with PerReplica, because the PerReplica concept was renamed and doesn't exist anymore.

* Combined imagenet and cifar-10 benchmarks

* Comments and epochs_between_evals.

* Added tuned tests and cleaned up benchmark flags

* Fix names.

* Return results and add images/sec hook.

* updated doc strings for return values.

* 128 to 256 batch for FP16 test

* added more doc strings to fix lint.

* Add num_packs flag for MirroredStrategy's cross device ops.

* fix parens

* Fix lint errors and make all_reduce_alg more robust.

* Set default num_packs to 1

* Update resnet_run_loop.py

* Update resnet_run_loop.py

* Update resnet_run_loop.py

* Update resnet_run_loop.py

* Update resnet_run_loop.py

* Revert "Revert " Ensure static shapes when enabling XLA in Resnet Keras model (#6508)" (#6517)"

This reverts commit cc9eef76.

* Set `batch_size` to keras.Input in non-eager mode.

Eager mode currently has OOM problem.

* Add comments for enable_eager flag.

* Always set drop_remainder=True.

* Only set drop_remainder=True for XLA.

Reason: break 1-gpu nightly test.

This reverts commit 371645fc.

* Update resnet_model.py

* Ensure static shapes when enabling XLA.

* Define `drop_remainder` as a variable.

* Handles per_replica_batch_size in non-XLA mode

* Remove trailing whitespace.

Co-authored-by: Jiri Simsa <jsimsa@google.com>

* Move distribution strategy creation before creating any ops, which is
required by multi-node collective ops in eager mode.

* Scale up learning rate according to num workers in ResNet50 w/
Estimator.

* Scale up LR in cifar.

* Fix a typo.

* Add num_workers to run param as well. Make num_worker optional in
params.

* Shard input for distribution strategy.

* Pass in input_context from real input_fn.

* Pass in input_context from real input_fn.

* Make pipeline id base 1 for better readability.

* optimizer back to compat.v1

* add doc string to fix lint